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HomeMy WebLinkAboutNicolette NEBA Expert Report Duke Energy Final_20160630NET ENVIRONMENTAL BENEFIT ANALYSIS OF PROPOSED REMEDIAL ALTERNATIVES TO ADDRESS ALLEGED RELEASES FROM COAL ASH IMPOUNDMENTS AT THE ALLEN, BUCK, CLIFFSIDE, AND MAYO STEAM STATIONS Expert Opinion of: Joseph P. Nicolette Prepared by: EPS 1050 Crown Pointe Parkway, Suite 550 Atlanta, Georgia 30338 Tel: 404-315-9113 June 30, 2016 NET ENVIRONMENTAL BENEFIT ANALYSIS OF PROPOSED REMEDIAL ALTERNATIVES TO ADDRESS ALLEGED RELEASES FROM COAL ASH IMPOUNDMENTS AT THE ALLEN, BUCK, CLIFFSIDE, AND MAYO STEAM STATIONS Expert Opinion of: Joseph P. Nicolette EPS Environmental Planning Specialists, Inc. 1050 Crown Pointe Pkwy, Suite 550 Atlanta, GA 30338 Tel: 404-315-9113 June 30, 2016 EPS NET ENVIRONMENTAL BENEFIT ANALYSIS OF PROPOSED REMEDIAL ALTERNATIVES TO ADDRESS ALLEGED RELEASES FROM COAL ASH IMPOUNDMENTS AT THE ALLEN, BUCK, CLIFFSIDE, AND MAYO STEAM STATIONS June 2016 TABLE OF CONTENTS 1 INTRODUCTION....................................................................................................... 1 1.1 Background................................................................................................1 1.2 Summary of Opinions................................................................................ 2 1.3 Qualifications............................................................................................. 4 1.3.1 General...........................................................................................4 1.3.2 NEBA and Site Remediation Experience ........................................ 4 1.3.3 Ecosystem Service Valuation.......................................................... 4 2 NEBA AND APPLICATION TO SITE REMEDIATION...................................................... 6 2.1 What is NEBA?.......................................................................................... 6 2.1.1 Why is a NEBA Analysis Necessary for Developing Appropriate Remedial Actions for the Allen, Buck, Cliffside, and Mayo Coal Ash Basins?...................................................................................................... 8 2.1.2 Summary.......................................................................................14 2.1.3 Understanding Risks and Injury .................................................... 15 3 NEBA EVALUATION METHODS AND ANALYSIS....................................................... 16 3.1 Alternative Construction Analysis............................................................ 17 3.2 Air Emissions........................................................................................... 17 3.3 Safety Risks............................................................................................. 18 3.4 Cost......................................................................................................... 18 3.5 Human Health.......................................................................................... 18 3.6 Ecological (Habitat Alteration).................................................................. 19 4 NEBA SUMMARY RESULTS................................................................................... 32 4.1 Risks Driving Remedial Alternative Selection .......................................... 32 4.2 NEBA Assessment Parameter Impacts ................................................... 35 4.2.1 Human Heath Implementation Risks ............................................. 38 4.2.2 Air Emissions................................................................................ 40 4.2.3 Ecological Services....................................................................... 42 4.2.4 Costs............................................................................................. 44 4.2.5 Each Site Data Combined............................................................. 46 DCN: HWIDENC001 i June 30, 2016 4.2.6 Cumulative Assessment................................................................ 50 5 OPINIONS............................................................................................................. 51 6 REFERENCES....................................................................................................... 55 7 EXPERT REPORTS REVIEWED................................................................................ 58 Appendices A Construction and Cost B Air Emissions C Health and Safety Risk D Human Health Risk E Ecological Services F Joseph Nicolette CV DCN: HWIDENC001 11 June 30, 2016 0 I INTRODUCTION I was retained on this project to conduct a net environmental benefit analysis (NEBA) of three remedial alternatives for the Duke Energy Carolinas, LLC or Duke Energy Progress LLC ("Duke Energy") Allen, Buck, Cliffside, and Mayo steam station sites. The three alternatives evaluated were monitored natural attenuation (MNA), cap -in -place (CIP), and comprehensive removal (Removal). The NEBA was used to compare how the remedial alternatives for each site differ with regard to their human and ecological chemical and physical risk profiles, environmental footprint (e.g., environmental and community benefits and costs); ecological habitat value; human recreational value; greenhouse gas emissions (GHG) and monetary costs. Background on the development of the proposed remedial alternatives is provided in the following section, followed by a summary of my opinions, an overview of NEBA, my qualifications, the NEBA analysis, and, based on the NEBA, my overall opinions regarding appropriate remedial alternatives for the Allen, Buck, Cliffside, and Mayo sites. I reserve the right to supplement my opinions should additional information become available. 1.1 Background Coal ash stored in unlined basins associated with the Duke Energy Allen, Buck, Cliffside, and Mayo sites has resulted in elevated metal concentrations in groundwater and limited on -site surface water features (e.g., seeps) at these sites. These sites are currently the subject of an enforcement action brought by the North Carolina Department of Environmental Quality (NCDEQ). Organizations represented by the Southern Environmental Law Center ("Intervenors") are also parties to this litigation. The litigation seeks to establish violations of state law concerning migration of constituents of interest from these basins to surface water and groundwater, and impose a remedy for such violations. After this litigation commenced, North Carolina enacted the Coal Ash Management Act (CAMA). CAMA, within the North Carolina General Assembly Session Law 2014-122, requires the owner of coal combustion waste surface impoundments (e.g., Duke Energy) to conduct groundwater monitoring, assessment and remedial activities at coal ash basins across the state, as necessary. Duke Energy was required to submit a Groundwater Assessment Plan (GAP) to NCDEQ by December 31, 2014. Comprehensive Site Assessment (CSA) documents that reported the results of site characterization activities were required to be submitted within 180 days of approval of the GAP. Information developed under the CSAs (HDR 2415a, 2015b, 2015c; Synterra 2015a) provided the data to be used to prepare Corrective Action Plans (CAPS) that were to be submitted to NCDEQ within 90 days of submittal of the CSA. An agreement between Duke Energy and NCDEQ resulted in breaking the CAP document into two parts: Part 1 and Part 2 (HDR 2015d, 2015f, 2016a, 2016b, 2016c; Synterra 2015b, 2016). The CAP documents evaluated the three DCN: HWIDENC001 I June 30, 2016 0 alternatives (for some criteria, only in a qualitative manner) for the remediation of on -site groundwater according to the following criteria: • Effectiveness; • Implementability/feasibility; • Environmental sustainability; • Cost; and • Stakeholder input. NCDEQ classified the impoundments as either High, Intermediate, or Low risk. CAMA specifies that any impoundments classified by NCDEQ as High be closed no later than December 31, 2019 by dewatering the waste and either a) excavating the ash and converting the impoundment to an industrial landfill, or b) excavating and transporting the waste off -site for disposal in an appropriately licensed landfill. Intermediate -risk impoundments are required to be closed similarly to high -risk impoundments, but under a closure deadline of December 31, 2024. Impoundments classified as Low by NCDEQ must be closed by December 31, 2029 either similarly to the high and intermediate -risk sites, or by dewatering to the extent practicable and capping the waste in place. The Allen, Buck, Cliffside, and Mayo sites were most recently proposed (May 18, 2016) to be classified as Intermediate by the NCDEQ, which would require closure by dewatering the waste and either a) excavating the ash and converting the impoundment to an industrial landfill, or b) excavating and transporting the waste off -site for disposal in an appropriately licensed landfill. In the May 18, 2016 proposed classifications, surface water, dam safety, and groundwater were ranked individually'. A summary of my opinions regarding the overall net benefit associated with the proposed remedies to address alleged releases to groundwater and surface water is presented below. 1.2 Summary of Opinions • The Intervenors failed to take a holistic view of all of the environmental and community impacts and risks (environmental footprint) associated with alternative implementation during their remedial evaluation stage. This has skewed their remedy selection and leads ' The state environmental department has proposed the risk classification for all coal ash ponds in North Carolina as intermediate based on current conditions and available data, except for those already designated high priority by the coal ash law. If all necessary modifications and dam repairs included in the Notice of Deficiencies had been made to Department of Environmental Quality's (DEQ's) satisfaction, and if nearby wells were determined to not be affected by the coal ash ponds to DEQ's satisfaction, or permanent alternate water was made available to nearby residents, then these basins would have been classified as low. DEQ takes the position that the coal ash law does not grant DEQ the authority to change a pond's classification based on new information after the final proposed classifications are released. DEQ will therefore recommend that the law be changed to reflect dam safety repairs or the removal of any threat to drinking water. DEQ states that it would consider these subsequent actions or impose such conditions and reclassify the basins if the state law granted this authority. DCN: HWIDENC001 2 June 30, 2016 0 them to advocate for a remedy (Removal) that is less protective of the environment and the community. • Intervenors do not consider the environmental footprint of the Removal alternative, nor do they consider methods to reduce the footprint of these alternatives at any of the four sites. Metrics that may have significant social and economic impacts (e.g., GHG emissions, extended truck traffic, pollutant emissions through local communities, energy and resource consumption) or ecological impacts (e.g., terrestrial habitat disturbance associated with removal, capping, or the development of off -site disposal units) were not considered quantitatively by the Intervenors. • Developing a remedy should employ a NEBA evaluation to develop sustainable remedial alternatives that manage site risks while maximizing benefits and minimizing costs to the public. • The impact of metals in groundwater on groundwater ecosystem service flows provided by each individual site is marginal, if any. • The Removal remedy that the Intervenors propose for the Allen, Buck, Cliffside, and Mayo sites does not appear to provide any net benefit to off -site ecological or human use services, over other alternatives. o The Removal alternative will create greater harm to the environment compared to other alternatives. o The Removal alternative will create greater risks to the local community (via the extensive truck traffic) and to workers compared to other alternatives. • Based upon my evaluation, the Removal alternative will create significantly greater risks (and nuisance) to the local community (via the extensive truck traffic) and to workers, compared to other alternatives. • In comparing the CIP alternative to the Removal alternative, the Removal alternative has a much greater environmental footprint and creates significantly more environmental and community harm. This is consistent with the Tennessee Valley Authority findings in their review of CIP and Removal alternatives for coal ash as part of their programmatic EIS (Tennessee Valley Authority 2016). • Intervenors base their arguments for Removal on risks based solely upon chemical concentrations of the constituents of interest (COIs) in groundwater and limited surface water seep areas. In doing so, they neglected to consider factors other than chemical concentrations during the implementation of their preferred remedial alternative that also have the potential to influence, either positively or negatively, human health and ecological risks and services. • There is no need to order closure, much less closure by Removal, to remedy the alleged violations in this case. A basic NEBA demonstrates that Intervenors' proposed remedy (Removal) is clearly disproportionate to the risk. DCN: HWIDENC001 3 June 30, 2016 0 1.3 Qualifications 1.3.1 General The basis for my opinions presented herein include my 30+ years of experience as an environmental consultant; educational background; research and professional experience in NEBA, remedial alternatives analysis, natural resource damage assessment (NRDA), and ecosystem service valuation; review of the CSA, CAP Part 1, and CAP Part 2 documents for each site (HDR 2015a through HDR 2015f; HDR 2016a through 2016c; Synterra 2015a, b; Synterra 2016); and analyses conducted at my direction. In addition, I conducted site visits at these four locations during the period of June 6-9, 2014. I hold an M.S. degree in Fisheries Management from the University of Minnesota (1983), a B.S. degree in Environmental Resource Management from Penn State University (1980), and am a certified fisheries scientist with the American Fisheries Society. My CV is provided in Appendix F. 1.3.2 NEBA and Site Remediation Experience I co-authored the first formalized NEBA framework for remediation and restoration of contaminated sites with co-authors from the United States Environmental Protection Agency (USEPA) and Oak Ridge National Laboratory in 2003 (Efroymson et al. 2003). The formalized NEBA framework has been recognized by the National Oceanic and Atmospheric Administration (NOAA), the USEPA, the USEPA Science Advisory Board (USEPA SAB)3, and the Australian Maritime Safety Authority. I have over 24 years of experience in the application of NEBA concepts and ecosystem service valuation approaches on over 75 projects with experience in over 16 countries. These projects have included NEBA approaches to compare land management alternatives and remediation alternatives for groundwater, sediment, and soils and at various sites. These projects have included work for the USEPA, the U.S. Department of Defense, and private industry. Example projects include Crab Orchard National Wildlife Refuge; NASA Edwards Air Force Base; NASA Marshall Space Flight Center; Army Base Realignment and Closure sites in Washington, Alabama, California, and Illinois; Prince William Sound, Alaska; Hudson River, New York; Troutdale, Oregon; Rocky Mountain Arsenal, CO; Homestead AFB, Florida; Tennessee Valley Authority, Tennessee; and Calcasieu River Estuary, Louisiana. 1.3.3 Ecosystem Service Valuation I pioneered the ecological service valuation method known as habitat equivalency analysis (HEA) prior to its codification into NRDA regulations in the United States. I have applied these methods 2 The natural resources provided by the earth's ecosystems serve as the building block upon which human well-being flows. Ecosystems represent a complex and dynamic array of animal, plant, and microbe along non -living physical elements interacting as a functioning unit. This gives rise to many benefits, known as ecosystem services, which are the benefits people obtain from naturally functioning ecosystems (Nicolette et al. 2013a). s United States Environmental Protection Agency (USEPA) Science Advisory Board (SAB). 2009. Valuing the Protection of Ecological Systems and Services (EPA-SAB-09-012). Washington, DC: USEPA Science Advisory Board. DCN: HWIDENC001 4 June 30, 2016 0 to a multitude of cases across the United States as well as internationally. My experience includes over 75 NRDA-related cases, spanning from the Exxon Valdez release up through the Deepwater Horizon incident. I was the lead author of a book chapter (published in March of 2013 by the Oxford Press) that provided an overview of the NRDA regulations and the development of HEA and resource equivalency analysis (REA) approaches (Nicolette et al. 2013b). The book was entitled "The E.U. Liability Directive: A Commentary". I led Chapter 9 that was entitled "Experience with Restoration of Environmental Damage". I am a Senior Principal and Ecosystem Services Practice Director with Environmental Planning Specialists, Inc. (EPS), in Atlanta, Georgia. I have served on the Steering and/or Planning Committee of the Community of Ecosystem Services sponsored by the United States Geological Survey since 2008. I bill $240 per hour through EPS for my time. DCN: HWIDENC001 5 June 30, 2016 EPS 2 NEBA AND APPLICATION TO SITE REMEDIATION NEBA has been identified by USEPA as an important decision -making tool in the Superfund program (USEPA SAB 2009). In addition, the USEPA Superfund program supports the adoption of `green site assessment and remediation', which is defined as the practice of considering all environmental impacts of studies, selection and implementation of a given remedy, and incorporating strategies to maximize the net environmental benefit of cleanup actions. USEPA has committed to prioritizing and emphasizing green remediation approaches and maximizing the net environmental benefit of Superfund cleanups (USEPA 2013). As examples, USEPA and other federal agencies used NEBA to guide remedial decision -making, including recent remediation work on the Kalamazoo River (USEPA 2012a). The NOAA and the United States Coast Guard (USCG) identify the use of NEBA to evaluate opportunities for no net loss of resources and habitats during oil spill response planning (Aurand et al. 2000; NOAA 2011), and have used NEBA to evaluate environmental responses on the Gulf of Mexico (NOAA 2011). The United States Army Environmental Center (USAEC) also identifies NEBA as an important tool for assessing natural resource injury (USAEC 2005). 2.1 What is NEBA? In a site remediation context, a NEBA is a framework for comparing site remedial alternatives that includes the incorporation of a broad array of human, social, economic and environmental metrics to demonstrate differences among the various remedial alternatives with regard to human and ecological chemical and physical risk profiles; community and socioeconomic benefits and costs; ecological habitat value; human recreational value effects; GHG emissions (i.e., carbon footprinting); and remedial costs. The formalized framework for NEBA, as recognized by the USEPA Science Advisory Board (USEPA SAB 2009), is provided in Efroymson et al. 2003 and 2004. A NEBA framework can be used to demonstrate the net positive or negative changes in ecosystem service (ecological, social, and economic) values between various remedial alternatives. A NEBA framework also is useful for assessing and comparing the sustainability of different remedial alternatives through the quantification of impacts over time associated with the implementation and long-term performance of each alternative (Efroymson et al. 2004; Nicolette et al. 2013). NEBA has been shown to be an objective approach for evaluating the risks, benefits, and tradeoffs associated with different alternatives for remediation and environmental restoration work (Efroymson et al. 2003, 2004; USEPA 2009a; NOAA 2011). The NEBA approach is consistent with USEPA risk management objectives (USEPA 2009b) and provides a framework to help USEPA comply with its policy, guidance, and direction, particularly with large scale remedial DCN: HWIDENCO01 6 June 30, 2016 0 programs where the benefits and impacts of potential alternatives are significant and need to be evaluated closely. A NEBA framework incorporates quantified analyses that consider changes in various metrics that influence ecosystem service values which, in turn, help to highlight differences between remedial alternatives. NEBA is a systematic process for quantifying and comparing the benefits and costs among competing alternatives; it does not rely solely on monetization but rather also includes non - monetary environmental metrics. A NEBA framework for comparing each remedial alternative should include, among others, the incorporation of a broad array of metrics to demonstrate associated changes in human and ecological chemical and physical risk profiles; community and socioeconomic issues; ecological habitat value; human recreational value effects; GHG emissions (i.e., carbon footprinting); and remedial costs. NEBA is similar to cost -benefit analysis (CBA), which USEPA and other federal agencies have used for decades to support decision -making. NEBA and CBA both consider time -accumulated service flows (i.e., benefits and costs over time); since these benefits and costs occur over varying time frames, they can be normalized to their net present value using a discount rate. The NEBA approach, however, moves beyond the traditional CBA approach by demonstrating environmental stewardship and sustainability through the quantification of environmental metrics. A NEBA typically involves the comparison of several management alternatives that may include: (1) leaving the site condition "as is"; (2) physically, chemically or biologically treating the site condition; (3) improving ecological value through restoration alternatives that do not directly focus on removal of site materials; or (4) a combination of those alternatives (Efroymson et al. 2004). Understanding these benefits, and how they may change among remedial actions, maximizes benefits to the environment and the public while managing costs and site risks. As stated in Efroymson et al., 2004, "This framework for NEBA should be useful when the balance of risks and benefits from remediation of a site is ambiguous. This ambiguity arises when the contaminated site retains significant ecological value, when the remedial actions are themselves environmentally damaging, when the ecological risks from the contaminants are relatively small, uncertain, or limited to a component of the ecosystem, and when remediation or restoration might fail." NEBA typically considers a broader range of environmental effects than the traditional human health and ecological risk assessment processes that drive remedial action decisions. Typically, these processes consider only the remedial alternatives' ability to limit exposure and human risks from a chemical condition. The effects on other ecosystem services (e.g., human use and ecological benefits) associated with implementation of a remedial alternative are typically not considered in the traditional remedy selection process. A NEBA shows formally the positive and negative effects on ecosystem services and/or surrogate metrics associated with a remedial action in relation to the incremental changes in risk. By considering the effects of a given remedial alternative on all services provided by the site, the net effects on all service flows are considered, including any potential loss of services. In some cases, for example, a remedial alternative may destroy or significantly degrade the ecological landscape and achieve little or no reduction in a A NEBA can also be referred to as a net ecosystem service analysis (MESA) (Nicolette et al. 2013). DCN: HWIDENCO01 7 June 30, 2016 0 ecological or human health risk. In simpler terms, NEBA can be used to determine, for each remedial alternative, whether "the cure is worse than the disease." 2.1.1 Why is a NEBA Analysis Necessary for Developing Appropriate Remedial Actions for the Allen, Buck, Cliffside, and Mayo Coal Ash Basins? 1. The effects of the Removal or CIP remedial alternatives on ecosystem services have not been fully quantified. As such, this can lead to remedial alternatives that can either: — cause more ecological injury through the destruction of habitat than the ecological injury projected by the risk assessment (e.g., "the cure is worse than the disease"), or; — provide a marginal benefit or no net increase in ecosystem service value for the effort expended; this is especially true when ecological risks from the contaminants are relatively small, uncertain, or limited to a component of the ecosystem. It is important to understand that within the regulatory cleanup framework (e.g., North Carolina), the selection of remedial alternatives to address the site condition is typically based upon the potential for the chemical to pose a human health and/or ecological risk (or in some circumstances, the remedial action is considered necessary simply to address impaired groundwater regardless of risk) and the cost of the alternative. Within this framework, the net effect that a remedial action may have on the ecosystem, positive or negative, is rarely formally quantified when considering among remedial alternatives. 2. NEBA Will Improve the Validity of the Remedial Evaluation Decision -Making Process to Stakeholders Intervenors have not fully considered the environmental impacts, health risks and socio- economic consequences of both the short-term and the long-term impacts of the remedial alternatives. Each certain or possible impact should have been included in a formal analysis using a NEBA framework. A NEBA is needed to provide a scientifically -sound basis for remedial decision -making. Specifically, and per USEPA guidance, the NEBA provides transparency and consistency in the evaluation and decision -making process so that stakeholders and the public are aware of the full extent of potential social, health, and environmental impacts of the proposed remedy. The Intervenors provide insufficient information to demonstrate that they adequately and transparently considered important factors with environmental, social and economic implications consistent with policy, guidance and direction. Specifically, they did not demonstrate (1) a preference for green remediation or a sustainable solution; (2) the environmental footprints of the various remedial alternatives; (3) the environmental and social risks associated with the various remedial alternatives; and (4) the consideration of net environmental, economic and social benefits. The court should not, therefore, adopt Intervenors proposed remedy. DCN: HWIDENCO01 8 June 30, 2016 0 3. NEBA Support in Decision -Making Mandated by USEPA NEBA has been conducted by several federal agencies, including USEPA, because it provides an approach for balancing the risks, benefits and tradeoffs associated with competing remedial alternatives in a transparent manner by which all stakeholders can understand the basis for a decision. The merits of using a NEBA framework (based on formally quantified metrics) for transparent decision -making associated with site remediation, compensatory restoration and ecosystem service tradeoffs have been discussed by various authors (Efroymson et al. 2003; 2004; Colombo et al. 2011; Nicolette, et al 2013). The need for transparency in decision -making is made apparent in the 1995 USEPA Risk Characterization Program memorandum by Carol Browner, former USEPA Administrator (USEPA 1995). In the Browner memorandum, the core values that the USEPA is striving to achieve are transparency, clarity, consistency and reasonableness. These same core values have been repeated often in USEPA guidance from the 1990s to the present. Excerpts from the Memorandum Written by Former USEPA Administrator, Carol Browner (USEPA, 1995) "First, we must adopt as values transparency in our decision -making process and clarity in communication with each other and the public regarding environmental risk and the uncertainties associated with our assessments of environmental risk. " "Second, because transparency in decision -making and clarity in communication will likely lead to more outside questioning of our assumptions and science policies, we must be more vigilant about ensuring that our core assumptions and science policies are consistent and comparable across programs, well grounded in science, and that they fall within a "zone of reasonableness. " While I believe that the American public expects us to err on the side of protection in the face of scientific uncertainty, I do not want our assessments to be unrealistically conservative. We cannot lead the fight for environmental protection into the next century unless we use common sense in all we do. " As an example, NEBA was used by the USEPA at the Woodlands Superfund site located in New Jersey where it was applied to assess the ecological impacts of the preferred remedial groundwater action. The NEBA demonstrated that the preferred remedy (pump and treat) would cause injury to the ecosystem while a lower cost alternative (air sparge/vapor extraction) would not cause injury and would save $87 million in remedial costs. The Record of Decision was subsequently changed to the air sparge/vapor extraction alternative supported by the NEBA. In this case, the downgradient portion of the plumes at both sites was allowed to naturally attenuate. The USEPA deemed the application of NEBA as successful in an agency -published report describing the remedial decision at the Woodlands Superfund site (Exhibit 1, USEPA 2001). DCN: HWIDENCO01 9 June 30, 2016 0 Exhibit 1. Example USEPA groundwater Record of Decision change based on a NEBA. Region 1690 " :.. (hound Rater state SuppOned the Fed- 150 hour S change The Site is Conn. a $0 Woodland%, NJ located in a [Alfa] area 1 99 (ROD -A) 7 1:99 and the local populaston Est'd Sa}ings = Route ?_' supports tau MnCdy S 976 M Rowe i32 change. Type of Change: From - ground water pump and treat: To - au spargingisod vapor extraction and natural attenuation - Factual Basis-. BTAG memo indicated that the ground water pump and treat system would dewater the nearby wellands. In addltwn. durmg remedial &-sign. the PRP successfully identified alteruatices that would meet ROD objectives ai much lower cost 4. A systematic, transparent, and scientifically -based approach for a fully informed remedial decision -making process is needed. A NEBA is a comparative analysis that incorporates ecosystem services values and provides stakeholders a basis to balance the risks, benefits and trade-offs associated with competing alternative management alternatives. Incorporation of ecosystem service valuation concepts with formally quantified values within an alternatives decision -making process provides decision makers with an opportunity to make informed choices about the net benefits of actions that affect the environment. By "informed," I mean an approach that is: — systematic, — transparent and understandable to stakeholders, — non -arbitrary, — scientifically -based and defendable, — quantitative in nature where possible, — based on internationally recognized concepts and approaches, and — considerate of all stakeholder concerns thus providing a holistic perspective to the decision -making process. NEBA incorporates the use of ecosystem service valuation concepts and methods that have been litigation -tested and upheld in federal court (e.g., HEA, REA) to evaluate changes in habitat value over time. In addition, a variety of human use economics -based models can be incorporated into a NEBA to evaluate changes in human use value (recreational, commercial, aesthetic, educational, scientific, etc.) over time. A NEBA can also incorporate implementation risks, chemical contamination risks, and a variety of proxy metrics (e.g. GHGs) to help in differentiating between alternatives. 5 The natural resources provided by the earth's ecosystems serve as the building block upon which human well-being flows. Ecosystems represent a complex and dynamic array of animal, plant, and microbe along non -living physical elements interacting as a functioning unit. This gives rise to many benefits, known as ecosystem services, which are the benefits people obtain from naturally functioning ecosystems. DCN: HWIDENC001 10 June 30, 2016 0 5. It is the public policy of the State of North Carolina to carefully consider the environmental impacts associated with each remedial alternative as part of the decision -making process for selection of a preferred remedy. This is consistent with the direction and policy of the State of North Carolina Environmental Policy Act (SEPA) and Federal USEPA policy, guidance, and direction for remedial decision -making. Determining the appropriate remedy for this case requires a consistent, detailed and transparent assessment of the environmental, social and economic impacts associated with remedial alternatives specified for each site. The following examples from the USEPA and the State of North Carolina illustrate the importance of providing consistent, adequate, clear, and transparent information. Example 1. Risk managers must assess and balance risks between site contaminants and proposed remedies (USEPA 1997). EPA Superfund Ecological Risk Assessment (ERA) Guidance (Step 8) specifically states that: "The risk manager must balance (1) residual risks posed by site contaminants before and after implementation of the selected remedy with (2) the potential impacts of the selected remedy on the environment independent of contaminant effects. " "In instances where substantial ecological impact will result from the remedy (e.g., dredging a wetland), the risk manager will need to consider ways to mitigate the impact of the remedy and compare mitigated impacts to the threats posed by the site contamination. " Example 2. This balance should be incorporated into the decision -making process for contaminated sites (USEPA 2005). "Project managers are encouraged to use the concept of comparing net risk reduction between alternatives as part of their decision -making process for contaminated sediment sites, within the overall framework of the NCP remedy selection criteria. Consideration should be given not only to risk reduction associated with reduced human and ecological exposure to contaminants, but also to risks introduced by implementing the alternatives [..] Evaluation of both implementation risk and residual risk are existing important parts of the NCP remedy selection process. By evaluating these two concepts in tandem, additional information may be gained to help in the remedy selection process. " DCN: HWIDENC001 11 June 30, 2016 Example 3. Feasibility Studies should include a comparison of environmental footprints (USEPA 2008). "Green remediation focuses on maximizing the net environmental benefit of cleanup, while preserving remedy effectiveness as part of the Agency's primary mission to protect human health and the environment [..] Key opportunities for integrating core elements ofgreen remediation can be found when designing and implementing cleanup measures. Regulatory criteria and standards serve as a foundation for building green practices. Key elements include reducing atmospheric release of toxic or priority pollutants, and reducing emissions of greenhouse gases that contribute to climate change." "In accordance with green remediation strategies, feasibility studies could include comparison of the environmental footprint expected from each cleanup alternative, including GHG emissions, carbon sequestration capability, and water drawdown (lowering of the water table or surface water levels). " Example 4: Green remediation technologies should be considered for response actions (USEPA, 2011 a; 2011 b). The USEPA Superfund program supports the adoption of "green site assessment and remediation", which is defined as the practice of considering all environmental impacts of studies, selection and implementation of a given remedy, and incorporating strategies to maximize the net environmental benefit of cleanup actions. USEPA has established a "Clean & Green" policy to enhance the environmental benefits of Superfund cleanups by promoting technologies and practices that are sustainable. The policy applies to all Superfund cleanups. Under this policy, certain green remediation technologies will serve as touchstones for response actions. Example 5: USEPA has identified a framework to quantify various metrics to help understand and reduce a project's environmental footprint (USEPA 2012b). "Green remediation strategies can include a detailed analysis in which components of a remedy are closely examined and large contributions to the footprint are identified. More effective steps can then be taken to reduce the footprint while meeting regulatory requirements driving the cleanup [...J. The term "environmental footprint" as referenced in the methodology comprehensively includes metrics such as energy use and water use as well as air emissions to fully represent the effects a cleanup project may have on the environment. " "The methodology is a general framework to help site teams understand the remedy components with the greatest influence on the project's environmental footprint. Quantifying the metrics can serve as an initial step in reducing the remedy footprint. DCN: HWIDENCO01 12 June 30, 2016 0 The overall process allows those involved in the remedial process to analyze a remedy from another perspective and potentially yields viable and effective improvements that may not have been identified otherwise. " Example 6: The State of North Carolina has declared, in their SEPA (§ 113A-3), that it shall be the continuing policy of the State of North Carolina to conserve and protect its natural resources (USEPA 2012). "The State of North Carolina has declared, in their State environmental policy (§ 113A-3), that it shall be the continuingpolicy of the State ofNorth Carolina to conserve and protect its natural resources and to create and maintain conditions under which man and nature can exist in productive harmony. Further, it shall be the policy of the State to seek, for all of its citizens, safe, healthful, productive and aesthetically pleasing surroundings; to attain the widest range of beneficial uses of the environment without degradation, risk to health or safety; and to preserve the important historic and cultural elements of our common inheritance. (1971, c. 1203, s. 3)." Consistent with this declaration and USEPA direction, the State of North Carolina Waste Management Division integrates their programs with federal cleanup programs. Example 7. Guidelines for establishing remediation goals at Resource Conservation and Recovery Act (RCRA) Hazardous Waste Sites (NCDEQ 2013) The Hazardous Waste Sites (HWS) goal is that RCRA facilities remediate all releases of hazardous waste or hazardous constituents to unrestricted use levels. For groundwater, the unrestricted use level is the North Carolina Division of Water Quality, 15A NCAC 2L groundwater standard (2L) or site -specific background concentration. For soil, the unrestricted use level is either the site -specific background concentration or the lowest of a soil screening level protective of groundwater and the health -based residential PSRG. Unrestricted use levels are the starting points for the HWS preliminary screening process. The HWS does recognize that, in some cases, it may be infeasible to remediate to unrestricted use levels. Example 8. Planning and Promoting Ecological Land Reuse of Remediated Sites (ITRC, 2006, State of North Carolina is a Member) "The Interstate Technology and Regulatory Council (ITRC) Ecological Land Reuse Team has developed this guidance document to promote ecological land reuse as an integrated part of site remediation strategies and as an alternative to conventional property development or redevelopment. This reuse may be achieved through a design that considers natural or green technologies or through more traditional cleanup remedies. The decision process presented here helps stakeholders to integrate future DCN: HWIDENCO01 13 June 30, 2016 0 land use and stakeholder input into an ecological land end -use -based remediation project. " "Ecological benefits have not traditionally been designed into, nor credited to, the value of the reusable land until successful remediation was completed. Now, natural and green technologies can improve the ecology of the site as long as they support the intent of the land's use and do not jeopardize the elimination or reduction of the human or environmental risk. Consideration of ecological benefits, as well as the end use of an environmentally impacted site, is an integral component of the remediation process. 2.1.2 Summary A NEBA should be conducted by the Court to determine the remedy for this case, since remedial decision -making policy and guidance obligates the regulatory authorities to evaluate the environmental and community impacts associated with potential remedial actions and dictates that the remedies should be sustainable and demonstrate a net environmental benefit to the public. USEPA has made "greener" cleanups part of its cleanup program mission, which the Agency has stated strives to reduce adverse impacts on the environment, use natural resources and energy efficiently, minimize or eliminate pollution at its source, use renewable energy and recycled materials whenever possible, and reduce waste to the greatest extent possible. According to USEPA, the practice of "green remediation" involves strategies that consider all environmental effects associated with remedy implementation at contaminated sites, and the selection of alternatives that maximize the net environmental benefit of cleanup actions (USEPA 2008). The use of a NEBA evaluation ensures that on a site -specific basis, decision -makers consider, at the remedy selection stage, not only the benefits of a remedial approach, but also the residual risks associated with the approach and the risks associated with implementing the remedial approach. This differs from the traditional approach of either considering implementation risks at the remedy implementation stage or assuming that remedial approaches will be 100% effective on implementation, thereby bypassing any consideration of residual risk. NEBA is consistent with the National Oil and Hazardous Substances Pollution Contingency Plan's (NCP) 9 criteria (40 CFR §300.430(e)(9)(iii)), which require evaluation and balancing of short-term and long-term risks and benefits, including residual risk. Failure to take a holistic view of all of the environmental and community impacts and risks associated with alternative implementation during the remedial evaluation stage can skew remedy selection and result in a remedy that is less protective of the environment and the community. The NEBA was conducted to examine key parameters associated with remedial alternatives proposed at the Allen, Buck, Cliffside, and Mayo sites. This analysis follows; however, prior to discussing the NEBA analysis, I provide a brief discussion on the difference between "perceived" risk, "real" risk, and injury. These distinctions are important when determining appropriate remedial actions. DCN: HWIDENC001 14 June 30, 2016 0 2.1.3 Understanding Risks and Injury It is important to understand the distinction between risk and injury and the ramifications that this distinction has on remedial alternative selection. In the 1998 USEPA Guidelines for Ecological Risk Assessment (EPA/630/R-95/002F) (an expansion and replacement for the 1992 ecological risk assessment guidelines), risk assessment is defined as "...a process that evaluates the likelihood that adverse ecological effects may occur or are occurring as a result of exposure to one or more stressors" (USEPA 1998). "Risks" result from the existence of a hazard and uncertainty about its expression. Uncertainty is defined as "Imperfect knowledge concerning the present or future state of the system under consideration; a component of risk resulting from imperfect knowledge of the degree of hazard or of its spatial and temporal pattern of expression". Since a "risk" evaluation typically looks at the "likelihood" of an adverse effect, it thus includes an implied level of uncertainty in an effect. Thus, simply put, a "risk" represents the "potential" that adverse effects may occur (with some level of uncertainty), not a definitive measure of observable effects. In many cases, uncertainty in risk assessment is handled through the use of layers of conservative assumptions (e.g., use of maximum concentrations, extended exposure periods, unrealistic exposure assumptions) that cumulatively may predict a risk when, in fact, no injury is occurring (a "perceived" risk). As such, there may be no adverse effects, and no "real" risks in an area where contaminant concentrations are above a criterion and remediation is being required. A key function in the strategic site assessment is to be able to understand and differentiate between what would be considered a "perceived" risk versus a "real" risk. The meaning of observable effect to a natural resource is very different than a "risk" to a natural resource. This distinction is important when it comes to understanding those effects that have been documented (quantified/measured) through actual field studies, versus those effects "that may have" or "potentially have" occurred, or those effects that "may" or "potentially" be occurring now and/or into the future. The differentiation between risk and observable effects (i.e., injury) has been made prominent based upon the NRDA regulations in CERCLA (43 CFR 11), where the public is to be compensated for natural resource injury that has occurred as a result of a release. Under NRDA, the lost natural resource services (injury) are quantified so that an appropriately scaled restoration program can be developed ("service -to -service" equivalency approach). In this approach, injury must be measured (with some level of certainty) and used to develop the scale of the restoration program. Thus, there is some certainty that there is indeed injury and therefore, that restoration or remediation is required and adequate. DCN: HWIDENCO01 15 June 30, 2016 3 NEBA EVALUATION METHODS AND ANALYSIS The NEBA evaluation for the Allen, Buck, Cliffside, and Mayo sites compared the Removal, CIP and MNA alternatives as to how they would affect, positively or negatively, ecosystem service values (human use and ecological) and associated metrics for each site; and therefore provide an improved understanding of the environmental footprint and community risks associated with the alternatives. In order to conduct the NEBA analysis, several key factors were identified and considered to help establish an understanding of the environmental footprint that would be associated with the various alternatives. These included: 1. chemical risks associated with contaminant exposures (ecological and human); 2. health and safety and implementation risks (mortality, injuries, illness); 3. physical impacts on the habitats and related ecological services associated with the actions; 4. GHG's and other priority air pollutants; 5. community Impacts (e.g., increased traffic, accidents); 6. human use value (groundwater, recreation, hunting, etc.); and 7. costs. These factors resulted in specific parameters that were considered to help assess predicted changes over time, given implementation of the remedial alternatives. For the analysis, I considered the following parameters: • human health - public off -site contaminant exposure (wader, boater, swimmer); • human health - on -site contaminant exposure (commercial industrial worker, construction worker, trespasser); • human health - fish consumption (recreational, subsistence fishermen); • human health - drinking water (i.e., from groundwater consumption); • implementation risks - truck traffic related accidents/mortality; • implementation risks - on -site worker safety; • ecological habitat services (terrestrial, aquatic, avian); • Groundwater services; • GHGs; • pollutant emissions (e.g., NOx, particulate matter) - truck traffic, on -site construction (yellow iron); • human recreational uses; and • capital costs including long-term operations and maintenance (O&M). DCN: HWIDENCO01 16 June 30, 2016 0 Although the above parameters were considered in the analysis, not all were able to be quantified given the lack of available information. Therefore, the NEBA presented herein is not fully comprehensive in the scope of the supporting quantitative evaluations — the most pertinent and impactful elements are quantified. Other elements, if considered, would push the NEBA further in the disparity amongst the remedial alternatives considered. Those parameters not quantified were considered from a qualitative standpoint in support of evaluating the environmental footprint and community risks associated with the remedial alternatives. The analysis is sufficiently robust, however, to require rejection of the Intervenors' proposed remedy. 3.1 Alternative Construction Analysis As part of the evaluation, it was necessary to evaluate the construction activities, equipment necessary, etc. that would be associated with the proposed remedial alternatives in order to understand those factors that would affect the environmental footprint and associated risks associated with the proposed remedial alternatives. A construction and cost analysis for the MNA, CIP and Removal alternatives was conducted for the Allen Steam Station, Buck Steam Station, Cliffside Steam Station and Mayo Steam Station. The three remedial alternatives evaluated were based on the remedial assessment completed by HDR Engineering, Inc. and SynTerra Corporation as detailed in the Corrective Action Plan, Part 2 for each respective steam station (HDR, 2016a, 2016b, 2016c; Synterra, 2016). The quantity of ash and land coverage of existing ash basins for each steam station were provided by Duke Energy. The construction analysis was subdivided into three categories: land disturbance, project duration, and cost. The detailed analysis of construction implementation details for the three alternatives, for each of the four sites considered, is provided in Appendix A. This analysis developed the following information in support of the various components of the quantitative evaluation: • Types of equipment used and duration [air emissions]; • Land disturbance footprint; • Road miles traveled [air emissions; safety risks]; • Dimensions of work zones [safety risks]; and • Costs. The quantification aspects of the NEBA were conducted within the following analyses: air emissions, safety risks, costs, human and ecological health, and ecological habitat alterations. Each of these analyses are discussed, in turn, in the sections below. 3.2 Air Emissions Air emissions can be divided into two separate categories, criteria pollutants and (GHGs). The criteria pollutants, which were established in the Clean Air Act of 1970, consist of carbon monoxide (CO), nitrogen oxides (NOX), ozone, sulfur oxides (SOX), particulate matter (PM), and lead. These pollutants were determined to cause adverse health and environmental effects when DCN: HWIDENC001 17 June 30, 2016 0 present above specific concentrations in the atmosphere. For the purposes of this evaluation, NOx and PM were evaluated based on their expected emissions from the activities involved in the remedial activities. Based on previous experience with air emissions from remedial activities, the impacts from the other criteria pollutants were expected to be minimal and so were not included in this evaluation. Greenhouse gases, which consist primarily of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and fluorinated gases, were also determined by USEPA to have adverse health and environmental effects due to their contribution to climate change. The quantitative evaluation of each of these types of air emissions for the three remedial alternatives, specific to each of the sites considered is provided in Appendix B. 3.3 Safety Risks The risk of life to workers and public due to construction activities for each alternative was quantified. National statistics were used to evaluate the fatality and non -fatality (injury/illness) risks, as well as property damage incidents associated with truck traffic and onsite implementation for the four sites considered. Results are presented in Appendix C. 3.4 Cost Costs for the three remedial alternatives, for each of the four site considered, are provided in Appendix A. 3.5 Human Health Potential impacts to humans in contact with chemicals in environmental media were estimated for 30 years after implementation of the three remedial alternatives. A current condition (i.e., year 2015-2016) risk assessment was conducted for each site. These risk assessments formed the basis of estimating the risks in the future for the three different remedial alternatives. It is important to note that the purpose of this risk analysis is to compare the three different remedial alternatives; the analysis does not necessarily represent the true risk remaining after implementation of a particular remedial action alternative given numerous assumptions and data extrapolations were necessary in order to conduct this "forward projection" analysis. The receptors evaluated included a commercial/industrial worker, construction worker, trespasser, boater, swimmer, and wader. There were two considerations in extrapolating the current risk assessment to the future condition: the first was to forward project into the future (30 years from implementation/completion of the remedial action); the second was to project (estimate) the changes to various environmental media due to a given remedial action. The detailed evaluation of human health risks projected under each remedial action alternative, for each of the sites considered, is provided in Appendix D. b There were insufficient data to evaluate subsistence and recreational fishermen. DCN: HWIDENC001 18 June 30, 2016 0 3.6 Ecological (Habitat Alteration) The purpose of this evaluation was to understand how the Removal and CIP alternatives might affect ecological habitat values associated with the Allen, Buck, Cliffside, and Mayo Steam Station sites. A geographic information system (GIS) analysis was conducted in order to quantify the dimensions of the major land cover types presently existing in those areas of each site (e.g., basins) that would be affected by implementation of either the CIP or Removal alternatives. Those basins, along with selected site specific photographs for each site, are presented for the Allen (Figures 1- 5), Buck (Figures 6-10), Cliffside (Figures 11-15), and Mayo (Figures 16-20) sites, respectively. Through GIS polygon analysis, the affected aerial dimensions of each land cover type that would undergo alternation for the cap -in -place and comprehensive removal alternatives were determined for each site. The primary method by which changes in ecological services (i.e., habitat quality) associated with the various alternatives was evaluated was through the use of the HEA methodology. Actions potentially affecting ecological services include both physical implementation impacts as well as impacts associated with chemical releases and/or residuals. This methodology requires the evaluation of available data to select and identify appropriate metric(s) that will represent overall habitat service flows from the environment. My evaluation included the direct habitat impacts as well as indirect impacts (i.e., influences on adjacent areas, etc.). Because many ecological habitat services are not traded in the marketplace, they do not have a direct monetary value. The HEA approach is a service quantification approach that evaluates ecological habitat service losses and gains based on non -monetary metrics over time. It is important to recognize that ecosystem services are not static measurements but represent a flow of benefits over time. The HEA approach uses an environmental metric to measure changes to ecological habitat services and focuses on quantifying the area (e.g., acres) and level of impact over time in units typically represented as service -acre -years (SAY's). We used the HEA methodology to quantify the relative impacts or benefits, independent from each other, associated with the remedial alternatives. The evaluation of ecological habitat impacts projected under each remedial action alternative, for each of the sites considered, is provided in Appendix E. DCN: HWIDENCO01 19 June 30, 2016 Figure 1 EPS Site Reconnaissance June 2016 Allen Steam Station Belmont, NC Figure Narrative Shows vantage point locations for photographs taken during EPS site reconnaissance on June 7, 2016. Notes Legend Primary Ash Basins Name Active Basin QInactive Basin Ash Storage Units Basin Name Ash Storage Retired Ash Basin Ash La — Structural Fill 1 Structural Fill 2 Site Visit Photo Locations N A 500 1,000 Feet Environmental Planning Specialists, Inc. EPS-11 Figure 2. Wooded forest habitat growing on top of old ash basin at Allen Steam Station (Photo 1 location on Figure 1). Figure 3. Vegetation growth, surface water, and wetland habitat in the active basin at Allen Steam Station (Photo 2 location on Figure 1) DCN: HWIDENCO01 21 June 30, 2016 EPS Figure 4. Permitted outfall to the Catawba River (Lake Wylie) at Allen Steam Station (Photo 3 location on Figure 1). Figure 5. Early growth vegetation in the active basin at the Allen Steam Station (Photo 4 location on Figure 1). DCN: HWIDENCO01 22 June 30, 2016 Figure 6 �--� EPS Site Reconnaissance June 2016 Buck Steam Station ,. Salisbury, NC Figure Narrative _ Shows vantage point locations for photographs taken during _- EPS site reconnaissance on June 8, 2016. Notes: Game lands are present around the site. These lands provide hunting, trapping and inland fishing opportunities for Note Heron the public. the Rookery Eagle Nesting Site. and ''%� Heron Rookery Site q J Legend s ��. Basins Cell 1 (Active) Cell 2 (Old Primary Cell) i " °�1 Eagle Nesting Site Cell 3 (Secondary Primary) f ( O Ash Fill Area (storage) 1 Game Land -W, Site Visit Photo Locations y pr 4 r N o rce. Esri, Digit D • be, G� eoEye, i-cubed, Earn soar Geographies, _ - CNES/Airbus DS, USDA, USG,, AM Get tipping, Aerogrid, ION, MR 0 500 1,000 - swisstopo, and the GIS User Community Feet Environmental Planning Specialists, Inc. 0 Figure 7. Wetland habitat and vegetation in active Cell 1 at Buck Steam Station (Photo 1 location on Figure 6). Figure 8. Wetland, surface water, and forest habitat over the Old Primary Ash Basin (Cell 2) at Buck Steam Station (Photo 2 location on Figure 6). DCN: HWIDENC001 24 June 30, 2016 EPS Figure 9. Wooded forest growth over the in the Old Primary Ash Basin (Cell 2) at Buck Steam Station (Photo 3 location on Figure 6). Figure 10. Heron Rookery located adjacent to the surface water at the Old Primary Ash Basin (Cell 2) at Buck Steam Station with active nests (Photo 4 location on Figure 6). DCN: HWIDENC001 25 June 30, 2016 Figure 11 EPS Site Reconnaissance June 2016 Cliffside Steam Station Cliffside, NC Figure Narrative Shows vantage point locations for photographs taken during EPS site reconnaissance on June 6, 2016. Notes: Figure 14 was taken near the location that Figure 12 was taken. Legend Basins Active Ash Basin Unit 5 Inactive Basin Units 1-4 Inactive Basin Ash Storage Area 1 Site Visit Photo Locatio N A 500 1,000 Feet Environmental Planning Specialists, Inc. LP Figure 12. The Broad River at Cliffside Steam Station (Near Photo 1 location on Figure 11). Figure 13. Vegetation growing on the active ash basin (hill in background) at Cliffside Steam Station (Photo 2 location on Figure 11). DCN: HWIDENCO01 27 June 30, 2016 Figure 14. Vegetation growth and wetlands/surface water habitat in the active ash basin at Cliffside Steam Station (Photo 3 location on Figure 11). Figure 15. The Broad River near the permitted outfall at the Cliffside Steam Station. (Photo 1 location on Figure 11). DCN: HWIDENCO01 28 June 30, 2016 4::v •: �� ♦.� cFL� Figure 16 EPS Site Reconnaissance June 2016 Mayo Steam Station Y ; Roxboro, NC Figure Narrative -'' Shows vantage point locations 3 - for photographs taken during EPS site reconnaissance on June 9, 2016. Notes: Note extensive - gamelands adjacent to the ash ponds. 2 r = Legend Active Ash Basin Active Basin Game Land Streams si✓ Site Visit Photo Locations N '+ t•1� DigitralGl be, GeoEye, i-cubed, Earthstar Geogr phics, CNES/Airbus D-, SDA, U 'EX, Getmapping, Aerognd, IGN, IGF, 0 500 1,000 swisstopo, and the GIS User ommunity Feet Environmental Planning Specialists, Inc. 0 Figure 17. Clam shells and healthy aquatic vegetation in the outlet of the active basin at Mayo Steam Station (Photo 1 location on Figure 16). Figure 18. Waterfowl on the surface water of the active basin at Mayo Steam Station (Photo 2 location on Figure 16). DCN: HWIDENCO01 30 June 30, 2016 EPS Figure 19. Wetland habitat in the active basin at Mayo Steam Station (Photo 3 location on Figure 16). Figure 20. Crutchfield Branch (Photo 4 location on Figure 16). DCN: HWIDENCO01 31 June 30, 2016 0 4 NEBA SUMMARY RESULTS The analyses developed as presented in Appendices A-E provide quantification of a variety of metrics that can be used to ascertain the relative environmental footprint that one remedial alternative may have in comparison to another. In addition, we can evaluate how risks change through the implementation of the alternatives. In pulling together the information developed to date, I have developed several opinions as to the risks and benefits that would be associated with implementation of the alternatives. Prior to presenting my opinions, I provide several data summaries of the information developed in support of my opinions. 4.1 Risks Driving Remedial Alternative Selection Developing an appropriate remedy is based on a solution that manages site risks while maximizing benefits to the public and minimizing cost. For the Allen, Buck, Cliffside, and Mayo sites, risks that the final remedy should look to manage include potential human health and ecological risks. The remedy should effect a meaningful change in reducing the human health and/or ecological risk that is the basis for the remedy. Without a meaningful change in a risk parameter, the remedy should look to minimize environmental harm and reduce cost. For the Allen, Buck, Cliffside, and Mayo sites, the "forward projection" of human health risks modeled across the progression of the more aggressive remedial action alternatives (MNA to CIP to Removal) at each of the four sites, is presented in Figures 21-24. DCN: HWIDENCO01 32 June 30, 2016 EPS Figure 21. Allen Steam Station - Human Health Risks High Risk 1 a, op 0.75 L Y U ✓i � G1 U dl Moderate 0 0.5 M Risk a al 0 0.25 0 a Low Risk NUA 0 Option 1. MNA Option 2. Cap -In -Place with MNA Option 3. Removal with MNA ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk a On -Site worker contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk [I Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk Figure 22. Buck Steam Station - Human Health Risks High Risk 1 W to c 0.75 x u -cs a m > Moderate 0.5 m Risk v d 0 0.25 a � a 0 ¢ ¢ ¢ a a a a ¢ Low Risk �_ r NUA 0 Option 1. MNA Option 2. Cap -In -Place with MNA Option 3. Removal with MNA ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑ On -Site worker contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk o Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk DCN: HWIDENCO01 33 June 30, 2016 Figure23. ClifFside SteamStati+on -Human Health Risks High Risk a Moderate m Risk v 4 Q Low Risk NUA G z Option 1. IVINA High Risk C Q C Option 2. Cap -In -Place with NNA 0 1 Cu rm c 0.75 t t� -o Y u a� 0.5 0 rn c 0 0.25 C fl Q C C 0 Option 3. Removal with MNA ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑ On -Site worker contaminant exposure - no unacceptable risk (NUA), low risk, m od erate risk, high risk o Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk Figure 24. Mayo Steam Station - Human Health Risks of Moderate + Risk ra v it 3 0 ¢ J ¢ ¢ ¢ a a a a Low Risk z z z z z z z z NUA Option 1. MNA Option 2. Cap -In -Place with MNA Option 3. Removal with MNA ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑On -Site worker contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk 1 0.75 u v U v 0.5 0 a m C: 0 0.25 o Q O L d 0 DCN: HWIDENCO01 34 June 30, 2016 0 As will be noted in these figures, there are no unacceptable off -site human health risks at any of the four sites. For the on -site scenarios, nearly all forward projected risks are below the most conservative risk thresholds (i.e., a Hazard Index of unity (1) for non -carcinogens, and an Excess Lifetime Cancer Risk (ELCR) of lE-6). The only identified risk above this lower threshold was for an on -site construction worker at the Buck, Cliffside and Mayo sites where the projected risk was between 2E-6 and 3E-6. For the sake of the NEBA exercise, this level of risk was considered as "low" even though it is within the EPA accepted risk range of lE-4 to lE-6. Therefore, a more reasonable scenario would likely drive the "perceived" risk within a "no unacceptable risk" range ("real" risk range) at the three sites. Given this, it is readily apparent that the implementation of the remedial alternatives of CIP or Removal provide no apparent benefit from a risk management standpoint. Groundwater Service Value It should be recognized that a groundwater aquifer has the potential to provide a variety of service flows (USEPA 1995). The services that potentially can be provided by groundwater are listed in Table 1, including those parameters that can be evaluated to understand effects to groundwater services. As can be seen in Table 1, adverse groundwater service effects appear to be marginal if any. Thus, the question arises, are the environmental and community risks that will arise from remedy implementation and the implementation costs worth incurring to manage what appears to be marginal levels of risk, if any? 4.2 NEBA Assessment Parameter Impacts I next consider how the remedial actions might affect other parameters that are important to human health, ecological resources, and community perspectives. Figures 25 through 45 demonstrate how the assessment parameters will be affected given the MNA, CIP and Removal alternatives. In these figures, the parameter with the greatest adverse impact is set to 1, and the corresponding parameter in the other alternative is presented proportional to the highest value. This allows us to graph multiple parameters with varying scales on the same graph. The higher the bars move upwards from the x axis, the greater the environmental impact. This approach allows for the alternatives to be evaluated and compared given all the assessment parameters. The charts that follow are presented in turn, for each of the following parameters: human health implementation risks, air emissions, ecological services, and costs (Figures 25-41). Each parameter is further presented by site as follows: Allen, Buck, Cliffside, and Mayo. Following these Figures, charts for each site with all assessment parameters displayed are presented in Figures 42-44). Additionally, a cumulative chart is provided that displays the combined effect across all four sites (Figure 45). The overall results are presented in in Table 2. 7 Fish consumption scenarios (recreational and subsistence) were not evaluated in the "forward projection" modeling as there is too little information to estimate whether any "true" risk exists for this scenario (not likely however). DCN: HWIDENCO01 35 June 30, 2016 Table 1. Groundwater Services, Effects, and Parameter Evaluation. FUNCTION: STORAGE OF WATER EFFECTS SERVICE FUNCTION: DISCHARGE TO EFFECTS SERVICE RESERVE (STOCK) EFFECT STREAMS/LAKES/WETLANDS EFFECT Change in Welfare from Increase or Decrease in Drinking Water through Surface Change in Welfare from Increase or Decrease in Drinking Water; Availability of Drinking Water, Change in Human No Water Supplies; Availability of Drinking Water, Change in Human No Health or Health Risks Health or Health Risks Water for Crop Irrigation; Change in Value of Crops or Production Costs, No Water for Crop Irrigation through Change in Value of Crops or Production Costs, No Change in Human Health or Health Risks Surface Water Supplies; Change in Human Health or Health Risks Change in Value of Livestock Products or Water for Livestock through Surface Change in Value of Livestock Products or Production Water for Livestock; Production Costs, Change in Human Health or No Water Supplies; Costs, Change in Human Health or Health Risks No Health Risks Water for Food Product Change in Value of Food Products or Production Water for Food Product Processing Change in Value of Food Products or Production Processing; Costs, Change in Human Health or Health Risks No through Surface Water Supplies; Costs, Change in Human Health or Health Risks No Water for Other Manufacturing Change in Value of Manufactured Goods or Water for Other Manufacturing Change in Value of Manufactured Goods or Processes; Production Costs; No Processes through Surface Water Production Costs; No Supplies; Heated Water for Geothermal Provision of Cooling Water for Power Plants; Change in Cost of Electricity Generation No Power Plants through Surface Water Change in Cost of Electricity Generation No Supplies Change in Cost of Maintaining Public or Private Cooling Water for Other Power Provision of Erosion, Flood, and Property, Change in Human Health or Health Risks Plants; Change in Cost of Electricity Generation No Storm Protection through Personal Injury Protection, Change in No Economic Output Attributable to Use of Surface Water Supplies for Disposing Wastes Change in Human Health or Health Risks Attributable Transport and Treatment of Wastes to Change in Surface Water Quality, Change in Water/Soil Support System Change in Cost of Maintaining Public or Private and Other By -Products of Human Animal Health or Health Risks Attributable to Change Preventing Land Subsidence; Property No Economic Activity through Surface in Surface Water Quality, Change in Economic Output No Water Supplies Attributable to Use of Surface Water Supplies for Disposing Wastes Erosion and Flood Control Change in Cost of Maintaining Public or Private Support of Recreational Swimming, Change in Quantity or Quality Recreational Activities, through Absorption of Surface Property No Boating, Fishing, Hunting, Trapping Change in Human Health or Health Risks No Water Run -Off; and Plant Gathering Medium for Wastes and Other Change in Human Health or Health Risks Support of Commercial Fishing, By -Products of Human Economic Attributable to Change in Ground water Quality No Hunting, Change in Value of Commercial Harvest or Costs No Activity; Trapping, Plant Gathering Change in Human Health or Health Risks Attributable to Change in Water Quality, Change Marginal, Support of On -Site Observation or Clean Water through Support of in Animal Health or Health Risks Attributable to if any, see Study of Fish, Wildlife, and Plants Change in Quantity or Quality of On -Site Observation Living Organisms; Change in Water Quality, Change in Value of Appendix for Leisure, Educational, or Scientific or No Economic Output or Productions Costs D Purposes P Study Activities Attributable to Change in Water Quality Passive or Non -Use Services Marginal, Support of Indirect, Off -Site Fish, Change in Quantity or Quality of Indirect, Off -Site (e.g., Existence or Bequest Change in Personal Utility if any Wildlife, and Plant Uses (e.g. Activities No Motivations). viewing wildlife photos) Change in Human Health or Health Risks Attributable Provision of Clean Air through to Support of Living Organisms Change in Air Quality, Change in Animal Health or No Health Risks Attributable to Change in Air Quality Change in Human Health or Health Risks Attributable to Marginal, Provision of Clean Water through Change in Water Quality, Change in Animal Health or if any, see Support of Living Organisms Health Risks Attributable to Change in Water Quality, Appendix Change in Value of Economic Output or Productions D Costs Attributable to Change in Water Quality Change in Human Health or Health Risks Attributable to Regulation of Climate through Change in Climate, Change in Animal Health or No Support of Plants Health Risks Attributable to Change in Climate, Change in Value of Economic Output or Production Costs Attributable to Change in Climate Provision of Non -Use Services (e.g., Existence Services) Associated with Marginal, Surface Water Body or Wetlands Change in Personal Utility or Satisfaction if any Environments or Ecosystems Supported by Ground water Table 2. Overall NEBA Summary Table. NEBA Considerations Human Health Pathways Ecological Habitat Services Social Costs Groundwater Public off -site On -Site Truck traffic Implementation Implementation GHG emissions/ 1 Truck traffic Terrestrial Aquatic Priority pollutant NEBA Framework (drinking water ( g contaminant contaminant Fish Consumption incidents risk incidents - property risks - project Truck Trips - Public communities communities carbon foot emissions Capital and O&M consumption) exposure2,4 exposure2,3 damage only accident rate Roads printing Environmental Justice Concerns Site Alternative Metric(s) Metric(s) Metric(s) Metric(s) Metric(s) Metric(s) Metric(s) Metric(s) Metric(s) Metric(s) Metric(s) Metric(s) no unacceptable HI/ELCR -no HI/ELCR -no no unacceptable risk, low risk, unacceptable risk, unacceptable risk, risk, low risk, Fatalities (F) and Fatalities (F) and HEA based lost HEA based lost Tons/yr and tons of Tons/yr of NOx, Net present value moderate risk, high low risk, moderate low risk, moderate moderate risk, high injuries/illnesses (1) Incidents injuries/illnesses (1) Trips -Public Roads DSAYs DSAYs CO2e emitted PM10/2.5 emitted in real dollars risk risk, high risk risk, high risk risk NUA NUA Baseline = 1) No Action (MNA Only) NUA Baseline Baseline Baseline 0 Baseline Baseline Baseline Baseline 0.004/-- 0.1/5E-08 $ 7,315,000 NUA NUA $ 154,833,594 Allen 2) Capping with MNA NUA F - 0.1 8 F - 0.07 140,401 2,481 2,813 6,453.24 tons/yr NOx - 29.20 0.002/-- 0.02/1E-08 1 - 2.8 1 - 9.57 33,508.54 tons PM10/2.5 - 1.01 Above Baseline NUA NUA F - 0.68 F - 0.2 23,505.31 tons/yr NOx - 138.43 $ 1,543,608,397 3) Removal with MNA NUA 56 963,704 5,186 1,086 0.001/-- 0.004/1E-10 1 - 19.2 1 - 61 383,819.67 tons PM10/2.5 - 5.91 Above Baseline NUA Low Risk Baseline = 1) No Action (MNA Only) NUA Baseline Baseline Baseline 0 Baseline Baseline Baseline Baseline 0.02/2E-07 0.2/2E-06 $ 7,315,000 NUA NUA $ 85,402,106 2) Capping with MNA NUA F - 0.06 5 F - 0.04 88,108 1,550 452 6,399.53 tons/yr NOx - 29.02 BUCK 0.01/4E-08 0.05/6E-07 1 - 1.76 1 - 6.04 21,118.45 tons PM10/2.5 - 1.01 Above Baseline NUA NUA $ 445,498,459 3) Removal with MNA NUA F - 0.19 16 F - 0.1 265,570 2,264 709 23,519.64 tons/yr NOx - 138.47 0.009/2E-08 0.0006/6E-09 1 - 5.29 1 - 19 110,384.29 tons PM10/2.5 - 5.91 Above Baseline NUA Low Risk Baseline = 1) No Action (MNA Only) NUA Baseline Baseline Baseline 0 Baseline Baseline Baseline Baseline 0.02/6E-07 0.2/2E-06 $ 9,872,500 NUA NUA $ 85,897,149 Cliffside 2) Capping with MNA NUA F - 0.06 5 F - 0.04 82,460 1,303 317 6,521.54 tons/yr NOx - 29.43 0.02/1E-07 0.04/6E-07 1 - 1.65 1 - 5.65 19,564.61 tons PM10/2.5 - 1.02 Above Baseline NUA NUA F - 0.28 F - 0.1 23,504.36 tons/yr NOx - 138.42 $ 648,986,943 3) Removal with MNA NUA 27 394,596 2,624 498 0.02/6E-OS 0.01/4E-09 1 - 7.86 1 - 27 162,560.70 tons PM10/2.5 - 5.91 Above Baseline NUA Low Risk Baseline = 1) No Action (MNA Only) NUA Baseline Baseline Baseline 0 Baseline Baseline Baseline Baseline 0.2/-- 0.3/3E-06 $ 7,315,000 NUA NUA F - 0.05 F - 0.03 6,399.69 tons/yr NOx - 29.02 $ 70,423,111 Mayo 2) Capping with MNA NUA 0.2/-- 0.09/7E-07 1 - 1.33 4 1 - 4.59 66,720 991 557 15,999.23 tons PM10/2.5 - 1.01 Above Baseline NUA NUA F - 0.23 F - 0.1 23,518.04 tons/yr NOx - 138.47 $ 547,889,455 3) Removal with MNA NUA 23 330,083 2,294 875 0.2/-- 0.0002/- I - 6.58 I - 23 136,357.55 tons PM10/2.5 - 5.91 Above Baseline Notes: Current Baseline: Parameters are measured as a change from the current condition for each site. Footnotes: 1 ) No Action (MNA Only): Current Baseline: Parameters are measured as a change from the current condition for each site. 1. Insufficent data to evaluate 2) Capping with MNA: 2. Highest Hazard Index / ELCR 30 years after implementation 3) Removal with MNA: NUA: No Unacceptable Risk: HI < 1; ELCR <1E-06 Abbreviations: Low Risk: 1 < HI < 3; 1E-06 < ELCR < 1E-05 DSAY: discounted service acre year Moderate Risk: 1 < HI < 3; 1E-05 < ELCR < 1E-04 GHG: greenhouse gas High Risk: HI > 3; ELCR > 1E-04 NEBA: Net Environmental Benefit Analysis 3. Construction worker, Commercial/Industrial Worker or Trespasser 0&M: operation and management 4. Swimmer, Boater, Wader Page 1 of 1 4.2.1 Human Heath Implementation Risks Figure 25. Allen Steam Station - Human Implementation Risks Ol N lD O '-I O lD f1 High Risk Y 0) Moderate Z1 Risk o M 0 N or v m N EPS N N U1 W W O N W 0! G! W N N Low Risk z z z Co m m m m z z z z z z NUA Option 1. MNA Option 2. Cap -In -Place with MNA Option 3. Removal with MNA ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑On -Site worker contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk ■ Truck traffic related accidents fatalities ■ Truck traffic related accidents Project Illness/Injury Implementation risks - project fatalities Implementation risks - project illness/injury Truck traffic related accidents - property damage Figure 26. Buck Steam Station - Human Health Implementation Risks m o m � � o Sri o ti .� High Risk Moderate Risk o � o K 3 v m a v v o c c c c c a a 'v 'v 'v "v "v a a a a a a D . 7 Low Risk z z m m m m m z z z z z z NUA Option 1. MNA Option 2. Cap -In -Place with MNA Option 3. Removal with MNA ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑On -Site worker contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk ■ Truck traffic related accidents fatalities ■ Truck traffic related accidents Project Illness/Injury Implementation risks - project fatalities Implementation risks - project illness/injury Truck traffic related accidents - property damage 0 0 1 v u N 0.5 a M C O 0.25 .L O Q O a 0 1 v c co 0.75 u v U u v 0.5 0 d 7E C O 0.25 0 DCN: HWIDENCO01 38 June 30, 2016 Figure 27. Cliffside Steam Station - Human Health Implementation Risks o o O n O N N High Risk Moderate +� Risk ca Cu z o c c c c c ¢ ¢ h h Low Risk z z m m 2 m m NUA Option 1. MINA 0 0 o z z z Option 2. Cap -In -Place with MNA Option 3. Removal with MNA EPS ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑ On -Site worker contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑ Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk ■ Truck traffic related accidents fatalities ■ Truck traffic related accidents Project Illness/Injury Implementation risks - project fatalities Implementation risks - project illness/injury Truck traffic related accidents - property damage Figure 28. Mayo Steam Station - Human Health Implementation Risks m o N lD N m O O D O N N High Risk or Moderate + Risk m N Y a a Low Risk z z m m m m m NUA Option 1. MINA m 0 C � m � a a a z z z Option 2. Cap -In -Place with MNA Option 3. Removal with MNA ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑On -Site worker contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk ■ Truck traffic related accidents fatalities ■ Truck traffic related accidents Project Illness/Injury Implementation risks - project fatalities Implementation risks - project illness/injury Truck traffic related accidents - property damage 1 v U0 C m 0.75 u 0.25 o Q O a` 0 1 v 00 c M 0.75 U v U v 0.5 0- 0 0.25 0 Q O a 0 DCN: HWIDENCO01 39 June 30, 2016 EPS 4.2.2 Air Emissions Figure 29. Allen Steam Station - Air Emissions m ao m High Risk 1 on c t 0.75 U � v U it v Q) Moderate 0.5 +--0- Risk a v � � � O o N 0.25 v v v O M Q a a a v a w a a a ^ a a a O Low Risk z z z Co m Co z z z z z z a NUA 0 Option 1. MNA Option 2. Cap -In -Place with MNA Option 3. Removal with MNA ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑ On -Site worker contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk ■ GHG emissions/ carbon foot -printing - Tons CO2e emitted Emissions from increased truck traffic & yellow iron operations - Ton/yr Nox Emissions from increased truck traffic & yellow iron operations - Ton/yr PM2.5 Figure 30. Buck Steam Station - Air Emissions m o ti ti � High Risk 1 v o.o c t 0.75 U � v U of N 0) Moderate 0.5 0 + Risk a ca Y C O N N o 0.25 O C C G a O J a v 'v v a a a a a a fl- Low Risk z z m m m z z z z z z a NUA 0 Option 1. MNA Option 2. Cap -In -Place with MNA Option 3. Removal with MNA ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑ On -Site worker contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑ Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk ■ GHG emissions/ carbon foot -printing - Tons CO2e emitted Emissions from increased truck traffic & yellow iron operations - Ton/yr Nox Emissions from increased truck traffic & yellow iron operations - Ton/yr PM2.5 DCN: HWIDENCO01 40 June 30, 2016 Figure 31. Cliffside Steam Station - Air Emissions High Risk Y VI j Moderate 'Z Risk 10 v a c 0 0.25 3 v v v 0 a a 'v 'v 'v a a a a a a 0 Low Risk z z m m m z z z z z z a NUA 0 Option 1. MNA Option 2. Cap -In -Place with MNA Option 3. Removal with MNA ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑ On -Site worker contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑ Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk ■ GHG emissions/ carbon foot -printing - Tons CO2e emitted Emissions from increased truck traffic & yellow iron operations - Ton/yr Nox Emissions from increased truck traffic & yellow iron operations - Ton/yr PM2.5 1 EPS Figure 32. Mayo Steam Station - Air Emissions m o m m .ti ti vi High Risk C) Moderate + Risk Ci of Y 3 v v v o c c c v w Low Risk z z m m m NUA Option 1. MNA O 0) N O m a a a z z z Option 2. Cap -In -Place with MNA Option 3. Removal with MNA 0.5 a 1 W ao c 0.75 v a� U U v 0.5 a c O 0.25 O a O a 0 ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑ On -Site worker contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk ■ GHG emissions/ carbon foot -printing - Tons CO2e emitted Emissions from increased truck traffic & yellow iron operations - Ton/yr Nox Emissions from increased truck traffic & yellow iron operations - Ton/yr PM2.5 DCN: HWIDENCO01 41 June 30, 2016 4.2.3 Ecological Services High Risk Cr W Moderate +J Risk v Y Ln Figure 33. Allen Steam Station - Ecological Services m 1 V N C EPS 1 v on c t 0.75 U v U N 0.5 a- c 0 0.25 0 C C Q a a a v v a a a a a a p Low Risk z z z m z z z z z z NUA 0 Option 1. MNA Option 2. Cap -In -Place with MNA Option 3. Removal with MNA ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑ On -Site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑ Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk ■ Terrestrial Habitat - HEA based lost DSAYs Aquatic Habitat - HEA based lost DSAYs High Risk Figure 34. Buck Steam Station - Ecological Services 1 0- v U OJ Moderate 0.5 0 Risk a � O 0.25 3 v w O o c c Q a a a a a a a a p Low Risk z z z z z z z z a NUA 0 Option 1. MNA Option 2. Cap -In -Place with MNA Option 3. Removal with MNA ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑On -Site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk ■ Terrestrial Habitat - HEA based lost DSAYs Aquatic Habitat - HEA based lost DSAYs DCN: HWIDENCO01 42 June 30, 2016 Figure 35. Cliffside Steam Station - Ecological Services High Risk m 1 EPS v tiA c 0.75 u o U � m U Moderate 0.5 0 + Risk a v f° of C: y O 0.25 3 v v O a J a v v a a a a a a O Low Risk z z m z z z z z z a NUA 0 Option 1. MNA Option 2. Cap -In -Place with MNA Option 3. Removal with MNA ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑ On -Site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑ Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk ■ Terrestrial Habitat - HEA based lost DSAYs Aquatic Habitat - HEA based lost DSAYs Figure 36. Mayo Steam Station - Ecological Services N N 00 High Risk Y 1 v tiA ro t 0.75 u v a- U N Q) Moderate 0.5 0 'Z Risk a Y O 0.25 .t 3 v v O o a a a 'v 'v a a a a a a O Low Risk z z m z z z z z z a NUA 0 Option 1. MNA Option 2. Cap -In -Place with MNA Option 3. Removal with MNA ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk []On -Site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk ■ Terrestrial Habitat - HEA based lost DSAYs Aquatic Habitat - HEA based lost DSAYs DCN: HWIDENCO01 43 June 30, 2016 4.2.4 Costs Figure 37. Allen Steam Station - Cost o High Risk Moderate + Risk ca v of EPS 1 a) bA c t 0.75 U a� U U v 0.5 0 o_ c o O N 0.25 ° O a a a a a a a a a fl- > O Low Risk z z z z z z z z z NUA 0 Option 1. MNA Option 2. Cap -In -Place with MNA Option 3. Removal with MNA ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑On -Site worker contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk ■ Capital - Net Present Value (Millions) Figure 38. Buck Steam Station - Cost oq a High Risk QJ Moderate 0.5 0 +� Risk a W c � � O m 0.25 .L o O a a a a a a a a O > > ^ > Low Risk z I—� z z z z ■ z z z a 1 NUA 0 Option 1. MNA Option 2. Cap -In -Place with MNA Option 3. Removal with MNA ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑On -Site worker contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk ■ Capital - Net Present Value (Millions) DCN: HWIDENCO01 44 June 30, 2016 of Figure 39. Cliffside Steam Station - Cost High Risk EPS 1 v b.0 C t 0.75 U v U N IV Moderate 0.5 0` +� Risk a co v C or O w 0.25 3 °n O 0 Q > > rn > Low Risk z z a z z z z z z a NUA 0 Option 1. MNA Option 2. Cap -In -Place with MNA Option 3. Removal with MNA ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑ On -Site worker contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑ Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk ■ Capital - Net Present Value (Millions) High Risk Figure 40. Mayo Steam Station - Cost N 1 v u v Q) Moderate 0 5 0 +-� Risk a ca a� C � o 0.25 t 3 O o Q Low Risk z r_1 z z z z z z z a NUA 0 Option 1. MNA Option 2. Cap -In -Place with MNA Option 3. Removal with MNA ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑On -Site worker contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk ■ Capital - Net Present Value (Millions) DCN: HWIDENCO01 45 June 30, 2016 4.2.5 Each Site Data Combined Figure 41. Allen Steam Station - Summary 0 N O ti W N O m (Yf 06 1 W 10 Ol N .4 l 't , M C� N O ri O lD V1 Vl M .--I V1 ah High Risk EPS v Moderate > ID 'Z Risk oo o � o � � o N ry O N W � DJ rl WWWWWWWWWW M th C C C C C C C C C C M Low Risk » > M » > z z z m m a a m m a m m zzz z z z m m m m m m m m Co m... NUA Option 1. MNA Option 2. Cap -In -Place with MNA Option 3. Removal with MNA ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑On -Site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk ■ Truck traffic related accidents fatalities ■ Truck traffic related accidents Project Illness/Injury Implementation risks - project fatalities Implementation risks - project illness/injury Truck traffic related accidents - property damage ■ Terrestrial Habitat - HEA based lost DSAYs Aquatic Habitat - HEA based lost DSAYs ■ GHG emissions/ carbon foot -printing - Tons CO2e emitted Emissions from increased truck traffic & yellow iron operations - Ton/yr Nox Emissions from increased truck traffic & yellow iron operations - Ton/yr PM2.5 ■ Capital - Net Present Value (Millions) 1 v on 0.75 rroo t U N u v 0.5 a C O O 0.25 C CL 0 DCN: HWIDENCO01 46 June 30, 2016 Figure 42. Buck Steam Station - Summary High Risk Q) Moderate +� Risk v 3 o v m m m m m w v a w c c c c c c c c c c Low Risk > j�� ���- „m z z m m m m m m m m m m �. mmmmmm.n NUA mmmm Option 1. MNA 0 a 0 0 o o 0 o v m O ry ry 0 N N zzzzz Option 2. Cap -In -Place with MNA m m a m n N m.� of �oNoom°iv Option 3. Removal with MNA EPS ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑On -Site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk ■ Truck traffic related accidents fatalities ■ Truck traffic related accidents Project Illness/Injury Implementation risks - project fatalities Implementation risks - project illness/injury Truck traffic related accidents - property damage ■ Terrestrial Habitat - HEA based lost DSAYs Aquatic Habitat - HEA based lost DSAYs ■ GHG emissions/ carbon foot -printing - Tons CO2e emitted Emissions from increased truck traffic & yellow iron operations - Ton/yr Nox Emissions from increased truck traffic & yellow iron operations - Ton/yr PM2.5 ■ Capital - Net Present Value (Millions) 1 v 0o 0.75 rroo U 4! u v 0.5 21 a O 0.25 0 a 0 DCN: HWIDENCO01 47 June 30, 2016 High Risk Figure 43. Cliffside Steam Station - Summary ti m O a Ln v W O N W N co m EPS Y m � O p[ m Q) Moderate Z1 Risk o ro 0 v � a Y. O _q Ui N V N O W rl � o vavvavavvv Low Risk y m m m m m m w o � 7)� D D z z m m m m m m m m m m O z z z z z z NUA Option 1. MNA Option 2. Cap -In -Place with MNA Option 3. Removal with MNA ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑On -Site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk ■ Truck traffic related accidents fatalities ■ Truck traffic related accidents Project Illness/Injury Implementation risks - project fatalities Implementation risks - project illness/injury Truck traffic related accidents - property damage ■ Terrestrial Habitat - HEA based lost DSAYs Aquatic Habitat - HEA based lost DSAYs ■ GHG emissions/ carbon foot -printing - Tons CO2e emitted Emissions from increased truck traffic & yellow iron operations - Ton/yr Nox Emissions from increased truck traffic & yellow iron operations - Ton/yr PM2.5 ■ Capital - Net Present Value (Millions) 1 v on 0.75 rroo t U v U N 0.5 a co C O O 0.25 C L a 0 DCN: HWIDENCO01 48 June 30, 2016 Figure 44. Mayo Steam Station - Summary V N a m 1.n m o m co N High Risk Y Moderate +� Risk ro v 3 o v w m w a m w v a w c c c c c c c c c c Low Risk > j F Z Z 15 „ M Z Z . . . . . . m . . m is mmmmmmmmmm.n NUA EPS Option 1. MNA Option 2. Cap -In -Place with MNA Option 3. Removal with MNA ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑On -Site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk ■ Truck traffic related accidents fatalities ■ Truck traffic related accidents Project Illness/Injury Implementation risks - project fatalities Implementation risks - project illness/injury Truck traffic related accidents - property damage ■ Terrestrial Habitat - HEA based lost DSAYs Aquatic Habitat - HEA based lost DSAYs ■ GHG emissions/ carbon foot -printing - Tons CO2e emitted Emissions from increased truck traffic & yellow iron operations - Ton/yr Nox Emissions from increased truck traffic & yellow iron operations - Ton/yr PM2.5 ■ Capital - Net Present Value (Millions) 1 v on 0.75 rroo t U v u v 0.5 21 a O 0.25 C a 0 DCN: HWIDENCO01 49 June 30, 2016 4.2.6 Cumulative Assessment Figure 45. Cumulative Assessment - All Sites High Risk Y Moderate +� Risk v Cr J N N N N N N N N N N C C C C C C C C C C Low Risk =<)F1<5 Z Z ro m (a ro ro (a m ro m ro M mmmmmmmmmm.n NUA Option 1. MNA ti N m �o v n N N � � 7 N V VT a a M Z Z Z ' Option 2. Cap -In -Place with MNA oo I r T oom o�m o��N ^?corm.--i Ni v�nmM ey M O e1 .ti ci 01 Vt N V} Option 3. Removal with MNA EPS ■ Public off -site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑On -Site contaminant exposure - no unacceptable risk (NUA), low risk, moderate risk, high risk ❑Groundwater (drinking water consumption) - no unacceptable risk (NUA), low risk, moderate risk, high risk ■Trucktraffic related accidents -fatalities ■ Truck traffic related accidents - Project Illness/Injury Implementation risks - project fatalities Implementation risks - project illness/injury Truck traffic related accidents - property damage ■ Terrestrial Habitat - HEA based lost DSAYs Aquatic Habitat - HEA based lost DSAYs ■ GHG emissions/ carbon foot -printing - Tons CO2e emitted Emissions from increased truck traffic & yellow iron operations - Ton/yr Nox Emissions from increased truck traffic & yellow iron operations - Ton/yr PM2.5 ■ Capital - Net Present Value (Millions) 1 v on 0.75 t U N N 0.5 ° 0- 0 O 0.25 C L d N DCN: HWIDENCO01 50 June 30, 2016 0 5 OPINIONS • The Intervenors failed to take a holistic view of all of the environmental and community impacts and risks (environmental footprint) associated with alternative implementation during their remedial evaluation stage. This has skewed their remedy selection and leads them to advocate for a remedy (Removal) that is less protective of the environment and the community. • Intervenors do not consider the environmental footprint of the Removal alternative, nor do they consider methods to reduce the footprint of these alternatives at any of the four sites. Metrics that may have significant social and economic impacts (e.g., GHG emissions, extended truck traffic, pollutant emissions through local communities, energy and resource consumption) or ecological impacts (e.g., terrestrial habitat disturbance associated with removal, capping, or the development of off -site disposal units) were not considered quantitatively by the Intervenors. • Developing a remedy for the violations alleged requires employment of a NEBA evaluation to develop sustainable remedial alternatives that manage site risks while maximizing benefits and minimizing costs to the public. • Intervenors do not address four critical aspects of large, complex projects and associated remedial evaluations: o Sustainability and green remediation practices were not evaluated quantitatively; o Social and community impacts were not evaluated quantitatively; o Environmental footprints such as carbon and other greenhouse gas emissions were not evaluated quantitatively; and, o Net environmental, economic and social benefits and costs were not evaluated either qualitatively or quantitatively. • Based upon the NEBA evaluation, the Intervenors have not considered their obligation to the public to develop sustainable remedial alternatives that manage site risks while maximizing benefits and minimizing costs to the public. • Based upon the NEBA presented herein, neither Removal or CIP is warranted as a remedy for the violations alleged. • The impact of metals in groundwater on groundwater ecosystem service flows provided by each individual site is marginal, if any. • The Removal remedy that the Intervenors propose for the Allen, Buck, Cliffside, and Mayo sites does not appear to provide any net benefit to off -site ecological or human use services, over other alternatives. o The Removal alternative will create greater harm to the environment compared to other alternatives. DCN: HWIDENCO01 51 June 30, 2016 0 o The Removal alternative will create greater risks to the local community (via the extensive truck traffic) and to workers compared to other alternatives. • Based upon my evaluation, the Removal alternative will create significantly greater risks (and nuisance) to the local community (via the extensive truck traffic) and to workers, compared to other alternatives. • Human health and ecological risk evaluations on which the site classifications and subsequent remedial alternatives for the Allen, Buck, Cliffside, and Mayo sites are based, are overly conservative and mainly represent "perceived" risks, not "real" risks. • The Removal alternative will not change human or ecological risk scenarios to any meaningful level compared to other alternatives. • A refinement of both the human and ecological risk assessments will reduce "perceived" risks substantially, providing for the consideration of non -intrusive alternatives for managing "real" risks, if any. • The marginal benefits to the "perceived" risk profile accomplished by the Intervenors' proposed remedy do not justify the extreme differences in the cost profile, costs which are largely borne by the communities served by Duke Energy. • Based upon my evaluation, the Removal alternative, if implemented, would create greater environmental and human health impacts at each of the four sites, compared to other alternatives. • In comparing the CIP alternative to the Removal alternative, the Removal alternative has a much greater environmental footprint and creates significantly more environmental and community harm. This is consistent with the Tennessee Valley Authority findings in their review of CIP and Removal alternatives for coal ash as part of their programmatic EIS (Tennessee Valley Authority 2016). • At all four sites, it appears that the following will occur should the Removal remedy advocated by the Intervenors' be implemented at any of the sites: o The Removal alternative will cause more ecological injury through the destruction of habitat than the ecological injury projected by the risk assessment (e.g., "the cure is worse than the disease"), and; o The Removal alternative provides no net increase in ecosystem service value for the effort expended; the remedial actions are selected to address marginal, uncertain, and localized/limited risks. • With regard to the Removal alternative, the Intervenors have failed to demonstrate clear and transparent consideration of the following often -stated Agency objectives: Objective #1: Remedies should protect human health and the environment. Remediation work should remove unacceptable risks to human health and to the environment, with due consideration of the costs, benefits and technical feasibility of the alternatives. DCN: HWIDENC001 52 June 30, 2016 0 Objective #2: Remedies require safe working practices. Remediation work should be safe for on -site workers, local communities and the environment. Objective #3: Remedial decision -making should consider sustainability. Remediation decisions should be made with regard to the current and future implications of environmental, social and economic factors. A sustainable remediation solution should deliver the maximum net benefit achievable. Objective #4: Remedial decisions and their underlying foundations should be transparent. Remediation decisions, including the assumptions and supporting data used to reach them; should be documented in a clear and easily understood format. Objective #S: Remedial decisions require good governance and stakeholder involvement. Remediation decisions should be made with regard to the views of stakeholders and follow a clear process in which they can participate. In situations where a non - optimal remediation decision must be made, because of other factors that are more influential, a clear and transparent record of indicating why such a decision was taken should be a minimum requirement in any decision making process. Objective #6: Remedial decisions are based on sound science. Decisions should be made on the basis of sound science, relevant and accurate data, and clearly explained assumptions. Decisions should be based on the best available information and should be justifiable and reproducible. • Aside from recognizing the traditional requirements associated with remedial alternative selection, namely protection of human health and the environment and compliance with applicable or relevant and appropriate requirements), the Intervenors failed to present evidence of an evaluation of the significant differences (if any) in the environmental footprint when comparing the CIP and Removal alternatives that meets the requirements prescribed by federal agency guidance (USEPA 2012b). • Intervenors base their arguments for Removal on risks based solely upon chemical concentrations of the constituents of interest (COIs) in groundwater and limited surface water seep areas. In doing so, they neglected to consider factors other than chemical concentrations during the implementation of their preferred remedial alternative that also have the potential to influence, either positively or negatively, human health and ecological risks and services. • The Allen, Buck, Cliffside, and Mayo sites meet the definition as to when a NEBA would be of value in evaluating site remedial alternatives$. The site information indicates that s "This framework for NEBA should be useful when the balance of risks and benefits from remediation of a site is ambiguous. This ambiguity arises when the contaminated site retains significant ecological value, when the remedial actions are themselves environmentally damaging, when the ecological risks from the contaminants are relatively DCN: HWIDENC001 53 June 30, 2016 0 these sites retain significant ecological value. The Removal alternative proposed by the Intervenors' will be environmentally damaging, the ecological risks from the COI's are relatively small, uncertain, or limited to a component of the ecosystem, and remediation or restoration might fail. • There is no need to order closure, much less closure by Removal, to remedy the alleged violations in this case. A basic NEBA demonstrates that Intervenors' proposed remedy (Removal) is clearly disproportionate to the risk. small, uncertain, or limited to a component of the ecosystem, and when remediation or restoration might fail." (Efroymson et al. 2004). DCN: HWIDENC001 54 June 30, 2016 0 6 REFERENCES Aurand, D., Walko, L., and Pond, R. 2000. Developing Consensus Ecological Risk Assessments: Environmental Protection in Oil Spill Response Planning A Guidebook. United States Coast Guard. Washington, DC. 148p. Colombo, F., Nicolette, J., Wenning, R., and Travers, M. 2012. Incorporating Ecosystem Valuation in the Assessment of Risk and Remedy Implementation. Chemical Engineering Transactions. 28, 55-60. DOI: 3303/CET1228010 Efroymson, R., Nicolette, J., and Suter, G. 2003. A Framework for Net Environmental Benefit Analysis for Remediation or Restoration of Petroleum -Contaminated Sites; ORNL/TM- 2003/17; Oak Ridge National Laboratory: Oak Ridge, TN, USA, 2003. Efroymson, R., Nicolette, J., and Suter, G. 2004. A Framework for Net Environmental Benefit Analysis for Remediation or Restoration of Contaminated Sites. Environmental Management. 34, 315-331. HDR Engineering, Inc. of the Carolinas (HDR). 2015a. Comprehensive Site Assessment Report, Allen Steam Station Ash Basin. August 23, 2015. HDR. 2015b. Comprehensive Site Assessment Report, Buck Steam Station Ash Basin. August 23, 2015. HDR. 2015c. Comprehensive Site Assessment Report, Cliffside Steam Station Ash Basin. August 18, 2015. HDR. 2015d. Corrective Action Plan Part 1, Allen Steam Station Ash Basin. November 20, 2015. HDR. 2015e. Corrective Action Plan Part 1, Buck Steam Station Ash Basin. November 20, 2015. HDR. 2015f. Corrective Action Plan Part 1, Cliffside Steam Station Ash Basin. November 16, 2015. HDR. 2016a. Corrective Action Plan Part 2, Allen Steam Station Ash Basin. February 19, 2016. HDR. 2016b. Corrective Action Plan Part 2, Buck Steam Station Ash Basin. February 19, 2016. HDR. 2016c. Corrective Action Plan Part 2, Cliffside Steam Station Ash Basin. February 12, 2016. Interstate Technology and Regulatory Council (ITRC). 2006. Planning and Promoting Ecological Land Reuse of Remediated Sites. Washington, DC: ITRC, Ecological Reuse Team. National Oceanic and Atmospheric Administration (NOAA). 2011. (Shigenaka, G.) Summary Report for Fate and Effects of Remnant Oil Remaining in the Environment. Annex M - Net Environmental Benefit Analysis. National Oceanic and Atmospheric Administration. DCN: HWIDENC001 55 June 30, 2016 0 Nicolette, J., Burr, S., and Rockel, M. 2013a. A Practical Approach for Demonstrating Environmental Sustainability and Stewardship through a Net Ecosystem Service Analysis. Sustainability 2013, 5, 2152-2177; doi:10.3390/su5052152, Published May 10, 2013. Nicolette, J., Goldsmith, B., Wenning, R., Barber, T., and Colombo, F. 2013b. Chapter 9: Experience with Restoration of Environmental Damage. In & L. Bergkamp (Ed.) & B. Goldsmith (Ed.), The EU Environmental Liability Directive: A Commentary. Oxford University Press. May 10, 2013. North Carolina Department of Environmental Quality (NCDEQ). 2013. Division of Waste Management, Hazardous Waste Section. Guidelines for Establishing Remediation Goals at RCRA Hazardous Waste Sites. December 11, 2013. Suter, G.W., II. 1993. Ecological Risk Assessment. Lewis Publishers, Boca Raton, Florida. SynTerra. 2015a. Comprehensive Site Assessment Report, Mayo Steam Electric Plant. September 2, 2015. SynTerra. 2015b. Corrective Action Plan Part 1, Mayo Steam Electric Plant. December 1, 2015. SynTerra. 2016. Corrective Action Plan Part 2, Mayo Steam Electric Plant. February 29, 2016. Tennessee Valley Authority. 2016. Final Ash Impoundment Closure Environmental Impact Statement Part I — Programmatic NEPA Review. Project Number: 2015-31, June 2016. United States Army Environmental Center. 2005. United States Army Natural Resource Injury Guidance. September. http://aec.army.mil/portals/3/cleanup/nri- _ uigde.pdf United States Environmental Protection Agency (USEPA) 1995. A Framework for Measuring the Economic Benefits of Groundwater. Office of Water and Office of Policy, Planning and Evaluation. EPA 230-B-95-003. October 1995. USEPA. 1997. Ecological Risk Assessment Guidance for Superfund: Process for Designing and Conducting Ecological Risk Assessments (EPA 540-R-97-006). Washington, DC: USEPA, OSWER. USEPA. 1998. Guidelines for Ecological Risk Assessment (EPA/630/R-95/002F). Washington, DC. USEPA 2001. Updating Remedy Decision at Select Superfund Sites. Biannual Summary Report FY 1998 and FY 1999. USEPA Office of Emergency Response (OERR) (EPA 540-R-0I- 00, OSWER 9355.0-76) March 2001, 84 pp. USEPA. 2005. Contaminated Sediment Remediation Guidance for Hazardous Waste Sites (EPA- 540-R-05-012, OSWER 9355.0-85). Washington, DC: USEPA, OSWER. USEPA. 2008. Green Remediation: Incorporating Sustainable Environmental Practices into Remediation of Contaminated Sites (EPA 542-R-08-002). Washington DC: USEPA, OSWER. DCN: HWIDENC001 56 June 30, 2016 0 USEPA. 2009a. Endangerment and Cause or Contribute Findings for Greenhouse Gases Under Section 202(a) of the Clean Air Act. Washington DC: Federal Register 74, no. 239: 66, 496-66, 546. USEPA. 2009b. Principles for Greener Cleanups. Washington DC: USEPA, OSWER. USEPA. 2011a. Greener Cleanups: Contracting and Administrative Toolkit. Washington, DC: USEPA, OSWER. USEPA. 201 lb. Deferral for CO2 emissions from bioenergy and other biogenic sources under the Prevention of Significant Deterioration (PSD) and Title V programs: Proposed rule (EPA- HQ-OAR-2011-0083). Washington DC: Federal Register 76, no. 54: 15, 249-15, 266. USEPA. 2012a. "Re: Proposed Order for Recovery of Submerged Oil." Letter to Enbridge Energy, Limited Partnership. 3 Oct. 2012. MS. N.p. https://www3.epa. og v/region5/enbridgespill/pdfs/20121003-cover-letter-re-proposed- order.pdf USEPA. 2012b. Methodology for Understanding and Reducing a Project's Environmental Footprint (EPA 542-R-12-002). Washington DC: USEPA, OSWER, Office of Superfund Remediation and Technology Innovation. USEPA. 2013. Region 4 Superfund Annual Report 2013. USEPA. http://www.el2a. _og v/region4/soerfund/images/allmedia/pdfs/ annualreport2013.pdf. USEPA Science Advisory Board (SAB). 2009. Valuing the Protection of Ecological Systems and Services (EPA-SAB-09-012). Washington, DC: USEPA Science Advisory Board. DCN: HWIDENC001 57 June 30, 2016 0 7 EXPERT REPORTS REVIEWED Bedient, Philip B. 29 February 2016. Expert Opinion of. Remediation of Soil and Groundwater at the Allen Steam Station Operated by Duke Energy Carolinas, LLC, Belmont, North Carolina. Bedient, Philip B. 29 February 2016. Expert Opinion of. Remediation of Soil and Groundwater at the Cliffside Steam Station Operated by Duke Energy Carolinas, LLC, Mooresboro, North Carolina. Campbell, Steven K. and Richard K. Spruill. 29 February 2016. Expert Report. Buck Steam Station, 1555 Dukeville Road, Salisbury, NC 28146. Campbell, Steven K. and Richard K. Spruill. May 12, 2016. Expert Report. Buck Steam Station, 1555 Dukeville Road, Salisbury, NC 28146. Campbell, Steven K. and Richard K. Spruill. May 12, 2016. Expert Report, Addendum #1. Buck Steam Station, 1555 Dukeville Road, Salisbury, NC 28146. Cosler, Douglas J. February 29, 2016. Expert Report of. Allen Steam Station Ash Basins Belmont, North Carolina. Cosler, Douglas J. February 29, 2016. Expert Report of. Cliffside Steam Station Ash Basins Mooresboro, North Carolina. Hutson, Mark A. February 2016. Expert Report of. Mayo Steam Electric Plant Roxboro, NC. Parette, Robert. May 13, 2016. Opinions of the Appropriateness of Monitored Natural Attenuation in Conjunction with Cap -in -Place at the Buck Steam Station, Salisbury, NC. DCN: HWIDENCO01 58 June 30, 2016 EPS APPENDIX A Remedial Alternatives Construction Analysis EPS REMEDIAL ALTERNATIVE CONSTRUCTION ANALYSIS A construction and cost analysis for three remedial alternatives, monitored natural attenuation (MNA), cap -in -place (with MNA), and comprehensive removal (with MNA) were assessed for the Allen Steam Station, Buck Steam Station, Cliffside Steam Station and Mayo Steam Station. The three remedial alternatives evaluated are based on the remedial assessment completed by HDR Engineering, Inc. and synTerra Corporation as detailed in the Corrective Action Plan, Part 2 for each respective steam station (HDR, 2016a; HDR, 2016b; HDR, 2016c; synTerra, 2016). The quantity of ash in basins and ash storage units for each steam station was provided by Duke Energy. Land coverage dimensions were determined via GIS polygon analysis from shape files provided by HDR and synTerra (as provided in the CAP Part 2 documents). The construction analysis is subdivided into three categories as summarized in tables following this text. Supporting calculation worksheets are appended to the end of this Appendix. EPS Al TABLE 1. LAND USE DISTURBANCE The first analysis category modeled the land use disturbance associated with each remedial alternative for each site, which established the basis for other tasks under the construction and cost analysis. Note NINA only is not included in Table 1 as no significant land use disturbance occurs under this scenario. The cap -in -place alternative involves construction of a synthetic cover system (i.e., a Subtitle D cap design) over the surface -area footprint of the ash basin(s). This alternative also involves the support of an off -site soil borrow pit to provide the material for use in the cap. The comprehensive removal alternative involves excavation of materials contained within the ash basin(s), and transport of material to a new off -site landfill construction specifically for the purpose of the particular site's ash disposal. Note that under this alternative, additional land disturbance is incurred associated with the offsite landfill. The following parameters or assumptions were applied in evaluating the land cover alteration associated with the cap -in -place and comprehensive removal alternatives. A1.1 Cap -in -Place Assumptions: 1. The size of each cap is set equal to the current footprint of existing ash basins. 2. The borrow material for cap construction is sourced from an off -site location 25 miles away. 3. To determine borrow area size, the borrow area is set to a maximum depth of 10 ft and a side slope of 3 ft/ft. 4. A 50 ft perimeter buffer strip is included in the borrow area footprint. A1.2 Comprehensive Removal Assumptions: 1. A conversion of 1.2 tons per cubic yard of ash is assumed based on a study by the Electric Power Research Institute (EPRI, 2009). 2. To determine landfill size, each landfill is limited to a maximum height of 100 feet (ft) and a side slope of 3.5 ft/ft. 3. A 300 ft perimeter buffer strip is included in the landfill footprint. 4. To determine borrow area size, the borrow area is set to a maximum depth of 10 ft and a side slope of 3 ft/ft. 5. The borrow area is assumed to exist on the same property parcel as the off -site landfill. It is assumed this distance is 1 mile (2 mile round trip) from the landfill proper. 6. A 50 ft perimeter buffer strip is included in the borrow area footprint. EPS A2 TABLE 2. PROJECT DURATION The second analysis category modeled the anticipated duration of each remedial alternative. The removal alternative comprises of three primary tasks, 1) construction of an off -site landfill, 2) excavation, transport and placement of ash into the constructed landfill, and 3) closure of the landfill with synthetic cap. The cap -in -place alternative comprises of cap construction activities, which is assumed to be limited by the rate at which capping material (i.e. soil and clay) can be imported and placed on -site. A2.1 Removal Assumptions: 1. Landfill construction assumes a per acre activity rate of 1 day for clear & grub, 28 days for earthwork, 7 days for survey control, 14 days for liner/leachate system installation, and 4 days for liner soil cover. Activities will occur concurrently; therefore, the rate of earthwork is the limiting factor for the construction rate. 2. Ash basin excavation, transport to landfill and placement assumes an annual haul rate of 540,000 tons based on 50 trucks with 2 loads per day at 20 tons apiece, operating 12 hours per day for 270 days per year. Haul distance assumes 25 miles to the landfill (50 miles round-trip). 3. Landfill closure assumes landfill capping rate is subject to import rate of cover material. Import of cover material assumes an annual haul rate of 648,000 CY based on 6 trucks with 10 loads per day at 40 CY apiece, operating 12 hours per day for 270 days per year. Haul distance assumes 1 mile from the on -site borrow area. A2.2 Cap -in -Place Assumptions: 1. Cap Construction: Assumes an annual haul rate of 540,000 tons based on 50 trucks with 2 loads per day at 20 tons apiece, operating 12 hours per day for 270 days per year. Haul distance assumes 25 miles to the landfill (50 miles round-trip). EPS A3 TABLE 3. COST SUMMARY OF ALTERNATIVE REMEDIAL ACTIONS The final analysis category projected expenditures to implement each remedial alternative based on the modeled parameters for land use disturbance and project duration. Construction material cost, labor and equipment rates are based on data from the Remedial Action Cost Engineering and Requirements System (RACER) Software Package, Version 11.1.12.0 and from person communications with industry expert (personal communication). Landfill construction cost is estimated based on economic data cited in U.S. Environmental Protection Agency research (EPA, 2014), which reports landfill construction cost ranges from $300,000 to $800,000 per acre. A cost per acre of $600,000 was applied to for the current cost analysis. C A4 REFERENCES US EPA, 2014. Municipal Solid Waste Landfills, Economic Impact Analysis for the Proposed New Subpart to the New Source Performance Standards. June 2014. EPRI, 2009. Coal Ash: Characteristics, Management and Environmental Issues. Electric Power Research Institute, September 2009. HDR, 2016a. Corrective Action Plan Part 2, Allen Steam Station Ash Basin, February 19, 2016. HDR, 2016b. Corrective Action Plan Part 2, Buck Steam Station Ash Basin, February 19, 2016. HDR, 2016c. Corrective Action Plan Part 2, Cliffside Steam Station Ash Basin, February 12, 2016. SynTerra, 2016. Corrective Action Plan Part 2, Mayo Steam Electric Plant, February 29, 2016 Table 1. Land Use Change Summary REMOVAL WITH MNA CAPPING WITH MNA Site Existing Ash Pond/ Ash Fill Area Volume of Ash Landfill Construction Area 1 Landfill Cap Barrow Material Area 2 Landfill Total CIP Barrow Material Areal Acre CY Acre Acres Acres Acres Allen 322 16,058,333 225 93 319 190 Buck 180 4,425,000 94 35 129 122 Cliffside 189 6,575,000 121 47 168 115 Mayo 153 5,500,000 108 41 149 94 Total: 844 32,558,333 549 217 766 521 Assumption: Ash density: 1.2 tons/CY Notes: MNA: Monitored Natural Attenuation CY: Cubic Yards CIP: Cap in Place 1. Estimated landfill footprint based on 100' height and 3.5 side slope, plus 300 foot buffer for development/construction support. 2. Borrow Area: Excavation area based on 10 depth, plus 50 foot buffer for development/construction support. Table 2. Project Duration Summary REMOVAL WITH MINA CAPPING WITH MNA Site Construction' Ash Excavation, Transport & Placennent2 Closure/Cap3 Cap Construction Years Work Days Hours Years Work Days Hours Years Work Days Hours Years Work Days Hours Allen 3.6 1,078 12,936 35.7 9,635 115,620 2.0 552 6,618 5.2 1,402 16,824 Buck 1.2 368 4,416 9.8 2,655 31,860 0.7 190 2,280 3.3 880 10,559 Cliffside 1.7 506 6,075 14.6 3,945 47,340 1.0 261 3,137 3.0 823 9,881 Mayo 1.5 438 5,254 12.2 3,300 39,600 0.8 229 2,746 2.5 666 7,995 Total: 8.0 2,390.0 28,680.0 72.4 19,535.0 234,420.0 4.6 1,231.7 14,780.5 14.0 3,771.6 45,258.7 Notes: CIP: Cap -in -place MNA: Monitored Natural Attenuation Model Assumptions: CAPPING WITH REMOVAL WITH MNA MNA Schedule Assumptions Construction Ash Excavation &Closure/Cap Cap Construction Transport Work Days/Week 6 6 6 6 Work Weeks/Yearl SO 45 45 45 Hours/Dayl 12 12 12 12 Construction Days/Year 300 270 270 270 1. Landfill Construction: Assumes a per acre activity rate of 1 day for clear & grub, 28 days for earthwork, 7 days for survey control, 14 days for liner/leachate system installation, and 4 days for liner soil cover. 2. Ash Basin Excavation & Transport to Landfill: Assumes an annual haul rate of 540,000 tons based on 50 trucks with 2 loads per day at 20 tons apiece, operating 12 hours per day for 270 days per year. Haul distance assumes 25 miles to landfill (50 miles round- trip). 3. Landfill Closure: Assumes landfill capping rate is subject to import rate of cover material. Assumes an annual haul rate of 648,000 CY based on 6 trucks with 10 loads per day at 40 CY apiece, operating 12 hours per day for 270 days per year. Haul distance assumes 1 miles from on -site borrow area. 4. CIP Cap Construction: Assumes an annual haul rate of 540,000 tons based on SO trucks with 2 loads per day at 20 tons apiece, operating 12 hours per day for 270 days per year. Haul distance assumes 25 miles to landfill (SO miles round-trip). Table 3. Cost Summary REMOVAL WITH MNA CAPPING WITH MNA Site Construction Ash Excavation, Transport & Placement Closure/Cap Monitoring (Site and Landfill) 10% Contingency TOTAL Cap Construction Monitoring (Site Only) 1ng Contingency TOTAL Allen $100,370,274 $1,213,386,592 $85,199,912 $12,070,941 $139,895,677.77 $1,550,923,397 $147,407,813 $6,650,000 $14,740,781.30 $162,148,594.28 Buck $37,589,262 $335,265,203 $29,901,706 $10,248,951 $40,275,617.09 $453,280,739 $84,288,278 $6,650,000 $8,428,827.83 $92,717,106.13 Cliffside $49,818,330 $497,518,815 $40,574,949 $12,850,460 $58,791,209.40 $659,553,763 $87,063,317 $8,975,000 $8,706,331.75 $95,769,649.20 Mayo $43,769,723 $415,854,735 $35,710,413 $10,394,177 $49,533,487.06 $555,262,535 $70,671,010 $6,650,000 $7,067,101.04 $77,738,111.42 Total: $231,547,589 $2,462,025,345 $191,386,980 $45,564,529 $288,495,991 $3,219,020,434 $389,430,419 $28,925,000 $38,943,042 $428,373,461 Site MNA Monitoring Site Only) 10% Contingency TOTAL Allen $6,650,000 $665,000 $7,315,000 Buck $6,650,000 $665,000 $7,315,000 Cliffside $8,975,000 $897,500 $9,872,500 Mayo $6,650,000 $665,000 $7,315,000 Total: $28,925,000 $2,892,500 $31,817,500 Notes: Construction and material costs developed from Remedial Action Cost Engineering and Requirements System (RACER) Software Package, Version 11.1.12.0 Project durations and equipment levels based on personal communication with industry expert MNA: Monitored Natural Attenuation Table S1. Road Travel Summary REMOVAL WITH MNA CAPPING WITH MNA Ash Transport Closure/Cap Material Liner Material Closure/Cap Material Liner Material Site Trips Miles' Trips Miles' Trips Miles3 Trips Miles' Trips Miles3 Allen 963,500 48,175,000 33,091 66,182 204 40,832 140,198 7,009,888 203 40,684 Buck 265,500 13,275,000 11,398 22,797 70 13,938 87,995 4,399,735 114 22,735 Cliffside 394,500 19,725,000 15,683 31,366 96 19,175 82,340 4,117,022 119 23,824 Mayo 330,000 16,500,000 13,730 27,460 83 16,585 66,623 3,331,152 97 19,361 Total: 1,953,5001 97,675,0001 73,903 1 147,8051 453 1 90,5291 377,1561 18,857,796 533 106,603 Notes: MNA: Monitored Natural Attenuation 1. 25 miles one-way, public roads. 2. 1 miles one-way, on -site (non-public road) travel only. 3. 100 miles one-way, public roads. Table S2. Liner Specifications Roll Dimensions 30 Mil 40 mil 60 mil 80 mil 100 mil 1. Width (feet): 23 23 23 23 23 2. Length (feet) 1000 750 500 375 300 3. Area (square feet): 23,000 17,250 11,500 8,625 6,900 4. Gross Weight (pounds, approx.) 3,470 3,470 3,470 3,470 3,470 Landfill Option Site Liner Area (ft2) Cap Area Liner Rolls Cap Rolls Truck Trips Miles Allen 6,708,033 7,378,836 583 642 204 40,832 Buck 2,289,784 2,518,762 199 219 70 13,938 Cliffside 3,150,210 3,465,231 274 301 96 19,175 Mayo 2,724,615 2,997,077 237 261 83 16,585 1,293 1,423 453 90,529 CIP Option Total Ash Basin Area Site Ash Basin (ft2) Rolls Truck Trips Miles (Acres) Allen 322 14,035,895 1,221 203 40,684 Buck 180 7,843,547 682 114 22,735 Cliffside 189 8,219,196 715 119 23,824 Mayo 153 6,679,392 581 97 19,361 3,198 533 106,603 Assumptions Rolls/Truck: 6 Travel Distance (mile): 100 Table S3. Monitoring Cost Estimate Landfill Monitoring Site Landfill Width (ft) # MWs Well Installation Days/Event Analytical Cost Labor Cost Equipment Cost Reporting Events/Year Annual Cost 100 Years Allen 2,590 22 $75,541 5 $6,474.97 $8,633.29 $1,618.74 $10,000.00 2 $53,454 $5,420,941.13 Buck 1,513 10 $35,308 3 $3,026.41 $4,035.21 $756.60 $10,000.00 2 $35,636 $3,598,951.37 Cliffside 1,775 12 $41,414 3 $3,549.77 $4,733.02 $887.44 $10,000.00 2 $38,340 $3,875,459.74 Mayo 1,651 11 $38,515 3 $3,301.28 $4,401.71 $825.32 $10,000.00 2 $37,057 $3,744,177.18 MNA Monitoring # MWs Days/Event Analytical Labor Cost Equipment Reporting Events/Year Annual Cost 100 Years Site Cost Cost Allen 30 8 $9,000.00 $12,000.00 $2,250.00 $10,000.00 2 $66,500 $6,650,000 Buck 30 8 $9,000.00 $12,000.00 $2,250.00 $10,000.00 2 $66,500 $6,650,000 Cliffside 45 11 $13,500.00 $18,000.00 $3,375.00 $10,000.00 2 $89,750 $8,975,000 Mayo 30 8 $9,000.00 $12,000.00 $2,250.00 $10,000.00 2 $66,500 $6,650,000 Assumptions: Well Network Size: Assumes one well every 300 feet on down -gradient side of landfill, and one well every 600 feet per side and up -gradient side. Analytical Cost/Well: $300.00 Sample Events/Year: 2 Day Labor Rate: $1,600.00 Equipment Dy Rate: $300.00 Wells Sampled/Day: 4 Reporting Cost: $10,000 Well Install Cost: $3,500 Table S4. Off -Site landfill Size and Cost Estimate Landfill Requirements Site Tons CY ft3 Allen 19,270,000 16,058,333 433,575,000 Buck 5,310,000 4,425,000 119,475,000 Cliffside 7,890,000 6,575,000 177,525,000 Mayo 6,600,000 5,500,000 148,500,000 Total: 39,070,000 32,558,333 879,075,000 Conversion tons per CY: 1.2 Landfill Parameters: Height (ft) 100 Slope: 3.5 Landfill Cost/Acre Low: $300,000 High: $600,000 Landfill Buffer Width Width (ft) 300 GOAL SEEK CALCULATION - LANDFILL GEOMETRY Height Bottom (Landfill Base) Top Volume (ft3) 51 (ft) B1 (ft2) S2 (ft) B2 (ft2) 100 2,590 6,708,033 1,890 3,572,051 433,575,000 100 1,513 2,289,784 813 661,299 119,475,000 100 1,775 3,150,210 1,075 1,155,374 177,525,000 100 i 1,651 2,724,6151 951 903,7181 148,500,000 OUTPUT Site Landfill Buffer Acres Total Acreage Base Area (ft2) Acres Low Cost High Cost Allen 6,708,033 154 $46,198,571 $92,397,143 71.3 225.3 Buck 2,289,784 53 $15,769,862 $31,539,723 41.7 94.3 Cliffside 3,150,210 72 $21,695,660 $43,391,319 48.9 121.2 Mayo 2,724,615 63 $18,764,568 $37,529,136 45.51 108.0 Total: I Total: 341 $102,428,6611 $204,857,321 207.41 548.8 Table S5. Estimated Duration of Landfill Construction Site Size (Acres) Construction days Years Const. Hours Allen 154 1078.0 3.6 12,936 Buck 53 368.0 1.2 4,416 Cliffside 72 506.2 1.7 6,075 Mayo 63 437.8 1.5 5,254 Total: 341 2,390 8 28,680 Activity Duration/acre (days) Clear/Grub 1 Excavation/Earthwork: 28 Limiting factor - Used as construction rate Surveying 7 Liner/Leachate System 14 Protective Soil Liner 4 Construction Teams (sets of equipment) # Construction Teams 4 Project Duration Details Days/Week 6 Weeks/Year 50 Hours/day 12 Construction days/year 300 Table S6. Landfill Closure Cost Worksheet (RACER) Site Imported Material Volume Import Duration (yrs) Trips Travel Miles Days Hours Allen 1,323,632 2.0 33,091 66,182 552 6,618 Buck 455,937 0.7 11,398 22,797 190 2,280 Cliffside 627,321 1.0 15,683 31,366 261 3,137 Mayo 549,210 0.8 13,730 27,460 229 2,746 Total: 2,956,100 4.6 73,903 147,805 1,232 14,781 Protect Duration Details Annual Haul Rate (CY): 648,000 # Trucks: 6 Truck CY: 40 Trips/Day: 10 Days/Week 6 Weeks/Year 45 Hours/day 12 Fill Distance (miles): 1 Assumption Folder Assembly Level Data Report (RACER) Phase Technology Assembly No. Assembly Description City UOM Materials Labor Equipment Equip. Units Extended Cost Allen Capping 17030423 Unclassified Fill, 6" Lifts, Off -Site, Includes Delivery, Spreading, and 633,317 CY 21.97 0.92 0.72 $14,950,478 Allen Capping 18050301 Loam or topsoil, imported topsoil, 6" deep, furnish and place 158,329 LCY 21.17 4.94 1.43 $4,359,413 Allen Capping 18050402 Seeding, Vegetative Cover 157 ACR 2,623.43 423.05 161.57 $503,729 Allen Capping 33080507 Clay, Low Permeability, 6"Lifts, Off -Site 531,986 CY 21.97 2.17 1.40 $13,584,019 Allen Capping 33080571 60 Mil Polymeric Liner, High -density Polyethylene 7,523,805 SF 0.40 0.20 0.01 $4,638,925 Allen Capping Barrow Excavation Equipment Allen Capping 17030233 Crawler -mounted, 3.125 CY, 245 Hydraulic Excavator 6,618 HR $0.00 $57.39 $79.54 4 $3,624,899 Allen Capping 17030706 D8 with U-Blade Bulldozer 6,618 HR $0.00 $59.86 $106.51 2 $2,202,127 Allen Capping 17030226 988, 7.0 CY, Wheel Loader 6,618 HR $0.00 $59.86 $84.96 2 $1,916,884 Allen Capping 17030295 35 Ton, 769, Off -highway Truck 6,618 HR $0.00 $55.72 $89.09 2 $1,916,752 Allen Capping CAP Const. Equipment Allen Capping 17030704 D6 with A -blade Bulldozer 6,618 HR $0.00 $59.86 $60.12 2 $1,588,094 Allen Capping 17030101 Rough Grading, D6 Dozer 759,977 SY $0.00 $0.28 $0.31 1 $448,386 Allen Capping 17030431 580K, 1.0 CY, Backhoe with Front-end Loader 6,618 HR $0.00 $59.86 $20.70 1 $533,159 Allen Capping 18050413 Watering with 3,000-Gallon Tank Truck, per Pass 157 ACR $154.66 $38.83 $36.99 1 $36,190 Allen Capping 17030233 Crawler -mounted, 3.125 CY, 245 Hydraulic Excavator 6,618 HR $0.00 $57.39 $79.54 1 $906,225 Allen Capping 33010505 50 KW, 120/240 VAC, Generator, Daily Rental 552 DAY $0.00 $0.00 $163.38 1 $90,106 Imported Material: 1,323,632 CY Direct Cost: $51,299,388 Direct+ $82,079,521 Buck Capping 17030423 Unclassified Fill, 6" Lifts, Off -Site, Includes Delivery, Spreading, and 218,152 CY 21.97 0.92 0.72 $5,152,556 Buck Capping 18050301 Loam or topsoil, imported topsoil, 6" deep, furnish and place 54,538 LCY 21.17 4.94 1.43 $1,501,639 Buck Capping 18050402 Seeding, Vegetative Cover 54 ACR 2,623.43 423.05 161.57 $173,524 Buck Capping 33080507 Clay, Low Permeability, 6"Lifts, Off -Site 183,247 CY 21.97 2.17 1.40 $4,679,137 Buck Capping 33080571 60 Mil Polymeric Liner, High -density Polyethylene #REF! SF 0.40 0.20 0.01 $1,323,205 Buck Capping Barrow Excavation Equipment Buck Capping 17030233 Crawler -mounted, 3.125 CY, 245 Hydraulic Excavator 2,280 HR $0.00 $57.39 $79.54 4 $1,248,629 Buck Capping 17030706 D8 with U-Blade Bulldozer 2,280 HR $0.00 $59.86 $106.51 2 $758,542 Buck Capping 17030226 988, 7.0 CY, Wheel Loader 2,280 HR $0.00 $59.86 $84.96 2 $660,288 Buck Capping 17030295 35 Ton, 769, Off -highway Truck 2,280 HR $0.00 $55.72 $89.09 2 $660,242 Buck Capping CAP Const. Equipment Buck Capping 17030704 D6 with A -blade Bulldozer 2,280 HR $0.00 $59.861 $60.12 2 $547,033 Buck lCapping 17030101 Rough Grading, D6 Dozer 261,796 SY $0.00 $0.281 $0.31 1 $154,459 Buck ICapping 17030431 580K, 1.0 CY, Backhoe with Front-end Loader 2,280 HR 1 $0.00 $59.861 $20.701 $183,651 Page 1 of 2 Table S6. Landfill Closure Cost Worksheet (RACER) Phase Technology Assembly No. Assembly Description Qty UOM Materials Labor Equipment Equip. Units Extended Cost Buck Capping 18050413 Watering with 3,000-Gallon Tank Truck, per Pass 54 ACR $154.66 $38.83 $36.99 1 $12,467 Buck Capping 17030233 Crawler -mounted, 3.125 CY, 245 Hydraulic Excavator 2,280 HR $0.00 $57.39 $79.54 1 $312,157 Buck Capping 33010505 50 KW, 120/240 VAC, Generator, Daily Rental 190 DAY $0.00 $0.00 $163.38 1 $31,038 Imported Material: 455,937 CY Direct Cost: $17,398,567 Direct+ $27,837,707 Cliffside Capping 17030423 Unclassified Fill, 6" Lifts, Off -Site, Includes Delivery, Spreading, and 300,154 CY 21.97 0.92 0.72 $7,089,377 Cliffside Capping 18050301 Loam or topsoil, imported topsoil, 6" deep, furnish and place 75,038 LCY 21.17 4.94 1.43 $2,066,098 Cliffside Capping 18050402 Seeding, Vegetative Cover 74 ACR 2,623.43 423.05 161.57 $238,744 Cliffside Capping 33080507 Clay, Low Permeability, 6"Lifts, Off -Site 252,129 CY 21.97 2.17 1.40 $6,438,001 Cliffside Capping 3308OS71 60 Mil Polymeric Liner, High -density Polyethylene 3,565,827 SF 0.40 0.20 0.01 $1,820,591 Cliffside Capping Barrow Excavation Equipment Cliffside Capping 17030233 Crawler -mounted, 3.125 CY, 245 Hydraulic Excavator 3,137 HR $0.00 $57.39 $79.54 4 $1,717,982 Cliffside Capping 17030706 D8 with U-Blade Bulldozer 3,137 HR $0.00 $59.86 $106.51 2 $1,043,675 Cliffside Capping 17030226 988, 7.0 CY, Wheel Loader 3,137 HR $0.00 $59.86 $84.96 2 $908,487 Cliffside Capping 17030295 35 Ton, 769, Off -highway Truck 3,137 HR $0.00 $55.72 $89.09 2 $908,424 Cliffside Capping CAP Const. Equipment Cliffside Capping 17030704 D6 with A -blade Bulldozer 3,137 HR $0.00 $59.86 $60.12 2 $752,660 Cliffside Capping 17030101 Rough Grading, D6 Dozer 360,193 SY $0.00 $0.28 $0.31 1 $212,514 Cliffside Capping 17030431 580K, 1.0 CY, Backhoe with Front-end Loader 3,137 HR $0.00 $59.86 $20.70 1 $252,685 Cliffside Capping 18050413 Watering with 3,000-Gallon Tank Truck, per Pass 74 ACR $154.66 $38.83 $36.99 1 $17,152 Cliffside Capping 17030233 Crawler -mounted, 3.125 CY, 245 Hydraulic Excavator 3,137 HR $0.00 $57.39 $79.54 1 $429,496 Cliffside Capping 33010505 50 KW, 120/240 VAC, Generator, Daily Rental 261 DAY $0.00 $0.00 $163.38 1 $42,705 Imported Material: 627,321 CY Direct Cost: $23,938,591 Direct + $38,301,746 Mayo Capping 17030423 Unclassified Fill, 6" Lifts, Off -Site, Includes Delivery, Spreading, and 262,780 CY 21.97 0.92 0.72 $6,206,633 Mayo Capping 18050301 Loam or topsoil, imported topsoil, 6" deep, furnish and place 65,695 LCY 21.17 4.94 1.43 $1,808,835 Mayo Capping 18050402 Seeding, Vegetative Cover 65 ACR 2,623.43 423.05 161.57 $209,005 Mayo Capping 33080507 Clay, Low Permeability, 6"Lifts, Off -Site 220,735 CY 21.97 2.17 1.40 $5,636,364 Mayo Capping 33080571 60 Mil Polymeric Liner, High -density Polyethylene 3,121,823 SF 0.40 0.20 0.01 $1,593,898 Mayo Capping Barrow Excavation Equipment Mayo Capping 17030233 Crawler -mounted, 3.125 CY, 245 Hydraulic Excavator 2,746 HR $0.00 $57.39 $79.54 4 $1,504,065 Mayo Capping 17030706 D8 with U-Blade Bulldozer 2,746 HR $0.00 $59.86 $106.51 2 $913,720 Mayo Capping 17030226 988, 7.0 CY, Wheel Loader 2,746 HR $0.00 $59.86 $84.96 2 $795,365 Mayo Capping 17030295 35 Ton, 769, Off -highway Truck 2,746 HR $0.00 $55.72 $89.09 2 $795,310 Mayo Capping CAP Const. Equipment Mayo Capping 17030704 D6 with A -blade Bulldozer 2,746 HR $0.001 $59.86 $60.12 2 $658,942 Mayo Capping 17030101 Rough Grading, D6 Dozer 315,326 SY $0.00 $0.28 $0.31 1 $186,042 Mayo Capping 17030431 580K, 1.0 CY, Backhoe with Front-end Loader 2,746 HR $0.00 $59.86 $20.70 1 $221,222 Mayo Capping 18050413 Watering with 3,000-Gallon Tank Truck, per Pass 65 ACR $154.66 $38.83 $36.99 1 $15,016 Mayo Capping 17030233 Crawler -mounted, 3.125 CY, 245 Hydraulic Excavator 2,746 HR $0.00 $57.39 $79.54 1 $376,016 Mayo Capping 33010505 50 KW, 120/240 VAC, Generator, Daily Rental 229 DAY $0.00 $0.00 $163.38 1 $37,387 Imported Material: 549,210 CY Direct Cost: $20,957,821 Direct+ $33,532,514 Page 2 of 2 Table S7.Off-Site Landfill Closure - Barrow Area Size INPUT Site Imported Material Volume (CY) ft3 Allen 1,323,632 35,738,072 Buck 455,937 12,310,297 Cliffside 627,321 16,937,679 Mayo 549,210 14,828,659 Total: 2,956,100 79,814,707 Barrow Parameters: Height (ft) 10 Slope: 3 Buffer Width Width (ft) 50 GOAL SEEK CALCULATION - BARROW PIT Height Bottom Top Volume (ft3) Si (ft) 61 (ft2) S2 (ft) B2 (ft2) 10 1,920 3,687,830 1,860 3,460,985 35,738,072 10 1,139 1,298,193 1,079 1,165,067 12,310,297 10 1,331 1,772,448 1,271 1,616,288 16,937,679 10 1,248 1,556,522 1,188 1,410,410 14,828,659 OUTPUT Barrow Area (ft2) Acres Buffer Acres Total Acreage 3,687,830 85 8.8 93.5 1,298,193 30 5.2 35.0 1,772,448 41 1 6.1 1 46.8 1,556,5221 36 1 5.7 1 41.5 Total: 191 1 25.9 1 217 Table S8. Excavation, Transportation and Placement of Ash Site Ash Tons Duration (yrs) Trips Travel Miles Days Hours Allen 19,270,000 35.69 963,500 48,175,000 9,635 115,620 Buck 5,310,000 9.83 265,500 13,275,000 2,655 31,860 Cliffside 7,890,000 14.61 394,500 19,725,000 3,945 47,340 Mayo 6,600,000 12.22 330,000 16,500,000 3,300 39,600 Total: 39,070,000 72.4 1,953,500 97,675,000 19,535 234,420 ject Duration Details/Assumptions: Annual Haul Rate (tons): 540,000 # On -road transport Trucks: 50 # Off -road transport trucks: 4 Truck (tons/load): 20 Truck Trips/Day: 2 Active Excavators: 4 Active Bulldozers: 2 Days/Week 6 Weeks/Year 45 Water Truck ($/day) $100 Landfill Distance (miles): 25 Hours per work day; 12 Hours per year: 3,240 Phase Name I Assembly No. Assembly Description Qty UOM Materials Labor Equipment SubBid Equip. Extended Cost Allen 1703028S 12 CY (20 Ton), Dump Truck 115,620 HR $0.00 55.72 33.41 $0.00 50 $515,260,530 Allen 17030233 Crawler -mounted, 3.125 CY, 245 Hydraulic 115,620 HR $0.00 $57.39 $79.54 $0.00 4 $63,327,386 Allen 17030706 D8 with U-Blade Bulldozer 115,620 HR $0.00 $59.86 $106.51 $0.00 2 $38,471,399 Allen 17030226 988, 7.0 CY, Wheel Loader 115,620 HR $0.00 $59.86 $84.96 $0.00 1 $16,744,088 Allen 17030295 35 Ton, 769, Off -highway Truck 115,620 HR $0.00 $55.72 $89.09 $0.00 4 $66,971,729 Allen 18050413 Watering with 3,000-Gallon Tank Truck, per Pass 9,635 DAY $0.00 $38.83 $100.00 $0.00 1 $1,337,627 Allen 17030431 580K, 1.0 CY, Backhoe with Front-end Loader 115,620 HR $0.00 $59.86 $20.70 $0.00 1 $9,314,347 Allen 33010505 10 KW, 120/240 VAC, Generator, Daily Rental 9,635 DAY $0.00 $0.00 $0.00 $163.38 1 $1,574,166 Allen 33230516 8" Submersible Pump Rental, Day 9,635 DAY $0.00 $0.00 $0.00 $48.60 2 $936,522 Landfill Spreading/Management Allen 17030704 D6 with A -blade Bulldozer 115,620 HR $0.00 $59.86 $60 $0 2 $27,744,175 Direct $741,681,970 Direct+ $1,186,691,152 Phase Name I Assembly No. Assembly Description Qty UOM Materials Labor Equipment SubBid Equip. Extended Cost Buck 17030285 12 CY (20 Ton), Dump Truck 31,860 HR $0.00 55.72 33.41 $0.00 50 $141,984,090 Buck 17030233 Crawler -mounted, 3.125 CY, 245 Hydraulic 31,860 HR $0.00 $57.39 $79.54 $0.00 4 $17,450,359 Buck 17030706 D8 with U-Blade Bulldozer 31,860 HR $0.00 $59.86 $106.51 $0.00 2 $10,601,096 Buck 17030226 988, 7.0 CY, Wheel Loader 31,860 HR $0.00 $59.86 $84.96 $0.00 1 $4,613,965 Buck 1703029S 35 Ton, 769, Off -highway Truck 31,860 HR $0.00 $55.72 $89.09 $0.00 4 $18,454,586 Buck 18050413 Watering with 3,000-Gallon Tank Truck, per Pass 2,655 DAY $0.00 $38.83 $100.00 $0.00 1 $368,594 Buck 17030431 580K, 1.0 CY, Backhoe with Front-end Loader 31,860 HR $0.00 $59.86 $20.70 $0.00 1 $2,566,642 Buck 33010505 10 KW, 120/240 VAC, Generator, Daily Rental 2,655 DAY $0.00 $0.00 $0.00 $163.38 1 $433,774 Buck 33230516 8" Submersible Pump Rental, Day 2,655 DAY $0.00 $0.00 $0.00 $48.60 2 $258,066 Landfill Spreading/Management Buck 17030704 D6 with A -blade Bulldozer 31,860 HR $0.00 $59.86 $60 $0 2 $7,645,126 Direct $204,376,298 Direct+ $327,002,077 Page 1 of 2 Table S8. Excavation, Transportation and Placement of Ash Phase Name I Assembly No. Assembly Description Qty UOM Materials Labor Equipment SubBid Equip. Extended Cost Cliffside 17030285 12 CY (20 Ton), Dump Truck 47,340 HR $0.00 55.72 33.41 $0.00 50 $210,970,710 Cliffside 17030233 Crawler -mounted, 3.125 CY, 245 Hydraulic 47,340 HR $0.00 $57.39 $79.54 $0.00 4 $25,929,065 Cliffside 17030706 D8 with U-Blade Bulldozer 47,340 HR $0.00 $59.86 $106.51 $0.00 2 $15,751,912 Cliffside 17030226 988, 7.0 CY, Wheel Loader 47,340 HR $0.00 $59.86 $84.96 $0.00 1 $6,855,779 Cliffside 17030295 35 Ton, 769, Off -highway Truck 47,340 HR $0.00 $55.72 $89.09 $0.00 4 $27,421,222 Cliffside 18050413 Watering with 3,000-Gallon Tank Truck, per Pass 3,945 DAY $0.00 $38.83 $100.00 $0.00 1 $547,684 Cliffside 17030431 580K, 1.0 CY, Backhoe with Front-end Loader 47,340 HR $0.00 $59.86 $20.70 $0.00 1 $3,813,710 Cliffside 33010505 10 KW, 120/240 VAC, Generator, Daily Rental 3,945 DAY $0.00 $0.00 $0.00 $163.38 1 $644,534 Cliffside 33230516 8" Submersible Pump Rental, Day 3,945 DAY $0.00 $0.00 $0.00 $48.60 2 $383,454 Landfill Spreading/Management Cliffside 17030704 D6 with A -blade Bulldozer 47,340 HR $0.00 $59.86 $60 $0 2 $11,359,706 Direct $303,677,776 Direct+ $485,884,442 Phase Name Assembly No. Assembly Description Qty UOM Materials Labor Equipment SubBid Equip. Extended Cost Mayo 17030285 12 CY (20 Ton), Dump Truck 39,600 HR $0.00 55.72 33.41 $0.00 50 $176,477,400 Mayo 17030233 Crawler -mounted, 3.125 CY, 245 Hydraulic 39,600 HR $0.00 $57.39 $79.54 $0.00 4 $21,689,712 Mayo 17030706 D8 with U-Blade Bulldozer 39,600 HR $0.00 $59.86 $106.51 $0.00 2 $13,176,504 Mayo 17030226 988, 7.0 CY, Wheel Loader 39,600 HR $0.00 $59.86 $84.96 $0.00 1 $5,734,872 Mayo 17030295 35 Ton, 769, Off -highway Truck 39,600 HR $0.00 $55.72 $89.09 $0.00 4 $22,937,904 Mayo 18050413 Watering with 3,000-Gallon Tank Truck, per Pass 3,300 DAY $0.00 $38.83 $10.00 $0.00 1 $161,139 Mayo 17030431 580K, 1.0 CY, Backhoe with Front-end Loader 39,600 HR $0.00 $59.86 $20.70 $0.00 1 $3,190,176 Mayo 3301050S 10 KW, 120/240 VAC, Generator, Daily Rental 3,300 DAY $0.00 $0.00 $0.00 $163.38 1 $539,154 Mayo 33230516 8" Submersible Pump Rental, Day 3,300 DAY $0.00 $0.00 $0.00 $48.60 2 $320,760 Landfill Spreading/Management Mayo 17030704 D6 with A -blade Bulldozer 39,600 HR $0.00 $59.86 $60 $0 2 $9,502,416 Direct $253,730,037 Direct+ $405,968,059 Page 2 of 2 Table S9. landfill Cost Summary Landfill Construction Design Landfill Construction Erosion/Control Engineering/Survey Acres (Landfill Site /Planning Cost Measures Oversight Site Total Foot Print) Cost/Acre Allen $5,000,000 $92,397,143 $450,689.48 $2,522,442 $100,370,274 225 $445,408 Buck $5,000,000 $31,539,723 $188,504.49 $861,034 $37,589,262 94 $398,816 Cliffside $5,000,000 $43,391,319 $242,427.43 $1,184,583 $49,818,330 121 $410,996 Mayo $5,000,000 $37,529,136 $216,041.52 $1,024,545 $43,769,723 108 $405,197 Total: $20,000,000 $204,857,321 $1,097,663 $5,592,605 $231,547,589 Ash Dig & Transport Design Erosion/Control Engineering/Survey Acres (Ash Site /Planning Ash Dig &Transport Measures Oversight Site Total Ponds) Cost/Acre Allen $1,000,000 $1,186,691,152 $644,439.62 $25,051,000 $1,213,386,592 322 $3,765,711 Buck $1,000,000 $327,002,077 $360,126.12 $6,903,000 $335,265,203 180 $1,861,932 Cliffside $1,000,000 $485,884,442 $377,373.55 $10,257,000 $497,518,815 189 $2,636,744 Mayo $1,000,000 $405,968,059 $306,675.47 $8,580,000 $415,854,735 153 $2,712,018 Total: $4,000,000 $2,405,545,730 $1,688,615 $50,791,000 $2,462,025,345 Landfill Closure/Cap Design Erosion/Control Engineering/Survey Site /Planning Closure Measures Oversight Site Total Acres (Barrow) Cost/Acre Allen $1,500,000 $82,079,021 $186,956.11 $1,433,935 $85,199,912 93 $911,443 Buck $1,500,000 $27,837,707 $70,067.45 $493,932 $29,901,706 35 $853,512 Cliffside $1,500,000 $38,301,746 $93,604.89 $679,598 $40,574,949 47 $866,941 Mayo $1,500,000 $33,532,514 $82,922.11 $594,977 $35,710,413 41 $861,300 Total: $6,000,000 $181,750,987 $433,551 $3,202,442 $191,386,980 Assumptions Erosion Meas. (per acre): $2,000 Eng/Survey Oversight Details Engineer (day) $1,500 Survey crew (day) $750 Days/Week 6 Weeks/Year 52 Hours/Day 12 Annual Hours 3744 Table S10. Cap Cost Worksheet (RACER) Folder Assembly Level Data Report (RACER) Phase Name Assembly Description Qty UOM Materials Labor Equipment Equip. Units Extended Cost Allen Unclassified Fill, 6" Lifts, Off -Site, Includes Delivery, Spreading, and Compaction 766,639 CY 21.97 0.92 0.72 $18,107,357 Allen Loam or topsoil, imported topsoil, 6" deep, furnish and place 191,660 LCY 21.17 4.94 1.43 $5,277,131 Allen Seeding, Vegetative Cover 190 ACR 2,623.43 423.05 161.57 $609,787 Allen Clay, Low Permeability, 6"Lifts, Off -Site 643,977 CY 21.97 2.17 1.40 $16,443,644 Allen 160 Mil Polymeric Liner, High -density Polyethylene 9,107,670 SF 0.401 0.20 0.01 1 $4,650,070 $45,087,989 Allen Unclassified Fill, 6" Lifts, Off -Site, Includes Delivery, Spreading, and Compaction 574,966 CY 21.97 0.92 0.72 $13,580,218 Allen Loam or topsoil, imported topsoil, 6" deep, furnish and place 143,742 LCY 21.17 4.94 1.43 $3,957,761 Allen Seeding, Vegetative Cover 143 ACR 2,623.43 423.05 161.57 $457,308 Allen Clay, Low Permeability, 6"Lifts, Off -Site 482,972 CY 21.97 2.17 1.40 $12,332,461 Allen 60 Mil Polymeric Liner, High -density Polyethylene 6,830,602 SF 0.40 0.20 0.01 $3,487,475 $33,815,224 Allen D6 with A -blade Bulldozer 16,824 HR $0.00 $59.86 $60.12 2 $4,037,022.58 Allen Rough Grading, D6 Dozer 921,406 SY $0.00 $0.28 $0.31 1 $543,629.37 Allen 580K, 1.0 CY, Backhoe with Front-end Loader 16,824 HR $0.00 $59.86 $20.70 1 $1,355,319.80 Allen Watering with 3,000-Gallon Tank Truck, per Pass 190.4 ACR $154.66 $38.83 $36.99 1 $43,877.19 Allen Crawler -mounted, 3.125 CY, 245 Hydraulic Excavator 16,824 HR $0.00 $57.39 $79.54 1 $2,303,673.54 Allen 50 KW, 120/240 VAC, Generator, Daily Rental 1,402 DAY $0.00 $0.00 $163.38 1 $229,055.10 $8,512,578 Imported Material: 2,803,955 CY Direct Cost: $87,415,791 Direct+ $139,865,265 Buck Unclassified Fill, 6" Lifts, Off -Site, Includes Delivery, Spreading, and Compaction 404,124 CY 21.97 0.92 0.72 $9,545,060 Buck Loam or topsoil, imported topsoil, 6" deep, furnish and place 101,031 LCY 21.17 4.94 1.43 $2,781,771 Buck Seeding, Vegetative Cover 100 ACR 2,623.43 423.05 161.57 $321,447 Buck Clay, Low Permeability, 6"Lifts, Off -Site 339,464 CY 21.97 2.17 1.40 $8,668,055 Buck 60 Mil Polymeric Liner, High -density Polyethylene 4,800,991 SF 0.40 0.20 0.01 $2,451,224 $23,767,557 Buck Unclassified Fill, 6" Lifts, Off -Site, Includes Delivery, Spreading, and Compaction 250,307 CY 21.97 0.92 0.72 $5,912,031 Buck Loam or topsoil, imported topsoil, 6" deep, furnish and place 62,577 LCY 21.17 4.94 1.43 $1,722,977 Buck Seeding, Vegetative Cover 62 ACR 2,623.43 423.05 161.57 $199,092 Buck Clay, Low Permeability, 6"Lifts, Off -Site 210,258 CY 21.97 2.17 1.40 $5,368,831 Buck 60 Mil Polymeric Liner, High -density Polyethylene 2,973,644 SF 0.40 0.20 0.01 $1,518,242 $14,721,173 Buck Unclassified Fill, 6" Lifts, Off -Site, Includes Delivery, Spreading, and Compaction 95,921 CY 21.97 0.92 0.72 $2,265,569 Buck Loam or topsoil, imported topsoil, 6" deep, furnish and place 23,980 LCY 21.17 4.94 1.43 $660,268 Buck Seeding, Vegetative Cover 24 ACR 2,623.43 423.05 161.57 $76,288 Buck Clay, Low Permeability, 6"Lifts, Off -Site 80,574 CY 21.97 2.17 1.40 $2,057,407 Buck 60 Mil Polymeric Liner, High -density Polyethylene 1,139,540 SF 0.40 0.20 0.01 $581,811 $5,641,342 Buck D6 with A -blade Bulldozer 10,559 HR $0.00 $59.86 $60.12 2 $2,533,825.03 Buck Rough Grading, D6 Dozer 591,673 SY $0.00 $0.28 $0.31 1 $349,086.94 Buck 580K, 1.0 CY, Backhoe with Front-end Loader 10,559 HR $0.00 $59.86 $20.70 1 $850,662.38 Buck Watering with 3,000-Gallon Tank Truck, per Pass 122.2 ACR $154.66 $38.83 $36.99 1 $28,175.36 Buck Crawler -mounted, 3.125 CY, 245 Hydraulic Excavator 10,559 HR $0.00 $57.39 $79.54 1 $1,445,893.74 Buck 50 KW, 120/240 VAC, Generator, Daily Rental 880 DAY $0.00 $0.00 $163.38 1 $143,765.74 $5,351,409 Imported Material: 1,759,894 CY Direct Cost: $49,481,481 Direct+ $79,170,369 Cliffside Unclassified Fill, 6" Lifts, Off -Site, Includes Delivery, Spreading, and Compaction 487,752 CY 21.97 0.92 0.72 $11,520,275 Cliffside Loam or topsoil, imported topsoil, 6" deep, furnish and place 121,938 LCY 21.17 4.94 1.43 $3,357,420 Cliffside Seeding, Vegetative Cover 121 ACR 2,623.43 423.05 161.57 $387,950 Cliffside Clay, Low Permeability, 6"Lifts, Off -Site 409,711 CY 21.971 2.171 1.401 $10,461,786 Page 1 of 2 Table S10. Cap Cost Worksheet (RACER) Folder Assembly Level Data Report (RACER) Phase Name Assembly Description Qty UOM Materials Labor Equipment Equip. Units Extended Cost Cliffside 60 Mil Polymeric Liner, High -density Polyethylene 5,794,488 SF 0.40 0.20 0.01 $2,958,470 $28,685,901 Cliffside Unclassified Fill, 6" Lifts, Off -Site, Includes Delivery, Spreading, and Compaction 58,357 CY 21.97 0.92 0.72 $1,378,340 Cliffside Loam or topsoil, imported topsoil, 6" deep, furnish and place 14,589 LCY 21.17 4.94 1.43 $401,697 Cliffside Seeding, Vegetative Cover 14 ACR 1 2,623.43 423.05 161.57 $46,421 Cliffside Clay, Low Permeability, 6"Lifts, Off -Site 49,020 CY 21.97 2.17 1.40 $1,251,697 Cliffside 60 Mil Polymeric Liner, High -density Polyethylene 693,280 SF 0.40 0.20 0.01 $353,965 $3,432,120 Cliffside Unclassified Fill, 6" Lifts, Off -Site, Includes Delivery, Spreading, and Compaction 241,838 CY 21.97 0.92 0.72 $5,712,016 Cliffside Loam or topsoil, imported topsoil, 6" deep, furnish and place 60,460 LCY 21.17 4.94 1.43 $1,664,685 Cliffside Seeding, Vegetative Cover 60 ACR 2,623.43 423.05 161.57 $192,355 Cliffside IClay, Low Permeability, 6"Lifts, Off -Site 203,144 CY 21.97 2.17 1.40 $5,187,193 Cliffside 60 Mil Polymeric Liner, High -density Polyethylene 2,873,040 SF 0.40 0.20 0.01 $1,466,877 $14,223,127 Cliffside D6 with A -blade Bulldozer 9,881 HR $0.00 $59.86 $60.12 2 $2,371,009.28 Cliffside Rough Grading, D6 Dozer 555,690 SY $0.00 $0.28 $0.31 1 $327,857.28 Cliffside 580K, 1.0 CY, Backhoe with Front-end Loader 9,881 HR $0.00 $59.86 $20.70 1 $796,001.45 Cliffside Watering with 3,000-Gallon Tank Truck, per Pass 114.8 ACR $154.66 $38.83 $36.99 1 $26,461.88 Cliffside Crawler -mounted, 3.125 CY, 245 Hydraulic Excavator 9,881 HR $0.00 $57.39 $79.54 1 $1,352,985.08 Cliffside 50 KW, 120/240 VAC, Generator, Daily Rental 823 DAY $0.00 $0.00 $163.38 1 $134,527.80 $5,008,843 Imported Material: 1,646,809 CY Direct Cost: $51,349,991 Direct+ $82,159,986 Mayo Unclassified Fill, 6" Lifts, Off -Site, Includes Delivery, Spreading, and Compaction 637,541 CY 21.97 0.92 0.72 $15,058,174 Mayo Loam or topsoil, imported topsoil, 6" deep, furnish and place 159,385 LCY 21.17 4.94 1.43 $4,388,490 Mayo Seeding, Vegetative Cover 158 ACR 2,623.43 423.05 161.57 $507,097 Mayo Clay, Low Permeability, 6"Lifts, Off -Site 535,534 CY 21.97 2.17 1.40 $13,674,622 Mayo 60 Mil Polymeric Liner, High -density Polyethylene 7,573,987 SF 0.40 0.20 0.01 $3,867,022 $37,495,405 Mayo D6 with A -blade Bulldozer 7,995 HR $0.00 $59.86 $60.12 2 $1,918,423.47 Mayo Rough Grading, D6 Dozer 455,264 SY $0.00 $0.28 $0.31 1 $268,605.82 Mayo 580K, 1.0 CY, Backhoe with Front-end Loader 7,995 HR $0.001 $59.86 $20.70 1 $644,058.16 Mayo Watering with 3,000-Gallon Tank Truck, per Pass 94.1 ACR $154.66 $38.83 $36.99 1 $21,679.60 Mayo Crawler -mounted, 3.125 CY, 245 Hydraulic Excavator 7,995 HR $0.00 $57.39 $79.54 1 $1,094,722.98 Mayo 50 KW, 120/240 VAC, Generator, Daily Rental 666 DAY $0.00 $0.001 $163.38 1 $108,848.71 $4,056,339 Imported Material: 1,332,461 CY Direct Cost: $41,551,744 Direct+ $66,482,791 Page 2 of 2 Table S11. Off -Site Cap Source Material - Barrow Area Size INPUT Site Imported Material Volume (CY) ft3 Allen 2,803,955 75,706,791 Buck 1,759,894 47,517,139 Cliffside 1,646,809 44,463,835 Mayo 1,332,461 35,976,436 Total: 19,463,717 525,520,366 Barrow Parameters: Height (ft) 10 Slope: 3 Buffer Width Width (ft) 50 GOAL SEEK CALCULATION - BORROW PIT Height Bottom Top Volume (ft3) S1 (ft) B1 (ft2) S2 (ft) B2 (ft2) 10 2,781 7,736,365 2,721 7,406,193 75,706,791 10 2,210 4,883,100 2,150 4,621,527 47,517,139 10 2,139 4,573,498 2,079 4,320,469 44,463,835 10 1,927 3,712,044 1,867 3,484,444 35,976,43 " OUTPUT Site Barrow Area (ft2) Acres Buffer Acres Total Acreage Allen 7,736,365 178 12.8 190.4 Buck 4,883,100 112 10.1 122.2 Cliffside 4,573,498 105 9.8 114.8 Mayo 3,712,044 85 8.8 94.1 Total: 1 480 41.6 521 Table 512. CIP Cost Summary Site Design* Construction Cost Dewatering/ Treatment Erosion/Control Measures Engineering/Survey Oversight Site Total Allen $1,000,000 $139,865,265 $2,252,966 $644,440 $3,645,142 $147,407,813 Buck $1,000,000 $79,170,369 $1,469,921 $360,126 $2,287,862 $84,288,278 Cliffside $1,000,000 $82,159,986 $1,385,107 $377,374 $2,140,851 $87,063,317 Mayo $1,000,000 $66,482,791 $1,149,345 $306,675 $1,732,199 $70,671,010 Total: $4,000,000 $367,678,411 $6,257,339 $1,688,615 $9,806,054 $389,430,419 *Planning, survey, permits Construction Cost Details Subtitle D (non -hazardous) cap design and construction Off -Site source of capping materials 40 Mil HDPE Liner Current scenario does not account for any consolidation of ash material Erosion/Control: Assumed cost of $2,000/acre Dewatering/Treatment Details System Mobilizatioi $150,000 Operation $3,000 day Utilization 50% Assume discharge under NPDES permit Eng/Survey Oversight Details Engineer $1,500 day Survey $750 day Days/Week 6 Weeks/Year 52 Imported Material Import Duration Site Volume (CY) (yrs) Trips Travel Miles Days Hours Allen 2,803,955 5.2 140,198 7,009,888 1,402 16,824 Buck 1,759,894 3.3 87,995 4,399,735 880 10,559 Cliffside 1,646,809 3.0 82,340 4,117,022 823 9,881 Mayo 1,332,461 2.5 66,623 3,331,152 666 7,995 Total: 7,543,119 14.0 377,156 18,857,796 3,772 45,259 Project Duration Details Annual Haul Rate (CY): 540,000 # Trucks: 50 Truck CY: 20 Trips/Day: 2 Days/Week 6 Weeks/Year 45 Hours/day 12 Fill Distance (miles): 25 Erosion Control & Measures $/acre 2000 Table S13. Projected Excavation Details for Stations of Interest Station Basin Approximate Volume' (Tons) Surface Area Perimeter (sq ft) (Ac) (ft) Allen Active Ash Basin 10,380,000 8,004,259 184 15,515 Inactive Ash Basin 6,160,000 6,031,636 138 13,466 Allen Basins Total 16,540,000 Ash Fill 1 430,000 Ash Fill 2 560,000 Ash Landfill and Subgrade 1,740,000 Allen Fills/Landfills Tota12 2,730,000 Allen Total 19,270,000 14,035,895 Buck Cell 1 (Additional Primary) 2,840,000 4,232,788 97 10,468 Cell (Old Primary) 1,950,000 2,613,463 60 11,339 Cell a (Secondary) 270,000 997,296 23 5,348 Buck Basins Total 5,060,000 Ash Fill Area 250,000 Buck Fills Tota12 250,000 Buck Total 5,310,000 7,843,547 180 Cliffside Active Ash Basin 5,040,000 5,089,416 117 19,329 Units 1-4 Inactive Ash Basin 300,000 622,773 14 3,136 Unit S Inactive Ash Basin 2,350,000 2,507,007 58 11,316 Cliffside Basins Total 7,690,000 Ash Storage Area 1 200,000 Cliffside Fills Tota12 200,000 Cliffside Total 7,890,000 8,219,196 E1'a Mayo Ash Pond 6,600,000 6,679,392 153 22,723 Mayo Total 6,600,000 6,679,392 153 Notes: 1. Ash quantities provided by Duke Energy. 2. Quantity includes ash located in permitted landfills, ash fills, and structural fills located on top of the ash basin(s). While these structures are not "CCR surface impoundments" under the Coal Ash Management Act and, therefore, not subject to the statute's closure provisions, this figure includes the quantities in these structures in the event Duke Energy is required to excavate them along with the basins. 3. Dimensions developed from GIS polygon analysis of ash basin configuration. EPS APPENDIX B Air Emissions Analysis of Remedial Alternatives 0 B1 INTRODUCTION The expected air emissions were estimated as part of the NEBA framework for the remedial alternatives under consideration. Air emissions can be divided into two separate categories, criteria pollutants and greenhouse gases (GHGs). 131.1 Criteria Pollutants The criteria pollutants, which were established in the Clean Air Act of 1970, consist of carbon monoxide (CO), nitrogen oxides (NOx), ozone, sulfur oxides (SOx), particulate matter (PM), and lead. These pollutants were determined to cause adverse health & environmental effects when present above specific concentrations in the atmosphere. For the purposes of this evaluation, NOx and PM were evaluated based on their expected emissions from the activities involved in the remedial activities. Based on previous experience with air emissions from remedial activities, the impacts from the other criteria pollutants were expected to be minimal and so were not included in this evaluation. For example, the sulfur content in the fuels, that are typically converted to SOx, have been reduced by regulations to the extent that they are nearly negligible. Regulations have also significantly reduced the VOC emissions from engines to the extent that they are relatively small by comparison with NOx emissions. In addition to the harmful effects of direct exposure to NOx, their emissions are also precursors in the formation of ozone (another criteria pollutant), acid rain, and fine particulate matter. Therefore, based on the anticipated quantity of NOx emissions combined with their environmental and health effects, the NOx emissions were estimated as part of this NEBA analysis. Particulate matter is subdivided into two categories: coarse particulate matter (particles that have a diameter of 10 microns or less) also known as PM10; and fine particulate matter (particles that have a diameter of 2.5 microns or less), also known as PM2.5. These are particles that generally pass through the throat and nose and enter the lungs. Once inhaled, these particles can affect the heart and lungs and cause serious health effects. Because the health effects and expected emissions of PM10 and PM2.5 are significant, they have also been estimated for this NEBA analysis. 131.2 Greenhouse Gases Greenhouse gases, which consist primarily of carbon dioxide (CO2), methane (CH4), nitrous oxide (N20), and fluorinated gases, were also determined by USEPA to have adverse health and environmental effects due to their contribution to climate change. Climate change is expected to result in public health risks associated with heat waves, increased smog, extreme weather events, and mosquito -spread diseases among others. Climate change is also expected to affect environmental factors, such as sea level and acidity. Each of the individual GHG pollutants has a Appendix B I June 30, 2016 Air Emissions Analysis of Remedial Alternatives EPS different Global Warming Potential (GWP), a relative measure of the heat trapped by each gas compared to CO2. The net effect of potential GHG emissions have been normalized to a standard called "CO2 equivalent" (CO2e) based on their relative GWPs. Appendix B 2 June 30, 2016 Air Emissions Analysis of Remedial Alternatives EPS B2 EMISSION ESTIMATION METHODOLOGY Air pollutant emissions resulting from the three remedial options were evaluated. As described above, the pollutants analyzed were restricted to nitrogen oxides (NOx), particulate matter (PM10/PM2.5), and greenhouse gases (GHG). These were selected as being the pollutants for which the impact to the environment would be significant, based on experience gained from analysis of similar projects. Appropriate emission factors were taken from a variety of regulator - approved sources, such as Off -Road Model Mobile Source Emission Factors from South Coast Air Quality Management District and Direct Emissions from Mobile Combustion Sources from USEPA Center for Corporate Climate Leadership. Each emission factor source is referenced in notes below the emissions tables appended to this Appendix report. B2.1 Monitoring Only For the first remedial alternative, which consists of leaving the ash in place and periodically monitoring the groundwater, it was assumed that the air emissions would be negligible. Further, even if the minimal emissions associated with monitoring were evaluated, these emissions would be duplicated for the other two options, as the monitoring would be required for all three options. Therefore, the first option represents the baseline for evaluating the emissions from the other two options. B2.2 Cap -in -Place (CIP) For the second option, capping -in -place, the emissions from the necessary on -site activities plus the emissions from the trucks transporting the cap material to the site were evaluated. The on -site activities consist of earth moving and compacting equipment, on -site trucks, a diesel generator, and the personal vehicles used by workers to commute to and from the site. For the truck emissions associated with the transportation of cap material, it was assumed that the material would be brought from a site 25 miles from the ash ponds. This evaluation focuses primarily on the direct emissions from the engines associated with the on -site activities and truck transport. Estimates for fugitive dust from unpaved roads and material handling are not included although it is expected that some of the dust would be PM10 or, to a significantly lesser extent, PM2.5. The duration of the project was determined based on the amount of material needed to cap the ash ponds. B2.3 Excavation and Landfilling The last option, excavation/landfilling, included emissions from on -site activities at both the ash pond location and the landfill site. The emissions from truck transport between the sites were also evaluated. It was assumed for estimating purposes that the landfill would be located 25 miles from Appendix B 3 June 30, 2016 Air Emissions Analysis of Remedial Alternatives 0 the ash ponds. The on -site activities at the ash pond includes equipment similar to what is needed for the CIP alternative. At the landfill, the analysis includes the activities required to construct the landfill, the operation of the landfill while ash is being received, and the closure of the landfill. The durations were determined based on the amount of material that would be transported during each phase. The analysis includes the direct emissions from the various activities and truck transport but does not include fugitive dust emissions. Appendix B 4 June 30, 2016 Air Emissions Analysis of Remedial Alternatives 0 B3 RESULTS The emissions estimates for each scenario are shown in the attached table, along with any assumptions and the sources of the emission factors. The results are reiterated in the following table. The PM10 and PM2.5 are combined as a single entry because the emission factor data does not distinguish between them. It can be assumed that the majority of the PM emissions are actually PM2.5, as they are they are the products of diesel fuel combustion, which primarily consists of very fine particulates. The GHG emissions are shown on an annual emissions basis (ton/yr) and cumulatively over the duration of the project, as their effects are cumulative unlike the criteria pollutants for which the impact is much shorter in duration. PM10/PM2.5 Cumulative NOx Emissions CO2e Emissions Scenario Emissions CO2e Emissions (tons/yr) (tons/yr) (tons/yr) (tons) Cap in Place 29.20 1.01 6,453.24 33,508.54 d Excavation & 138.43 5.91 23,505.31 383,819.67 Landfill Cap in Place 29.02 1.01 6,399.53 21,118.45 x U oa Excavation & Landfill 138.47 5.91 23,519.64 110,384.29 Cap in Place 29.43 1.02 6,521.54 19,564.61 w U Excavation & Landfill 138.42 5.91 23,504.36 162,560.70 Cap in Place 29.02 1.01 6,399.69 15,999.23 o Excavation & Landfill 138.47 5.91 23,518.04 136,357.55 Cap in Place 116.67 4.05 25,774.00 90,190.83 0 F- Excavation & 553.79 23.62 94,047.36 793,122.22 Landfill PSD Significant 40 15/10 NA NA Threshold GHG Reporting NA NA 25,000 NA H Threshold Appendix B 5 June 30, 2016 Air Emissions Analysis of Remedial Alternatives 0 As shown in the table, the NOx emissions from the CIP scenario are substantial, about 117 tons/yr, which is well above the significance level of 40 tons/yr established in the federal Prevention of Significant Deterioration (PSD) regulations. Under the PSD rules, a project with emissions at this level would require an extensive permitting process, including air dispersion modeling to demonstrate that the emissions would not cause an exceedance of the National Ambient Air Quality Standards. The emissions from the excavation/landfill option are even more significant and are typically about 554 tons/yr. The PM10/PM2.5 emissions for the CIP option are less than the NOx emissions, but the PSD significance thresholds are also much lower for this pollutant. As shown, the PMIO/PM2.5 emissions from the CIP alternative are estimated to be about 4 ton/yr and for the excavation/landfill alternative are about 24 ton/yr. It is important to remember that these value only includes particulate matter from fuel combustion. If the fugitive dust emissions from material handling and unpaved roads were added, recognizing that some of this particulate matter would be larger than 10 microns, the total PM10/PM2.5 emissions would be much higher. Finally, the estimated GHG emissions from the CIP and excavation/landfill alternatives are also fairly significant. Although there are few regulations limiting GHG emissions at this time, USEPA has begun requiring sources that emit 25,000 tons or more of CO2e annually to report those emissions to USEPA. This effort is being pursued by USEPA in order to develop a database of major emitters and in anticipation of future regulations. The GHG emissions from the CIP alternative exceed this threshold for both the CIP and excavation/landfill options on an annual basis (25,774 ton/yr and 94,047 ton/yr, respectively). Additionally, it is important to note that there are also other indirect GHG emissions from the remediation, as there are GHG emissions associated with the production and delivery of the fuels used by the various equipment associated with the alternatives. A more complete analysis would likely show that the carbon footprint (direct plus indirect GHG emissions) for these alternatives are much higher than these estimates. Based on the estimated air emissions and their comparisons with regulatory thresholds, it is apparent that the air quality impact from capping in place would be considerably higher than monitoring the ash ponds without capping. The air emissions from excavation of the ash and transporting it to a landfill would be even more substantial. Appendix B 6 June 30, 2016 Air Emissions Analysis of Remedial Alternatives REMOVAL WITH MNA CAPPING WITH MNA Site NOx PM10/PM2.5 CO2e Cumulative CO2e NOx PM10/PM2.5 CO2e Cumulative CO2e Emissions Emissions Emissions Emissions Emissions Emissions Emissions Emissions (tons/yr) (tons/yr) (tons/yr) (tons) (tons/yr) (tons/yr) (tons/yr) (tons) Allen 138.43 5.91 23,505.31 383,819.67 29.20 1.01 6,4S3.24 33,508.54 Buck 138.47 5.91 23,519.64 110,384.29 29.02 1.01 6,399.53 21,118AS Cliffside 138.42 5.91 23,504.36 162,S60.70 29.43 1.02 6,S21.54 19,564.61 Mayo 138.47 5.91 23,518.04 136,357.55 29.02 1.01 6,399.69 1S,999.23 Total 553.79 23.62 94,047.36 793,122.22 116.67 4.05 25,774.00 90,190.83 Thresholds PSD Significant 40 15/10 NA NA 40 15/10 NA NA Threshold GHG Reporting NA NA 25,000 NA NA NA 25,000 NA Threshold Option 1: Monitor Only Since similar monitoring would be required for each option for the project duration, similar emissions are expected for each and the emissions are not included in this estimate . It is expected that the emissions contributions from personal vehicles due to monitoring would be insignificant compared to the other emission sources. Option 2: Cap in Place (CIP) CIP Duration = 12 hr/day 270 day/yr 5.2 years 140198 truck trips carrying cap material 203 truck trips carrying liner material Usage Emission Factors Emissions Cumulative Emissions NOx PM10/PM2.5 CO2 CH4 (tons/yr) (tons) Activity Value Units Value Units Value Units Value Units Value Units NOx PM10/PM2.5 CO2e CO2e 2 Dozers' 6480 hr/yr 2.0891 Ib/hr 0.0858 Ib/hr 239 Ib/hr 0.0234 Ib/hr 6.77 0.28 776.26 4030.71 Tractor' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 563.07 Wheeled Backhoe' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 563.07 Water Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 2190.53 Vibratory Roller' 3240 hr/yr 0.5273 Ib/hr 0.0353 Ib/hr 67 Ib/hr 0.0071 Ib/hr 0.85 0.06 108.83 565.09 Tracked Excavator' 3240 hr/yr 0.6603 Ib/hr 0.0332 Ib/hr 120 Ib/hr 0.0089 Ib/hr 1.07 0.05 194.76 1011.30 Maint/Service Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 2190.53 55 KVA Diesel Generator' 3240 hr/yr 0.6102 Ib/hr 0.0431 Ib/hr 77.9 Ib/hr 0.0073 Ib/hr 0.99 0.07 126.49 656.82 15 Personal Vehicles2,3,1,5,6 121500 miles/yr 0.95 g/mile 0.0045 g/mile 410 g/mile 0.0452 g/mile 0.13 0.00 55.10 286.11 Truck Transport- Cap Material''8'9'10 27000 truck trips/yr 459.55 g/truck trip 10.75 g/truck trip 138000 g/truck trip 0.255 g/truck trip 13.68 0.32 4107.41 21327.78 Truck Transport - Liner Material''8.9,10,11 39 truck trips/yr I 1838.20 g/truck trip 1 43.00 Ig/truck trip I 552000 g/truck trip 1 1.02 g/truck trip 0.08 0.00 23.79 123.53 1 29.20 1.01 6,453.24 33,508.54 Option 3 Landfill Construction Duration = Excavation Duration = Closure Duration = Excavation and Landfill 12 hr/day 300 day/yr 3.6 years 204 truck trips carrying liner material (construction and closure) 12 hr/day 270 day/yr 35.7 years 963500 truck trips carrying ash 12 hr/day 270 day/yr 2.0 years 33091 truck trips carrying closure material Usage Emission Factors Emissions Cumulative Emissions NOx PM10/PM2.5 CO2 CH4 (tons/yr) (tons) Activity Value Units Value Units Value Units Value Units Value Units NOx PM10/PM2.5 CO2e CO2e EXCAVATION 2 Tracked Excavators' 6480 hr/yr 0.6603 Ib/hr 0.0332 Ib/hr 120 Ib/hr 0.0089 Ib/hr 2.14 0.11 389.52 13905.90 2 Dozers' 6480 hr/yr 2.0891 Ib/hr 0.0858 Ib/hr 239 Ib/hr 0.0234 Ib/hr 6.77 0.28 776.26 27712.32 Wheeled Loader' 3240 hr/yr 0.7114 Ib/hr 0.0375 Ib/hr 109 Ib/hr 0.0089 Ib/hr 1.15 0.06 176.94 6316.77 2 Dump Trucks' 6480 hr/yr 0.0587 Ib/hr 0.0024 Ib/hr 7.6 Ib/hr 0.0008 Ib/hr 0.19 0.01 24.69 881.39 Water Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 15060.55 Wheeled Backhoe' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 3871.26 2 6-8" Pumps' 6480 hr/yr 0.3830 Ib/hr 0.0239 Ib/hr 49.6 Ib/hr 0.0051 Ib/hr 1.24 0.08 161.12 5751.88 55 KVA Diesel Generator' 3240 hr/yr 0.6102 Ib/hr 0.0431 Ib/hr 77.9 Ib/hr 0.0073 Ib/hr 0.99 0.07 126.49 4515.82 15 Personal Vehicles2,3,4,5 121500 miles/yr 0.95 g/mile 0.0045 g/mile 410 g/mile 0.0452 g/mile 0.13 0.001 55.101 1967.09 Usage Emission Factors Emissions Cumulative Emissions NOx PM10/PM2.5 CO2 CH4 (tons/yr) (tons) Activity Value Units Value Units Value Units Value Units Value Units NOx PM10/PM2.5 CO2e CO2e MATERIAL TRANSPORT Truck Transport - Ash''s,9,10 26989 truck trips/yr 459.55 g/truck trip 10.75 g/truck trip 138000 g/truck trip 0.2550 g/truck trip 13.67 0.32 4105.70 146573.51 Truck Transport- Closure Material''$'9'10 16546 truck trips/yr 18.38 g/truck trip 0.43 g/truck trip 5520 g/trucktrip 0.0102 g/truck trip 0.34 0.01 100.68 201.36 Truck Transport - Liner Material"s'9,10,i1 36 truck trips/yr 1838.20 g/truck trip 43 g/truck trip 552000 g/truck trip 1.0200 g/truck trip 0.07 0.00 22.17 124.13 LANDFILL CONSTRUCT. 8 Tracked Excavators' 25920 hr/yr 0.6603 Ib/hr 0.0332 Ib/hr 120 Ib/hr 0.0089 Ib/hr 8.56 0.43 1558.08 8725.27 8 Dozers' 25920 hr/yr 2.0891 Ib/hr 0.0858 Ib/hr 239 Ib/hr 0.0234 Ib/hr 27.07 1.11 3105.02 17388.12 4 Vibratory Compactors' 25920 hr/yr 0.568 Ib/hr 0.0234 Ib/hr 123 Ib/hr 0.0065 Ib/hr 7.36 0.30 1596.19 8938.64 4 Wheeled Loaders' 12960 hr/yr 0.7114 Ib/hr 0.0375 Ib/hr 109 Ib/hr 0.0089 Ib/hr 4.61 0.24 707.76 3963.47 8 Dump Trucks' 25920 hr/yr 0.0587 Ib/hr 0.0024 Ib/hr 7.6 Ib/hr 0.0008 Ib/hr 0.76 0.03 98.76 553.03 4 Water Trucks' 12960 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 8.63 0.30 1687.46 9449.76 4 Wheeled Backhoes' 12960 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 2.64 0.17 433.76 2429.03 4 6-8" Pumps' 25920 hr/yr 0.3830 Ib/hr 0.0239 Ib/hr 49.6 Ib/hr 0.0051 Ib/hr 4.96 0.31 644.47 3609.02 4 Maint/Service Trucks' 12960 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 1 8.63 0.30 1687.46 9449.76 4 55 KVA Diesel Generator' 12960 hr/yr 0.6102 Ib/hr 0.0431 Ib/hr 77.9 Ib/hr 0.0073 Ib/hr 3.95 0.28 505.97 2833.46 100 Personal Vehicles2'3'4,5 810000 miles/yr 0.95 g/mile 0.0045 g/mile 410 g/mile 0.0452 g/mile 0.85 0.00 367 2057.09 LANDFILL FILLING 2 Dozers' 6480 hr/yr 2.0891 Ib/hr 0.0858 Ib/hr 239 Ib/hr 0.0234 Ib/hr 6.77 0.28 776.26 27712.32 Tractor' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 3871.26 Wheeled Backhoe' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 3871.26 Water Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 15060.55 Vibratory Roller' 3240 hr/yr 0.5273 Ib/hr 0.0353 Ib/hr 67 Ib/hr 0.0071 Ib/hr 0.85 0.06 108.83 3885.14 Tracked Excavator' 3240 hr/yr 0.6603 Ib/hr 0.0332 Ib/hr 120 Ib/hr 0.0089 Ib/hr 1.07 0.05 194.76 6952.95 Maint/Service Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 15060.55 55 KVA Diesel Generator' 3240 hr/yr 0.6102 Ib/hr 0.0431 Ib/hr 77.9 Ib/hr 0.0073 Ib/hr 1 0.99 0.07 126.49 4515.82 15 Personal Vehicles2'3'4'5'6 121500 miles/yr 0.95 g/mile 0.0045 g/mile 410 g/mile 0.0452 g/mile 0.13 0.00 55.10 1967.09 LANDFILL CLOSURE 2 Dozers' 6480 hr/yr 2.0891 Ib/hr 0.0858 Ib/hr 239 Ib/hr 0.0234 Ib/hr 6.77 0.28 776.26 1552.51 Tractor' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 216.88 Wheeled Backhoe' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 216.88 Water Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 843.73 Vibratory Roller' 3240 hr/yr 0.5273 Ib/hr 0.0353 Ib/hr 67 Ib/hr 0.0071 Ib/hr 0.85 0.06 108.83 217.66 Tracked Excavator' 3240 hr/yr 0.6603 Ib/hr 0.0332 Ib/hr 120 Ib/hr 0.0089 Ib/hr 1.07 0.05 194.76 389.52 Maint/Service Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 843.73 55 KVA Diesel Generator' 3240 hr/yr 0.6102 Ib/hr 0.0431 Ib/hr 77.9 Ib/hr 0.0073 Ib/hr 0.99 0.07 126.49 252.99 15 Personal Vehicles2'3'4'5'6 121500 miles/yr 0.95 g/mile 0.0045 g/mile 410 g/mile 0.0452 Ig/mile 1 0.13 0.00 55.10 110.20 138.43 5.91 23,505.31 383,819.67 Usage Emission Factors I Emissions Cumulative Emissions NOx PM10/PM2.5 CO2 CH4 (tons/yr) (tons) Activity Value I Units I Value I Units Value I Units Value I Units I Value I Units I NOx I PM10/P 2.51 CO2e I CO2e 1. South Coast Air Quality Management District Off -Road Model Mobile Source Emission Factors for a 2016 fleet average from http://www.agmd.gov/home/regulations/ceqa/air-quality-analysis-handbook/off-road-mobile-source- emission-factors. 2. USEPA Center for Corporate Climate Leadership Greenhouse Gas Inventory Guidance: Direct Emissions from Mobile Combustion Sources Tier 1 Gasoline Light -Duty Truck emission factors (0.0452 g CH4/mile) from https://www.epa.gov/sites/prod uction/files/2016-03/documents/mobi leem issions_3_2016. pdf. 3. Personally Operated Vehicle (POV) use is estimated as: -Excavation: 15 persons/day * 30 miles/person * 270 day/yr (it is assumed each person travels 15-miles one-way to work); -Landfill Construction: 25 persons/day * 30 miles/person * 270 day/yr. 4. USDOT Bureau of Transportation Statistics Average Fuel Efficiency of U.S. Light Duty Vehicles for calendar year 2014 (21.4 miles/gal) for POV CO2 emissions from http://www. rita. dot.gov/bts/sites/rita.dot.gov. bts/files/publications/nati onal_transportation_statisti cs/htm I/table_04_23. htm I. S. 100-yr GWP for CH4 (25 kg CO2e/kg CH4) from Intergovernmental Panel on Climate Change (IPCC), Fourth Assessment Report (AR4), 2007. 6. USEPA Office of Transportation and Air Quality Average Annual Emissions and Fuel Consumption forGasoline-FueledPassenger Cars and Light Trucks light -duty trucks emissions estimates for the in -use fleet as of 2008 based on https://www3.epa.gov/otaq/consumer/42OfO8O24.pdf. 7. It is assumed that the landfill location is 25 miles away, so 1 truck trip carrying ash is 50 miles, and 20-ton trucks are used. It is assumed that the landfill cap/closure material for Option 3 is taken from an adjacent land parcel, thus 1 truck trip carrying cap/closure material is taken to be 2 miles. It is assumed that the landfill cap/closure material for Option 2 is taken from a site 25 miles aways, so 1 truck trip carrying cap/closure material for Option 2 is taken to be 50 miles. 8. USEPA Office of Transportation and Air Quality Average In -Use Emissions from Heavy -Duty Trucks NOx and PM10/PM2.5 emission factors approximated as a diesel Heavy -Duty Vehicle Class Villa (33,001-60,000 lb GVWR--since it is for 20 ton (40,000 lb) material +truck) --based on the in -use fleet from July 2008 from https://www3.epa.gov/otaq/consumer/420f08027.pdf. 9. USEPA Center for Corporate Climate Leadership Emission Factors for Greenhouse Gas Inventories greenhouse gas emission factors approximated as heavy duty vehicles for 1960-present from Table 4 of https://www.epa.gov/sites/production/files/2015-12/documents/emission-factors_nov_2015.pdf, dated November 2015. 10. It is assumed that the weight of the vehicle + load is approximately 60,000 lb (20-ton load and 10-ton truck). Diesel heavy duty combination truck CO2 emission factor for MY2014/Class 8/Day Cab is 92 g CO2/ton-mile taken from https://www.gpo.gov/fdsys/pkg/FR-2011-09-15/pdf/2011-20740.pdf (EPA/DOT Federal Register Vol. 76, No. 179, 15 September 2011). -92 g CO2/ton-mile * (30 ton*x mile)/truck trip = y g CO2/truck trip 11. It is assumed that the liner is transported 100 miles to the site/landfill on public roads and approximately 10 tons of liner are transported per trip (3240 lb/roll * 1 ton/2000 lb * 6 rolls/truck trip = 10 ton/truck trip). Option 1: Monitor Only Since similar monitoring would be required for each option for the project duration, similar emissions are expected for each and the emissions are not included in this estimate . It is expected that the emissions contributions from personal vehicles due to monitoring would be insignificant compared to the other emission sources. Option 2: Cap in Place (CIP) CIP Duration = 12 hr/day 270 day/yr 3.3 years 87995 truck trips carrying cap material 114 truck trips carrying liner material Usage Emission Factors Emissions Cumulative Emissions NOx PM10/PM2.5 CO2 CH4 (tons/yr) (tons) Activity Value Units Value Units Value Units Value Units Value Units NOx PM10/PM2.5 CO2e CO2e 2 Dozers' 6480 hr/yr 2.0891 Ib/hr 0.0858 Ib/hr 239 Ib/hr 0.0234 Ib/hr 6.77 0.28 776.26 2561.64 Tractor' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 357.85 Wheeled Backhoe' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 357.85 Water Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 1392.15 Vibratory Roller' 3240 hr/yr 0.5273 Ib/hr 0.0353 Ib/hr 67 Ib/hr 0.0071 Ib/hr 0.85 0.06 108.83 359.13 Tracked Excavator' 3240 hr/yr 0.6603 Ib/hr 0.0332 Ib/hr 120 Ib/hr 0.0089 Ib/hr 1.07 0.05 194.76 642.71 Maint/Service Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 1392.15 55 KVA Diesel Generator' 3240 hr/yr 0.6102 Ib/hr 0.0431 Ib/hr 77.9 Ib/hr 0.0073 Ib/hr 0.99 0.07 126.49 417.43 15 Personal Vehicles2,3,1,5,6 121500 miles/yr 0.95 g/mile 0.0045 g/mile 410 g/mile 0.0452 g/mile 0.13 0.00 55.10 181.83 Truck Transport- Cap Material''8'9'10 26665 truck trips/yr 459.55 g/truck trip 10.75 g/truck trip 138000 g/truck trip 0.255 g/truck trip 13.51 0.32 4056.47 13386.34 Truck Transport - Liner Material''8.9,10,11 35 truck trips/yr I 1838.20 g/truck trip 1 43.00 Ig/truck trip I 552000 g/truck trip 1 1.02 g/truck trip 0.07 0.00 21.02 69.37 1 29.02 1.01 6,399.53 21,118.45 Option 3 Landfill Construction Duration = Excavation Duration = Closure Duration = Excavation and Landfill 12 hr/day 300 day/yr 1.2 years 70 truck trips carrying liner material (construction and closure) 12 hr/day 270 day/yr 9.8 years 265500 truck trips carrying ash 12 hr/day 270 day/yr 0.7 years 11398 truck trips carrying closure material Usage Emission Factors Emissions Cumulative Emissions NOx PM10/PM2.5 CO2 CH4 (tons/yr) (tons) Activity Value Units Value Units Value Units Value Units Value Units NOx PM10/PM2.5 CO2e CO2e EXCAVATION 2 Tracked Excavators' 6480 hr/yr 0.6603 Ib/hr 0.0332 Ib/hr 120 Ib/hr 0.0089 Ib/hr 2.14 0.11 389.52 3817.30 2 Dozers' 6480 hr/yr 2.0891 Ib/hr 0.0858 Ib/hr 239 Ib/hr 0.0234 Ib/hr 6.77 0.28 776.26 7607.30 Wheeled Loader' 3240 hr/yr 0.7114 Ib/hr 0.0375 Ib/hr 109 Ib/hr 0.0089 Ib/hr 1.15 0.06 176.94 1734.02 2 Dump Trucks' 6480 hr/yr 0.0587 Ib/hr 0.0024 Ib/hr 7.6 Ib/hr 0.0008 Ib/hr 0.19 0.01 24.69 241.95 Water Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 4134.27 Wheeled Backhoe' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 1062.70 2 6-8" Pumps' 6480 hr/yr 0.3830 Ib/hr 0.0239 Ib/hr 49.6 Ib/hr 0.0051 Ib/hr 1.24 0.08 161.12 1578.95 55 KVA Diesel Generator' 3240 hr/yr 0.6102 Ib/hr 0.0431 Ib/hr 77.9 Ib/hr 0.0073 Ib/hr 0.99 0.07 126.49 1239.64 15 Personal Vehicles2,3,4,5 121500 miles/yr 0.95 g/mile 0.0045 g/mile 410 g/mile 0.0452 g/mile 0.13 0.001 55.101 539.99 Usage Emission Factors Emissions Cumulative Emissions NOx PM10/PM2.5 CO2 CH4 (tons/yr) (tons) Activity Value Units Value Units Value Units Value Units Value Units NOx PM10/PM2.5 CO2e CO2e MATERIAL TRANSPORT Truck Transport - Ash''s,9,10 27092 truck trips/yr 459.55 g/truck trip 10.75 g/truck trip 138000 g/truck trip 0.2550 g/truck trip 13.72 0.32 4121.38 40389.48 Truck Transport- Closure Material''$'9'10 16283 truck trips/yr 18.38 g/truck trip 0.43 g/truck trip 5520 g/trucktrip 0.0102 g/truck trip 0.33 0.01 99.08 69.36 Truck Transport - Liner Material"s'9,10,i3 37 truck trips/yr 1838.20 g/truck trip 43 g/truck trip 552000 g/truck trip 1.0200 g/truck trip 0.07 0.00 22.42 42.60 LANDFILL CONSTRUCT. 8 Tracked Excavators' 25920 hr/yr 0.6603 Ib/hr 0.0332 Ib/hr 120 Ib/hr 0.0089 Ib/hr 8.56 0.43 1558.08 2960.36 8 Dozers' 25920 hr/yr 2.0891 Ib/hr 0.0858 Ib/hr 239 Ib/hr 0.0234 Ib/hr 27.07 1.11 3105.02 5899.54 4 Vibratory Compactors' 25920 hr/yr 0.568 Ib/hr 0.0234 Ib/hr 123 Ib/hr 0.0065 Ib/hr 7.36 0.30 1596.19 3032.75 4 Wheeled Loaders' 12960 hr/yr 0.7114 Ib/hr 0.0375 Ib/hr 109 Ib/hr 0.0089 Ib/hr 4.61 0.24 707.76 1344.75 8 Dump Trucks' 25920 hr/yr 0.0587 Ib/hr 0.0024 Ib/hr 7.6 Ib/hr 0.0008 Ib/hr 0.76 0.03 98.76 187.63 4 Water Trucks' 12960 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 8.63 0.30 1687.46 3206.17 4 Wheeled Backhoes' 12960 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 2.64 0.17 433.76 824.13 4 6-8" Pumps' 25920 hr/yr 0.3830 Ib/hr 0.0239 Ib/hr 49.6 Ib/hr 0.0051 Ib/hr 4.96 0.31 644.47 1224.49 4 Maint/Service Trucks' 12960 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 8.63 0.30 1687.46 3206.17 4 55 KVA Diesel Generator' 12960 hr/yr 0.6102 Ib/hr 0.0431 Ib/hr 77.9 Ib/hr 0.0073 Ib/hr 3.95 0.28 505.97 961.35 100 Personal Vehicles2'3'4'5 810000 miles/yr 0.95 g/mile 0.0045 g/mile 410 g/mile 0.0452 g/mile 0.85 0.00 367 697.94 LANDFILL FILLING 2 Dozers' 6480 hr/yr 2.0891 Ib/hr 0.0858 Ib/hr 239 Ib/hr 0.0234 Ib/hr 6.77 0.28 776.26 7607.30 Tractor' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 1062.70 Wheeled Backhoe' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 1062.70 Water Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 4134.27 Vibratory Roller' 3240 hr/yr 0.5273 Ib/hr 0.0353 Ib/hr 67 Ib/hr 0.0071 Ib/hr 0.85 0.06 108.83 1066.51 Tracked Excavator' 3240 hr/yr 0.6603 Ib/hr 0.0332 Ib/hr 120 Ib/hr 0.0089 Ib/hr 1.07 0.05 194.76 1908.65 Maint/Service Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 4134.27 55 KVA Diesel Generator' 3240 hr/yr 0.6102 Ib/hr 0.0431 Ib/hr 77.9 Ib/hr 0.0073 Ib/hr 0.99 0.07 126.49 1239.64 Personal Vehicles2,3,4,5,6 121500 miles/yr 0.95 g/mile 0.0045 g/mile 410 g/mile 0.0452 g/mile 0.13 0.00 55.10 539.99 LANDFILL CLOSURE 2 Dozers' 6480 hr/yr 2.0891 Ib/hr 0.0858 Ib/hr 239 Ib/hr 0.0234 Ib/hr 6.77 0.28 776.26 543.38 Tractor' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 75.91 Wheeled Backhoe' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 75.91 Water Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 295.30 Vibratory Roller' 3240 hr/yr 0.5273 Ib/hr 0.0353 Ib/hr 67 Ib/hr 0.0071 Ib/hr 0.85 0.06 108.83 76.18 Tracked Excavator' 3240 hr/yr 0.6603 Ib/hr 0.0332 Ib/hr 120 Ib/hr 0.0089 Ib/hr 1.07 0.05 194.76 136.33 Maint/Service Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 295.30 55 KVA Diesel Generator' 3240 hr/yr 0.6102 Ib/hr 0.0431 Ib/hr 77.9 Ib/hr 0.0073 Ib/hr 0.99 0.07 126.49 88.55 Personal VehicleS1,3,4,1'6 121500 miles/yr 0.95 g/mile 0.0045 g/mile 410 g/mile 0.0452 g/mile 0.13 0.00 55.10 38.57 138.47 5.91 23,519.64 110,384.29 Usage Emission Factors I Emissions Cumulative Emissions NOx PM10/PM2.5 CO2 CH4 (tons/yr) (tons) Activity Value I Units I Value I Units Value I Units Value I Units I Value I Units I NOx I PM10/P 2.51 CO2e I CO2e 1. South Coast Air Quality Management District Off -Road Model Mobile Source Emission Factors for a 2016 fleet average from http://www.agmd.gov/home/regulations/ceqa/air-quality-analysis-handbook/off-road-mobile-source- emission-factors. 2. USEPA Center for Corporate Climate Leadership Greenhouse Gas Inventory Guidance: Direct Emissions from Mobile Combustion Sources Tier 1 Gasoline Light -Duty Truck emission factors (0.0452 g CH4/mile) from https://www.epa.gov/sites/prod uction/files/2016-03/documents/mobi leem issions_3_2016. pdf. 3. Personally Operated Vehicle (POV) use is estimated as: -Excavation: 15 persons/day * 30 miles/person * 270 day/yr (it is assumed each person travels 15-miles one-way to work); -Landfill Construction: 25 persons/day * 30 miles/person * 270 day/yr. 4. USDOT Bureau of Transportation Statistics Average Fuel Efficiency of U.S. Light Duty Vehicles for calendar year 2014 (21.4 miles/gal) for POV CO2 emissions from http://www. rita. dot.gov/bts/sites/rita.dot.gov. bts/files/publications/nati onal_transportation_statisti cs/htm I/table_04_23. htm I. S. 100-yr GWP for CH4 (25 kg CO2e/kg CH4) from Intergovernmental Panel on Climate Change (IPCC), Fourth Assessment Report (AR4), 2007. 6. USEPA Office of Transportation and Air Quality Average Annual Emissions and Fuel Consumption forGasoline-FueledPassenger Cars and Light Trucks light -duty trucks emissions estimates for the in -use fleet as of 2008 based on https://www3.epa.gov/otaq/consumer/42OfO8O24.pdf. 7. It is assumed that the landfill location is 25 miles away, so 1 truck trip carrying ash is 50 miles, and 20-ton trucks are used. It is assumed that the landfill cap/closure material for Option 3 is taken from an adjacent land parcel, thus 1 truck trip carrying cap/closure material is taken to be 2 miles. It is assumed that the landfill cap/closure material for Option 2 is taken from a site 25 miles aways, so 1 truck trip carrying cap/closure material for Option 2 is taken to be 50 miles. 8. USEPA Office of Transportation and Air Quality Average In -Use Emissions from Heavy -Duty Trucks NOx and PM10/PM2.5 emission factors approximated as a diesel Heavy -Duty Vehicle Class Villa (33,001-60,000 lb GVWR--since it is for 20 ton (40,000 lb) material +truck) --based on the in -use fleet from July 2008 from https://www3.epa.gov/otaq/consumer/420f08027.pdf. 9. USEPA Center for Corporate Climate Leadership Emission Factors for Greenhouse Gas Inventories greenhouse gas emission factors approximated as heavy duty vehicles for 1960-present from Table 4 of https://www.epa.gov/sites/production/files/2015-12/documents/emission-factors_nov_2015.pdf, dated November 2015. 10. It is assumed that the weight of the vehicle + load is approximately 60,000 lb (20-ton load and 10-ton truck). Diesel heavy duty combination truck CO2 emission factor for MY2014/Class 8/Day Cab is 92 g CO2/ton-mile taken from https://www.gpo.gov/fdsys/pkg/FR-2011-09-15/pdf/2011-20740.pdf (EPA/DOT Federal Register Vol. 76, No. 179, 15 September 2011). -92 g CO2/ton-mile * (30 ton*x mile)/truck trip = y g CO2/truck trip 11. It is assumed that the liner is transported 100 miles to the site/landfill on public roads and approximately 10 tons of liner are transported per trip (3240 lb/roll * 1 ton/2000 lb * 6 rolls/truck trip = 10 ton/truck trip). Option 1: Monitor Only Since similar monitoring would be required for each option for the project duration, similar emissions are expected for each and the emissions are not included in this estimate . It is expected that the emissions contributions from personal vehicles due to monitoring would be insignificant compared to the other emission sources. Option 2: Cap in Place (CIP) CIP Duration = 12 hr/day 270 day/yr 3.0 years 82340 truck trips carrying cap material 119 truck trips carrying liner material Usage Emission Factors Emissions Cumulative Emissions NOx PM10/PM2.5 CO2 CH4 (tons/yr) (tons) Activity Value Units Value Units Value Units Value Units Value Units NOx PM10/PM2.5 CO2e CO2e 2 Dozers' 6480 hr/yr 2.0891 Ib/hr 0.0858 Ib/hr 239 Ib/hr 0.0234 Ib/hr 6.77 0.28 776.26 2328.77 Tractor' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 325.32 Wheeled Backhoe' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 325.32 Water Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 1265.59 Vibratory Roller' 3240 hr/yr 0.5273 Ib/hr 0.0353 Ib/hr 67 Ib/hr 0.0071 Ib/hr 0.85 0.06 108.83 326.48 Tracked Excavator' 3240 hr/yr 0.6603 Ib/hr 0.0332 Ib/hr 120 Ib/hr 0.0089 Ib/hr 1.07 0.05 194.76 584.28 Maint/Service Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 1265.59 55 KVA Diesel Generator' 3240 hr/yr 0.6102 Ib/hr 0.0431 Ib/hr 77.9 Ib/hr 0.0073 Ib/hr 0.99 0.07 126.49 379.48 15 Personal Vehicles2,3,1,5,6 121500 miles/yr 0.95 g/mile 0.0045 g/mile 410 g/mile 0.0452 g/mile 0.13 0.00 55.10 165.30 Truck Transport- Cap Material''8'9'10 27447 truck trips/yr 459.55 g/truck trip 10.75 g/truck trip 138000 g/truck trip 0.255 g/truck trip 13.90 0.33 4175.35 12526.06 Truck Transport - Liner Material''8.9,10,11 40 truck trips/yr I 1838.20 g/truck trip 1 43.00 Ig/truck trip I 552000 g/truck trip 1 1.02 g/truck trip 0.08 0.00 24.14 72.41 1 29.43 1.02 6,521.S4 19,564.61 Option 3 Landfill Construction Duration = Excavation Duration = Closure Duration = Excavation and Landfill 12 hr/day 300 day/yr 1.7 years 96 truck trips carrying liner material (construction and closure) 12 hr/day 270 day/yr 14.6 years 394500 truck trips carrying ash 12 hr/day 270 day/yr 1.0 years 15683 truck trips carrying closure material Usage Emission Factors Emissions Cumulative Emissions NOx PM10/PM2.5 CO2 CH4 (tons/yr) (tons) Activity Value Units Value Units Value Units Value Units Value Units NOx PM10/PM2.5 CO2e CO2e EXCAVATION 2 Tracked Excavators' 6480 hr/yr 0.6603 Ib/hr 0.0332 Ib/hr 120 Ib/hr 0.0089 Ib/hr 2.14 0.11 389.52 5687.01 2 Dozers' 6480 hr/yr 2.0891 Ib/hr 0.0858 Ib/hr 239 Ib/hr 0.0234 Ib/hr 6.77 0.28 776.26 11333.33 Wheeled Loader' 3240 hr/yr 0.7114 Ib/hr 0.0375 Ib/hr 109 Ib/hr 0.0089 Ib/hr 1.15 0.06 176.94 2583.33 2 Dump Trucks' 6480 hr/yr 0.0587 Ib/hr 0.0024 Ib/hr 7.6 Ib/hr 0.0008 Ib/hr 0.19 0.01 24.69 360.46 Water Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 6159.22 Wheeled Backhoe' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 1583.21 2 6-8" Pumps' 6480 hr/yr 0.3830 Ib/hr 0.0239 Ib/hr 49.6 Ib/hr 0.0051 Ib/hr 1.24 0.08 161.12 2352.31 55 KVA Diesel Generator' 3240 hr/yr 0.6102 Ib/hr 0.0431 Ib/hr 77.9 Ib/hr 0.0073 Ib/hr 0.99 0.07 126.49 1846.81 15 Personal Vehicles2,3,4,5 121500 miles/yr 0.95 g/mile 0.0045 g/mile 410 g/mile 0.0452 g/mile 0.13 0.001 55.101 804.47 Usage Emission Factors Emissions Cumulative Emissions NOx PM10/PM2.5 CO2 CH4 (tons/yr) (tons) Activity Value Units Value Units Value Units Value Units Value Units NOx PM10/PM2.5 CO2e CO2e MATERIAL TRANSPORT Truck Transport - Ash''s,9,10 27021 truck trips/yr 459.55 g/truck trip 10.75 g/truck trip 138000 g/truck trip 0.2550 g/truck trip 13.69 0.32 4110.53 60013.75 Truck Transport- Closure Material''$'9'10 15683 truck trips/yr 18.38 g/truck trip 0.43 g/truck trip 5520 g/trucktrip 0.0102 g/truck trip 0.32 0.01 95.43 95.43 Truck Transport - Liner Material"s'9,10,i3 36 truck trips/yr 1838.20 g/truck trip 43 g/truck trip 552000 g/truck trip 1.0200 g/truck trip 0.07 0.00 21.64 58.42 LANDFILL CONSTRUCT. 8 Tracked Excavators' 25920 hr/yr 0.6603 Ib/hr 0.0332 Ib/hr 120 Ib/hr 0.0089 Ib/hr 8.56 0.43 1558.08 4206.83 8 Dozers' 25920 hr/yr 2.0891 Ib/hr 0.0858 Ib/hr 239 Ib/hr 0.0234 Ib/hr 27.07 1.11 3105.02 8383.56 4 Vibratory Compactors' 25920 hr/yr 0.568 Ib/hr 0.0234 Ib/hr 123 Ib/hr 0.0065 Ib/hr 7.36 0.30 1596.19 4309.70 4 Wheeled Loaders' 12960 hr/yr 0.7114 Ib/hr 0.0375 Ib/hr 109 Ib/hr 0.0089 Ib/hr 4.61 0.24 707.76 1910.96 8 Dump Trucks' 25920 hr/yr 0.0587 Ib/hr 0.0024 Ib/hr 7.6 Ib/hr 0.0008 Ib/hr 0.76 0.03 98.76 266.64 4 Water Trucks' 12960 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 8.63 0.30 1687.46 4556.13 4 Wheeled Backhoes' 12960 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 2.64 0.17 433.76 1171.14 4 6-8" Pumps' 25920 hr/yr 0.3830 Ib/hr 0.0239 Ib/hr 49.6 Ib/hr 0.0051 Ib/hr 4.96 0.31 644.47 1740.06 4 Maint/Service Trucks' 12960 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 8.63 0.30 1687.46 4556.13 4 55 KVA Diesel Generator' 12960 hr/yr 0.6102 Ib/hr 0.0431 Ib/hr 77.9 Ib/hr 0.0073 Ib/hr 3.95 0.28 505.97 1366.13 100 Personal Vehicles2'3'4'5 810000 miles/yr 0.95 g/mile 0.0045 g/mile 410 g/mile 0.0452 g/mile 0.85 0.00 367 991.81 LANDFILL FILLING 2 Dozers' 6480 hr/yr 2.0891 Ib/hr 0.0858 Ib/hr 239 Ib/hr 0.0234 Ib/hr 6.77 0.28 776.26 11333.33 Tractor' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 1583.21 Wheeled Backhoe' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 1583.21 Water Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 6159.22 Vibratory Roller' 3240 hr/yr 0.5273 Ib/hr 0.0353 Ib/hr 67 Ib/hr 0.0071 Ib/hr 0.85 0.06 108.83 1588.88 Tracked Excavator' 3240 hr/yr 0.6603 Ib/hr 0.0332 Ib/hr 120 Ib/hr 0.0089 Ib/hr 1.07 0.05 194.76 2843.50 Maint/Service Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 6159.22 55 KVA Diesel Generator' 3240 hr/yr 0.6102 Ib/hr 0.0431 Ib/hr 77.9 Ib/hr 0.0073 Ib/hr 0.99 0.07 126.49 1846.81 Personal Vehicles2,3,4,5,6 121500 miles/yr 0.95 g/mile 0.0045 g/mile 410 g/mile 0.0452 g/mile 0.13 0.00 55.10 804.47 LANDFILL CLOSURE 2 Dozers' 6480 hr/yr 2.0891 Ib/hr 0.0858 Ib/hr 239 Ib/hr 0.0234 Ib/hr 6.77 0.28 776.26 776.26 Tractor' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 108.44 Wheeled Backhoe' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 108.44 Water Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 421.86 Vibratory Roller' 3240 hr/yr 0.5273 Ib/hr 0.0353 Ib/hr 67 Ib/hr 0.0071 Ib/hr 0.85 0.06 108.83 108.83 Tracked Excavator' 3240 hr/yr 0.6603 Ib/hr 0.0332 Ib/hr 120 Ib/hr 0.0089 Ib/hr 1.07 0.05 194.76 194.76 Maint/Service Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 421.86 55 KVA Diesel Generator' 3240 hr/yr 0.6102 Ib/hr 0.0431 Ib/hr 77.9 Ib/hr 0.0073 Ib/hr 0.99 0.07 126.49 126.49 Personal VehicleS1,3,4,1'6 121500 miles/yr 0.95 g/mile 0.0045 g/mile 410 g/mile 0.0452 g/mile 0.13 0.00 55.10 55.10 138.42 5.91 23,504.36 162,560.70 Usage Emission Factors I Emissions Cumulative Emissions NOx PM10/PM2.5 CO2 CH4 (tons/yr) (tons) Activity Value I Units I Value I Units Value I Units Value I Units I Value I Units I NOx I PM10/P 2.51 CO2e I CO2e 1. South Coast Air Quality Management District Off -Road Model Mobile Source Emission Factors for a 2016 fleet average from http://www.agmd.gov/home/regulations/ceqa/air-quality-analysis-handbook/off-road-mobile-source- emission-factors. 2. USEPA Center for Corporate Climate Leadership Greenhouse Gas Inventory Guidance: Direct Emissions from Mobile Combustion Sources Tier 1 Gasoline Light -Duty Truck emission factors (0.0452 g CH4/mile) from https://www.epa.gov/sites/prod uction/files/2016-03/documents/mobi leem issions_3_2016. pdf. 3. Personally Operated Vehicle (POV) use is estimated as: -Excavation: 15 persons/day * 30 miles/person * 270 day/yr (it is assumed each person travels 15-miles one-way to work); -Landfill Construction: 25 persons/day * 30 miles/person * 270 day/yr. 4. USDOT Bureau of Transportation Statistics Average Fuel Efficiency of U.S. Light Duty Vehicles for calendar year 2014 (21.4 miles/gal) for POV CO2 emissions from http://www. rita. dot.gov/bts/sites/rita.dot.gov. bts/files/publications/nati onal_transportation_statisti cs/htm I/table_04_23. htm I. S. 100-yr GWP for CH4 (25 kg CO2e/kg CH4) from Intergovernmental Panel on Climate Change (IPCC), Fourth Assessment Report (AR4), 2007. 6. USEPA Office of Transportation and Air Quality Average Annual Emissions and Fuel Consumption forGasoline-FueledPassenger Cars and Light Trucks light -duty trucks emissions estimates for the in -use fleet as of 2008 based on https://www3.epa.gov/otaq/consumer/42OfO8O24.pdf. 7. It is assumed that the landfill location is 25 miles away, so 1 truck trip carrying ash is 50 miles, and 20-ton trucks are used. It is assumed that the landfill cap/closure material for Option 3 is taken from an adjacent land parcel, thus 1 truck trip carrying cap/closure material is taken to be 2 miles. It is assumed that the landfill cap/closure material for Option 2 is taken from a site 25 miles aways, so 1 truck trip carrying cap/closure material for Option 2 is taken to be 50 miles. 8. USEPA Office of Transportation and Air Quality Average In -Use Emissions from Heavy -Duty Trucks NOx and PM10/PM2.5 emission factors approximated as a diesel Heavy -Duty Vehicle Class Villa (33,001-60,000 lb GVWR--since it is for 20 ton (40,000 lb) material +truck) --based on the in -use fleet from July 2008 from https://www3.epa.gov/otaq/consumer/420f08027.pdf. 9. USEPA Center for Corporate Climate Leadership Emission Factors for Greenhouse Gas Inventories greenhouse gas emission factors approximated as heavy duty vehicles for 1960-present from Table 4 of https://www.epa.gov/sites/production/files/2015-12/documents/emission-factors_nov_2015.pdf, dated November 2015. 10. It is assumed that the weight of the vehicle + load is approximately 60,000 lb (20-ton load and 10-ton truck). Diesel heavy duty combination truck CO2 emission factor for MY2014/Class 8/Day Cab is 92 g CO2/ton-mile taken from https://www.gpo.gov/fdsys/pkg/FR-2011-09-15/pdf/2011-20740.pdf (EPA/DOT Federal Register Vol. 76, No. 179, 15 September 2011). -92 g CO2/ton-mile * (30 ton*x mile)/truck trip = y g CO2/truck trip 11. It is assumed that the liner is transported 100 miles to the site/landfill on public roads and approximately 10 tons of liner are transported per trip (3240 lb/roll * 1 ton/2000 lb * 6 rolls/truck trip = 10 ton/truck trip). Option 1: Monitor Only Since similar monitoring would be required for each option for the project duration, similar emissions are expected for each and the emissions are not included in this estimate . It is expected that the emissions contributions from personal vehicles due to monitoring would be insignificant compared to the other emission sources. Option 2: Cap in Place (CIP) CIP Duration = 12 hr/day 270 day/yr 2.5 years 66623 truck trips carrying cap material 97 truck trips carrying liner material Usage Emission Factors Emissions Cumulative Emissions NOx PM10/PM2.5 CO2 CH4 (tons/yr) (tons) Activity Value Units Value Units Value Units Value Units Value Units NOx PM10/PM2.5 CO2e CO2e 2 Dozers' 6480 hr/yr 2.0891 Ib/hr 0.0858 Ib/hr 239 Ib/hr 0.0234 Ib/hr 6.77 0.28 776.26 1940.64 Tractor' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 271.10 Wheeled Backhoe' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 271.10 Water Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 1054.66 Vibratory Roller' 3240 hr/yr 0.5273 Ib/hr 0.0353 Ib/hr 67 Ib/hr 0.0071 Ib/hr 0.85 0.06 108.83 272.07 Tracked Excavator' 3240 hr/yr 0.6603 Ib/hr 0.0332 Ib/hr 120 Ib/hr 0.0089 Ib/hr 1.07 0.05 194.76 486.90 Maint/Service Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 1054.66 55 KVA Diesel Generator' 3240 hr/yr 0.6102 Ib/hr 0.0431 Ib/hr 77.9 Ib/hr 0.0073 Ib/hr 0.99 0.07 126.49 316.23 15 Personal Vehicles2,3,1,5,6 121500 miles/yr 0.95 g/mile 0.0045 g/mile 410 g/mile 0.0452 g/mile 0.13 0.00 55.10 137.75 Truck Transport- Cap Material''8'9'10 26649 truck trips/yr 459.55 g/truck trip 10.75 g/truck trip 138000 g/truck trip 0.255 g/truck trip 13.50 0.32 4054.04 10135.10 Truck Transport - Liner Material''8.9,10,11 39 truck trips/yr I 1838.20 g/truck trip 1 43.00 Ig/truck trip I 552000 g/truck trip 1 1.02 g/truck trip 0.08 0.00 23.61 59.02 1 29.02 1.01 6,399.69 15,999.23 Option 3 Landfill Construction Duration = Excavation Duration = Closure Duration = Excavation and Landfill 12 hr/day 300 day/yr 1.5 years 83 truck trips carrying liner material (for construction and closure) 12 hr/day 270 day/yr 12.2 years 330000 truck trips carrying ash 12 hr/day 270 day/yr 0.8 years 13730 truck trips carrying closure material Usage Emission Factors Emissions Cumulative Emissions NOx PM10/PM2.5 CO2 CH4 (tons/yr) (tons) Activity Value Units Value Units Value Units Value Units Value Units NOx PM10/PM2.5 CO2e CO2e EXCAVATION 2 Tracked Excavators' 6480 hr/yr 0.6603 Ib/hr 0.0332 Ib/hr 120 Ib/hr 0.0089 Ib/hr 2.14 0.11 389.52 4752.15 2 Dozers' 6480 hr/yr 2.0891 Ib/hr 0.0858 Ib/hr 239 Ib/hr 0.0234 Ib/hr 6.77 0.28 776.26 9470.32 Wheeled Loader' 3240 hr/yr 0.7114 Ib/hr 0.0375 Ib/hr 109 Ib/hr 0.0089 Ib/hr 1.15 0.06 176.94 2158.67 2 Dump Trucks' 6480 hr/yr 0.0587 Ib/hr 0.0024 Ib/hr 7.6 Ib/hr 0.0008 Ib/hr 0.19 0.01 24.69 301.20 Water Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 5146.74 Wheeled Backhoe' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 1322.95 2 6-8" Pumps' 6480 hr/yr 0.3830 Ib/hr 0.0239 Ib/hr 49.6 Ib/hr 0.0051 Ib/hr 1.24 0.08 161.12 1965.63 55 KVA Diesel Generator' 3240 hr/yr 0.6102 Ib/hr 0.0431 Ib/hr 77.9 Ib/hr 0.0073 Ib/hr 0.99 0.07 126.49 1543.22 15 Personal Vehicles2,3,4,5 121500 miles/yr 0.95 g/mile 0.0045 g/mile 410 g/mile 0.0452 g/mile 0.13 0.001 55.101 672.23 Usage Emission Factors Emissions Cumulative Emissions NOx PM10/PM2.5 CO2 CH4 (tons/yr) (tons) Activity Value Units Value Units Value Units Value Units Value Units NOx PM10/PM2.5 CO2e CO2e MATERIAL TRANSPORT Truck Transport - Ash''s,9,10 27049 truck trips/yr 459.55 g/truck trip 10.75 g/truck trip 138000 g/truck trip 0.2550 g/truck trip 13.70 0.32 4114.89 50201.62 Truck Transport- Closure Material''$'9'10 17163 truck trips/yr 18.38 g/truck trip 0.43 g/truck trip 5520 g/trucktrip 0.0102 g/truck trip 0.35 0.01 104.43 83.55 Truck Transport - Liner Material"s'9,10,i1 36 truck trips/yr 1838.20 g/truck trip 43 g/truck trip 552000 g/truck trip 1.0200 g/truck trip 0.07 0.00 21.96 50.51 LANDFILL CONSTRUCT. 8 Tracked Excavators' 25920 hr/yr 0.6603 Ib/hr 0.0332 Ib/hr 120 Ib/hr 0.0089 Ib/hr 8.56 0.43 1558.08 3583.59 8 Dozers' 25920 hr/yr 2.0891 Ib/hr 0.0858 Ib/hr 239 Ib/hr 0.0234 Ib/hr 27.07 1.11 3105.02 7141.55 4 Vibratory Compactors' 25920 hr/yr 0.568 Ib/hr 0.0234 Ib/hr 123 Ib/hr 0.0065 Ib/hr 7.36 0.30 1596.19 3671.23 4 Wheeled Loaders' 12960 hr/yr 0.7114 Ib/hr 0.0375 Ib/hr 109 Ib/hr 0.0089 Ib/hr 4.61 0.24 707.76 1627.85 8 Dump Trucks' 25920 hr/yr 0.0587 Ib/hr 0.0024 Ib/hr 7.6 Ib/hr 0.0008 Ib/hr 0.76 0.03 98.76 227.14 4 Water Trucks' 12960 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 8.63 0.30 1687.46 3881.15 4 Wheeled Backhoes' 12960 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 2.64 0.17 433.76 997.64 4 6-8" Pumps' 25920 hr/yr 0.3830 Ib/hr 0.0239 Ib/hr 49.6 Ib/hr 0.0051 Ib/hr 4.96 0.31 644.47 1482.28 4 Maint/Service Trucks' 12960 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 1 8.63 0.30 1687.46 3881.15 4 55 KVA Diesel Generator' 12960 hr/yr 0.6102 Ib/hr 0.0431 Ib/hr 77.9 Ib/hr 0.0073 Ib/hr 3.95 0.28 505.97 1163.74 100 Personal Vehicles2'3'4'5 810000 miles/yr 0.95 g/mile 0.0045 g/mile 410 g/mile 0.0452 g/mile 0.85 0.00 367 844.88 LANDFILL FILLING 2 Dozers' 6480 hr/yr 2.0891 Ib/hr 0.0858 Ib/hr 239 Ib/hr 0.0234 Ib/hr 6.77 0.28 776.26 9470.32 Tractor' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 1322.95 Wheeled Backhoe' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 1322.95 Water Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 5146.74 Vibratory Roller' 3240 hr/yr 0.5273 Ib/hr 0.0353 Ib/hr 67 Ib/hr 0.0071 Ib/hr 0.85 0.06 108.83 1327.70 Tracked Excavator' 3240 hr/yr 0.6603 Ib/hr 0.0332 Ib/hr 120 Ib/hr 0.0089 Ib/hr 1.07 0.05 194.76 2376.08 Maint/Service Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 5146.74 55 KVA Diesel Generator' 3240 hr/yr 0.6102 Ib/hr 0.0431 Ib/hr 77.9 Ib/hr 0.0073 Ib/hr 1 0.99 0.07 126.49 1543.22 Personal Vehicles2,3,4,5,6 121500 miles/yr 0.95 g/mile 0.0045 g/mile 410 g/mile 0.0452 g/mile 0.13 0.00 55.10 672.23 LANDFILL CLOSURE 2 Dozers' 6480 hr/yr 2.0891 Ib/hr 0.0858 Ib/hr 239 Ib/hr 0.0234 Ib/hr 6.77 0.28 776.26 621.00 Tractor' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 86.75 Wheeled Backhoe' 3240 hr/yr 0.4070 Ib/hr 0.0258 Ib/hr 66.8 Ib/hr 0.0055 Ib/hr 0.66 0.04 108.44 86.75 Water Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 337.49 Vibratory Roller' 3240 hr/yr 0.5273 Ib/hr 0.0353 Ib/hr 67 Ib/hr 0.0071 Ib/hr 0.85 0.06 108.83 87.06 Tracked Excavator' 3240 hr/yr 0.6603 Ib/hr 0.0332 Ib/hr 120 Ib/hr 0.0089 Ib/hr 1.07 0.05 194.76 155.81 Maint/Service Truck' 3240 hr/yr 1.3322 Ib/hr 0.0459 Ib/hr 260 Ib/hr 0.0164 Ib/hr 2.16 0.07 421.86 337.49 55 KVA Diesel Generator' 3240 hr/yr 0.6102 Ib/hr 0.0431 Ib/hr 77.9 Ib/hr 0.0073 Ib/hr 0.99 0.07 126.49 101.19 Personal VehicleS1,3,4,1'6 121500 miles/yr 0.95 g/mile 0.0045 g/mile 410 g/mile 0.0452 g/mile 0.13 0.00 55.10 44.08 138.47 5.91 23,518.04 136,357.55 Usage Emission Factors I Emissions Cumulative Emissions NOx PM10/PM2.5 CO2 CH4 (tons/yr) (tons) Activity Value I Units I Value I Units Value I Units Value I Units I Value I Units I NOx I PM10/P 2.51 CO2e I CO2e 1. South Coast Air Quality Management District Off -Road Model Mobile Source Emission Factors for a 2016 fleet average from http://www.agmd.gov/home/regulations/ceqa/air-quality-analysis-handbook/off-road-mobile-source- emission-factors. 2. USEPA Center for Corporate Climate Leadership Greenhouse Gas Inventory Guidance: Direct Emissions from Mobile Combustion Sources Tier 1 Gasoline Light -Duty Truck emission factors (0.0452 g CH4/mile) from https://www.epa.gov/sites/prod uction/files/2016-03/documents/mobi leem issions_3_2016. pdf. 3. Personally Operated Vehicle (POV) use is estimated as: -Excavation: 15 persons/day * 30 miles/person * 270 day/yr (it is assumed each person travels 15-miles one-way to work); -Landfill Construction: 25 persons/day * 30 miles/person * 270 day/yr. 4. USDOT Bureau of Transportation Statistics Average Fuel Efficiency of U.S. Light Duty Vehicles for calendar year 2014 (21.4 miles/gal) for POV CO2 emissions from http://www. rita. dot.gov/bts/sites/rita.dot.gov. bts/files/publications/nati onal_transportation_statisti cs/htm I/table_04_23. htm I. S. 100-yr GWP for CH4 (25 kg CO2e/kg CH4) from Intergovernmental Panel on Climate Change (IPCC), Fourth Assessment Report (AR4), 2007. 6. USEPA Office of Transportation and Air Quality Average Annual Emissions and Fuel Consumption forGasoline-FueledPassenger Cars and Light Trucks light -duty trucks emissions estimates for the in -use fleet as of 2008 based on https://www3.epa.gov/otaq/consumer/42OfO8O24.pdf. 7. It is assumed that the landfill location is 25 miles away, so 1 truck trip carrying ash is 50 miles, and 20-ton trucks are used. It is assumed that the landfill cap/closure material for Option 3 is taken from an adjacent land parcel, thus 1 truck trip carrying cap/closure material is taken to be 2 miles. It is assumed that the landfill cap/closure material for Option 2 is taken from a site 25 miles aways, so 1 truck trip carrying cap/closure material for Option 2 is taken to be 50 miles. 8. USEPA Office of Transportation and Air Quality Average In -Use Emissions from Heavy -Duty Trucks NOx and PM10/PM2.5 emission factors approximated as a diesel Heavy -Duty Vehicle Class Villa (33,001-60,000 lb GVWR--since it is for 20 ton (40,000 lb) material +truck) --based on the in -use fleet from July 2008 from https://www3.epa.gov/otaq/consumer/420f08027.pdf. 9. USEPA Center for Corporate Climate Leadership Emission Factors for Greenhouse Gas Inventories greenhouse gas emission factors approximated as heavy duty vehicles for 1960-present from Table 4 of https://www.epa.gov/sites/production/files/2015-12/documents/emission-factors_nov_2015.pdf, dated November 2015. 10. It is assumed that the weight of the vehicle + load is approximately 60,000 lb (20-ton load and 10-ton truck). Diesel heavy duty combination truck CO2 emission factor for MY2014/Class 8/Day Cab is 92 g CO2/ton-mile taken from https://www.gpo.gov/fdsys/pkg/FR-2011-09-15/pdf/2011-20740.pdf (EPA/DOT Federal Register Vol. 76, No. 179, 15 September 2011). -92 g CO2/ton-mile * (30 ton*x mile)/truck trip = y g CO2/truck trip 11. It is assumed that the liner is transported 100 miles to the site/landfill on public roads and approximately 10 tons of liner are transported per trip (3240 lb/roll * 1 ton/2000 lb * 6 rolls/truck trip = 10 ton/truck trip). EPS APPENDIX C Traffic and Implementation Risk Analysis C C I OVERVIEW The risk to the health and life of workers and the public due to construction activities for each alternative should be considered during the selection process. Additionally, the risk of property damage should also be evaluated. National statistics were used to evaluate the fatality, injury/illness, and property damage risks associated with truck traffic and onsite implementation for the Allen, Buck, Cliffside, and Mayo facilities. EPS C2 TRUCK TRAFFIC RISK The risks of fatality and injury due to truck traffic associated with ash removal and capping -in - place are presented on Table 1 of this appendix. Data on annual fatalities and injuries associated with large -truck traffic and large -truck miles traveled per year was taken from the National Highway Traffic Safety Administration's (NHTSA) large truck traffic safety fact sheet for 2014 (NHTSA, 2016). This is the most recent data available and accounts for fatalities and injuries for truck occupants as well as non -truck occupants. Averages of the calculated fatality and injury rates for the years 2010 through 2014 were also examined and found to be similar to the 2014 rates. The reference values used for the truck traffic risk calculations are given in Table 2 of this appendix. Total truck driving miles was calculated for the construction analysis for each facility. Significantly more truck miles are associated with the ash removal option than with the capping - in -place option. Transporting borrow pit and liner material was also considered in these calculations. In addition to the risk of health and life to the workers and the public, the risk of property damage resulting from truck traffic was also considered for both alternatives. Data for property damage only (PDO) crashes involving large trucks in 2014 (Federal Motor Carrier Safety Administration, 2016) was used to estimate the expected number of PDO incidents for both alternatives at each facility. The results are presented in Table 1 and the reference values are included in Table 2. EPS C3 IMPLEMENTATION RISK In addition to truck traffic risks, there are substantial risks associated with performing the excavation, landfill construction, and cap construction. Occupational risks calculated for remedial alternatives implementation are also presented on Table 1. Data on occupational fatalities and nonfatal injuries and illnesses was gathered from the Bureau of Labor Statistics (BLS) National Consensus of Fatal Occupational Injuries in 2014 (Preliminary Results) News Release (BLS, 2015a), Fatal Occupational Injuries, Total Hours Worked, and Rates of Fatal Occupational Injuries by Selected Worker Characteristics, Occupations, and Industries, Civilian Workers, 2014 (BLS, 2015b), and Employee -Reported Workplace Injuries and Illnesses, 2014 News Release (BLS, 2015c). The reference values used for the occupational risk calculations are given in Table 3. Data from the forestry and logging industry (NAICS code 113) was used to calculate the fatality and injury/illness risks associated with clearing wooded land for borrow pits and the proposed landfills (if applicable). According to the BLS, "Industries in the Forestry and Logging subsector grow and harvest timber on a long production cycle (i.e., of 10 years or more)" and include logging equipment operators and heavy truck and tractor -trailer drivers. Mature tree removal at this caliber is expected to occur at each facility where land will be cleared for a borrow pit and to prepare for landfill construction. While a landfill area will only be cleared for the removal option, a borrow pit will be needed for both alternatives (removal and capping -in -place). If additional tree removal will be needed that has not been accounted for in either the borrow or proposed landfills areas, the fatality and injury/illness risks will increase. Data from the construction industry (NAICS code 23) was used to calculated the fatality and injury/illness risks associated with excavation, landfill construction, and cap construction work, where relevant. According to the BLS, "The construction sector comprises establishments primarily engaged in the construction of buildings or engineering projects. Establishments primarily engaged in the preparation of sites for new construction and establishments primarily engaged in subdividing land for sale as building sites also are included in this sector." Occupations falling in this category include construction laborers, operating engineers, first -line supervisors, and heavy truck and tractor -trailer drivers. The excavation of the ash basins, construction of an offsite landfills, and the construction of an in -place cap all fall under this industry and therefore construction -based risk statistics were used for these project phases in this evaluation. Time and manpower to clear the land and complete construction for each alternative was estimated based on scope and is outlined in the separate Construction Analysis appendix. Here, a ratio of two days to clear one acre of wooded land was used to calculate the total hours worked clearing the borrow pit and proposed landfill areas. EPS C4 SAMPLE CALCULATIONS C4.1 Truck Traffic Risk Calculations C4.1.1 Fatalities due to Truck Traffic Ft,2014 Ft = x Dt Dt,2014 C4.1.2 Injuries due to Truck Traffic I t,2014 It = x Dt Dt,2014 C4.1.3 PDO Truck Crashes Pt,2014 Pt = x Dt Dt,2014 C4.2 Implementation Risk Calculations C4.2.1 Fatalities due to Implementation F FL,2014XT +XTFC,2014 L- _L c TL,2014 TC,2014 C4.2.2 Injuries and Illnesses due to Implementation IIL,2014 X T + IC,2014 X T t _ - L C TL,2014 TC,2014 C4.3 Definitions Ft = Fatalities resulting from truck traffic, including workers and non -workers (number of persons) Ft,2014 = Fatalities resulting from large truck traffic, including truck occupants and other people, in the United States in 2014 (number of persons) (NHTSA, 2016) Dt,2014 = Large -truck miles traveled in the United States in 2014 (miles) (NHTSA, 2016) Dt = Total truck distance traveled (miles), calculated (see Construction Analysis appendix) C It = Injuries resulting from truck traffic, including workers and non -workers (number of cases) It,2014 = Injuries resulting from large truck traffic, including truck occupants and other people, in the United States in 2014 (number of cases) (NHTSA, 2016) Pt = Property damage only (PDO) truck crashes resulting from large truck traffic (number of incidents) (FMCSA, 2016) P t,2014 = PDO crashes involving large trucks in the United States in 2014 (number of incidents) (FMCSA, 2016) F; = Fatalities resulting from implementation of the selected remedy (number of persons) FL,2014 = Occupational fatalities in the forestry and logging industry in the United States in 2014 (number of persons) (BLS, 2015b) TL,2014 = Total hours worked in the forestry and logging industry in the United States in 2014 (hours) (BLS, 2015b) TL = Total hours worked clearing wooded land (hours), calculated based on a clearing rate of 2 acres per day and a crew size of 17 persons (see Construction Analysis appendix) Fc,2014 = Occupational fatalities in the construction industry in the United States in 2014 (number of persons) (BLS, 2015b) Tc,2014 = Total hours worked in the construction industry in the United States in 2014 (hours) (BLS, 2015b) Tc = Total hours worked completing remedy (hours), calculated (see Construction Analysis appendix) I; = Injuries and illnesses resulting from implementation of the selected remedy (number of cases) IL,2014 = Nonfatal occupational injuries and illnesses in the forestry and logging industry in the United States in 2014 (number of cases) (BLS, 2015c) Ic,2014 = Nonfatal occupational injuries and illnesses in the construction industry in the United States in 2014 (number of cases) (BLS, 2015c) EPS C5 RESULTS The calculations show that removing the ash and constructing an offsite landfill will produce significantly more fatalities and injuries or illnesses for each site considered when compared to the alternate option of capping in place. With the highest risk of personal injury (workers and the general public) and personal property damage (i.e., road accidents) over the lifespan of the project (see Table 1), the Allen facility should expect approximately 19 injuries to the general public resulting from traffic accidents and 61 worker injuries from construction activities associated with the ash removal option. Alternately, approximately 3 injuries to the general public and 10 worker injuries from construction activities are estimated to result from -capping -in place for Allen. If all four facilities proceed with the comprehensive removal alternative, then a total of approximately 39 injuries to the general public and 131 worker injuries should be expected, along with 114 incidents of public property damage resulting from truck crashes on public roads. The capping -in - place alternative exhibits less risk with an estimated total of 7.5 injuries to the public and 26 injuries to workers across all four sites. With the MNA alternative, the incident rates are negligible as the risks associated with trucking and construction are removed. See Table 1 for a summary of estimated injury and traffic incident rates for the Allen, Buck, Cliffside, and Mayo facilities. C C6 REFERENCES Bureau of Labor Statistics (BLS). (2015a). National Consensus of Fatal Occupational Injuries in 2014 (Preliminary Results) News Release, available September 17, 2015. U.S. Department of Labor. BLS. (2015b). Fatal occupational injuries, total hours worked, and rates of fatal occupational injuries by selected worker characteristics, occupations, and industries, civilian workers, 2014. Retrieved from the Bureau of Labor Statistics website: http://www.bls.gov/iif/oshwc/cfoi/cfoi rates2014hb.pd BLS. (2015c). Employer -Reported Workplace Injuries and Illnesses - 2014 News Release, available October 29, 2015. U.S. Department of Labor. Federal Motor Carrier Safety Administration (FMCSA). (2016). Large Truck and Bus Crash Facts 2014. Retrieved from the Federal Motor Carrier Safety Administration website: htlps://www.fmcsa.dot.gov/ National Highway Traffic Safety Administration (NHTSA). (2016). Traffic Safety Facts, Large Trucks, 2014 Data, DOT HS 812 279, Revised May 2016. U.S. Department of Transportation. Washington, D.C. Table 1. Summary of Traffic and Implementation Risks REMOVAL WITH MNA CAPPING WITH MNA Site Estimated Fatalities from Truck Traffic 1 Estimated Fatalities from Implementation 2'3 Estimated Injuries or Illnesses from Truck Traffic 1 Estimated Injuries or Illnesses from Implementation 4'6 Estimated Large- Truck Crashes Resulting in PDO Estimated Fatalities from Truck Traffic 1 Estimated Fatalities from Implementation 2,3 Estimated Injuries or Illnesses from Truck Traffic 1 Estimated Injuries or Illnesses from Implementation 4,5 Estimated Large - Truck Crashes Resulting in PDO Allen 0.68 0.23 19.2 61 56 0.10 0.07 2.8 9.6 8 Buck 0.19 0.08 5.3 19 16 0.06 0.04 1.8 6.0 5 Cliffside 0.28 0.11 7.9 27 23 0.06 0.04 1.6 5.7 5 Mayo 0.23 0.09 6.6 23 19 0.05 0.03 1.3 4.6 4 Total: 11.4 0.5 38.9 131 114 0.3 0.2 7.5 25.8 22 Table 2. Truck Traffic Risk Reference Values Large -truck miles Number of truck Number of "other Number of truck Number of "other Total number of PDO Crashes Total number of traveled - 2014 occupant fatalities - people" fatalities - 6 occupant injuries - people" injuries - injuries per truck Involving Large millions 1 2014 1 2014 1 fatalities per mile 20141 20141 mile 6 Trucks - 2014 9 279,132 657 3,246 1.40E-08 27,000 84,000 3.98E-07 326,000 Table 3. Implementation Risk Reference Values Injuries/Illnesses - Injuries/Illnesses Injuries/illnesses Fatalities - 2014 Total hours worked Fatalities per hour Industry 2,3 3 7 2014 (total incident rate - per hour worked - (total number) (millions) - 2014 worked - 2014 4 s s number)2014 2014 Forestry and logging 92 180 5.11E-07 2,800 5.1 2.55E-05 (NAICS code 113) Construction 874 18,168 4.81E-08 200,900 3.6 1.80E 05 (NAICS code 23) Notes: MINA - Monitored Natural Attenuation PDO - Property Damage Only Values represent total expected fatalities and injuries or illnesses over the estimated lifespan of the project, excluding post MINA activities (1) - Fact Sheet, Traffic Safety Facts 2014 Data. NHTSA's National Center for Statistics and Analysis, DOT HS 812 279, Washington, DC, revised May 2016. (2) - Bureau of Labor Statistics. National Consensus of Fatal Occupational Injuries in 2014 (Preliminary Results) News Release. Table 2. Fatal occupational injuries by industry and selected event or exposure, 2014. September 17, 2015. (3) - Bureau of Labor Statistics. Fatal occupational injuries, total hours worked, and rates of fatal occupational injuries by selected worker characteristics, occupations, and industries, civilian workers, 2014. (4) - Bureau of Labor Statistics. Employer -Reported Workplace Injuries and Illnesses - 2014 News Release. Table 2. Numbers of nonfatal occupational injuries and illnesses by case type and ownership, selected industries, 2014. October 29, 2015. (5) - Bureau of Labor Statistics, Employer -Reported Workplace Injuries and Illnesses - 2014 News Release. Table 1. Incidence rates of nonfatal occupational injuries and illnesses by case type and ownership, selected industries, 2014. October 29, 2015. (6) - Calculated from data provided in reference (1) (7) - Calculated from data provided in reference (3) (8) - Calculated from data provided in references (4) and (5) (9) - Federal Motor Carrier Safety Administration Analysis Division, Large Track and Bus Crash Facts 2014. March 2016. EPS APPENDIX D Human Health Contaminant Exposure C DI INTRODUCTION Potential impacts to humans in contact with chemicals in environmental media was estimated for 30 years after implementation of the three corrective action options (Monitored Natural Attenuation (MNA), Capping, and Excavation). A current condition (i.e., year 2015-2016) risk assessment was conducted for each site. These risk assessments formed the basis of estimating the risks' in the future for the three different corrective actions. It is important to note that the purpose of this analysis is to compare the three different corrective action scenarios. The purpose is not to accurately determine the actual expected risk for each scenario. The current condition risk assessments evaluated the following environmental media (Area of Wetness (AOW)/seep soil, AOW/seep water, groundwater, sediment, surface water and fish tissue). The receptors evaluated included a commercial/industrial worker, construction worker, trespasser, boater, swimmer, wader, recreational fisher and subsistence fisher. One exposure scenario and one chemical were excluded from this risk evaluation for the Net Environmental Benefit Analysis. Fish consumption was excluded because the risk assessment for this scenario presented in the Corrective Action Plan (CAP) documents was not based on fish tissue data and the assessment as presented was overly -conservative and not representative of the true risk. Lead was excluded because the risks due to lead are calculated differently than for other chemicals and they are not additive with other chemicals. There were two considerations in extrapolating the current risk assessment to the future condition. The first was to forward project into the future (30 years from implementation/completion of the corrective action). The second was to project the changes due to each corrective action. The information available for each site varied. Accordingly, for some environmental media different techniques were used to estimate the projected potential risks. ' Note, in risk evaluations calculations are performed differently for carcinogenic and non -carcinogenic chemicals. The Hazard Index (HI) is the measurement used to determine the hazard of non -carcinogens. The Excess Lifetime Cancer Risk (ELCR) is the measurement used for carcinogenic chemicals. For the purpose of the text of this document the term "risk" is used to generically represent both the cancer risk and hazard. However, the associated tables do differentiate between cancer risk (ELCR) and non -carcinogenic hazard (HI). Appendix D 1 Jun 30, 2016 Human Health Contaminant Exposure C D2 AOW SOIL AND AOW WATER This evaluation was conducted the same for each site. It was assumed that the concentrations (and, thus, risk) for the AOWs would not change from now to 30 years in the future. For the MNA scenario it was assumed that the risk would be the same as the current risk. The CAPs indicate that most if not all AOWs would not be present under the capping scenario. However, to be conservative it was assumed that 75% of the AOWs would be removed. Accordingly, the projected MNA risk would be 25% of the current risk. Under the excavation scenario all contaminants in soil would be removed, thus there would be no contaminants in any future AOWs. Accordingly, there is no projected excavation risk. Table IA and Table 2 show the calculation of the projected risks for the AOW/seep Soil and Water, respectively. The Current Risk Assessment values for AOW/seep soil for Allen and Buck were taken from their respective risk assessments. The current values for Cliffside and Mayo were revised. The revised risk calculations are shown in Table IB. Cliffside was revised to remove outliers of arsenic and cobalt that were skewing the dataset and overestimating the risk. Mayo was revised to include more data (from perimeter soil samples and ash basin ash samples) as the original dataset had only 4 samples and was not representative of a realistic exposure. EPS D3 GROUNDWATER Groundwater modeling was conducted for each site projecting concentrations in groundwater into the future for the three different scenarios. However, different amounts and types of information were provided for the groundwater model output for each site. Accordingly, the projection of risks due to groundwater into the future for each scenario for each site was conducted differently. D3.1 Allen and Cliffside The CAP Part 2 report for Cliffside presented results of a groundwater model predicting concentrations under all three scenarios. The output provided contained excel spreadsheets with concentrations of each of the chemical under each scenario for specific wells at the compliance boundary for many years. The concentrations for these wells for 30-years into the future (after the action is taken) was extracted. The results from all the wells presented were averaged to determine estimated groundwater concentrations 30 years after each corrective action was completed. These concentrations were then multiplied by the Risk Based Concentrations (RBC) from the risk assessment (included in the CAP Part 2) to determine the projected risk 30 years in the future for each scenario. Not all chemicals included in the risk assessment were evaluated in the groundwater model. For these chemicals, the concentrations 30 years into the future were estimated by multiplying the current groundwater concentrations in the risk assessment by a ratio, which was based on the future modeled groundwater concentrations for the modeled chemicals divided by the groundwater concentrations used in the risk assessment. Table 3A shows the risk calculations for the future scenarios. The only receptor with exposure to groundwater is the construction worker. Allen is similar to Cliffside in that the output of groundwater modeling available was the same so that modeled concentrations from individual wells could be obtained for 30 years after the different correction actions. However, the CAP Part 2 report for Allen did not include the excavation scenario. The groundwater model presented in the CAP Part 1 report did include the excavation scenario. The ratio of the excavation model to the MNA model from the CAP Part 1 report was used to estimate the excavation concentrations based on the MNA model results from the CAP Part 2 report. Otherwise the risk estimations for Allen were conducted in the same manner as for Cliffside and are summarized in Table 3B. D3.2 Buck The groundwater model output for Buck was not sufficiently detailed to perform the same analysis as was conducted for Allen and Cliffside. Graphs depicting the change in concentrations over time for the three scenarios were presented for some wells and a few chemicals. These graphs for the wells closest to the downgradient edge of the plume were used to estimate an overall increase or decrease in concentrations for the different scenarios. The MNA scenario is the same scenario as the current risk assessment. However, the MNA scenario needed to be projected 30 years into the C future. The graphs were used to determine the ratio between the concentrations for the MNA scenario 30 years from now to the concentration for the year of the risk assessment. The MNA concentrations 30 years from now was on average 1.5 times the concentrations from the year of the risk assessment. Thus, the estimated risks for the MNA scenario was determined by multiply the current risk values by 1.5. For the Cap scenario the graphs were used to estimate the ratio of the concentrations from the Cap scenario to the MNA scenario 30 years from now. The average of these ratios (0.5) was multiplied by the MNA risk to estimate the Cap risk. The same process was conducted for the Excavation scenario using a multiple of 0.6. A summary of the results is presented in Table 3C. DU Mayo The output from the groundwater model for Mayo was presented as a series of figures depicting the extent of the plume at different dates for each scenario. The changes in plume size between these figures were used to estimate the projected risk between the different scenarios 30 years into the future. The risk for the MNA scenario was estimated by multiplying the current risk by 1.5. The risk for the Cap scenario was estimated by multiplying the MNA risk by 0.25. The risk for the Excavation scenario was estimated by multiplying the Cap risk by 0.1. A summary of the results is presented in Table 3D. EPS D4 SEDIMENT AND SURFACE WATER - ON -SITE The on -site sediment and surface water exposure pathways were evaluated the same way for all sites. It was assumed that the concentrations (and, thus, risk) for on -site sediment and surface water would not change from now to 30 years from now. For the MNA scenario it was assumed that the risk is the same as in the current risk assessment. Under the Cap and Excavation scenarios there is minimal if any risk into the future. However, to be conservative for the Cap scenario it was assumed that the risk is 25% of the MNA risk. It assumed there is no risk under the Excavation scenario. Table 4 shows the projected risk calculations for on -site soil and Table 5 for on -site surface water. EPS D5 SEDIMENT - OFF -SITE The off -site sediment exposure pathway was evaluated the same way for all sites. It was assumed that the concentrations (and, thus, risk) for off -site sediment would not change from now to 30 years from now. For the MNA scenario it was assumed that the risk is the same as in the current risk assessment. Under the Cap and Excavation scenarios there is minimal if any risk into the future. However, to be conservative it was assumed that the risk under capping would be 25% of the MNA risk and the risk under excavation would be 10% of the MNA risk. Table 6 shows the projected risk calculations for off -site soil. D6 SURFACE WATER - OFF -SITE D6.1 Allen and Cliffside The CAP Part 2 Report for Allen and Cliffside presented surface water mixing models that estimated the current surface water concentrations based on the current modeled groundwater concentrations. The estimated groundwater concentrations presented in Table 3A and 3B were used as input to the surface water model to estimate surface water concentrations 30 years from implementation of the three different corrective action scenarios. These surface water concentrations were then multiplied by the RBCs from the risk assessments for each site to estimate the risk of the different scenarios in the future. A summary of these calculations is presented in Table 7A and Table 7B for Allen and Cliffside, respectively. D6.2 Buck The CAP Part 2 Report for Buck presented a surface water mixing model that estimated the current surface water concentrations based on the current modeled groundwater concentrations. However, the analytes that are constituents of potential concern (COPC) for surface water were not included in the groundwater or surface water model. Thus, the projected risks for the three scenarios in the future were estimated by applying factors to the current risks presented in the risk assessment. The results of the surface water model give a ratio of 1.05 for the 30-year MNA surface water concentration to the current surface water concentration. Thus, the 30-year MNA surface water concentrations for the COPCs were determined by multiplying the current risk value by 1.05. For the 30-year Cap and Excavate scenarios factors of 0.93 and 0.94 (respectively) were estimated by dividing the projected surface water concentrations for Cap and Excavation by the MNA concentration. Accordingly, the 30-year Cap and Excavate scenarios for the COPCs were estimated by multiplying the 30-year MNA risk by 0.93 and 0.94, respectively. These calculations are summarized in table 7C. D6.3 Mayo The Mayo CAP Part 2 Report did not include a surface water model. In order to project the risk for each scenario in the future, the ratios from current year to 2030 for MNA (1.05) and for MNA to Cap (0.96) or Excavate (0.94) were estimated from the surface water models from the other sites. The results of the projects for Mayo are shown in Table 7D. V-S D7 SUMMARY Table 8A and Table 8B show the combined risks for each scenario where the risk for each medium is summed to determine the total risk for each receptor. Figures are shown showing the combined risks for each receptor in each scenario compared to acceptable risk criteria (HI of 1 and 3 and ELCR of I E-5 and I E-4). This information is further summarized on Table 9 where the maximum risk for on -site exposure (maximum risk between commercial/industrial worker, construction worker and trespasser) and maximum risk for off -site exposure (boater, swimmer and wader) is presented. Table 1A. AOW/Seep Soil Hazard Index ELCR Current Risk 30-year 30-year 30-year Current Risk 30-year 30-year 30-year Site Receptor Assessment MNA Cap Excavate Assessment MNA Cap Excavate Allen Commerical/Industrial Worker 0.095 0.095 0.024 -- 3.6E-10 3.6E-10 9.1E-11 -- Construction Worker 0.014 0.014 0.0036 -- 7.0E-12 7.0E-12 1.8E-12 -- Trespasser 0.030 0.030 0.0076 -- 1.3E-11 1.3E-11 3.3E-12 -- Boater -- -- -- Swimmer -- -- -- -- -- Wader -- -- -- Buck Commerical/Industrial Worker 0.19 0.19 0.048 -- 2.2E-06 2.2E-06 5.5E-07 -- Construction Worker 0.049 0.049 0.012 -- 6.6E-08 6.6E-08 1.7E-08 -- Trespasser 0.062 0.062 0.016 -- 2.7E-07 2.7E-07 6.8E-08 -- Boater -- -- -- Swimmer -- -- -- -- -- Wader -- -- -- Cliffside 1 Commerical/Industrial Worker 0.11 0.11 0.03 -- 2.3E-06 2.3E-06 5.7E-07 -- Construction Worker 0.04 0.04 0.009 -- 7.0E-08 7.0E-08 1.7E-08 -- Trespasser 0.03 0.03 0.009 -- 2.9E-07 2.9E-07 7.2E-08 -- Boater -- -- -- Swimmer -- -- -- -- -- Wader -- -- -- Mayo 1 Commerical/Industrial Worker 0.34 0.34 0.09 -- 2.9E-06 2.9E-06 7.3E-07 -- Construction Worker 0.26 0.26 0.064 -- 8.9E-08 8.9E-08 2.2E-08 -- Trespasser 0.11 0.11 0.028 -- 3.7E-07 3.7E-07 9.2E-08 -- Boater -- -- -- Swimmer -- -- -- -- -- Wader I -- -- -- 1 Concentrations for current scenario updated from the original Risk Assessment (see Table 1B) -- Not Applicable 30-year MNA: same as current risk assessment 30-year Cap: 0.25 x 30-year MNA 30-year Excavate: 0 Table 1B - Revised AOW/Seep Soil Current Condition Risk Calculations Cliffside Receptor Analyte EPC (mg/kg) HI RBC ELCR RBC HI 3 ELCR 4 Commerical/Industrial Worker Aluminum 15674 1 1.20E+06 0.013 Commerical/Industrial Worker Arsenic 6.88 2 4.80E+02 3.00E+02 0.014 2.3E-06 Commerical/Industrial Worker Cobalt 22.24 2 3.50E+02 8.30E+06 1.60E+05 0.064 2.7E-10 0.0090 Commerical/Industrial Worker Manganese 1447 1 Commerical/Industrial Worker Vanadium 39.87 1 5.80E+03 0.0069 Total 0.11 2.3E-06 Construction Worker Aluminum 15674 1 1.50E+06 0.010 _ Construction Worker Arsenic 6.88 2 6.40E+02 9.90E+03 0.011 6.9E-08 Construction Worker Cobalt 22.24 2 4.40E+03 4.30E+08 0.0051 5.2E-12 Construction Worker Manganese 1447 1 2.00E+05 0.0072 Construction Worker Vanadium 39.87 1 1.50E+04 0.0027 Total 0.036 7.0E-08 Trespasser Aluminum 156741 3.60E+06 0.0044 Trespasser Arsenic 6.88 2 1.50E+03 2.40E+03 0.0046 2.9E-07 Trespasser 1.10E+03 2.30E+08 0.020 9.7E-12 Cobalt Manganese 22.24 2 14471 Trespasser 5.00E+05 0.0029 Trespasser Vanadium 39.871 _ 1.80E+04 0.0022 Total 0.034 2.9E-07 Mayo Receptor Analyte EPC (mg/kg) HI RBC ELCR RBC HI 3 ELCR 4 Commerical/Industrial Worker Arsenic 8.8 2 4.80E+02 0.018 2.9E-06 Commerical/Industrial Worker Cobalt 1112 _3.00E+02 3.50E+02 8.30E+06 0.031 1.3E-10 Commerical/Industrial Worker Iron 209217 1 8.20E+05 0.26 Commerical/Industrial Worker Manganese 6066 1 1.60E+05 0.038 Total 0.34 2.9E-06 Construction Worker Arsenic 8.8 2 6.40E+02 9.90E+03 0.014 8.9E-08 Construction Worker Cobalt ill 4.40E+03 4.30E+08 0.0025 2.6E-12 Construction Worker Iron 209217 1 1.00E+06 0.21 Construction Worker Manganese 6066`1 2.00E+05 0.030 Total 0.26 8.9E-08 Trespasser Arsenic 8.8 2 1.54E+03 2.40E+03 0.0057 3.7E-07 Cobalt Trespasser 112 1.10E+03 2.30E+08 0.010 4.8E-12 Iron 2.50E+06 Trespasser 209217 1 0.084 Trespasser 0.012 Manganese 60661 5.00E+05 Total 0.11 3.7E-07 1 Original EPC from risk assessment 2 Revised EPC. Cliffside: with statistical outliers removed from dataset. Mayo: with additional data added to the dataset from perimeter soil samples and ash basin ash 3 EPC / HI RBC 4 EPC / ELCR RBC x 1E-04 EPC: Exposure Point Concentration RBC: Risk -based Concentration from Risk Assessment Table 2. AOW/Seep Water Hazard Index ELCR Current Risk 30-year 30-year 30-year Current Risk 30-year 30-year 30-year Site Assessment MNA Cap Excavate Assessment MNA Cap Excavate Allen Commerical/Industrial Worker 0.00025 0.00025 0.000062 -- 2.1E-09 2.1E-09 5.4E-10 -- Construction Worker -- -- -- -- -- Trespasser 0.011 0.011 0.0028 -- 5.3E-08 5.3E-08 1.3E-08 -- Boater -- -- -- Swimmer -- -- -- -- -- Wader -- -- -- Buck Commerical/Industrial Worker 0.0021 0.0021 0.00053 -- 2.4E-08 2.4E-08 6.0E-09 -- Construction Worker -- -- -- -- -- Trespasser 0.13 0.13 0.033 -- 6.4E-07 6.4E-07 1.6E-07 -- Boater -- -- -- Swimmer -- -- -- -- -- Wader -- -- -- Cliffside Commerical/Industrial Worker 0.0036 0.0036 0.00090 -- 7.5E-08 7.5E-08 1.9E-08 -- Construction Worker -- -- -- -- -- Trespasser 0.12 0.12 0.031 -- 2.1E-06 2.1E-06 5.3E-07 -- Boater -- -- -- Swimmer -- -- -- -- -- Wader -- -- -- Mayo Commerical/Industrial Worker 0.000020 0.000020 0.0000050 -- -- -- -- -- Construction Worker 0.00000060 0.00000060 0.00000015 -- -- -- -- -- Trespasser 0.0000020 0.0000020 0.00000050 -- -- -- -- -- Boater -- -- -- Swimmer -- -- -- -- -- Wader I -- -- -- -- Not Applicable 30-year MNA: same as current risk assessment 30-year Cap: 0.25 x 30-year MNA 30-year Excavate: 0 Table 3A. Cliffside Groundwater Risk Const Wkr Const Wkr 30 Year Modeled Groundwater 2 30 Year Estimated HI 3 30 Year Estimated ELCR 4 MNA Cap Excavate MNA Cap Excavate MNA Cap Excavate Analyte HI RBC ELCR RBC µg/L µg/L µg/L Aluminum 1 96000000 27163 27138 26743 0.00028 0.00028 0.00028 Antimony 17000 5.0 5.0 4.9 0.00029 0.00029 0.00029 Arsenic 29000 450000 11 10 10 0.00037 0.00036 0.00036 2.4E-09 2.3E-09 2.3E-09 Barium 5000000 94 74 76 0.000019 0.000015 0.000015 Beryllium 480000 1.1 1.0 1.0 0.0000023 0.0000022 0.0000022 Boron 19000000 307 257 212 0.000016 0.000014 0.000011 Chromium (total) 8600000 10 10 10 0.0000011 0.0000011 0.0000011 Chromium VI 28000 76000 1.4 1.4 1.4 0.000050 0.000051 0.000051 1.9E-09 1.9E-09 1.9E-09 Cobalt 330000 22 20 20 0.000066 0.000062 0.000061 Manganese 1 2200000 9681 9672 9531 0.0044 0.0044 0.0043 Mercury 1 50000 0.5 0.5 0.5 0.000010 0.000010 0.000010 Molybdenum 1 480000 91 91 89 0.00019 0.00019 0.00019 Nickel 1000000 14 12 12 0.000014 0.000012 0.000012 Strontium 1 190000000 7723 7715 7603 0.000041 0.000041 0.000040 Vanadium 960000 5.2 5.0 5.0 0.000005 0.000005 0.000005 Zinc 1 31000000 153 153 151 0.0000050 0.0000049 0.0000049 Total for Construction Worker 0.0058 0.0057 0.0057 4.2E-09 4.2E-09 4.2E-09 1 Not included in groundwater model. Concentrations estimated based on ratio of modeled analytes to current concentrations used in risk assessment. 2 Average of modeled concentrations from wells included in the model (which were all at the compliance boundary) 3 Modeled groundwater concentration / HI RBC 4 Modeled groundwater concentration / ELCR RBC x 1E-04 RBC: Risk -based concentration from risk assessement Table 3B. Allen Groundwater Risk Const Wkr Const Wkr 30 Year Modeled Groundwater 2 30 Year Estimated HI 3 30 Year Estimated ELCR 4 MNA Cap Excavate MNA Cap Excavate MNA Cap Excavate Analyte HI RBC ELCR RBC µg/L µg/L µg/L Aluminum 1 96000000 8128 7146 2735 0.000085 0.000074 0.000028 Antimony 17000 1.3 1.0 0.4 0.000075 0.000056 0.000022 Arsenic 29000 450000 4 4 1 0.00015 0.00013 0.000022 9.9E-10 8.2E-10 1.4E-10 Barium 5000000 191 173 59 0.000038 0.000035 0.000012 Beryllium 1 480000 4.0 3.6 1.4 0.0000084 0.0000074 0.0000028 Boron 19000000 438 370 0 0.000023 0.000019 0.0 Chromium (total) 8600000 10 11 0 0.0000012 0.0000013 0.0 Cobalt 330000 11.5 10.4 2.5 0.000035 0.000031 0.0000077 Manganese 1 2200000 9355 8224 3147 0.0043 0.0037 0.0014 Mercury 1 50000 253 222 85 0.0051 0.0044 0.0017 Molybdenum 1 480000 46.1 40.6 15.5 0.000096 0.000085 0.000032 Nickel 1 1000000 1222 1074 411 0.00122 0.00107 0.00041 Selenium 480000 1 1 0 0.0000016 0.0000015 0.00000044 Vanadium 960000 23.1 22.9 14.8 0.000024 0.000024 0.000015 Zinc 1 31000000 50 44 17 0.0000016 0.0000014 0.00000054 Total for Construction Worker 1 0.0111 0.0097 0.0037 9.9E-10 8.2E-10 1.4E-10 1 Not included in groundwater model. Concentrations estimated based on ratio of modeled analytes to current concentrations used in risk assessment. 2 Average of modeled concentrations from wells included in the model (which were all at the compliance boundary) 3 Modeled groundwater concentration / HI RBC 4 Modeled groundwater concentration / ELCR RBC x 1E-04 RBC: Risk -based concentration from risk assessement Table 3C. Buck Groundwater Risk Hazard Index ELCR Current Risk 30-year 30-year 30-year Current Risk 30-year 30-year 30-year Receptor Assessment MNAl CapZ Excavate3 Assessment MNAl Cape Excavate3 Construction Worker 0.0007 0.0011 0.0005 0.00063 7.0E-09 1.1E-08 5.3E-09 6.3E-09 1 30-year MNA = Current Risk Assessment x 1.5 2 30-year Cap = 30-year MNA x 0.5 2 30-year Cap = 30-year MNA x 0.6 Table 3D. Mayo Groundwater Risk Hazard Index ELCR Current Risk 30-year 30-year 30-year Current Risk 30-year 30-year 30-year Receptor Assessment MNAl CapZ Excavate3 Assessment MNAl Cape Excavate3 Construction Worker 1.00E-03 0.0015 0.0004 0.00015 -- -- -- -- 1 30-year MNA = Current Risk Assessment x 1.5 2 30-year Cap = 30-year MNA x 0.25 2 30-year Cap = 30-year MNA x 0.1 -- Not applicable Table 4. On -Site Sediment Hazard Index ELCR Current Risk 30-year 30-year 30-year Current Risk 30-year 30-year 30-year Site Receptor Assessment MNA Cap Excavate Assessment MNA Cap Excavate Allen Commerical/Industrial Worker 0.000043 0.000043 0.000011 -- -- -- -- -- Construction Worker -- -- -- -- -- -- -- -- Trespasser 0.00059 0.00059 0.00015 -- -- -- -- -- Boater -- -- -- Swimmer -- -- -- -- -- Wader -- -- -- Buck Commerical/Industrial Worker 0.00031 0.00031 0.000078 -- 2.6E-09 2.6E-09 6.5E-10 -- Construction Worker -- -- -- -- -- -- -- -- Trespasser 0.0044 0.0044 0.0011 -- 2.5E-08 2.5E-08 0.0000 -- Boater -- -- -- Swimmer -- -- -- Wader -- -- -- Cliffside Commerical/Industrial Worker 0.000091 0,000091 0.000023 -- 9.1E-09 9.1E-09 2.3E-09 -- Construction Worker -- -- -- -- -- -- -- -- Trespasser 0.0018 0.0018 0.0005 -- 8.7E-08 8.737E-08 0.0000 -- Boater -- -- -- Swimmer -- -- -- -- -- Wader -- -- -- Mayo Commerical/Industrial Worker 0.00050 0.00050 0.00013 -- 2.0E-08 2.0E-08 5.0E-09 -- Construction Worker -- -- -- -- -- -- -- -- Trespasser 0.0080 0.0080 0.0020 -- 1.0E-07 0.0000001 0.0000 -- Boater -- -- -- Swimmer -- -- -- -- -- Wader I-- -- -- -- Not Applicable 30-year MNA: same as current risk assessment 30-year Cap: 0.25 x 30-year MNA 30-year Excavate: 0 Table 5. On -Site Surface Water Hazard Index ELCR Current Risk 30-year 30-year 30-year Current Risk 30-year 30-year 30-year Site Receptor Assessment MNA Cap Excavate Assessment MNA Cap Excavate Allen Commerical/Industrial Worker 0.00015 0.00015 0.000036 -- -- -- -- -- Construction Worker -- -- -- -- -- Trespasser 0.0043 0.0043 0.0011 -- -- -- -- -- Boater -- -- -- Swimmer -- -- -- -- -- Wader -- -- -- Buck Commerical/Industrial Worker 0.000061 0.000061 0.000015 -- -- -- -- -- Construction Worker -- -- -- -- -- Trespasser 0.0013 0.0013 0.0003 -- -- -- -- -- Boater -- -- -- Swimmer -- -- -- -- -- Wader -- -- -- Cliffside Commerical/Industrial Worker 0.000017 0.000017 0.000004 -- -- -- -- -- Construction Worker -- -- -- -- -- Trespasser 0.00037 0.00037 0.00009 -- -- -- -- -- Boater -- -- -- Swimmer -- -- -- -- -- Wader -- -- -- Mayo Commerical/Industrial Worker 0.00070 0.00070 0.00018 -- -- -- -- -- Construction Worker -- -- -- -- -- Trespasser 0.020 0.020 0.0050 -- -- -- -- -- Boater -- -- -- Swimmer -- -- -- -- -- Wader I-- -- -- -- Not Applicable 30-year MNA: same as current risk assessment 30-year Cap: 0.25 x 30-year MNA 30-year Excavate: 0 Table 6. Off -Site Sediment Hazard Index ELCR Current Risk 30-year 30-year 30-year Current Risk 30-year 30-year 30-year Site Receptor Assessment MNA Cap Excavate Assessment MNA Cap Excavate Allen Commerical/Industrial Worker -- -- -- -- -- -- -- -- Construction Worker -- -- -- -- -- -- -- -- Trespasser -- -- -- -- -- Boater 0.00017 0.00017 0.000042 1.7E-OS -- -- -- -- Swimmer 0.0018 0.00176 0.00044 1.8E-04 -- -- -- -- Wader 0.0018 0.0018 0.00044 1.8E-04 -- -- -- -- Buck Commerical/Industrial Worker -- -- -- -- -- -- -- -- Construction Worker -- -- -- -- -- -- -- -- Trespasser -- -- -- -- -- Boater 0.0014 0.0014 0.00035 1.4E-04 1.6E-08 1.6E-08 0.0000 1.6E-09 Swimmer 0.013 0.013 0.0033 1.3E-03 1.7E-07 1.7E-07 4.3E-08 1.7E-08 Wader 0.013 0.013 0.0033 1.3E-03 6.6E-08 6.6E-08 1.7E-08 6.6E-09 Cliffside Commerical/Industrial Worker -- -- -- -- -- -- -- -- Construction Worker -- -- -- -- -- -- -- -- Trespasser -- -- -- -- -- Boater 0.0010 0.0010 0.0003 1.0E-04 5.5E-08 5.533E-08 0.0000 5.5E-09 Swimmer 0.0070 0.0070 0.0017 7.0E-04 5.9E-07 5.9E-07 1.5E-07 5.9E-08 Wader 0.0040 0.0040 0.0010 4.0E-04 2.3E-07 2.3E-07 5.8E-08 2.3E-08 Mayo Commerical/Industrial Worker -- -- -- -- -- -- -- -- Construction Worker -- -- -- -- -- -- -- -- Trespasser -- -- -- -- -- Boater 0.00030 0.00030 0.000075 3.0E-05 -- -- -- -- Swimmer 0.0030 0.0030 0.00075 3.0E-04 -- -- -- -- Wader 0.0030 0.0030 0.00075 3.0E-04 -- -- -- -- -- Not Applicable 30-year MNA: same as current risk assessment 30-year Cap: 0.25 x 30-year MNA 30-year Excavate: 0 Table 7A. Off -Site Surface Water: Allen 2046 MNA 2046 Cap 2046 Excavate Model CGW CSW Chronic Model CGW CSW Chronic Model CGW CSW Chronic Analyte Criver (µg/L) (µg/L) (µg/L) (µg/L) (µg/L) (µg/L) (µg/L) Aluminum 251 8128 12 63 0.30 7146 10 58 0.30 2735 2.5 38 0.26 Cobalt 0.25 Manganese 5 1 9355 48 8224 43 3147 20 Zinc 0.0051 50 0.241 44 0.211 17 0.083 1 Not included in surface water model. Cf1Vef based on 1/2 of typical detection limits. Cs = (QGWXCGW + QriverXCriver)/ (Qgw + Qriver) QGW (cfs) 0.64 Qriver(Cf5) 137.0882 Model CGW from Table 3B Receptor Analyte HI RBC ELCR RBC 30 Year Surface Water 30 Year Estimated HI 1 30 Year Estimated ELCR 2 MNA Cap Excavate µg/L µg/L µg/L MNA Cap Excavate MNA Cap Excavate Boater Aluminum 5.6E+07 -- 4.2E+04 -- 3.1E+05 -- 2.8E+07 -- 63 58 38 0.30 0 0 1.1E-06 1.0E-06 6.7E-07 -- -- -- -- -- -- -- -- -- -- -- -- Boater ICobalt 7.2E-06 7.1E-06 6.2E-06 Boater Manganese 48 43 20 1.6E-04 1.4E-04 6.3E-05 8.5E-09 7.5E-09 3.0E-09 Boater Zinc 0.24 0 0 Total 1.6E-04 1.5E-04 7.0E-05 -- -- -- Swimmer Swimmer Aluminum Cobalt 1.1E+06 -- 3.5E+02 -- 63 58 38 0.30 0 0 5.7E-05 5.3E-05 3.4E-05 8.6E-04 8.5E-04 7.4E-04 -- -- -- -- -- -- Swimmer Manganese 4.1E+04 -- 48 43 20 1.2E-03 1.1E-03 4.8E-04 Swimmer Zinc 3.4E+05 -- 0.24 0 0 7.0E-07 6.2E-07 2.5E-07 -- -- -- Total 2.1E-03 2.0E-03 1.3E-03 -- -- -- Wader Wader Aluminum 1.2E+06 -- 3.6E+02 - 9.0E+04 -- 3.6E+05 - 63 58 38 0.30 0 0 5.2E-05 4.8E-05 3.1E-05 8.4E-04 8.2E-04 7.2E-04 -- -- -- -- -- -- Cobalt Wader Wader Manganese 48 43 20 0.24 0 01 5.4E-04 4.8E-04 2.2E-04 6.6E-07 5.8E-07 2.3E-07 -- -- -- -- -- -- Zinc Total I 1.4E-03 1.4E-03 9.7E-04 -- -- -- 1 Modeled surface water concentration / HI RBC 2 Modeled surface water concentration / ELCR RBC x 1E-04 RBC: Risk -based concentration from risk assessement Table 7B. Off -Site Surface Water: Cliffside 2046 MNA 2046 Cap 2046 Excavate Analyte Criver Model CGW CSW Chronic Model CGW CSW Chronic Model CGW CSW Chronic (µg/L) (µg/L) (µg/L) (µg/L) (µg/L) (µg/L) (µg/L) Aluminum 251 27163 9681 1678 594 27138 1676 26743 9531 1652 585 Manganese 5 1 9672 594 Zinc 0.0051 153 9.34 151 9.20 153 9.35 1 Not included in surface water model. C,1Vef based on 1/2 of typical detection limits. These calculations are for Suck Creek, which is more sensitive than the Broad River. Csw = (QGWXCGW + QriverXCriver)/ (C gw + Qriver) CtcW (cfs) 0.14 Qriver (Cf5) 2.159 Model CGW from Table 3B Receptor Analyte HI RBC ELCR RBC 30 Year Surface Water 30 Year Estimated HI 1 30 Year Estimated ELCR 2 MNA Cap Excavate µg/L µg/L µg/L MNA Cap Excavate MNA Cap Excavate Boater Boater Aluminum Manganese 5.6E+07 - 3.1E+05 -- 2.8E+07 -- _ 1678 594 9.35 1676 594 9 1652 585 9 3.0E-05 3.0E-05 3.0E-05 -- -- -- -- -- -- -- -- -- 1.9E-03 1.9E-03 1.9E-03 Boater Zinc 3.3E-07 3.3E-07 3.3E-07 Total 1.9E-03 1.9E-03 1.9E-03 -- -- -- Swimmer Swimmer Aluminum 1.1E+06� _ -- 4.1E+04 - 3.4E+05 - 1678 594 9.35 1676 594 9 1652 585 9 1.5E-03 1.4E-02 1.5E-03 1.4E-02 1.5E-03 1.4E-02 -- -- -- -- -- -- -- -- -- Manganese Swimmer Zinc 2.7E-05 2.7E-05 2.7E-05 Total I 1.6E-02 1.6E-02 1.6E-02 -- -- -- Wader Wader Wader Aluminum 1.2E+06 - 9.0E+04 -- 3.6E+05 - 1678 594 9.35 1676 594 9 1652 585 9 1.4E-03 1.4E-03 1.4E-03 -- -- -- Manganese 6.6E-03 6.6E-03 2.6E-05 2.6E-05 6.5E-03 2.6E-05 -- -- -- -- -- -- Zinc Total 8.0E-03 8.0E-03 7.9E-03 -- -- -- 1 Modeled surface water concentration / HI RBC 2 Modeled surface water concentration / ELCR RBC x 1E-04 RBC: Risk -based concentration from risk assessement Table 7C. Off -Site Surface Water: Buck Receptor Hazard Index ELCR Current Risk 30-year 30-year Assessment MNA1 Cape 30-year Excavate3 Current Risk 30-year 30-year 30-year Assessment MNA1 Cape Excavate3 Boater 0.00099 0.0010 0.0010 0.00098 -- -- -- -- -- -- -- -- -- -- -- -- Swimmer Wader 0.0077 0.0081 0.0075 0.0075999 0.0035 0.0037 0.0034 0.0035 1 30-year MNA = Current Risk Assessment x 1.05 2 30-year Cap = 30-year MNA x 0.93 2 30-year Cap = 30-year MNA x 0.94 Factors were determined by projecting the results of the surface water model into the future for each scenario for the analytes used in the surface water model, which were not the COPCs for surface water. Table 7D. Off -Site Surface Water: Mayo Hazard Index ELCR Current Risk 30-year 30-year 30-year Current Risk 30-year 30-year 30-year Receptor Assessment MNAl Capz Excavate3 Assessment MNAl Capz Excavate3 Boater 0.02 0.021 0.020 0.020 -- -- -- -- Swimmer 0.2 0.21 0.20 0.20 -- -- -- -- Wader 0.1 0.11 0.10 0.10 -- -- -- -- 1 30-year MNA = Current Risk Assessment x 1.05 2 30-year Cap = 30-year MNA x 0.96 2 30-year Cap = 30-year MNA x 0.94 Factors were determined by projecting the results of the surface water model into the future for each scenario for the analytes used in the surface water model, Table 8A. Total Hazard Index for Each Receptor and Scenario 30-Year MNA - HI AOW AOW Groundwater On -Site On -Site Off -Site Off -Site Total Site Receptor Soil Water Sediment Surface Water Sediment Surface Water HI Allen Commerical/Industrial Wkr 9.5E-02 2.5E-04 -- 4.3E-05 1.5E-04 -- -- 9.6E-02 Construction Worker 1.4E-02 -- 1.1E-02 -- -- -- -- 2.5E-02 Trespasser 3.0E-02 1.1E-02 -- 5.9E-04 4.3E-03 -- -- 4.6E-02 Boater -- -- -- -- -- 1.7E-04 1.6E-04 3.3E-04 Swimmer -- -- -- -- -- 1.8E-03 2.1E-03 3.9E-03 Wader -- -- -- -- -- 1.8E-03 1.4E-03 3.2E-03 Buck Commerical/Industrial Wkr 1.9E-01 2.1E-03 -- 3.1E-04 6.1E-05 -- -- 1.9E-01 Construction Worker 4.9E-02 - 1.1E-03 - -- -- -- 5.0E-02 Trespasser 6.2E-02 1.3E-01 -- 4.4E-03 1.3E-03 -- -- 2.0E-01 Boater -- -- -- -- -- 1.4E-03 1.0E-03 2.4E-03 Swimmer -- -- -- -- -- 1.3E-02 8.1E-03 2.1E-02 Wader -- -- -- -- -- 1.3E-02 3.7E-03 1.7E-02 Cliffside Commerical/Industrial Wkr 1.1E-01 3.6E-03 -- 9.1E-05 1.7E-05 -- -- 1.1E-01 Construction Worker 3.6E-02 -- 5.8E-03 -- -- -- -- 4.2E-02 Trespasser 3.4E-02 1.2E-01 -- 1.8E-03 3.7E-04 -- -- 1.6E-01 Boater - - - -- - 1.0E-03 1.9E-03 2.9E-03 Swimmer -- -- -- -- -- 7.0E-03 1.6E-02 2.3E-02 Wader -- - - -- - 4.0E-03 8.0E-03 1.2E-02 Mayo Commerical/Industrial Wkr 3.4E-01 2.0E-05 -- 5.0E-04 7.0E-04 -- -- 3.4E-01 Construction Worker 2.6E-01 6.0E-07 1.5E-03 -- -- -- -- 2.6E-01 Trespasser 1.1E-01 2.0E-06 -- 8.0E-03 2.0E-02 -- -- 1.4E-01 Boater -- -- -- - -- 3.0E-04 2.1E-02 2.1E-02 Swimmer -- -- -- -- -- 3.0E-03 2.1E-01 2.1E-01 Wader -- -- -- -- -- 3.0E-03 1.1E-01 1.1E-01 30-Year Cap - HI AOW AOW Groundwater On -Site On -Site Off -Site Off -Site Total Site Receptor Soil Water Sediment Surface Water Sediment Surface Water HI Allen Commerical/Industrial Wkr 2.4E-02 6.2E-05 -- 1.1E-05 3.6E-05 -- -- 2.4E-02 Construction Worker 3.6E-03 -- 9.7E-03 -- -- -- -- 1.3E-02 Trespasser 7.6E-03 2.8E-03 -- 1.5E-04 1.1E-03 -- -- 1.2E-02 Boater -- -- - -- -- 4.2E-05 1.5E-04 1.9E-04 Swimmer -- -- -- -- -- 4.4E-04 2.0E-03 2.4E-03 Wader -- -- -- -- -- 4.4E-04 1.4E-03 1.8E-03 Buck Commerical/Industrial Wkr 4.8E-02 5.3E-04 -- 7.8E-05 1.5E-05 -- -- 4.8E-02 Construction Worker 1.2E-02 -- 5.3E-04 -- -- -- -- 1.3E-02 Trespasser 1.6E-02 3.3E-02 -- 1.1E-03 3.3E-04 -- -- 4.9E-02 Boater -- -- -- -- -- 3.5E-04 9.7E-04 1.3E-03 Swimmer -- -- -- -- -- 3.3E-03 7.5E-03 1.1E-02 Wader -- -- -- -- -- 3.3E-03 3.4E-03 6.7E-03 Cliffside Commerical/Industrial Wkr 2.7E-02 9.0E-04 -- 2.3E-05 4.3E-06 -- -- 2.8E-02 Construction Worker 9.0E-03 -- 5.7E-03 -- - -- -- 1.5E-02 Trespasser 8.6E-03 3.1E-02 -- 4.6E-04 9.3E-05 -- -- 4.0E-02 Boater -- - -- -- - 2.5E-04 1.9E-03 2.2E-03 Swimmer -- -- -- -- -- 1.7E-03 1.6E-02 1.8E-02 Wader -- - -- - -- 1.0E-03 8.0E-03 9.0E-03 Mayo Commerical/Industrial Wkr 8.6E-02 5.0E-06 -- 1.3E-04 1.8E-04 -- -- 8.6E-02 Construction Worker 6.4E-02 1.5E-07 3.8E-04 -- -- -- -- 6.4E-02 Trespasser 2.8E-02 5.0E-07 -- 2.0E-03 5.0E-03 -- -- 3.5E-02 Boater -- - - -- - 7.5E-05 2.0E-02 2.0E-02 Swimmer -- - -- -- - 7.5E-04 2.0E-01 2.0E-01 Wader -- -- -- -- -- 7.5E-04 1.0E-01 1.0E-01 30-Year Excavate - HI AOW AOW Groundwater On -Site On -Site Off -Site Off -Site Total Site Receptor Soil Water Sediment Surface Water Sediment Surface Water HI Allen Commerical/Industrial Wkr -- -- -- -- -- -- -- 0.0E+00 Construction Worker -- -- 3.7E-03 -- -- -- -- 3.7E-03 Trespasser -- -- -- -- -- -- -- 0.0E+00 Boater -- -- -- -- -- 1.7E-05 7.0E-05 8.7E-05 Swimmer -- -- -- -- -- 1.8E-04 1.3E-03 1.4E-03 Wader -- -- -- -- -- 1.8E-04 9.7E-04 1.1E-03 Buck Commerical/Industrial Wkr -- -- -- -- -- -- -- 0.0E+00 Construction Worker -- -- 6.3E-04 -- -- -- -- 6.3E-04 Trespasser -- -- -- -- -- -- -- 0.0E+00 Boater -- -- -- -- -- 1.4E-04 9.8E-04 1.1E-03 Swimmer -- -- -- -- -- 1.3E-03 7.6E-03 8.9E-03 Wader -- -- - - - 1.3E-03 3.5E-03 4.8E-03 Cliffside Commerical/Industrial Wkr -- -- -- -- -- -- -- 0.0E+00 Construction Worker -- -- 5.7E-03 -- - -- -- 5.7E-03 Trespasser -- -- -- -- -- -- -- 0.0E+00 Boater -- - -- -- - 1.0E-04 1.9E-03 2.0E-03 Swimmer -- -- -- -- - 7.0E-04 1.6E-02 1.6E-02 Wader -- -- -- -- -- 4.0E-04 7.9E-03 8.3E-03 Mayo Commerical/Industrial Wkr -- -- -- -- -- -- -- 0.0E+00 Construction Worker -- -- 1.5E-04 - - -- -- 1.5E-04 Trespasser -- -- -- -- -- -- -- 0.0E+00 Boater -- -- - -- -- 3.0E-05 2.0E-02 2.0E-02 Swimmer -- -- -- -- -- 3.0E-04 2.0E-01 2.0E-01 Wader -- -- - -- - 3.0E-04 9.9E-02 9.9E-02 Table 8B. Total ELCR for Each Receptor and Scenario 30-Year MNA - ELCR AOW AOW Groundwater On -Site On -Site Off -Site Off -Site Total Site Receptor Soil Water Sediment Surface Water Sediment Surface Water ELCR Allen Commerical/Industrial Wkr 3.6E-10 2.1E-09 -- -- -- -- -- 2.5E-09 Construction Worker 7.0E-12 -- 9.9E-10 - -- -- - 1.0E-09 Trespasser 1.3E-11 5.3E-08 - -- -- -- - 5.3E-08 Boater -- - - -- -- -- - Rec Fisher -- - -- -- -- -- - Sub Fisher -- -- -- - -- - - Swimmer -- - - -- -- - - Wader Buck Commerical/Industrial Wkr 2.2E-06 2AE-08 - 2.6E-09 - - - 22E-06 Construction Worker 6.6E-08 - 1.1E-08 - - - - 7.7E-08 9.4E-07 Trespasser 2.7E-07 6.4E-07 - 2.5E-08 - - - Boater -- - - - - 1.6E-08 - 1.6E-08 Rec Fisher -- - - 0.0E+00 O.OE+00 0.0E+00 - Sub Fisher -- - - 0.0E+00 0.0E+00 0.0E+00 - 1.7E-07 Swimmer -- - - - - 1.7E-07 - Wader 6.6E-08 6.6E-08 Cliffside Commerical/Industrial Wkr 2.3E-06 7.5E-08 - 9.1E-09 - - - 2.4E-06 Construction Worker 7.0E-08 - 4.2E-09 - - - - 7.4E-08 2.5E-06 Trespasser 2.9E-07 2.1E-06 - 8.7E-08 - - - Boater -- - - - - 5.5E-08 - 5.5E-08 Rec Fisher -- - - 0.0E+00 0.0E+00 0.0E+00 - Sub Fisher -- - - 0.0E+00 0.0E+00 0.0E+00 - 5.9E-07 Swimmer -- - - - - 5.9E-07 - Wader 2.3E-07 2.3E-07 Mayo Commerical/Industrial Wkr 2.9E-06 - - 2.0E-08 - - - 3.0E-06 Construction Worker 8.9E-08 - - - - - - 8.9E-08 4.7E-07 Trespasser 3.7E-07 - - 1.0E-07 - - - Boater -- - - - - - - Rec Fisher -- - - 0.0E+00 O.OE+00 0.0E+00 - Sub Fisher -- -- -- 0.0E+00 0.0E+00 0.0E+00 - Swimmer -- - -- -- - - - Wader 30-Year Cap -ELCR AOW AOW Groundwater On -Site On -Site Off -Site Off -Site Total Site:: Receptor Sol[ Water Sediment Surface Water Sediment Surface Water ELCR Allen Commerical/Industrial Wkr 9.1E-11 5.4E-10 - - - - - 6.3E-10 Construction Worker 1.8E-12 - - - - - - 1:5E-12 1.4E-08 Trespasser 3.3E-12 1.3E-08 8.2E-10 - - - - Boater -- - - - - - Rec Fisher -- - - - - - Sub Fisher -- - - - - - - Sw1mmer -- - - - - - - Wader Buck Commerical/Industrial Wkr 5.5E-07 6.0E-09 - 6.5E-10 - - - 5.6E-07 Construction Worker 1.7E-08 - 5.3E-09 - - - - 2.2E-08 2.3E-07 Trespasser 6.8E-08 1.6E-07 - 6.3E-09 - - - Boater -- - - - - 4.0E-09 - 4.0E-09 Rec Fisher -- - - 0.0E+00 0.0E+D0 0.0E+DO - Sub Fisher -- - - 0.0E+0D 0.0E+00 0.0E+DO - 4.3E-08 Swimmer -- - - - - 4.3E-08 - Wader 1.7E-08 1.7E-08 Cliffside Commerical/Industrial Wkr 5.7E-07 1.9E-08 - 2.3E-09 - - - 5.9E-07 Construction Worker 1.7E-08 - 4.2E-09 - - - - 2.22E-OS 62E-07 Trespasser 72E-08 5.3E-07 - 2.2E-08 - - - Boater -- - - - - 1.4E-08 - 1.4E-08 Rec Fisher -- - - 0.0E+00 0.0E+D0 0.0E+DO - Sub Fisher -- - - 0.0E+0D 0.0E+00 0.0E+DO - 1.5E-07 Swimmer -- - - - - 1.5E-07 - Wader 5.8E-08 5.8E-08 Mayo Commerical/Industrial Wkr 7.3E-07 - - 5.0E-09 - - - 7.4E-07 Construction Worker 2.2E-08 - - - - - - 2.2E-08 12E-07 Trespasser 92E-08 - - 2.5E-08 - - - Boater -- - - - - - - Rec Fisher -- - - 0.0E+00 0.0E+D0 0.0E+DO - Sub Fisher - - - 0.0E+00 0.0E+00 0.0E+DO - Swimmer - - - - - - - Wader 30-Year Excavate - ELCR AOW AOW Groundwater On -Site On -Site Off -Site Off -Site Total Site Receptor Soil Water Sediment Surface Water Sediment Surface Water ELCR Allen Commerical/Industrial Wkr -- -- O.0E+00 -- -- -- -- -- Construction Worker -- -- 1.4E-10 -- -- -- - 1.4E-10 -- Trespasser -- -- -- -- -- Boater -- -- -- -- -- - - Rec Fisher -- -- -- -- -- - - Sub Fisher -- -- - -- -- - Swimmer -- - - -- - - Wader Buck Commerical/Industrial Wkr -- - 0.0E+00 - - - - Constructian Worker -- - 6.3E-09 - - - - 6.3E-09 Trespasser -- - - - - - - Runter -- - - - - 1.6E-09 - 1.26E-09 Rec Fisher -- - - 0.0E+00 0.0E+00 0.0E+00 - Sub Fisher -- - - 0.0E+00 O.OE+DO 0.0E+DO - 1.7E-08 Swimmer -- - - - - 1.7E-08 - Wader 6.6E-09 6.6E-09 Cliffside Commerical/Industrial Wkr -- - 0.0E+00 - - - - Construction Worker - - 4.2E-09 - - - - 4.2E-09 Trespasser -- - - - - - - Boater -- - - - - 5.5E-09 - 5.5E-09 Rec Fisher -- - - 0.0E+00 0.0E+00 0.0E+00 - Sub Fisher -- - - 0.0E+00 O.OE+00 0.0E+DO - 5.9E-08 Swimmer -- - - - - 5.9E-08 - Wader 2.3E-08 2.3E-08 Mayo Commerical/Industrial Wkr -- - 0.0E+00 - - - - Construction Worker -- - - - - - - -- Trespasser - - - - - - - Boater - - - - -- - - Rec Fisher - - - 0.0E+00 0.0E+00 0.0E+00 - 5ub Fisher - - - 0.0E+00 0.0E+00 0.0E+00 Swimmer - - - - - - Wader Table 9. Summary of Risks for Each Scenario Allen Hazard Index ELCR On -Site Off -Site On -Site Off -Site MNA 0.1 0.004 5 E-08 -- Cap 0.02 0.002 1E-08 -- Excavate 1 0.004 0.001 1E-10 -- Buck Hazard Index ELCR On -Site Off -Site On -Site Off -Site MNA 0.2 0.02 2 E-06 2 E-07 Cap 0.05 0.01 6E-07 4E-08 Excavate 1 0.0006 0.009 6E-09 2E-08 Cliffside Hazard Index ELCR On -Site Off -Site On -Site Off -Site MNA 0.2 0.02 2E-06 6E-07 Cap 0.04 0.02 6E-07 1E-07 Excavate 1 0.01 0.02 4E-09 6E-08 Mayo Hazard Index ELCR On -Site Off -Site On -Site Off -Site MNA 0.3 0.2 3E-06 -- Cap 0.09 0.2 7E-07 -- Excavate 1 0.0002 0.2 -- -- On-Site: Maximum of commercial/industrial worker, construction worker and trespasser Off -Site: Maximum of boater, swimmer and wader 3 2.5 2 1.5 1 0.5 0 3 2.5 2 1.5 1 0.5 0 Allen -HI-30Yr MNA Cap Excavation ■ Commerical/Industrial Wkr ■ Construction Worker ■ Trespasser ■ Boater ■ Swimmer ■ Wader Buck - HI - 30 Yr MNA Cap Excavation ■Commerical/Industrial Wkr ■ Construction Worker ■Trespasser ■Boater ■Swimmer 0Wader Cliffside - HI - 30 Yr 3 2.5 2 1.5 1 0.5 0 M NA Cap Excavation ■ Commerical/Industrial Wkr ■ Construction Worker ■Trespasser ■ Boater ■ Swimmer ■ Wader Mayo -HI-30Yr 3 2.5 2 1.5 1 0.5 ■_■. _ ■__ _ a :■ _ ■ M NA Cap Excavation ■Commerical/Industrial Wkr ■ Construction Worker ■Trespasser ■Boater ■Swimmer 0Wader 1.00E-04 8.00E-05 6.00E-05 4.00E-05 2.00E-05 0.00E+00 1.00E-04 8.00E-05 6.00E-05 4.00E-05 2.00E-05 0.00E+00 Allen - ELCR - 30 Yr MNA Cap Excavation ■ Commerical/Industrial Wkr ■ Construction Worker ■ Trespasser ■ Boater ■ Swimmer ■ Wader Buck - ELCR - 30 Yr MNA Cap Excavation ■ Commerical/Industrial Wkr ■ Construction Worker ■ Trespasser ■ Boater ■ Swimmer ■ Wader 1.00E-04 8.00E-05 6.00E-05 4.00E-05 2.00E-05 0.00E+00 1.00E-04 8.00E-05 6.00E-05 4.00E-05 2.00E-05 0.00E+00 Cliffside - ELCR - 30 Yr MNA Cap Excavation ■ Commerical/Industrial Wkr ■ Construction Worker ■ Trespasser ■ Boater ■ Swimmer ■ Wader Mayo - ELCR - 30 Yr MNA Cap Excavation ■ Commerical/Industrial Wkr ■ Construction Worker ■ Trespasser ■ Boater ■ Swimmer ■ Wader Updated EPC Calculations Cliffside: Computation of New EPC Matrix Location Sample ID Constituent Result Result Flag for ProLICL AOW Soil S-11 S-11 SD 20150629 Arsenic 310.0 1 detect AOW Soil S-10 S-10 SD 20150627 Arsenic 250.0 1 detect AOW Soil CLFSP051 CLFSP051_SD_20150627 Arsenic 51.4 1 detect AOW Soil CLFTD052 CLFTD052_SD_20150627 Arsenic 33.2 1 detect AOW Soil CLFTDO04 CLFTDO04_SD_20150627 Arsenic 18.5 1 detect AOW Soil S-19 S-19 SD 20150603 Arsenic 10.4 1 detect AOW Soil S-3 S-3 SD 20150627 Arsenic 5.0 1 detect AOW Soil S-20 S-20 SD 20150627 Arsenic 4.4 1 detect AOW Soil S-1 S-1 SD 20150627 Arsenic 16.0 0 non -detect AOW Soil S-14 S-14 SD 20150602 Arsenic 15.9 0 non -detect AOW Soil S-17 S-17 SD 20150603 Arsenic 15.8 0 non -detect AOW Soil CLFSP059 CLFSP059_SD_20150627 Arsenic 14.3 0 non -detect AOW Soil S-18 S-18 SD 20150603 Arsenic 13.6 0 non -detect AOW Soil S-2 S-2 SD 20150603 Arsenic 13.5 0 non -detect AOW Soil S-12 S-12 SD 20150627 Arsenic 12.5 0 non -detect AOW Soil CLFSP061 CLFSP061_SD_20150629 Arsenic 10.8 0 non -detect AOW Soil CLFTDO05 CLFTD005_SD_20150709 Arsenic 10.3 0 non -detect AOW Soil S-15 S-15 SD 20150602 Arsenic 10.3 0 non -detect AOW Soil S-4 S-4 SD 20150629 Arsenic 10.3 0 non -detect AOW Soil S-5 S-5 SD 20150709 Arsenic 9.5 0 non -detect AOW Soil S-2 S-2 SD 20150627 Arsenic 9.2 0 non -detect AOW Soil CLFSTR065 CLFSTR065_SD_20150627 Arsenic 9.1 0 non -detect AOW Soil CLFSP058 CLFSP058_SD_20150627 Arsenic 8.8 0 non -detect AOW Soil S-16 S-16 SD 20150602 Arsenic 8.8 0 non -detect AOW Soil S-6 S-6 SD 20150627 Arsenic 8.8 0 non -detect AOW Soil S-9 S-9 SD 20150627 Arsenic 8.8 0 non -detect AOW Soil S-7 S-7 SD 20150627 Arsenic 7.6 0 non -detect AOW Soil S-8 S-8 SD 20150629 Arsenic 6.4 0 non -detect AOW Soil S-22 S-22 SD 20150627 Arsenic 6.0 0 non -detect User Selected Options Date/Time of Computation From File Full Precision Outlier Tests for Selected Uncensored Variables 6/21 /2016 10:05.21 AM As_Co.xls OFF Rosner's Outlier Test for Arsenic Mean 31.35 Standard Deviation 69.94 Number of data 29 Number of suspected outliers 4 Potential Obs. Test Critical Critical # Mean sd outlier Number value value (5%) value (1 %) 1 31.35 68.72 310 1 4.055 2.89 3.22 2 21.4 45.76 250 2 4.996 2.88 3.2 3 12.93 9.499 51.4 3 4.049 2.86 3.18 4 11.45 5.69 33.2 4 3.821 2.84 3.16 For 5% significance level, there are 4 Potential Outliers Potential outliers are: 310, 250, 51.4, 33.2 For 1 % Significance Level, there are 4 Potential Outliers Potential outliers are: 310, 250, 51.4, 33.2 Matrix Location Sample ID Constituent Result Result Flag for ProUCL AOW Soil CLFTDO04 CLFTD004_SD_20150627 Cobalt 926.0 1 detect AOW Soil S-17 S-1 7SD20150603 Cobalt 95.8 1 detect AOW Soil S-18 S-18 SD 20150603 Cobalt 88.3 1 detect AOW Soil S-2 S-2 SD 20150603 Cobalt 81.6 1 detect AOW Soil S-19 S-1 9SD20150603 Cobalt 50.4 1 detect AOW Soil S-11 S-1 1SD20150629 Cobalt 45.2 1 detect AOW Soil S-2 S-2 SD 20150627 Cobalt 45.0 1 detect AOW Soil S-3 S-3 SD 20150627 Cobalt 33.6 1 detect AOW Soil CLFTD052 CLFTDO52_SD_20150627 Cobalt 29.0 1 detect AOW Soil CLFSP051 CLFSP051_SD_20150627 Cobalt 26.3 1 detect AOW Soil S-9 S-9 SD 20150627 Cobalt 26.0 1 detect AOW Soil CLFSP061 CLFSP061_SD_20150629 Cobalt 20.3 1 detect AOW Soil S-6 S-6 SD 20150627 Cobalt 11.6 1 detect AOW Soil S-1 S-1 SD 20150627 Cobalt 11.0 1 detect AOW Soil S-20 S-20 SD 20150627 Cobalt 8.4 1 detect AOW Soil S-22 S-22 SD 20150627 Cobalt 7.2 1 detect AOW Soil S-10 S-10 SD 20150627 Cobalt 6.9 1 detect AOW Soil CLFSTR065 CLFSTR065_SD_20150627 Cobalt 6.2 1 detect AOW Soil S-5 S-5 SD 20150709 Cobalt 5.6 1 detect AOW Soil CLFSP058 CLFSP058_SD_20150627 Cobalt 4.8 1 detect AOW Soil S-14 S-14 SD 20150602 Cobalt 15.9 0 non -detect AOW Soil CLFSP059 CLFSP059_SD_20150627 Cobalt 14.3 0 non -detect AOW Soil S-12 S-12 SD 20150627 Cobalt 12.5 0 non -detect AOW Soil CLFTDO05 CLFTD005_SD_20150709 Cobalt 10.3 0 non -detect AOW Soil S-15 S-1 5SD20150602 Cobalt 10.3 0 non -detect AOW Soil S-4 S-4 SD 20150629 Cobalt 10.3 0 non -detect AOW Soil S-16 S-16 SD 20150602 Cobalt 8.8 0 non -detect AOW Soil S-7 S-7 SD 20150627 Cobalt 7.6 0 non -detect AOW Soil S-8 S-8 SD 20150629 Cobalt 6.4 0 non -detect Outlier Tests for Selected Uncensored Variables User Selected Options Date/Time of Computation 6/21/2016 10:05:21 AM From File As Co.xls Full Precision OFF Rosner's Outlier Test for Cobalt Mean 56.06 Standard Deviation 169.2 Number of data 29 Number of suspected outliers 4 Potential Obs. Test Critical Critical # Mean sd outlier Number value value (5%) value (1 %) 1 56.06 166.3 926 1 5.231 2.89 3.22 2 24.99 25.92 95.8 2 2.732 2.88 3.2 3 22.36 22.31 88.3 3 2.956 2.86 3.18 4 19.83 18.35 81.6 4 3.366 2.84 3.16 For 5% significance level, there are 4 Potential Outliers Potential outliers are: 926, 95.8, 88.3, 81.6 For 1 % Significance Level, there are 4 Potential Outliers Potential outliers are: 926, 95.8, 88.3, 81.6 A B C I D I E I F I G I H I I I J K L 1 ProUCL Analysis: UCL Statistics for Data Sets with Non -Detects 2 User Selected Options 3 Date/Time of Computation 6/23/2016 8:23:26 PM 4 From File Cliffside_As.xls 5 Full Precision OFF 6 Confidence Coefficient 95% 7 Number of Bootstrap Operations 2000 8 Arsenic - Cliffside Facility AOW Soil 9 10 General Statistics 11 Total Number of Observations 25 Number of Distinct Observations 20 12 Number of Detects 4 Number of Non -Detects 21 13 Number of Distinct Detects 4 Number of Distinct Non -Detects 16 14 Minimum Detect 4.4 Minimum Non -Detect 6 151 Maximum Detect 18.5 Maximum Non -Detect 16 16 Variance Detects 42.68 Percent Non -Detects 84% 17 Mean Detects 9.575 SD Detects 6.533 18 Median Detects 7.7 CV Detects 0.682 19 Skewness Detects 1.141 Kurtosis Detects 0.224 20 Mean of Logged Detects 2.088 SD of Logged Detects 0.671 21 22 Normal GOF Test on Detects Only 23 Shapiro Wilk Test Statistic 0.877 Shapiro Wilk GOF Test 24 5% Shapiro Wilk Critical Value 0.748 Detected Data appear Normal at 5% Significance Level 25 Lilliefors Test Statistic 0.258 Lilliefors GOF Test 26 5% Lilliefors Critical Value 0.443 Detected Data appear Normal at 5% Significance Level 271 Detected Data appear Normal at 5% Significance Level 28 29 Kaplan -Meier (KM) Statistics using Normal Critical Values and other Nonparametric UCLs 30 Mean 5.594 Standard Error of Mean 0.752 31 SD 2.975 95% KM (BCA) UCL N/A 32 95% KM (t) UCL 6.88 95% KM (Percentile Bootstrap) UCL N/A 331 95% KM (z) UCL 6.83 95% KM Bootstrap t UCL N/A 34 90% KM Chebyshev UCL 7.849 95% KM Chebyshev UCL 8.87 35 97.5% KM Chebyshev UCL 10.29 99% KM Chebyshev UCL 13.07 36 37 Gamma GOF Tests on Detected Observations Only 38 A-D Test Statistic 0.355 Anderson -Darling GOF Test 39 5% A-D Critical Value 0.659 Detected data appear Gamma Distributed at 5% Significance Level 40 K-S Test Statistic 0.296 Kolmogrov-Smirnoff GOF 41 5% K-S Critical Value 0.397 Detected data appear Gamma Distributed at 5% Significance Level 42 Detected data appear Gamma Distributed at 5% Significance Level 43 44 Gamma Statistics on Detected Data Only 451 k hat (MLE) 3.072 k star (bias corrected MLE) 0.935 46 Theta hat (MLE) 3.117 Theta star (bias corrected MLE) 10.24 47 nu hat (MLE) 24.58 nu star (bias corrected) 7.477 48 MLE Mean (bias corrected) 9.575 MLE Sd (bias corrected) 9.904 49 50 Gamma Kaplan -Meier (KM) Statistics 51 k hat (KM) 3.536 nu hat (KM) 176.8 52 Approximate Chi Square Value (176.82, a) 147.1 Adjusted Chi Square Value (176.82, (i) 145.2 53 95% Gamma Approximate KM-UCL (use when n> 50) 6.726 95% Gamma Adjusted KM-UCL (use when n<50) 6.811 54 A B C D I E I F I G I H I I J K L 55 Gamma ROS Statistics using Imputed Non -Detects 56 GROS may not be used when data set has > 50% NDs with many tied observations at multiple DLs 57 GROS may not be used when kstar of detected data is small such as < 0.1 581 For such situations, GROS method tends to yield inflated values of UCLs and BTVs 59 For gamma distributed detected data, BTVs and UCLs may be computed using gamma distribution on KM estimates 60 Minimum 1.864 Mean 5.349 61 Maximum 18.5 Median 4.676 62 SD 3.169 CV 0.593 631 k hat (MLE) 5.039 k star (bias corrected MLE) 4.461 64 Theta hat (MLE) 1.061 Theta star (bias corrected MLE) 1.199 65 nu hat (MLE) 252 nu star (bias corrected) 223.1 66 MLE Mean (bias corrected) 5.349 MLE Sd (bias corrected) 2.532 67 Adjusted Level of Significance (0) 0.0395 68 Approximate Chi Square Value (223.06, a) 189.5 Adjusted Chi Square Value (223.06, R) 187.4 691 95% Gamma Approximate UCL (use when n> 50) 6.297 95% Gamma Adjusted UCL (use when n<50) N/A 70 71 Lognormal GOF Test on Detected Observations Only 72 Shapiro Wilk Test Statistic 0.912 Shapiro Wilk GOF Test 73 5% Shapiro Wilk Critical Value 0.748 Detected Data appear Lognormal at 5% Significance Level 74 Lilliefors Test Statistic 0.262 Lilliefors GOF Test 75 5% Lilliefors Critical Value 0.443 Detected Data appear Lognormal at 5% Significance Level 76 Detected Data appear Lognormal at 5% Significance Level 77 78 Lognormal ROS Statistics Using Imputed Non -Detects 79 Mean in Original Scale 5.651 Mean in Log Scale 1.657 80 SD in Original Scale 2.998 SD in Log Scale 0.345 81 95% t UCL (assumes normality of ROS data) 6.676 95% Percentile Bootstrap UCL 6.723 82 95% BCA Bootstrap UCL 7.298 95% Bootstrap t UCL 8.957 83 95% H-UCL (Log ROS) 6.338 84 85 UCLs using Lognormal Distribution and KM Estimates when Detected data are Lognormally Distributed 86 KM Mean (logged) 1.648 95% H-UCL (KM -Log) 6.192 871 KM SD (logged) 0.326 95% Critical H Value (KM -Log) 1.83 88 KM Standard Error of Mean (logged) 0.093 89 90 DL/2 Statistics 91 DL/2 Normal DL/2 Log -Transformed 92 Mean in Original Scale 6.058 Mean in Log Scale 1.717 931 SD in Original Scale 3.117 SD in Log Scale 0.388 94 95% t UCL (Assumes normality) 7.125 95% H-Stat UCL 6.961 95 DL/2 is not a recommended method, provided for comparisons and historical reasons 96 97 Nonparametric Distribution Free UCL Statistics 98 Detected Data appear Normal Distributed at 5% Significance Level 99 100 Suggested UCL to Use 101 95% KM (t) UCL 6.88 95% KM (Percentile Bootstrap) UCL N/A 102 Warning: One or more Recommended UCL(s) not available! 103 104 Note: Suggestions regarding the selection of a 95% UCL are provided to help the user to select the most appropriate 95% UCL. 1051 Recommendations are based upon data size, data distribution, and skewness. 106 These recommendations are based upon the results of the simulation studies summarized in Singh, Maichle, and Lee (2006). 107 However, simulations results will not cover all Real World data sets; for additional insight the user may want to consult a statistician. A B C I D I E I F I G I H I I I J K L 1 ProUCL Analysis: UCL Statistics for Data Sets with Non -Detects 2 User Selected Options 3 Date/Time of Computation 6/23/2016 8:40:48 PM 4 From File Cliffside_AsCo.xls 5 Full Precision OFF 6 Confidence Coefficient 95% 7 Number of Bootstrap Operations 2000 8 Cobalt - Cliffside Facility AOW Soil 9 10 General Statistics 11 Total Number of Observations 25 Number of Distinct Observations 23 12 Number of Detects 16 Number of Non -Detects 9 13 Number of Distinct Detects 16 Number of Distinct Non -Detects 7 14 Minimum Detect 4.8 Minimum Non -Detect 6.4 151 Maximum Detect 50.4 Maximum Non -Detect 15.9 16 Variance Detects 250.5 Percent Non -Detects 36% 17 Mean Detects 21.09 SD Detects 15.83 18 Median Detects 15.95 CV Detects 0.75 19 Skewness Detects 0.667 Kurtosis Detects -0.959 20 Mean of Logged Detects 2.747 SD of Logged Detects 0.831 21 22 Normal GOF Test on Detects Only 23 Shapiro Wilk Test Statistic 0.866 Shapiro Wilk GOF Test 24 5% Shapiro Wilk Critical Value 0.887 Detected Data Not Normal at 5% Significance Level 25 Lilliefors Test Statistic 0.226 Lilliefors GOF Test 26 5% Lilliefors Critical Value 0.222 Detected Data Not Normal at 5% Significance Level 271 Detected Data Not Normal at 5% Significance Level 28 29 Kaplan -Meier (KM) Statistics using Normal Critical Values and other Nonparametric UCLs 30 Mean 15.82 Standard Error of Mean 2.934 31 SD 14.16 95% KM (BCA) UCL 20.84 32 95% KM (t) UCL 20.84 95% KM (Percentile Bootstrap) UCL 21 331 95% KM (z) UCL 20.65 95% KM Bootstrap t UCL 21.87 34 90% KM Chebyshev UCL 24.62 95% KM Chebyshev UCL 28.61 35 97.5% KM Chebyshev UCL 34.14 99% KM Chebyshev UCL 45.01 36 37 Gamma GOF Tests on Detected Observations Only 38 A-D Test Statistic 0.641 Anderson -Darling GOF Test 39 5% A-D Critical Value 0.752 Detected data appear Gamma Distributed at 5% Significance Level 40 K-S Test Statistic 0.181 Kolmogrov-Smirnoff GOF 41 5% K-S Critical Value 0.218 Detected data appear Gamma Distributed at 5% Significance Level 42 Detected data appear Gamma Distributed at 5% Significance Level 43 44 Gamma Statistics on Detected Data Only 451 k hat (MLE) 1.806 k star (bias corrected MLE) 1.509 46 Theta hat (MLE) 11.68 Theta star (bias corrected MLE) 13.98 47 nu hat (MLE) 57.79 nu star (bias corrected) 48.29 48 MLE Mean (bias corrected) 21.09 MLE Sd (bias corrected) 17.17 49 50 Gamma Kaplan -Meier (KM) Statistics 51 k hat (KM) 1.248 nu hat (KM) 62.42 52 Approximate Chi Square Value (62.42, a) 45.24 Adjusted Chi Square Value (62.42, (i) 44.25 53 95% Gamma Approximate KM-UCL (use when n> 50) 21.83 95% Gamma Adjusted KM-UCL (use when n<50) 22.32 54 A B C D I E I F I G I H I I J K L 55 Gamma ROS Statistics using Imputed Non -Detects 56 GROS may not be used when data set has > 50% NDs with many tied observations at multiple DLs 57 GROS may not be used when kstar of detected data is small such as < 0.1 581 For such situations, GROS method tends to yield inflated values of UCLs and BTVs 59 For gamma distributed detected data, BTVs and UCLs may be computed using gamma distribution on KM estimates 60 Minimum 0.676 Mean 15.35 61 Maximum 50.4 Median 7.2 62 SD 14.84 CV 0.967 631 k hat (MLE) 1.207 k star (bias corrected MLE) 1.089 64 Theta hat (MLE) 12.71 Theta star (bias corrected MLE) 14.09 65 nu hat (MLE) 60.37 nu star (bias corrected) 54.46 66 MLE Mean (bias corrected) 15.35 MLE Sd (bias corrected) 14.71 67 Adjusted Level of Significance (0) 0.0395 68 Approximate Chi Square Value (54.46, a) 38.5 Adjusted Chi Square Value (54.46, (i) 37.59 691 95% Gamma Approximate UCL (use when n> 50) 21.71 95% Gamma Adjusted UCL (use when n<50) 22.24 70 71 Lognormal GOF Test on Detected Observations Only 72 Shapiro Wilk Test Statistic 0.905 Shapiro Wilk GOF Test 73 5% Shapiro Wilk Critical Value 0.887 Detected Data appear Lognormal at 5% Significance Level 74 Lilliefors Test Statistic 0.168 Lilliefors GOF Test 75 5% Lilliefors Critical Value 0.222 Detected Data appear Lognormal at 5% Significance Level 76 Detected Data appear Lognormal at 5% Significance Level 77 78 Lognormal ROS Statistics Using Imputed Non -Detects 79 Mean in Original Scale 15.96 Mean in Log Scale 2.44 80 SD in Original Scale 14.36 SD in Log Scale 0.792 81 95% t UCL (assumes normality of ROS data) 20.88 95% Percentile Bootstrap UCL 20.69 82 95% BCA Bootstrap UCL 21.23 95% Bootstrap t UCL 22.44 83 95% H-UCL (Log ROS) 22.58 84 85 UCLs using Lognormal Distribution and KM Estimates when Detected data are Lognormally Distributed 86 KM Mean (logged) 2.421 95% H-UCL (KM -Log) 21.99 871 KM SD (logged) 0.787 95% Critical H Value (KM -Log) 2.24 88 KM Standard Error of Mean (logged) 0.166 89 90 DL/2 Statistics 91 DL/2 Normal DL/2 Log -Transformed 92 Mean in Original Scale 15.43 Mean in Log Scale 2.349 931 SD in Original Scale 14.72 SD in Log Scale 0.868 94 95% t UCL (Assumes normality) 20.47 95% H-Stat UCL 23.08 95 DL/2 is not a recommended method, provided for comparisons and historical reasons 96 97 Nonparametric Distribution Free UCL Statistics 98 Detected Data appear Gamma Distributed at 5% Significance Level 99 100 Suggested UCL to Use 101 95% KM (Percentile Bootstrap) UCL 21.00 95% GROS Adjusted Gamma UCL 22.24 102 95% Adjusted Gamma KM-UCL 22.32 103 104 Note: Suggestions regarding the selection of a 95% UCL are provided to help the user to select the most appropriate 95% UCL. 1051 Recommendations are based upon data size, data distribution, and skewness. 106 These recommendations are based upon the results of the simulation studies summarized in Singh, Maichle, and Lee (2006). 107 However, simulations results will not cover all Real World data sets; for additional insight the user may want to consult a statistician. Sample ID Sample Collection Date Matrix Arsenic ProUCL Flag Cobalt ProUCL Flag AB-01 (4-5) 03/19/2015 ash 3.1 0 3.1 0 AB-01 (34.3-35) 03/19/2015 ash 2.9 1 4.2 1 AB-01 (48-50) 03/19/2015 ash 2.6 1 3.4 1 AB-02 (4-5) 03/18/2015 ash 4.4 0 4.4 0 AB-02 (18-20) 03/18/2015 ash 3.3 1 4.5 1 AB-02 (32.9-33.7) 03/18/2015 ash 3.4 1 4.7 1 AB-03 (4-5) 03/18/2015 ash 3.3 0 3.3 0 AB-03 (21.6-23.4) 03/18/2015 ash 3.3 1 4 1 AB-04 (4-5) 03/17/2015 ash 3.5 0 3.5 0 AB-04 (33-35) 03/17/2015 ash 13.4 1 13.6 1 AB-04 (33-35) DUP 03/17/2015 ash 17.0 1 19.7 1 AB-04 (53-55) 03/17/2015 ash 10.0 1 12.9 1 ABMW-01 (1-1.5) 06/02/2015 ash 16.6 1 7.6 j 1 ABMW-01 (11.5-12) 06/02/2015 ash 26.2 1 8.3 1 MW-03BR (0.8-1.25) 02/05/2015 soil 3.0 0 5.4 1 MW-03BR (14.75-15) 02/05/2015 soil 2.5 0 4.3 1 MW-03BR (17-18) 02/05/2015 soil 2.7 0 3.6 1 MW-07BR (1-2) 04/23/2015 soil 5.1 0 5.1 0 MW-08BR (0.75-1.25) 02/11/2015 soil 3.2 0 21.3 1 MW-08BR (25.5-26) 02/11/2015 soil 2.7 0 32.9 1 MW-08BR (30.5-31) 02/11/2015 soil 2.7 0 7.4 1 MW-13BR (0-2) 04/12/2015 soil 6.5 0 6.5 0 MW-13BR (52-54) 04/13/2015 soil 5.1 0 6.6 1 MW-13BR (52-54) DUP 04/13/2015 soil 5.9 0 7.2 1 MW-15BR (0.5-1) 05/09/2015 soil 6.2 0 3.9 1 MW-16BR (1-2) 04/25/2015 soil 6.5 0 6 j 1 MW-16BR (23-24) 04/26/2015 soil 5.0 0 4.6 1 MW-16BR (33-35) 04/25/2015 soil 7.5 0 12 1 MW-16BR (54.5-55.5) 04/25/2015 soil 5.2 0 19.6 1 SB-05 (1-2) 03/04/2015 soil 1.8 1 2.8 1 SB-05 (2.3-3.3) 03/04/2015 soil 1.1 1 1.7 1 SB-05 (8-9) 03/04/2015 soil 1.0 1 2.2 1 SB-06 (1-2) 03/04/2015 soil 1.2 1 2.5 1 SB-07 (1-2) 05/07/2015 soil 5.8 0 5.9 1 SB-07 (4.5-7.5) 05/07/2015 soil 6.0 0 6 0 S-01 05/12/2015 seep_soil 56.4 1 14.4 0 5-02 05/13/2015 seep_soil 6.8 0 16.4 1 S-02B 05/13/2015 seep_soil 6.3 0 4.7 1 5-08 05/13/2015 seep_soil 8.7 0 40.6 1 Notes: Ash and perimeter soil data records were added to the AOW_soil data to recompute the EPC (expand the # of data records for a more robust ProUCL statistical evaluation) A B C D E F G H J K L 1 ProUCL: UCL Statistics for Data Sets with Non -Detects 2 User Selected Options 3 Date/Time of Computation 6/23/2016 5:19:22 PM 4 From File Duke_EPC.xls 5 Full Precision OFF 6 Confidence Coefficient 95% 7 Number of Bootstrap Operations 2000 8 Arsenic 9 101 General Statistics 11 Total Number of Observations 38 Number of Distinct Observations 32 12 Number of Detects 15 Number of Non -Detects 23 13 Number of Distinct Detects 14 Number of Distinct Non -Detects 19 14 Minimum Detect 1 Minimum Non -Detect 2.5 151 Maximum Detect 56.4 Maximum Non -Detect 7.5 16 Variance Detects 218.3 Percent Non -Detects 60.53% 17 Mean Detects 10.68 SD Detects 14.77 18 Median Detects 3.3 CV Detects 1.383 19 Skewness Detects 2.409 Kurtosis Detects 6.559 201 Mean of Logged Detects 1.622 SD of Logged Detects 1.259 21 22 Normal GOF Test on Detects Only 23 Shapiro Wilk Test Statistic 0.682 Shapiro Wilk GOF Test 24 5% Shapiro Wilk Critical Value 0.881 Detected Data Not Normal at 5% Significance Level 251 Lilliefors Test Statistic 0.289 Lilliefors GOF Test 26 5% Lilliefors Critical Value 0.229 Detected Data Not Normal at 5% Significance Level 27 Detected Data Not Normal at 5% Significance Level 28 29 Kaplan -Meier (KM) Statistics using Normal Critical Values and other Nonparametric UCLs 30 Mean 5.304 Standard Error of Mean 1.686 31 1 SD 9.985 95% KM (BCA) UCL 8.162 32 95% KM (t) UCL 8.149 95% KM (Percentile Bootstrap) UCL 8.44 33 95% KM (z) UCL 8.077 95% KM Bootstrap t UCL 11.5 34 90% KM Chebyshev UCL 10.36 95% KM Chebyshev UCL 12.65 35 97.5% KM Chebyshev UCL 15.83 99% KM Chebyshev UCL 22.08 36 371 Gamma GOF Tests on Detected Observations Only 38 A-D Test Statistic 0.735 Anderson -Darling GOF Test 39 5% A-D Critical Value 0.773 Detected data appear Gamma Distributed at 5% Significance Level 40 K-S Test Statistic 0.277 Kolmogrov-Smirnoff GOF 41 5% K-S Critical Value 0.23 Detected Data Not Gamma Distributed at 5% Significance Level 42 Detected data follow Appr. Gamma Distribution at 5% Significance Level 43 44 Gamma Statistics on Detected Data Only 45 k hat (MLE) 0.796 k star (bias corrected MLE) 0.681 46 Theta hat (MLE) 13.42 Theta star (bias corrected MLE) 15.68 47 nu hat (MLE) 23.88 nu star (bias corrected) 20.44 48 MLE Mean (bias corrected) 10.68 MLE Sd (bias corrected) 12.94 49 A B C D E I F I G I H I J K L 50 Gamma Kaplan -Meier (KM) Statistics 51 k hat (KM) 0.282 nu hat (KM) 21.45 52 Approximate Chi Square Value (21.45, a) 11.92 Adjusted Chi Square Value (21.45, R) 11.63 53 95% Gamma Approximate KM-UCL (use when n> 50) 9.539 95% Gamma Adjusted KM-UCL (use when n<50) 9.781 54 55 Gamma ROS Statistics using Imputed Non -Detects 56 GROS may not be used when data set has > 50% NDs with many tied observations at multiple DLs 57 GROS may not be used when kstar of detected data is small such as < 0.1 58 For such situations, GROS method tends to yield inflated values of UCLs and BTVs 59 For gamma distributed detected data, BTUs and UCLs may be computed using gamma distribution on KM estimates 601 Minimum 0.01 Mean 4.298 61 Maximum 56.4 Median 0.01 62 SD 10.49 CV 2.44 63 k hat (MLE) 0.217 k star (bias corrected MLE) 0.218 64 Theta hat (MLE) 19.78 Theta star (bias corrected MLE) 19.74 65 nu hat (MLE) 16.51 nu star (bias corrected) 16.54 661 MLE Mean (bias corrected) 4.298 MLE Sd (bias corrected) 9.212 67 Adjusted Level of Significance ((3) 0.0434 68 Approximate Chi Square Value (16.54, a) 8.348 Adjusted Chi Square Value (16.54, R) 8.106 69 95% Gamma Approximate UCL (use when n> 50) 8.519 95% Gamma Adjusted UCL (use when n<50) 8.773 70 71 Lognormal GOF Test on Detected Observations Only 721 Shapiro Wilk Test Statistic 0.927 Shapiro Wilk GOF Test 73 5% Shapiro Wilk Critical Value 0.881 Detected Data appear Lognormal at 5% Significance Level 74 Lilliefors Test Statistic 0.224 Lilliefors GOF Test 75 5% Lilliefors Critical Value 0.229 Detected Data appear Lognormal at 5% Significance Level 76 Detected Data appear Lognormal at 5% Significance Level 77 781 Lognormal ROS Statistics Using Imputed Non -Detects 79 Mean in Original Scale 5.275 Mean in Log Scale 0.952 80 SD in Original Scale 10.12 SD in Log Scale 0.98 81 95% t UCL (assumes normality of ROS data) 8.043 95% Percentile Bootstrap UCL 8.241 82 95% BCA Bootstrap UCL 9.388 95% Bootstrap t UCL 12.13 83 95% H-UCL (Log ROS) 6.137 84 85 UCLs using Lognormal Distribution and KM Estimates when Detected data are Lognormally Distributed 86 KM Mean (logged) 0.934 95% H-UCL (KM -Log) 6.276 87 KM SD (logged) 1.006 95% Critical H Value (KM -Log) 2.402 88 KM Standard Error of Mean (logged) 0.197 89 901 DL/2 Statistics 91 DL/2 Normal DL/2 Log -Transformed 92 Mean in Original Scale 5.65 Mean in Log Scale 1.126 93 SD in Original Scale 9.996 SD in Log Scale 0.918 94 95% t UCL (Assumes normality) 8.386 95% H-Stat UCL 6.655 95 DL/2 is not a recommended method, provided for comparisons and historical reasons 96 97 Nonparametric Distribution Free UCL Statistics 98 Detected Data appear Approximate Gamma Distributed at 5% Significance Level 99 100 Suggested UCL to Use 101 95% KM (t) UCL 8.149 95% GROS Adjusted Gamma UCL 8.773 102 95% Adjusted Gamma KM-UCL 9.781 103 104 Note: Suggestions regarding the selection of a 95% UCL are provided to help the user to select the most appropriate 95% UCL. 105 Recommendations are based upon data size, data distribution, and skewness. 106 These recommendations are based upon the results of the simulation studies summarized in Singh, Maichle, and Lee (2006). 107 However, simulations results will not cover all Real World data sets; for additional insight the user may want to consult a statistician. A B C D E F G H J K L 108 ProUCL: UCL Statistics for Data Sets with Non -Detects 109 User Selected Options 110 Date/Time of Computation 6/23/2016 5:19:22 PM 111 From File Duke_EPC.xls 112 Full Precision OFF 113 Confidence Coefficient 95% 114 Number of Bootstrap Operations 2000 115 Cobalt 116 117 General Statistics 1181 Total Number of Observations 39 Number of Distinct Observations 37 119 Number of Detects 31 Number of Non -Detects 8 120 Number of Distinct Detects 30 Number of Distinct Non -Detects 8 121 Minimum Detect 1.7 Minimum Non -Detect 3.1 122 Maximum Detect 40.6 Maximum Non -Detect 14.4 123 Variance Detects 83.31 Percent Non -Detects 20.51% 1241 Mean Detects 9.5 SD Detects 9.128 125 Median Detects 5.9 CV Detects 0.961 126 Skewness Detects 2.054 Kurtosis Detects 4.294 127 Mean of Logged Detects 1.918 SD of Logged Detects 0.793 128 129 Normal GOF Test on Detects Only 1301 Shapiro Wilk Test Statistic 0.742 Shapiro Wilk GOF Test 131 5% Shapiro Wilk Critical Value 0.929 Detected Data Not Normal at 5% Significance Level 132 Lilliefors Test Statistic 0.262 Lilliefors GOF Test 133 5% Lilliefors Critical Value 0.159 Detected Data Not Normal at 5% Significance Level 134 Detected Data Not Normal at 5% Significance Level 135 1361 Kaplan -Meier (KM) Statistics using Normal Critical Values and other Nonparametric UCLs 137 Mean 8.218 Standard Error of Mean 1.376 138 SD 8.426 95% KM (BCA) UCL 10.86 139 95% KM (t) UCL 10.54 95% KM (Percentile Bootstrap) UCL 10.46 140 95% KM (z) UCL 10.48 95% KM Bootstrap t UCL 11.4 141 90% KM Chebyshev UCL 12.34 95% KM Chebyshev UCL 14.21 1421 97.5% KM Chebyshev UCL 16.81 99% KM Chebyshev UCL 21.91 143 144 Gamma GOF Tests on Detected Observations Only 145 A-D Test Statistic 1.16 Anderson -Darling GOF Test 146 5% A-D Critical Value 0.762 Detected Data Not Gamma Distributed at 5% Significance Level 147 K-S Test Statistic 0.181 Kolmogrov-Smirnoff GOF 148 5% K-S Critical Value 0.16 Detected Data Not Gamma Distributed at 5% Significance Level 149 Detected Data Not Gamma Distributed at 5% Significance Level 150 151 Gamma Statistics on Detected Data Only 152 k hat (MLE) 1.647 k star (bias corrected MLE) 1.509 153 Theta hat (MLE) 5.768 Theta star (bias corrected MLE) 6.295 1541 nu hat (MLE) 102.1 nu star (bias corrected) 93.57 155 MLE Mean (bias corrected) 9.5 MLE Sd (bias corrected) 7.733 156 157 Gamma Kaplan -Meier (KM) Statistics 158 k hat (KM) 0.951 nu hat (KM) 74.2 159 Approximate Chi Square Value (74.20, a) 55.36 Adjusted Chi Square Value (74.20, (i) 54.72 160 95% Gamma Approximate KM-UCL (use when n>=50) 11.01 95% Gamma Adjusted KM-UCL (use when n<50) 11.14 161 162 Gamma ROS Statistics using Imputed Non -Detects 163 GROS may not be used when data set has > 50% NDs with many tied observations at multiple DLs 164 GROS may not be used when kstar of detected data is small such as < 0.1 165 For such situations, GROS method tends to yield inflated values of UCLs and BTVs 11661 For gamma distributed detected data, BTVs and UCLs may be computed using gamma distribution on KM estimates A B C D E F G H I J K L 167 Minimum 0.01 Mean 7.834 168 Maximum 40.6 Median 4.6 169 SD 8.788 CV 1.122 170 k hat (MLE) 0.742 k star (bias corrected MLE) 0.702 171 Theta hat (MLE) 10.56 Theta star (bias corrected MILE) 11.16 172 nu hat (MLE) 57.87 nu star (bias corrected) 54.75 173 MLE Mean (bias corrected) 7.834 MLE Sd (bias corrected) 9.35 174 Adjusted Level of Significance ((3) 0.0437 175 Approximate Chi Square Value (54.75, a) 38.75 Adjusted Chi Square Value (54.75, (3) 38.22 176 95% Gamma Approximate UCL (use when n>=50) 11.07 95% Gamma Adjusted UCL (use when n<50) 11.22 177 178 Lognormal GOF Test on Detected Observations Only 179 Shapiro Wilk Test Statistic 0.956 Shapiro Wilk GOF Test 180 5% Shapiro Wilk Critical Value 0.929 Detected Data appear Lognormal at 5% Significance Level 181 Lilliefors Test Statistic 0.131 Lilliefors GOF Test 182 5% Lilliefors Critical Value 0.159 Detected Data appear Lognormal at 5% Significance Level 1831 Detected Data appear Lognormal at 5% Significance Level 184 185 Lognormal ROS Statistics Using Imputed Non -Detects 186 Mean in Original Scale 8.173 Mean in Log Scale 1.742 187 SD in Original Scale 8.542 SD in Log Scale 0.8 188 95% t UCL (assumes normality of ROS data) 10.48 95% Percentile Bootstrap UCL 10.48 189 95% BCA Bootstrap UCL 10.98 95% Bootstrap t UCL 11.61 190 95% H-UCL (Log ROS) 10.44 191 192 UCLs using Lognormal Distribution and KM Estimates when Detected data are Lognormally Distributed 193 KM Mean (logged) 1.748 95% H-UCL (KM -Log) 10.41 194 KM SD (logged) 0.793 95% Critical H Value (KM -Log) 2.179 1951 KM Standard Error of Mean (logged) 0.132 196 197 DL/2 Statistics 198 DL/2 Normal DL/2 Log -Transformed 199 Mean in Original Scale 8.145 Mean in Log Scale 1.716 200 SD in Original Scale 8.585 SD in Log Scale 0.84 201 95% t UCL (Assumes normality) 10.46 95% H-Stat UCL 10.72 202 DL/2 is not a recommended method, provided for comparisons and historical reasons 203 204 Nonparametric Distribution Free UCL Statistics 205 Detected Data appear Lognormal Distributed at 5% Significance Level 206 2071 Suggested UCL to Use 208 95% KM (BCA) LICLI 10.86 209 210 Note: Suggestions regarding the selection of a 95% UCL are provided to help the user to select the most appropriate 95% UCL. 211 Recommendations are based upon data size, data distribution, and skewness. 212 These recommendations are based upon the results of the simulation studies summarized in Singh, Maichle, and Lee (2006). 12131 However, simulations results will not cover all Real World data sets; for additional insight the user may want to consult a statistician. EPS APPENDIX E Ecological Habitat Services 0 ECOLOGICAL HABITAT SERVICES The purpose of this evaluation was to understand how the Removal and CIP alternatives might affect ecological habitat values associated with the Allen, Buck, Cliffside, and Mayo Steam Station sites. A geographic information system (GIS) analysis was conducted in order to quantify the dimensions of the major land cover types presently existing in those areas of each site (e.g., basins) that would be affected by implementation of either the CIP or Removal alternatives. The land cover types were established based upon review of GIS files data provided by HDR and SynTerra, aerial photography and site visits. The land cover types included the following: Terrestrial Habitats • Bottomland Hardwoods • Mixed Hardwoods • Mixed Pines • Oak Hickory Hardwoods • Pine Plantation • Early Successional • Scrub Shrub • Ash Wetlands • Emergent • Forested • Open Water Other • Parking/Roads • Other features Through GIS polygon analysis, the projected affected aerial dimensions of each land cover type that would undergo alteration by either the CIP or Removal alternatives were determined for each site, including a 30-foot work zone buffer around each basin that was would be affected by each alternative. These data were grouped by major habitat type for purposes of the evaluation (Table E-1). The focus was on how the terrestrial and wetland/surface water habitats will be affected given implementation of the alternatives. Appendix E 1 June 30, 2016 Ecological Habitat Services EPS Table E-1. Acreage of the selected major habitat types under the current site condition that would be physically impacted through implementation of the CIP or Removal alternatives. Site Allen Buck Cliffside Mayo Total Evaluation Area (acres) 342 196 195 181 Terrestrial Early Successional/Shrub Scrub 99.9 52.1 42.2 42.5 Forest 34.2 36.3 1 25.1 11.0 Open Field -Grass 72.9 34.2 51.5 14.0 Wetland Wetland and Surface Water 98.0 64.5 1 44.6 �79.2 The evaluation area includes only those areas that were projected to be impacted by the remedial actions, including a 30 foot buffer around the outside of each basin. Appendix E 2 June 30, 2016 Ecological Habitat Services EPS El ECOSYSTEM SERVICES ASSESSMENT METHODOLOGY An integral part of the NEBA approach is to understand the value of the ecosystem services being provided within the study area. Ecosystem services can be viewed as both ecological and human use services. It is important to understand that many human use services are provided by the presence of the ecological services. For example, state game lands provide recreational hunting and birdwatching activities that would otherwise not be present if the ecology and habitat was not in place. Thus, actions adversely affecting ecological habitats can result in significant human use service losses while actions that enhance habitats can result in increased human use value. Importantly, the net environmental benefits will be assessed using the current status of the environment (the project baseline) against which the potential change in ecosystem service values associated with each of the remedial alternatives will be compared. Since these benefits and costs occur over varying time frames, they can be normalized to their net present value using a time discount rate. The discount rate is the rate at which the public is indifferent to consuming goods now or sometime in the future. NEBA incorporates the use of ecological service valuation methods such as habitat equivalency analysis (HEA) and resource equivalency analysis (REA) to evaluate changes in habitat value over time. E1.1 Ecological Services The primary method by which changes in ecological services (i.e., habitat quality) associated with the various alternatives was evaluated was through the use of the HEA methodology. Actions potentially affecting ecological services include both physical implementation impacts as well as impacts associated with chemical releases and/or residuals. This methodology requires the evaluation of available data to select and identify appropriate metric(s) that will represent overall habitat service flows from the environment. Our evaluation focused on the direct habitat impacts as well as indirect impacts (i.e., influences on adjacent areas, etc.). Because many ecological habitat services are not traded in the marketplace, they do not have a direct monetary value. The HEA approach is a service quantification approach that evaluates ecological habitat service losses and gains based on non -monetary metrics over time. It is important to recognize that ecosystem services are not static measurements but represent a flow of benefits over time. The HEA approach uses an environmental metric to measure changes to ecological habitat services and focuses on quantifying the area (e.g., acres) and level of impact over time in units typically represented as service -acre -years (SAY's). We used the HEA methodology to quantify the relative impacts or benefits, independent from each other, associated with the remedial alternatives. Appendix E 3 June 30, 2016 Ecological Habitat Services 0 Ecological service losses and gains can be measured using a variety of indicator metrics and units. In many cases, the quantification of changes in ecological service flows is based upon selecting or developing an indicator (can be one or more metrics) that acts as a surrogate to represent the ecological service flows provided over time by the habitat and expressing the changes in services (for that indicator) under the different alternatives as a percentage change from a baseline or reference condition. Ecosystem studies, literature -based information, or a combination thereof, can be used to estimate how ecosystem services may change given an action. The metrics or indicators are selected based upon the site, habitats, and the scientific knowledge. The metric(s) are typically incorporated into a curve that represents the loss or gain of services over time. Aside from a base year, the ecological service calculations also involve a discount rate that allows for the gains and losses to be evaluated from a net present value (NPV) standpoint. Within the HEA methodology, calculations of ecological service losses and gains associated with a project are computed over time and represent time accumulated service flows. In these cases, the units are typically displayed as a discounted service acre year (dSAY) which represents an ecological habitat value. The discount rate is the rate at which the public is indifferent to consuming goods now or sometime in the future. We used a 3% discount rate, this rate commonly used in NRDA cases across the country. In evaluating ecological habitat service flows, applications of the HEA methodology must account for the absolute difference in the flow of services from the ecological resources resulting from an action and how those services are distributed over time. In addition, for any given action, it is likely that the ecological habitat service loss or gain may not always be constant over time. An assessment of the shape of the various ecological habitat service flow curves over time is thus necessary. Quantifying the ecological service value of a given action can be conducted prior to project implementation through the use of projected metrics based on scientific data and professional judgment. Use of the HEA methodology has been upheld in U.S. federal court' as an appropriate method to evaluate changes in ecological habitat services associated with actions and incorporated into the European Union Environmental Liabilities Directive (EU ELD). The analysis of changes in ecological services was conducted by examining how changes in the existing habitats will occur given implementation of the CIP and Removal alternatives. The analysis conducted herein addressed the major terrestrial, wetland/surface water, and grass/field habitats likely to be affected. Terrestrial —Forest, Shrub Scrub, and Grass/Open Field Analysis For the terrestrial analysis, three ecological service flow graphics were developed for each site per habitat type. The curves represent how the service level might change over time (the flow of value) given the implementation of each alternative. United States v Melvin Fisher , 977 F. Supp 1193 (SD Fla 1997); United States, et al v Great Lakes Dredge and Dock Co, Docket no 97- 02510-CV-EBD (SD Fla 2001). Appendix E 4 June 30, 2016 Ecological Habitat Services 0 E1.2 Baseline "Current" Condition For the baseline terrestrial condition at each site, the following assumption was made: The flow of ecological service value is consistent with the above -ground biomass of the forest, and increases as the forest moves through successional stages, as projected through time in Figure E 1 (adapted from McMahon et al. 2010). That is, as the vertical structure and complexity increases, so does the ecological value and biodiversity. "Protecting forests will not only preserve biodiversity and defend the rights of forest communities, it's also one of the quickest and cost effective ways of halting climate change" (Greenpeace 2016). 100 0 v U > 75 L v V) U U Up 50 O O U W 4- 0 25 L O U_ 011 1 1 1 0 50 100 150 200 250 Years Figure E1. Graphic showing the relationship between above -ground biomass (AGB) and stand age of multiple forest plots in a temperate deciduous forest in Edgewater, MD. Adapted from McMahon et al. (2010). In addition, in a typical forest the diversity increases as succession proceeds to climax as shown in Figure E2 (Enviroscience 2016). Appendix E 5 June 30, 2016 Ecological Habitat Services young mature forest climax Y;"<r� 20 40 60 80 EPS old growth — — — — • — moist coniferous forest ♦ ♦ ♦ pine forest Stages of Succession 100 Figure E2. Graphic showing the relationship between diversity and production of forest at different stages of succession. Reproduced from Enviroscience (2008). The assumption of the projected forest value based upon Figure E1 was kept consistent with all alternatives. This will keep the comparison of flow changes across alternatives consistent. The baseline ecosystem service value for forest habitat at the Allen site is depicted in Figure E3. Note that the value estimate starts at an approximate age of 25 years for the forest stands being evaluated. This is conservative in estimating the baseline value as the tree stands are likely older on average than 25 years of age. Additionally, the flow of value, for all sites and scenarios was calculated over a 200-year period, 2016-2216. For shrub -scrub habitat, the start time on the successional curve was adjusted to 5 years. Appendix E 6 June 30, 2016 Ecological Habitat Services 100 0 V) v v 75 i N to ro u 50 3b O Current age Ou of tree stands w n 25 N i O LL 0 0 t2016 / On -site baseline value (34.2 acres) 539 dSAYs Baseline forest ecological services projected into future 50 100 150 200 250 t2216 Years EPS Figure E3. Ecological service flow framework used to evaluate service flow changes on forest habitats at the Allen Steam Station — depicts current baseline of ecosystem service flows into the future. This curve shows the conceptual model under which the forest dSAY value (539) was calculated. The value was discounted at a 3% discount rate. This represents the loss of service value projected into the future with removal of the forest habitat. For the grass/open field habitats, they would convert to grass under the CIP alternative, or regenerate to forest under the Removal alternative. EU CIP Alternative The next step was to evaluate how ecological services would change given implementation of the CIP and Removal options, for each site. For the CIP alternative, the "red" area in Figure E4 represents the loss of forest habitat value when that forest habitat is removed for the cap construction. The tree removal for the CIP alternative at the Allen site will take several years to be completed, so the initial loss is calculated over that removal time frame. As can be seen in Figure E4, when 36.4 acres of forest habitat are removed, the future stream of benefits that the forest provides is also lost. The on -site forest loss is -505 dSAYs. It should be kept in mind that a significant amount of cover material will need to be brought into each site as part of the CIP alternative. In the case of the Allen site, it was estimated that, as part of the construction evaluation provided in Appendix A, 190 acres of forest off -site Appendix E 7 June 30, 2016 Ecological Habitat Services 0 would need to be removed to provide an area from which to obtain enough cover material to support the CIP development. The need for fill material and off -site forest impacts results in an estimated loss of -2,992 dSAYs. However, positive ecological services will be generated from the cap grass habitat once it is completed (Figure E4) and natural regeneration of the forest in the area where the landfill cover material was obtained. Mowed grass habitat is significantly less valuable forest habitat. Based upon Figures E1 and E2, I assumed that mowed grass habitat has one -tenth the value of forest habitat. This is likely an overestimate of the value of the grass habitat compared to the forest habitat (based upon diversity, carbon, etc.) but I was conservative in order to not overestimate the loss of forest habitat value from the CIP alternative. The estimated positive ecological service value of the grass cap for the Allen site is 91 dSAYs. In addition, I did not calculate the edge effects that would result from conversion of forest to grass, so was conservative in estimating the loss of value associated with the CIP alternative. As shown in Figure E4, the grass value remains constant after it is in place as it is a mowed and maintained habitat. In addition, the off -site forest area that was impacted to gain cover material will naturally recover. It will therefore provide positive ecological service value that would follow the same curve shape as depicted in Figure El but the recovery start point will be delayed several years for each site depending upon the pace of removal for the CIP development. The time that this recovery would start is based upon the years in which the cover material would be removed from the offsite area and ranges from 3-6 years depending upon the site. The estimated gain in forest value provided by the regeneration of the forest at the off -site cover material area was estimated to be 1,844 dSAYs. The overall estimated net loss of forest ecological service value for the Allen Station was -1,562 dSAYs. 0 100 75 50 25 0 0 Lost on -site forest ecological services projected into the future On -site forest loss (34.2 acres) -505 dSAYs Off -site forest loss (190 acres) -2,992 dSAYs FV44 Off -site forest regeneration (190 acres) 1,844 dSAYs 0#4 Net Loss -1,562 dSAYs Grass cap ecological services with cap -in -place 91 dSAYs t2016 50 100 150 200 t2216 250 Years Appendix E 8 June 30, 2016 Ecological Habitat Services EPS Figure E4. Ecological service flow framework used to evaluate service flow changes, on forest habitats that will be associated with the "Cap -in -Place" option, at the Allen Steam Station. E1.4 Removal Alternative I next estimated the net change in ecological value for the Removal alternative. The ecosystem service curve for this scenario is presented in Figure E5. 100 0 Ln v U 75 L v up 50 O O v w 4- 25 L O 1.L Lost on -site forest ecological services projected into the future V On -site forest losses (34.2 acres) -505 dSAYs On -site forest regeneration (34 acres) 135 dSAYs Off -site forest losses (319 acres) -5,024 dSAYs Off -site forest regeneration (93 acres) 903 dSAYs 011 I 0 50 100 150 200 t2016 t2216 Years Figure E5. Ecological service flow framework used to evaluate service flow changes, on forest habitats that will be associated with the "Removal" option, at the Allen Steam Station. As can be seen in Figure E5, the on -site forest losses with the Removal alternative remain at -505 dSAYs, but do get offset to some extent as the end condition after removal is a natural forest through regeneration as depicted by the "green" area. The on -site regeneration value is 135 dSAYs. Similar to the CIP alternative, there will also be offsite forest ecological losses associated with the creation of a new landfill disposal area plus the additional loss of ecological habitat associated with cover material area of about -5,024 BAYS. The off -site cover material area will gain some ecological value as it naturally regenerates (903 dSAYs). These data are depicted in Figure E5. The overall estimated net loss of forest ecological service value for the Allen Station under the Removal alternative was -4,491 dSAYs. This process was conducted for all four sites, and habitat type groupings (forest, shrub -scrub, wetland/surface water, and grass/open field). Appendix E 9 June 30, 2016 Ecological Habitat Services 0 E2 ANALYSIS RESULTS The approach taken in my analysis was developed to approximate the relative change in ecosystem service value from one alternative to another, for the general categories evaluated based upon similar assumptions where applicable. The dSAY calculations are approximate values and not intended to be exact, but enough to identify impacts and differences between alternatives. The ecosystem service value curves for all four stations, habitat types, and remedial alternatives are depicted in Figures E3 through E50 (at the end of this appendix). The figures provide a representation of multiple actions affecting ecosystem service values, both positive and negative given actions affecting ecological services. The results of the analysis, across all sites are presented in Table E2. Table E2. Summary of changes in ecological service value associated with the CIP and Removal alternatives. Table E2. Summary of changes in ecological service value associated with the CIP and Removal alternatives (units in dSAYs). Ecosystem service changes associated with each action, by site and habitat type. Allen Allen Allen Allen Buck Buck Buck Buck Cliffside Cliffside Cliffside Cliffside Mayo Mayo Mayo Mayo Forest SS/ES Wetland Grass Forest SS/ES Wetland Grass Forest SS/ES Wetland Grass Forest SS/ES Wetlan Grass MNA Action 539 1,129 3,356 250 572 587 2,191 117 395 474 1,541 176 173 474 2,705 48 CAP In Place Onsite Cap Grass 91 266 261 194 103 139 170 91 71 112 120 137 32 112 210 37 Onsite Removal (505) (1,129) (3,074) (250) (554) (587) (622) (117) (383) (474) (437) (176) (168) (474) (767) (48) Offsite Removal (2,992) (1,921) (1,811) (1,480) Offsite Regeneration 1,844 1,296 1,221 998 SubTotal (1,562) (863) (2,813) (56) (1,076) (448) (452) (26) (902) (362) (317) (39) (618) (362) (557) (11) Removal Onsite Removal (505) (1,129) (2,037) (250) (554) (587) (1,330) (117) (383) (474) (935) (176) (168) (474) (1,642) (48) Onsite Regeneration 135 396 951 288 313 206 621 135 186 166 437 204 87 166 767 55 Offsite Removal (5,024) (2,032) (2,646) (2,347) Offsite Regeneration 903 372 499 1 1 1 435 SubTotal (4,491) (733) (1,086) 38 (1,901) (381) (709) 18 (2,344) (308) (499) 28 (1,993) (308) (875) 7 Forest, early successional, and grass habitat values were combined and are presented in Table E3. These habitat values (forest and early successional) fall along the same successional curve, and grass habitats were quality adjusted and can be compared through time, with differing starting points in the successional progression. DSAY values between different habitat types may not be equivalent and when combining DSAYs between habitat types this must be considered and need to take into account quality differences between the habitat types being compared. For my analysis, I grouped the wetland/surface water habitat value changes together with the terrestrial values. This assumes that 1 dSAY of a wetland/surface water is equivalent to 1 dSAY of the terrestrial habitat. Wetland/surface water habitat is likely more valuable than forest habitat as mitigation ratios often ask for more riparian buffer acreage (e.g., 10:1) to offset wetland impacts. However, to be conservative and not overestimate the impacts from the remedial actions, I have kept them on a 1 to 1 basis in Table E3. Appendix E 10 June 30, 2016 Ecological Habitat Services 0 In order to put dSAY values into perspective, approximately 34.2 dSAYs are provided by one acre of habitat, Therefore, one can infer the impact of the lost dSAYs by dividing by 34.2. As such and as presented in Table E3, the CIP and Removal alternatives each would result, given the calculations herein, in the permanent removal of approximately 309 and 457 acres of habitat value into perpetuity, respectively. This loss includes any ecological value as well as any associated human use values. Based on the conservatism in the analysis, these losses are likely higher. Table E3. Changes (i.e., losses) in habitat value associated with the alternatives by site. Site Allen Buck Cliffside Mayo Total MNA Cap in Place (dSAY loss) (5,294) (2,002) (1,620) (1,548) (10,464) Removal (dSAY loss) (6,272) (2,973) (3,122) (3,169) (15,536) MNA - - - - Cap in Place (acres lost into perpetuity) (155) (59) (47) (45) (306) Removal (acres lost into perpetuity) (183) (87) (91) (93) (454) Appendix E 11 June 30, 2016 Ecological Habitat Services EPS E3 HUMAN RECREATIONAL SERVICES For this evaluation, since the site is retained under private property, human recreational on -site services were not considered. However, it should be recognized that game land areas can provide significant human use value. In addition, as no off -site impacts were identified, off -site human recreational service values were not evaluated. Appendix E 12 June 30, 2016 Ecological Habitat Services 0 E4 REFERENCES Enviroscience. 2008. Web Accessed June 23, 2016. http://envirosci.net/I I I/succession/succession.htm Greenpeace. Web Accessed June 23, 2016. http://www. rg eenpeace.org/international/en/campaigns/forests/solutions/ McMahon, S. M., Parkera, G. G., and Millera, D. R. 2010. Evidence for a recent increase in forest growth. PNAS, Edited by William H. Schlesinger, Institute of Ecosystem Studies, Millbrook, NY, February 23, 2010, Vol. 107, No. 8, 3611-3615. United States v. Melvin Fisher, 977 F. Supp 1193 (SD Fla 1997); United States, et al v Great Lakes Dredge and Dock Co, Docket no 97-02510-CV-EBD (SD Fla 2001). Appendix E 13 June 30, 2016 Ecological Habitat Services C Forest Curves 100 0 cn U 'j 75 N (0 U W 50 O O U W a--+ N 25 O LL W 0 50 100 150 200 250 Yea rs Figure E1. Graphic showing the relationship between above -ground biomass (AGB) and stand age of multiple forest plots in a temperate deciduous forest in Edgewater, MD. Adapted from McMahon et al. (2010). 0 N U L c� U O 0 U w V) N L O 100 75 50 25 W On -site baseline value (34.2 acres) 539 cISAYs Baseline forest ecological services projected into future 0 50 100 150 200 250 t2016 t2216 Yea rs Figure E3. Ecological service flow framework used to evaluate service flow changes on forest habitats at the Allen Steam Station — depicts current baseline of ecosystem service flows into the future. 0 cn v U (3) V) c� U .O O U W cn v O LL 100 Lost on -site forest ecological 75 services projected into the future 50 25 0 1 1 I 0 50 tzo16 On -site forest loss (34.2 acres) -505 dSAYs Off -site forest loss (190 acres) -2,992 dSAYs Off -site forest regeneration (190 acres) 1,8 Grass cap ecological services with cap -in -place 91 dSAYs Will Years 150 200 250 t2216 Figure E4. Ecological service flow framework used to evaluate service flow changes, on forest habitats that will be associated with the "Cap -in -Place" option, at the Allen Steam Station. y 100 0 Lost on -site forest ecological 75 services projected into the future a� V) c� U 'pip 50 On -site forest losses (34.2 acres) -505 dSAYs O On -site forest regeneration (34 acres) 135 dSAYs O U w Off -site forest losses (319 acres) -5,024 dSAYs 4-1 a 25 Off -site forest regeneration (93 acres) 903 dSAYs O LL 0 0 50 100 150 200 t2016 t2216 Yea rs Figure E5. Ecological service flow framework used to evaluate service flow changes, on forest habitats that will be associated with the "Removal" option, at the Allen Steam Station. 100 0 cn U 'j 75 N On -site baseline value (36.3 acres) 572 cISAYs U .W 50 O Current age Ostands Baseline forest ecological UJ N services projected into future a) 25 LL 0 50 0 t2016 100 150 200 250 t2216 Yea rs Figure E6. Ecological service flow framework used to evaluate service flow changes on forest habitats at the Buck Steam Station — depicts current baseline of ecosystem service flows into the future. 100 Lost forest ecological services projected into the future U 'j 75 N ca U 'O 50 On -site forest loss with tree clearance (36.3 acres) -554 dSAY O Off -site forest loss (122 acres) -1,921 dSAY U Off -site Forest Regeneration (122 acres) 1,296 dSAY w 4-1 N25 • ; , 1 • i O Grass cap ecological services with cap -in -place 103 dSAYs 0 0 50 100 150 200 t2016 t2216 Yea rs Figure ET Ecological service flow framework used to evaluate service flow changes, on forest habitats that will be associated with the "Cap -in -Place" option, at the Buck Steam Station. Wis] y 100 I*- y a Lost on -site forest ecological U services projected into the future 'j 75 N U On -site forest losses (36 acres) -554 dSAYs O50 On -site forest regeneration (36 acres) 313 dSAYs O w Off -site forest losses (129 acres) -2,032 dSAYs N 25 Off -site forest regeneration (35 acres) 372 dSAYs a� O 0- 0 t2016 t2216 50 100 150 200 250 Yea rs Figure E8. Ecological service flow framework used to evaluate service flow changes, on forest habitats that will be associated with the "Removal" option, at the Buck Steam Station. 100 0 cn U 'j 75 N ca U .W 50 O Current age O of tree U w stands 4-1 N 25 O LL 0 0 t2016 On -site baseline value (25 acres) 395 dSAYs Baseline forest ecological services projected into future Yea rs 150 200 250 t2216 Figure E9. Ecological service flow framework used to evaluate service flow changes on forest habitats at the Cliffside Steam Station — depicts current baseline of ecosystem service flows into the future. 100 Lost forest ecological services a projected into the future U 'j 75 N ca U O50 On -site forest loss with tree clearance (25 acres) -383 dSAYs p Off -site forest loss (115 acres) -1,811 dSAYs w Off -site forest regeneration (115 acres) 1,221 dSAYs 4-1 LL Grass cap ecological services with cap -in -place 71 dSAYs 0 50 100 150 200 t2016 t2216 Yea rs Figure E10. Ecological service flow framework used to evaluate service flow changes, on forest habitats that will be associated with the "Cap -in -Place" option, at the Cliffside Steam Station. y 100 ONO Lost on -site forest ecological y services projected into the future U 'j 75 N ca W 50 On -site forest losses (25 acres) -383 dSAYs O On -site forest regeneration (25 acres) 186 dSAYs O U w Off -site forest losses (168 acres) -2,646 dSAYs v 25 Off -site forest regeneration (47 acres) 499 dSAYs a� O 0- 0 t2016 t2216 50 100 150 200 250 Yea rs Figure Ell. Ecological service flow framework used to evaluate service flow changes, on forest habitats that will be associated with the "Removal" option, at the Cliffside Steam Station. 100 0 cn U 'j 75 N ca U .W 50 O Current age O of tree U w stands 4-1 N 25 O LL 0 0 t2016 On -site baseline value (11 acres) 173 dSAYs Baseline forest ecological services projected into future 50 100 150 Yea rs 200 250 t2216 Figure E12. Ecological service flow framework used to evaluate service flow changes on forest habitats at the Mayo Steam Station — depicts current baseline of ecosystem service flows into the future. 100 0 N Lost forest ecological services U projected into the future 'j 75 c6 U .� 50 On -site forest loss with tree clearance (11 acres) -168 dS O Off -site forest loss (94 acres) -1,480 dSA U Off -site forest regeneration (94 acres) 998 dSA w `n 25 (UNet Loss. dSAYs O U_ Grass cap ecological services with cap -in -place 32 dSAYs 0 s 0 50 100 150 200 t2016 t2216 Years Figure E13. Ecological service flow framework used to evaluate service flow changes, on forest habitats that will be associated with the "Cap -in -Place" option, at the Mayo Steam Station. y W Wiz 100 0 N Lost on -site forest ecologicalCli y U services projected into the future 'j 75 N U On -site forest losses (11 acres) -168 dSAYs O50 On -site forest regeneration (11 acres) 87 dSAYs O w Off -site forest losses (149 acres) -2,347 dSAYs N 25 Off -site forest regeneration (41 acres) 435 dSAYs aU O 0 0 t2016 t2216 50 100 150 200 250 Yea rs Figure E14. Ecological service flow framework used to evaluate service flow changes, on forest habitats that will be associated with the "Removal" option, at the Mayo Steam Station. Wetland/Surface Water Curves 100 75 U O O 50 U W 25 ca N 0 es 0 50 100 150 200 t2016 t2216 Yea rs alit Figure E15. Ecological service flow framework used to evaluate service flow changes on wetland/surface water habitats at the Allen Steam Station — depicts current baseline of ecosystem service flows into the future. 0 100 0 N U L cn 75 U .O O 50 U W 25 ca N 0 0 50 t2016 WIN Yea rs 150 Baseline ecological services 200 t2216 w M1111 Figure E16. Ecological service flow framework used to evaluate service flow changes, to wetland/surface water habitats that will be associated with the "Cap -in -Place" option, at the Allen Steam Station. cBaseline ecological services 100 MUM U On -site wetland loss (98 acres) -2,037 dSAYs cn 75 U O O 50 U w On -site forest regeneration (98 acres) 951 dSAYs 0 25 C N 710W 0 f_ 0 t2016 t2216 50 100 150 200 250 Yea rs Figure E17. Ecological service flow framework used to evaluate service flow changes, on wetland/surface water habitats that will be associated with the "Removal' option, at the Allen Steam Station. 0 100 Ln N U L cn 75 U O O 50 U W 25 ca N 0 0 50 100 150 200 t2016 t2216 Yea rs Figure E18. Ecological service flow framework used to evaluate service flow changes on wetland/surface water habitats at the Buck Steam Station — depicts current baseline of ecosystem service flows into the future. alit 0 100 0 N U L cn 75 U O O 50 U W 25 ca N 0 0 50 t2016 WIN Yea rs 150 Baseline ecological services 200 250 t2216 Figure E19. Ecological service flow framework used to evaluate service flow changes, to wetland/surface water habitats that will be associated with the "Cap -in -Place" option, at the Buck Steam Station. cBaseline ecological services 100 MUM U On -site wetland loss (64 acres) -1,330 dSAYs cn 75 U O O 50 U w On -site forest regeneration (64 acres) 621 dSAYs 0 25 N 7 0 t2016 t2216 50 100 150 200 250 Yea rs Figure E20. Ecological service flow framework used to evaluate service flow changes, on wetland/surface water habitats that will be associated with the "Removal' option, at the Buck Steam Station. 0 100 Ln N U L cn 75 U O O 50 U W 25 ca N 0 0 50 100 150 200 t2016 t2216 Yea rs Figure E21. Ecological service flow framework used to evaluate service flow changes on wetland/surface water habitats at the Cliffside Steam Station — depicts current baseline of ecosystem service flows into the future. alit 0 100 0 N U L cn 75 U O O 50 U W 25 ca N 0 0 50 t2016 WIN Yea rs 150 Baseline ecological services 200 250 t2216 Figure E22. Ecological service flow framework used to evaluate service flow changes, to wetland/surface water habitats that will be associated with the "Cap -in -Place" option, at the Cliffside Steam Station. cBaseline ecological services 100 MUM U On -site wetland loss (45 acres) -935 dSAYs Q Ln 75 U O O 50 U w On -site forest regeneration (45 acres) 437 dSAYs 0 25 C 0 0 t2016 t2216 50 100 150 200 250 Yea rs Figure E23. Ecological service flow framework used to evaluate service flow changes, on wetland/surface water habitats that will be associated with the "Removal' option, at the Cliffside Steam Station. 100 75 U O O 50 U W 25 ca N 0 0 50 100 150 200 t2016 t2216 Yea rs alit Figure E24. Ecological service flow framework used to evaluate service flow changes on wetland/surface water habitats at the Mayo Steam Station — depicts current baseline of ecosystem service flows into the future. 0 100 0 N U L cn 75 U O O 50 U W 25 ca N 0 0 50 t2016 WIN Yea rs 150 Baseline ecological services 200 250 t2216 Figure E25. Ecological service flow framework used to evaluate service flow changes, to wetland/surface water habitats that will be associated with the "Cap -in -Place" option, at the Mayo Steam Station. cBaseline ecological services 100 MUM U On -site wetland loss (79 acres) -1,642 dSAYs cn 75 U O O 50 U w On -site forest regeneration (79 acres) 767 dSAYs 0 25 N 0 0 t2016 t2216 50 100 150 200 250 Yea rs Figure E26. Ecological service flow framework used to evaluate service flow changes, on wetland/surface water habitats that will be associated with the "Removal' option, at the Mayo Steam Station. Early Successional/Shrub/Scrub Curves � o U � N \ U_ O •> •N � U U U >, O i Q M U W W 100 75 50 25 W On -site baseline value (100 acres) 1,129 dSAYs Current age of Baseline ecological services vegetation projected into future 0 t2016 t2216 50 100 150 200 250 Yea rs Figure E27. Ecological service flow framework used to evaluate service flow changes on early succession/scrub habitats at the Allen Steam Station — depicts current baseline of ecosystem service flows into the future. 100 � o U Ln 75 \ U_ •> O •N � v N Ln v 50 U =3 • U_ (/) bn O i O M U 25 w w W 0 t2016 t2216 50 100 150 200 250 Yea rs Figure E28. Ecological service flow framework used to evaluate service flow changes, to early succession/scrub habitats that will be associated with the "Cap -in -Place" option, at the Allen Steam Station. 100 � o U Ln 75 \ U '> 0 •N v L Ln 50 v U =3 U 'aA v) 0 i 0 M U 25 w w 0 0 Lost on -site ecological services projected into the future Current age of vegetation t2o16 On -site losses (100 acres) -1,129 dSAYs On -site forest regeneration (100 acres) 396 dSAYs 1 50 100 150 200 250 tzz16 Yea rs Figure E29. Ecological service flow framework used to evaluate service flow changes, to early succession/scrub habitats that will be associated with the "Removal" option, at the Allen Steam Station. 100 o U 75 \ U_ �> O On -site baseline value (52 acres) 587 dSAYs U _ 50 U =3 ru .V Curren/age000000 � aA of Baseline ecological services >, o= o vegeta projected into future w w 25 0 0 t2016 t2216 50 100 150 200 250 Yea rs Figure E30. Ecological service flow framework used to evaluate service flow changes on early succession/scrub habitats at the Buck Steam Station — depicts current baseline of ecosystem service flows into the future. 100 � o U Ln 75 \ U_ •> O •N � v N Ln v 50 U =3 • U_ (/) bn O i O M U 25 w w W 0 t2016 t2216 50 100 150 200 250 Yea rs Figure E31. Ecological service flow framework used to evaluate service flow changes, to early succession/scrub habitats that will be associated with the "Cap -in -Place" option, at the Buck Steam Station. 100 � o U Ln 75 \ U '> O •N v L Ln 50 v U =3 U 'aA v) O i O M U 25 w w 0 0 Lost on -site ecological services projected into the future Current age of vegetation t2o16 50 well] On -site losses (52 acres) -587 dSAYs On -site regeneration (52 acres) 206 dSAYs Yea rs 1 150 200 250 tzz16 Figure E32. Ecological service flow framework used to evaluate service flow changes, to early succession/scrub habitats that will be associated with the "Removal" option, at the Buck Steam Station. 100 o U N 75 \ � U _ O > On -site baseline value (42 acres) 474 dSAYs U _ 50 U = .V Current age V) aA of Baseline ecological services >, = o 0 vegetation projected into future w w 25 0 50 100 0 t2016 150 200 250 t2216 Yea rs Figure E33. Ecological service flow framework used to evaluate service flow changes on early succession/scrub habitats at the Cliffside Steam Station — depicts current baseline of ecosystem service flows into the future. 100 � o U Ln 75 \ U_ •> O •N � v N Ln v 50 U =3 • U_ (/) bn O i O M U 25 w w W 0 t2016 t2216 50 100 150 200 250 Yea rs Figure E34. Ecological service flow framework used to evaluate service flow changes, to early succession/scrub habitats that will be associated with the "Cap -in -Place" option, at the Cliffside Steam Station. 100 � o U Ln 75 \ U '> O •N v L Ln 50 v U =3 U 'aA v) O i O M U 25 w w 0 0 Lost on -site ecological services projected into the future Current age of vegetation t2o16 50 well] On -site losses (42 acres) -474 dSAYs On -site regeneration (42 acres) 166 dSAYs Yea rs 1 150 200 250 tzz16 Figure E35. Ecological service flow framework used to evaluate service flow changes, to early succession/scrub habitats that will be associated with the "Removal" option, at the Cliffside Steam Station. 100 o U 75 \ U � _ O > On -site baseline value (42 acres) 474 dSAYs U _ 50 U = .V Current age V) aA of Baseline ecological services >, o = 0 vegetation projected into future w w 25 0 0 t2016 t2216 50 100 150 200 250 Yea rs Figure E36. Ecological service flow framework used to evaluate service flow changes on early succession/scrub habitats at the Mayo Steam Station — depicts current baseline of ecosystem service flows into the future. 100 � o U Ln 75 \ U_ •> O •N � v N Ln v 50 U =3 • U_ (/) bn O i O M U 25 w w W 0 t2016 t2216 50 100 150 200 250 Yea rs Figure E37. Ecological service flow framework used to evaluate service flow changes, to early succession/scrub habitats that will be associated with the "Cap -in -Place" option, at the Mayo Steam Station. 100 � o U Ln 75 \ U '> O •N v L Ln 50 v U =3 U 'aA v) O i O M U 25 w w 0 0 Lost on -site ecological services projected into the future Current age of vegetation t2o16 50 well] On -site losses (42 acres) -474 dSAYs On -site regeneration (42 acres) 166 dSAYs Yea rs 1 150 200 250 tzz16 Figure E38. Ecological service flow framework used to evaluate service flow changes, to early succession/scrub habitats that will be associated with the "Removal" option, at the Mayo Steam Station. Grass Curves 100 0 N N U 'j 75 N cn M U 'Ep 50 O O U W 0 0 25 L (7 111 0 t2016 t2216 50 100 150 200 250 Yea rs Figure E39. Ecological service flow framework used to evaluate service flow changes on grass habitats at the Allen Steam Station — depicts current baseline of ecosystem service flows into the future. 100 0 N aJ U •� 75 N V) ca U ' 50 O O U w L 25 On -site grass loss L 1 0 1 —1- 0 50 t2016 On -site grass loss (72.9 acres) -250 dSAYs On -site grass gain (72.9 acres) 194 dSAYs Net Loss -56 dSAYs Grass cap ecological services gain with cap -in -place well] Years Mil 200 250 t2216 Figure E40. Ecological service flow framework used to evaluate service flow changes, on grass habitat that will be associated with the "Cap -in -Place" option, at the Allen Steam Station. Grass converts to grass. 100 0 c0 U W 50 O O U W a-1 N 25 LL W Gained on -site forest ecological services projected into the future Lost on -site grass ecological services projected into the future KIII7 On -site forest gains (72.9 acres) 288 dSAYs On -site grass losses (72.9 acres) -250 dSAYs Yea rs 150 200 tzz16 Figure E41. Ecological service flow framework used to evaluate service flow changes, on grass habitats that will be associated with the "Removal" option, at the Allen Steam Station. Grass habitat converts to forest. 100 0 N N U 'j 75 N cn M U 'Ep 50 O O U W 0 0 25 L (7 W 0 t2016 t2216 50 100 150 200 250 Yea rs Figure E42. Ecological service flow framework used to evaluate service flow changes on grass habitats at the Buck Steam Station — depicts current baseline of ecosystem service flows into the future. 100 0 Ln N U •� 75 a) cn ca U •Eb 50 O O On -site grass loss (34.2 acres) -117 dSAYs w On -site grass gain (34.2 acres) 91 dSAYs v`ni 25 On -site grass loss Net Loss -26 dSAYs ca � 1 Grass cap ecological services gain with cap -in -place 0 0 50 100 150 200 t2016 t2216 Yea rs Figure E43. Ecological service flow framework used to evaluate service flow changes, on grass habitat that will be associated with the "Cap -in -Place" option, at the Buck Steam Station. Grass converts to grass. alit 100 0 Ln (, U 75 a) V) M U •Eb 50 O O U W v4-ai 25 a, LL W Gained on -site forest ecological services projected into the future Lost on -site grass ecological services projected into the future KIII7 On -site forest gains (34.2 acres) 135 dSAYs On -site grass losses (34.2 acres) -117 dSAYs Yea rs 150 200 tzz16 Figure E44. Ecological service flow framework used to evaluate service flow changes, on grass habitats that will be associated with the "Removal" option, at the Buck Steam Station. Grass converts to forest. 100 0 N N U 'j 75 N cn M U 'Ep 50 O O U W 0 0 25 L (7 111 0 t2016 t2216 50 100 150 200 250 Yea rs Figure E45. Ecological service flow framework used to evaluate service flow changes on grass habitats at the Cliffside Steam Station — depicts current baseline of ecosystem service flows into the future. 100 0 Ln N U •� 75 a) cn ca U •Eb 50 O O On -site grass loss (51.5 acres) -176 dSAYs w On -site grass gain (51.5 acres) 137 dSAYs v`ni 25 On -site grass loss Net Loss -39 dSAYs ca � 1 Grass cap ecological services gain with cap -in -place 0 0 50 100 150 200 t2016 t2216 Yea rs Figure E46. Ecological service flow framework used to evaluate service flow changes, on grass habitat that will be associated with the "Cap -in -Place" option, at the Cliffside Steam Station. Grass converts to grass. alit 100 0 Ln (, U 75 a) V) M U •Eb 50 O O U W v4-ai 25 a, LL W Gained on -site forest ecological services projected into the future Lost on -site grass ecological services projected into the future will] On -site forest gains (51.5 acres) 204 dSAYs On -site grass losses (51.5 acres) -176 dSAYs Yea rs 150 200 tzz16 Figure X47. Ecological service flow framework used to evaluate service flow changes, on grass habitats that will be associated with the "Removal' option, at the Cliffside Steam Station. Grass converts to forest. 100 0 N N U 'j 75 N cn M U 'Ep 50 O O U W 0 0 25 L (7 W 0 t2016 t2216 50 100 150 200 250 Yea rs Figure E48. Ecological service flow framework used to evaluate service flow changes on grass habitats at the Mayo Steam Station — depicts current baseline of ecosystem service flows into the future. 100 0 N aJ U •� 75 N V) ca U ' 50 O O U w L 25 On -site grass loss L 1 0 1 —1- 0 50 t2o16 On -site grass loss (14 acres) -48 dSAYs On -site grass gain (14 acres) 37 dSAYs Net Loss -11 dSAYs Grass cap ecological services gain with cap -in -place well] Years Mil 200 250 t2216 Figure E49. Ecological service flow framework used to evaluate service flow changes, on grass habitat that will be associated with the "Cap -in -Place" option, at the Mayo Steam Station. Grass converts to grass. 100 0 Ln (, U 75 a) V) M U •Eb 50 O O U W v4-ai 25 a, LL W Gained on -site forest ecological services projected into the future Lost on -site grass ecological services projected into the future KIII7 On -site forest gains (14 acres) 55 dSAYs On -site grass losses (14 acres) -48 dSAYs Yea rs 150 200 tzz16 Figure E50. Ecological service flow framework used to evaluate service flow changes, on grass habitats that will be associated with the "Removal" option, at the Mayo Steam Station. Grass converts to forest. EPS APPENDIX F Joseph Nicolette CV Joseph Nicolette Fields of Competence Risk management and net environmental benefit analysis (NEBA) Natural resource damage assessment (NRDA) Ecosystem service valuation Site remediation alternatives analysis Ecological and human health risk assessment evaluation CERCLA project coordination Oil spill response and planning Aquatic ecology Experience Summary Expertise: Joseph Nicolette has over 30 years of experience in the environmental consulting field with a focus on NRDA, NEBA, ecosystem service valuation, site risk management, remediation alternatives analysis, project coordination and agency relations, and aquatic ecology. He is a Senior Principal and Ecosystem Services Practice Leader at Environmental Planning Specialists, Inc. (EPS). Joe has made demonstrated contributions both nationally and internationally in developing what is known as the net environmental benefits analysis (NEBA) or net ecosystem service analysis (MESA) approaches. He co- authored the first formalized NEBA framework recognized by the United States Environmental Protection Agency (USEPA), the USEPA Science Advisory Board (USEPA SAB), the National Oceanic and Atmospheric Administration (NOAA). Joe has focused on NEBA applications to site risk management and decision -making, incorporating ecosystem service valuation, over the past 24 years. He provides strategic advice and oversight for projects to help balance the risks, benefits and tradeoffs associated with competing alternatives (e.g., remediation actions, mitigation and restoration, oil spill response, site restoration, and decommissioning actions). He has contributed to multiple environmental assessments associated with oil and chemical releases, and has managed NEBA, NRDA and CERCLA issues on behalf of the responsible party. His role has been to provide technical direction and assist the client in coordination with Natural Resource Trustee Agencies (State and Federal), the United States Environmental Protection Agency (USEPA), state agencies, and other stakeholders in the risk management and NRDA processes. Joseph has participated in NRDA projects since 1990. He is recognized for his NRDA experience and role in pioneering NRDA injury and restoration ecological 2 economics approaches such as habitat equivalency analysis (HEA). He has been involved in the conduct of impact assessments for over 50 hazardous releases and has led projects on behalf of the client, including agency negotiations. These include assessments associated with oil and chemical releases (including petroleum products, PCB's, DDT, dioxin, metals, etc.) and damage assessments associated with releases under US NRDA regulations and the EU Environmental Liabilities Directive (ELD). He has contributed to multiple environmental assessments associated with oil releases from the Exxon Valdez up to and including the Deepwater Horizon Incident. Mr. Nicolette has managed over 50 NRDA related projects. His services include client strategy development regarding natural resource injury and environmental assessment; ecological and human health risk characterization; mitigation and remedial planning, restoration valuation and planning, allocation modeling, agency negotiations, technical assessment design, and technical data review, assessment, and interpretation. Joseph has also worked with several law firms to provide expert witness and litigation support for multiple NRDA related environmental cases. He has provided written affidavits to support his technical findings and provided testimony through deposition by the Department of Justice. He has also served as the Project Coordinator and/or risk management lead on behalf of the responsible party at multiple Superfund (CERCLA) sites. He has experience with sites in EPA Regions 2, 3, 4, 5, 6, 8 and 10. These sites were primarily related to polychlorinated biphenyls (PCBs), metals, dioxin, and TCE releases to a variety of media such as soil, sediment, surface water and groundwater. These projects involved the assessment of ecological and human health risks and the feasibility of various remedies to manage potential risks. Mr. Nicolette has also provided PRP identification and remedial/NRDA liability allocation support for industrial clients. He has developed and reviewed allocation models for specific sites, determining estimates of the portion of remedial and NRDA liability associated with various PRP's at the site. Joseph is also trained in hierarchical relational scientific database design, structuring and management. 0 Joseph Nicolette Credentials M.S. Fisheries Management, 1983. University of Minnesota - related fields: statistics, computer applications, and aquatic invertebrates B.S. Environmental Resources Management, 1980. Pennsylvania State University Certifications Certified Fisheries Scientist, no. 2,042, since 1992 Career 2016-Present Senior Principal and Ecosystem Service Practice Director Joes role is to provide strategic consultation for projects directed at managing client environmental liabilities (e.g., NEBA, NRDA, oil spill response, offshore decommissioning, remediation, risk management) and assets, including natural resource valuations of land parcels. 2009-2016 Principal, Global Ecosystem Services Director, Ramboll Environ Provided strategic consultation for projects directed at managing client environmental liabilities (e.g., NEBA, NRDA, oil spill response, offshore decommissioning, remediation, risk management) and assets, including natural resource valuations of land parcels. 1999-2009 Vice President, Natural Resource Liability and Asset Management, C112M HILL Provided strategic consultation for projects directed at managing client environmental liabilities (e.g., NEBA, NRDA, oil spill response, offshore decommissioning, remediation, risk management) and assets, including natural resource valuations of land parcels. 1998-1999 President, Nicolette Environmental, Inc. NRDA Project Strategy, Management, and Negotiations 3 1990-1998 Senior Consultant, ENTRIX Inc. Oil Spill Response and Damage Assessment Advisor 1984-1990 Operations Supervisor, Adirondack lakes Survey Corporation Oversight of field operations, data management, and QA/QC 1983-84 North Carolina State University Acid Deposition Program. Database Manager. Developed the Fish Information Network as part of acid deposition studies in the United States. Professional Affiliations USGS sponsored ACES (a community of ecosystem services) — Steering/Planning Committees 2008-present Net Environmental Benefits Analysis Joe's contributions to the development of NEBA approaches, aside from co-authoring the first formalized NEBA framework, includes the conduct of national and international presentations and workshops (Brazil, Canada, Germany, Great Britain, Italy, Malaysia, Norway, Scotland, South Africa, Sweden, Thailand, United States, etc.) related to ecosystem service valuation, benefits assessment (i.e., NEBA), risk management, project development, resource equivalency, oil spill response and planning, site remediation and damage assessment. Joes success in this area also includes: ✓ Recognition of, and incorporation of the formalized NEBA framework by the USEPA in a report by the USEPA Science Advisory Report entitled "Valuing the Protection of Ecological Systems and Services", (2009). ✓ A chapter regarding NEBA that was integrated into Interstate Technical Regulatory Council Guidance (2006). ✓ Pilot studies evaluating appropriate metrics for NEBA for site cleanup, restoration, and remediation on behalf of the US Environmental Protection Agency (2008). ✓ Guidance for the London Energy Institute (2009 and 2010, London) on the Environmental Liability Directive and on establishing ecological baselines 0 Joseph Nicolette N N I (pre -incident) using NEBA concepts. NEBA support for the Oil and Gas Producers (2015-2016) (OGP) Joint Industry Group (JIP) related to oil spill response and planning in the Arctic. He was the lead author of a book chapter (published in March of 2013 by the Oxford Press) that provided an overview of the U.S. natural resource damage assessment regulations and the development of resource equivalency analysis approaches. The book was entitled "The E.U. Liability Directive: A Commentary". He led Chapter 9 that was entitled "Experience with Restoration of Environmental Damage". The chapter also discusses the similarities and differences of the U.S. NRDA rules in relation to the ELD. Led the first NEBA studies for decommissioning of offshore oil and gas platforms in Australia and the North Sea (2014-Present). Multiple published papers and reports related to NEBA and ecosystem service valuation. Multiple client projects using NEBA and ecosystem service valuation approaches to address remedial risk issues and manage costs Joseph has served on the Planning and Steering committees for the ACES Conferences (A Community of Ecosystem Services), working with the United States Geological Survey Conference leaders since 2008. Participated in the conduct of numerous environmental assessments associated with the development of restoration and remedial decisions. This includes the ecosystem service valuation of compensatory restoration projects related to NRDA or other resolutions. Example Projects Armstrong World Industries CERCLA OU-1 Site, Macon, Georgia. Joe served as the EPA Project Coordinator on behalf of AWI and provided oversight of Operable Unit 1 (OU-1) evaluations, including the development of the environmental engineering/cost analysis (EE/CA), remedial design/remedial action, Post - Closure, NRDA/NEBA considerations, and supporting field sampling. He assisted in negotiations with the USEPA and State agencies and coordinated OU-1 CERCLA activities, on behalf of AWI, with the USEPA and GAEPD. The primary contaminant of concern was PCBs. He provided oversight of the site characterization efforts including the development of study plans, the L, ecological and human health risk assessments, and the site remedy with a focus on the managing potential PCB risks. BP Deepwater Horizon. Joe served as a Project Technical Lead related to selected damage assessment (NRDA) issues associated with the Deepwater Horizon Gulf of Mexico oil release. Crab Orchard National Wildlife Refuge, PCB CERCLA/NRDA Site, Marion, Illinois. Joe led PCB site characterization, remedial alternatives analysis through NEBA, remediation implementation, and NRDA liability management). Joseph served as the CERCLA Project Coordinator jointly working with two PRP's (Schlumberger and the USFWS). He helped negotiate environmental remedial and NRDA associated liabilities related to historical PCB releases at the site. In serving as the Project Coordinator, he coordinated closely with the federal (USFWS, USEPA) and state (IL DNR, IL EPA) regulatory agencies. This was a unique case in that the USFWS was a PRP and at the same time, a trustee for natural resources. Innovative risk management data collection and analysis approaches were used to support risk management decision -making and included incremental sampling study design and surface weighted area concentration (SWAG) analysis A NEBA-based approach using resource equivalency analysis was used as part of the remedial and damage assessment resolution, saving the client over $10 million dollars. Edwards Air Force Base (EAFB) and NASA CERCLA Site, California. NRDA and remedial NEBA negotiations. Joe provided NRDA and remedial NEBA support to EAFB as the responsible party at this site. Joseph developed an overarching CERCLA NRDA and NEBA strategy to EAFB and participated in agency negotiations. Major issues were related to groundwater and surface soil contamination. A NEBA was used to demonstrate that the presumptive pump and treat remedy would provide no net benefit to the public. A less intrusive MNA alternative was instituted and reduced project costs by $65 million. USEPA OSWER NEBA Studies, Colorado and Florida Joe served as the Principle Investigator for a project evaluating the metrics that can be used to evaluate changes in ecosystem services associated with site remediation and local development. This work was conducted for the 0 Joseph Nicolette USEPA OSWER group as it relates to evaluating the net environmental benefit (NEBA) associated with remedial actions at superfund sites. The USEPA was evaluating the use of NEBA to help demonstrate the benefits and environmental stewardship associated with site remediation and in the selection of site management alternatives. These studies were conducted for the USEPA at Rocky Mountain Arsenal, Colorado, and Homestead AFB, Florida. This project was directed at a "real world" evaluation of metrics that can be used to evaluate changes in ecosystem service values associated with site cleanup decision making. This project was titled "Demonstrating the Net Environmental Benefit of Site Cleanup; An Evaluation of Ecological and Economic Metrics at Two Superfund Sites" was conducted for the USEPA Superfund group as it relates to evaluating the net environmental benefit associated with remedial actions at Superfund sites and appropriate metrics. This work was developed to be consistent with the Millennium Assessment ecological service categorization. Texas: For Region 6 USEPA, Joe supported the conduct of NEBAs to evaluate remedial alternatives at two orphan sites in Texas. The NEBAs were used to demonstrate the benefits1limpacts associated with remediation. These are described below. NEBA for USEPA: Jasper Creosoting Company Superfund Site, Texas: An ecological risk assessment (ERA) for a wetland indicated low to medium risks for benthic invertebrates and a subset of upper trophic level receptors associated with exposure to dioxin and polycyclic aromatic hydrocarbons in sediment. Joe supported the development of a NEBA for USEPA Region 6 to quantify the net present value of the ecological services associated with no further action and six remedial alternatives involving monitoring, phytoremediation and combinations of full removal, partial removal and wetland enhancement. The NEBA demonstrated that monitored natural attenuation coupled with phytoremediation would provide the greatest net environmental benefit at the least cost and decrease ecological risks over time. The cost of this alternative was estimated to be more than $2 million less than the most intrusive remedy. s NEBA for USEPA: State Marine Superfund Site, Texas: Joe supported the development of a NEBA for the USEPA to evaluate remedial alternatives for marginal ecological risks to benthic invertebrates identified in intertidal sediments. The baseline level of ecological service associated with no further action and monitored natural attenuation was evaluated as well as the net change in ecological services that would be expected with a sediment removal action. The NEBA results indicated that the loss of ecological services associated with no further action would be minor, if injury was occurring, and that the intrusive remedy would create a greater net ecological service loss because of impacts to habitat. In this case, no further action was selected as the preferred alternative as the NEBA demonstrated that expenditure of more than $6 million on sediment removal would not be protective of the environment. Army Base Realignment and Closure (BRAG) Strategy Studies Joe served as the Principal Investigator for the U.S. Department of the Army of four studies designed to understand site development and reuse strategies and integration of natural resource values to maximize the benefits associated with site natural resources and remedial strategies. For these studies, potential site remedial alternatives were evaluated in conjunction with potential reuse scenarios to reflect the interdependencies between remediation and property reuse. The goal for each site was to identify the combination of remedial action and re -use that would provide for the protection of human health and the environment while providing the greatest net ecological and human use value. This series of studies conducted over two years and included the following four Army BRAC sites: Camp Bonneville, Washington; Savanna Army Depot, Illinois; Fort Ord, California; and Fort McClellan, Alabama. The Department of the Army used the information from these analyses to develop preferred land management and remedial actions for each site that minimize remedial costs while maximizing natural resource values to the public. Passaic River, New Jersey. Joe developed a preliminary example NEBA evaluation for Passaic River remedial alternatives (large scale sediment dredging) as proposed by the USEPA. The overall goal was to demonstrate the 0 Joseph Nicolette environmental and human health cost/benefit of proposed intrusive remediation. Tennessee EIS Support Eco Asset Valuation: Comparing Economic Development and Conservation of over 300 Land Parcels Joe was the technical lead for a land eco-valuation project for a confidential client. In partnership with the client's Resource Stewardship Division, Joseph led a Land Valuation Pilot for —14,000 acres that surrounded a reservoir in Tennessee. This project was conducted as a NEBA in support of the client's NEPA documentation (e.g., supporting the preferred alternative) that will drive the management of properties surrounding the reservoir. As part of this work, he evaluated and compared the environmental (e.g., natural resource) value and real estate (e.g., "highest and best" land use) value associated with the parcels located within the project area given the three proposed land use alternative scenarios (e.g. no -action, balanced conservation and recreation, and balanced development and recreation). This analysis resulted in the development of a 4th preferred alternative that maximized ecosystem service values (ecological and human use) back to the public. Brownfield Site NEBA Options Analysis. Joe provided oversight of the incorporation of a NEBA framework for a large Brownfield site. He collaboratively worked with the City of Milwaukee, the public, regulatory agencies and prospective developers as part of the City of Milwaukee's Menomonee Valley Industrial Center Redevelopment Program to create a sustainability-focused brownfield's success. Through the use of a NEBA framework, he was able to evaluate the development plans for the Brownfield site and modify/tweak the designs to maximize ecological and human use values to the public. We were able to demonstrate that the revised plan for site restoration and remediation activities created ecological, recreational and aesthetic creation/enhancement benefits estimated at over $120MM of value to the public. In addition, the NEBA also assisted in identifying over $25MM in value - engineered cost savings and new-found revenue streams from the beneficial re -use of materials. NEBA for PCB Related Dam Removal at Lake Hartwell, South Carolina. Joe assisted a client in evaluation of the ecological impacts associated with the removal of two rel dams in South Carolina. The dam removals were being conducted as part of an NRDA settlement associated with PCB releases. Joe evaluated ongoing fish tissue PCB concentrations associated with MNA activities. Lower Passaic River, PRP Group NRDA Negotiations and Strategy Advisement, New Jersey. Joe supported a group of Cooperating PRP's in developing a strategy and negotiating with the State and federal trustees regarding the Lower Passaic River NRDA. Joseph directly interfaced with the Companies and the State and federal Trustees. His role was to assist the PRP's in developing a strategy to manage their NRDA liability at the site. He assisted with conducting preliminary evaluations of ecological and human use injury and compensatory restoration project identification. LCP CERCLA Site, Brunswick, Georgia, PCB releases. Joe provided remedial NEBA analysis and NRDA support associated with PCB releases for a client in Brunswick, GA. His role was to evaluate ecological risk data for the purposes of integrating select areas of the estuary into a NEBA sediment management approach. In addition, he also evaluated injury assessment data and developed potential compensatory restoration options as part of this project. He participated in agency meetings and discussions regarding remedial and NRDA issues. Delaware River, Pennsylvania, PCBs. Joe supported a PRP on ecological risk and NRDA issues associated with historical PCB releases at the Metals Bank site. Specifically, he evaluated potential impacts of PCBs on aquatic invertebrates, fish, and birds. A resource equivalency analysis approach was used as part of the damage assessment. Joe evaluated agency impact analyses and developed appropriate compensatory restoration. Kalamazoo River, Michigan, PCBs. Joe assisted a client on the Kalamazoo River in the evaluation of historical PCB concentrations, potential impacts to fish and terrestrial biota, development of appropriate SWAC concentrations, and remedial alternative evaluations. Joe evaluated fish tissue PCB concentrations and examined trends of PCBs in fish along the river. Sediment PCB Remedial NEBA Evaluation. Confidential Client, Portland, Oregon. Joe provided Joseph Nicolette technical and strategic support to a client on the Willamette River to settle its NRDA mitigation liability. Wisconsin Tissue, Fox River PCBs. Joe provided technical and strategic advice to Wisconsin Tissue regarding the Fox River NRDA and participated in trustee negotiations on behalf of Wisconsin Tissue. Confidential Client, Calumet River CERCLA, Allocation, Remedial, and NRDA Support. Joe represented a potentially responsible party (PRP) with remedial and NRDA liabilities associated with historical PCBs, metals, etc. releases. In this context, Joe reviewed allocation issues, HEA model runs, sediment ecological and water toxicity data, and sediment and water column concentration data to assist the client in responding to demands of the natural resource trustees and other PRPs. Allocation Modeling. Confidential Clients, New York, New Jersey, and Indiana: Joe has provided PRP identification and remedial liability allocation support for two major industrial clients in the states of New York, New Jersey, and Indiana. The overall purpose of these projects was to identify potential PRPs and rank them by estimated contribution in relation to one another. Based upon the information gathered during this work (e.g., volumes released, parameter toxicity, geographic location), supported the development of allocation models and the approach for determining estimates of the portion of remedial liability associated with each PRP. Colonial Pipeline Company. Senior NRDA Advisor (1993-Present). Oil spill response, planning and assessment. Joe's responsibilities included overall project management (of oil release impact assessments, including participation in company planning and drills) for Colonial from a technical and administrative aspect, including project strategy, coordination with the governmental regulatory agencies, management of contractors, cost control and management, task management, field study coordination, risk management, technical expertise, and report generation. In addition, he provides overall management and oversight of necessary compensatory restoration required as part of a spill settlement, if any. 7 Example emergency releases Joe has responded to and/or provided expertise for environmental assessments include the following: o BP, Deepwater Horizon, Gulf of Mexico o BP barge release, Elizabeth River, VA o Sugarland Run, Potomac River o Darling Creek, Louisiana o Lookout Mountain, Tennessee o Tennessee River/Goose Creek, Knoxville o Reedy River, South Carolina o San Jacinto River, Texas o Pine Bend, Tennessee o Simpsonville, South Carolina o Sun Oil, barge release, Delaware River, DE o Maritrans, tug barge release, Mississippi River, MN o Amoco Pipeline, pipeline release, Whiting, Illinois o Southern Pacific Railroad, tanker car (release of metum sodium, Sacramento River, CA) o Buckeye Pipeline release, Allegheny River, PA o Buckeye Pipeline release, Quinnipiac River, CT o EXXON, tanker, Prince William Sound, Valdez, AK o Plantation Pipeline, pipeline release, Rich Fork, NC o Plantation Pipeline, pipeline release, Ward Road, GA o Conoco. Calcasieu Estuary, Louisiana o Unocal, Guadalupe, CA o Union Pacific Railroad, 7 train derailment releases, UT, CO o Petrobras, pipeline release, Brazil Rainforest o CITGO, pipeline release, Texas o Multiple smaller releases Alabama, Marshall Space Flight Center. NRDA, NEBA, and site remediation of groundwater contamination at NASA Marshall Space Flight Center CERCLA Site in Alabama. Joe was retained to provide oversight of a NRDA for the NASA Marshall Space Flight Center in Huntsville, Alabama. Joseph assisted in coordinating the assessment with the natural resource trustees and provided technical support for determining the potential levels of injury and potential scale of compensatory restoration. This project also entailed the use of NEBA to evaluate potential remedial alternatives associated with the site. 0 Joseph Nicolette Koch Site, Hastings, Minnesota Using natural resource economics -based approaches, Joe supported an assessment of the value to the public of converting a land asset along the Mississippi River to a public recreational use to obtain a consent decree, facilitate site closure, and generate substantial goodwill with the public. The fair market value of the property was $215,000 and the recreational value, given the transfer scenario, was approximately $30 million (see following press release). "Plans Announced for Koch Riverfront Park; City to Complete Bike Trail, Develop Park with Koch Petroleum Group's Gift of Land, April 20, 2000 5:46 PM EDT HASTINGS, Minn., April 20 /PRNewswire/ -- At a ceremony held today on the west bank of the Mississippi River, the City of Hastings and Koch Petroleum Group unveiled a joint initiative to develop a bike trail and a community park on riverfront property currently owned by Koch. David Robertson, president of Koch Petroleum Group, announced the company's donation of 43 acres of Mississippi riverfront land to the city. Hastings Mayor Mike Werner also announced that the city will name the area Koch Riverfront Park. The announcement ceremony, hosted by Werner and Robertson, was held at the riverfront site immediately northwest of downtown Hastings. "We sincerely hope that generations of area families will enjoy the park, and that it will serve as a gathering place that will connect community members to each other and to the river, " said Robertson. The appraised market value of the land is $215,000; its potential recreation value to the public is estimated at $30 million based on a recent study... " RMC, Troutdale Superfund Site, Oregon — CERCLA, NEBA, CWA, NRDA. Joe provided strategic advice regarding the evaluation of remediation options for RMC's 16-acre NPDES process water pond ("Company Lake"). Regulatory agencies stated a desire to declare the pond a Water of the State. Such a determination would have eliminated RMC's ability to use the pond as part of the plant's NPDES wastewater management system. This would have required closing the plant or constructing a replacement storm water treatment system. Joseph supported a collaborative negotiation process that resulted in integrating pond remediation, continued pond use for NPDES purposes, plant storm water system upgrades, and an informal settlement of NRD claims. Direct savings to RMC in reduced project scope are estimated at more than $1 million. Indirect savings associated with retaining use of Company Lake are believed to exceed $10 million. Land Transfer and Restoration Strategy, Confidential Client, Utah Joe contributed to a study whose purpose was to determine the status of real estate, contamination and natural resource conditions for a large and complex industrial site in Utah. This analysis served to develop a path forward to demonstrate and maximize the value of a site by integrating site cleanup with site reuse and future development. This study considered and integrated Property Reuse Alternatives, Alternative Remedial Approaches, Natural Resource Assets, and Liability Transfer Options. The results of the study provided the high-level framework to establish a reasonable performance target for the eventual reuse of the site and helped the client to maximize the overall ecological and human use values that the site could provide under various reuse scenarios. For most large, complex sites there is an initial degree of uncertainty associated with defining environmental remediation and the potential impact on the real estate and ecological assets of the site. The study process was developed to establish high-level opportunities for value enhancement through the development of a proactive strategy to reduce this uncertainty and effectively reposition the property for sale and future site reuse. This study was a key step in the process to ensure that the site is proactively repositioned, sold and redeveloped. The study also reduced the uncertainty associated with existing environmental remediation and restoration and provides insight into the environmental issues associated with site reuse. Tactical Oil Response Plans. Joe has led the development of tactical response plans for Colonial Pipeline for multiple locations in the SE United States. These plans have been developed with NEBA concepts in mind to assist Colonial in managing response efforts in the event of a release. Joe supports Colonial in annual spill drill events. His role is to help Colonial Pipeline manage environmental impacts and liabilities during an event, including NEBA considerations in OSRP. Pipeline Mitigation Projects Joe led a NEBA to evaluate the mitigation requirements for a pipeline expansion project in Louisiana. This project demonstrated that the compensatory restoration was 60% less than that 0 Joseph Nicolette requested by the USAGE. This reduced the client project costs substantially. The results were published in the Oil and Gas Pipeline Magazine. For the same client, Joe led a NEBA to evaluate the mitigation requirements for a pipeline expansion project in Texas. This project demonstrated that the compensatory restoration was significantly less (greater than 50% less) than what was estimated to be requested by the USACE. This analysis incorporated the resource equivalency approaches and consideration of socioeconomic benefits Mitigation Bank Prospectus Development and Financial Cash Flow Analysis For a confidential client, Joe led the evaluation of the remedial liability and buffer land wetland and stream mitigation banking value associated with the clients property. Based on the evaluation, ENVIRON staff calculated the credit values for both the wetland and stream banking opportunities and developed a mitigation bank prospectus that was negotiated with the US Army Corps of Engineers. As with any mitigation banking project, the primary uncertainties in determining the cost and return of a project include: the quantity of credits that can be realized, the market available for the credits, the sale price of the credits and the cost of restoration or creating the credits. Joe conducted an analysis to understand the mitigation banking uncertainties and minimize these uncertainties. A financial cash flow analysis was conducted to bound the project uncertainties. The mitigation bank cash flow financial analysis was conducted based upon our understanding of the credit values, demand and potential site uplift (i.e., number of credits to be allocated). This analysis provided our client with the information necessary to evaluate the business case for the bank as more refined information became available. As such, it provided an "off ramp" should any information arise that would make the mitigation bank non -desirable from a financial perspective. The cash flow analysis was a critical step to determine viability of the project. The cost and returns were predicted under conservative, likely and aggressive scenarios. Confidential Railroad, Litigation Support (Utah, Nevada, Colorado). Joe was retained by a major railroad to support a major litigation case. Joe Nicolette served as a a technical expert for the railroad providing expertise related to the environmental effects of chemical releases to the environment (tanker car releases associated with derailments along multiple sites: diesel and sulfuric acid releases) and the NRDA process. He evaluated multiple sites regarding NRDA liability and provided strategic advice to the client regarding potential liabilities. This case was settled prior to deposition. Confidential Paper Company: Private and Public Interest Property Valuation Joe led a study for a large paper company to evaluate the natural resource value associated with buffer properties surrounding a mill that was scheduled for closure. The client was looking to capture eco-asset values not typically considered in fair market valuations. The evaluation considered multiple land parcels for several hundred acres of property and provided an assessment of the "private" and "public" interest value associated with the property. Using this information, the client was able to negotiate the sale of the property, manage site liabilities, and capture value greater than a fair market value. This project included an evaluation of wetland and stream mitigation banking value including calculation and estimation of potential credit values and potential cash -flow for the site. The eco- asset valuation estimated the minimum private interest value (based on the ranges developed in this memorandum) was estimated to be about $7,500,000. The FMV of the property was in the range of $400,000. The overall public interest value was estimated to be $5,200,000. Client used this analysis to inform negotiations regarding the sale of their property. Confidential Client, Los Angeles Harbor, California — NRDA. Joe assisted in an in-depth statistical analysis of sediment toxicity PCB and DDT concentrations. This project entailed a thorough analysis of sediment concentration threshold data and the applicability of these data to represent thresholds in sediments of the Los Angeles Harbor area. This analysis was conducted to provide a sound scientific basis to refute the apparent effects threshold (AET) levels developed by a NOAA expert witness. Statistical Analysis, Lower Bayou d7nde and Calcasieu River Estuary. Joe examined the spatial and temporal trends of hexachlorobenzene, and hexachlorobutadiene Joseph Nicolette (HCB and HCBD, respectively), PCBs, and mercury in sediments and biological tissues in these waterways. Evaluated trend data in response to National Oceanic and Atmospheric Administration (NOAA) comments regarding analysis of the data. These data will be used in creating a proposed restoration -based compensation approach for the site using NEBA, HEA, and ROA. American Petroleum Institute Aquatic Toxicity Studies. Joe participated in a study to evaluate the toxicity of petroleum products on fish, invertebrates, algae, and zooplankton for use in NRDA's. The purpose of the project was to conduct a critical review of toxicity values for the purposes of identifying potential risks associated with released oil. This study screened about 8,000 references published on the fate and effects of petroleum in the environment. The study evaluated and compared toxicity values for a range of petroleum substances from crude oil to petroleum products. This analysis identified the large disparity between acute (LC50) values for a given compound for the same species and identified the likely causes associated with methods used to derive toxicity values. Conoco, Clooney Loop, Louisiana. Assisted in development of a net environmental benefits analysis to demonstrate that a less intrusive remedy for ethylene dichloride coupled with wetlands restoration resulted in greater environmental benefit than dredging remedy. Resulted in a cost savings of $19 million to Conoco. Exxon Valdez, Alaska. When the supertanker Exxon Valdez struck a rock reef and spilled approximately 11 million gallons of crude oil into Prince William Sound, Joe participated in a project to evaluate the impacts of the oil release on invertebrate populations in selected Prince William Sound areas. The purpose of the work was to collect data to contribute to a NEBA. Data were collected to describe the epifauna and epiflora, cryptic fauna, and infauna of oiled and reference beaches and to relate the composition, numbers, and vertical distribution of infauna to the amounts of subsurface oil. International Project Examples Landmark Enforcement Case, U.K. Joe provided technical oversight (2013) for a recent landmark 10 enforcement undertaking in the U.K. for a water pollution offence. This was the first enforcement undertaking accepted by the environment agency for a pollution incident. Joe led the technical effort for the client's external legal representation in assessing suitable actions to compensate for the environmental impacts of a river pollution incident. The technical basis for the resolution was the use of a resource equivalency analysis approach (i.e., habitat equivalency analysis — HEA). The Environment Agency accepted an enforcement undertaking as a practical alternative to a prosecution and fine. This was the first case in the U.K. to use this approach. Lago Maggiore, Italy Joe led a Comparative Analysis (CA) (e.g. a NEBA) to provide a formal quantification of the change in key ecosystem service values that would be associated with the implementation of a remedial action and compares those changes to costs and predicted changes in risk. The goal of a CA is to provide information to support the selection of remedial alternatives that maximize benefits to the public, while managing site risks and costs. Offshore Decommissioning O&G platforms and FPSO's in the North Sea and Western Australia Confidential Clients Joe was the Principal Investigator leading NEBA efforts associated with the decommissioning of offshore O&G platforms and FPSO's (Australia and the North Sea). These NEBA projects entail an understanding of the environmental conditions of the site; subsea infrastructure design, removal alternatives that can be considered for the site, ROV analysis, fate and transport of potential chemical releases and baseline conditions, ecological and human risk assessment evaluations for both potential chemical (NORM, petroleum, mercury, etc.) releases and physical impacts associated with removal alternative implementation; associated GHG emissions of removal alternatives; human use evaluation of the sites (e.g., commercial, recreational fisheries); Marine life and CITES evaluations; baseline and predictive impact assessment; and worker health and safety. Arctic NEBA, Oil and Gas Industry. Joe worked as part of an integrated team to develop a NEBA tool for oil spill response and planning for the Oil and Gas Producers 0 Joseph Nicolette (OGP) Joint Industry Group (JIP) related to oil spill response and planning in the Arctic. The overall goal of the Phase 2 Project is to help evaluate oil spill response technologies, their application in the Arctic environment, and develop a decision -making tool to limit environmental and social impacts in the event of an oil release. Ecosystem Services Analysis of Chlorpyrifos Use in Citrus Production, Spain Joe led an analysis of the effects on ecosystem services (ES) of using an agricultural insecticide (chlorpyrifos) in citrus orchards in south- eastern Spain to identify risk management actions and understand the consequences of the hypothetical situation of discontinued use of chlorpyrifos. Farmers rely on chlorpyrifos to limit the occurrence of skin blemishes on the surface of citrus fruit caused by scale insects. Citrus fruit is graded for sale based on size, shape and lack of skin blemishes under European marketing regulations; farmers only make a profit when unblemished fruit is sold in Europe. ES, including income, were compared for the current status and the hypothetical scenario. The study concluded that discontinuation of chlorpyrifos use would result in significant economic losses for the region when only a small change in current orchard management, such as using a vegetated conservation area for risk mitigation, could offset potential ecological impacts while maintaining farmer's income and the recreational value of orchards to local people. The study informed advocacy discussions between the client, scientists and policy makers responsible for pesticide authorisation in Europe. Ecosystem Services Analysis of an Agricultural Molluscicide used in the UK Joe led an analysis of the effects on ecosystem services (ES) of using an agricultural molluscicide in winter wheat and oilseed rape production in the UK. These are key crops and farmers in north- western Europe rely on the control of slugs using pelleted molluscicides for crop protection. The study compared marketing leading molluscicides and presented the costs and benefits of each across a range of ecosystem services, including crop production, habitat services (e.g. indicator farmland species), soil and drinking water services. The findings informed advocacy discussions with pesticide regulators at a national level and had implications for other environmental policies, such as the European Water Framework Directive. it Ecosystem Services Analysis of 1,3-Dichloropropene (1,3-D) Use in Tomato Production, Italy The use of 1,3- D as a soil fumigant for the control of damaging nematode pests in tomato production in Southern Italy and Sicily was evaluated using an ecosystem services approach. 1,3- D is currently approved for emergency use in Italian tomato cultivation, although its use is widespread and well established amongst growers. A field visit and interviews with farmers underpinned the socio-economic aspects of the study. The findings compared traditional chemical controls (1,3-D) with perceived `green' approaches to cultivation, such as soil solarisation (covering soil with plastic) and biofumigation (use of biological fumigants). The study provided an analysis of the costs and benefits to the environmental and socio-economic services for each alternative, which could be used to inform policy makers. Database Design and Structuring Examples Gulf of Mexico Baseline Database System Development - Deepwater Horizon (2010-2015). Based on his experience in NRDA combined with his large scale relational database training, design, and management, Joseph was assigned to serve as technical lead for the development of a baseline information management system incorporating ecological, chemical, physical, socioeconomic, and GIS mapping data in the Gulf of Mexico. This database system was used to evaluate the potential for natural resource damages associated with ecological and human use services in the Gulf of Mexico, and incorporated data from Florida, Alabama, Louisiana, Mississippi, and Texas. North Carolina Acid Deposition Program: USEPA Acid Deposition and Fisheries Populations in the Northeast U.S. Joe designed this database system that contains chemical, physical and biological data on fish populations potentially impacted by acidic deposition. The development of this complex database required integration and coordination of multiple databases across multiple U.S. states and development of a database structure to allow data transfer. The database was used by the USEPA in evaluating acid deposition. Adirondack Lakes Acid Deposition Data Management System. This is a nationally recognized database which incorporated physical, biological and chemical data Joseph Nicolette associated with over 1,700 lakes in New York State. The database was funded by the State of New York Department of Environmental Conservation (NYDEC) and the Empire State Electric Energy Corporation (ESEERCO). The Adirondack Lake database was used primarily for acid deposition assessment by the EPA and the National Acid Precipitation Assessment Program (NAPAP). Selected Publications and Project Reports Deacon, S., Norman, S., Nicolette, J., Reub, G., Greene, G., Osborn, R., and Andrews, P. 2015. Integrating ecosystem services into risk management decisions: Case study with Spanish citrus and the insecticide chlorpyrifos. Science of the Total Environment. Elsevier publishing. 505 (2015) 732-739. http://dx.doi.org/10.1016/j.scitotenv.2014.10.034 Nicolette, J., Burr, S., and Rockel, M. 2013. A Practical Approach for Demonstrating Environmental Sustainability and Stewardship through a Net Ecosystem Service Analysis. Sustainability 2013, 5, 2152-2177; doi:10.3390/su5052152, Published May 10, 2013. Nicolette, J., Goldsmith, B., Wenning, R., Barber, T., and Colombo, Fabio. 2013, Experience with Restoration of Environmental Damage. Chapter 9 of the book entitled "The E.U. Liability Directive: A Commentary", edited by L. Berkamp and B. Goldsmith. Published March 14, 2013, Oxford Press. Pages 181-219. Colombo, F., Nicolette, J., Wenning, R., and M. Travers. 2012. Incorporating Ecosystem Service Valuation in the Assessment of Risk and Remedy Implementation. Chemical Engineering Transactions, 28, 55-60 DOI: 10.3303/CET1228010 Nicolette, J. Rockel, M. and D. Pelletier. 2011. Incorporating Ecosystem Service Valuation into Remedial Decision -Making: Net Ecosystem Service Analysis. American Bar Association. Superfund and Natural Resource Damage Litigation Committee Newsletter, December 2011, Vol. 7. No. 1. S. Deacon, A. Goddard, N. Eury, and J. Nicolette. 2010. Assessing risks to ecosystems: Using a net 12 environmental benefit analysis framework to assist with environmental decision -making. In Restoration and Recovery: Regenerating Land and Communities. British Land Reclamation Society. Whittles Publishing, Scotland, U.K. pages 164-175. A. Campioni, J. Nicolette and V. Magar. 2010. Riparazione danno ambientale: valutazione tecnica/economica. Rifiuti e bonifiche: www.ambientesicurezza.ilsole24ore.com. Pages 57- 60. S. Deacon, N. Eury, J. Nicolette, and M. Travers. 2010. The European Environmental Liability Directive and Comparisons with U.S. Natural Resource Damage Assessment Regulations. Air and Waste Management Association. Nicolette, J. 2008. Contributing Author. Use of net environmental benefit analysis to demonstrate the Net Benefit of Site Cleanup Actions: An Evaluation of Ecological and Economic Metrics at Two Superfund Sites. US. EPA Pilot Studies Report. Nicolette, J. 2003-2004 Contributing Author.: Net environmental benefit analyses (NEBA) for 4 Army BRAC Sites: Fort McClellan, AL, Savanna Army Depot, IL and Camp Bonneville, WA. Four separate technical study reports). Technical Reports. Efroymson, R., Nicolette, J. and Suter, G. 2004. A Framework for net environmental benefit analysis for remediation or restoration of contaminated sites. Environmental Management. September 2004. Vol. 34: (3): pp 315-331. Efroymson, R.A., Nicolette, J. and Suter II, G.W. (2003). A Framework for Net Environmental Benefit Analysis for Remediation or Restoration of Petroleum -Contaminated Sites. Oak Ridge National Laboratory (TM-2003/17), Oak Ridge, Tennessee. Nicolette, J., Rockel, M. and Kealy M. 2001. Quantifying ecological changes helps determine best mitigation. Pipeline and Gas Industry Magazine. September 2001. 0 Joseph Nicolette CH2MHILL/Marstel-Day, Inc. Contributing Author (2003-2004): Net environmental benefit analyses (NEBA) for Fort McClellan, AL, Savanna Army Depot, IL and Camp Bonneville, WA. Technical Reports. CH2MHILL. Contributing Author (2003-2004): Net environmental benefit analyses for OU6 and OU1 at Edwards Air Force Base, CA. Technical Reports. CH2M Hill. 2000. Environmental Assessment and Closure Report, Goose Creek, Knoxville, Tennessee Release Site. Prepared for the Tennessee Department of Environmental Conservation on Behalf of Colonial Pipeline Company, Inc. CH2M Hill. 2000. Draft Response Action Plan. Terminal Road Site, Newington, Virginia. Prepared for the Plantation Pipeline Company. February 4. CH2M Hill. 2000. Contributing Author. Benthic Macroinvertebrate Sampling Plan, Rich Fork Release, North Carolina. Prepared for the Plantation Pipeline Company. CH2M Hill. 2000. Contributing Author. Evaluation of the Koch Hastings terminal Site with Regards to an NRDA. Prepared for Koch Petroleum. CH2M Hill. 1999-2001. Contributing Author. Quarterly Monitoring Reports aor the Darling Creek, Louisiana Site. Prepared for Colonial Pipeline Company, Inc. Quarterly Reports Beginning in First Quarter of 1999 Through 2000. CH2M Hill. 1999. Contributing Author. Net Environmental Benefits Analysis of Treatability Studies. Prepared for NASA (Marshall Space Flight Center) Superfund Site. CH2M Hill and Peacock, B. 1999. Contributing Author. Final Restoration Plan and Environmental Assessment. Prepared for Colonial Pipeline and the Natural Resource Trustees. Final Restoration Plan for the Sugarland Run Oil Spill, NRDA case. 13 CH2M Hill 1999. Contributing Author. Preliminary Natural Resources Asset and Liability Management Study. Prepared for Pacific Gas and Electric Company (PG&E). Entrix, Nicolette Environmental, Inc., and Peacock, B. 1998. Draft Restoration Plan and Environmental Assessment. Prepared for Colonial Pipeline and the Natural Resource Trustees. Draft Restoration Plan (for public review) for the Sugarland Run Oil Spill, NRDA case. Entrix and Nicolette Environmental, Inc. 1998c. Reedy River Fisheries Population Assessment. Conducted as Part of the Natural Resource Damage Assessment, Report (Volumes I and II). Prepared for the Natural Resource Trustees and Colonial Pipeline Company, Inc. Project Number 773712. Draft. April 3. Entrix and Nicolette Environmental, Inc. 1998d. Reedy River Ecological Assessment Studies: Conducted as part of the Reedy River NRDA. Prepared for the Natural Resource Trustees and Colonial Pipeline Company, Inc. Entrix. 1998a. Contributing Author. Water Quality Assessment: Conducted in Response to the Reedy River Incident. Prepared for the Natural Resource Trustees and Colonial Pipeline Company, Inc. Project Number 773712. Draft April 3. Entrix. 1998b. Contributing Author. Reedy River Aquatic Macroinvertebrate Assessment: Conducted as Part of the Natural Resource Damage Assessment. Prepared for the Natural Resource Trustees and Colonial Pipeline Company, Inc. Project Number 773712. Draft April 3. Entrix. 1998c. Contributing Author. Preliminary Recreational Lost Use and Compensatory Restoration Assessment: Conducted as Part of the Natural Resource Damage Assessment. Prepared for the Natural Resource Trustees and Colonial Pipeline Company, Inc. Project Number 773712. Draft April 3. Entrix and Nicolette Environmental, Inc. 1998a. Reedy River Corbicula Population and PAH Bioaccumulation Assessment: Conducted as Part of 0 Joseph Nicolette the Natural Resource Damage Assessment. Prepared for the Natural Resource Trustees and Colonial Pipeline Company, Inc. Project Number 773712. Draft. April 3. Entrix and Nicolette Environmental, Inc. 1998b. Reedy River Crayfish Population and PAH Bioaccumulation Assessment — Oct 1996 and Sep 1997: Conducted as Part of the Natural Resource Damage Assessment. Prepared for the Natural Resource Trustees and Colonial Pipeline Company, Inc. Project Number 773712. Draft. Apr 3. Entrix and Nicolette Environmental, Inc. 1997. Quality Assurance Project Plan for the Reedy River Incident. Entrix. 1996a. Contributing Author. Reedy River Natural Resource Damage Assessment: Current Status and Proposed Approach. Prepared for the Natural Resource Trustees and Colonial Pipeline Company, Inc. Project Number 773712. Draft September 11. Entrix. 1996b. Contributing Author. Assessing Potential Injury to Wildlife Along the Reedy River: A discussion Prepared as Part of the Natural Resource Damage Assessment. Prepared for the Natural Resource Trustees and Colonial Pipeline Company, Inc. Project Number 773712. Draft October 15. Entrix. 1996. Contributing Author. Natural Resource Credit Analysis for a Confidential Site. Based upon the Habitat Equivalency Analysis Framework. Confidential Client. Markarian, R.K., J.P. Nicolette, T. Barber, and L. Giese. 1995. A Critical Review of Toxicity Values and an Evaluation of the Persistence of Petroleum Products for Use in Natural Resource Damage Assessments. American Petroleum Institute (API) Publication Number 4594. January. Nicolette, J.P., T.R. Barber, R.K. Markarian, T.W. Cervino, and D.V. Pearson. 1995. A Cooperative Natural Resource Damage Assessment (NRDA) case study: Coloial pipeline release, Reston, Virginia. Proceedings: Toxic Substances in Water Environments. Assessment and Control: Water Environment Federation. Pp. 7-21 to 7-32. May. 14 Entrix. 1995. Contributing Author. Analytical Chemistry Results: Oyster and Sediment Samples from the Quinnipiac River, Connecticut. Prepared for Buckeye Pipeline Company and the Connecticut State Department of Agriculture. Entrix. 1995.c Contributing Author. Habitat Equivalency Analysis: Overview, Definition of Terms, and Debit Calculations for Sugarland Run, NRDA. Prepared for Colonial Pipeline Company and the Natural Resource Trustees. Entrix. 1994. Contributing Author. Addendum to the Fisheries Injury Assessment for Sugarland Run. Conducted as Part of the Natural Resource Damage Assessment. Prepared for Colonial Pipeline Company, Inc. and the Natural Resource Trustees. Entrix, 1994. Contributing Author. Mass Balance of Spilled Diesel: Simulation Modeling. Conducted as Part of the Natural Resource Damage Assessment. Prepared for Colonial Pipeline Company, Inc. and the Natural Resource Trustees. Entrix. 1994. Contributing Author. Fish Population Survey and Injury Assessment for Sugarland Run: Conducted as Part of the Natural Resource Damage Assessment. Prepared for Colonial Pipeline Company, Inc. and the Natural Resource Trustees. Entrix. 1994. Contributing Author. Benthic Macroinvertebrate Survey of Sugarland Run, Broad Run, and the Potomac River: Conducted as Part of the Natural Resource Damage Assessment. For Colonial Pipeline Company, Inc. and the Natural Resource Trustees. Entrix. 1994. Contributing Author. Corbicula PAH Bioaccumulation Study for Sugarland Run and the Potomac River: Conducted as Part of the Natural Resource Damage Assessment. For Colonial Pipeline Company, Inc. and the Natural Resource Trustees. Entrix. 1994. Contributing Author. Preliminary Survey: Analytical Chemistry Data Analysis for Sugarland Run and the Potomac River: Conducted as Part of the Natural Resource Damage Assessment. Prepared 0 Joseph Nicolette for Colonial Pipeline Company, Inc. and the Natural Resource Trustees. Entrix. 1994. Contributing Author. Vegetation Survey and Natural Communities of the Potomac River from Lowes Island to Chicamuxen Creek. Prepared for Colonial Pipeline Company, Inc., the USEPA, and the Natural Resource Trustees. Entrix. 1993. Contributing Author. NRDA Study Plan and Status: Sugarland Run and the Potomac River: Prepared for Colonial Pipeline Company, Inc. and the Natural Resource Trustees. Entrix. 1993. Contributing Author. Response Action Plan for Remediation of Spilled Diesel. Prepared for Colonial Pipeline Company, Inc. Entrix. 1993. Contributing Author. Quality Assurance Project Plan. Supplement to the Response Action Plan. Specific action to address Section 10.1 of the EPA Unilateral Order. Prepared for the Colonial Pipeline Company, Inc. Entrix. 1993. Contributing Author. Preliminary Survey Plan for Sugarland Run and the Potomac River in response to the EPA Administrative Order regarding the Sugarland Run oil spill. Prepared for Colonial Pipeline Company, Inc. and the NR Trustees. Entrix. 1993. Contributing Author. Sediment and Water Quality Toxicity Tests for Sugarland Run. Conducted as part of the Natural Resource Damage Assessment. Prepared for Colonial Pipeline Company, Inc. and the Natural Resource Trustees. Entrix. 1992. Contributing Author. Oil Spill Response and Impact Assessment Report for British Petroleum (BP). Norfolk, Virginia Crude Oil Release. Entrix. 1992. Oil and Oil Product Toxicity Database User's Guide for OILTOX. Prepared for the American Petroleum Institute. Entrix. 1991. Contributing Author. Instream Flow Study and Gravel Enhancement on the White Salmon 15 River, Washington. FERC Relicensing Agency Review Document. Entrix. 1990. Contributing Author. Biological Data Report on the Intertidal Communities of Prince William Sound. Prepared for Exxon. Part of an evaluation of the environmental costs associated with oil cleanup in Prince William Sound as a result of the Exxon Valdez Oil Spill. EA Engineering, Science and Technology, Inc. 1989. Contributing Author. Pilot Study Report -Predation by Piscivorous Fish in the Lower Tuolumne River, 1989. 1991. Report for the Turlock -Modesto Irrigation District. EA Engineering, Science and Technology, Inc. 1989. Contributing Author. An Evaluation of the Effect of Gravel Ripping on Redd Distribution in the Lower Tuolumne River. Report for the Turlock -Modesto Irrigation District. EA Engineering, Science and Technology, Inc. 1989. Contributing Author. Tuolumne River Summer Flow Study. Report for the Turlock -Modesto Irrigation District. EA Engineering, Science and Technology, Inc. 1989. Contributing Author. Chinook Salmon Redd Excavations. Report for the Turlock -Modesto Irrigation District. EA Engineering, Science and Technology, Inc. 1989. Contributing Author. Adirondack Lakes Study: 1984-1987: An Evaluation of Fish Communities and Water Chemistry. Adirondack Lakes Survey Corp., NY. Final Report. Nicolette, J.P. and G.R. Spangler. 1986. Contributing Author. Population Characteristics of Adult Pink Salmon in Two Minnesota Tributaries to Lake Superior. Journal of Great Lakes Research. 12(4):237-250. Bagdovitz, M., W. Taylor, J. Nicolette, and G.R. Spangler. 1986. Contributing Author. Pink Salmon Populations in the U.S. Waters of Lake Superior, Joseph Nicolette 1981-1984. Journal of Great Lakes Research. 12(1):72-81. Nicolette, J.P. 1984. Contributing Author. A Three - Year -Old Pink Salmon In An Odd -Year Run in Lake Superior." North American Journal of Fisheries Management. Vol. 4(1):130-132. Baker, J, Harvey, T. and J.P. Nicolette. 1984. Contributing Author. Compilation of Available Data on the Status of Fish Populations in Regions of the Eastern U.S. Final Report to the Environmental Protection Agency, Corvallis, OR. Nicolette, J.P. 1983. Contributing Author. Population Dynamics of Pink Salmon in Two Minnesota Tributaries to Lake Superior. M.S. Thesis. University of Minnesota, St. Paul, MN. 94 p. 0