Quarry Permit Ap 92-10 Noise

STEWART
ACOUSTICAL
CONSULTANTS
July 13, 2020
Mr. Brian Wrenn
Director - Division of Energy, Mineral and Land Resources
NC Department of Environmental Quality
612 Mail Service Center
Raleigh, North Carolina 27699-1612
and
Mr. Daniel Sams
Regional Engineer -Wilmington Regional Office
NC Department of Environmental Quality
127 Cargo Dr Extension
Wilmington, NC 28405
Phone: [phone]
7330 Chapel Hill Rd, Ste 201
Raleigh, NC 27607
www.sacnc.com
Re: Permit Application 92-10 - Comments on Noise of Wake Stone Quarry Expansion at
Umstead Park
Dear Mr. Wrenn and Mr. Sams:
I am Noral D. Stewart, PhD, President and Senior Principal Consultant, Stewart Acoustical
Consultants, 7330 Chapel Hill Road, Raleigh, NC 27607. 1 am writing as a consultant to the
Mattox Law Firm and their client The Umstead Coalition.
I have attached a one -page summary of my expertise and educational background (1)*. 1 received
my education - specialized in acoustics - in the Department of Mechanical and Aerospace
Engineering at N. C. State University. Aside from a brief 4-year hiatus, I have been continually
involved in the study and application of acoustics since 1967. 1 am a Fellow of the Acoustical
Society of America and ASTM International, a Past President of the National Council of Acoustical
Consultants, and the fourth recipient of the Laymon Miller Medal for Excellence in Acoustical
Consulting.
My consulting experience includes substantial work for two major quarry companies on many
sites, some work for a third, and two projects for the NC Division of Land Resources Management
(NCDLR) regarding quarry issues. The first of those involved the original hearings before the
Mining Commission for the Wake Stone Quarry in 1980 when I made measurements and
recordings in support of the NCDLR case to deny the permit.
My quarry experience has involved many appearances before local government boards in support
of quarry permit applications and a few against. In those cases, it was always my experience that
the burden of proof was on the applicant to demonstrate that the proposal met the criteria for
approval.
In both cases where I worked for NCDLR in 1980 and in 2000 for Mr. Stephen Conrad and Mr.
Charles Gardner, I was clearly told that that it was the responsibility of the state mining regulatory
agency to protect park lands from significant impacts from noise, though the state agency deferred
to local regulators regarding noise impacts on other properties.
* References listed at end and attached.
Member Firm - National Council of Acoustical Consultants

Quarry Noise Impact Page 2 July 13, 2020
Based on my experience with quarries and review of the permit application (92-10), it is my
professional opinion that the quarry expansion as proposed will have a significant adverse
impact on Umstead State Park due to noise. It is also my professional opinion that the
permit application as submitted does not present adequate steps to prevent such impact
or evidence that the steps proposed will prevent such impact.
There are two kinds of park spaces: one of which is used for sound producing activities such as
sports competitions or concerts, and another which is a place for quiet contemplation and
enjoyment of the quiet sounds of nature. Umstead is of this second type though challenged by
neighboring activities. For National Parks of this nature, the National Park Service (NPS) has
developed a strong plan for monitoring sound and development of park specific plans to control
the intrusion of man-made sounds from either outside the park or by park visitors. A summary of
NPS's monitoring work as of 2010 (2) and a more recent update (3) are attached along with the
NPS policy on noise (4). The NPS emphasizes the audibility of intrusive sounds which depends
on the level of the intrusive sound relative to the existing background sound, and for some sounds,
distinctive characteristics of those sounds. For instance, backup alarms, some drills, and jaw
crushers crushing hard rock, produce distinctive sounds that can be heard at much lower levels
than non -distinctive sound. Therefore, NPS has not established sound criteria as absolute sound
levels. This corresponds to the recognition by manufacturers of products that it is the way that a
product sounds, its sound quality, that is more important than sound level unless the level is very
high. Today, in the environmental noise world, this is incorporated in the concept of the
"soundscape," or making an environment sound the way people expect it to sound.
When the existing quarry was approved by the Mining Commission, several conditions were
attached intended to reduce noise impact on the park. There was no requirement that the quarry
carefully evaluate the design of barriers or berms, or machinery modifications or equipment
locations on the sound propagation. There is no evidence this was ever done. There was
discussion about the drawing of noise contours showing the propagation of sound during the
approval process, but such was a very difficult process at that time, and it was not required. While
one condition was location of processing equipment at the lowest practical elevation, the
proposed and approved location was at a high elevation. When viewed from across Crabtree
Creek north of the pit, there is a high rise from the creek that might be assumed to be a berm.
However, it is the original topography and there is a similar rise in elevation on the north side of
the creek. Appropriately high berms were built to the east of the pit.
For the first year after the opening of the quarry in 1982 and 83, the Division of Parks and
Recreation conducted extensive sound monitoring in the park and concluded that the quarry was
clearly heard and noise levels and their impact on the park were increasing. The mining regulatory
agencies have visited the quarry since opening and generally reported compliance with the
conditions of the permit. However, the people doing these inspections have no acoustical
expertise or qualifications to evaluate whether the steps taken are acoustically effective and have
not monitored sound.
It has been almost 40 years since the quarry was first permitted. The world and our knowledge
and ability to deal with noise has changed greatly. In 1980 it was thought that vegetation was
much more effective in attenuation sound than it actually is. We now know that much of the
benefit attributed to vegetation is actually due to soft ground. We also know that most or all of
the noise buffering benefits of soft ground are lost when a berm or barrier is inserted, so a net
benefit of the barrier must be evaluated. In 1980, there was some understanding of the effects of
winds and temperature gradients to curve sound upward or downward. However, this knowledge
had not been widely applied to berms and barriers. We now have a much better understanding

Quarry Noise Impact Page 3 July 13, 2020
of atmospheric effects related to winds and temperature gradients that cause the sound to curve
upward or downward. We know that at distances beyond 1000 feet from a source, the sound
level can vary by more than 20 dB due to variations in atmospheric conditions. In some cases, a
sound can go from inaudible to very clear in a period of less than 30 minutes as atmospheric
conditions change. These atmospheric effects can make low barriers and berms totally ineffective
during the times when the sound propagation is strongest. In 1980, we did not have modern
backup alarms designed to minimize long range propagation and concentrate the warning where
it is actually needed. Finally, 40 years ago, plotting contours of sound propagation was
impractical. Today we have computers and extremely capable software designed to do just this.
In sum, we now have much greater ability to evaluate the spread of sound and to figure out ways
to limit that spread. The industry standard for noise evaluation has progressed from the pencil
and paper calculations of 1980 to computer calculations undreamed of then. Among the general
sound analysis programs that can be applied to a quarry are SoundPlan and Cadna-A. The
Oregon Department of Transportation with the help of the US DOT has developed a computer
model specifically to evaluate noise from quarries (5). Today it is common to use such computer
programs to model the noise propagation from proposed quarries to surrounding areas. Two
papers are attached (6,7) that discuss modern modeling and investigation of quarry noise.
So, what do we have before us in Wake Stone's mining permit application? There are many
problems that are simply not properly evaluated and addressed.
1. The site is clearly a very challenging one. Quarry companies I have worked with whenever
possible prefer a site where they can have a large buffer on their property around the
operation. This site has insufficient land area to provide adequate buffers and Wake Stone
proposes to place the pit right at the boundaries with very small buffer areas, even smaller
than those for the existing pit.
2. The site begs for high berms on the order of 40 feet to protect adjacent areas, yet only 15-
foot berms are proposed. Such berms at least during early stages of the quarry clearing
and operation will have little benefit beyond a few hundred feet from them much of the
time.
3. Crabtree creek presents a challenge as it creates a gap in any berm protection plan.
Sound from trucks crossing the bridge and possibly other sound could propagate down
the channel created by the creek valley reflecting from the water in the creek.
4. There is a road around the north crest of the existing pit approved in 1991 that is now
proposed to be improved so it can be used for heavy haul trucks. A 14-foot highway type
noise wall is proposed. However, as with highway barriers, this will have little if any benefit
much of the time a few hundred feet away in the park across the creek. This road needs
to follow a lower elevation path so the high crest where it is proposed will serve as a
barrier.
5. One of the major irritating sounds from quarries is backup alarms. The traditional alarms
generate a tonal beep at around 1200 Hz. This propagates as a distinct noticeable sound
for long distances. I remember hearing such alarms clearly a half -mile away in 1980 when
doing measurements around a quarry to prepare for hearings. These alarms not only
irritate people who have no need to hear them, they also create confusion among people
who do need to be warned when they are always hearing many alarms. New alarms use
high-pitched sound over a broader frequency range that is directional to the area behind
the truck and does not spread strongly over long distance. DEMLR should consider
mandating these modern alarm systems, at least for off -road vehicles that remain on site.

Quarry Noise Impact Page 4 July 13, 2020
6. Wake Stone proposes to keep existing processing equipment at its current location, and
some of it at a point of high elevation where it is difficult to shield the park from it. Although
the equipment is not moving, the granting of the permit modification will extend these noise
impacts for at least 20 years beyond the life of the existing Triangle Quarry. This
equipment is at the location indicated in the initial permit, though that permit also said the
"the plant shall be located at the lowest feasible elevation." The primary and secondary
crushers have been located lower in the pit but much equipment remains at the high
elevation. Nothing is said about evaluating the impact of it on the park or anything that
could be done to reduce that impact. Some of this equipment remaining up higher is of
types that have been enclosed at other quarries to reduce noise.
7. Since the original quarry opened, new criteria for blast ground vibration, more restrictive
than those of 1980, have been developed and imposed on quarries. These and the
"overpressure" or sound criteria for blasting are based on preventing structural damage
rather than disturbance of natural quiet. Quarries do have ways to control the peak of the
vibration and sound by spreading the blast over a short period and typically do that. This
is a sensitive location, not only due to the park but also due to an extremely close house.
There is nothing in the permit application about any extra care in blasting due to these
conditions. The application says blasting will be controlled to avoid damage to structures
500 feet away. However, the closest home is potentially within 300 feet of a blast site for
which a permit is sought and even closer to the less defined "future reserves."
Wake Stone's current mining permit application is devoid of an adequate noise mitigation plan.
Wake Stone must be required to submit a carefully developed analysis evaluating noise impacts
and including strategies to implement effective controls to minimize those noise impacts. The
plan must be developed and documented by recognized experts hired by the proponents and
reviewed by recognized experts hired by the agency. The permit must not be granted unless it
can clearly be concluded that adequate conditions are imposed and will be enforced to assure no
significant adverse impact on the park.
Sincerely,
STEWART ACOUSTICAL CONSULTANTS
Noral D. Stewart, PhD FASA FASTM INCE
Attachments
1. Resume Noral D. Stewart
2. An assessment of noise audibility and sound levels in U.S. National Parks
3. Anthropogenic noise in US national parks — sources and spatial extent
4. NPS Director's order #47: Soundscape preservation and noise management
5. Quarry noise model (Oregon DOT)
6. Proactive noise control at a rock quarry next to a residential neighborhood
7. Novel approach to visualization of SoundPLAN data for analysis of mining noise

STEWART
ACOUSTICAL
CONSULTANTS
Noral D. Stewart
Senior Principal Consultant
Phone: [phone]
7330 Chapel Hill Rd, Ste 201
Raleigh, NC 27607
www.sacnc.com
Education: BSME (with honors) - 1969, MSME - 1974, PhD - 1981
Department of Mechanical & Aerospace Engineering, North Carolina State University at Raleigh
Specialization in acoustics and noise control at all levels
Honors and Recognitions: Laymon N. Miller Medal for Excellence in Acoustical Consulting
Fellow of the Acoustical Society of America Award of Merit- Fellow of ASTM International
NCSU Mechanical and Aerospace Engineering - Hall of Fame - 2013 Inaugural Class
ASTM E33 Wallace Waterfall Award Phi Kappa Phi Tau Beta Pi Pi Tau Sigma
Technical and Professional Society Membership and Activities
National Council of Acoustical Consultants
President - 00-02, Pres. Elect 98-00, VP 96-98, Board of Directors 94-04
Bylaws Chair 05-current Long -Range Planning Committee - 00-Life, Chair 02-04
Acoustical Society of America
Member - Technical Committee on Architectural Acoustics 96-20
Member - Technical Committee on Noise 95-10, 11-18
North Carolina Chapter Chair 79-80, Sec -Treasurer 77-79, 83-91, Treasurer 91-96
Institute of Noise Control Engineering
Co -Chairman - 1981 National Conference - NOISE-CON81
Papers review for Noise Control Engineering Journal
ASME International
Papers review for various technical divisions
Aircraft Noise Subcommittee, Transportation Research Board
Standards Activities
ASTM International Committee E33 on Building and Environmental Acoustics
Vice Chair - 04-09, 12-17 - Chair - Subcommittee E33.05 on Research 11 -
Chair - Task Groups for standards E336, E1686, E557, E1332, and E2964
Acoustics Proposal Review Committee of Facilities Guideline Institute (Medical Facilities) 2015 -
ASA-ANSI Committee S12 Working Group on Classroom Acoustics Standard S12.60
Consulting Experience: Consulting since 1977, Full time 1981-2016.
Consulting activities have covered the broad spectrum of problems involved in architectural
acoustics, community and environmental noise, and industrial noise control. Largest acoustical
consulting firm headquartered in the area between Washington, DC and Atlanta, GA.
Expert Witness: Over 30 cases in Federal and state courts of NC, SC, & TN, and in arbitration.
Publications and Presentations:
Co -Author of chapter on Community Noise in the AIHA Noise Manual
Six Refereed Journal Papers Seven papers in Proceedings and Magazines
Sixteen Invited and Ten Contributed Presentations at National & International Conferences
Primary Author of Two ASTM standards and major revisions of another
Ten training classes including national teleconference on Community Noise for ASHA
Thirty Presentations to Regional Organizations
Special Invited Activities:
Co -Editor of Proceedings and Co -Chair of Noise -Con 81 National Noise Control Conference
Principal Noise Control Expert 1990 NIH Consensus Conference on Noise and Hearing Loss
Represented all major acoustical organizations before International Codes Council 2011
Invited by National Academies of Engineering to discuss future of industrial noise control in 2014
Member Firm - National Council of Acoustical Consultants

Landscape Ecol (2011) 26:1297-1309
DOI 10.1007/s 10980-011-9643-x
RESEARCH ARTICLE
An assessment of noise audibility and sound levels in U.S.
National Parks
Emma Lynch • Damon Joyce • Kurt Fristrup
Received: 2 December 2010 / Accepted: 10 August 2011 / Published online: 25 August 2011
© Springer Science+Business Media B.V. (outside the USA) 2011
Abstract Throughout the United States, opportuni-
ties to experience noise -free intervals are disappearing.
Rapidly increasing energy development, infrastructure
expansion, and urbanization continue to fragment the
acoustical landscape. Within this context, the National
Park Service endeavors to protect acoustical resources
because they are essential to park ecology and central
to the visitor experience. The Park Service monitors
acoustical resources in order to determine current
conditions, and forecast the effects of potential man-
agement decisions. By community noise standards,
background sound levels in parks are relatively low. By
wilderness criteria, levels of noise audibility are
remarkably high. A large percentage of the noise
sources measured in national parks (such as highways
or commercial jet traffic) originates outside park
boundaries and beyond the management jurisdiction
of NPS. Many parks have adopted noise mitigation
plans, but the regional and national scales of most noise
sources call for conservation and management efforts
on similar scales.
Keywords National parks • Acoustical monitoring
Noise • Acoustical resources • Natural quiet
E. Lynch (®) • D. Joyce • K. Fristrup
U.S. National Park Service, Natural Sounds and Night
Skies Division, 1201 Oakridge Drive, Suite 100,
Fort Collins, CO 80525, USA
e-mail: Emma—[e-mail]
Introduction
Anthropogenic noise is arguably one of the least
understood and most common threats to resources in
national parks. Burgeoning energy development,
infrastructure expansion, and urbanization create
expansive noise footprints that fragment the acous-
tical landscape and restrict naturally quiet conditions
to relatively brief intervals of the day in many
protected natural areas. Acoustical resources are
conserved or restored by the National Park Service
(NPS) because they are crucial to ecological integrity
and important for visitor experience. NPS is required
by law and management policies to protect the
acoustical environment.
Stewardship of acoustical resources requires sys-
tematic acoustical monitoring to determine the cur-
rent status of resources, identify trends in resource
conditions, and inform management decisions regard-
ing desired future conditions. This paper summarizes
the acoustical conditions in several parks in the
National Park system, and identifies salient patterns
in these data.
Acoustical resource management in the National
Park Service
The need for resource protection in national parks
was first articulated in the National Park Service
Organic Act of 1916, which stated that the purpose of
national parks is "... to conserve the scenery and the
Q Springer

1298
Landscape Ecol (2011) 26:1297-1309
natural and historic objects and the wild life therein
and to provide for the enjoyment of the same in such
manner and by such means as will leave them
unimpaired for the enjoyment of future generations."
The first Congressional reference to acoustical
resources is in the Grand Canyon Enlargement Act of
1975, which explicitly identified "natural quiet" as a
resource to be protected for future generations. In this
case, Congress recognized the conflict between the
demand for air tours over Grand Canyon and the
resource degradation that visitors on the ground
experienced. The Redwoods Act of 1978 addressed
potential conflicts between visitor use and resource
protection by affirming that, "the protection, man-
agement, and administration of these areas shall be
conducted in light of the high value and integrity of
the National Park System and shall not be exercised
in derogation of the values and purposes for which
these various areas have been established, except as
may have been or shall be directly and specifically
provided by Congress." In 1987, Congress focused
specific attention on aircraft flights over park lands
when it passed the National Parks Overflights Act
(Public Law 100-91). This act mandated that the Park
Service conduct a number of studies related to the
effects of overflights on parks, and directed the NPS
to report results to Congress. The Natural Sounds
Program, a national NPS office, was established in
2000, with the passing of the National Parks Air Tour
Management Act (NPATMA). NPATMA mandated
that FAA and NPS jointly develop Air Tour Man-
agement Plans (ATMPs) for more than 106 parks
where commercial air tours operate.
Effects of noise on visitor experience
The founding documents of the NPS state that parks
were created for the purpose of preserving resources
for the enjoyment of present and future generations.
Like scenic vistas, clean air, or pristine bodies of
water, natural sounds are considered a precious
natural resource worthy of protection by the NPS.
Any "noise," or human -caused sound that masks or
degrades natural sounds is a threat to the acoustical
environment (which we define as the complete set of
physical sound resources intrinsic to a park). Many
people visit national parks with the hope and expec-
tation of experiencing natural sounds, and noise
degrades their chance to experience the cultural,
�L Springer
historical, and natural features that parks offer. A
1998 survey of the American public revealed that 72
percent of respondents thought that providing oppor-
tunities to experience natural quiet and the sounds of
nature was a very important reason for having national
parks, while another 23 percent thought that it was
somewhat important (Haas and Wakefield 1998. In
another survey specific to park visitors, 91 percent of
respondents considered enjoyment of natural quiet
and the sounds of nature to be compelling reasons for
visiting national parks (McDonald et al. 1995).
The need to preserve the acoustical environment for
the benefit of the visitor experience is further amplified
in wilderness areas, where managers endeavor to
provide opportunities for solitude. Separation from the
sights and noise originating outside wilderness is one
of the primary indicators used to gauge success in
wilderness preservation (Dawson 2004). Noise can
even affect visitors who are not actively listening to the
environment, and who may not explicitly perceive the
noise. For instance, a 2008 study found that noise from
traffic and aircraft caused involuntary physiological
responses (increased blood pressure and heart rate) in
sleeping humans (Haralabidis et al. 2008). Parks are
critical fora for education, and can inform visitors of
all ages about the importance of natural resource
protection. However, noise can elevate ambient sound
levels in parks above the recommended conditions for
classrooms (35 dB(A), ANSI Standard 512.60), mak-
ing it difficult for park educational and interpretive
presentations to reach their audience. Degraded com-
munication can also elevate risk for park staff or
visitors who are engaged in potentially hazardous
activities, when shared information and coordinated
actions are essential for safety (e. g. search and rescue,
climbing, or canyoneering).
Effects of noise on wildlife
Hearing is likely more vital for wildlife than park
visitors. In addition to auditory communication, ani-
mals rely on sounds to gather many kinds of important
environmental information. Adventitious sounds can
alert attentive listeners to the location, identity, and
behavior of other animals, including predators, com-
petitors exploiting an important resource, rivals in
mating systems, and potential prey. Physical environ-
mental features may also be revealed by the sounds
they produce (changing weather, flowing water, fire).

Landscape Ecol (2011) 26:1297-1309
1299
Noise can interfere with animal acoustical aware-
ness in several ways. Very loud sounds can temporar-
ily deafen animals. Less dramatic noise events can
distract attention or introduce clutter to the acoustical
environment. Noise adds energy to existing sound
levels, effectively reducing the range at which signals
can be detected, identified, and localized (masking).
Masking can take place even if the animals do not react
to, or even perceive, the noise source. In general,
sounds are more easily masked by other sounds with
similar acoustical properties (e.g. center frequency,
bandwidth). It should be noted that the effects of
masking extend beyond intentional communication
(between members of a single species). Many verte-
brate species have been shown to "eavesdrop" on the
communications between other species, as in the case
of gray squirrels (Sciurus caroninensis) listening to
blue jay (Cyanocitta cristata) calls to determine risk of
cache pilfering (Schmidt and Ostfeld 2008).
Prolonged exposure to noise has been shown to
cause wildlife to avoid certain areas, reducing already
limited potential habitat. Sonoran pronghorn antelope,
mule deer, and sage grouse have been shown to
preferentially select habitat with less noise from human
activity (Landon et al. 2003; Sawyer et al. 2006;
Doherty et al. 2008). Studies of songbird behavior and
ecology near oil and gas development found a signif-
icant reduction in pairing success, bird density, and bird
species diversity caused by noise (Habib et al. 2007;
Bayne et al. 2008). Development inside national parks
is managed to avoid unacceptable impacts to resources,
but noise can have substantial effects on habitat quality,
species distribution and demographic parameters.
To adequately understand and protect acoustical
resources, the park service conducts acoustical mon-
itoring to determine the status of acoustic resources,
track trends in resource conditions, and inform
management decisions. This paper presents monitor-
ing and analysis protocols, summarizes the acoustical
conditions in several parks in the National Park
system, identifies significant patterns in these data,
and discusses ways parks have incorporated acousti-
cal data into management actions.
Methods
Acoustical monitoring equipment is widely utilized
to ensure compliance with industrial health and
safety, community environmental standards, and
architectural standards for indoor spaces. The Natural
Sounds and Night Skies Division of the Park Service
utilizes similar instruments, but high standards for
resource condition and visitor experience call for
different monitoring practices and objectives. Fur-
thermore, the harsh weather conditions encountered
during long deployments in national parks (ranging
from summer in Death Valley National Park and
Preserve to winter in Kenai Fjords National Park),
and high probability of wildlife encounters demand
entirely new system configurations. These monitoring
systems gather long-term data about acoustical con-
ditions in parks and provide vital metrics such as
existing- and natural- ambient sound levels.
Equipment
National Park Service acoustical monitoring equip-
ment has evolved over three distinct generations. All
three types were employed in the collection of data
referenced in this paper. The common denominators
among the generations are ANSI Type 1 sound level
meters (SLMs) using '/z" measurement microphones.
Microphones were deployed with environmental
housings and wind screens at approximately 1.5 m
above ground (approximating the average height of
the human ear). Each second, the SLMs collected 33
1/3 octave sound pressure level (SPL) measurements
(in decibels, or dB) from 12.5 to 20,000 Hz, which
encompasses the nominal range of human hearing.
Generation I acoustical monitoring equipment con-
sisted of a Larson Davis 824 SLM streaming data to a
laptop computer. An anemometer was collocated to
record local wind speeds over the monitoring period.
The generation I systems were powered by at least
three 35Ah lead acid batteries and photovoltaic panels.
Deployment locations for these systems were limited
by weight (approximately 90 kg), solar exposure, and
power requirements (approximately 12 W). The
weight of these systems required at least four people
for deployment and recovery. Furthermore, they
experienced a high rate of data loss due to serial
communication conflicts. NPS required the capacity to
identify prominent noise sources and the stations were
developed to make audio recordings as well as measure
sound levels. The laptop software was programmed to
save 10-s uncompressed audio recordings every 2 min.
This sampling scheme was required due to limited
Q Springer

1300
Landscape Ecol (2011) 26:1297-1309
storage space, but also ensured that every aircraft
overflight event would span at least two recordings.
Generation II introduced several substantial
improvements. In 2007, the laptop was replaced with
a personal digital assistant (PDA), reducing the
power consumption to approximately 2.5 W. The
reduction in power consumption allowed the use of
fewer batteries, resulting in a system weight of
approximately 11 kg. The PDA used an optimized
software interface between SLM and PDA, resulting
in negligible data loss. Generation II acoustical
monitoring stations included anemometers, but these
data were collected by a separate data logger.
Another notable improvement was the introduction
of a continuous audio recorder. Audio input from the
microphone was delivered to a 60 GB hard disk -
based MP3 audio recorder. These audio data provided
more complete and detailed records of all sounds at
each site. Unfortunately, the hard disk MP3 audio
recorders proved unreliable; extremes of temperature
and humidity often caused them to fail.
Generation III, introduced in 2008, employed a
new SLM, the Larson Davis 831. This unit possesses
its own 2 GB internal memory, as well as USB storage
capabilities. With these storage options, the PDA
became superfluous. In addition, the introduction of
solid state MP3 audio recorders, with no moving parts,
proved far more reliable in inclement weather. In
2010, we configured the SLMs to accept instantaneous
wind speed, wind direction, temperature, and humid-
ity, from attached sensors. This eliminated the need
for an additional data logger, and eliminated the need
to resynchronize data collected by independent
devices. Future acoustical monitoring systems may
be much more capable. In cooperation with Colorado
State University's Electrical and Computer Engineer-
ing Department, the Natural Sounds and Night Skies
Division is developing small SPL meters capable of
multichannel acoustic data collection, real-time beam -
forming to resolve direction of arrival, real-time
detection for acoustical events of interest, and wire-
less communications to provide regular summaries of
conditions and equipment status.
Study areas and site selection
This report summarizes data collected at 189 sites in
43 national parks (there are a total of 393 park units in
the National Park system). The number of sites
�L Springer
monitored in each park depended largely upon the
variation in major land cover types, or the number of
distinct management zones within the park. Areas with
similar attributes (vegetation, topography, land cover,
elevation, and climate) possess similar natural sound
sources, and hence can be considered representative of
a given soundscape. Additional criteria for site selec-
tion included avoidance of problematic conditions:
large, reflective surfaces such as cliff walls, persistent
masking sources such as rivers or waterfalls, and
heavily traveled roads or trails, for security. For
concision, parks are referred to in figures by 4-letter
codes, but a list of full park names is listed in Table 1.
Though the variability of SPLs over time and space
in national parks is not fully understood, each additional
dataset provides insight into natural variability. The
monitoring period used for collection of these data is
based on a preliminary statistical study that evaluated
long-term datasets from Bryce Canyon National Park
and Arches National Park. Based on the study, 25 days
was found to be adequate to account for annual
variation in sound level within 3 dB (Iyer 2005). Iyer's
findings are supported by the observation that this
period is generally sufficient to capture a representative
sample of weather conditions at a given site.
Off -site listening and visual analysis to identify
sound sources
A limited amount of on -site listening and data logging
was conducted at most monitoring sites. These obser-
vations, performed by experienced technicians, iden-
tify the common sound sources that can be heard at the
site by an attentive listener. Monitoring equipment has
made 30 days of continuous data relatively easy to
gather. The resulting volumes of data demand efficient
data reduction methods that yield audibility statistics
comparable to what is obtained by intensive listening
in the field.
Audibility denotes the capacity of a sound to be
perceived by an animal with normal hearing. Audibil-
ity is influenced by the hearing ability of the animal,
the masking effects of other sound sources, and by the
frequency content and amplitude of the sound. Two
distinct methods were developed to rapidly measure
the audibility of sound sources at each site. The goal of
our audibility analyses was to determine how often
anthropogenic sounds were perceptible by humans at

Landscape Ecol (2011) 26:1297-1309
1301
Table 1 List of full park
Park code
Park name
Population
names, their abbreviations,
and population size within
DRTO
Dry Tortugas National Park
374
16.1 km (10 mile) of park
boundary
SAND
Sand Creek Massacre National Historic Park
3,022
GRBA
Great Basin National Park
3,078
ORPI
Organ Pipe Cactus National Monument
3,296
BRCA
Bryce Canyon National Park
3,861
CIRO
City of Rocks National Reserve
4,040
DENA
Denali National Park & Preserve
7,523
KEFJ
Kenai Fjords National Park
8,272
GRSA
Great Sand Dunes National Park & Preserve
8,437
ELMO
El Morro National Monument
9,059
NOCA
North Cascades National Park Complex
10,710
BADL
Badlands National Park
11,600
DEPO
Devils Postpile National Monument
11,835
PEFO
Petrified Forest National Park
17,404
MORU
Mount Rushmore National Memorial
19,995
SEKI
Sequoia and Kings Canyon National Park
24,051
YOSE
Yosemite National Park
24,779
ELMA
El Malpais National Monument
25,438
MORA
Mount Rainier National Park
25,558
DEVA
Death Valley National Park
26,514
GLCA
Glen Canyon National Recreation Area
26,612
GRCA
Grand Canyon National Park
27,200
CAHA
Cape Hatteras National Seashore
29,542
ROMO
Rocky Mountain National Park
31,614
LAMR
Lake Meredith National Recreation Area
35,078
HALE
Haleakala National Park
37,721
CALO
Cape Lookout National Seashore
42,107
Population sizes as of 2009,
ZION
Zion National Park
42,201
within 16.1 km (10 mile) of
the park boundary are also
ACAD
Acadia National Park
42,883
reported. Anomalous
HAVO
Hawai'i Volcanoes National Park
48,213
population reports (such as
MOJA
Mojave National Preserve
54,337
374 people within 16.1 km
OLYM
Olympic National Park
86,161
of Dry Tortugas National
Park) can be attributed to
PORE
Point Reyes National Seashore
150,309
the intersection of large
MONO
Monocacy National Battlefield
219,373
U.S. census block borders
BITH
Big Thicket National Preserve
295,806
(which in rural areas are
often as large as counties)
GRSM
Great Smoky Mountains National Park
311,960
with the park boundary
MUWO
Muir Woods National Monument
403,547
buffer. Any blocks which
LAKE
Lake Mead National Recreation Area
710,556
intersect park boundary
EVER
Everglades National Park
859,237
buffers were included in the
total population count,
SAAN
San Antonio Missions National Historic Park
954, 350
occasionally producing
MIMA
Minute Man National Historic Park
1,160,446
overestimates of nearby
GOGA
Golden Gate National Recreation Area
2,487,768
population size
each site so that we
might determine
what the ambient." One of the methods for rapid
calculation of
acoustical environment
would be like without noise. audibility involves listening to a subsample
of the
We call this baseline ambient sound level the "natural audio data; the other involves visual inspection of
IL Springer

1302
Landscape Ecol (2011) 26:1297-1309
spectrograms. Both of these analyses were performed
in an office environment.
At sites where anthropogenic noise was rarely
audible (such as remote backcountry sites) noise
events were identified visually by technicians, using
spectrograms generated from SPL data. Spectrograms
are plots which display sound level as a function of
time and frequency. By plotting daily spectrograms
for each site (see Fig. 1), analyzers can quickly
examine many samples within the measurement
period. We've determined that most anthropogenic
sounds possess recognizable sound signatures. Thus,
we were able to manually identify and catalog each
event, indicating its begin and end time, as well as the
frequencies it spanned, maximum level, and sound
exposure level (a single number representing the total
equivalent energy of a sound, in dB, over a given
period of time, abbreviated SEL).
In datasets with continuous audio, we confirmed
identification of events with uncertain sound
signatures by playing back corresponding audio files.
We used the total percent time anthropogenic sounds
were audible to calculate the natural ambient sound
level for each hour.
For locations where many noise sources were
audible at once (such as sites near roads or trails),
visual detection of simultaneous events proved dif-
ficult. In these cases, technicians listened to daily
samples (10 s every 2 min) from the audio data. For
each 10 s sound sample, all audible sound sources
were identified. This information was compiled to
calculate a total percent time audible value for each
sound source, which was in turn used to calculate the
natural ambient sound level for each hour. To avoid
limitations imposed by the office environment, such
as the confounding sounds of conversation or HVAC,
we used over -ear, noise canceling headphones when
cataloging audible events. Results from visual anal-
ysis and auditory analysis of the same dataset were
found to be comparable.
Fame (min)
Sou11d Pressur8 Lml (Q)
Fig. 1 24 h spectrogram, annotated with jet aircraft events
This 24 h spectrogram displays 1/3 octave band SPLs for all
hours of the day. The x-axis represents time in 5 min
increments, with 2 h displayed on each line. The y-axis
represents the logarithmic frequency scale ranging from 12.5 to
20,000 Hz. The z-axis (tone, ranging from black to white)
describes unweighted SPLs from —9 to 90 dB. On this scale,
�L Springer
quiet intervals appear dark while loud events appear white. The
white boxes drawn on the plot highlight just 10 of the many jet
aircraft overflights. The morning bird chorus is distinguishable
as a series of subtle dots near 4,000 Hz, starting near the end of
the 5th hour. Thunder claps appear as sharp, white spikes in the
middle of the day

Landscape Ecol (2011) 26:1297-1309
1303
Calculation of metrics
No single metric is adequate to characterize acoustic
resources. Furthermore, each park has unique char-
acteristics and legislative requirements, so one set of
metrics may not meet the needs of all parks.
Accordingly, the Natural Sounds and Night Skies
Division works with several metrics. Acoustical
studies in national parks use SPL data, spectral data,
audibility data, source identification data, and mete-
orological data.
Background sound levels are a fundamental prop-
erty of the acoustical environment, because they
determine the minimum amplitude of acoustical
signals that can be detected, identified, and localized.
The median ambient sound level (L50) represents an
average background level that includes all sound
sources (both natural and anthropogenic); the NPS
calls this quantity the existing ambient sound level.
The median ambient sound level is preferred over the
mean ambient sound level because it is not unduly
affected by unusual events, and because the proba-
bility of exceeding this level is known (50%). The
natural ambient metric (Lnat) estimates the desired
condition for many parks. It is an estimate of what the
median ambient sound levels for a site would be in the
absence of all extrinsic (or anthropogenic) sources.
The NPS method of calculating Lnat does not simply
remove all intervals in which noise is audible. While it
may seem logical to do so, this method is flawed
because in some cases (e.g. windy locations), quiet
periods are the only time noise events are audible.
Thus, removing the intervals where noise was audible
would also remove the quietest moments. In some
cases, this method produces nonsensical results where
estimates of Lnat exceed L50: how can adding noise
result in a lower median level? Instead, NPS presently
estimates Lnat by removing the loudest p percent of the
data in each hour (where p is the percent of the time
when anthropogenic noise is audible), and computing
the median of the remaining SPL measurements. The
calculation identifies the exceedance level, L,{, which
represents the L50 value that would have existed in the
absence of noise. Algebraically, the calculation is:
100 — p
x = 2 +p
For example, if human caused sounds are present
30% of the hour, p = 30, x = 65, and the Lnat for that
hour is equal to the L65, or the median sound level
exceeded 65% of the time during the hour. This formula
could underestimate natural sound levels when loud
natural events, like thunder, are numerous. However, it
is unlikely that this bias will persist over a 25 day
measurement period (NPS 2005). This Lnat estimate
ensures that Lnat levels are always lower than L50 levels.
The audibility of both natural and anthropogenic sounds
varies substantially throughout the day, so ambient
values are calculated on an hourly basis. In addition,
NPS measures wind speed in order to determine when
sound level measurements are unreliable. Wind causes
flow noise around the microphone enclosure, inflating
sound level measurements above the levels that would
be measured if the microphone were not present. At
present, NPS does not utilize sound level measurements
when the wind speed exceeds 5 m/s.
The NPS emphasizes changes in background
sound levels because this effect of noise can be
translated directly into lost hearing opportunities. In
most environments, the energy from a sound source is
distributed over the surface of hemispheres that
increase in size as the sound propagates away from
its origin. This effect, called spherical spreading loss,
causes the sound level to decrease by 6 dB for each
doubling of distance from the source. Therefore, to
compensate for a 6 dB increase in background sound
level, a listener would have to be half as far away
from the source to detect it. A 12 dB increase in
background levels causes a 75% reduction in detec-
tion distance. For animals that rely upon sounds to
warn them of danger, this loss of alerting distance can
have dire consequences. Other animals —and many
park visitors —use hearing to search for items of
interest. The search area is proportional to the square
of the maximum detection distance, so each 6 dB
increase in background level causes a 75% reduction
in listening area. Note that these listening area effects
do not necessarily correlate with measures of per-
ceived loudness in humans. Many references state
that each 10 dB increase in SPL causes a doubling of
perceived loudness (Crocker 1997), but a 10 dB
increase is equivalent to moving the sound source
more than three times closer to the listener.
The above paragraph addresses the issue of detec-
tion, but all of its points also apply to the degradation
of information content in the received signal. This
information includes species and individual identity,
behavioral context, and location. Numerous studies
IL Springer

1304
Landscape Ecol (2011) 26:1297-1309
have investigated the degree to which physical envi-
ronments and signal characteristics interact to limit the
range at which this information can be perceived
(Marten and Marler 1977; Marten et al. 1977).
Cursory inspection of the hourly metrics across
sites revealed general patterns that appeared to be
shared by most —but not all —sites. The existence of
exceptional sites recommended a median polish
procedure for analysis, rather than a linear model or
ANOVA. Median polish is a computational technique
for robustly decomposing a two-way table into an
additive model consisting of overall, row, column,
and residual effects (Tukey 1977). In our application,
we focus on the column effects, which capture shared
diel patterns in noise values across all sites.
Results
Measured levels of hourly noise audibility are
presented for 93 sites in 22 parks in Fig. 2a, and
the overall picture attests to the ubiquity of audible
noise in national parks.
A median polish applied to the data in Fig. 2a
estimates the median noise audibility across all sites
and hours to be over 28%. Even the quietest sites in
this dataset (Kenai Fjords National Park, City of
Rocks National Reserve) experience audible noise
more than 5% of most daytime hours (Fig. 2a).
Periods of quiet can be found at most sites, during the
hours between 0000 and 0600. But most sites exhibit
high noise audibility from 0700 to 2200 h, even in
relatively remote settings. The high levels of noise in
Yosemite relative to Sequoia Kings Canyon provide
an informative contrast. Many of the sites in Sequoia
Kings Canyon had rushing water nearby, so it is
possible that this constant sound source prevented
detection of noise events. Yosemite lies beneath two
high traffic aircraft routes (east —west traffic for the
San Francisco Bay Area, north —south traffic between
southern California and the Pacific Northwest), and it
tends to have quieter natural ambient levels that
enhance detection of distant noise sources.
In this figure, parks are ordered by total population
size within a 16.1 km (10 mile) buffer of their
boundaries, such that the parks near the least
populated areas appear on the left, and parks near
the most populated areas appear on the right. Though
the parks in the least populated areas do display
smaller time audible percentages, the vast majority of
sites display a consistent pattern of audibility,
independent of the size of the nearby population.
A
70 9A 90_. 60 Time audible I%j
Hour
al Yalo & 9 1 g-- 1 9 a� A m
a
Fig. 2 Hourly percent time audible for human -caused noise
sources. a Results of off -site noise audibility analysis for 93
sites in 22 parks. Park names are arranged on the horizontal
axis, while hours of the day are shown on the vertical axis. The
beginnings and ends of site groupings are marked by tick
marks. Parks are ordered from left to right by total population
within a 16.1 km (10 mile) buffer of park boundaries; parks
�L Springer
B
02 0.6 1.0 1.4
Multiplier
with the smallest population nearby are on the left, while parks
with the largest nearby population are placed on the right.
Percent time audible for noise is symbolized by the tone of
each block, with the scale displayed at the top of the figure.
b Diel trend of audibility for all noise (in black) and aircraft
noise (in white). These deviations were computed using a
median polish procedure

Landscape Ecol (2011) 26:1297-1309
1305
This pattern suggests that the most commonly audible
noise source must be something other than that
caused by the surrounding communities. Figure 2b
shows that the general pattern of noise audibility in
parks tracks the activity cycles of humans, and that
the pattern of all noise audibility is nearly identical to
the pattern of aircraft noise alone. The aircraft "rush
hour" is a bit later than the peak of commuter traffic
in cities, with a peak between 0900 and 1000 h. A
lesser peak also occurs in the early evening, which
corresponds to airport departures after normal busi-
ness hours. These audibility results probably under-
state afternoon traffic levels, because winds tend to be
stronger and more prevalent in the afternoon and act
to reduce the audibility of aircraft noise.
A few sites in national parks suffer from degraded
noise environments comparable to urban settings.
Two notable sites, one in Yosemite National Park,
and one in Minute Man National Historic Park
exhibited very high audibility across all hours (in
Fig. 2a, these sites stand out as the brightest in their
respective parks). The site in Minute Man National
Historic Park, near Concord, Massachusetts, was
situated close to highway Route 2A and Hanscom
Field airport, while the site in Yosemite National
Park was located in Yosemite Village ("The Mall").
The Mall is one of the most congested areas in the
park during the day; the high nocturnal noise
audibility was due to HVAC in nearby buildings.
Many national parks have zones like Yosemite
Village, which are designed to provide important
services for large numbers of visitors (see Fig. 2,
Kenai Fjords National Park, for audibility statistics
from another visitor facilities zone). Future designs
for such sites can plausibly provide the same services
and preserve a quieter environment.
The sites which deviated from the normal pattern
of audibility each have unique stories. Zion National
Park, Lake Mead National Recreation Area, and
Mojave National Preserve all have notable late night
(0000-0400) audibility, due to train and aircraft
activity near Las Vegas. The sites in Organ Pipe
Cactus National Monument are near the Mexican
border, and these sites experience noise from inten-
sive border patrol activity, particularly in the evening
and early morning hours.
While Fig. 2 reveals the patterns of audibility in
national parks, it does not provide insight into sound
levels. Audibility provides a sensitive measure of the
temporal extent of noise events, but it provides no
information about loudness. Figure 3 displays three
measures of sound level—L90, L50, and L01—from
189 sites in 43 parks. As in Fig. 2, sites are ordered
by total population size within a 16.1 km (10 mile)
buffer of their boundaries, such that the parks near the
least populated areas appear on the left, and parks
near the most populated areas appear on the right.
These metrics represent an estimate of background
ambient sound level, the median ambient level, and
the magnitude of loud events, respectively. These
values are A -weighted sound levels computed from
1/3rd octave spectrum level measurements from 12.5
to 800 Hz (see ASA Specification for Sound Level
Meters DF for details on these terms). The range of
frequencies used in Fig. 3 spans most transportation
noise energy, so these measurements provide the
clearest indication of the potential impacts of noise
and the capacity of the local acoustical environment
to mask other transportation noise. Full spectrum
dB(A) measurements are inappropriate to evaluate
the potential impacts of transportation noise because
they encompass all frequencies, low to high. High
frequency natural sounds can substantially inflate
environmental sound levels, yet these sounds cannot
mask transportation noise.
While the exceedence levels in Fig. 3a vary widely
among parks, panel 3B reveals that a common pattern of
natural ambient sound levels does exist. A salient
feature of Fig. 3 is the similarity of the three panels with
the L90 and L50 patterns being nearly identical. Median
polishing of the data in these three figures yielded the
diel patterns displayed on the right hand side of each
panel, and the following overall median sound levels
across all sites and hours of the day: L90 = 21.8 dB(A),
L50 = 24.6 dB(A), L01 = 40.6 dB(A). In addition to
approximately 4 dB increase in level, the L50 panel
exhibited a stronger afternoon increase in sound levels
than the L90 panel.
As in Fig. 2, exceptional patterns in the data can
be related to exceptional conditions at the sites. The
highest L01 levels in Fig. 3 correspond to dense urban
settings in Golden Gate and San Antonio Missions,
unusual conditions at Rocky Mountain National Park
(the "Thunder in the Rockies motorcycle rally"), and
frequent aircraft activity over Lake Mead (helicopter
transport of Grand Canyon air tourists over Indian
Pass). The Rocky Mountain National Park data are a
fairly accurate representation of acoustical conditions
Q Springer

1306
Landscape Ecol (2011) 26:1297-1309
A
Fig. 3 Measured background, median, and peak levels of
sounds between 20 and 800 Hz, in dB(A). a Measured hourly
exceedence levels from 189 acoustical monitoring sites in 42
parks. Parks are displayed on the horizontal axis, and hours of
the day are shown on the vertical axis. Parks are ordered left to
right, from smallest population size to largest population size
within 16.1 km (10 mile) of the park boundary. The tone of
each block represents sound level as measured by the integral of
A -weighted energy between 20 and 800 Hz. These measure-
ments focus attention on the frequencies covering most of the
transportation noise energy. Darker tones symbolize quieter
near any busy park road during periods of high
visitation. However, not all high sound levels are
attributable to noise. At sites in Olympic National
Park, Cape Lookout National Seashore, and North
Cascades National Park, ambient sound levels are
naturally high because of the sounds of waves or
cascading streams (sites such as these appear mono-
chromatic in this figure). In this sense, the term
"natural quiet" offers an incomplete image of desired
conditions because the powerful sounds of water are
quintessential to the character of these places.
A comparison of Figs. 2 and 3 shows that high
levels of audible noise do not always coincide with
high ambient sound levels. City of Rocks is note-
worthy for low audibility and ambient sound levels;
part of this national reserve was originally identified
�L Springer
gp dWAj.2"W HZ p
P
Hour
04
a 08 09 10 11 12
$ Multiplier
sound levels while brighter tones symbolize louder sound
levels. The L90 represents the hourly levels exceeded 90% of the
time during the monitoring period, and is often used to
approximate background ambient sound levels in community
settings. The L50 represents the hourly levels exceeded 50% of
the time during the monitoring period. The L01 represents the
hourly levels exceeded 1% of the time during the monitoring
period, and summarizes the sound levels for the loudest events
that were measured at the site. Fields with hash marks indicate
hours without data. b The overall diel trends produced by
median polish of L01, L50, and L90 data in (a)
in legislation as "Silent City of Rocks." However,
many sites in Grand Canyon, Lake Mead, Yosemite,
and Zion exhibit low ambient sound levels but
extensive durations of audible noise. These sites
illustrate the delicate nature of exceptionally quiet
locations: their pristine character is most susceptible
to noise from distant sources. Several sites in Kenai
Fjords and Sequoia Kings Canyon show that rela-
tively high ambient levels due to natural sounds can
be coupled with limited extents of audible noise.
Discussion
A comprehensive 1982 EPA survey assessing the noise
climate in residential areas revealed that 87 percent of

Landscape Ecol (2011) 26:1297-1309
1307
the urban population of the United States was exposed
to a day -night sound level over 55 dB, and an
additional 53% was exposed to a day -night sound
level over 60 dB (day -night sound level is a standard
community -noise metric, defined as 24 h average
sound level, with a 10 dB penalty added for noise
levels occurring between 10 p.m. and 7 a.m.) (EPA
1982). Collectively, park monitoring data show that
most park sites have relatively low background sound
levels, and are generally quieter than most urban or
suburban communities. But despite their quiet back-
ground sound levels, extrinsic noise is audible in many
parks for significant fractions of the day. High traffic
locations in parks present the most degraded acoustical
environments, due to the density of visitors, the mode
of transporting visitors within parks, and noise from
buildings and other park infrastructure. Many remote
sites also have high levels of audibility, because very
distant sound sources can be audible against low
background sound levels. The quietest sites in parks are
the most vulnerable to noise intrusions.
There are several reasons for NPS to pursue noise
management. First, noise management is rooted in
NPS management policies: "the natural ambient
sound level —that is, the environment of sound that
exists in the absence of human -caused noise —is the
baseline condition and the standard against which
current conditions in a soundscape will be measured
and evaluated" (NPS 2006). NPS management pol-
icies (2006) also state that: "culturally appropriate
sounds are important elements of the national park
experience in many parks." In NPS areas, "the
Service will preserve soundscape resources and
values of the parks to the greatest extent possible to
protect opportunities for appropriate transmission of
cultural and historic sounds that are fundamental
components of the purposes and values for which the
parks were established" (ibid).
Moreover, protected natural and cultural areas
preserve increasingly rare sanctuaries for the public to
fully experience natural sounds and solitude. Quiet
settings at cultural sites or memorials enhance the
contemplative or reverent atmosphere. Quiet is also an
essential attribute of outstanding settings for teaching
and interpretive presentations. Children are especially
prone to distraction, and have more difficulty than adults
in understanding speech in noisy locations. In attempt-
ing to preserve outstanding acoustical conditions, NPS
confronts an accelerating historical trend. Rapid energy
development, infrastructure expansion, and urbaniza-
tion are fragmenting the acoustical landscape.
Degraded listening opportunities also affect innu-
merable aspects of ecosystem function. From the
perspective of resource preservation and restoration, it
is understandable if noise management pales in
comparison to ensuring the survival of threatened and
endangered species. Nonetheless, an emergent body of
literature suggests that these concerns are often linked.
For wildlife, noise pollution intensifies the ecological
stress that habitat fragmentation has caused (Barber
et al. 2010). Hearing is the universal alerting sense; it
remains active even in sleeping animals.
Fortunately, the benefits of noise management can
be measured and perceived immediately; a noise
source quieted, displaced, or removed is readily
apparent. However, ecosystem recovery from noise
exposure and changes in visitor expectations and use
patterns may progress on much longer time scales. NPS
enjoys a unique obligation and opportunity to translate
the principles governing architectural design for out-
standing indoor acoustics into park architectures that
preserve authentic conditions. Design options like
noise barriers between parking areas and scenic
overlooks may provide significant improvements over
current conditions. In the longer term, transportation
networks inside parks can be reshaped to reduce their
impacts to acoustic resources and visitor listening
opportunities.
Lamentably, much of the noise measured in national
parks comes from sources outside park boundaries and
beyond the management jurisdiction of NPS. The
regional and national scales of these noise sources call
for conservation and management efforts on the same
scales.
As shown in Table 2, the NPS has made a number
of significant achievements in the realm of sound-
scape management and noise mitigation. Muir Woods
National Monument declared a permanent "quiet
zone" in Cathedral Grove, after social science
research revealed that such signage was supported
by an overwhelming majority of park visitors and that
the resulting reduction in sound levels was equivalent
to halving the number of visitors in the park. Mass
transit has become an increasingly attractive option to
parks like Zion National Park and Devils Postpile
National Monument, allowing them to provide access
to large numbers of visitors while diminishing
impacts to resources. When Zion National Park
Q Springer

1308 Landscape Ecol (2011) 26:1297-1309
Table 2 Noise management techniques, as applied in national parks
Park Mitigation method(s)
Acadia National Park Currently using baseline ambient data to fulfill legislative mandate by managing air tours over
national parks
Big Thicket National Preserve Used baseline ambient data to persuade energy development company to erect berms between
park and directional drilling operations
Devils Postpile National Currently using acoustical data to determine impacts of new mass transit options (buses)
Monument
Grand Canyon National Park Utilized baseline ambient data in completed draft Air Tour Management Plan. Currently seeking
public comment
Great Sand Dunes National Park
Cited data in an injunction of proposed oil and gas exploration in adjacent national wildlife refuge
& Preserve
Haleakala National Park
Currently using baseline ambient data to fulfill legislative mandate by managing air tours over
national parks
Hawai'i Volcanoes National
Using baseline ambient data to fulfill legislative mandate by managing air tours over national
Park
parks
Kenai Fjords National Park
Established desired future conditions and soundscape quality standards for Exit Glacier
Management Plan
Lake Mead National Recreation
Currently using baseline ambient data to fulfill legislative mandate by managing air tours over
Area
national parks
Minute Man National Historic
Established desired future conditions and soundscape quality standards. Drafted Soundscape
Park
Management Plan to manage park -wide acoustical environment, currently under park review
Mojave National Preserve
Monitored areas below flight paths between to document baseline conditions prior to the
construction of a nearby major airport
Mount Rainier National Park
Currently using baseline ambient data to fulfill legislative mandate by managing air tours over
national parks
Mount Rushmore National Currently using baseline ambient data to fulfill legislative mandate by managing air tours over
Memorial national parks
Muir Woods National Designated permanent `quiet zone,' based on the findings of various acoustical monitoring studies
Monument
North Cascades National Park Incorporated protection of natural sounds into wilderness management plan
Complex
Organ Pipe Cactus National Used baseline ambient conditions to determine effects of border patrol installations on the
Monument soundscape and the endangered Sonoran Pronghorn, a species which inhabits the park
Sand Creek Massacre National Gathered baseline ambient data in order to incorporate protection of natural sounds into the
Historic Site park's first general management plan. Worked with Colorado Air National Guard to assess
impacts of military overflights
Sequoia and Kings Canyon Currently using baseline ambient data to fulfill legislative mandate by managing air tours over
National Parks national parks
Yosemite National Park Incorporated developed desired conditions and standards of quality for soundscapes in Merced
River Plan. Considered soundscape as a resource to be protected and incorporated into future
plans
Zion National Park Used acoustical data to quantify benefits of shuttle system in Zion Canyon. Finalized a
soundscape management plan which included desired future conditions, soundscape objectives,
and standards of quality
instituted a shuttle bus system to reduce summer
congestion on park roads, the park received visitor
comments expressing appreciation for the quieter
conditions. Numerous parks have begun drafting Air
Tour Management plans to mitigate noise from air
�L Springer
tour operations. Acoustical monitoring data has even
been cited in court decisions as a reason to halt oil
and gas exploration near parks. These efforts to
mitigate noise in parks are an encouraging trend. The
NPS has a unique opportunity to educate and engage

Landscape Ecol (2011) 26:1297-1309
1309
the public on issues like noise pollution, air quality,
and climate change, but effective resolution will
require partnerships that transcend park boundaries
and institutional barriers to cooperation.
Acknowledgments We thank acoustical technicians, Ric
Hupalo, Skip Ambrose, Dave Schirokauer, Ericka Pilcher,
Charlotte Formichella, Dave Stack, Katherine Warner, Daniel
Mennitt, Jessica Briggs, and Cecilia Leumas for the many field
and office hours they spent collecting and analyzing the data in
this report. We also greatly appreciate the assistance provided
by park personnel in data collection efforts. Thanks to Kirk
Sherrill and David Hollema from the Natural Resource
Stewardship and Science (NRSS) Inventory and Monitoring
Program for GIS assistance. We also thank technicians at Wyle
Laboratory for the role they played in data collection, and our
partner agency, the Volpe National Transportation Systems
Center, for data collection assistance, as well as monitoring and
analysis protocol development.
References
Acoustical Society of America (1983) American National
Standard Specification for Sound Level Meters. ANSI
Standard 51.4-1983, 17 Feb 1983. Rev 2006
Barber J, Crooks K, Fristrup K (2010) The costs of chronic
noise exposure for terrestrial organisms. Trends Ecol Evol
25:180-189
Bayne EM, Habib L, Boutin S (2008) Impacts of chronic
anthropogenic noise from energy -sector activity on
abundance of songbirds in the boreal forest. Consery Biol
22:1186-1193
Crocker MJ (1997) Encyclopedia of acoustics. Wiley, New
York
Dawson C (2004) Monitoring outstanding opportunities for
solitude. Int J Wilderness 10(3):12-29
Doherty KE, Naugle DE, Walker BL, Graham JM (2008)
Greater sage -grouse winter habitat selection and energy
development. J Wildl Manag 72:187-195
Environmental Protection Agency (1982) National Ambient
Noise Survey. Office of Noise Abatement and Control,
Washington, DC
Haas GE, Wakefield TJ (1998) National Parks and the Amer-
ican public: a summary report of the National Parks
Conservation Association, conducted by Colorado State
University, Fort Collins, CO
Habib L, Bayne EM, Boutin S (2007) Chronic industrial noise
affects pairing success and age structure of ovenbirds
Seiurus aurocapilla. J Appl Ecol 44:176-184
Haralabidis A, Dimakopoulou K, Vigna-Tagliand F et al
(2008) Acute effects of night-time noise exposure on
blood pressure in populations living near airports. Ear
Heart J 29:658-664
Iyer H (2005) Determination of adequate measurement periods
(temporal sampling). Draft report to Natural Sounds
Program, Fort Collins, CO
Landon DM, Krauseman PR, Koenen KKG, Harris LK (2003)
Pronghorn use of areas with varying sound pressure lev-
els. Southwest Nat 48:725-728
Marten K, Marler P (1977) Sound transmission and its sig-
nificance for animal vocalization. 1. Temperate habitats.
Behav Ecol Sociobiol 2:271-290
Marten K, Quine D, Marler P (1977) Sound transmission and
its significance for animal vocalization. 2. Tropical forest
habitats. Behav Ecol Sociobiol 2:291-302
McDonald CD, Baumgartner RM, Iachan R (1995) Aircraft
management studies. USDI Report 94-2 Denver, CO
National Park Service (2005) Acoustic and soundscape studies
in National Parks: Draft, Fort Collins, CO
National Park Service (2006) Management Policy 4.9:
Soundscape Management. US Government Printing
Office, Washington DC
Sawyer H, Nielson RM, Lindzey F, McDonald LL (2006)
Winter habitat selection of mule deer before and during
development of a natural gas field. J Wildl Manag
70:396-403
Schmidt KA, Ostfeld RS (2008) Eavesdropping squirrels
reduce their future value of food under the perceived
presence of cache robbers. Am Nat 171:386-393
Tukey J (1977) Exploratory data analysis. Addison-Wesley,
Reading
IL Springer

RESEARCH COMMUNICATIONS 559
Check for
updates I
Anthropogenic noise in US national parks -
sources and spatial extent
Rachel T Buxton'*, Megan F McKenna 2, Daniel Mennitt3, Emma Browne, Kurt Fristrup2, Kevin R Crooks', Lisa M Angeloni4,
and George Wittemyert
In an era of unprecedented environmental change, US national parks are refuges of natural ecosystems and facilitate connections
between humans and nature. However, anthropogenic noise is an increasingly pervasive threat in these parks. To diagnose noise
levels and sources, we analyzed thousands of hours of acoustic recordings collected across park units and summarized results from
continental -scale sound models. We found that anthropogenic noise was audible in 37% of park recordings, and that parks with
high road density and those in close proximity to large airports experienced a greater number of noise events. The most common
noise sources were aircraft and road vehicles, but, when present, trains and watercraft generated the loudest noise levels. Sound
models show that anthropogenic noise caused a tenfold increase in median sound levels in 36% of parks, and loud areas were often
localized. Our analysis identifies situations where noise management would yield the greatest benefits to park visitors and wildlife.
Front Ecol Environ 2019; 17(10): 559-564, doi:10.1002/fee.2112
The US National Park Service (NPS) was established over a
century ago to conserve natural and cultural resources. As
the first system of federally protected areas in the world, US
national parks have shaped a global standard for protected
areas. Since NPS's inception, the US population has more than
tripled, road and aircraft traffic have become widespread, and
80% of the US population now lives in urban areas (Barber
et al. 2010). In this context, national parks represent refuges of
ecological integrity and provide increasingly important oppor-
tunities for people to establish personal connections with natu-
ral environments (Miller 2005; Machlis and McNutt 2015).
This rapid increase in infrastructure, transportation net-
works, and human activity has resulted in the widespread dis-
tribution of anthropogenic noise (hereafter "noise"), even in
the most remote protected areas of the US (Figure 1; Buxton
et al. 2017a). At high levels of exposure, noise annoys people
and contributes to health problems (Basner et al. 2014). At
lower levels of exposure, noise reduces the benefits of experi-
encing natural sounds, which include increased relaxation,
restored attention, improved mood, and reduced stress
(Benfield et al. 2014; Abbott et al. 2016). Noise also affects
wildlife, masking critical sounds (including incidental signals
such as the sound of predators approaching) and increasing
perceived risk, causing changes in behavior, physiology, and
fitness (reviewed in Shannon et al. [20161). Moreover, the
responses of individual species to noise extend through eco-
logical interactions to alter community structure and ecosys-
tem function (Francis et al. 2012). Despite its known impacts
on natural systems, noise is rarely considered alongside other
pervasive threats to protected areas (Butchart et al. 2010).
'Dept. of Fish, Wildlife and Conservation Biology, Colorado State University,
Fort Collins, CO *([e-mail]); ZNatural Sounds and
Night Skies Division, National Park Service, Fort Collins, CO, 3Dept. of
Electrical and Computer Engineering, Colorado State University, Fort
Collins, CO; ¢Dept. of Biology, Colorado State University, Fort Collins, CO
Congressional concerns about noise in national parks have
been expressed through legislation since 1975, and NPS policy
requires the management of noise and conservation of acoustic
resources (NPS 2006). Accordingly, NPS has been identifying
noise sources, measuring not only how often they are heard
but also sound levels at hundreds of sites over the past two dec-
ades, resulting in a unique, spatially diverse acoustic dataset.
This study is the first comprehensive analysis of all noise
sources in national parks across the US. More specifically, we
identify the causes of continental -scale patterns of noise expo-
sure (Buxton et al. 2017a) by analyzing the identities and char-
acteristics of noise sources audible in national park units and
relating these outputs to landscape -scale summaries of acous-
tic conditions inside national parks. The results document
(1) the loudest and most frequent sources of noise and the
anthropogenic features associated with them, (2) which of
these sources predict landscape levels of noise estimated using
geospatial models, and (3) summaries of these noise metrics
across different protection categories (ie park type, wilderness
areas, and critical habitat of US endangered species). We relate
this diagnosis of noise across different park contexts with
emerging approaches to mitigate noise pollution, aiming to
identify management strategies that preserve or restore natural
soundscape experiences for park visitors and wildlife.
■ Methods
Noise sources
A team of trained acoustic technicians identified, cate-
gorized, and measured characteristics of noise by listening
to and observing spectrograms of recordings from 251
sites in 66 park units (WebPanel 1). At 168 sites with
This is an open access article under the terms of the Creative Commons Attribution
License, which permits use, distribution and reproduction in any medium, provided
the original work is properly cited.
© 2019 The Authors. Frontiers in Ecology and the Environment published by Wiley Periodicals Inc. on behalf of the Ecological Society of America.

RT Buxton et al.
a
z
a
z
Figure 1. Some examples of anthropogenic noise sources in US national parks include (a) aircraft (Theodore Roosevelt National Park, North Dakota), (b)
vehicles (Denali National Park and Preserve, Alaska), (c) trains (Cuyahoga Valley National Park, Ohio), and (d) watercraft (Glacier Bay National Park, Alaska).
high rates of noise events, categories of noise (WebTable 1)
were identified as present or absent in 10-second samples
of audio taken every 2 minutes (except for sites in Alaska's
Denali National Park; see WebPanel 1). The audibility
of a noise source was calculated as the proportion of
acoustic samples where the noise event was observed
during an hour of sampling. At 96 sites with low rates
of noise events and little overlap between events, tech-
nicians measured the following characteristics of all noise
events observed throughout recordings: sound exposure
level (SEL), maximum sound pressure level (max SPL),
and duration (minutes) (for definitions, see WebPanel
2). We estimated audibility for these sites by calculating
the probability that a noise event intersects a 10-second
sample across each 2-minute interval based on the dura-
tion of the event (WebPanel 1). Note that both types of
analyses were conducted at 13 sites.
To compare audibility among noise categories (WebTable
1), we fitted the most parsimonious model structure, a gener-
alized linear mixed model (GLMM) with a quasi -Poisson
error structure with the log of the total number of acoustic
samples included as an offset term. Because we found substan-
tial temporal autocorrelation in model residuals for SEL, max
SPL, and duration of noise events, these were compared
between categories using second -order autoregressive, inte-
grated, moving average (ARIMA) mixed models with a
Gaussian error structure. All models incorporated each cate-
gory of noise, season, a variable controlling for the front- or
backcountry placement of the recorder, and morning hours
(0700-0900) as fixed variables, with date nested within site
nested within park as a random effect.
Using data from national parks within the contiguous US,
we used an information —theoretic approach to investigate
which landscape -level anthropogenic features explain differ-
ences in audibility of all noise sources across sites. We con-
structed three global quasi -Poisson GLMMs, each of which
included uncorrelated combinations of distance to, size of, or
density of anthropogenic features, and variables for park
designation type, wilderness areas, and critical habitats of US
endangered species (covariates are described in WebTable 2).
For the park designation variable we assigned each of the 21
types of national park units, based on distinctive manage-
ment attributes, into one of four possible designations:
(1) national parks, preserves, and reserves; (2) recreation
areas; (3) cultural parks; and (4) national monuments
(WebPanel 1). We included recording date nested within site
nested within park as a random effect in each model. We
chose the model with the lowest Akaike's information crite-
a
7.
a
z
Front Ecol Environ doi: 10. 1002/fee.2112

Noise in US national parks
rion (AIC) score, and considered covariates from this model
with the greatest parameter estimates with 95% confidence
intervals that did not overlap zero to be the most influential
(Burnham and Anderson 2002). All model procedures are
described in detail in WebPanel 1.
Noise exceedance
To quantify acoustic conditions in parks within the con-
tiguous US, we extracted national estimates of noise from
a previously published geospatial model (Mennitt and
Fristrup 2016). These models were generated by machine -
learning algorithms to analyze the relationship between
acoustic measurements at 492 sites across the contiguous
US and geospatial features, including vegetation, topog-
raphy, climate, hydrology, and anthropogenic activity. In
addition to predicting expected sound levels, the models
predicted natural sound levels by minimizing anthropo-
genic factors. Furthermore, the difference between these
values is an estimate of the amount that anthropogenic
sound energy raises existing sound levels above natural
levels (Mennitt et al. 2014). We used this difference, termed
"noise exceedance", as a metric of noise because it meas-
ures changes in sound levels due to anthropogenic noise
(detailed definition in WebPanel 2; Buxton et al. 2017a).
Noise exceedance values were predicted from A -weighted
sound levels (a method of summarizing sound levels across
frequencies) emphasizing sound energy at frequencies at
which many vertebrates have their most sensitive hearing
thresholds, and averaged over summer daytime hours,
representing seasonal listening conditions (WebPanel 1).
Sites in Alaska and Hawaii were included in audibility
analyses, but predictions of noise exceedance were limited
to sites in the contiguous US.
We summarized noise exceedance, examining median noise
exceedance within each park unit and the proportion of pixels
experiencing noise exceedance above 3 decibels (dB) and 10
dB within each park unit. Exceedances of 3 dB and 10 dB cor-
respond to a doubling and tenfold increase, respectively, in
acoustic energy, and to a 50% and 90% decrease, respectively, in
the spatial extent of acoustic signal detection (ie "listening
area"; Barber et al. 2010) in many vertebrates (Buxton et al.
2017a). In addition to masking important acoustic informa-
tion, noise exceedance in this range reduces visitor enjoyment
of parks through annoyance and interference with natural
quiet and natural sounds (Rapoza et al. 2015). Moreover, the
scholarly literature published over the past two decades
demonstrates that noise exceedance in this range affects wild-
life species richness, reproductive success, behavior, and physi-
ology (Shannon et al. 2016).
Comparison across management designations
We compared noise audibility and exceedance (1) across
park types; (2) in wilderness areas within parks, non -
wilderness areas within parks, and wilderness areas outside
parks; and (3) in critical habitat of US endangered species
within parks, non -designated areas within parks, and critical
habitat outside parks. Fitting models to compare noise
exceedance among park management designations was unfea-
sible given the dataset's large size (n > 5 million) and the
prohibitively large matrices needed to account for high spatial
autocorrelation. Accordingly, we used a bootstrapped pro-
cedure (described in WebPanel 1). To compare audibility
of noise in recordings across land designations, we used
the predictions from GLMMs described above.
To examine the relationship between noise exceedance and
characteristics of noise sources at each site, we built linear mod-
els predicting noise exceedance at each recorder location from
mean audibility, SEL, max SPL, and duration of each category of
noise. We considered noise category models with the highest R2
and lowest AIC (corrected for small sample size: AICc) as the
best predictors of audibility and other noise metrics.
■ Results
Audibility of the loudest and most frequent noise sources
Analysis of 1,440,999 acoustic samples from 46,789 hours
of recordings in US national park units identified ten
common noise sources (Figure 2). Although noise sources
varied among park units, aircraft noise was heard at all
sites and was the predominant source at most sites
(Figure 2). The second most common noise source was
ground -based vehicles (WebFigure 1). Another common
category was sounds from people (eg voices, footsteps;
WebFigure 1). Some noise sources were geographically
limited; for example, train sounds were more common
in western parks (Figure 2).
Model results, accounting for different sampling across
locations and time, indicated that noise was audible in 37% of
acoustic samples (WebFigure 1). Noise events were most com-
mon between the hours 0700-0900 and during the summer
(July —September; WebTable 3). Aircraft and vehicle sounds
were the most common noise sources, audible in 3 1 % of sam-
ples, ranging from sites with no noise audible to 100% of sam-
ples containing aircraft or vehicle noises (WebTable 4). Aircraft
noise was predominantly (73%) from jets, and 43% of ground -
based vehicle noise was from automobiles (car, truck, and bus
engines and tires on pavement; WebFigure 1). People sounds
were mainly (89%) voices (WebFigure 1).
Among the subset (38%) of recordings for which acoustic
parameters could be measured for individual noise events (n =
51,754 noise events), watercraft sounds had the highest SEL
(mean ± standard deviation [SD]: 64.6 ± 4.9 dB) and the long-
est duration (mean ± SD: 19.9 ± 1.7 seconds; WebTable 5), and
train sounds had the highest max SPL (mean ± SD: 48.7 ± 13.4
dB). Anthropogenic features that were associated with higher
audibility of noise sources included the density of roads within
the park, and the distance to and traffic volume of nearby air-
ports (WebTable 6).
Front Ecol Environ doi:10.1002/fee.2112

RT Buxton et al.
4 1.
O Noise sources
• Aircraft
M Vehicle
Watercraft
i O Oversnow
O Train
� Grounds care
People
Domestic animal
fit:. O Building sounds
Construction Park boundaries
Figure 2. Ten common noise sources were identified through analysis of recordings made
in US national park units. The proportion of each type of noise source observed in the
recordings indicated the regional presence of some sources (eg trains) and the prevalence
of others (eg vehicles, aircraft). The "oversnow" category includes sound from snowmobiles,
snow coaches, snow groomers, and snow planes.
Spatial extent of noise predicted from geospatial models
The median noise exceedance, measured within each park
unit, ranged from 0 dB to 29.1 dB (WebTable 7). Median
noise exceedance was greater than 3 dB in 77% of 364 park
units (48% of total park area), representing a doubling in
acoustic energy, and median noise exceedance was greater
than 10 dB in 36% of park units (2% of park area; Figure 3;
WebTable 7), representing a tenfold increase in acoustic energy.
Park units with the highest 10% median noise exceedance
had high levels of noise exceedance across the entire park
area (Figure 3). Conversely, noise exceedance values were less
than 3 dB across most of the park area in park units with
the lowest 10% median noise exceedance. Of all noise sources
detected in the acoustic recordings, audibility of ground vehi-
cles was most strongly related to predicted noise exceedance
from geospatial models (parameter estimate ± standard error:
8.64 ± 0.94, R2 = 0.30; WebTable 8). In contrast, duration
of audible aircraft noise events was negatively related to noise
exceedance (-0.04 ± 0.01, R2 = 0.34), because aircraft can be
heard from greater distances at quieter sites.
Noise across different protective categories
Examination of noise across types of park units revealed higher
noise exceedance and audibility in park units designated to
preserve cultural or historic resources (WebFigure 2; WebTable
9). When other landscape variables were taken
into account, noise audibility was highest in parks
designated for recreation (WebTable 6). Natural
resource parks had the lowest noise exceedance
and audibility (WebFigure 2).
Fewer noise events were audible at recording
sites inside relative to outside wilderness areas
(WebFigure 2), even when other landscape fac-
tors were accounted for (WebTable 6). Likewise,
median noise exceedance was lower inside NPS
wilderness relative to non -wilderness areas
(WebFigure 2; WebTable 9). Noise exceedance
in NPS wilderness was slightly elevated, but not
significantly, relative to designated wilderness
in other protected lands (eg Forest Service
land; WebTable 9).
Audibility of noise sources was similar
between designated critical habitats of US endan-
gered species and non -designated areas within
national park units. Similarly, noise exceedance
did not differ significantly between critical and
non -critical habitats within park boundaries
(WebFigure 2; WebTable 9). Noise exceedance
was slightly higher in critical habitats outside of
park boundaries as compared with that inside
park boundaries (WebTable 9).
■ Discussion
The negative effects of noise on a range of animal species,
ecological communities, and human visitors in protected areas
are well documented (Rapoza et al. 2015; Shannon et al.
2016). Noise that overlaps in frequency with important natural
signals compromises a primary sensory system for wildlife
(Swaddle et al. 2015) and people. We assessed the common
noise sources in US national parks, derived from 46,789 hours
of recordings, and how these sources relate to noise levels
predicted by geospatial models. We found that the most
common sources of noise were aircraft, road vehicles, and
people sounds, with vehicles accounting for much of the
variation in median noise exceedance. Our large-scale assess-
ments of the spatial distribution, common sources, and levels
of noise provide insights for spatial planning to implement
the numerous existing tools for reducing noise (NRC 2010).
The most common noise sources require distinct man-
agement approaches. Aircraft noise was spatially extensive
and was audible for longer in areas with low noise exceed-
ance, likely because low ambient sound levels make it easier
to hear all sounds (Lynch et al. 2011). The vast spatial extent
of air transportation networks combined with the regulatory
role of the Federal Aviation Administration (FAA) means
that management of aircraft noise necessitates a collabora-
tive approach. NPS has partnered with the FAA to limit the
spread of noise in parks by routing flights over road corri-
dors (FAA 2012); collaborative mitigation approaches such
Front Ecol Environ doi: 10. 1002/fee.2112

Noise in US national parks
Figure 3. Median model -predicted noise exceedance within each national park unit in the contiguous US indicates large variability in the levels of noise
across units. In the top graph, boxes (25th-75th percentile) and whiskers (2nd-98th percentile) for all park units (n = 396) are overlayed on colors repre-
senting levels <3 dB (dark blue), 3-6 dB (cyan), 6-10 dB (yellow -green), and >10 dB (yellow) (representing a reduction in listening area of less than 50%,
50-75%, 75-90%, and greater than 90%, respectively). Parks were generally inundated with noise >10 dB (eg parks within the boundaries of
Washington, DC), had high noise exceedance in spatially restricted areas (eg noise exceedance was >10 dB in a small area in Death Valley National Park
that experiences high traffic and visitation), or experience near -natural acoustic environments (eg exceedance was <3 tlB in 85% of remote areas in Great
Sand Dunes National Park).
as this can be replicated in areas where our analysis identi-
fied high audibility of aircraft. Noise from road vehicles was
more spatially restricted, but when present was a chronic
source of noise that drove high levels of noise exceedance.
Considering road vehicle noise when devising transporta-
tion plans (eg shuttle systems, speed limits) and designing
park infrastructure (eg "quiet pavement") could be a key
strategy for reducing its effects (Lynch et al. 2011). Finally,
we found widespread sounds produced by people. In the
context of visitor conversation and ranger interpretive ses-
sions, voices are intrinsic to park values and visitor experi-
ence; yet even when appropriate to the setting, these sounds
affect wildlife (eg Buxton et al. 2017b). In areas where
reduced visitor sounds would further enhance natural
resources (eg wildlife -viewing areas), designation of quiet
zones can markedly improve conditions (Francis et al. 2017).
Quiet zones have the additional benefit of enhancing the
visitor experience in these places (Stack et al. 2011).
Geospatial model predictions of noise levels revealed the
ubiquity of noise in national parks. Although NPS lands are
among the quietest of US protected areas (Buxton et al. 2017a),
we found high overall median noise exceedance (>10 dB) in
one-third of US national park units (2% of all park area). These
levels of noise have been shown to affect the body condition
(Phillips et al. 2018), behavior (Klett-Mingo et al. 2016), and
fitness (Schroeder et al. 2012) of many wildlife species.
Ultimately, this level of noise exceedance can affect ecosystem
services, altering processes like seed dispersal and pollination
(Francis et al. 2012). Moreover, we found increases in sound
levels of 3 dB due to noise in almost half of all park area
(pooling among all parks) — levels known to alter, for example,
avian song performance, with detrimental outcomes for com-
petition and pairing success (Davidson et al. 2017). Often, high
noise exceedance was limited to small areas within a park unit.
Our assessment of the distribution and levels of noise within
and among parks indicates areas where management of noise
would generate substantial benefits.
Noise management strategies will depend on the manage-
ment designation of the park, as well as the relationship between
noise sources and visitor experience. Inside national park
boundaries, noise was lower in wilderness than in areas without
such protective designations. The exclusion of motorized vehi-
cles, among the most prevalent noise sources in our analysis, is
critical to maintaining near -natural conditions available in wil-
derness areas managed to provide "outstanding opportunities
for solitude" (Watson et al. 2015). Noise was high in cultural
parks (military, memorial, or historic sites), which are relatively
small (<135 km2) and usually close to large cities. Because these
sites are often embedded within more developed landscapes
outside park jurisdiction, collaborative approaches for noise
diminution are needed in such contexts. We note that models of
noise exceedance generally underestimate the highest sound
levels and overestimate the lowest sound levels (Mennitt and
Fristrup 2016), suggesting that parks identified as loud are likely
Front Ecol Environ doi:10.1002/fee.2112

RT Buxton et al.
louder than estimated and quiet parks are likely quieter than
estimated. In most cases noise exceedance is underestimated,
and as such represents a conservative estimate of noise levels.
Although conditions in national park units are typically
quieter than conditions in their surrounding landscapes, NPS
has legislative mandates to manage parks to superlative
standards of resource quality and visitor experience. As park
units consider accommodating higher levels of visitation,
substantial challenges include designing transportation plans
and park infrastructure that conserve or restore soundscapes.
On large landscape scales, noise management will likely
require collaboration with partners to reduce noise arriving
from outside park boundaries. The variety of noise sources
and their spatial distribution across park contexts emphasize
the need for diverse strategies informed by local knowledge
and partnerships to conserve natural soundscapes for park
visitors, wildlife, and ecological processes.
■ Acknowledgements
We thank numerous park staff and Colorado State University
(CSU) research associates for placing and servicing acoustic
recorders; J Job, C White, D Joyce, and CSU-NPS acoustical
technicians for collecting and analyzing acoustic data; and
B Gerber for statistical advice.
C References
Abbott LC, Taff D, Newman P, et al. 2016. The influence of natural
sounds on attention restoration. J Park Rec Admin 34: 5-15.
Barber JR, Crooks KR, and Fristrup KM. 2010. The costs of chronic
noise exposure for terrestrial organisms. Trends Ecol Evol 25:
180-89.
Basner M, Babisch W, Davis A, et al. 2014. Auditory and non -auditory
effects of noise on health. Lancet 383: 1325-32.
Benfield JA, Taff BD, Newman P, et al. 2014. Natural sound facilitates
mood recovery. Ecopsychology 6: 183-88.
Burnham KP and Anderson DR. 2002. Model selection and multi -
model inference: a practical information -theoretic approach. New
York, NY: Springer-Verlag.
Butchart SHM, Walpole M, Collen B, et al. 2010. Global biodiversity:
indicators of recent declines. Science 328: 1164-68.
Buxton RT, McKenna MF, Mennitt DJ, et al. 2017a. Noise pollution is
pervasive in US protected areas. Science 356: 531-33.
Buxton RT, McKenna MF, Galvan R, et al. 2017b. Visitor noise at a
nesting colony alters the behavior of a coastal seabird. Mar Ecol-
Prog Ser 570: 233-46.
Davidson BM, Antonova G, Dlott H, et al. 2017. Natural and anthro-
pogenic sounds reduce song performance: insights from two
emberizid species. Behav Ecol 28: 974-82.
FAA (Federal Aviation Administration). 2012. Final FAA RNAV and
RNP procedures at Denver International Airport and Rocky
Mountain Metropolitan Airport environmental assessment and
Finding for No Significant Impact (FONSI) and Record of
Decision (ROD). Washington, DC: FAA. https://bit.ly/2m9woF6.
Viewed 20 May 2019.
Francis CD, Kleist NJ, Ortega CP, et al. 2012. Noise pollution alters
ecological services: enhanced pollination and disrupted seed dis-
persal. P Roy Soc Lond B Bio 279: 2727-35.
Francis CD, Newman P, Taff BD, et al. 2017. Acoustic environments
matter: synergistic benefits to humans and ecological communi-
ties. JEnviron Manage 203: 245-54.
Klett-Mingo JI, Pav6n I, and Gil D. 2016. Great tits, Parus major,
increase vigilance time and reduce feeding effort during peaks of
aircraft noise. Anim Behav 115: 29-34.
Lynch E, Joyce D, and Fristrup K. 2011. An assessment of noise audi-
bility and sound levels in US national parks. Landscape Ecol 26:
1297-309.
Machlis G and McNutt M. 2015. Parks for science. Science 348: 1291.
Mennitt D, Sherrill K, and Fristrup K. 2014. A geospatial model of
ambient sound pressure levels in the contiguous United States. J
Acoust Soc Am 135: 2746-64.
Mennitt DJ and Fristrup KM. 2016. Influence factors and spatiotem-
poral patterns of environmental sound levels in the contiguous
United States. Noise Control Eng 64: 342-53.
Miller JR. 2005. Biodiversity conservation and the extinction of expe-
rience. Trends Ecol Evol 20: 430-34.
NPS (National Park Service). 2006. National Park Service manage-
ment policies. Washington, DC: NPS. www.nps.gov/policy/
mp2006.pdf. Viewed 20 May 2019.
NRC (National Research Council). 2010. Technology for a quieter
America. Washington, DC: The National Academies Press.
Phillips JN, Gentry KE, Luther DA, et al. 2018. Surviving in the city:
higher apparent survival for urban birds but worse condition on
noisy territories. Ecosphere 9: e02440.
Rapoza A, Sudderth E, and Lewis K. 2015. The relationship between
aircraft noise exposure and day -use visitor survey responses in
backcountry areas of national parks. J Acoust Soc Am 138: 2090-
105.
Schroeder J, Nakagawa S, Cleasby IR, et al. 2012. Passerine birds
breeding under chronic noise experience reduced fitness. PLoS
ONE 7: e39200.
Shannon G, McKenna MF, Angeloni LM, et al. 2016. A synthesis of
two decades of research documenting the effects of noise on wild-
life. Biol Rev Camb Philos 91: 982-1005.
Stack DW, Peter N, Manning RE, et al. 2011. Reducing visitor noise
levels at Muir Woods National Monument using experimental
management. J Acoust Soc Am 129: 1375-80.
Swaddle JP, Francis CD, Barber JR, et al. 2015. A framework to assess
evolutionary responses to anthropogenic light and sound. Trends
Ecol Evol 30: 550-60.
Watson A, Martin S, Christensen N, et al. 2015. The relationship
between perceptions of wilderness character and attitudes toward
management intervention to adapt biophysical resources to a
changing climate and nature restoration at Sequoia and Kings
Canyon National Parks. Environ Manage 56: 653-63.
• Supporting Information
Additional, web -only material may be found in the online
version of this article at http://onlinelibrary.wiley.com/doi/10.
1002/fee.2112/suppinfo
Front Ecol Environ doi: 10. 1002/fee.2112

National al Park Service

San Diego, CA
NOISE -CON 2019
2019 August 26-28
Quarry Noise Model
Juliet A. Page, Alexander Oberg, Aaron Hastings, Gary Baker Kira Glover -Cutter
Volpe National Transportation Systems Center, US DOT Oregon Department of Transportation
55 Broadway, Cambridge, MA 02142 Salem, OR 97301 555 13'h St. NE, Suite 2
[e-mail] Salem, OR 97301
ABSTRACT
The Oregon Department of Transportation (ODOT) sponsored the development of a tool to help
predict noise exposure in areas surrounding ODOT aggregate source sites (rock quarries) by the
USDOT's Volpe Center. The Quarry Noise Model (QNM)' has three inter -communicating
components: QNM Graphical Information System (GIS) Module, Noise Database and Acoustic
Engine. The GIS Module represents the Graphical User Interface (GUI) for QNM and harnesses
both custom and existing tools within ESRI's ArcGIS to streamline the analysis of quarry noise
scenarios. Two types of parameters are included in the noise database: classification parameters,
which are used to identify the source, and emission parameters, which characterize the emitted
noise. The Acoustic Engine leverages the Advanced Acoustic Model (AAM)2 which has been
updated to include the ability to model operations from equipment distributed over a quadrilateral
area, calculation of attenuation due to surrounding foliage, and a simplified blast model for
prediction of initial blast overpressure. This paper describes QNM, its acoustic database, and the
enhancements and adaptations made to AAM in support of QNM development.
1. INTRODUCTION
Large regions of Oregon rely heavily on material from Oregon Department of Transportation
(ODOT) quarries for projects such as road maintenance. Recently, the potential listing of sage
grouse as an endangered species prompted ODOT to consider whether activities from ODOT
aggregate source sites would comply with potential Oregon Department of Fish and Wildlife
(ODFW) recommended mitigation. Given that noise levels of ODOT quarry operations have yet
to be fully investigated, a need was identified to collect, analyze, and model quarry noise data for
development of a methodology that can be applied throughout the state to determine potential noise
impacts within noise sensitive species habitats. The key objective of this project was to develop a
GIS-based noise model of ODOT quarry operations that can be visually mapped in relation to
noise sensitive receivers.
QNM has three inter -communicating components at its heart: QNM Noise Database, Acoustic
Engine, and QNM GIS Module. The QNM GIS Module was developed expressly for the purpose
of identifying, in a georeferenced fashion, quarry operations that create noise. The GIS Module
streamlines the running of the Acoustic Engine using these data and receives the resultant noise
calculations that may be displayed within the GIS environment.
2. QNM GIS MODULE
The QNM GIS Module represents the Graphical User Interface (GUI) for QNM. The GIS Module
harnesses both custom and existing tools within ESRI's ArcGIS interface to streamline the process
of creating and running quarry noise scenarios. The QNM Manual' provides detailed instructions.
Below is a brief overview of the QNM GIS Module functionality.

Quarry Noise Model
Page, Oberg, Hastings, Baker and Glover -Cutter
• Create and populate the scenario inputs geodatabase with quarry geographic data, including
points, routes and areas that host quarry operations;
• Prepare Elevation, Impedance and Foliage Data;
• Define Operations (Point, Route, Area, and Blast); and
• Run the Advanced Acoustic Model (AAM) and process AAM Outputs.
A. QNM Toolbar
The QNM GIS Module consists of existing ArcGIS 10.5.1 functionality, which must be installed
on the user's computer, along with two Python -based ArcMap add -ins: Quarry Noise Toolbar and
Quarry Noise Toolbox. The Quarry Noise Toolbar and Toolbox are designed for users with some
prior experience using ArcMap.
B. Preparing the Quarry Noise Scenario GIS Data
Before running the quarry noise module, the user must prepare the input data associated with the
quarry. The first step in this process is to run the Create Input Geodatabase tool within the Quarry
Noise Toolbox. The purpose of this tool is to generate a template scenario input geodatabase that
contains empty feature classes that will store the quarry features (points, road segments, routes,
and areas), along with other supporting feature classes and tables. The output of this tool is a
geodatabase called scenario_inputs.gdb, which includes the following GIS layers and tables:
• Points, Road Segments and Areas: Store the three categories of geographic data where
quarry noise operations can occur;
• Routes and Routes_ Description: Store the information related to the series of road segments
over which a quarry noise operation might travel;
• Area of Interest (AOI): Utilized by QNM GIS Module to calculate the scenario's analysis
extent and clip elevation, impedance and foliage data during preparation;
• Operations table: Store the linkage between a noise source and the geography the noise source
will be utilizing in the scenario;
• Foliage: Optional polygon feature class for identifying areas where foliage impact on sound
propagation is to be computed;
• Points of Interest (POI): Optional feature class for storing points where the user would like
to determine specific noise impacts; and
• Quarry Boundary: Optional feature class used to help calculate the `worst -case scenario'
directionality of point noise sources.
C. Digitizing the Quarry Noise Scenario
The most time consuming part of the scenario preparation is the digitization of the quarry
geography using built-in ArcMap functionality. While it is possible to use existing GIS and CAD
data for the quarry to help populate these datasets, it is important that the data fit into the template
generated by the Create Input Geodatabase Tool.
D. Quarry Noise Toolbar
The purpose of the Quarry Noise Toolbar is to utilize the ArcMap interface to define routes that
will be utilized by quarry noise sources and AAM for the purposes of modeling noise. There are
three components to the toolbar: "Initialize Route", "Prepare Route from Segments", and "Finalize
Route." Within this tool, the user can select the road segments that make up a route. Road
segments must be selected in the order they are traversed, and a road segment must neighbor the
previously selected road segment. Figure 1 shows an example of what the ArcMap display might
look like after populating a quarry scenario with actual GIS data.
NoiseCon 2019, San Diego, CA, August 26-28, 2019

Quarry Noise Model
Page, Oberg, Hastings, Baker and Glover -Cutter
Figure 1: ArcMap display after quarry scenario digitization
E. Defining Quarry Operations
Once the Quarry Noise Scenario digitization is finalized, the user is ready to define quarry
operations. There are a series of four tools in the Quarry Noise Toolbox that streamline the process
of defining a quarry operation by allowing the user to link a noise source to the geography that a
noise source is utilizing. These four tools, used to specify the various activities at a quarry, are
described in detail in the QNM manual', and include:
• Define a Point Operation: Used for stationary equipment with user defined heading or worst
case scenario that orients the loudest noise to the closest quarry boundary;
• Define a Route Operation: Used for equipment traversing routes- user defines the speed at
each node along the route;
• Define an Area Operation: Used for distributed operations- user defines total operations and
number of points along the primary (longest) and secondary (shortest) quadrilateral and
distributes operations spatially and azimuthally over that area; and
• Define a Blast Operation: Used to separately model blast operation airborne noise- user
defines the state of confinement factor and instantaneous charge per hole.
F. Prepare Elevation, Impedance and Foliage Data
The purpose of these tools is to generate elevation, impedance and foliage data compatible with
QNM. Before running the tools, the user must have a GIS-based raster elevation dataset. The user
can use pre-existing elevation data or generate their own using other more detailed data, such as
quarry -based CAD data.
• Prepare Elevation Data: Clips elevation data to the area of interest (AOI), re -projects
elevation data to the user -defined spatial reference, and converts into the AAM format;
• Prepare Impedance Data: Generates impedance data in AAM format using a GIS-based raster
dataset of impedance data; land cover data may be reclassified using ArcMap's Reclassify
tool into ground impedance values per guidance in the AAM User Manual; and
• Prepare Foliage Data: Generates foliage height data in AAM format; unlike elevation and
impedance data, foliage data are not required for running QNM, however including it can
enhance the accuracy of analyses in areas where foliage may impact noise propagation.
G. Prepare Noise Model Inputs and Running AAM
The purpose of the Prepare Noise Model Inputs tool is to prepare the AAM input deck based on
all quarry geography and operation information. The user must specify meteorological data,
NoiseCon 2019, San Diego, CA, August 26-28, 2019

Quarry Noise Model Page, Oberg, Hastings, Baker and Glover -Cutter
including temperature, pressure and relative humidity. The tool then creates a batch file that
enables the user to run AAM outside of the QNM GIS Module.
H. Process Outputs
After AAM runs using the batch file, the Process Outputs tool converts AAM output files into
GIS-formatted files that can be viewed in ArcMap. The GIS files generated include:
• Point feature classes representing noise at POIs;
• Raster datasets representing noise over the AOI for each noise measurement; and
• Polyline feature classes representing noise contours over the extent of the AOI.
The output feature classes and raster datasets from this tool can be added to a map in ArcMap for
analysis and visualization. Figure 2 is an example within the ArcMap interface.
fi Fdt Ywx BwbWa Mw, S.Mcti.n Cwa .a fuYw,in VhW.e I p
- o x
Gdbp
Fyyi.�3iu.w��LPNIJodw.,
�CFJ\ipapaM
=3 a
=o CNMSJe13
w3 gCpppygµbr��4
aa� po,pmyyy�
Cl F.kp�loi]}umie,M,P[.
x � fiikpc iNi-MS
B Fwq fe®9H� [Mlwe:
..�F.i�1W j.MIL
��Fdbp�Feil31
� Wkp� M3 i3O
Fy�glp,jyl0 mMdn
=�F�bp�,Wi}50
C9 WkR fos3.S5C ca.ta.s
:�Fdbry fy1]S�
f3 qd�. I W 3.Ib0 aantwa
■qu�ipi3}uaX
a rdar• rer� 3 unx m.a..
n Fi�w+.IW1.5nd. iVl
�aH�,sm�iw
•-�ChVSen, &n,
Figure 2: Example Noise Output within the ArcMap Environment
3. QNM NOISE DATABASE
This section describes the methodology used to develop the QNM Noise Database and includes
information about the noise data parameters, origins, standardization, aggregation and
organization. There are a total of 474 pieces of equipment in the database with effective source
heights identified. The data are organized into 1000, 2000 and 3000 series, depending on the
quality and confidence of the data sources. The data contain one-third octave band, free -field data
at a reference distance of 1000 ft. and source height with four ordinal angles of directivity, where
applicable and available.
A. Noise Data Parameters
Noise data parameters are required in order to identify and accurately model noise emissions and
propagation. Two types of parameters are included in the noise database: classification parameters,
used to identify the source, and emission parameters, which characterize the physics of the emitted
noise. The classification parameters are: Source ID, Source Name, Source Mode and Operation.
The emission parameters are: Source Height, Noise Metric, Orientation and the Z-weighted One-
third Octave Band Levels from 10 Hz to 10 kHz.
B. Noise Data Origins
The QNM has an expandable database allowing for newly measured sources to be added, however
the initial database was developed using three main sources of existing data. The first was a set of
reports, copies of which ODOT provided to the Volpe Center. The second was a single report
NoiseCon 2019, San Diego, CA, August 26-28, 2019

Quarry Noise Model Page, Oberg, Hastings, Baker and Glover -Cutter
generated specifically for ODOT, thus representing specific equipment found at ODOT sites. The
third was a set of source data collected for the National Highway Cooperative Research (NCHRP)
Project 25-49 for an update to the Federal Highway Administration's (FHWA) Roadway
Construction Noise Model (RCNM).3 As none of these data sets were developed for the specific
purpose of supplying the QNM with noise emission data, the degree of suitability varied across
the sources. The QNM manual summarizes the compatibility of the various data sets.
C. Standardization of Noise Data
For each noise source, the noise data were standardized to Z-weighted, one-third octave band
sound pressure levels at the source's height, 100 feet from the source. When sufficient data were
available, the sources were converted to the free -field (i.e. ground effects removed) using AAM.
Correction to the reference distance of 100 feet was accomplished by subtracting the spherical
spreading attenuation at the measured distance and adding the spherical spreading attenuation at
100 feet. Attenuations were computed using AAM (note this correction uses the AAM acoustic
module, as does QNM, to propagate the sound from the reference distance to receptor locations).
Only one-third octave band and octave band data were considered. Estimation of one-third octave
band levels from overall levels was not performed. One-third octave band data were estimated
from octave band data using a spline interpolation. Some original data were reported using
A -weighting. Because AAM utilizes Z-weighting data, these were converted from A- to Z-
weighting. In other cases, reported levels were in sound power level rather than sound pressure
level; these were converted to sound pressure level at 100 feet.
D. Aggregation and Organization of Noise Data
Noise data are organized in the database in three series:
• 1000 Series - 77 sources: RCNM and ODOT data (appended with PV for Pleasant Valley)
• 2000 series - 314 sources: each entry represents a single piece of equipment and activity
• 3000 series - 70 sources: aggregated sources based on combinations of 1000 and 2000 series
The QNM manual and associated database codex file provides details on each data source, quality
and aggregation procedure. The database has been processed into a NetCDF format file for use
with AAM, and QNM includes a tool for users to add their own custom data sources.
7. QNM ACOUSTIC ENGINE
Quarry Mode functionality, including use of the Quarry Noise Database, was initially incorporated
into AAM Version 2.3. The QNM GIS module creates an ASCII input file (.INQ extension) that
consists of keyword controlled options. The new SETUP QUARRY keyword accepts a range of
possible geometric parameters including points, roads, routes, areas and operational types,
including moving, static and distributed quarry and blast operations. The QNM manual provides
details of the input file structure in case a user wants to make modifications manually outside of
the features available in the QNM GIS module.
Recent additions to AAM physical modeling include the ability to model operations from
equipment that is distributed across a quadrilateral area, the addition of attenuation due to
surrounding foliage and the calculation of airborne blast noise from quarry operations. These
selected keyword features and physical modeling additions in AAM are described below.
A. Distributed Operations
The DISTROQOPS keyword is used in AAM to define distributed operations over quadrilateral
areas in the quarry, with inputs as illustrated in Figure 3. The user defines the number of
operational/fractional hours (with user -defined split between daytime and night time) and the total
number of a pieces of equipment operating. Noise exposure is accumulated using the user defined
total time. The area is divided up into points which are used to model the equipment over that area.
NoiseCon 2019, San Diego, CA, August 26-28, 2019

Quarry Noise Model Page, Oberg, Hastings, Baker and Glover -Cutter
The user can specify how many points are used for the noise calculations. An example with a
distributed quarry operation (DIRTMOVE01) is illustrated in Figure 3. The area is divided into a
mesh of 8 x 5 points (black rimmed blue dots). These 40 points will be used acoustically to model
noise from the equipment.
• .51 DIRTMO VED1
•
• P012
•
Figure 3: Example of distributed operations with acoustic modeling nodes shown.
B. Foliage Attenuation Modeling
The AAM foliage model utilizes the method described in ISO 9613-2, 1996.4 The method applies
only in instances where tree and shrub foliage is sufficiently dense to completely block the view
along the propagation path and when it is impossible to see a short distance through the foliage.
The .FOL binary file is structured similarly to the .ELV and .IMP files and contains trees or shrub
foliage height for only those regions meeting the foliage density criteria.
The effective propagation distance df used to compute attenuation through dense foliage is the
summation of the line of sight blockage near the source (di) and receiver (d2), along a ray
propagating at an angle of 15' (Figure 4) with respect to the horizon. For the purposes of
computing foliage attenuation, the ground is treated as a straight line between the source and
receiver locations (local terrain effects are ignored in computing di and d2) in AAM. The 15' ray
path is modeled as an arc and computed independently at the source and receiver sides. The rays
may not connect in the middle if the source and receiver heights are different. The curved ray path
radius is assumed to be 5 km as noted in the standard. A height comparison is made at incremental
distances until the ray exits the foliage and the ray path propagation distances through the foliage
(di and d2) are calculated and summed. Subsequent secondary ray reentry into foliage due to the
presence of taller dense foliage is not considered. The ISO standard defines attenuation for octave
bands. Within AAM these are applied to each of the one-third octave bands within the associated
octave band.
Figure 4: Geometry for computing foliage df=dr+d2 parameter. (Source: ISO9613-2)
C. Quarry Blast Noise Modeling
AAM includes a simple model for airblast noise modeling for quarry operations. Airblast is an
airborne shock wave that results from explosive detonation and is dependent on the explosive
charge weight per hole and the confinement factor (an indication of the degree of explosive
confinement). This may be used to estimate the overpressure from airblast operations in an
aggregate manner and does not take into account specific blasting sequences, depths or orientation
or the phasing that may result. The simple blast model adopted for QNM encompasses four parts:
NoiseCon 2019, San Diego, CA, August 26-28, 2019

Quarry Noise Model
Page, Oberg, Hastings, Baker and Glover -Cutter
• Airblast overpressure calculation from the user defined maximum blast charge, based on the
Minnesota recommended models, a derivative of the Linehan and Wiss methodology"
with the propagation exponent modified to remove ground effect;
• Ground effect computed based on local terrain using the AAM methodology';
• Blast event duration, as specified by the user; and
• Normalized spectrum application based on published Tunisia empirical data.
The equation P = x �Qo 3� Bexp presents the method for calculating the blast overpressure, where P is
the overpressure (kPa), K is the state of confinement factor (3.3 indicates fully confined and 185
indicates unconfined explosions), Bexp is the Blast Propagation Exponent, Q is the maximum
instantaneous charge (lb) and R is the distance from the charge (m). (The metric relation is
presented here for consistency with literature; units of Ft are used in AAM with appropriate
conversions applied). The propagation coefficient in the Minnesota model has Bexp = -1.2,
however this has been updated to Bexp =-0.8552 in QNM to remove the ground effect so that it
can be replaced with the AAM calculations to take into account terrain effects.
The state of confinement factor (K) � can be empirically derived for a particular quarry if blast data
are available. Analysis of Tunisia quarry noise measurement data suggests a suitable empirical
value of K=6.5 for the state of confinement factor, for the conditions experienced during those
blasting events, and a Bexp of-0.8552. Data and analysis is provided in the QNM manual.' While
this simplified blast noise model implemented in QNM is omnidirectional, propagation takes into
account the surrounding terrain and shielding, as well as the effects of nearby foliage, and applies
them spectrally to the blast noise at the receiver location. A side by side example of blast noise
predictions considering propagation over flat and natural varying terrain is provided in Figure 5.
One can see the impact terrain shielding has on the signal, but also the places where terrain actually
amplifies the sound. Generally, noise reductions are in areas where line of sight between source
and receiver is broken by the intervening terrain, and areas that increase in noise are often the side
of a hill that is facing the blast.
108DOOC1 PSFina%' 1 2 3 4 5 6 7 8 9 101112 1080000 PSFinax' 1 2 3 4 5 6 7 8 9 101112
1078000 1076000
1075000 1076000
s
2060000 2062000 2064000 2006000 2068C 2060000 2062000 2064000 2066000 20680DO
Figure 5: Comparison of Two Blast Noise Events Propagated over Flat (left) and Natural (right) Terrain.
Using the Tunisia data, the QNM blast propagation exponent was determined) by matching the
slope of the curve of overpressure levels with distance to the empirical data. The curve was then
adjusted by varying the confinement factor to match the overpressure amplitudes. This resulted in
a propagation exponent of-0.8552 and a state of confinement factor of 6.5. The scatter in the
I Confinement is a controllable variable in the blast design which can be optimized to maximize yield while
minimizing noise, vibration and fly rock. It is often the result of the amount of stemming material in the drill hole
surrounding the explosive, the burden and spacing of the drill holes, and the type and amount of spacing, but can also
depend on the local geology, rock type and placement of the drill holes relative to an open face.
NoiseCon 2019, San Diego, CA, August 26-28, 2019

Quarry Noise Model Page, Oberg, Hastings, Baker and Glover -Cutter
predictions is commensurate with the scatter in the empirical data. One could use the same
procedure with QNM analysis for any quarry to obtain empirical based values for Bexp and K.
The QNM overpressure is capped at a minimum distance of 250 ft. from the blast location. Due to
the exponential formulation of the simple blast model, excessive and unrealistic overpressure
values would otherwise result for shorter distances. A reference blast spectrum$ was also obtained
for QNM metric calculation. Because the primary energy of the blast is below 10 Hz, and AAM
only includes 10 Hz to 10 kHz data, additional energy from the 6.3 and 8 Hz bins was lumped into
the 10 Hz band so that it could be accounted for in cumulative metrics. This simplified
methodology in QNM does not account for the actual non -linear signature evolution of the blast
pressure -time history as it propagates to the receiver. A limitation of this method is that the nominal
blast spectrum, derived from a distance of 1000 ft., is assumed to apply to the full area of interest
being calculated using QNM. In reality, non -linear propagation effects will transform the pressure
time history with distance and change the spectral content, moving energy from one band to
another. Accounting for this kind of non -linear propagation is beyond the capabilities of the current
QNM model. It is recommended that for critical points of interest, empirical data be used to check
that the assumed spectrum is applicable at that distance and the predicted results are valid. By
using the DIAGNOSTICS keyword in power -user mode, AAM will output the analytical spectra
employed in the model for checking against empirical data.
SUMMARY
The QNM model has been developed for the ODOT. It is available as a derivative work from the
Volpe National Transportation Systems Center, U.S. Department of Transportation for users with
approved NASA AAM Software Usage Agreements. Contact the authors for further information.
ACKNOWLEDGEMENTS
The authors express their gratitude to the Oregon DOT for funding this work, and especially to
Kira Glover -Cutter for her project leadership, attention to detail and tireless model testing. Many
thanks are also extended to Scott Billings for his guidance and to the advisory panel for their input
and feedback, which helped shape the Quarry Noise Model. Finally we also extend thanks to the
other Volpe team members who supported this project, especially Anjuliee Mittelman.
REFERENCES
1. Page, J.A., Oberg, A., Hastings, A., and Baker, G., "Quarry Noise Model User Guide and Technical Reference,"
DOT-VNTSC-ODOT-18-01, US Department of Transportation, December 2018.
2. Bradley, K.A., Hobbs, C., Wilmer, C., Czech, J.J., "Advanced Acoustic Model Technical Reference and User
Manual", Wyle Research Report WR 16-08, April 2017.
3. El-Aassar, A., Alexander, A., Carpenter, S.P., Bowen, D., Hastings, A., "Development of a Highway Construction
Noise Prediction Model Final Report", NCHRP Project No. 25-49, September 2019. h!Ws://gpps.trb.or crosfeed/
trbnetproj ectdisplay.asp?prof ectid=3 889
4. ISO 1996. International Standard Organization (ISO) "Acoustics — Attenuation of sound during propagation
outdoors — Part 2: General method of calculation", 9613-2, Dec 15, 1996, p.15.
5. Minnesota, 2015. Minnesota State Website, Dyno-Noble manufacturer information, accessed 13 Oct. 2018.
https://www.leg.state.nm.us/docs/2015/other/150681/PFEISref l/Dyno%20Nobel%202010.pdf
6. Linehan & Wiss, 1982. "Vibration and Air Blast Noise from Surface Coal Mine Blasting", Mining Engineering
Journal 34, 1982, pp. 391-395.
7. Aloui, Monia, Yannick Bleuzen, Elhoucine Essefi, Chedly Abbes, 2016. "Ground Vibrations and Air Blast
Effects Induced by Basting in Open Pit Mines: Case of Metlaoui Mining Basin, Southwestern Tunisia", J. Geol
Geophys 2016, 5:3. http://dx.doi.org/10.4172/2381-8719.1000247
NoiseCon 2019, San Diego, CA, August 26-28, 2019

inEer.noiie
'2012
august 19-22
AL new ycek city. wo
o-j.��-' �ti� woea•.oi�t�.
Proactive noise control at a rock quarry next to a residential
neighborhood
Marc C. Wallace')
Ryan T. Callahan)
Tech Environmental, Inc.
303 Wyman Street
Suite 295
Waltham, MA 02451
An existing 60 year old quarry operation located in the northeastern United States sought
state approval to reconfigure and upgrade their rock crushing equipment. Changes
included moving some of their equipment closer to a residential neighborhood abutting the
facility. The residential development was built during the housing boom in the early part of
the last decade. Property owners had signed deeds acknowledging potential noise and dust
issues with the adjacent quarry. The quarry owner has proactively worked with the
neighbors to minimize noise and dust emissions from their existing operations. Many of the
neighbors were concerned about noise from the relocated plant, a growing concern in
today's society as new residential developments are built next to industrial facilities.
Placing loud industrial equipment near homes poses unique challenges for noise control
engineers and communities. Tech Environmental worked closely with the owner and the
community to develop and evaluate various types of sound mitigation strategies. This
approach allowed the quarry to demonstrate compliance with state and town noise limits
and appease most of the neighbors. Working with noise professionals and applying the
correct sound mitigation allows industrial activities to coexist with nearby residents.
1 INTRODUCTION
A quarry owner needed to upgrade their nonmetallic mineral processing plant (crushing
plant) and relocate the secondary and tertiary crushers closer to the primary crusher. The existing
crushing plant was over 60 years old and was in need of significant improvements. The relocated
secondary and tertiary plants would be moved approximately 400 meters north of the existing
location, closer to a residential neighborhood. The quarry owner sought State and local approval
to reconfigure and upgrade their rock crushing equipment closer to a residential neighborhood
abutting the facility. As one would expect, many of the neighbors were concerned about noise
from the relocated plant. Fortunately, since taking ownership of the quarry, the owner has
a) email: [e-mail]
b) email: [e-mail]

proactively worked with the Town and the neighbors to minimize noise and dust emissions from
its existing operations. Tech Environmental worked closely with the owner and the community
to develop and evaluate various types of sound mitigation strategies and a compliance
monitoring program to comply with State and Town noise limits while trying to appease most of
the adjacent neighbors.
This paper will discuss the technical approaches used to demonstrate that the relocated
crushing plant complies with State and local regulations, the sound mitigation measures
implemented and the efforts to work with the community.
2 DESCRIPTION OF QUARRY OPERATIONS
A quarry owner operates a crushing plant in the northeast United States, which has been in
existence for over 60 years; the quarry produces crushed stone, gravel and sand of various sizes
and quantities. The quarry owner purchased the property from the previous owner in 2000. The
owner wanted to reconfigure and upgrade their existing crushing operation to improve overall
crushing operations and to obtain access to future reserves of rock deposit onsite. The primary
crusher remained in the same location, but the secondary and tertiary crushers, located in the
southern portion of the site, were moved approximately 400 m closer to the primary crusher in
the center of the site. The primary upgrades to the plant included removing two 1.8 meter (m) x
4.9 m screens; replacing one 1.8 m x 4.9 m screen and three existing 1.5 m x 4.9 m screens with
one 2.4 m x 7.3 m Double Deck Diester Screen, two 2.4 m x 7.3 m Double Deck Diester Screens
and one 2.4 m x 7.3 m Triple Deck Diester Screen. The quarry produces about 1,000,000
metric tons per year of crushed stone and sand products. Figure 1 presents a site plan showing
the original facility location and property boundaries and Figure 2 shows the new locations of the
crushing plants.
3 ACOUSTIC MODELING ANALYSIS
Tech Environmental performed an acoustic modeling analysis as part of the state air quality
permit required for relocating the existing plant. To predict the sound impact on nearby residents
and noise sensitive areas resulting from the movement of existing equipment to a new location
within the quarry, the Cadna-A acoustic model was employed. Cadna-A is a comprehensive 3D
noise modeling program based on ISO 9613-2. The model used terrain features of the quarry and
surrounding area imported from the State GIS database. This was an especially important step
due to the extreme elevation differences of quarry activities and bordering residents. The
residents nearest to the relocated activity are elevated and look down into the quarry. This
scenario makes noise mitigation challenging. Modeled ground absorption was conservatively
assumed as 0.5 (not entirely absorptive or reflective) except in areas of pavement or rock, which
was assumed to be zero (completely reflective). Sound power levels calculated from actual
measurements taken within the quarry were used to represent the relocated equipment. Once the
position of all the equipment was input into the model, calculations were made at specific
receptors (the closest residents to the north, west, and noise sensitive areas to the east). Figure 3
shows the 10 receptor locations used in the acoustic model representing site property boundaries
and nearest residential and industrial properties.
In addition to specific receptor information, a grid of results was calculated. Earth berms
proposed by the quarry owner to reduce the visual and sound impacts from the relocated plant
were also included as part of the model. In addition to including the crushers, screens and
conveyors, front end loaders, haul trucks and backup alarms were also included in the model to

provide a complete picture of the sound sources in the quarry. Based on the initial modeling
results, it was clear that although the earth berms would significantly reduce potential sound
level increases to the nearest neighbors, additional sound attenuation of the relocated crushers
and screens was needed. Noise barriers of various dimensions and orientations were evaluated in
over 20 model simulations. The results of the additional noise barrier modeling analysis revealed
that a three -sided wall on the secondary and tertiary crushers and screens and a single -sided wall
on the two new 2.4 in x 7.3 in Double Deck Screens would be needed to provide additional
shielding. The effect these modeled barriers had on the sound contours allowed the optimization
of both the size and placement of the barriers.
4 SOUND MITIGATION MEASURES
As discussed above, the quarry owner proposed to build three earth berms around the site.
The quarry owner built a 30-foot-high earth berm along the northern portion of the site shielding
the residential neighborhood. The second earth berm will extend along a portion of the eastern
site boundary and a third earth berm adjacent to an industrial park will be along a portion of the
southern boundary adjacent to a two-lane state roadway (See Figure 4). The second and third
earth berms are near wetland areas and will be constructed over time upon receiving approval
from the Town's wetlands protection commission. The critical earth berm closest to the
residential neighborhood shielded most of the residences closest to the new location of the
secondary and tertiary crushers and screens. In addition, the loudest sound sources, the
secondary and tertiary crushers, would also be located at the bottom of the quarry floor
approximately 40 feet below the elevation of the nearest homes. As discussed above, the acoustic
modeling analysis also included a three -sided wall on the secondary crusher and screen, a two-
sided wall on the two tertiary crushers, and a single -sided wall on the two new 2.4 in x 7.3 in
Double Deck Screens to provided additional shielding. These sound absorbing walls were
attached to the steel framing of each unit. The quarry operator also arranged aggregate
stockpiles in the shape of kidney beans to reduce sound from facility operations. All of these
measures are part of the quarry owner's Best Management Practices (BMP) to reduce sound
levels at noise -sensitive areas within 305 in that could be impacted by a source. Additional
sound mitigation included rubberized chutes to reduce rock on metal sound impacts.
The acoustical modeling performed for the project required that the sound walls must
achieve an overall 12-dBA reduction. To achieve the required sound reduction, the minimum
STC rating of 30 was needed with a minimum NRC rating of 0.95. The quarry owner asked us to
investigate vendors who could provide sound wall panels that were light weight and capable of
being installed directly onto the steel framing of the crushers and screens. Koch Industries, Inc.
met these design and sound reduction requirements. The panels installed are 102 millimeters
(mm) thick. Each panel is constructed of a back sheet that consists of solid 16 gauge galvanized
steel, and a front sheet that consists of perforated 22 gauge galvanized steel. The top and bottom
side channels are 18 gauge galvanized steel. Each panel is filled with 64 kg/m3 density mineral -
fiber insulation with a total density of each panel of 50 kg/m3.
The sound panels were installed on three sides on both the secondary and tertiary crushers
and single panels were installed on the screens to block sound directed at the residential
neighborhood based on the results of the acoustic modeling analysis.

5 WORKING WITH THE COMMUNITY
The quarry owner purchased the property from another quarry operator in 2000. The site is
surrounded by industrial land on the west and east sides and by a strip of undeveloped land on
the north side. The land to the north of the of site was rezoned from industrial to residential in
the early part of the last decade which allowed for a residential development to be built with 42
new homes. Each property owner's deed includes language acknowledging that they are
purchasing property adjacent to a quarry that may cause fugitive dust and noise issues. Over the
years, the quarry owner has been proactive in working with the Town and the neighborhood to
minimize dust and noise conditions as part of their blasting and crushing operations.
The quarry owner needed to obtain a building permit from the Town planning board to be
able to relocate their quarry processing plant. As part of the process, the quarry operator and
Tech Environmental held an open house at quarry for the planning board and for any Town
residents to take a tour of the facility and to show proposed plans and drawings of the relocated
quarry plant. As part of the building permit process, the planning board wanted an
environmental consulting firm to conduct a third -party review of all engineering and
environmental permitting documents including any acoustic studies. The quarry owner and
Tech Environmental held an additional site visit and walk through the adjacent neighborhood, so
that the planning board and their consultant could get a better understanding of the project.
Over a dozen meetings were held with planning board to discuss the project and make
recommendations on minimizing the environmental impacts. The initial meetings were attended
by dozens of concerned citizens. As the permitting process moved forward, the quarry owner and
Tech Environmental made several presentations and worked with the planning board and their
consultant to address their concerns. To ensure that relocated plant and sound mitigation
measures will meet the State and local sound limits, a sound compliance monitoring protocol
was developed to implement periodic sound testing throughout the first year of operation. Most
of those who live in the residential neighborhood were satisfied that the quarry owner had met
most of their concerns, however a small, but vocal group of residences were not satisfied and
would hold judgment until the compliance monitoring program was implemented.
6 COMPREHENSIVE SOUND COMPLIANCE MONITORING
After all mitigation measures were installed and normal operation of the quarry
commenced, it was necessary to demonstrate compliance with both Town and State noise
regulations. Per the requirements of the Sound Compliance Monitoring Protocols, created by
Tech Environmental and approved by the Town's own noise consultant and the State
environmental protection agency, three consecutive months of sound compliance monitoring was
performed during the first full year of operations followed by semi-annual monitoring the second
year of operation. The protocol presented the method for sound compliance monitoring. The
objective was to confirm that the relocated plant, once in operation, complied with both the
Town and the State noise regulations. Per the requirements of these monitoring protocols, sound
compliance monitoring was performed when the primary, secondary and tertiary crushers were
all in full operation. The quarry owner notified the Town prior to each monitoring session, so
that planning board members or residents could observe the monitoring. Sound compliance
measurements were taken at 10 locations representing three site property boundaries, five
residential -zoned properties, and two industrial -zoned properties. These locations represented
both background monitoring and modeling locations used in the acoustic modeling analysis (See
Figure 3).

All sound level measurements were made with two ANSI Type 1 real-time sound analyzers
(ANSI Standard S I A) that were calibrated to NIST standards within the previous 12 months and
were field -calibrated with an ANSI Type 1 calibrator. The Larson Davis 824 and Bruel & Kjaer
(B&K 2250) sound analyzer microphones were tripod -mounted at a height of 2 m, in accordance
with ANSI Standard 512.18-1994 and equipped with wind screens. Measurements were not
made during periods of precipitation or when winds exceed 5 m/s (II mph). Concurrent
observations of audible activity at the quarry, other industrial sites and other noise -producing
sources were recorded by the sound engineer on field data sheets.
The sound compliance monitoring recorded the A -weighted L90 sound level and L90 whole
and one-third octave band levels for a minimum of 15 minutes at each monitoring location in
order to demonstrate compliance with the State regulations. The Town noise regulations did not
specify what sound metric was to be used. For the purposes of this monitoring program, the
equivalent sound level Leq was selected. The regulations specified different limits for
"continuous noise" and "sporadic noise." The "continuous" residential and industrial sound
limits are 50 dBA and 65 dBA, respectively, and the "Sporadic" sound limits are 10-dBA higher.
Sporadic noise is defined as sound lasting one minute or less. Therefore, the sound compliance
monitoring recorded fifteen (15) one -minute A -weighted Leq sound level measurements at each
monitoring location and those levels were compared to the Sporadic and Continuous Sound
Limits.
After each compliance monitoring session, a sound compliance monitoring report was
submitted to the Town and State for their review. Both the monthly and semi-annual compliance
monitoring reports demonstrated that continuous and sporadic sound levels generated from the
quarry were below the Town and State noise regulations. Even so, the quarry owner continued
to make additional sound attenuation improvements on their operations, and as a result,
measured sound levels in the adjacent neighborhood were lower during the second year semi-
annual compliance monitoring.
The quarry owner and Tech Environmental attended planning board meetings that were
open to the public to discuss the results of the monthly monitoring reports. Many of the residents
were satisfied, but, some residents living closest to the quarry were skeptical of the results of the
compliance monitoring. They believed that the monitoring procedures did not capture highest
sound impacts from the quarry and that compliance monitoring was being performed on days
when the quarry was operating at less than maximum capacity. As discussed above, the quarry
owner made additional sound mitigation improvements at the site and they continue to meet with
residents when noise complaints are reported to the plant manager. In the second year of
operation, there have been less noise complaints from neighborhood residents; overall sound
levels have been lower, and when possible the quarry owner has shut down operations on
weekends to accommodate when a resident has a special event occurring outdoors (i.e.,
graduation, birthday party, etc.). Recently, State regulators attended the semi-annual compliance
monitoring session. They were quite impressed with the amount of sound mitigation measures
implemented at the site and how quiet the facility sounded within the adjacent neighborhood.
7 CONCLUSIONS
A quarry owner wanted to reconfigure and upgrade their rock crushing equipment and
move it closer to a residential neighborhood abutting the facility. The Town and nearby residents
were concerned about noise from the relocated plant. As part of the permitting process, Tech
Environmental worked closely with the quarry owner and the community to develop and
evaluate various types of sound mitigation strategies to demonstrate compliance with State and

Town noise limits and to try to appease most of the neighbors concerns about potential noise
impacts. Through extensive acoustic modeling, an array of sound mitigation measures were
designed and installed, which included earth berms, sound walls, and rubberized conveyors and
chutes. Monthly and semi-annual compliance monitoring was conducted that demonstrated
compliance with State and local noise regulations. Most of the neighborhood residents are
satisfied with the steps that the quarry owner has taken to minimize sound impacts to the
neighborhood. The quarry owner continues to make improvements at the site in order to be a
good neighbor with the residents.

i _77"7 .
I_j
�r
r Original Facility
Location 1 J
_ } T ti . _` i
Fig. I - Site Plan Showing Original Facility Location

Fig. 2 — Site Plan Showing New Location of Crushing Plant

Fig. 3 — Noise Monitoring Locations

Fig. 4 — Earth Berm Locations

Grand Rapids, MI
NOISE -CON 2017
2017 June 12-14
Novel Approach to Visualization of SoundPLAN Data for
Analysis of Mining Noise
Mike Raley
Acoustics By Design
321 SW 41h Avenue, Suite 700
Portland
OR 97204
[e-mail]
ABSTRACT
This paper describes a novel approach to using Google Earth in conjunction with SoundPLAN
when conducting an environmental noise analysis of aggregate mines and quarries. Google Earth
can be used to quickly visualize large data sets generated by SoundPLAN and to determine what
areas of the site require noise control measures for the mining equipment. The graphics created
by this method can be used to develop a noise -driven mining plan and to help explain the noise
control measures to the project team and the public. Other useful GIS tools that work with Google
Earth and SoundPLAN are also discussed.
1. INTRODUCTION
In aggregate mine and quarry projects, the analysis area can cover many acres. SoundPLAN makes
it possible to analyze a large number of potential source locations, predict the maximum noise
levels from the operation, and determine the maximum required noise mitigation. However,
identifying which source locations require mitigation and which do not can be difficult using
SoundPLAN's results tables. This paper describes how to use Google Earth to visualize the
SoundPLAN results in a way that makes it easy to identify where mitigation measures are required.
The resulting visualization(s) can be used to develop a mining plan that allows the operator to
defer the implementation of noise control measures, possibly by many years.
Generation of the visualization begins with the creation of a grid of noise sources in
SoundPLAN. The grid covers the analysis area, and the grid spacing can be adjusted based on the
size of the area and the level of detail needed in the analysis. Once the grid is created and properly
set up, the SoundPLAN analysis is run to generate predicted noise levels from each source location.
The SoundPLAN results table is exported to Excel and converted to a format that can be displayed
in Google Earth. Opening the resulting file in Google Earth creates a grid of color -coded points
that show where mitigation is and is not needed. The point grid can be used to verify the analysis
results and develop a noise -driven mining plan. Details of each step are discussed below.
2. SOUNDPLAN ANALYSIS
Creating the Google Earth visualization beings with the prediction of sound levels from a large
number of source locations. To do this, a grid of point sources is created in SoundPLAN (see
Figure I below). The grid can be created using a script in AutoCAD, or by drawing a line in
SoundPLAN, dividing the line into points, and converting the points to point sources.

Visualization of SoundPLANResults in Google Earth Raley
�:. E•i 1Alw WdYrw.rM1 GcuTeel. iooh Oaimic.neeon av�n YFwry HNc
. r f o ') ¢s. Zm.m� rtrroo N `S 0 0 u.v.w. -] � • N •v..aao..� }} .�kpmw.
c .i.er3s��
a.
rb- .: ��ni�iuutYul n s 1 r r ����ti',��t1���,<m �v�rurrr� r i � r-' �r��^»w:�rrrr r Ir•��.� �� �f � ' t � r r i � .
9
� I. fir'• � � 'I �f?.fh�;� �,i
r
J
Figure 1: Example of a source grid as used in SoundPLAN.
Once the grid of sources has been defined, the analysis is run as usual, and a large table of results
is generated (see Figure 2 below). For large sites with tight source grid spacing, there could be
hundreds, if not thousands, of source locations.
MEMENUftim
rat - I 1 1 Yeti I M. 4. ..r 1 =.. I r. . o: @ I m.
Laurejwood Quarry Expansions 10
i}
Mean propagation - 2110 - Phase 1 - Scenario 1 - Rock Drill -
sr„
L50
RND FI sou"
0 j,
LW S Adly
Agr
MPr Amm A01 Amso
dLr4tl Lr
'
1
?31
dBA) m .dB
dB
dB dB dB dB
.dB �dB[A1
Receiver R 1 Fl G d L54 83.9
s�
1 G Rxk
57
120.
86.8.3
47,4
-0.5
0.0
-t.<
D.0
-F.3
0.0
68 .9
1! G�Rxk
58
120.
88.5
47.4
-0.5
0.0
-t.2
0.0
-Z.3
0.0
$8.9
•
t G Rxk
50
120.
$9.3
47.8
4.5
0.0
-1.2
0.0
-2.4
0.0
88.4
t G Rock
0120.
71.0
-48.0
-0.5
0.0
-1.2
0.0
-2.4
0.0
68.1
I..{jij
i
1 G Rack
57
120.
73.9
-48.4
-0-5
0.0
-1.3
0.0
-2.3
0.0
87.8
t G Rock
207
120.
74.9
-W.5
-0.5
0.0
-1.3
0.0
-2.0
0.0
67A
1 G Rock
45
120.
75.5
48.9
.05
a
•1.3
0.0
.2.3
0.0
67.2
1 G Rock
48
120.
79.4
49.0
-0.5
aD
-1-3
0.0
-2.2
0.0
67.1
1; G Roe
28
120.
75.5
48.5
-0.5
-D2
AA
0.0
-2.6
0.0
67.0
11 G Rock
208
120.
82.7
49.3
-0.5
0.0
-1A
0.0
-2.5
0.0
86.5
1
1 G Rak
210
120.
82.3
-49.3
-0-5
0.13
-1,4
0.0
-2.8
0.0
66.2
•
1 G Rock
14
120.
90.1
-50.1
-0.5
0.0
-1.5
0.0
-2.2
0.0
65.70
? ,nil
1 G Rock
So
120.
918
-W-5
-0-5
0.0
-1-5
0-0
-2-2
0.0
65-7
1 G Rock
31
120.
90.4
40.1
-0.5
aO
` -13
0.0
•2A
0.0
85.8
t ;` G iRock
49
120.
94.5
-50.5
-0.8
0.0
-1.6
0A
I -2.2
0.0
BSA
M
GjR.ock
209
120.
$6.2
d9.7
-0.5
-OA
-1A
0.0
-2.8
0.0
65.2
Eij
t G Rock
44
120.
98.8
-50.7
-0.6
0.0
-1A
0.0
-2.3
0.0
65.1
1 ! G Rock
262
120.
92.2
-50.3
-0.5
0.0
-1.5
0.0
-3.2
0.0
84.7
M
1! GjRoek
219
120.
102.5
-51.2
-0.6
0.0
A
0.0
-2.4
0.0
84.4
11 GjRoek
204
120.
95.7
-50.6
-0.6
-0.3
-1.7
0.0
-2.6
0.0
64A
7� 0JkWk
34
120.
100.1
-51.0
-0.5
0.D
-1 -G
0.0
-2.6
0.0
64A
Figure 2: Example of SoundPLAN results showing the large table of results even for a single receiver.
Identifying the loudest source location using the results tables is easy enough, but identifying
which areas on the site generate noise levels above or below a noise limit can be very difficult. To
quickly identify where noise mitigation is needed, it is necessary to visualize the SoundPLAN
results based on each noise source location.
NOISE-CON2017, Grand Rapids, MI, June 12-14, 2017

Visualization of SoundPLANResults in Google Earth Raley
3. PREPARATION OF RESULTS FOR VISUALIZATION
The first step to visualizing the SoundPLAN results is to translate them into a format that can be
easily manipulated and that can easily interface with other programs, especially Google Earth.
Excel is the chosen format because it is widely used and because there are tools available for
translating Excel data into a format (a KML file) that can be displayed in Google Earth.
SoundPLAN also provides a built-in function for exporting results data to Excel.
SoundPLAN's results tables include a list of the predicted sound levels at each receiver from
each source location. Visualizations can be generated for a single receiver or for all receivers. For
a single receiver, export only the results data for that receiver. For all receivers, or a group of
receivers, export the results data for all receivers in question and filter the data to include only the
loudest predicted level for each source location.
Once the results data has been exported and filtered, it needs to be properly formatted for
import into Google Earth. There are several programs that will convert Excel data into a KML
file, one of the file types used in Google Earth. It is best to pick a converter and determine the file
formatting needed for that converter. Acoustics By Design (ABD) uses the Excel to KML
converter provided by Earth Point(https://www.earthpoint.us/ExcelToKml.aspx).
._ s 7 u - - •-B- - - `�wwe scener • 5 -w �aro�..:T,i., �.•;
� dlfa+nW Pa:rn - •, Rs'>�ee+0• t3nM- sry1M-
nw«~aa
HU
A a
C U
E
E
G
1� 1 MMw l.anpigrdl Name
kallCebr
PelMlen
LtlNlnle OM[1lpllen
2
3r4A
IOT41MTI dmE 5W7901m1
O3
581
3
i Red
iJr 493877 SIriE 5027927 mF
0.3
58.7
Of ayaald hnL :A¢1v2,w.
tl a
MAP
5 '
d Rud
1. a9285d 3 F 50?M nJ�
0 3
Sr?
a_
r Rnd
mT 1*.W me r0? 9a+mr
0,
M 1)7
r
BRed
IOT d9187f 3mE �8N,mF
03
42.1
6 '
9 Ra]
1Sr dgpg7l dmE 50?7961 nN
0.3
59
9 `
14 we0
101 ayyyl, amE �1Y[r96, mn
tl 3
L9 1
10 '
11
F i0PA1219nJ
O.1
6d i
11 '
12 Red
IRr ag3gS7 ZE 50 WIW
03
63.3
12 `
IsFed
10T aK957 E t;Z`70r�0
3
44.4
1$ '
11 Rea
tOT 19 r ]niE 502805&M
0 3
M 1
1� '
IS wed
101 agygv,M6 WP95 x
03
56
15'Iat
d45HS5 2wF 50?791LLM
03
'7 S
16
17 Rea
1ST a93g55.dmE 50QiY}JnN
03
62
I r' '
fie wed
I. oBa€vs wf S9Qrp w
o a
sr 9
1$
19 Rue
IN 0. 71 2 50?8R W
0 $
09
19
20IiuO
IOi a938B9nE 5�14]Bn/1
03
01
20 '
2t Rod
10r iy34M 1rFf= 50?7918
0 3
W
2f
22 Re0
1ST t96e85.en'C 50Q79i&M
0.3
- 5
& '
23 wetl
WT a9oeas Z 50P/9GAnM
0 3
62.1
IN i9C�99B Ind 502M9
03
6d 9
2a
25 R
1Sr mew 5 e 402r913M
0 3
95A
35 `
24 Rea
'QT'"M 31£ 5W7902n+
0 3
41
M '
27 Ree
10T t9085BmE SQZ79Q3HJ
O 3
0.i
21
29 wed
10T a9aen 1- 59¢atlrlmF•
4 a
&r 9
719 r
79 Fed
IQT t13= rF.5 9�M77r. •
a 3
Od 6
29
W R90
JOT496971 311E 5VQ8PV11M
03
64A
z w.. AT p
2 acn..ysw��.n
.. ,... ,• , � �� sas rhea aw;,„�:
- - FiM•51W+-
e.e.
S T 11 V W % Y 2 AA AB
Sheell k_U Shell .
+ - L.
Figure 3: Example Excel file for conversion to KML format.
At the very least, the Excel file will need to include the coordinates for each source location. Earth
Point's converter will accept coordinates in many formats such as LatLon and UTM. To visualize
where mitigation is needed, the icon representing each source location should be color -coded to
quickly group source locations by the noise levels they generate at a receiver or receivers. The
color of each source location can be set separately in the Excel spreadsheet. Two colors can be
used to identify sources above and below a threshold, such as a state or county noise limit.
Additional colors can also be used if desired. For instance, a third color can be added for source
locations that produce noise levels within five dB of the threshold, indicating source locations that
may exceed the noise limit when combined with other noise sources.
Additional information can be added to the KML file to increase the usefulness of the Google
Earth visualization. For instance, the name of the icon for a source location can be set to the
NOISE-CON2017, Grand Rapids, MI, June 12-14, 2017

Visualization of SoundPLANResults in Google Earth Raley
predicted noise level from that source location. This makes it possible to quickly see the predicted
noise level from a particular source location, and it is therefore easy to identify spatial trends in
the predicted noise levels. Also, if a visualization is generated for all receivers so that it depicts
the maximum predicted noise level for each source location, the receiver with the highest noise
level from the source could be added to the source description. This can help identify which
sources are affecting which receivers.
4. VISUALIZATION OF THE DATA IN GOOGLE EARTH
Once the data has been correctly formatted, the file can be converted using the chosen Excel to
KML converter and then opened in Google Earth. Figure 4 shows an example of the resulting
color -coded point grid in Google Earth.
Figure 4: Color -coded point grid as viewed in Google Earth.
The resulting grid can be used to quickly identify areas where noise mitigation measures are
needed. It can also be used to determine which source locations affect which receivers. For
instance, the example below in Figure 6 shows a comparison of the point grid for all receivers and
the point grid for only receivers R2 and R3. An error check of the prediction results can also be
done using the point grid. For instance, if the graphic for receivers R2 and R3 showed a few red
icons in the northwest corner of the site, this would seem anomalous and would need to be
investigated to ensure the accuracy of the predictions.
NOISE-CON2017, Grand Rapids, MI, June 12-14, 2017

Visualization of SoundPLANResults in Google Earth Raley
Figure 5: Comparison of point grid for all receivers and only receivers R2 and R3.
Visualizations can also be used to evaluate the effectiveness of a berm and highlight potential
issues with the use of a berm. For example, in Figure 6 below, comparing the point grids for
predictions with and without berms shows that a berm provides effective mitigation for receivers
R2 and R3, but not for receiver R1.
Figure 6: Comparison of point grids for predictions with and without noise berms.
Using Google Earth to visualize the terrain, as shown in Figure 7, it is clear that the berm for R1
is ineffective because it is in a low-lying area and the land rises sharply just south of the berm
location.
NOISE-CON2017, Grand Rapids, MI, June 12-14, 2017

Visualization of SoundPLANResults in Google Earth Raley
Figure 7: Using Google Earth to visualize why a berm is ineffective.
Finally, the point grids in Google Earth can be used to quickly and accurately define various zones
where different levels of noise mitigation are required. These zones can be used to develop a
phased mining plan that allows the operator to defer noise control measures. As an example,
Figure 8 shows how the previous visualizations were used to develop four distinct zones. The
operator could begin work on Zone 1 without the need for any mitigation (except for in a very
small portion of the southwest corner). After working on Zone 1, they could move to Zone 2 and
only have to construct a small portion of the total berms required for the site. This would allow
the operator to slowly build the berms over time, rather than being required to build the berms all
at once before mining began. Construction of berms can be time consuming and expensive, so
delaying the berm construction and spreading it over multiple phases would be a significant benefit
to the operator.
Figure 8: Depiction of noise control zones based on point grids in Google Earth.
NOISE-CON2017, Grand Rapids, MI, June 12-14, 2017

Visualization of SoundPLANResults in Google Earth
5. CONCLUSIONS
Raley
Google Earth can be a powerful tool for visualizing SoundPLAN results. Creating a grid of point
sources in SoundPLAN and visualizing the results in Google Earth can allow the user to verify
calculation results, determine where mitigation is needed, evaluate the effectiveness of mitigation
measures such as berms, and develop a noise -driven mining plan. The Google Earth visualizations
can also be very helpful when trying to communicate the results of SoundPLAN modeling to a
client.
ACKNOWLEDGEMENTS
Thanks to Kerrie Standlee for teaching me about mines, quarries, their noise sources, and how to
mitigate them. Thanks to Valerie Smith for helping me develop this analysis method, and
especially for her help developing some of the "Excel magic" that I use in this procedure
NOISE-CON2017, Grand Rapids, MI, June 12-14, 2017