Technical Support Document (TSD) for the EPA’s Proposed Finding that Lead Emissions from Aircraft Engines that Operate on Leaded Fuel Cause or Contribute to Air Pollution that May Reasonably Be Anticipated to Endanger Public Health and Welfare


Technical Support Document (TSD)
for the EPA's Proposed Finding that
Lead Emissions from Aircraft Engines
that Operate on Leaded Fuel Cause or
Contribute to Air Pollution that May
Reasonably Be Anticipated to Endanger
Public Health and Welfare

SEPA

United States
Environmental Protection
Agency

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Technical Support Document (TSD)
for the EPA's Proposed Finding that
Lead Emissions from Aircraft Engines
that Operate on Leaded Fuel Cause or
Contribute to Air Pollution that May
Reasonably Be Anticipated to Endanger
Public Health and Welfare

Assessment and Standards Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency

United States
Environmental Protection
^1	Agency

EPA-420-R-22-025
October 2022

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The data presented in this document supports the EPA's Proposed Finding that Lead
Emissions from Aircraft Engines that Operate on Leaded Fuel Cause or Contribute to Air
Pollution that May Reasonably Be Anticipated to Endanger Public Health and Welfare. This
TSD includes an overview of information regarding piston-engine aircraft and the use of leaded
aviation gasoline, the inventory of lead emissions from piston-engine aircraft, concentrations of
lead in air attributable to emissions from piston-engine aircraft, an overview of the fate and
transport of emissions of lead from piston-engine aircraft and information regarding populations
residing near and attending school near airports, including consideration of environmental
justice. Appendices to this TSD include EPA's two peer-reviewed reports titled, "Model-
extrapolated Estimates of Airborne Lead Concentrations at U.S. Airports"1 and "National
Analysis of the Populations Residing Near or Attending School Near U.S. Airports,"2 and EPA's
two Program Overviews reporting on airport lead monitoring.3'4'5

The EPA is proposing to find that lead air pollution may reasonably be anticipated to
endanger the public health and welfare within the meaning of section 231(a) of the Clean Air
Act. The EPA is also proposing to find that engine emissions of lead from certain aircraft cause
or contribute to the lead air pollution that may reasonably be anticipated to endanger public
health and welfare under section 231(a) of the Clean Air Act.

The proposed findings, if finalized, would not themselves apply new requirements to entities
other than EPA and FAA. Specifically, if the EPA issues final findings that lead emissions from
covered aircraft engines cause or contribute to air pollution which may reasonably be anticipated
to endanger public health or welfare, only then would EPA, under section 231 of the Clean Air
Act, promulgate aircraft engine emission standards for that air pollutant. In contrast to the
findings, those standards would apply to and have an effect on other entities outside the federal
government. Such findings also would trigger the FAA's statutory mandate to prescribe
standards for the composition or chemical or physical properties of an aircraft fuel or fuel
additive to control or eliminate aircraft emissions which EPA has decided endanger public health
or welfare under section 231(a) of the Clean Air Act.

Even in the event this proposed action is finalized, it is premature to speculate on the scope,
applicability, timing, and nature of any subsequent rulemakings by EPA and FAA. The impact of
any subsequent rulemaking cannot be evaluated with any reasonable amount of certainty at this
point. We also understand that industry may now be taking, or in the future could take, voluntary
actions related to aircraft lead emissions. As noted by the National Academies of Sciences,
Engineering, and Medicine (NAS) in 2021, there are a number of regulatory and non-regulatory
options to mitigate lead emissions from aircraft, including the potential use of existing unleaded

1 EPA (2020) Model-Extrapolated Estimates of Airborne Lead Concentrations at U.S. Airports. EPA,
Washington, DC, EPA-420-R-20-003, 2020. EPA responses to peer review comments on the report are available at
https://nepis.epa. gov/Exe/ZyPDF.cgi?Dockey=P100YIWD.pdf.

2 EPA (2020) Analysis of the Populations Residing Near or Attending School Near U.S. Airports. EPA-420-R-
20-001, available at https://nepis.epa.gov/Exe/Zv PDF.cgi?Dockey=P100YG4A.pdf. and in the EPA responses to
peer review comments on the report, available here: https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100YISM.pdf.

3 EPA (2013) Program Update: Airport Lead Monitoring. EPA, Washington, DC, EPA-420-F-13-032, 2013.
Available at: https://nepis.epa.gov/Exe/ZyPDF.cgi/P100GNLC.PDF?Dockey=P100GNLC.PDF.

4 EPA (2015) Program Overview: Airport Lead Monitoring. EPA, Washington, DC, EPA-420-F-15-003, 2015.
Available at: https://nepis.epa.gov/Exe/ZyPDF.cgi/P100LJDW.PDF?Dockey=P100LJDW.PDF.

5 The appendices duplicate information included in those reports and also include two substantive footnotes in
Appendix A, and a few editorial changes and corrections.

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fuel options for the piston-engine aircraft fleet certified to operate safely on such fuels; potential
future lead-free fuels and propulsion systems; and changes to operations and practices at
airports.6 Even if industry were to take such actions, however, industry's current independent,
voluntary behavior, and industry's potential future action or inaction in response to the outcome
of this proposed determination, is immaterial to this proposed action, which is limited to
determining whether lead emissions from covered aircraft engines cause or contribute to air
pollution which may reasonably be anticipated to endanger the public health and welfare within
the meaning of CAA section 231(a).

Although not part of the supporting rationale for this action, for informational purposes only,
we provide here reference to FAA and industry's evaluation of unleaded fuels to replace leaded
aviation gasoline. The FAA currently has two integrated initiatives focused on transitioning
safely away from the use of leaded fuel: The Piston Aviation Fuels Initiative (PAFI), and the
FAA-industry partnership to Eliminate Aviation Gasoline Lead Emissions (EAGLE). 7'8'9

Table of Contents:

A. Piston-Engine Aircraft and the Use of Leaded Aviation Gasoline

B. Emissions of Lead from Piston-Engine Aircraft

C. Concentrations of Lead in Air Attributable to Emissions from Piston-Engine Aircraft

D. Fate and Transport of Emissions of Lead from Piston-Engine Aircraft

E. Consideration of Environmental Justice and Children in Populations Residing Near
Airports

Appendices

A. EPA Report: Model-Extrapolated Estimates of Airborne Lead Concentrations at U.S.
Airports.

B. EPA Report: Analysis of the Populations Residing Near or Attending School Near U.S.
Airports.

C. Program Update: Airport Lead Monitoring. 2013.

D. EPA Program Overview: Airport Lead Monitoring. 2015.

6 National Academies of Sciences, Engineering, and Medicine 2021. Options for Reducing Lead Emissions from
Piston-Engine Aircraft. Washington, DC: The National Academies Press, https://doi.org/10.17226/26050.

7 FAA (2012) Unleaded Avgas Transition Rulemaking Committee: Findings & Recommendations. Available at:
https://www.faa.gov/regulations_policies/rulemaking/committees/documents/media/UATARC-1312011.pdf

8 FAA Piston Aviation Fuel Initiative. Information is available at: https://www.faa.gov/about/initiatives/avgas/;

9 FAA EAGLE Initiative. Information is available at: https://www.faa.gov/unleaded.

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Overview of Aircraft Lead Emissions

We summarize here background information that provides context for the proposed finding
that lead emissions from certain engines used in aircraft cause or contribute to air pollution that
may reasonably be anticipated to endanger public health and welfare. This includes information
on the population of aircraft that have piston engines, information on the use of leaded aviation
gasoline (avgas) in aircraft, physical and chemical characteristics of lead emissions from engines
used in aircraft that can operate on leaded fuel, concentrations of lead in air from these engine
emissions, and the fate and transport of lead emitted by engines used in such aircraft. We also
include here an analysis of populations residing near and attending school near airports and an
analysis of potential environmental justice implications with regard to residential proximity to
runways where piston-engine aircraft operate.

This proposal draws extensively from the EPA's scientific assessments for lead, which are
developed as part of the EPA's periodic reviews of the air quality criteria10 for lead and the lead
NAAQS.11 These scientific assessments provide a comprehensive review, synthesis, and
evaluation of the most policy-relevant science that builds upon the conclusions of previous
assessments. In the information that follows, we discuss and describe scientific evidence
summarized in the most recent assessment, the EPA 2013 Lead Integrated Science Assessment
(ISA)12 as well as information summarized in previous EPA Air Quality Criteria Documents
(AQCDs), including the 1977, 1986, and 2006 AQCDs.131415

As described in the 2013 Lead ISA, lead emitted to ambient air is transported through the air
and is distributed from air to other environmental media through deposition.16 Lead emitted in
the past can remain available for environmental or human exposure for extended time in some
areas.17 Depending on the environment where it is deposited, it may to various extents be
resuspended into the ambient air, integrated into the media on which it deposits, or transported in

10 Under section 108(a)(2) of the CAA, air quality criteria are intended to "accurately reflect the latest scientific
knowledge useful in indicating the kind and extent of all identifiable effects on public health or welfare which may
be expected from the presence of [a] pollutant in the ambient air ...." Section 109 of the CAA directs the
Administrator to propose and promulgate ' 'primary" and ' 'secondary'' NAAQS for pollutants for which air quality
criteria are issued. Under CAA section 109(d)(1), EPA must periodically complete a thorough review of the air
quality criteria and the NAAQS and make such revisions as may be appropriate in accordance with sections 108 and
109(b) of the CAA. A fuller description of these legislative requirements can be found, for example, in the ISA (see
2013 Lead ISA, p. lxix).

11 Section 109(b)(1) defines a primary standard as one "the attainment and maintenance of which in the
judgment of the Administrator, based on such criteria and allowing an adequate margin of safety, are requisite to
protect the public health." A secondary standard, as defined in section 109(b)(2), must "specify a level of air
quality the attainment and maintenance of which, in the judgment of the Administrator, based on such criteria, is
requisite to protect the public welfare from any known or anticipated adverse effects associated with the presence of
[the] pollutant in the ambient air.''

12 EPA (2013) ISA for Lead. EPA, Washington, DC, EPA/600/R-10/075F, 2013.

13 EPA (1977) AQC for Lead. EPA, Washington, DC, EPA-600/8-77-017 (NTIS PB280411), 1977.

14 EPA (1986) AQC for Lead. EPA, Washington, DC, EPA-600/8-83/028aF-dF (NTIS PB87142386), 1986.

15 EPA (2006) AQC for Lead. EPA, Washington, DC, EPA/600/R-5/144aF, 2006.

16 EPA (2013) ISA for Lead. Section 3.1.1. "Pathways for Pb Exposure." p. 3-1. EPA, Washington, DC,
EPA/600/R-10/075F, 2013.

17 EPA (2013) ISA for Lead. Section 3.7.1. "Exposure." p. 3-144. EPA, Washington, DC, EPA/600/R-
10/075F, 2013.

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surface water runoff to other areas or nearby waterbodies.18 Lead in the environment today may
have been airborne yesterday or emitted to the air long ago.19 Over time, lead that was initially
emitted to air can become less available for environmental circulation by sequestration in soil,
sediment and other reservoirs.20

The multimedia distribution of lead emitted into ambient air creates multiple air-related
pathways of human and ecosystem exposure. These pathways may involve media other than air,
including indoor and outdoor dust, soil, surface water and sediments, vegetation and biota. The
human exposure pathways for lead emitted into air include inhalation of ambient air or ingestion
of food, water or other materials, including dust and soil, that have been contaminated through a
pathway involving lead deposition from ambient air.21 Ambient air inhalation pathways include
both inhalation of air outdoors and inhalation of ambient air that has infiltrated into indoor
environments.22 The air-related ingestion pathways occur as a result of lead emissions to air
being distributed to other environmental media, where humans can be exposed to it via contact
with and ingestion of indoor and outdoor dusts, outdoor soil, food and drinking water.

The scientific evidence documents exposure to many sources of lead emitted to the air that
have resulted in higher blood lead levels, particularly for people living or working near sources,
including stationary sources, such as mines and smelters, and mobile sources, such as cars and
trucks when lead was a gasoline additive.23'24'25'26'27'28 Similarly, with regard to emissions from
engines used in piston-engine aircraft there have been studies reporting positive associations of
children's blood lead levels with proximity to airports and activity by piston-engine aircraft,29'30
thus indicating potential for children's exposure to lead from aircraft engine emissions. A recent
study evaluating cardiovascular mortality rates in adults 65 and older living within a few
kilometers and downwind of runways, while not evaluating blood lead levels, found higher

18 EPA (2013) ISA for Lead. Section 6.2. "Fate and Transport of Pb in Ecosystems." p. 6-62. EPA,
Washington, DC, EPA/600/R-10/075F, 2013.

19 EPA (2013) ISA for Lead. Section 2.3. "Fate and Transport of Pb." p. 2-24. EPA, Washington, DC,
EPA/600/R-10/075F, 2013.

20 EPA (2013) ISA for Lead. Section 1.2.1. "Sources, Fate and Transport of Ambient Pb;" p. 1-6. Section2.3.
"Fate and Transport of Pb." p. 2-24. EPA, Washington, DC, EPA/600/R-10/075F, 2013.

21 EPA (2013) ISA for Lead. Section 3.1.1. "Pathways for Pb Exposure." p. 3-1. EPA, Washington, DC,
EPA/600/R-10/075F, 2013.

22 EPA (2013) ISA for Lead. Sections 1.3. "Exposure to Ambient Pb." p. 1-11. EPA, Washington, DC,
EPA/600/R-10/075F, 2013.

23 EPA (2013) ISA for Lead. Sections 3.4.1. "Pb in Blood." p. 3-85; Section 5.4. "Summary." p. 5-40. EPA,
Washington, DC, EPA/600/R-10/075F, 2013.

24 EPA (2006) AQC for Lead. Chapter 3. EPA, Washington, DC, EPA/600/R-5/144aF, 2006.

25 EPA (1986) AQC for Lead. Section 1.11.3. EPA, Washington, DC, EPA-600/8-83/028aF-dF (NTIS
PB87142386), 1986.

26 EPA (1977) AQC for Lead. Section 12.3.1.1.

600/8-77-017 (NTIS PB280411), 1977.

27 EPA (1977) AQC for Lead. Section 12.3.1.2.

600/8-77-017 (NTIS PB280411), 1977.

28 EPA (1977) AQC for Lead. Section 12.3.1.1.

600/8-77-017 (NTIS PB280411), 1977.

29 Miranda et. al., 2011. A Geospatial Analysis of the Effects of Aviation Gasoline on Childhood Blood Lead
Levels. Environmental Health Perspectives. 119:1513-1516.

30 Zahran et. al., 2017. The Effect of Leaded Aviation Gasoline on Blood Lead in Children. Journal of the
Association of Environmental and Resource Economists. 4(2):575-610.

"Air Exposures." p. 12-10.
"Air Exposures." p. 12-10.
"Air Exposures." p. 12-10.

EPA, Washington, DC, EPA-
EPA, Washington, DC, EPA-
EPA, Washington, DC, EPA-

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mortality rates in adults living near single-runway airports in years with more piston-engine air
traffic, but not in adults living near multi-runway airports, suggesting the potential for adverse
adult health effects near some airports.31

A. Piston-Engine Aircraft and the Use of Leaded Aviation Gasoline

Aircraft operating in the U.S. are largely powered by either turbine engines or piston engines,
although other propulsion systems are in use and in development. Turbine-engine powered
aircraft and a small percentage of piston-engine aircraft (i.e., those with diesel engines) operate
on fuel that does not contain a lead additive. Covered aircraft engines is defined here as any
aircraft engine that is capable of using leaded aviation gasoline. Covered aircraft, which are
predominantly piston-engine powered aircraft, operate on leaded avgas. Examples of covered
aircraft include smaller piston-powered aircraft such as the Cessna 172 (single-engine aircraft)
and the Beechcraft Baron G58 (twin-engine aircraft), as well as the largest piston-engine
aircraft—the Curtiss C-46 and the Douglas DC-6. Additionally, some rotorcraft, such as the
Robinson R44 helicopter, light-sport aircraft, and ultralight vehicles can have piston engines that
operate using leaded avgas.

Lead is added to avgas in the form of tetraethyl lead. Tetraethyl lead helps boost fuel octane,
prevents engine knock, and prevents valve seat recession and subsequent loss of compression for
engines without hardened valves. There are three main types of leaded avgas: 100 Octane,
which can contain up to 4.24 grams of lead per gallon (1.12 grams of lead per liter), 100 Octane
Low Lead (100LL), which can contain up to 2.12 grams of lead per gallon (0.56 grams of lead
per liter), and 100 Octane Very Low Lead (100VLL), which can contain up to 0.71 grams of lead
per gallon (0.45 grams of lead per liter).32 Currently, 100LL is the most commonly available and
most commonly used type of avgas.33 Tetraethyl lead was first used in piston-engine aircraft in
1927.34 Commercial and military aircraft in the U.S. operated on 100 Octane leaded avgas into
the 1950s, but in subsequent years, the commercial and military aircraft fleet largely converted to
turbine-engine powered aircraft which do not use leaded avgas.35'36 The use of avgas containing
approximately 4 grams of lead per gallon continued in piston-engine aircraft until the early 1970s
when 100LL became the dominant leaded fuel in use.

There are two sources of data from the federal government that provide annual estimates of
the volume of leaded avgas supplied and consumed in the U.S.: the Department of Energy,
Energy Information Administration (DOE EIA) provides information on the volume of leaded

31 Klemick et. al., 2022. Cardiovascular Mortality and Leaded Aviation Fuel: Evidence from Piston-Engine Air
Traffic in North Carolina. International Journal of Environmental Research and Public Health. 19(10):5941.

32 ASTM International (May 1, 2021) Standard Specification for Leaded Aviation Gasolines D910-21.

33 National Academies of Sciences, Engineering, and Medicine (NAS). 2021.Options for Reducing Lead
Emissions from Piston-Engine Aircraft. Washington, DC: The National Academies Press.
https://doi.org/10.17226/26050.

34 Ogston 1981. A Short History of Aviation Gasoline Development, 1903-1980. Society of Automotive
Engineers, p. 810848.

35 U.S. Department of Commerce Civil Aeronautics Administration. Statistical Handbook of Aviation (Years
1930-1959). https://babel.hathitnist.org/cgi/pt?id=mdp.39015027813032&view=lup&seq=899.

36 U.S. Department of Commerce Civil Aeronautics Administration. Statistical Handbook of Aviation (Years
1960-1971). https://babel.hathitnist.org/cgi/pt?id=mdp.39015004520279&view=lup&seq=9&skin=202L

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avgas supplied in the U.S.,37 and the Federal Aviation Administration (FAA) provides
information on the volume of leaded avgas consumed in the U.S.38 Over the ten-year period
from 2011 through 2020, DOE estimates of the annual volume of leaded avgas supplied
averaged 184 million gallons, with year-on-year fluctuations in fuel supplied ranging from a 25
percent increase to a 29 percent decrease. Over the same period, from 2011 through 2020, the
FAA estimates of the annual volume of leaded avgas consumed averaged 196 million gallons,
with year-on-year fluctuations in fuel consumed ranging from an eight percent increase to a 14
percent decrease. The FAA forecast for consumption of leaded avgas in the U.S. ranges from
185 million gallons in 2026 to 179 million gallons in 2041, a decrease of three percent in that
period.39 As described later in this section, while the consumption of leaded avgas is expected to
decrease three percent from 2026 to 2041, FAA projects increased activity at some airports and
decreased activity at other airports out to 2045.

The FAA's National Airspace System Resource (NASR)40 provides a complete list of
operational airport facilities in the U.S. Among the approximately 19,600 airports listed in the
NASR, approximately 3,300 are included in the National Plan of Integrated Airport Systems
(NPIAS) and support the majority of piston-engine aircraft activity that occurs annually in the
U.S.41 While less aircraft activity occurs at the remaining airports, that activity is conducted
predominantly by piston-engine aircraft. Approximately 6,000 airports have been in operation
since the early 1970s when the leaded fuel being used contained up to 4.24 grams of lead per
gallon of avgas.42 The activity by piston-engine aircraft spans a range of purposes, as described
further below. In Alaska this fleet of aircraft currently play a critical role in the transportation
infrastructure.

37 DOE. EIA. Petroleum and Other Liquids; Supply and Disposition. Aviation Gasoline in Annual Thousand
Barrels. Fuel production volume data obtained from

https://www.eia.gov/dnav/pet/pet sum snd a eppv mbbt a cur-1.htm and

https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=C400000001&f= A on Dec., 30, 2021.

38 Department of Transportation (DOT). FAA. Aviation Policy and Plans. FAA Aerospace Forecast Fiscal
Years 2009-2025. p. 81. Available at http://www.faa.gov/data research/aviation/aerospace forecasts/2009-2025/
media/2009%20Forecast%20Doc.pdf. This document provides historical data for 2000-2008 as well as forecast data.

39 DOT. FAA. Aviation Policy and Plans. Table 23. p. 111. FAA Aerospace Forecast Fiscal Years 2021-2041.
Available at https://www.faa.gov/sites/faa.gov/files/data_research/aviation/aerospace_forecasts/FY2021-

4 l_FAA_Aerospace_Forecast.pdf.

40 See FAA. NASR. Available at
https://www.faa.gov/air_traffic/flight_info/aeronav/aero_data/eNASR_Browser/.

41 FAA (2020) National Plan of Integrated Airport Systems (NPIAS) 2021-2025 Published by the Secretary of
Transportation Pursuant to Title 49 U.S. Code, Section 47103. Retrieved on Nov. 3, 2021 from:

https://www.faa.gov/airports/planning eapaeity/npias/euiTent/media/.NPIAS~202.1.~2025~Narrative.pdf.

42 See FAA's NASR. Available at
https://www.faa.gov/air_traffic/flight_info/aeronav/aero_data/eNASR_Browser/.

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As of 2019, there were 171,934 piston-engine aircraft in the U.S.43 This total includes
128,926 single-engine aircraft, 12,470 twin-engine aircraft, and 3,089 rotorcraft.44 The average
age of single-engine aircraft in 2018 was 46.8 years and the average age of twin-engine aircraft
in 2018 was 44.7 years old.45 In 2019, 883 new piston-engine aircraft were manufactured in the
U.S. some of which are exported.46 For the period from 2019 through 2041, the fleet of fixed
wing47 piston-engine aircraft is projected to decrease at an annual average rate of 0.9 percent,
and the hours flown by these aircraft is projected to decrease 0.9 percent per year from 2019 to
2041.48 An annual average growth rate in the production of piston-engine powered rotorcraft of
0.9 percent is forecast, with a commensurate 1.9 percent increase in hours flown in that period by
piston-engine powered rotorcraft.49 There were approximately 664,565 pilots certified to fly
general aviation aircraft in the U.S. in 2021.50 This included 197,665 student pilots and 466,900
non-student pilots. In addition, there were more than 301,000 FAA Non-Pilot Certificated
mechanics.51

Piston-engine aircraft are used to conduct flights that are categorized as either general aviation
or air taxi. General aviation flights are defined as all aviation other than military and those
flights by scheduled commercial airlines. Air taxi flights are short duration flights made by
small commercial aircraft on demand. The hours flown by aircraft in the general aviation fleet
are comprised of personal and recreational transportation (67 percent), business (12 percent),
instructional flying (8 percent), medical transportation (less than one percent), and the remainder
includes hours spent in other applications such as aerial observation and aerial application.52

43 FAA. General Aviation and Part 135 Activity Surveys - CY 2019. Chapter 1: Historical General Aviation
and Air Taxi Measures. Table 1.1 - General Aviation and Part 135 Number of Active Aircraft By Aircraft Type 2008-2019.
Retrieved on Dec.. 27, 2021 at https://www.faa.gov/data research/aviation data statistics/general aviation/CY20.1.9/.
Separately, FAA maintains a database of FAA-registered aircraft and as of January 6, 2022 there were 222,592 piston-
engine aircraft registered with FAA. See: https://registry .faa.gov/aircraftinquiry/.

FAA. General Aviation and Part 135 Activity Surveys - CY 2019. Chapter 1: Historical General Aviation
and Air Taxi Measures. Table 1.1 - General Aviation and Part 135 Number of Active Aircraft By Aircraft Type 2008-2019.

Retrieved on Dec., 27, 2021 at https://www.faa.gov/data research/aviation data statistics/general aviation/CY20.1.9/.

45 General Aviation Manufacturers Association (GAMA) (2019) General Aviation Statistical Databook and
Industry Outlook,p.27. Retrieved on October 7, 2021 from: GAMA 2019Databook Final~2020~03~20.pdf

46 GAMA (2019) General Aviation Statistical Databook and Industry Outlook, p. 16. Retrieved on October 7,
2021 from: GAMA 2019Databook Final-2020-03-20.pdf.

47 There are both fixed-wing and rotary-wing aircraft; and airplane is an engine-driven, fixed-wing aircraft and a
rotorcraft is an engine-driven rotary-wing aircraft.

48 See FAA Aerospace Forecast Fiscal Years 2021-2041. p. 28. Available at
data research/aviation/aerospace forecasts/FY20 ospace Forecast.pdf.

49 FAA Aerospace Forecast Fiscal Years 2021-2041. Table 28. p. 116., and Table 29. p. 117. Available at

https://www.faa.gov/sites/faa.gov/files/data research/aviation/aerospace forecasts/FY2021-

4.1. FAA Aerospace Forecast.pdf.

FAA. U.S. Civil Airmen Statistics. 2021 Active Civil Airman Statistics. Retrieved from U.S. Civil Airmen
Statistics | Federal Aviation Administration (faa.gov) on May 20, 2022.

^' FAA. U.S. Civil Airmen Statistics. 2021 Active Civil Airman Statistics. Retrieved from U.S. Civil Airmen
Statistics | Federal Aviation Administration (faa.gov) on May 20, 2022.

^ FAA. General Aviation and Part 135 Activity Surveys-CY 2019. Chapter 1: Historical General Aviation
and Air Taxi Measures. Table 1.4 - General Aviation and Part 135 Total Hours Flown By Actual Use 2008-2019 (Hours in
Thousands). Retrieved on Dec., 27, 2021 at https://www.faa.gov/data research/aviation data statistics/general aviation/
CY20.1.9/.

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Aerial application for agricultural activity includes crop and timber production, which involve
fertilizer and pesticide application and seeding cropland. In 2019, aerial application in
agriculture represented 883,600 hours flown by general aviation aircraft, and approximately 17.5
percent of these total hours were flown by piston-engine aircraft.53

Approximately 71 percent of the hours flown that are categorized as general aviation activity
are conducted by piston-engine aircraft, and 17 percent of the hours flown that are categorized as
air taxi are conducted by piston-engine aircraft.54 From the period 2012 through 2019, the total
hours flown by piston-engine aircraft increased nine percent from 13.2 million hours in 2012 to
14.4 million hours in 2019.55'56

As noted earlier, the U.S. has a dense network of airports where piston-engine aircraft
operate, and a small subset of those airports have air traffic control towers which collect daily
counts of aircraft operations at the facility (one takeoff or landing event is termed an
"operation"). These daily operations are provided by the FAA in the Air Traffic Activity System
(AT ADS).57 The AT ADS reports three categories of airport operations that can be conducted by
piston-engine aircraft: Itinerant General Aviation, Local Civil, and Itinerant Air Taxi. The sum
of Itinerant General Aviation and Local Civil at a facility is referred to as general aviation
operations. Piston-engine aircraft operations in these categories are not reported separately from
operations conducted by aircraft using other propulsion systems (e.g., turboprop). Because
piston-engine aircraft activity generally comprises the majority of general aviation activity at an
airport, general aviation activity is often used as a surrogate measure for understanding piston-
engine activity.

In order to understand the trend in airport-specific piston-engine activity in the past ten years,
we evaluated the trend in general aviation activity. We calculated the average activity at each of
the airports in ATADS over three-year periods for the years 2010 through 2012 and for the years
2017 through 2019. We focused this trend analysis on the airports in ATADS because these data
are collected daily at an airport-specific control tower (in contrast with annual activity estimates
provided at airports without control towers). There were 513 airports in ATADS for which data
were available to determine annual average activity for both the 2010-2012 period and the 2017-
2019 time period. The annual average operations by general aviation at each of these airports in
the period 2010 through 2012 ranged from 31 to 346,415, with a median of 34,368; the annual
average operations by general aviation in the period from 2017 through 2019 ranged from 2,370

53 FAA. General Aviation and Part 135 Activity Surveys - CY 2019. Chapter 3: Primary and Actual Use.
Table 3.2 - General Aviation and Part 135 Total Hours Flown by Actual Use 2008-2019 (Hours in Thousands).
Retrieved on Mar., 22, 2022 at

https://www.faa.gov/data research/aviation data statistics/general aviation/CY20.1.9/.

54 FAA. General Aviation and Part 135 Activity Surveys - CY 2019. Chapter 3: Primary and Actual Use.
Table 3.2 - General Aviation and Part 135 Total Hours Flown by Actual Use 2008-2019 (Hours in Thousands).
Retrieved on Mar., 22, 2022 at

https://www.faa.gov/data research/aviation data statistics/general aviation/CY2019/.

55 FAA. General Aviation and Part 135 Activity Surveys - CY 2019. Chapter 3: Primary and Actual Use.
Table 1.3 - General Aviation and Part 135 Total Hours Flown by Aircraft Type 2008-2019 (Hours in Thousands).
Retrieved on Dec., 27, 2021 at

https://www.faa.gov/data research/aviation data statistics/general aviation/CY20.1.9/.

56 In 2012, the FAA Aerospace Forecast projected a 0.03 percent increase in hours flown by the piston-engine
aircraft fleet for the period 2012 through 2032. FAA Aerospace Forecast Fiscal Years 2012-2032. p.53.

57 See FAA's Air Traffic Activity Data. Available at https://aspm.faa.gov/opsnet/svs/airport.asp.

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to 396,554, with a median of 34,365. Of the 513 airports, 211 airports reported increased general
aviation activity over the period evaluated.58 The increase in the average annual number of
operations by general aviation aircraft at these 211 facilities ranged from 151 to 136,872 (an
increase of two percent and 52 percent, respectively).

While national consumption of leaded avgas is forecast to decrease three percent from 2026 to
2045, this change in fuel consumption is not expected to occur uniformly across airports in the
U.S. The FAA produces the Terminal Area Forecast (TAF), which is the official forecast of
aviation activity for the 3,300 U.S. airports that are in the NPIAS.59 For the 3,306 airports in the
TAF, we compared the average activity by general aviation at each airport from 2017-2019 with
the FAA forecast for general aviation activity at those airports in 2045. The FAA forecasts that
activity by general aviation will decrease at 234 of the airports in the TAF, remain the same at
1,960 airports, and increase at 1,112 of the airports. To evaluate the magnitude of potential
increases in activity for the same 513 airports for which we evaluated activity trends in the past
ten years, we compared the 2017-2019 average general aviation activity at each of these airports
with the forecasted activity for 2045 in the TAF.60 The annual operations estimated for the 513
airports in 2045 ranges from 2,914 to 427,821 with a median of 36,883. The TAF forecasts an
increase in activity at 442 of the 513 airports out to 2045, with the increase in operations at those
facilities ranging from 18 to 83,704 operations annually (an increase of 0.2 percent and 32
percent, respectively).

B. Emissions of Lead from Piston-Engine Aircraft

This section describes the physical and chemical characteristics of lead emitted by covered
aircraft, and the national, state, county and airport-specific annual inventories of these engine
emissions of lead. Information regarding lead emissions from motor vehicle engines operating
on leaded fuel is summarized in prior AQCDs for Lead, and the 2013 Lead ISA also includes
information on lead emissions from piston-engine aircraft.61'62'63 Lead is added to avgas in the
form of tetraethyl lead along with ethylene dibromide, both of which were used in leaded
gasoline for motor vehicles in the past. Therefore, the summary of the science regarding
emissions of lead from motor vehicles presented in the 1997 and 1986 AQCDs for Lead is
relevant to understanding some of the properties of lead emitted from piston-engine aircraft and
the atmospheric chemistry these emissions are expected to undergo. Recent studies relevant to
understanding lead emissions from piston-engine aircraft have also been published and are
discussed here.

58 Geidosch. Memorandum to Docket EPA-HQ-OAR-2022-0389. Past Trends and Future Projections in
General Aviation Activity and Emissions. June 1, 2022. Docket ID EPA-HQ-2022-0389.

59 FAA's TAF Fiscal Years 2020-2045 describes the forecast method, data sources, and review process for the
TAF estimates. The documentation for the TAF is available at

https://taf.faa.gov/Downloai ¦nmmarvFY2020-2045 .pdf.

60 The TAF is prepared to assist the FAA in meeting its planning, budgeting, and staffing requirements. In
addition, state aviation authorities and other aviation planners use the TAF as a basis for planning airport
improvements. The TAF is available on the Internet. The TAF database can be accessed at:

https://taf.faa.gov.

61 EPA (1977) AQC for Lead. EPA, Washington, DC, EPA-600/8-77-017 (NHS PB280411), 1977.

62 EPA (1986) AQC for Lead. EPA, Washington, DC, EPA-600/8-83/028aF-dF (NTIS PB87142386), 1986.

63 EPA (2013) ISA for Lead. Section 2.2.2.1 "Pb Emissions from Piston-engine Aircraft Operating on Leaded
Aviation Gasoline and Other Non-road Sources." p. 2-10. EPA, Washington, DC, EPA/600/R-10/075F, 2013.

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1. Physical and Chemical Characteristics of Lead Emitted by Piston-Engine Aircraft

As with motor vehicle engines, when leaded avgas is combusted, the lead is oxidized to form
lead oxide. In the absence of the ethylene dibromide lead scavenger in the fuel, lead oxide can
collect on the valves and spark plugs, and if the deposits become thick enough, the engine can be
damaged. Ethylene dibromide reacts with the lead oxide, converting it to brominated lead and
lead oxybromides. These brominated forms of lead remain volatile at high combustion
temperatures and are emitted from the engine along with the other combustion by-products.64
Upon cooling to ambient temperatures these brominated lead compounds are converted to
particulate matter. The presence of lead dibromide particles in the exhaust from a piston-engine
aircraft has been confirmed by Griffith (2020) and is the primary form of lead emitted by engines
operating on leaded fuel.65 In addition to lead bromides, ammonium salts of other lead halides
were also emitted by motor vehicles and would be expected in the exhaust of piston-engine
aircraft.66

Uncombusted alkyl lead was also measured in the exhaust of motor vehicles operating on
leaded gasoline and is therefore likely to be present in the exhaust from piston-engine aircraft.67
Alkyl lead is the general term used for organic lead compounds and includes the lead additive
tetraethyl lead. Summarizing the available data regarding emissions of alkyl lead from piston-
engine aircraft, the 2013 Lead ISA notes that lead in the exhaust that might be in organic form
may potentially be 20 percent (as an upper bound estimate).68 In addition, tetraethyl lead is a
highly volatile compound and therefore, a portion of tetraethyl lead in fuel exposed to air will
partition into the vapor phase.69

Particles emitted by piston-engine aircraft are in the submicron size range (less than one
micron in diameter). The Swiss Federal Office of Civil Aviation (FOCA) published a study of
piston-engine aircraft emissions including measurements of lead.70 The Swiss FOCA reported
the mean particle diameter of particulate matter emitted by one single-engine piston-powered
aircraft ranged from 0.049 to 0.108 microns under different power conditions (lead particles
would be expected to be present, but these particles were not separately identified in this study).
The particle number concentration ranged from 5.7xl06 to 8.6xl06 particles per cm3. The
authors noted that these particle emission rates are comparable to those from a typical diesel

64 EPA (1986) AQC for Lead. EPA, Washington, DC, EPA-600/8-83/028aF-dF (NTIS PB87142386), 1986.

65 Griffith 2020. Electron microscopic characterization of exhaust particles containing lead dibromide beads
expelled from aircraft burning leaded gasoline. Atmospheric Pollution Research 11:1481-1486.

66 EPA (1986) AQC for Lead. Volume 2: Chapters 5 & 6. EPA, Washington, DC, EPA-600/8-83/028aF-dF
(NTIS PB87142386), 1986.

67 EPA (2013) ISA for Lead. Table 2-1. "Pb Compounds Observed in the Environment." p. 2-8. EPA,
Washington, DC, EPA/600/R-10/075F, 2013.

68 EPA (2013) ISA for Lead. Section 2.2.2.1 "Pb Emissions from Piston-engine Aircraft Operating on Leaded-
Aviation Gasoline and Other Non-road Sources." p. 2-10. EPA, Washington, DC, EPA/600/R-10/075F, 2013.

69 Memorandum to Docket EPA-HQ-OAR-2022-0389. Potential Exposure to Non-exhaust Lead and Ethylene
Dibromide. June 15, 2022. Docket ID EPA-HQ-2022-0389.

70 Swiss FOCA (2007) Aircraft Piston Engine Emissions Summary Report. 33-05-003 Piston Engine
Emissions Swiss FOCA Summary. Report_070612_rit. Available at

https://www.bazl.admin.ch/bazl/en/home/specialists/regulations-and-guidelines/environment/pollutant-
emissions/aircraft-engine-emissions/report~appendices~database-and-data-sheets.html.

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passenger car engine without a particle filter.71 Griffith (2020) collected exhaust particles from a
piston-engine aircraft operating on leaded avgas and examined the particles using electron
microscopy. Griffith reported that the mean diameter of particles collected in exhaust was 13
nanometers (0.013 microns) consisting of a 4 nanometer (0.004 micron) lead dibromide particle
surrounded by hydrocarbons.

2. Inventory of Lead Emitted by Piston-Engine Aircraft

Lead emissions from covered aircraft are the largest single source of lead to air in the U.S. in
recent years, contributing over 50 percent of lead emissions to air starting in 2008 (Table l).72 In
2017, approximately 470 tons of lead were emitted by engines in piston-powered aircraft, which
constituted 70 percent of the annual emissions of lead to air in that year.73 Lead is emitted at and
near thousands of airports in the U.S. as described in Section A. The EPA's method for
developing airport-specific lead estimates is described in the EPA's Advance Notice of Proposed
Rulemaking on Lead Emissions from Piston-Engine Aircraft Using Leaded Aviation Gasoline74
and in the document titled "Calculating Piston-Engine Aircraft Airport Inventories for Lead for
the 2008 National Emissions Inventory."75 The EPA's National Emissions Inventory (NEI)
reports airport estimates of lead emissions as well as estimates of lead emitted in-flight, which
are allocated to states based on the fraction of piston-engine aircraft activity estimated for each
state. These inventory data are briefly summarized here at the state, county, and airport level.76

Table 1. Piston-Engine Emissions of Lead to Air

2008

2011

2014

2017

Piston-engine emissions of lead to air, tons

560

490

460

470

Total U.S. lead emissions, tons

950

810

720

670

Piston-engine emissions as a percent of the total U.S. lead inventory

59%

60%

64%

70%

71 Swiss FOCA (2007) Aircraft Piston Engine Emissions Summary Report. 33-05-003 Piston Engine
Emissions Swiss FOCASummary. Report_070612_rit. Section 2.2.3.a. Available at
https://www.bazl.admin.ch/bazl/en/home/specialists/regulations-and-guidelines/environment/pollutant-
emissions/aircraft-engine-emissions/report~appendices~database-and-data-sheets.html.

72 The lead inventories for 2008, 2011 and 2014 are provided in the U.S. EPA (2018b) Report on the
Environment Exhibit 2. Anthropogenic lead emissions in the U.S. Available at
https://cfpnb.epa.gOv/roe/i ndicator.cfm?i=.1.3#2.

73 EPA 2017 NEI. Available at https://www.epa.gov/air-emissions-inventories/2017-national-emissions-
inventory-nei-data.

74 Advance Notice of Proposed Rulemaking on Lead Emissions from Piston-Engine Aircraft Using Leaded
Aviation Gasoline. 75 FR 2440 (April 28, 2010).

75 Airport lead annual emissions data used were reported in the 2017 NEI. Available at https://www.epa.gov/air-
emissions-inventories/2017-national-emissions-inventory-nei-data. The methods used to develop these inventories
are described in EPA (2010) Calculating Piston-Engine Aircraft Airport Inventories for Lead for the 2008 NEI.
EPA, Washington, DC, EPA-420-B-10-044, 2010. (Also available in the docket for this action, EPA-HQ-OAR-
2022-0389).

76 The 2017 NEI utilized 2014 aircraft activity data to develop airport-specific lead inventories. Details can be
found on page 3-17 of the document located here: https://www.epa.gov/sites/defanlt/files/202.1.-
02/doeiiments/nei2017 tsd_fulHan2021.pdf#page=70&zoom=100.68.633.

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At the state level, the EPA estimates of lead emissions from piston-engine aircraft range from
0.3 tons (Rhode Island) to 50.5 tons (California), 47 percent of which is emitted in the landing
and takeoff cycle and 53 percent of which the EPA estimates is emitted in-flight, outside the
landing and takeoff cycle.77 Among the counties in the U.S. where the EPA estimates engine
emissions of lead from covered aircraft, lead inventories range from 0.00005 tons per year to 4.1
tons per year and constitute the only source of air-related lead in 1,140 counties (the county
estimates of lead emissions include the lead emitted during the landing and takeoff cycle and not
lead emitted in-flight).78 In the counties where engine emissions of lead from aircraft are the
sole source of lead to these estimates, annual lead emissions from the landing and takeoff cycle
ranged from 0.00015 to 0.74 tons. Among the 1,872 counties in the U.S. with multiple sources
of lead, including engine emission from covered aircraft, the contribution of aircraft engine
emissions ranges from 0.0006 to 0.26 tons, comprising 0.0065 to 99.98 percent of the county
total, respectively.

The EPA estimates that among the approximately 20,000 airports in the U.S., airport lead
inventories range from 0.00005 tons per year to 0.9 tons per year.79 In 2017, the EPA's NEI
includes 638 airports where the EPA estimates engine emissions of lead from covered aircraft
were 0.1 ton or more of lead annually. Using the FAA's forecasted activity in 2045 for the
approximately 3,300 airports in the NPIAS (as described in Section A), the EPA estimates
airport-specific inventories may range from 0.00003 tons to 1.28 tons of lead (median of 0.03
tons), with 656 airports estimated to have inventories above 0.1 tons in 2045.80

We estimate that piston-engine aircraft have consumed approximately 38.6 billion gallons of
leaded avgas in the U.S. since 1930, excluding military aircraft use of this fuel, emitting
approximately 113,000 tons of lead to the air.81

77 Lead emitted in-flight is assigned to states based on their overall fraction of total piston-engine aircraft
operations. The state-level estimates of engine emissions of lead include both lead emitted in the landing and
takeoff cycle as well as lead emitted in-flight. The method used to develop these estimates is described in EPA
(2010) Calculating Piston-Engine Aircraft Airport Inventories for Lead for the 2008 NEI, available here:
https://nepis.epa.gOv/Exe/ZyPDF.cgi/P 1009113.PDF?Dockey=P1009I13.PDF.

78 Airport lead annual emissions data used were reported in the 2017 NEI. Available at https://www.epa.gov/air-
emissions-inventories/2017-national-emissions-inventory-nei-data. In addition to the triennial NEI, the EPA
collects from state, local, and Tribal air agencies point source data for larger sources every year (see
https://www.epa.gov/air-emissions-inventories/air-emissions-reporting-requirements-aerrfor specific emissions
thresholds). While these data are not typically published as a new NEI, they are available publicly upon request and
are also included in https://www.epa.gov/air-emissions-modeling/emissions-modeling-platforms that are created for
years other than the triennial NEI years. County estimates of lead emissions from non-aircraft sources used in this
action are from the 2019 inventory. There are 3,012 counties and statistical equivalent areas where EPA estimates
engine emissions of lead occur.

79 See EPA 2017 NEI. Available at https://www.epa.gov/air-emissions-inventories/2017-national-emissions-
invcnton-nci-data

80 EPA used the method describe in EPA (2010) Calculating Piston-Engine Aircraft Airport Inventories for Lead
for the 2008 NEI to estimate airport lead inventories in 2045. This document is available here:
https://nepis.epa.gov/Exe/ZyPDF.cgi/P1009I13.PDF?Dockey=P1009I13.PDF.

81 Geidosch. Memorandum to Docket EPA-HQ-OAR-2022-0389. Lead Emissions from the use of Leaded
Aviation Gasoline from 1930 through 2020. June 1,2022. Docket ID EPA-HQ-2022-0389.

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C. Concentrations of Lead in Air Attributable to Emissions from Piston-Engine
Aircraft

In this section, we describe the concentrations of lead in air resulting from emissions of lead
from covered aircraft. Air quality monitoring and modeling studies for lead at and near airports
have identified elevated concentrations of lead in air from piston-engine aircraft exhaust at, and
downwind of, airports where these aircraft are active.82'83'84'85'86,87 This section provides a
summary of the literature regarding the local-scale impact of aircraft emissions of lead on
concentrations of lead at and near airports, with specific focus on the results of air monitoring for
lead that the EPA required at a subset of airports and an analysis conducted by the EPA to
estimate concentrations of lead at 13,000 airports in the U.S., titled "Model-extrapolated
Estimates of Airborne Lead Concentrations at U.S. Airports."88

Gradient studies evaluate how lead concentrations change with distance from an airport where
piston-engine aircraft operate. These studies indicate that concentrations of lead in air are
estimated to be one to two orders of magnitude higher at locations proximate to aircraft
emissions, compared to nearby locations not impacted by a source of lead air emissions

82 Carr et. al., 2011. Development and evaluation of an air quality modeling approach to assess near-field
impacts of lead emissions from piston-engine aircraft operating on leaded aviation gasoline. Atmospheric
Environment, 45 (32), 5795-5804. DOI: http://dx.doi.org/.1.0. .1.016/i.atmosenv.20.1. .1..07.017.

83 Feinberg et. al., 2016. Modeling of Lead Concentrations and Hot Spots at General Aviation Airports. Journal
of the Transportation Research Board, No. 2569, Transportation Research Board, Washington, D.C., pp. 80-87.
DOI: 10.3141/2569-09.

84 Municipality of Anchorage (2012). Merrill Field Lead Monitoring Report. Municipality of Anchorage
Department of Health and Human Services. Anchorage, Alaska. Available at

http://www.mniii.org/Departments/healtIi/Admin/environ.ment/AirO/Docnments/MerriH%20FieM%20Lead%20Mon
itoring%20Studv 2012/MerriH%20Field%20Lead%20Studv%20Report%20-%20final.pdf.

85 Environment Canada (2000) Airborne Particulate Matter, Lead and Manganese at Buttonville Airport. Conor
Pacific Environmental Technologies for Environmental Protection Service. Ontario.

86 Fine et. al., 2010. General Aviation Airport Air Monitoring Study. South Coast Air Quality Management
District. Available at http://www.aamd.gov/docs/defanit-sonrce/air-analitv/air-analitY-monitoring-stndies/general-
aviatim-stHiMstod^^

87 Lead emitted from piston-engine aircraft in the particulate phase would also be measured in samples collected
to evaluate total ambient PM2.5 concentrations.

88 EPA (2020) Model-extrapolated Estimates of Airborne Lead Concentrations at U.S. Airports. EPA,
Washington, DC. EPA-420-R-20-003, 2020. Available at

https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100YG52.pdf. EPA responses to peer review comments on the
report are available at https://nepis.epa.gov/Exe/ZvPDF.cgi?Dockev=P100YIWD.pdf. These documents are also
available in the docket for this action (Docket EPA-HQ-OAR-2022-0389). This report is Appendix A in this TSD.

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(concentrations for periods of approximately 18 hours to three-month averages).89'90'91'92'93'94
The magnitude of lead concentrations at and near airports is highly influenced by the amount of
aircraft activity (i.e., the number of take-off and landing operations, particularly if concentrated
at one runway) and the time spent by aircraft in specific modes of operation. The most
significant emissions in terms of ground-based activity, and therefore ground-level
concentrations of lead in air, occur near the areas with greatest fuel consumption where the
aircraft are stationary and running.95'96'97 For piston-engine aircraft these areas are most
commonly locations in which pilots conduct engine tests during run-up operations prior to take-
off (e.g., magneto checks during the run-up operation mode). Run-up operations are conducted
while the brakes are engaged so the aircraft is stationary and are often conducted adjacent to the
runway end from which the aircraft will take off. Additional modes of operation by piston-
engine aircraft, such as taxiing or idling near the runway, may result in additional hotspots of
elevated lead concentration (e.g., start-up and idle, maintenance run-up).98

The lead NAAQS was revised in 2008.99 The 2008 decision revised the level, averaging time
and form of the standards to establish the current primary and secondary standards, which are
both 0.15 micrograms per cubic meter of air, in terms of consecutive three-month average of lead
in total suspended particles.100 In conjunction with strengthening the lead NAAQS in 2008, the
EPA enhanced the existing lead monitoring network by requiring monitors to be placed in areas
with sources such as industrial facilities and airports with estimated lead emissions of 1.0 ton or
more per year. Lead monitoring was conducted at two airports following from these
requirements (Deer Valley Airport, AZ and the Van Nuys Airport, CA). In 2010, the EPA made

89 These studies report monitored or modeled data for averaging times ranging from approximately 18 hours to
three-month averages.

90 Carr et. al., 2011. Development and evaluation of an air quality modeling approach to assess near-field
impacts of lead emissions from piston-engine aircraft operating on leaded aviation gasoline. Atmospheric
Environment, 45 (32), 5795-5804. DOI: http://dx.doi.org/.1.0. .1.016/i.atmosenv.20.1. .1..07.017.

91 Heiken et. al., 2014. Quantifying Aircraft Lead Emissions at Airports. ACRP Report 133.
http://www.nap.edu/catalog/22142/quantifying-aircraft-lead-emissions-at-airports.

92 Hudda, et. al., 2022. Substantial Near-Field Air Quality Improvements at a General Aviation Airport
Following a Runway Shortening. Environmental Science & Technology. DOI: 10.1021/acs.est.lc06765.

93 Fine et. al., 2010. General Aviation Airport Air Monitoring Study. South Coast Air Quality Management
District, http://www.aqmd.gov/docs/default-source/air-quality/air-quality-monitoring-studies/general-aviation-
study/study-of-air-toxins-near-van-nuys-and-santa-monica-airport.pdf.

94 EPA (2020) Model-extrapolated Estimates of Airborne Lead Concentrations at U.S. Airports. EPA,
Washington, DC, EPA-420-R-20-003, 2020.

95 EPA (2010) Development and Evaluation of an Air Quality Modeling Approach for Lead Emissions from
Piston-Engine Aircraft Operating on Leaded Aviation Gasoline. EPA, Washington, DC, EPA-420-R-10-007, 2010.
https://nepis.epa.gOv/Exe/ZvPDF.cgi/P 1007H4Q.PDF?Dockev=Pl007H4Q.PDF.

96 EPA (2020) Model-extrapolated Estimates of Airborne Lead Concentrations at U.S. Airports. EPA,
Washington, DC, EPA-420-R-20-003, 2020. EPA responses to peer review comments on the report are available at
https://nepis.epa. gov/Exe/ZyPDF.cgi?Dockey=P100YIWD.pdf.

97 Feinberg et. al., 2016. Modeling of Lead Concentrations and Hot Spots at General Aviation Airports. Journal
of the Transportation Research Board, No. 2569, Transportation Research Board, Washington, D.C., pp. 80-87.
DOI: 10.3141/2569-09.

98 Feinberg et. al., 2016. Modeling of Lead Concentrations and Hot Spots at General Aviation Airports. Journal
of the Transportation Research Board, No. 2569, Transportation Research Board, Washington, D.C., pp. 80-87.
DOI: 10.3141/2569-09.

99 73 FR 66965 (Nov. 12, 2008).

100 40 CFR 50.16 (Nov. 12, 2008).

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further revisions to the monitoring requirements such that state and local air quality agencies are
now required to monitor near industrial facilities with estimated lead emissions of 0.50 tons or
more per year and at airports with estimated emissions of 1.0 ton or more per year.101 As part of
this 2010 requirement to expand lead monitoring, the EPA also required a one-year monitoring
study of 15 additional airports with estimated lead emissions between 0.50 and 1.0 ton per year
in an effort to better understand how these emissions affect concentrations of lead in the air at
and near airports. Further, to help evaluate airport characteristics that could lead to ambient lead
concentrations that approach or exceed the lead NAAQS, airports for this one-year monitoring
study were selected based on factors such as the level of piston-engine aircraft activity and the
predominant use of one runway due to wind patterns.

As a result of these requirements, state and local air authorities collected and certified lead
concentration data for at least one year at 17 airports with most monitors starting in 2012 and
generally continuing through 2013. The data presented in Table 2 are based on the certified data
for these sites and represent the maximum concentration monitored in a rolling three-month
average for each location.102

Table 2. Lead Concentrations Monitored at 17 Airports in the U.S.

Airport, State

Lead Design Value,103
Hg/m3

Auburn Municipal Airport, WA

0.06

Brookhaven Airport, NY

0.03

Centennial Airport, CO

0.02

Deer Valley Airport, AZ

0.04

Gillespie Field, CA

0.07

Harvey Field, WA

0.02

McClellan-Palomar Airport, CA

0.17

Merrill Field, AK

0.07

Nantucket Memorial Airport, MA

0.01

Oakland County International Airport, MI

0.02

Palo Alto Airport, CA

0.12

Pry or Field Regional Airport, AL

0.01

Reid-Hillview Airport, CA

0.10

Republic Airport, NY

0.01

San Carlos Airport, CA

0.33

Stinson Municipal, TX

0.03

Van Nuys Airport, CA

0.06

Monitored lead concentrations violated the lead NAAQS at two airports in 2012: the
McClellan-Palomar Airport and the San Carlos Airport. At both of these airports, monitors were
located in close proximity to the area at the end of the runway most frequently used for pre-flight
safety checks (i.e., run-up). Alkyl lead emitted by piston-engine aircraft would be expected to

101 75 FR 81226 (Dec. 27, 2010).

102 EPA (2015) Program Overview: Airport Lead Monitoring. EPA, Washington, DC, EPA-420-F-15-003,
2015. Available at: https://nepis.epa.gov/Exe/ZyPDF.cgi/P100LJDW.PDF?Dockey=P100LJDW.PDF. This
document is Appendix D in this TSD.

103 A design value is a statistic that summarizes the air quality data for a given area in terms of the indicator,
averaging time, and form of the standard. Design values can be compared to the level of the standard and are
typically used to designate areas as meeting or not meeting the standard and assess progress towards meeting the
NAAQS.

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partition into the vapor phase and would not be collected by the monitoring conducted in this
study, which is designed to quantitatively collect particulate forms of lead.104

Airport lead monitoring and modeling studies have identified the sharp decrease in lead
concentrations with distance from the run-up area and therefore the importance of considering
monitor placement relative to the run-up area when evaluating the maximum impact location
attributable to lead emissions from piston-engine aircraft. The monitoring data in Table 2 reflect
differences in monitor placement relative to the run-up area as well as other factors; this study
also provided evidence that air lead concentrations at and downwind from airports could be
influenced by factors such as the use of more than one run-up area, wind speed, and the number
of operations conducted by single- versus twin-engine aircraft.105

The EPA recognized that the airport lead monitoring study provided a small sample of the
potential locations where emissions of lead from piston-engine aircraft could potentially cause
concentrations of lead in ambient air to exceed the lead NAAQS. Because we anticipated that
additional airports and conditions could lead to exceedances of the lead NAAQS at and near
airports where piston-engine aircraft operate, and in order to understand the range of lead
concentrations at airports nationwide, we developed an analysis of 13,000 airports in the peer-
reviewed report titled, "Model-extrapolated Estimates of Airborne Lead Concentrations at U.S.
Airports."106 This report provides estimated ranges of lead concentrations that may occur at and
near airports where leaded avgas is used. The study extrapolated modeling results from one
airport to estimate air lead concentrations at the maximum impact area near the run-up location
for over 13,000 U.S. airports.107 The model-extrapolated lead estimates in this study indicate
that some additional U.S. airports may have air lead concentrations above the NAAQS at this
area of maximum impact. The report also indicates that, at the levels of activity analyzed at the
13,000 airports, estimated lead concentrations decrease to below the standard within 50 meters
from the location of highest concentration.

To estimate the potential ranges of lead concentrations at and downwind of the anticipated
area of highest concentration at airports in the U.S., the relationship between piston-engine
aircraft activity and lead concentration at and downwind of the maximum impact site at one

104 As noted earlier, when summarizing the available data regarding emissions of alkyl lead from piston-engine
aircraft, the 2013 Lead ISA notes that an upper bound estimate of lead in the exhaust that might be in organic form
may potentially be 20 percent (2013 Lead ISA, p. 2-10). Organic lead in engine exhaust would be expected to
influence receptors within short distances of the point of emission from piston-engine aircraft. Airports with large
flight schools and/or facilities with substantial delays for aircraft queued for takeoff could experience higher
concentrations of alkyl lead in the vicinity of the aircraft exhaust.

105 The data in Table 2 represent concentrations measured at one location at each airport and monitors were not
consistently placed in close proximity to the run-up areas. Monitored concentrations of lead in air near airports are
highly influenced by proximity of the monitor to the run-up area. In addition to monitor placement, there are
individual airport factors that can influence lead concentrations (e.g., the use of multiple run-up areas at an airport,
fleet composition, and wind speed). The monitoring data reported in Table 2 reflect a range of lead concentrations
indicative of the location at which measurements were made and the specific operations at an airport.

106 EPA (2020) Model-Extrapolated Estimates of Airborne Lead Concentrations at U.S. Airports. EPA,
Washington. DC. EPA-420-R-20-003, 2020,

107 In this study, the EPA defined the maximum impact site as 15 meters downwind of the tailpipe of an aircraft
conducting run-up operations in the area designated for these operations at a runway end. The maximum impact
area was defined as approximately 50 meters surrounding the maximum impact site.

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airport was applied to piston-engine aircraft activity estimates for each U.S. airport.108 This
approach for conducting a nationwide analysis of airports was selected due to the impact of
piston-engine aircraft run-up operations on ground-level lead concentrations, which creates a
maximum impact area that is expected to be generally consistent across airports. Specifically,
these aircraft consistently take off into the wind and typically conduct run-up operations
immediately adjacent to the take-off runway end, and thus, modeling lead concentrations from
this source is constrained by variation in a few key parameters. These parameters include: 1)
total amount of piston-engine aircraft activity, 2) the proportion of activity conducted at one
runway end, 3) the proportion of activity conducted by multi-piston-engine aircraft, 4) the
duration of run-up operations, 5) the concentration of lead in avgas, 6) wind speed at the model
airport relative to the extrapolated airport, and 7) additional meteorological, dispersion model, or
operational parameters. These parameters were evaluated through sensitivity analyses as well as
quantitative or qualitative uncertainty analyses. To generate robust concentration estimates, the
EPA evaluated these parameters, conducted wind-speed correction of extrapolated estimates, and
used airport-specific information regarding airport layout and prevailing wind directions for the
13,000 airports.109

Results of this national analysis show that model-extrapolated three-month average lead
concentrations in the maximum impact area may potentially exceed the lead NAAQS at airports
with activity ranging from 3,616 - 26,816 Landing and Take-Off events (LTOs) in a three-month
period.110 The lead concentration estimates from this model-extrapolation approach account for
lead engine emissions from aircraft only, and do not include other sources of air-related lead.
The broad range in LTOs that may lead to concentrations of lead exceeding the lead NAAQS is
due to the piston-engine aircraft fleet mix at individual airports such that airports where the fleet
is dominated by twin-engine aircraft would potentially reach concentrations of lead exceeding
the lead NAAQS with fewer LTOs compared with airports where single-engine aircraft dominate
the piston-engine fleet.111 Model-extrapolated three-month average lead concentrations from
aircraft engine emissions were estimated to extend to a distance of at least 500 meters from the
maximum impact area at airports with activity ranging from 1,275 - 4,302 LTOs in that three-
month period.112 In a separate modeling analysis at an airport at which hundreds of take-off and
landing events by piston-engine aircraft occur per day, the EPA found that modeled 24-hour

108 Prior to this model extrapolation study, the EPA developed and evaluated an air quality modeling approach
(this study is available here: https://nepis.epa.gov/Exe/ZvPDF.cgi/P1007H40.PDF?Dockev=P1007H4Q.PDF). and
subsequently applied the approach to a second airport and again performed an evaluation of the model output using
air monitoring data (this second study is available here:
https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100YG52.pdf).

109 EPA (2020) Model-extrapolated Estimates of Airborne Lead Concentrations at U.S. Airports. EPA,
Washington. DC. EPA-420-R-20-003, 2020. Available at

110 EPA (2020) Model-extrapolated Estimates of Airborne Lead Concentrations at U.S. Airports. Table 6. p. 53.
EPA. Washington. DC. EPA-420-R-20-003, 2020,

111 See methods used in EPA (2020) Model-extrapolated Estimates of Airborne Lead Concentrations at U.S.
Airports. Table 2. p.23. EPA, Washington, DC, EPA-420-R-20-003, 2020.

112 EPA (2020) Model-extrapolated Estimates of Airborne Lead Concentrations at U.S. Airports, Table 6. p.53.
EPA. Washington. DC. EPA-420-R-20-003, 2020,

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concentrations of lead were estimated above background extending almost 1,000 meters
downwind from the runway.113

Model-extrapolated estimates of lead concentrations in the EPA report "Model-extrapolated
Estimates of Airborne Lead Concentrations at U.S. Airports" were compared with monitored
values and show general agreement, suggesting that the extrapolation method presented in this
report provides reasonable estimates of the range in concentrations of lead in air attributable to
three-month activity periods of piston-engine aircraft at airports. The assessment included
detailed evaluation of the potential impact of run-up duration, the concentration of lead in avgas,
and the impact of meteorological parameters on model-extrapolated estimates of lead
concentrations attributable to engine emissions of lead from piston-powered aircraft.
Additionally, this study included a range of sensitivity analyses as well as quantitative and
qualitative uncertainty analyses.

The EPA's model-extrapolation analysis of lead concentrations from engine emissions
resulting from covered aircraft found that the lowest annual airport emissions of lead estimated
to result in air lead concentrations approaching or potentially exceeding the NAAQS was 0.1
tons per year. There are key pieces of airport-specific data that are needed to fully evaluate the
potential for piston-engine aircraft operating at an airport to cause concentrations of lead in the
air to exceed the lead NAAQS, and the EPA's report "Model-extrapolated Estimates of Airborne
Lead Concentrations at U.S. Airports" provides quantitative and qualitative analyses of these
factors.114 The EPA's estimate of airports that have annual lead inventories of 0.1 ton or more
are illustrative of, and provide one approach for an initial screening evaluation of locations
where engine emissions of lead from aircraft increase localized lead concentrations in air.
Airport-specific assessments would be needed to determine the magnitude of the potential range
in lead concentrations at and downwind of each facility.

As described in Section A, the FAA forecasts 0.9 percent decreases in piston-engine aircraft
activity out to 2041, however these decreases are not projected to occur uniformly across
airports. Among the more than 3,300 airports in the FAA TAF, the FAA forecasts both
decreases and increases in general aviation, which is largely comprised of piston-engine aircraft.
If the current conditions on which the forecast is based persist, then lead concentrations in the air
may increase at the airports where general aviation activity is forecast to increase.

In addition to airport-specific modeled estimates of lead concentrations, the EPA also
provides annual estimates of lead concentrations for each census tract in the U.S. as part of the
Air Toxics Screening Assessment (AirToxScreen).115 The census tract concentrations are
averages of the area-weighted census block concentrations within the tract. Lead concentrations
reported in the AirToxScreen are based on emissions estimates from anthropogenic and natural

113 Carr, et. al., 2011. Development and evaluation of an air quality modeling approach to assess near-field
impacts of lead emissions from piston-engine aircraft operating on leaded aviation gasoline. Atmospheric
Environment45: 5795-5804.

114 EPA (2020) Model-extrapolated Estimates of Airborne Lead Concentrations at U.S. Airports. Table 6. p.53.
EPA, Washington, DC, EPA-420-R-20-003, 2020JPA responses to peer review comments on the report are
available here: https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100YIWD.pdf.

115 See EPA's most recent AirToxScreen. Available at https://www.epa.gov/AirToxScreen.

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sources, including aircraft engine emissions.116 The 2017 AirToxScreen provides lead
concentration estimates in air for 73,449 census tracts in the U.S.117 Lead emissions from piston-
engine aircraft comprised more than 50 percent of these census block area-weighted lead
concentrations in over half of the census tracts, which included tracts in all 50 states, as well as
Puerto Rico and the Virgin Islands.

D. Fate and Transport of Emissions of Lead from Piston-Engine Aircraft

This section summarizes the chemical transformation that piston-engine aircraft lead
emissions are anticipated to undergo in the atmosphere and describes what is known about the
deposition of piston-engine aircraft lead, and potential impacts on soil, food, and aquatic
environments.

1. Atmospheric Chemistry and Transport of Emissions of Lead from Piston-Engine
Aircraft

Lead emitted by piston-engine aircraft can have impacts in the local environment and, due to
their small size (i.e., typically less than one micron in diameter),118'119 lead-bearing particles
emitted by piston engines may disperse widely in the environment. However, lead emitted
during the landing and takeoff cycle, particularly during ground-based operations such as start-
up, idle, preflight run-up checks, taxi and the take-off roll on the runway, may deposit to the
local environment and/or infiltrate into buildings.120 Depending on ambient conditions (e.g.,
ozone and hydroxyl concentrations in the atmosphere), alkyl lead may exist in the atmosphere
for hours to days121 and may therefore be transported off airport property into nearby
communities.

Lead halides emitted by motor vehicles operating on leaded fuel were reported to undergo
compositional changes upon cooling and mixing with the ambient air as well as during transport,
and we would anticipate lead bromides emitted by piston-engine aircraft to behave similarly in
the atmosphere. The water-solubility of these lead-bearing particles was reported to be higher

116 These concentration estimates are not used for comparison to the level of the Lead NAAQS due to different
temporal averaging times and underlying assumptions in modeling. The AirToxScreen estimates are provided to
help state, local and Tribal air agencies and the public identify which pollutants, emission sources and places they
may wish to study further to better understand potential risks to public health from air toxics. There are
uncertainties inherent in these estimates described by the EPA, some of which are relevant to these estimates of lead
concentrations; however, these estimates provide perspective on the potential influence of piston-engine emissions
of lead on air quality. See https://www.epa.gov/AirToxScreen/airtoxscreen-limitations.

117 As airports are generally in larger census blocks within a census tract, concentrations for airport blocks
dominate the area-weighted average in cases where an airport is the predominant lead emissions source in a census
tract.

118 Swiss FOCA (2007) Aircraft Piston Engine Emissions Summary Report. 33-05-003 Piston Engine
Emissions Swiss FOCA Summary. Report_070612_rit. Available at

https://www.bazl.admin.ch/bazl/en/home/specialists/regulations-and-guidelines/environment/pollutant-
emissions/aircraft-engine-emissions/report~appendices~database-and-data-sheets.html.

119 Griffith 2020. Electron microscopic characterization of exhaust particles containing lead dibromide bads
expelled from aircraft burning leaded gasoline. Atmospheric Pollution Research 11:1481-1486.

120 EPA (2013) ISA for Lead. Section 1.3. "Exposure to Ambient Pb." p. 1-11. EPA, Washington, DC,
EPA/600/R-10/075F, 2013.

121 EPA (2006) AQC for Lead. Section E.6. p. 2-5. EPA, Washington, DC, EPA/600/R-5/144aF, 2006.

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for the smaller lead-bearing particles.122 Lead halides emitted in motor vehicle exhaust were
reported to break down rapidly in the atmosphere via redox reactions in the presence of
atmospheric acids.123 Tetraethyl lead has an atmospheric residence time ranging from a few
hours to a few days. Tetraethyl lead reacts with the hydroxyl radical in the gas phase to form a
variety of products that include ionic trialkyl lead, dialkyl lead and metallic lead. Trialkyl lead is
slow to react with the hydroxyl radical and is quite persistent in the atmosphere.124

2. Deposition of Lead Emissions from Piston-Engine Aircraft and Soil Lead

Concentrations to Which Piston-Engine Aircraft May Contribute

Lead is removed from the atmosphere and deposited on soil, into aquatic systems and on other
surfaces via wet or dry deposition.125 Meteorological factors (e.g., wind speed, convection, rain,
humidity) influence local deposition rates. With regard to deposition of lead from aircraft engine
emissions, the EPA modeled the deposition rate for aircraft lead emissions at one airport in a
temperate climate in California with dry summer months. In this location, the average lead
deposition rate from aircraft emissions of lead was 0.057 milligrams per square meter per
year.126

Studies summarized in the 2013 Lead ISA suggest that soil is a reservoir for contemporary
and historical emissions of lead to air.127 Once deposited to soil, lead can be absorbed onto
organic material, can undergo chemical and physical transformation depending on a number of
factors (e.g., pH of the soil and the soil organic content), and can participate in further cycling
through air or other media.128 The extent of atmospheric deposition of lead from aircraft engine
emissions would be expected to depend on a number of factors including the size of the particles
emitted (smaller particles, such as those in aircraft emissions, have lower settling velocity and
may travel farther distances before being deposited compared with larger particles), the
temperature of the exhaust (the high temperature of the exhaust creates plume buoyancy), as well
as meteorological factors (e.g., wind speed, precipitation rates). As a result of the size of the lead
particulate matter emitted from piston-engine aircraft and as a result of these emissions occurring
at various altitudes, lead emitted from these aircraft may distribute widely through the
environment.129 Murphy et. al. (2008) reported weekend increases in ambient lead monitored at
remote locations in the U.S. that the authors attributed to weekend increases in piston-engine
powered general aviation activity.130

122 EPA (1977) AQC for Lead. Section 6.2.2.1. EPA, Washington, DC, EPA-600/8-77-017, 1977.

123 EPA (2006) AQC for Lead. Section E.6. EPA, Washington, DC, EPA/600/R-5/144aF, 2006.

124 EPA (2006) AQC for Lead. Section 2. EPA, Washington, DC, EPA/600/R-5/144aF, 2006.

125 EPA (2013) ISA for Lead. Section 1.2.1. "Sources, Fate and Transport of Ambient Pb;" p. 1-6; and Section
2.3. "Fate and Transport of Pb." p. 2-24 through 2-25. EPA, Washington, DC, EPA/600/R-10/075F, 2013.

126 Memorandum to Docket EPA-HQ-OAR-2022-0389. Deposition of Lead Emitted by Piston-engine Aircraft.
June 15, 2022. Docket ID EPA-HQ-2022-0389.

127 EPA (2013) ISA for Lead. Section 2.6.1. "Soils." p. 2-118. EPA, Washington, DC, EPA/600/R-10/075F,
2013.

128 EPA (2013) ISA for Lead. Chapter 6. "Ecological Effects of Pb." p. 6-57. EPA, Washington, DC,
EPA/600/R-10/075F, 2013.

129 Murphy, et. al., 2008. Weekly patterns of aerosol in the United States. Atmospheric Chemistry and Physics.
8:2729-2739.

130 Lead concentrations collected as part of the Interagency Monitoring of Protected Visual Environments
(IMPROVE) network and the National Oceanic and Atmospheric Administration (NOAA) monitoring sites.

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Heiken et. al. (2014) assessed air lead concentrations potentially attributable to resuspended
lead that previously deposited onto soil relative to air lead concentrations resulting directly from
aircraft engine emissions.131 Based on comparisons of lead concentrations in total suspended
particulate (TSP) and fine particulate matter (PM2.5) measured at the three airports, coarse
particle lead was observed to account for about 20-30 percent of the lead found in TSP. The
authors noted that based on analysis of lead isotopes present in the air samples collected at these
airports, the original source of the lead found in the coarse particle range appeared to be from
aircraft exhaust emissions of lead that previously deposited to soil and were resuspended by wind
or aircraft-induced turbulence. Results from lead isotope analysis in soil samples collected at the
same three airports led the authors to conclude that lead emitted from piston-engine aircraft were
not the dominant source of lead in soil in the samples measured at the airports they studied. The
authors note the complex history of topsoil can create challenges in understanding the extent to
which aircraft lead emissions impact soil lead concentrations at and near airports (e.g., the source
of topsoil can change as a result of site renovation, construction, landscaping, natural events such
as wildfire and hurricanes, and other activities). Concentrations of lead in soil at and near
airports servicing piston-engine aircraft have been measured using a range of
approaches.132'133'134'135'136'137 Kavouras et. al. (2013) collected soil samples at three airports and
reported that construction at an airport involving removal and replacement of topsoil complicated
interpretation of the findings at that airport and that the number of runways at an airport may
influence resulting lead concentrations in soil (i.e., multiple runways may provide for more wide-
spread dispersal of the lead over a larger area than that potentially affected at a single-runway
airport).

3. Potential for Lead Emissions from Piston-Engine Aircraft to Impact Agricultural

Products

Studies conducted near stationary sources of lead emissions (e.g., smelters) have shown that
atmospheric lead sources can lead to contamination of agricultural products, such as
vegetables.138'139 In this way, air lead sources may contribute to dietary exposure pathways.140
As described in Section A, piston-engine aircraft are used in the application of pesticides,

131 Heiken et. al., 2014. ACRP Web-Only Document 21: Quantifying Aircraft Lead Emissions at Airports.
Contractor's Final Report for ACRP 02-34. Available at http://www.trb.org/Piiblications/Blnrbs/172599.aspx.

132 McCumber and Strevett 2017. A Geospatial Analysis of Soil Lead Concentrations Around Regional
Oklahoma Airports. Chemosphere 167:62-70.

133 Kavouras, et. al., 2013. Bioavailable Lead in Topsoil Collected from General Aviation Airports. The
Collegiate Aviation Review International 31(l):57-68. Available at https://doi.org/10.22488/okstate. .1.8. .1.00438

134 Heiken et. al., 2014. ACRP Web-Only Document 21: Quantifying Aircraft Lead Emissions at Airports.
Contractor's Final Report for ACRP 02-34. Available at http://www.trb.org/Piiblications/Blnrbs/172599.aspx.

135 EPA (2010) Development and Evaluation of an Air Quality Modeling Approach for Lead Emissions from
Piston-Engine Aircraft Operating on Leaded Aviation Gasoline. EPA, Washington, DC, EPA-420-R-10-007, 2010.
https://nepis.epa.gOv/Exe/ZyPDF.cgi/P 1007H4Q.PDckey=P1007H4Q.PDF.

136 Environment Canada (2000) Airborne Particulate Matter, Lead and Manganese at Buttonville Airport.
Toronto, Ontario, Canada: Conor Pacific Environmental Technologies for Environmental Protection Service,
Ontario Region.

137 Lejano and Ericson 2005. Tragedy of the Temporal Commons: Soil-Bound Lead and the Anachronicity of
Risk. Journal of Environmental Planning and Management. 48(2):301-320.

138 EPA (2013) ISA for Lead. Section 3.1.3.3. "Dietary Pb Exposure." p. 3-20 through 3-24. EPA, Washington,
DC, EPA/600/R-10/075F, 2013.

139 EPA (2006) AQC for Lead. Section 8.2.2. EPA, Washington, DC, EPA/600/R-5/144aF.

140 EPA (2006) AQC for Lead. Section 8.2.2. EPA, Washington, DC, EPA/600/R-5/144aF.

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fertilizers and seeding crops for human and animal consumption and as such, provide a potential
route of exposure for lead in food. To minimize drift of pesticides and other applications from
the intended target, pilots are advised to maintain a height between eight and 12 feet above the
target crop during application.141 The low flying height is needed to minimize the drift of the
fertilizer and pesticide particles away from their intended target. An unintended consequence of
this practice is that exhaust emissions of lead have a substantially increased potential for directly
depositing on vegetation and surrounding soil. Lead halides, the primary form of lead emitted by
engines operating on leaded fuel,142 are slightly water soluble and, therefore, may be more
readily absorbed by plants than other forms of inorganic lead.

The 2006 AQCD indicated that surface deposition of lead onto plants may be significant.143
Atmospheric deposition of lead provides a pathway for lead in vegetation as a result of contact
with above-ground portions of the plant.144'145'146 Livestock may subsequently be exposed to
lead in vegetation (e.g., grasses and silage) and in surface soils via incidental ingestion of soil
while grazing.147

4. Potential For Lead Emissions from Piston-Engine Aircraft to Impact Aquatic

Ecosystems

As discussed in Section 6.4 of the 2013 Lead ISA, lead bioaccumulates in the tissues of
aquatic organisms through ingestion of food and water or direct uptake from the environment
(e.g., across membranes such as gills or skin).148 Alkyl lead, in particular, has been identified by
the EPA as a Persistent, Bioaccumulative, and Toxic (PBT) pollutant.149 There are 527 seaport
facilities in the U.S., and landing and take-off activity by seaplanes at these facilities provides a
direct pathway for emission of organic and inorganic lead to the air near/above inland waters and
ocean seaports where these aircraft operate.150 Inland airports may also provide a direct pathway
for emission of organic and inorganic lead to the air near/above inland waters. Lead emissions
from piston-engine aircraft operating at seaplane facilities as well as airports and heliports near

141 O'Connor-Marer. Aerial Applicator's Manual: A National Pesticide Applicator Certification Study Guide, p.
40. National Association of State Departments of Agriculture Research Foundation. Available at
https://www.agaviation.org/Files/ReiatedEntities/Aerial Applicators Maniial.pdf.

142 The additive used in the fuel to scavenge lead determines the chemical form of the lead halide emitted;
because ethylene dibromide is added to leaded aviation gasoline used in piston-engine aircraft, the lead halide
emitted is in the form of lead dibromide.

143 EPA (2006) AQC for Lead. pp. 7-9 and AXZ7-39. EPA, Washington, DC, EPA/600/R-5/144aF.

144 EPA (2006) AQC for Lead. p. AXZ7-39. EPA, Washington, DC, EPA/600/R-5/144aF.

145 EPA (1986) AQC for Lead. Sections 6.5.3. EPA, Washington, DC, EPA-600/8-83/028aF-dF (NTIS
PB87142386), 1986.

146 EPA (1986) AQC for Lead. Section 7.2.2.2.1 .EPA, Washington, DC, EPA-600/8-83/028aF-dF (NTIS
PB87142386), 1986.

147 EPA (1986) AQC for Lead. Section 7.2.2.2.2. EPA, Washington, DC, EPA-600/8-83/028aF-dF (NTIS
PB87142386), 1986.

148 EPA (2013) ISA for Lead. Section 6.4.2. "Biogeochemistry and Chemical Effects of Pb in Freshwater and
Saltwater Systems." p. 6-147. EPA, Washington, DC, EPA/600/R-10/075F, 2013.

149 EPA (2002) Persistent, Bioaccumulative, and Toxic Pollutants (PBT) Program. PBT National Action Plan for
Alkyl-Pb. Washington, DC. June. 2002.

150 See FAA's NASR. Available at
https://www.faa.gov/air_traffic/flight_info/aeronav/aero_data/eNASR_Browser/.

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water bodies can enter the aquatic ecosystem by either deposition from ambient air or runoff of
lead deposited to surface soils.

In addition to deposition of lead from engine emissions by piston-powered aircraft, lead may
enter aquatic systems from the pre-flight inspection of the fuel for contaminants that pilots
conduct. While some pilots return the checked fuel to their fuel tank or dispose of it in a
receptacle provided on the airfield, some pilots discard the fuel onto the tarmac, ground, or
water, in the case of a fuel check being conducted on a seaplane. Lead in the fuel discarded to
the environment may evaporate to the air and may be taken up by the surface on which it is
discarded. Lead on tarmac or soil surfaces is available for runoff to surface water. Tetraethyl
lead in the avgas directly discarded to water will be available for uptake and bioaccumulation in
aquatic life. The National Academy of Sciences Airport Cooperative Research Program (ACRP)
conducted a survey study of pilots' fuel sampling and disposal practices. Among the 146 pilots
responding to the survey, 36 percent indicated they discarded all fuel check samples to the
ground regardless of contamination status and 19 percent of the pilots indicated they discarded
only contaminated fuel to the ground.151 Leaded avgas discharged to the ground and water
includes other hazardous fuel components such as ethylene dibromide.152

E. Consideration of Environmental Justice and Children in Populations Residing Near
Airports

This section provides a description of how many people live in close proximity to airports
where they may be exposed to airborne lead from aircraft engine emissions of lead (referred to
here as the "near-airport" population). This section also provides the demographic composition
of the near-airport population, with attention to implications related to environmental justice (EJ)
and the population of children in this near-source environment. Consideration of EJ implications
in the population living near airports is important because blood lead levels in children from low-
income households remain higher than those in children from higher income households, and the
most exposed Black children still have higher blood lead levels than the most exposed non-
Hispanic White children.153 154 155

Executive Orders 12898 (59 FR 7629, February 16, 1994) and 14008 (86 FR 7619, February
1, 2021) direct federal agencies, to the greatest extent practicable and permitted by law, to make
achieving EJ part of their mission by identifying and addressing, as appropriate,
disproportionately high and adverse human health or environmental effects of their programs,
policies, and activities on people of color populations and low-income populations in the United

151 National Academies of Sciences, Engineering, and Medicine 2014. Best Practices for General Aviation
Aircraft Fuel-Tank Sampling. Washington, DC: The National Academies Press, https://doi.org/10.17226/22343.

152 Memorandum to Docket EPA-HQ-OAR-2022-0389. Potential Exposure to Non-exhaust Lead and Ethylene
Dibromide. June 15, 2022. Docket ID EPA-HQ-2022-0389.

153 EPA (2013) IS A for Lead. Section 5.4. "Summaiy." p. 5-40. EPA, Washington, DC, EPA/600/R-10/075F,
2013.

154 EPA. America's Children and the Environment. Summary of blood lead levels in children updated in 2022,
available at https://www.epa.gov/americaschildrenenviron.ment/biomon.itoring-lead. Data source: Centers for
Disease Control and Prevention, National Report on Human Exposure to Environmental Chemicals. Blood Lead
(2011 -2018). Updated March 2022. Available at

https://www.cdc.gov/exposurereport/report/pdf/cgroup2_LBXBPB_2011-p.pdf.

155 The relative contribution of lead emissions from covered aircraft engines to these disparities has not been
determined and is not a goal of the evaluation described here.

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States. The EPA defines environmental justice as the fair treatment and meaningful involvement
of all people regardless of race, color, national origin, or income with respect to the
development, implementation, and enforcement of environmental laws, regulations, and policies.

Our consideration of EJ implications here is focused on describing baseline conditions using
the most recent year for which demographic data are available. The analysis described here
provides information regarding whether some demographic groups are more highly represented
in the near-airport environment compared with people who live farther from airports.

Residential proximity to airports implies that there is an increased potential for exposure to lead
from covered aircraft engine emissions.156 As described in Section C, several studies have
measured higher concentrations of lead in air near airports with piston-engine aircraft activity.
Additionally, as noted in Section A, two studies have reported increased blood lead levels in
children with increasing proximity to airports.157'158

We first summarize here the literature on disparity with regard to those who live in proximity
to airports. Then we describe the analyses the EPA has conducted to evaluate potential disparity
in the population groups living near runways where piston-engine aircraft operate compared to
those living elsewhere.

Numerous studies have found that environmental hazards such as air pollution are more
prevalent in areas where people of color and low-income populations represent a higher fraction
of the population compared with the general population, including near transportation
sources.I59-160-161-162-163 xhe literature includes studies that have reported on communities in
close proximity to airports that are disproportionately represented by people of color and low-
income populations. McNair (2020) described nineteen major airports that underwent capacity
expansion projects between 2000 and 2010, thirteen of which had a large concentration or
presence of persons of color, foreign-born persons or low-income populations nearby.164
Woodburn (2017) reported on changes in communities near airports from 1970-2010, finding

156 Residential proximity to a source of a specific air pollutant(s) is a widely used surrogate measure to evaluate
the potential for higher exposures to that pollutant (EPA Technical Guidance for Assessing Environmental Justice in
Regulatory Analysis. Section 4.2.1). Data presented in Section C demonstrate that lead concentrations in air near
the runup area can exceed the lead NAAQS and concentrations decrease sharply with distance from the ground-
based aircraft exhaust and vary with the amount of aircraft activity at an airport. Not all people living within 500
meters of a runway are expected to be equally exposed to lead.

157 Miranda et. al., 2011. A Geospatial Analysis of the Effects of Aviation Gasoline on Childhood Blood Lead
Levels. Environmental Health Perspectives. 119:1513-1516.

158 Zahran et. al., 2017. The Effect of Leaded Aviation Gasoline on Blood Lead in Children. Journal of the
Association of Environmental and Resource Economists. 4(2):575-610.

159 Rowangould 2013. A census of the near-roadway population: public health and environmental justice
considerations. Transportation Research Part D 25:59-67. http://dx.doi.Org/10.1016/j.trd.2013.08.003

160 Marshall, et. al., 2014. Prioritizing environmental justice and equality: diesel emissions in Southern
California. Environmental Science & Technology 48: 4063-4068. https://doi.org/10.1021/es405167f

161 Marshall 2008. Environmental inequality: air pollution exposures in California's South Coast Air Basin.
Atmospheric Environment 21:5499-5503. https://doi.Org/10.1016/j.atmosenv.2008.02.005

162 Tessum et. al., 2021. PM2.5 polluters disproportionately and systemically affect people of color in the United
States. Science Advances 7:eabf4491.

163 Mohai et. al., 2009. Environmental justice. Annual Reviews 34:405-430. https://doi.org/10.1146/annurev-
environ-082508-094348.

164 McNair 2020. Investigation of environmental justice analysis in airport planning practice from 2000 to 2010.
Transportation Research Part D 81:102286.

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suggestive evidence that at many hub airports over time, the presence of marginalized groups
residing in close proximity to airports increased.165 Rissman et. al. (2013) reported that with
increasing proximity to the Hartsfield-Jackson Atlanta International Airport, exposures to
particulate matter were higher, and there were lower home values, income, education, and
percentage of white residents.166

The EPA used two approaches to understand whether some members of the population (e.g.,
children five and under, people of color, indigenous populations, low-income populations)
represent a larger share of the people living in proximity to airports where piston-engine aircraft
operate compared with people who live farther away from these airports. In the first approach,
we evaluated people living within, and children attending school within, 500 meters of all of the
approximately 20,000 airports in the U.S., using methods described in the EPA's report titled
"National Analysis of the Populations Residing Near or Attending School Near U.S.

Airports."167 In the second approach, we evaluated people living near the NPIAS airports in the
conterminous 48 states. As noted in Section A, the NPIAS airports support the majority of
piston-engine aircraft activity that occurs in the U.S. Among the NPIAS airports, we compared
the demographic composition of people living within one kilometer of runways with the
demographic composition of people living at a distance of one to five kilometers from the same
airports.

The distances analyzed for those people living closest to airports (i.e., distances of 500 meters
and 1,000 meters) were chosen for evaluation following from the air quality monitoring and
modeling data presented in Section C. Specifically, the EPA's modeling and monitoring data
indicate that concentrations of lead from piston-engine aircraft emissions can be elevated above
background levels at distances of 500 meters over a rolling three-month period. On individual
days, concentrations of lead from piston-engine aircraft emissions can be elevated above
background levels at distances of 1,000 meters on individual days downwind of a runway,
depending on aircraft activity and prevailing wind direction.168'169'170

Because the U.S. has a dense network of airports, many of which have neighboring
communities, we first quantified the number of people living and children attending school
within 500 meters of the approximately 20,000 airports in the U.S. The results of this analysis
are summarized at the national scale in the EPA's report titled "National Analysis of the

165 Woodburn 2017. Investigating neighborhood change in airport-adjacent communities in multiairport regions
from 1970 to 2010. Journal of the Transportation Research Board, 2626, 1-8.

166Rissman et. al., 2013. Equity and health impacts of aircraft emissions at the Hartfield-Jackson Atlanta
International Airport. Landscape and Urban Planning, 120: 234-247.

167 EPA (2020) Model-extrapolated Estimates of Airborne Lead Concentrations at U.S. Airports. EPA,
Washington, DC, EPA-420-R-20-003, 2020. EPA responses to peer review comments on the report are available at
https://nepis.epa. gov/Exe/ZyPDF.cgi?Dockey=P100YISM.pdf.

168 EPA (2020) Model-extrapolated Estimates of Airborne Lead Concentrations at U.S. Airports. EPA,
Washington, DC, EPA-420-R-20-003, 2020.

169 Carr et. al., 2011. Development and evaluation of an air quality modeling approach to assess near-field
impacts of lead emissions from piston-engine aircraft operating on leaded aviation gasoline. Atmospheric
Environment, 45 (32), 5795-5804. DOI: http://dx.doi.org/.1.0. .1.016/i.atmosenv.20.1. .1..07.017.

170 We do not assume or expect that all people living within 500m or 1,000m of a runway are exposed to lead
from piston-engine aircraft emissions, and the wide range of activity of piston-engine aircraft at airports nationwide
suggests that exposure to lead from aircraft emissions is likely to vary widely.

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Populations Residing Near or Attending School Near U.S. Airports."171 From this analysis, the
EPA estimates that approximately 5.2 million people live within 500 meters of an airport
runway, 363,000 of whom are children age five and under. The EPA also estimates that 573
schools attended by 163,000 children in kindergarten through twelfth grade are within 500
meters of an airport runway.172

In order to identify potential disparities in the near-airport population, we first evaluated
populations at the state level. Using the U.S. Census population data for each State in the U.S.,
we compared the percent of people by age, race and indigenous peoples (i.e., children five and
under, Black, Asian, and Native American or Alaska Native) living within 500 meters of an
airport runway with the percent by age, race, and indigenous peoples comprising the state
population.173 Using the methodology described in Clarke (2022), the EPA identified states in
which children, Black, Asian, and Native American or Alaska Native populations represent a
greater fraction of the population compared with the percent of these groups in the state
population.174 Results of this analysis are presented in the following tables.175 This state-level
analysis presents summary information for a subset of potentially relevant demographic
characteristics. We present data in this section regarding a wider array of demographic
characteristics when evaluating populations living near NPIAS airports.

Among children five and under, there were three states (Nevada, South Carolina, and South
Dakota), in which the percent of children five and under living within 500 meters of a runway
represent a greater fraction of the population by a difference of one percent or greater compared
with the percent of children five and under in the state population (Table 3).

171 In this analysis, we included populations living in census blocks that intersected the 500-meter buffer around
each runway in the U.S. Potential uncertainties in this approach are described in our report National Analysis of the
Populations Residing Near or Attending School Near U.S. Airports. EPA-420-R-20-001, available at
https://nepis.epa.gov/Exe/ZvPDF.cgi?Dockev=P100YG4A.pdf. and in the EPA responses to peer review comments
on the report, available here: https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100YISM.pdf.

172 EPA (2020) National Analysis of the Populations Residing Near or Attending School Near U.S. Airports.
EPA-420-R-20-001. Available at https://nepis.epa.gov/Exe/ZvPDF.cgi?Dockev=P100YG4A.pdf.

173 Clarke. Memorandum to Docket EPA-HQ-OAR-2022-0389. Estimation of Population Size and
Demographic Characteristics among People Living Near Airports by State in the United States. May 31, 2022.
Docket ID EPA-HQ-2022-0389.

174 Clarke. Memorandum to Docket EPA-HQ-OAR-2022-0389. Estimation of Population Size and
Demographic Characteristics among People Living Near Airports by State in the United States. May 31, 2022.
Docket ID EPA-HQ-2022-0389.

175 These data are presented in tabular form for all states in this memorandum located in the docket: Clarke.
Memorandum to Docket EPA-HQ-OAR-2022-0389. Estimation of Population Size and Demographic
Characteristics among People Living Near Airports by State in the United States. May 31, 2022. Docket ID EPA-
HQ-2022-0389.

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Table 3. The Population of Children Five Years and Under Within 500 Meters of an Airport Runway
Compared to the State Population of Children Five Years and Under.

State

Percent of
Children Aged
Five Years and
Under Within 500
Meters

Percent of
Children Aged
Five Years and
Under Within the
State

Number of Children
Aged Five Years and
Under Within 500
Meters

Number of
Children Aged
Five Years and
Under in the State

Nevada

10%

8%

1000

224,200

South Carolina

9%

8%

400

361,400

South Dakota

11%

9%

3,000

71,300

There were nine states in which the Black population represented a greater fraction of the
population living in the near-airport environment by a difference of one percent or greater
compared with the state as a whole. These states were California, Kansas, Kentucky, Louisiana,
Mississippi, Nevada, South Carolina, West Virginia, and Wisconsin (Table 4).

Table 4. The Black Population Within 500 Meters of an Airport Runway and the Black Population, by State.

State

Percent Black

Percent Black

Black Population

Black Population in



Within 500

Within the State

Within 500 Meters

the State



Meters







California

8%

7%

18,981

2,486,500

Kansas

8%

6%

1,240

173,300

Kentucky

9%

8%

3,152

342,800

Louisiana

46%

32%

14,669

1,463,000

Mississippi

46%

37%

8,542

1,103,100

Nevada

12%

9%

1,794

231,200

South Carolina

31%

28%

10,066

1,302,900

West Virginia

10%

3%

1,452

63,900

Wisconsin

9%

6%

4,869

367,000

There were three states with a greater fraction of Asians in the near-airport environment
compared with the state as a whole by a difference of one percent or greater: Indiana, Maine,
and New Hampshire (Table 5).

Table 5. The Asian Population Within 500 Meters of an Airport Runway and the Asian Population, by State.

State

Percent Asian
Within 500
Meters

Percent Asian
Within the
State

Asian Population
Within 500 Meters

Asian Population
in the State

Indiana

4%

2%

1,681

105,500

Maine

2%

1%

406

13,800

New Hampshire

4%

2%

339

29,000

Among Native Americans and Alaska Natives, there were five states (Alaska, Arizona,
Delaware, South Dakota, and New Mexico) where the near-airport population had greater
representation by Native Americans and Alaska Natives compared with the portion of the
population they comprise at the state level by a difference of one percent or greater. In Alaska,
as anticipated due to the critical nature of air travel for the transportation infrastructure in that

27

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state, the disparity in residential proximity to a runway was the largest; 16,000 Alaska Natives
were estimated to live within 500 meters of a runway, representing 48 percent of the population
within 500 meters of an airport runway compared with 15 percent of the Alaska state population
(Table 6).

Table 6. The Native American and Alaska Native Population Within 500 Meters of an Airport Runway and
the Native American and Alaska Native Population, by State.

State

Percent Native
American and
Alaska Native
Within 500 Meters

Percent Native
American and
Alaska Native
Within the State

Native American
and Alaska Native
Population Within
500 Meters

Native American
and Alaska Native
Population in the
State

Alaska

48%

15%

16,020

106,300

Arizona

18%

5,017

335,300

Delaware

112

5,900

New Mexico

21%

10%

2,265

208,900

South Dakota

22%

1,606

72,800

In a separate analysis, the EPA focused on evaluating the potential for disparities in
populations residing near the NPIAS airports. The EPA compared the demographic composition
of people living within one kilometer of runways at 2,022 of the approximately 3,300 NPIAS
airports with the demographic composition of people living at a distance of one to five
kilometers from the same airports.176'177 In this analysis, over one-fourth of airports (i.e., 515)
were identified at which children under five were more highly represented in the zero to one
kilometer distance compared with the percent of children under five living one to five kilometers
away (Table 7). There were 666 airports where people of color had a greater presence in the
zero to one kilometer area closest to airport runways than in populations farther away. There
were 761 airports where people living at less than two-times the Federal Poverty Level
represented a higher proportion of the overall population within one kilometer of airport runways
compared with the proportion of people living at less than two-times the Federal Poverty Level
among people living one to five kilometers away.

176 For this analysis, we evaluated the 2,022 airports with a population of greater than 100 people inside the zero
to one kilometer distance to avoid low population counts distorting the assessment of percent contributions of each
group to the total population within the zero to one kilometer distance.

177 Kamal et. al., Memorandum to Docket EPA-HQ-OAR-2022-0389. Analysis of Potential Disparity in
Residential Proximity to Airports in the Conterminous United States. May 24, 2022. Docket ID EPA-HQ-2022-
0389. Methods used are described in this memo and include the use of block group resolution data to evaluate the
representation of different demographic groups near-airport and for those living one to five kilometers away.

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Table 7. Number of Airports (Among the 2,022 Airports Evaluated) With Disparity for Certain Demographic
Populations Within One Kilometer of an Airport Runway in Relation to the Comparison Population Between

One and Five Kilometers from an Airport Runway.

Number of Airports with Disparity3

Demographic Group

Total Airports
with Disparity

Disparity
1-5%

Disparity
5-10%

Disparity
10-20%

Disparity
20%+

Children under five years of age

515

507

People with income less than twice the
Federal Poverty Level

761

307

223

180

People of Color (all races, ethnicities and
indigenous peoples)

666

377

126

123

Non-Hispanic Black

405

240

Hispanic

551

402

Non-Hispanic Asian

268

243

Non-Hispanic Native American or Alaska
Native178

144

130

Non-Hispanic Hawaiian or Pacific
Islander

Non-Hispanic Other Race

Non-Hispanic Two or More Races

226

To understand the extent of the potential disparity among the 2,022 NPIAS airports, Table 7
provides information about the distribution in the percent differences in the proportion of
children, individuals with incomes below two-times the Federal Poverty Level, and people of
color living within one kilometer of a runway compared with those living one to five kilometers
away. For children, Table 7 indicates that for the vast majority of these airports where there is a
higher percentage of children represented in the near-airport population, differences are
relatively small (e.g., less than five percent). For the airports where disparity is evident on the
basis of poverty, race and ethnicity, the disparities are potentially large, ranging up to 42 percent
for those with incomes below two-times the Federal Poverty Level, and up to 45 percent for
people of color.179

There are uncertainties in the results provided here inherent to the proximity-based approach
used. These uncertainties include the use of block group data to provide population numbers for
each demographic group analyzed, and uncertainties in the Census data, including from the use
of data from different analysis years (e.g., 2010 Census Data and 2018 income data). These
uncertainties are described, and their implications discussed in Kamal et. al. (2022).180

178 This analysis of 2,022 NPIAS airports did not include airports in Alaska.

179 Kamal et. al., Memorandum to Docket EPA-HQ-OAR-2022-0389. Analysis of Potential Disparity in
Residential Proximity to Airports in the Conterminous United States. May 24, 2022. Docket ID EPA-HQ-2022-
0389.

180 Kamal et. al., Memorandum to Docket EPA-HQ-OAR-2022-0389. Analysis of Potential Disparity in
Residential Proximity to Airports in the Conterminous United States. May 24, 2022. Docket ID EPA-HQ-2022-
0389.

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The data summarized here indicate that there is a greater prevalence of children under five
years of age, an at-risk population for lead effects, within 500 meters or one kilometer of some
airports compared to more distant locations. This information also indicates that there is a
greater prevalence of people of color and of low-income populations within 500 meters or one
kilometer of some airports compared with people living more distant. If such differences were to
contribute to disproportionate and adverse impacts on people of color and low-income
populations, they could indicate a potential EJ concern. Given the number of children in close
proximity to runways, including those in EJ populations, there is a potential for substantial
implications for children's health.

30

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Appendix A:

Model-extrapolated Estimates of Airborne
Lead Concentrations at U.S. Airports

A-l

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Table of Contents

Abbreviations	A-3

Summary	A-4

1.	Introduction	A-6

1.1	Use of Leaded Avgas in Piston-Engine Aircraft	A-6

1.2	Lead Concentrations in Air from Leaded Avgas Use in Piston-Engine Aircraft at
Individual Airports	A-7

1.3	Characterizing Maximum Impact Area Lead Concentrations from Piston-Engine Activity
at U.S. Airports	A-8

2.	Air Quality Modeling of Lead from Piston-Engine Aircraft at a Model Airport	A-10

2.1	Overview of Air Quality Modeling at a Model Airport	A-10

2.2	Air Quality Model Performance at a Model Airport	A-ll

2.3	Yearlong Air Quality Modeling to Develop AQFs at a Model Airport	A-13

3.	Method to Calculate Model-Extrapolated Lead Concentrations Nationwide	A-14

3.1	Calculation of AQFs for Piston-Engine Aircraft Activity and Lead Concentrations	A-14

3.2	National Analysis Methods	A-17

3.3	Evaluation of Airports for Potential Lead Concentrations Above the Lead NAAQS ... A-37

3.3.1	Sensitivity Analysis of Airport-Specific Parameters that Influence Potential for Lead
Concentrations to be Above the NAAQS	A-37

3.3.2	Airport-Specific Activity Data	A-39

3.3.3	Airport-Specific Criteria for Identifying Potential Lead Levels Above the NAAQS ... A-43

3.4	Characterization of Uncertainty of Cross-Airport Parameters that Influence the
Potential for Lead Concentratins to be Above the NAAQS for Lead	A-51

4. Model-Extrapolated Lead Concentrations: Results and Uncertainty Characterization... A-55

4.1	Ranges of Lead Concentrations in Air at Airports Nationwide	A-55

4.2	Airports with Potential Lead Concentrations Above the Lead NAAQS with Unrestricted
Access Within 50 m of the Maximum Impact Site	A-62

4.3	Quantitative Uncertainty Analysis of Concentrations of Lead in Air at Airports: The
Influence of Run-up Time and Avgas Lead Concentration	A-66

4.3.1	National Analysis and Airport-Specific Monte Carlo Results	A-66

4.3.2	Comparison of Model-Extrapolated Concentrations From the Airport-Specific Activity
Analysis with Monte Carlo Bounds to Monitored Concentrations in the Maximum
Impact Area	A-68

4.4	Qualitative Characterization of Uncertainty and Variability in Model-Extrapolated Lead
Concentrations from National and Airport-Specific Activity Analyses	A-71

4.4.1	Meteorological Parameters	A-71

4.4.2	AERMOD and AERSURFACE Parameters	A-73

A-2

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4.4.3 Operational Parameters
References	

A-74

A-75

Appendix A to the Report Model-extrapolated Estimates of Airborne Lead Concentrations at
U.S. Airports: Supplemental Information on Detailed Air Quality Modeling at a Model
Airport

Appendix B to the Report Model-extrapolated Estimates of Airborne Lead Concentrations at
U.S. Airports: Supplemental Data for Piston-Engine Aircraft Activity and Model-Extrapolated
Lead Contraction Gradients

Appendix C to the Report Model-extrapolated Estimates of Airborne Lead Concentrations at
U.S. Airports: Uncertainty Characterization

Abbreviations

Air Quality (AQ)

Air Quality Factor (AQF)

Air Taxi (AT)

Air Traffic Activity Data System (ATADS)

Airport Cooperative Research Program (ACRP)

American Meteorological Society/Environmental Protection Regulatory Model (AERMOD)
Clean Air Act (CAA)

US Environmental Protection Agency (EPA)

US Federal Aviation Administration (FAA)

General Aviation (GA)

General Aviation and Air Taxi Activity Survey (GAATA)

Landing and take-off operations (LTOs)

Multi-Engine (ME)

National Ambient Air Quality Standard (NAAQS)

National Academies of Sciences (NAS)

National Emissions Inventory (NEI)

One hundred octane low lead (100LL)

Reid-Hillview Airport of Santa Clara County (RHV)

Santa Monica Municipal Airport (SMO)

Single-Engine (SE)

Terminal Area Forecast (TAF)

Tetraethyl lead (TEL)

Touch-and-Go (T&G)

A-3

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Summary

The main objective of the analyses presented in this report is to estimate the potential
ranges of lead concentrations at and downwind of the anticipated area of highest
concentration at airports in the US. To accomplish this objective, the relationship between
piston-engine aircraft activity and lead concentration at and downwind of the maximum impact
site at one airport was applied to piston-engine aircraft activity estimates for each US airport.
This approach for conducting a nationwide analysis of airports was selected due to the
dominant impact of piston-engine aircraft run-up operations on ground-level lead
concentrations, which creates a maximum impact area that is expected to be generally
consistent across airports. Specifically, these aircraft consistently take-off into the wind and
typically conduct run-up operations immediately adjacent to the take-off runway end, and thus,
modeling lead concentrations from this source is constrained to variation in a few key
parameters. These parameters include: 1) total amount of piston-engine aircraft activity, 2) the
proportion of activity conducted at one runway end, 3) the proportion of activity conducted by
multi-piston-engine aircraft, 4) the duration of run-up operations, 5) the concentration of lead
in avgas, 6) wind speed at the model airport relative to the extrapolated airport, and 7)
additional meteorological, dispersion model, or operational parameters. These parameters
were evaluated through sensitivity analyses across airports or using quantitative or qualitative
uncertainty analyses.

Results of the national analysis show that model-extrapolated 3-month average lead
concentrations in the maximum impact area range from less than 0.0075 |-ig/m3 up to 0.475
l-ig/m3 at airports nationwide. The range of model-extrapolated concentrations in the maximum
impact area aligns with expectations from previous monitoring at airports that showed
exceedances of the lead NAAQS in the maximum impact area of some airports.181 Results of the
national analysis also demonstrate and quantify the gradient in lead concentrations with the
highest concentrations in locations closer to the maximum impact area than those further
downwind.

For the subset of airports where estimated lead concentrations could potentially be above
the lead NAAQS, the analysis was further refined using a set of sensitivity analyses and airport-
specific data. This airport-specific analysis identified some airports where model-extrapolated
lead concentration estimates suggest the potential for piston-engine aircraft activity to cause
lead concentrations above the lead NAAQS in the area of maximum impact with unrestricted
public access. Lead concentration estimates in this analysis should not be used to evaluate
attainment of the lead NAAQS.

Overall, comparisons of both national and airport-specific model-extrapolated
concentrations to monitored values show general agreement and suggest that the
extrapolation method presented in this report provides reasonable estimates of the range in
concentrations of lead in air attributable to peak activity periods of piston-engine aircraft at

181 For additional information on monitoring data collected at airports see: https://www.epa,gov/regulations-
emissions-vehicles-and-engines/airport-lead-inyentorjes-air-qualitv-monitoring-ajr.

A-4

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airports. Uncertainty in the national and airport-specific activity analyses were evaluated using
a Monte Carlo analysis, which characterized how variability in run-up duration and avgas lead
concentrations influence model-extrapolated lead concentrations. Results showed that model-
extrapolated lead concentrations may increase at airports with average run-up durations that
are longer than the average run-up duration observed at the model airport, even if the avgas
lead concentration is lower than that used in the national analysis. Additional, qualitative
analyses were used to evaluate sources of uncertainty that were not addressed in sensitivity or
Monte Carlo analyses.

Quantitative and qualitative evaluations of meteorological parameters that can impact
model-extrapolated concentrations focused on adjusting concentrations to reflect site-specific
wind speeds (See Section 3.2 for details) and evaluating changes in wind direction, mixing
height, and temperature. While the wind speed adjustment did not meaningfully impact the
range of concentrations in the maximum impact area of US airports, this adjustment does have
an important impact on model-extrapolated concentrations at individual airports, particularly
at those airports where wind speeds during the maximum activity period differ significantly
from those observed at the model airport. As discussed in Section 4.4.1, minimal uncertainty is
expected in model-extrapolated concentrations due to shifts in wind direction given that most
airports are built with the predominate runway facing into the wind. It is also anticipated that
mixing height has a minimal impact on uncertainty in model-extrapolated concentrations at the
maximum impact area, because of the dominant impact of the very localized run-up emissions
at this location and the fact that GA and AT aircraft activity occurs almost entirely during the
day when vertical mixing is greatest. At downwind locations, mixing height may play a larger
role and would be an important variable to examine when evaluating individual airports,
particularly those with mixing height characteristics significantly different from the model
airport. Finally, ambient temperature and other microclimate or meteorological variables are
not expected to meaningfully impact nationwide results, however, there is more uncertainty in
model-extrapolated concentrations at airports that have maximum activity periods during
meteorological conditions not observed at the model airport.

Additional sources of potential uncertainty that were evaluated qualitatively included
dispersion modeling inputs and operational parameters. While dispersion modeling inputs such
as surface roughness, Bowen Ratio, and albedo may result in some uncertainty at downwind
locations, their impact on variability near the maximum impact site is mitigated due to
consistency in on-airport characteristics and land-use requirements immediately downwind of
runways based on landing and take-off safety requirements. As with meteorological
parameters, the appropriateness of dispersion modeling inputs used in this analysis for
individual airports with meaningful differences in land use of the areas immediately
surrounding a runway would need to be considered on a case-by-case basis. Differences in
operational parameters (e.g., piston/turboprop split and single-engine/multi-engine split,
distribution of aircraft engine types operating at the airport, diurnal activity patterns) are not
expected to contribute significantly to uncertainty in extrapolated concentration estimates for
airports nationwide; however, in modeling individual airports, national fleet and operational
data should be supplemented with local data where available and feasible.

A-5

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The model-extrapolated lead concentrations provided in this report reflect only lead
concentrations in air attributable to piston-engine aircraft activity and only at the area of
maximum concentration and downwind of that location. Additional analyses, which are outside
of the scope set by the objective of this report, would be necessary to evaluate concentrations
of lead in air at other areas at and near airports. In addition, to understand total lead
concentrations in air, other airborne sources of lead (e.g., nearby industrial sources, sources
contributing to local background concentrations) would need to be considered. Understanding
total lead exposure, which is relevant for understanding blood lead levels, would also need to
consider exposure to lead from additional media (e.g., soil, drinking water).

1. Introduction

The United States (US) Environmental Protection Agency (EPA) is evaluating the air quality
impact of emissions of lead from piston-engine aircraft operating on leaded fuel. One
component of the evaluation includes conducting an analysis of concentrations of lead in air at
and downwind of airports. This analysis was conducted to provide an understanding of the
potential range in lead concentrations in air at the approximately 13,000 airports with piston-
engine aircraft activity in the US. This report describes the methods that the EPA used to
estimate these lead concentrations and presents the results of this analysis along with a
quantitative uncertainty analysis. Background information is presented immediately below in
order to provide a general understanding of the use of leaded fuel in aircraft, and the state of
the science on modeling concentrations of lead in air from aircraft emissions at individual
airports. Subsequent sections provide details on the analysis approach for airports nationwide.

1.1 Use of Leaded Avgas in Piston-Engine Aircraft

Emissions of lead from aircraft operating on leaded aviation gasoline (avgas) are the largest
source of lead released into the atmosphere in the US, accounting for 62% of lead (456 tons) in
the 2014 National Emissions Inventory (NEI) (USEPA 2016a). Leaded avgas is used in piston-
engine aircraft, of which there are approximately 140,000 in the US (FAA 2014) . These aircraft
operate at most of the approximately 20,000 US airport facilities (approximately 13,000 of
which are airports, while the remainder are heliports, balloon ports, and other facility types)
(FAA 20 17).182,183 Piston-engine aircraft conduct approximately 32 million landing and take-off

182 This report focuses on fixed-wing piston-engine airplane activity at airports. Facility types other than airports
are not included in this report; seaports and water runways at airports are both excluded from analyses in this
report, and rotorcraft operations at airports are not included in this report. Appendix B provides some information
on conducting additional rotorcraft analyses in the future.

183 Data on airport facilities was downloaded from FAA Air Traffic Activity Data System (ATADS) at
httpi//aspm,faa,gov/opsnet/sys/Airport,asp on 13 February 2014.

A-6

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operations (LTOs) annually (USEPA 2011).184 Most piston-engine aircraft operations fall into the
categories of either General Aviation (GA) or Air Taxi (AT) activity. GA is defined as the
operation of civilian aircraft for purposes other than commercial, such as passenger or freight
transport, including personal, business and instructional flying; AT is scheduled or on-demand
services that carry limited payload and/or passengers (FAA 2012).

Piston-engine aircraft rely on lead as an additive to avgas to help boost fuel octane and
prevent engine knock, as well as prevent valve seat recession and subsequent loss of
compression for engines without hardened valves.185 Lead is added to the fuel in the form of
tetraethyl lead (TEL) along with ethylene dibromide, which acts as a lead scavenger to prevent
lead deposits on valves and spark plugs. Currently one hundred octane low lead (100LL), which
contains up to 2.12 grams of lead per gallon, is the most commonly used type of avgas in the
US, although FAA survey data reports limited use of a leaded avgas containing 4.24 grams of
lead per gallon, known as "100 Octane," and unleaded avgas (FAA 2015). Lead is not added to
jet fuel, which is used in commercial aircraft, most military aircraft, and other turbine-engine
aircraft.

1.2 Lead Concentrations in Air from Leaded Avgas Use in Piston-Engine Aircraft at Individual

Airports

Lead emissions from piston-engine aircraft operating on leaded avgas increase
concentrations of lead in air at and downwind of airports (Environment Canada 2000, Fine et.
al. 2010, Carr et. al. 2011, Anchorage DHHS 2012, Feinberg et. al. 2016). Gradient studies
evaluating lead concentrations near airports where piston-engine aircraft operate indicate that
concentrations of lead in air are one to two orders of magnitude higher at locations proximate
to aircraft emissions compared to locations approximately 500- to 1000-meters downwind (Fine
et. al. 2010, USEPA 2010a, Carr et. al. 2011, Feinberg et. al. 2016). The most significant
emissions in terms of ground-based activity, and therefore ground-level concentrations of lead
in air, occur near the areas with greatest fuel consumption where the aircraft are stationary for
a period of time (USEPA 2010a, Carr et. al. 2011, ICF 2014, Feinberg et. al. 2016). For piston-
engine aircraft these areas are most commonly locations in which pilots conduct engine tests
during run-up operations prior to take-off (i.e., magneto checks during the run-up operation
mode). Run-up operations are typically conducted adjacent to the runway end from which

184 Piston-engine aircraft conduct two types of operational cycles, or cycle-types. These cycle-types include: 1) a
full landing-and-take-off operation (full LTO) during which the pilot conducts all pre-flight engine checks and
completes full take-off and landing operations, and 2) a touch-and-go operation (T&G) during which the pilot
briefly touches down on a runway before taking-off again almost immediately in order to practice take-off and
landing procedures. This is a training exercise most commonly performed by student pilots. Throughout this
report, "cycle-type" is used to refer to the full LTO and T&G categories, while "LTOs" is used to refer more
generally to all cycle-types (i.e., both full LTO and T&G).

185 Minimum octane requirements as well as other carefully controlled fuel parameters in avgas prevent the
general use of unleaded motor vehicle fuel in piston-engine aircraft.

A-7

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aircraft take-off and the brakes are engaged so the aircraft is stationary.186 As a result of the
aircraft being stationary, duration of run-up, and high fuel consumption rate, emissions from
run-up activity are the largest contributor to local maximum atmospheric lead concentrations;
run-up emissions are estimated to contribute over 80% of the lead concentrations at and
immediately downwind of the area where the run-up mode of operation occurs, even though
this mode of operation does not have the highest fuel consumption rate (Appendix A). Hence,
the area adjacent to the runway end at which run-up operations most frequently occur is
identified here as the maximum impact site for lead concentrations.187,188

1.3 Characterizing Maximum Impact Area Lead Concentrations from Piston-Engine Activity at

U.S. Airports

The understanding of piston-engine aircraft lead emissions and resulting concentrations in
air was developed through detailed monitoring and modeling studies at individual airports.
However, conducting detailed air quality monitoring or modeling for lead at each of the 13,000
US airports is not feasible; thus, the analysis of concentrations of lead in air at and downwind of
airports nationwide is based on detailed air quality modeling at a representative, model airport.
The modeling results were used to develop factors that relate piston-engine aircraft activity to
concentrations of lead in air. The factors, termed Air Quality Factors (AQFs), were used in
conjunction with estimates of piston-engine aircraft activity at airports nationwide to calculate
model-extrapolated concentrations at and downwind of each US airport.

The rationale for this approach is based on the consistent set of parameters required for the
safe operation of a piston-engine aircraft. Specifically, piston-engine aircraft consistently
conduct run-up operations prior to take-off, and the run-up activity has the following
characteristics: 1) run-up operations require high fuel consumption rates while the aircraft is
stationary, and thus are the location of the maximum impact site for lead concentrations, 2) the
location of run-up activity occurs in a designated area proximate to the runway end from which
aircraft take-off, and 3) the runway end used for take-off, and hence the location of run-up
operations, can be identified using wind direction since piston-engine aircraft takeoff into the
wind.

186	A single "runway" has a magnetic heading designation for each "runway end" in order to distinguish which
direction the aircraft is taking off from or landing on to; we use "runway end" throughout this report.

187	For purposes of this report and the underlying analysis, the maximum impact site is defined as 15 meters
downwind of the tailpipe of an aircraft conducting run-up operations in the area designated for these operations
at a runway end. The maximum impact area is the approximately 50 meters surrounding the maximum impact site.
The downwind gradient is the approximately 500-meter area that extends from the maximum impact site.
Additional characterization of the maximum impact site, area, and downwind gradient is provided in Section 2.

188	While run-up operations are most frequently the location of the maximum impact site of aircraft lead
emissions at airports, at some airports other operations such as taxi or idling near the runway may result in a
hotspot of emissions. This report focuses on run-up as the location of the maximum impact site in an effort to
characterize concentrations of lead in air at the location of maximum impact for most US airports. Additional
analyses would be necessary to more specifically characterize concentrations of lead in air at individual airports.

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This analysis focuses on the maximum impact areas at airports nationwide (i.e., the 50
meters surrounding the maximum impact site adjacent to run-up operations). Notably, the
maximum impact area lead concentration estimates provided in this report are based on
average values for several key input variables; thus, the concentrations are not "worst-case"
estimates (i.e., they do not reflect the use of the maximum values for all the key input
parameters). For each US airport, model-extrapolated lead concentrations are calculated as 3-
month average values to maintain consistency with the form of the National Ambient Air
Quality Standard (NAAQS) for lead (i.e., a maximum 3-month average of 0.15 |-ig/m3) (National
primary and secondary ambient air quality standards for lead 40 CFR 50.12, USEPA 2016b).
Importantly, while model-extrapolated concentrations are calculated and presented in a
manner consistent with the lead NAAQS, these results should not be used to determine
attainment of the lead NAAQS at individual airports. Information on the process that EPA, the
states, and the Tribes follow to determine whether or not an area is meeting the NAAQS for
lead is described on the EPA website (USEPA). Lead concentration estimates presented in this
report are provided to inform an understanding of the potential range of impacts that lead
emissions from piston-engine aircraft alone may have on air quality in close proximity to this
source of lead. Due to the inherent uncertainties in extrapolating relationships between
concentration and activity from one well-characterized model airport to others, uncertainty and
variability in model-extrapolated lead concentrations is characterized.

This document is organized to first provide the methods and results of detailed air quality
modeling of lead at a model airport (Section 2). Section 3 describes how the modeling results
were used to develop a quantitative relationship between piston-engine aircraft activity and
lead concentrations; this section further provides the methodology to estimate piston-engine
aircraft activity at airports nationwide, which is used to calculate lead concentrations at airports
nationwide based on the relationship between activity and lead concentrations. Section 3 also
presents methods to identify a subset of airports for more in-depth analyses using airport-
specific data. Section 4 presents the model-extrapolated lead concentrations that result from
combining piston-engine aircraft activity estimates with the relationship between activity and
lead concentrations in the maximum impact area and locations downwind at each airport
nationwide. In addition, Section 4 characterizes uncertainty and variability in these model-
extrapolated lead concentrations.

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2. Air Quality Modeling of Lead from Piston-Engine Aircraft at a Model Airport

To characterize concentrations of lead in air at and downwind of the maximum impact area
of airports nationwide, EPA first conducted detailed air quality modeling at a model airport. The
results of this detailed air quality modeling were used to develop factors, known as AQFs, which
provide quantitative relationships between piston-engine aircraft activity and lead
concentrations at and downwind of the maximum impact site at the modeled airport. The AQFs
were subsequently applied to estimates of aircraft activity at other airports across the country
in order to calculate model-extrapolated lead concentrations at and downwind of the
maximum impact area of airports nationwide. In this section we briefly explain the overall
approach for the detailed air quality modeling at the model facility, summarize the model
performance, and then discuss how the air quality modeling was conducted to develop the
AQFs.

2.1 Overview of Air Quality Modeling at a Model Airport

In order to characterize local-scale air quality impacts of lead at a model airport, EPA applied
the air quality model that is used for EPA and Federal Aviation Administration (FAA) regulatory
analysis of near-field gradients of primary pollutants such as lead, namely the American
Meteorological Society (AMS)/EPA Regulatory Model (AERMOD).189,190 Since AERMOD had not
been previously applied to modeling lead emissions from piston-engine aircraft activity, EPA
developed the necessary model inputs and parameters, including: piston-engine aircraft
parameters (i.e., sub-daily time-in-mode activity, dispersion due to aircraft turbulent wake,
allocation of approach and climb-out emissions at altitude) and emissions characteristics of
non-aircraft sources (e.g., nearby roads) (USEPA 2010a, Carr et. al. 2011). These model inputs
were developed and first applied at a GA airport (Santa Monica Airport, SMO) that was selected
due to the availability of previously collected lead monitoring data, which indicated elevated
concentrations of lead in air at and near the runway (Fine et. al. 2010). Additional monitoring
data were collected in parallel to the development of AERMOD modeling inputs in order to
evaluate model performance. Details regarding the AERMOD inputs, model performance, and
results are published elsewhere (USEPA 2010a, Carr et. al. 2011).

The foundational work to establish AERMOD inputs for modeling lead emissions from piston-
engine aircraft at SMO provided an understanding of the key characteristics of the relationship
between aircraft activity and concentrations of lead in air. Some of the key findings from this
work, included: 1) piston-engine aircraft operations increase ground-level concentrations of
lead, with the largest concentrations resulting from engine checks prior to take-off (i.e., run-up
operations), 2) lead concentrations attributable to piston-engine aircraft decrease with
increasing distance from the run-up location, such that the maximum impact location is

189	AERMOD is a steady-state plume model that incorporates air dispersion based on planetary boundary layer
turbulence structure and scaling concepts, including treatment of both surface and elevated sources, and both simple
and complex terrain. Additional details about AERMOD are available at: https://www.epa.gov/scram/air~qualitv~
dispersion-modeling-preferred-and-recommended-models

190	The FAA inventory tool for air emissions and noise, Aviation Environmental Design Tool (AEDT), does not
include lead emissions (https://aedt.faa.gov/Documents/AEDT 2b NEPA Guidance.pdf).

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immediately adjacent to the run-up area at a runway end, and 3) above-background lead
concentrations occur up to 900 and 450 meters downwind of the maximum impact location on
a daily and average 3-month basis, respectively (USEPA 2010a, Carr et. al. 2011). The National
Academies of Sciences (NAS) Airport Cooperative Research Program (ACRP) subsequently
conducted a similar study of airport lead concentrations at three airports and similarly
identified run-up as a critical operation mode to evaluate when modeling the impact of piston-
engine aircraft lead emissions on ground-based lead concentrations (Heiken et. al. 2014,
Feinberg et. al. 2016). These findings presented a clear approach for conducting air quality
modeling at an airport, which would be used as a model facility for developing AQFs and
subsequently characterizing concentrations of lead in air at and downwind of airports
nationwide.

Reid-Hillview Airport of Santa Clara County (RHV) was selected as a representative GA
airport for use as the model airport.191 To apply AERMOD at the model airport, aircraft and
meteorological data, similar to those collected at SMO, were collected at RHV. Specifically, data
collected at this facility included: 1) number and type of piston-engine aircraft LTOs, 2) time in
each operating mode, 3) time-of-day and day-of-week patterns of aircraft activity, 4) the
concentration of lead in avgas, and 5) meteorological data (i.e., wind direction, wind speed,
mixing height, temperature). These inputs were collected first for a seven-day period in order
to characterize model performance at the model airport through comparisons of modeled and
monitored concentrations. After characterizing model performance, additional activity and
meteorology data were collected to model a yearlong period, which was then used to develop
AQFs. Information on model performance at the model facility is presented immediately below
in Section 2.2; information on the yearlong modeling is in Section 2.3. Appendix A provides
details on specific AERMOD inputs at the model airport study, as well as information regarding
the piston-engine aircraft modeled at the model airport compared to the national piston-
engine aircraft fleet.

2.2 Air Quality Model Performance at a Model Airport

Comparisons of modeled and monitored daily average concentrations at the model airport
were conducted over a seven-day period at three monitoring sites (upwind, 60 meters
downwind, and at the maximum impact site). The daily average was over 15 hours, from the
hours of 7 a.m. to 10 p.m. local time, representing the time when the airport was operational.
The overall R2 value across the three monitoring sites regressed against the paired modeled
concentrations was 0.83, as shown in Figure 1. At the maximum impact site, the model tended
to under-predict monitored concentrations for the seven days of comparison conducted, but
was generally within 20% of monitored values and was within the 2:1 and 1:2 lines for all but
one monitored value.192 The generally good agreement between modeled and monitored

191 RHV is considered generally representative of GA airports based on several factors, including: type of piston-
engine aircraft operations, runway configuration, fleet composition of piston-driven aircraft engine technology
types, and diurnal profile of piston-engine aircraft activity (see Appendices A and B for comparisons of RHV fleet
and diurnal profiles relative to other GA airports).

192 Agreement with monitored concentrations within a factor of two is a common model evaluation criterion
Chang, J. and S. Hanna (2004). Air quality model performance evaluation. Meteorology and Atmospheric Physics,

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concentrations was also observed in previous studies comparing AERMOD air quality dispersion
model output with on-site monitoring data for lead at airports (Carr et. al. 2011; Feinberg et. al.
2016). As observed in these other studies, modeled lead concentrations can be both slightly
over- and underestimates of on-site monitored values, and the performance observed for the
model airport is considered to be aligned with prior work. We focused on understanding
discrepancy between modeled and monitored concentrations on the few days when the
discrepancy was greater than 20%. For these days, sensitivity analyses were conducted to
identify possible reasons for the divergence. Details on the sensitivity analyses are presented in
Appendix A, but generally showed that run-up location, run-up duration, and relative levels of
multi-engine aircraft activity explained instances when the model under- or over-predicted
monitored concentrations; uncertainty and variability in monitored values are not evaluated
here, but also contribute to the divergence in these comparisons with modeled data. In
addition, variability in emission rates for a given engine and across engine types will also
contribute to variability in measured concentrations, as discussed in Section 4.4. The
application of a 3-month averaging time is expected to minimize the impact of individual days
in which the model may have over- or under-predicted lead concentrations. Comparisons
between model-extrapolated concentrations, based on the AQFs developed at the model
airport, and monitored concentrations at airports other than the model airport are presented in
Section 4.

Model-to-Monitor Comparison

Monitored Pb Coric., |Jg/m3

O Maximum Impact Site 1:1

A Upwind 2:1 and 1:2

O 60m Downwind

Figure 1. Comparison of modeled and monitored daily average concentrations at three sites at the model airport

during a 7-day period.

87 (1), 167-196, Luecken, D., W. Hutzell and G. Gipson (2006). Development and analysis of air quality modeling
simulations for hazardous air pollutants. Atmospheric Environment, 40 (26), 5087-5096.

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The model performance at the model airport confirmed previous work showing that a
limited set of parameters influence concentration in the maximum impact site, and supported
moving forward with the development of AQFs to characterize the relationship between piston-
engine aircraft activity and lead concentrations at and downwind of a maximum impact area.

2.3 Yearlong Air Quality Modeling to Develop AQFs at a Model Airport

This section provides general information used to model yearlong concentrations of lead in
air that were subsequently used to calculate 3-month average AQFs at the model facility.

Details regarding inputs to AERMOD including aircraft emission inventories, source
parameterization, meteorological inputs, and receptor placement are provided in Appendix A.

As noted above, air quality modeling for this work built on prior piston-engine aircraft
modeling in which aircraft- and airport-specific parameterizations were used in AERMOD to
evaluate near-field gradients in ambient lead concentrations. Inputs in the yearlong modeling
included 1) a detailed inventory for emissions of lead from piston-engine aircraft (i.e., aircraft
activity, source locations, and lead emission rates), 2) meteorological data, 3) a dense receptor
grid, and 4) piston-engine aircraft characterization and parameterization. Using previously
published modeling methods, which are further described in Appendix A, Section 1.5, aircraft
lead emissions were modeled as volume sources. The parameterization of aircraft lead
emissions at the model airport included aircraft wake turbulence, and plume rise from ground-
based aircraft emissions. Specific values for the initial vertical and horizontal dispersion by
operation mode are provided in Appendix A.

Aircraft activity data for the yearlong modeling at the model facility used on-site
observations in conjunction with on-site daily operations data collected by FAA.193 Hourly
aircraft activity profiles were developed from on-site observations for single-engine and multi-
engine aircraft conducting either full landing and take-off or touch-and-go operation cycles.
Time spent in each mode (i.e., start-up, idle, taxi, run-up, take-off and landing) was recorded
during the days of observation and was used along with fuel consumption rates by mode to
calculate emissions by mode. Source locations for all modes of aircraft activity (i.e., start-up,
idle, taxi, run-up, take-off and landing) are described in Appendix A; emissions at altitude were
represented using volume sources at 50-meter intervals up to approximately 500 meters and
release heights for ground-based activity were 0.5 meters.

Surface and upper-air meteorological data (from stations 10 km, and 55 km away from the
model facility, respectively) were processed using AERMOD's meteorological preprocessor,
AERMET, to produce hourly data on mixing heights, stability, wind direction, wind speed,
temperature, and precipitation. The wind direction data were used to identify the runway end
from which piston-engine aircraft took off during each hour of each day in the year of modeling

193 As discussed in Section 3, FAA data does not indicate which aircraft operations are conducted by piston-
engine aircraft, compared to turboprop or other engine types. Rather activity is reported as specific to GA or AT,
which can be used to estimate activity specific to piston-engine aircraft based on national averages or airport-
specific data. For the model airport, data collected at the airport during the model-to-monitor comparison
evaluation provided inputs to appropriately allocate GA and AT aircraft activity to piston-engine activity. For
additional information see Appendix A.

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(2010). Surface characteristics and AERSURFACE parameterization are described in the
Appendix A.

To identify the spatial extent of elevated lead concentrations within the vicinity of the
airport, 2,250 receptor locations were used, with the most densely located receptors placed at
50-meter intervals at and near ground-based aircraft activity, as well as out to 1 km downwind
from run-up and take-off activity. Receptor spacing was at 100-meter intervals at other
locations within the 1 km perimeter of the runway centroid, and increased to 200 meters after
2 km.

Results of the yearlong model run provided daily lead concentrations at and downwind of
the maximum impact site that are attributable to piston-engine aircraft activity (i.e., do not
include background lead concentrations from other sources). These daily average lead
concentrations were used to calculate 3-month, rolling-average lead concentrations. As
detailed in Section 3 below, the 3-month, rolling average lead concentrations were then used to
calculate AQFs that relate piston-engine aircraft activity over 3-month periods to lead
concentrations at and downwind of the maximum impact site. The combination of the AQFs
and activity estimates at other US airports provides model-extrapolated lead concentrations for
a national analysis of lead concentrations at and downwind of maximum impact areas at
airports nationwide.194

3. Method to Calculate Model-Extrapolated Lead Concentrations Nationwide

In this section we discuss the methods for calculating model-extrapolated lead
concentrations at US airports. Section 3.1 provides the AQFs developed from the yearlong air
quality modeling at the model airport discussed above. Section 3.2 provides the methodology
for estimating activity at each airport and shows how we use activity estimates for each airport
in combination with the AQFs to develop a national analysis of model-extrapolated
concentrations of lead attributable to piston-engine aircraft at and downwind of the maximum
impact area at approximately 13,000 US airports. This national analysis uses US average
statistics for the fraction of GA and AT activity conducted by piston-engine aircraft. This analysis
is further refined using airport-specific data for a subset of airports as described in Section 3.3.
Section 3.4 then describes quantitative Monte Carlo uncertainty analyses for both the national
and airport-specific analyses.

3.1 Calculation of AQFs for Piston-Engine Aircraft Activity and Lead Concentrations

The AQFs were calculated for the different piston-engine aircraft cycle types and engine
classes. Specifically, piston-engine GA and AT aircraft perform two types of operational cycles:
1) full LTOs, in which aircraft start or end the operation in a full stop outside of the active

194 As stated in Section 1 we define maximum impact site as the 15 meters immediately adjacent to run-up and
the maximum impact area as the 50 meters surrounding the maximum impact site. 'Maximum impact site' is used
in the context of the model airport and 'maximum impact area' is used in the context of airports for which we
calculated model-extrapolated lead concentrations.

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runway, and 2) T&Gs, in which aircraft land and take-off without coming to a full stop.195
Further, fixed-wing piston-engine GA and AT aircraft can be subdivided into two classes, single-
engine (SE) and multi-engine (ME) planes. Due to differences in fuel consumption and time in
each operational mode between aircraft classes and cycle-types, respectively, an AQF was
calculated specific to each aircraft class (i.e., single- or multi-engine, SE or ME) and cycle-type
(i.e., full LTO or T&G). Accordingly, four different types of AQFs (i.e., SE full LTO, SE T&G, ME full
LTO, ME T&G) were calculated for nine specific receptor sites at and downwind of the
maximum impact site, which was the runway end at which LTOs most frequently occurred at
the model airport facility. The AQFs are calculated as the ratio of the average lead
concentration over rolling 3-month time periods to piston-engine aircraft LTOs at the most
frequently used runway end over the same 3-month period.196 For example, the SE full LTO AQF
at the maximum impact site is the ratio of the 3-month average modeled lead concentration
(|ag/m3) attributed to SE LTO at the model airport maximum impact site and the number of full
LTOs conducted by SE piston aircraft at the most frequently used runway end in the same 3-
month period (Equation l).197

3-month average modeled lead concentration ()

Eq. 1: SE full LTO AQF at maximum impact site =	^

# of full SE LTOs during 3-month period

The specific steps to calculate AQFs at and downwind of the maximum impact site are:

1.	Calculate average modeled daily lead concentrations at each of the nine receptor locations
over fourteen consecutive one-month periods separately for emissions from each aircraft
class and cycle-type (e.g., SE T&G, ME full LTO).

2.	Calculate rolling 3-month average modeled lead concentrations at each of the nine receptor
locations by averaging across monthly average concentrations attributable to each aircraft
class and cycle-type (e.g., SE T&G, ME full LTO).

3.	Sum piston-engine activity by cycle-type and aircraft class (e.g., SE T&G, ME full LTO) in the
3-month periods.

4.	Divide each 3-month average ambient lead concentration at each receptor site for each
cycle-type and aircraft class by the corresponding total number of LTOs separated by cycle-
type and aircraft class (e.g., ambient lead concentration from SE full LTO emissions at 50 m
during July - Sept. 2011 / # of SE full LTOs during July - Sept. 2011).

5.	Calculate the average AQF across the 12 rolling 3-month periods separately for each aircraft
class and operation-type pair at each of the nine receptor locations (e.g., average of the 12,
3-month AQFs for SE full LTOs at the 50-meter receptor site).

195	As noted in Footnote 3, for simplicity, both types of LTOs (i.e., full LTO and T&G) are referred to as LTOs,
while "cycle-type" is used to denote the categories of full LTO and T&G.

196	As noted in Section 1, this analysis uses 3-month average lead concentrations to allow for comparisons with
the 3-month average concentration set for the lead NAAQS USEPA (2016b). Review of the National Ambient Air
Quality Standards for Lead EPA-HQ-OAR-2010-0108; FRL-9952-87-OAR.

197	Both full LTO and T&G AQFs include concentration attributable to emissions from aircraft operating in all
modes (e.g., taxi, take-off, run-up), with the exception that T&G AQFs do not include the lead concentration due
run-up emissions.

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As Steps 1 through 4 above describe, for each aircraft class and operation-type pair 12 AQFs
were calculated for each set of 3 consecutive months in a 14-month period. The set of 12 AQFs
for each aircraft class and operation type were used to evaluate variability in AQFs due to
changes in meteorology over a 14-month period.198 In order to average across the largest range
in meteorology inputs to AQFs (e.g., wind speed), the resulting 12 AQFs were averaged to
provide a single 3-month AQF for each aircraft class, operation-type, and location combination
(Table 1). The extent to which meteorology variability included in the modeling to calculate
AQFs is representative of the range of meteorology at airports across the country is discussed
further in Section 4.

Table 1. Average of the 12 rolling 3-month AQFs (jig Pb/m3/LTO) at and downwind of the maximum impact site199

AQFs

Distance (meters)

Max

Impact

Site

50 m

100 m

150 m

200 m

250 m

300 m

400 m

500 m

SE Full
LTO

1.5xl0"5

3.5xl0"6

1.6xl0"6

l.lxlO"6

9.2xl0"7

7.6xl0"7

5.5xl0"7

4.0xl0"7

2.9xl0"7

SE T&G

1.7xl0"7

1.6xl0"7

1.7xl0"7

1.3xl0"7

1.2xl0"7

l.OxlO"7

8.0x10 s

6.1xl0"8

5.5xl0"8

ME Full

9.0xl0"5

2.3xl0"5

l.lxlO"5

8.2xl0"6

6.6x10 s

5.5xl0"6

4.0xl0"6

3.0xl0"6

2.2xl0"6

ME T&G

6.8xl0"7

5.0xl0"7

4.5xl0"7

3.3xl0"7

2.7xl0"7

2.2xl0"7

1.7xl0"7

1.3xl0"7

1.2xl0"7

When each AQF is multiplied by the number of corresponding LTOs (full LTOs or T&Gs) that
occur at the most frequently used runway end during a 3-month period, the sum of the
products equals the lead concentration over the 3-month period at each of the nine locations.
The concentration of lead in air, [Pb]Air, is calculated by Equation 2, where Avgas[Pb] is the
concentration of lead in fuel and PA is piston activity for the given engine and operation type.
The next section describes how the number of piston-engine LTOs, specific to aircraft class and
operation-types, was estimated for each US airport in order to calculate 3-month average
model-extrapolated concentrations of lead in air at each airport.

198	Variation in the rolling 3-month average AQFs for full LTOs is generally +/-25% of the mean across all 12
AQFs. Specifically, rolling 3-month average AQFs for SE full LTOs vary from 28% greater to 14% less than the
associated mean AQFs. For ME full LTOs, the individual rolling 3-month AQFs vary from 23% greater to 13% less
than the associated mean AQFs. The variation is consistent across locations. While ME aircraft typically have two
engines, ME AQFs are more than double the equivalent SE AQFS due to greater fuel consumption of their engines
and differences in time-in-modes. The T&G AQFs are one to two orders of magnitude smaller than the full LTO
AQFs in the same location, and variability between AQFs is somewhat larger by percentage (46% greater to 16%
less than the associated mean AQFs) but smaller in absolute terms.

199	Additional information on the relationships between AQFs and distances downwind is available in Appendix

C.

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Eq 2 200'201*

[Pb]Air=

Avgas[Pb]^£

Tpu [(PAse,fuMxAQFse FuN)+(PAse t&gxAQFse t&g)+(PAme fuNxAQFme fun)+(PAme,t&gxAQFME( t&g)]

2.12 2-I-f
gal

3.2 National Analysis Methods

This section summarizes the approach and rationale for the national analysis of lead
concentrations at and downwind of the maximum impact area at US airports. At a high-level,
this approach entails estimating piston-engine aircraft activity at each runway end of each
airport, and then combining activity estimates from the most actively used runway end in a 3-
month period with the AQFs presented in the previous section. The following text describes, in
brief, the methods used to estimate 3-month maximum piston-engine aircraft activity at each
runway end for airports nationwide; the detailed methods for this analysis are provided in
Table 2.

Airport-specific piston-engine aircraft activity data are not collected by FAA or reported by
airports in a national data source. Rather, piston-engine aircraft activity is reported by FAA as
part of GA and AT activity, which can also include jet-engine aircraft activity. To estimate
piston-engine activity, we used national datasets as described in Appendix B and FAA survey
data regarding the national average for number of hours flown by piston-engine GA or AT
aircraft nationwide.202 Specifically, the percent of hours flown by piston-engine aircraft
categorized as GA (72%) and, separately, AT (23%) was used to estimate the number of LTOs
conducted by piston-engine aircraft at US airports that report GA and AT LTOs (e.g., if an airport
reports 100 GA LTOs and 10 AT LTOs, then 72 and 2 LTOs would be attributed to piston-engine
aircraft for each respective category). For airports that do not report LTOs conducted by GA and
AT, EPA expanded on an FAA method to estimate LTOs using data on the number of aircraft

2°° per the description in the above text, the concentration of lead in air is calculated at nine distances starting
immediately adjacent to run-up out to 500 meters downwind.

201	The scalar for the concentration of lead in avgas is used to normalize the lead concentration to the ASTM
specification for 100 LL (ASTM International (2016). Standard Specification for Leaded Aviation Gasolines.
httpsi/Zcoropass,astro,org/EPU/htrol annot,cgi?D910+19). The impact of variability in avgas lead concentrations
on model-extrapolated lead concentrations is discussed in Section 3.4.

202	Data on hours flown by piston-engine aircraft is consistent with activity data (LTOs), but activity data are
reported as number of LTOs conducted by piston-engine aircraft in both GA and AT categories, whereas hours
flown data are reported for piston-engine aircraft in GA and, separately, AT categories. Piston-engine aircraft flew
65.8% of hours categorized as GA and AT combined compared to conducting 65.7% of LTOs categorized as GA and
AT combined. Piston-engine aircraft flew 72% of hours categorized as GA, and, separately, 23% of those
categorized as AT.

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based at the airport (i.e., aircraft that are air worthy and operational that are based at an
airport for the majority of the year, commonly referred to as "based aircraft").203 This approach
to estimate piston-engine LTOs is routinely applied in the EPA National Emissions Inventory and
is documented in full on the EPA website.204 The national analysis of lead concentrations at and
downwind of airports nationwide used these annual piston-engine LTO estimates to calculate
the number of piston-engine LTOs at each runway end of US airports over 3-month rolling
periods as described below (Figure 2).205 For this analysis, annual piston-engine LTO estimates
from 2011 formed the basis of calculating activity at each runway end over 3-month rolling
periods. Additional discussion on piston-engine activity in 2011 compared to other recent years
is provided in Appendix B, Section 1. For a subset of airports, airport-specific data were used to
provide an additional estimate piston-engine LTOs, as detailed in Section 3.3.

Annual GA and, separately, AT piston-engine LTOs at each US airport were separated into
the four categories of the aircraft classes and cycle-types: SE full LTO, SE T&G, ME full LTO, and
ME T&G, based on FAA data for GA and AT activity. Next, annual LTOs in each of these four
categories at each airport were temporally allocated into daily and then hourly periods based
on a combination of daily activity data from FAA and observations of hourly activity patterns at
the model airport. The allocation of annual to daily piston-engine aircraft activity was
accomplished by calculating a daily fraction of activity (i.e., GA or AT LTOs on a given
day/annual GA or AT LTOs) for each airport. The daily fraction was then multiplied by the
number of piston-engine LTOs in each of the four aircraft class and cycle-type categories. The
resulting number of daily LTOs in each category was then allocated to each hour of each day
based on a diurnal profile (i.e., fraction of daily LTOs per hour) from the model airport
described in Section 2.2. Appendix B provides additional information on the diurnal profile
observed at the model facility compared to observations at other airports.

203	When airports do not report LTOs specific to GA and AT activity, then the number of aircraft that can use
leaded fuel (i.e., SE, ME, helicopters, and ultralight vehicles) that are based at a given airport was used to help
estimate the number of LTOs conducted by each category of activity (GA or AT) out of the total number of LTOs
conducted at that airport. Airports lacking data on both the number of LTOs and the number of based aircraft were
assigned 1 LTO per year based on a review of available information. For more information, see Sections 4a and 4b
of: http://nepis,epa,gov/Exe/ZvPDF,cgi/P1009113,PDF?Dockev=P1009113,PDF.

204	See Sections 4 and 6a of: http://nepi$,epa.gov/Exe/Zy PDF,cgi/P1009113,PPF?Pockey=P1009113, PDF

205	The method used to estimate piston-engine aircraft activity at specific runway ends has inherent uncertainty
from both underlying operational data and local airport traffic patterns. Nevertheless, comparisons of the
methodology presented here to airport-specific observations and data suggest that this method is appropriate for
estimating piston-engine specific activity (See Section 3.3). EPA acknowledges that there are other methods to
estimate piston-engine specific activity (Heiken et. al. 2016), and that the national analysis focuses on activity
estimates during a single year (2011), which does not capture the annual variability in piston-engine aircraft
activity at each airport due to local circumstances or national trends.

A-18

-------
Annual (2011) jet and piston
LTOs per airport

(GAand AT)

Bin by:

Engine type (piston/jet)
Aircraft class (SE/ME)
Cycle type (full LTO/T&G)

o

_£Z

aj

4-J

E

Fraction annual LTOs into daily
LTOs

FAA Daily
Activity Data
(500 airports)

Fraction daily LTOs into hourly
LTOs

(SE full LTO, SE T&G, ME full LTO, ME T&G)

>
H3

C

CD
>

O
H3

O

E

Assign hourly LTOs to runways

(SE full LTO, SE T&G, ME full LTO, ME T&G)

Sum hourly LTOs to
daily LTOs per runway

(SE full LTO, SE T&G, ME full LTO, ME T&G)

C

0
E

1

m
aj

Sum daily LTOsto
LTOs per 3-months per runway

(SE full LTO, SE T&G, ME full LTO, ME T&G)

ID most active runway during
3-month period

(SE full LTO, SE T&G, ME full LTO, ME T&G)

"a

rc	^

aj	^

—	o

aj	mzz

.n	rc

c

Q)
U

c
o

u

U

# of LTOs on most active runway

per 3-months *

(3-month average ng Pb/m3/ LTO)

3-month average ng Pb/m3
@ 9 distances from run-up

Figure 2. Overview of method to estimate piston-engine aircraft activity at airports nationwide. Center rectangles
represent main calculation steps, while colors denote different spatial granularity. Grey cylinders represent input

datasets. See Table 2 for details.

A-19

-------
With the number of piston-engine LTOs (categorized as SE full LTO, SE T&G, ME full LTO, ME
T&G) per hour at each airport, the next step was to assign LTOs to specific runway ends at each
airport. Hourly LTOs were assigned to the runway end at which piston-engine activity would
occur based on wind direction data since piston-engine aircraft take-off and land into the wind
(See Appendix B for additional information on runway assignment and wind direction data).206
Hourly LTOs per runway end were then summed to daily and, subsequently, rolling 3-month
totals (aircraft class and cycle-type categories were maintained when aggregating up to 3-
month LTOs). The total piston-engine LTOs per runway end in a 3-month period was then used
to identify the most active runway at each airport. Next, the number of 3-month LTOs on the
most active runway is multiplied by the appropriate AQF (e.g., number of 3-month SE full LTOs x
SE full LTO AQF at maximum impact site) (Figure 3). As depicted in Equation 2, summing across
the products from each of the four aircraft class and cycle-type categories provides a 3-month
average, model-extrapolated concentration of lead in the maximum impact area and eight
downwind locations for each of the approximately 13,000 airports. These model-extrapolated
3-month average lead concentrations are: 1) attributable to aircraft using leaded avgas, and 2)
located at each of the nine specified distances at each US airport.

Activity
Estimate

(# of 3-month LTOs
on most active
runway at each
airport)

AQFs

(maximum impact
site and eight
downwind distances)

Full	Full

SE-	* SE-

Piston	Piston

AQF	Activity

T&G T&G
SE- „ SE-
Piston Piston
AQF Activity



>1

Full

Full

ME- „

ME-

Piston

Piston

AQF Activity

T&G T&G
ME- * ME-
Piston Piston
AQF Activity

Estimated
Ambient [Pb]

Avgas [Pb]/
2.12

Figure 3. Visualization of approach for calculating extrapolated lead concentrations by multiplying emission factors
(AQFs) by activity estimates for each airport nationwide using Equation 2.

206 While piston-engine aircraft may conduct run-up and take-off on an alternative runway (i.e., not one facing
into the wind) due to activity levels, weather, noise restrictions, or other airport operational considerations, wind
is the primary driver of active runway selection Lohr, G. W. and D. M. Williams (2008). Current practices in runway
configuration management (RCM) and arrival/departure runway balancing (ADRB). NASA/TM-2008-215557 NASA.
https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/2009001Q329.pdf. Therefore, prevailing wind direction is an
appropriate indicator for identifying which runway and direction piston-engine aircraft conduct take-off and
landing operations. Runways are built to allow the maximum possible days of flying by taking into account the
dominant wind direction(s) experienced at the airport; thus, the runway end(s) predominantly used for piston-
engine aircraft take-off can be identified.

A-20

-------
While several meteorological, geographical, and operational parameters may vary from
conditions at the model airport or from the national default parameters used across the
national analysis described above, wind speed is one meteorological parameter that clearly
affects local concentration profiles of atmospheric aerosols. The model-extrapolated
concentrations at and downwind of the maximum impact site as characterized in the approach
above can be adjusted to better consider meteorological conditions by using inverse wind
speed data over the 3-month maximum period. Specifically, the near-field concentration of a
non-reactive pollutant scales with , where u is wind speed and angled brackets imply a
time average (Barrett and Britter 2008). If the wind speed at the model airport is v and the wind
speed at a specific airport is u, then the wind-adjusted concentration would be the model-
extrapolated concentration estimated by the methodology detailed above multiplied by the
ratio of average inverse wind speeds /. If the wind speed at the specific airport is, in
general, higher than the wind speed at the model airport where the AQFs were derived, then
 would be less than  resulting in a lower concentration per activity at the specific
airport than the AQF. Utilizing the same wind data that was used to assign operations to
specific runways, model-extrapolated concentrations at airports nationwide can be adjusted for
wind-speed, thereby appropriately characterizing concentrations at airports with significantly
higher or lower wind speeds than the model airport. For the wind speed adjustment, wind
speeds from 6am to 11pm207 were averaged over the entire year at the model airport and for
the 3-month maximum activity period at each US airport. As the inverse of wind speed tends
toward infinity as wind speed tends toward zero, 0.5 m/s is chosen as a minimum allowable
wind speed; this choice also aligns with ASOS station wind detection limits. Further details of
the wind-adjustment approach are provided in Appendix A.

Results of the national analysis method and wind speed adjustment described here, and
detailed in Table 2, are provided in Section 4. Additional quantitative and qualitative
assessments of uncertainty from other potentially influential parameters, such as avgas lead
concentration and seasonality of operational profiles are discussed in Section 3.4.

207 These are the modeled hours from opening through one hour past closing for each airport, reflecting the
times when atmospheric lead concentrations are expected to be highest.

A-21

-------
Table 2. Steps to Calculate Airport Facility Specific Piston-Engine Aircraft Lead Concentrations

Step
#

Step208

Description

Rationale

Data

Source209

Steps 1-7 Objective: Estimate how much piston-engine activity occurred at each U.S. airport on an hourly basis, by
engine, and operation type.

Estimate how
much activity is
conducted by
piston-engine
aircraft annually

Estimate the annual number of
piston-engine LTOs210 in defined
categories (i.e., GA and AT)

Only piston-engine aircraft use leaded
avgas, thus we needed to estimate how
much of the total activity at an airport
was specific to piston-engine aircraft,
rather than turbine-engine aircraft.

While several data sources provide
airport-specific aircraft activity data
(separately for General Aviation (GA) and
Air Taxi (AT) activity), none specifically
identify the number of piston-engine

2011 NEI

GA and AT

piston-

engine

annual

LTOs211

(USEPA

2011)

208 Each step in this table was carried out for the 13,153 airports in the US. Heliports and rotorcraft activity at airports were not included in this analysis; see
Appendix B for additional information. For each of the 13,153 airports included in the analysis, calculations were completed for each day of 2011 and January-
February 2012; however, annual estimates of piston-engine specific LTOs were only available for 2011, and thus estimates of piston-engine aircraft LTOs from
January - February 2011 were used as surrogate activity data in the first two months of 2012. Based on the 2010 FAA Terminal Area Forecast (TAF), GA activity
levels were similar between 2011 and 2012 (5% lower activity in 2012 than 2011) (https://taf.faa.gov/).

209 Additional information on available FAA data sources is presented in Appendix B.

210 An aircraft operation is defined as any landing or takeoff event, therefore, to calculate LTOs, operations are divided by two. Most data sources from FAA
report aircraft activity in numbers of operations. Our air quality factors (AQFs), described in step 13, are in units of concentration per LTO, therefore for the
purposes of this analysis, operations need to be converted to LTO events.

211 The EPA 2011 NEI estimates annual GA and AT piston-engine LTOs that occur at each airport nationwide. These estimates were the starting point for this
national analysis of lead concentrations at and downwind of maximum impact sites at airports nationwide. The general approach to estimate piston-engine
aircraft LTOs in the 2011 NEI is briefly outlined here with more details are available in Sections 1, 3,4, and 6a of the NEI documentation USEPA. (2011). "2011
National Emissions Inventory (NEI) Data." 2017, from http://www.epa.gov/air-emissions-inventories/2011-national-emissions-inventorv-nei-data. In particular,
the 2011 NEI used based aircraft, reported as single- or multi-engine, to develop more airport-specific piston-engine LTOs at airports with the potential for lead
air emissions inventories greater than 0.50 tons per year. In the national analysis, based aircraft are similarly used to develop more airport-specific results for
airports with model-extrapolated concentrations in the upper range of those nationwide (see Section 3.3 for details).

A-22

-------
Step

Step208

Description

Rationale

Data

Source209

aircraft LTOs that occur at each U.S.

airport facility.

For GA activity

The national average percent of GA

Multiplying GA LTOs at an airport by the

2011 NEI

activity that was conducted by

national average of GA LTOs conducted

GA piston-

piston-engines (72%), according to

by piston-engine aircraft was necessary to

engine

the 2010 FAA GAATA report, was

estimate the annual number of GA

annual

multiplied by total GA LTOs at each

piston-engine LTOs that occurred at each

LTOs &

airport.

FAA
GAATA,
2010 (FAA
2010)

For AT activity

The national average percent of AT

Multiplying AT LTOs at an airport by

2011 NEI

activity that was conducted by

the national average of AT LTOs

AT piston-

piston-engines (23%), according to

conducted by piston-engine aircraft was

engine

the 2010 FAA GAATA report, was

necessary to estimate the annual number

annual

multiplied by total AT LTOs at each

of AT piston-engine LTOs that occurred at

LTOs &

airport.

each airport.

(FAA
2010)

Result: Annual number of GA piston-engine LTOs and AT piston-engine LTOs at each U.S. airport

Estimate how

Estimate the number of total annual

Different aircraft classes and cycle-types

much of the

piston-engine LTOs that are

have different fuel consumption rates,

annual piston-

conducted by specific aircraft

and therefore different quantities of lead

engine aircraft

classes (i.e., SE and ME for specific

emissions.

activity is

cycle-types (i.e., Full LTO and T&G)

conducted by

at each airport.

each piston-

engine aircraft

class, performing

A-23

-------
Step

Step208

Description

Rationale

Data

#







Source209



different cycle-









types







2a

For GA piston-

Multiply the annual number of GA

Fractioning GA piston-engine activity into

Step la &



engine LTOs

piston-engine LTOs (from Step la)

4 combinations of aircraft and cycle-types

(FAA





by the national fraction of annual

(i.e., 68% SE Full LTO, 23% SE T&G, 8% ME

2010)(Tab





GA activity conducted by each

Full LTO, 2% ME T&G) allows us to

le 1.4)212





aircraft class and cycle-type (i.e., SE

categorize LTOs by sub-type of GA piston-







Full LTO, SE T&G, ME Full LTO, ME

engine activity which is important since







T&G).

each sub-type impacts the resulting
concentrations differently.



2b

For AT piston-

Multiply the annual number of AT

Fractioning AT piston-engine activity into

Step lb &



engine LTOs

piston-engine LTOs (from Step lb)

4 combinations of aircraft classes and

(FAA





by the national fraction of annual AT

cycle-types (i.e., 57% SE Full LTO, 0% SE

2010)





activity conducted by each aircraft

T&G, 43% ME Full LTO, 0% ME T&G)

(Table 1.4)





class and cycle-type (i.e., SE Full LTO,

allows us to categorize LTOs by sub-type







SE T&G, ME Full LTO, ME T&G).

of AT piston-engine activity, which is
important since each sub-type impacts
the resulting concentrations differently.



Result: Annual number of piston-engine LTOs at each U.S. airport categorized as: 1) GA SE Full LTO, 2) GA SE T&G, 3) GA

ME Full LTO, 4) GA ME T&G, 5) AT SE Full LTO, 6) AT SE T&G, 7) AT ME Full LTO, 8) AT ME T&G.



3

At the U.S.

Approximately 500 airports have air

Steps 1-2 provide annual piston-engine

ATADS



towered airports,

traffic control towers (i.e., are

activity; however, aircraft activity varies



212 The 2011 FAA GAATA report was not published, therefore the 2010 FAA GAATA report was used for this step. Based on a comparison of the 2010 and
2012 FAA GAATA reports, engine and operation type splits were very similar between 2010 and 2012 (<1% difference in any category between 2012 than 2010)
(httpsi//www,faa,gov/data research/aviation data statistics/general aviation/). See Section 4 for additional discussion on uncertainty and variability in data
used in this analysis. The full LTOs and T&Gs fractions were based on the number of hours flown for GA or AT activities where T&Gs were defined as the
percent of "instructional" hours and full LTOs were defined as the percent of all remaining hours (e.g., total GA hours flown - instructional hours). The amount
of instructional activity will vary by airport. For instance, T&G activity was 4.5 to 29% and 0 to 35% of total SE and ME LTOs, respectively at airports for which
EPA has conducted onsite observational surveys (see Appendix C for survey details).

A-24

-------
Step
#

Step208

Description

Rationale

Data

Source209



estimate what
fraction of annual
activity occurred
on each day of
the analysis
(separately for GA
and AT)213

"towered airports") and therefore
have daily activity counts (separate
for GA and AT). At each of these
airports we developed separate GA
and AT daily activity profiles, or
fractions of annual activity that
occurred during each day of the
analysis. These daily activity profiles
will later be applied to all U.S.
airports (see Step 5).

by month, day, and hour. Because of this
temporal variability, identifying the
maximum 3-month period of activity
necessitates that we apportion the
annual activity data to daily activity (this
step) and subsequently (in the following
steps) further apportion daily data to
each hour of the day.



3a

For GA LTOs

At each towered airport, divide daily
GA LTOs for each day included in the
analysis by annual GA LTOs to reach
the daily fraction of GA LTOs at each
towered airport.

Dividing daily by annual GA activity
produces a daily GA activity profile for
each towered airport.

ATADS

3b

For AT LTOs

At each towered airport, divide daily
AT LTOs for each day included in the
analysis by annual AT LTOs to reach
the daily fraction of AT LTOs at each
towered airport.

Dividing daily by annual AT activity
produces a daily AT activity profile for
each towered airport.

ATADS

Result: Daily Activity Profiles, separately for GA and AT activity, at each towered airport for each day in the analysis.

4

For each non-
towered U.S.
airport, identify

Use latitude/longitude data and a
distance formula to determine the
closest towered airport to each non-
towered U.S. airport.214 These data

Data to develop daily activity profiles are
only available for airports that report
daily activity data (i.e., towered airports).
To apportion each airport's annual

FAA 5010

213	For example, the number of GA operations at each towered airport on January 1, 2011 (from ATADS dataset) were divided by each airport's respective
total number of GA operations in 2011. All operational data were converted to LTOs by dividing by two (i.e., two operations is one LTO).

214	For two airports with (latitude, longitude) pairs of (LatA, LongA) and (LatB, LongB), the distance between them will be:

distance (km) = R*arccos[cosd(LatA)*cosd(LatB)*cosd(LongB-LongA)+sind(LatA)*sind(LatB)] where R is the radius of the spherical approximation of Earth.

A-25

-------
Step

Step208

Description

Rationale

Data

Source209

its closest

will be used in combination with the

activity to individual days, we apply the

towered airport

daily activity profiles calculated in
step 3 to estimate daily piston
activity at each U.S. airport.

daily profile from the towered airport
closest in distance to the non-towered
airport. To do so, we first determine the
closest towered airport for each non-
towered U.S. airport.215

Result: Identification of the closest towered airport for each non-towered airport in the U.S.

Estimate the

Multiply each airport's annual

The GA and AT daily activity profiles (step

number of daily

activity (step 2) by the daily activity

3) allow us to apportion annual activity

piston-engine

profile (step 3) for its closest

into daily activity.

LTOs at all U.S.

towered airport. This is done

airports

separately for GA and AT.

For GA LTOs

Multiply each airport's annual

Daily activity data are only available for

Steps 2a &

piston-engine GA activity (for each

the combined set of all GA aircraft engine

of the 4 types: 1) GA SE Full LTO, 2)

& operation types (i.e., SE Full LTO, SE

GA SE T&G, 3) GA ME Full LTO, 4) GA

T&G, ME Full LTO, ME T&G), thus, we use

ME T&G) by the GA daily activity

the same GA daily activity profile for each

profile for its closest towered

of the 4 subsets of GA activity at all

airport.

airports.

For AT LTOs

Multiply each airport's annual

Similar to GA, daily activity data are only

Steps 2b

piston-engine AT activity (for each

available for all types of AT aircraft engine

& 3b

of the 4 types: 1) AT SE Full LTO, 2)

& operation types (i.e., SE Full LTO, SE

AT SE T&G, 3) AT ME Full LTO, 4) AT

T&G, ME Full LTO, ME T&G) combined,

215 Airport towers at the 500 most active airports in the U.S. report the number of total operations on each day, which are recorded in the FAA ATADS
database. For airport facilities without ATADS data, we used activity data from the nearest ATADS facility as a surrogate for the airport facility without daily
activity data (distances between ATADS facility and surrogates: Mean 64 km, Max 672 km, 25th % 28 km, 75th % 79 km, 90th % 128 km, 95th % 169km, 99th %
292 km). The closest towered airport to a towered airport will be itself. Note that primary airports (i.e., airports with mainly commercial jet activity) were not
used as surrogates since these airports likely have a distinctly different activity profile than GA airports. (See Appendix B for additional details on the ATADS
database.)

A-26

-------
Step

Step208

Description

Rationale

Data

#







Source209





ME T&G) by the AT daily activity

thus, we use the same AT daily activity







profile for its closest towered

profile for each of the 4 subsets of AT







airport.

activity at all airports.



Result: Number of daily piston-engine LTOs at each U.S. airport categorized as: 1) GA SE Full LTO, 2) GA SE T&G, 3) GA ME

Full LTO, 4) GA ME T&G, 5) AT SE Full LTO, 6) AT SE T&G, 7) AT ME Full LTO, 8) AT ME T&G.



6

Sum the number

Sum the daily number of GA and AT

The concentration of lead emissions is

Step 5



of daily LTOs by

LTOs across aircraft engine and

related to the type of aircraft engine and





aircraft engine

operation type (i.e., SE Full LTO, SE

operation type, thus there is no





type & operation

T&G, ME Full LTO, ME T&G).

distinction in terms of emissions between





mode



a SE Full LTO conducted as GA vs. AT.
Understanding levels of GA vs. AT activity
was necessary to appropriately apportion
annual GA and AT activity into specific
piston engine and operation types.



6a

For SE full LTO

Sum the # of GA SE full LTOs & # of
AT SE full LTOs for each day at each
airport.





6b

For SE T&G

Same as Step 6a but for SE T&G.





6c

For ME full LTO

Same as Step 6a but for ME full LTO.





6d

For ME T&G

Same as Step 6a but for ME T&G.





Result: Number of daily piston-engine LTOs at each U.S. airport categorized as: 1) SE Full LTO, 2) SE T&G, 3) ME Full LTO,

4) ME T&G







7

Estimate the

For each day at each U.S. airport,

Step 6 results in daily piston-engine

Model



number of LTOs

multiply the number of daily piston-

activity; however, aircraft activity varies

airport



that occurred

engine LTOs (separated into 1) SE

by month, day, and hour. Because of this

(see



during each hour

Full LTO, 2) SE T&G, 3) ME Full LTO,

temporal variability, identifying the

Section 2



of each day (i.e.,

4) ME T&G) by the corresponding

maximum 3-month period of activity

&



the distribution of

hourly activity profile (i.e., % of daily

necessitates that we apportion the daily

Appendix



LTOs across

aircraft LTOs that occurred during

activity data to hourly activity (this step).



A-27

-------
Step
#

Step208

Description

Rationale

Data

Source209



facility

operational hours
of the day)

each operational hour) from the
model airport. There are separate
profiles for each engine type (1) SE
Full LTO, 2) SE T&G, 3) ME Full LTO,
4) ME T&G) by weekday/weekend
status.216

(e.g., If 30% of SE Full LTOs occurred
during Hour 5 on a weekday at the
representative facility, and 10 SE
Full LTOs occurred at a given facility
on Day 1 (a weekday) of the
analysis, then 3 SE Full LTOs would
be assigned to Hour 5 of Day 1 at
the given facility).

Subsequently (in the following step), we
use wind direction data to apportion the
hourly data to specific runway ends at
each airport.

A) & Step
6

7a

For weekdays



Since data we collected suggests that the
distribution of piston-engine aircraft
activity can vary between weekend and
weekdays, we used an activity
distribution representative of weekday
activity, and separately, an activity
distribution for weekend activity.

Appendix
A & Step 6

7a i

ForSE Full LTO

Multiply % of SE Full LTOs that
occurred in each operational hour of
a weekday at a representative





216 For more information on the distribution of LTOs over operational hours at the model airport see Appendix A. We characterize the influence of using a
different distribution of LTOs across the day on estimates of ambient lead in Appendix B.

A-28

-------
Step
#

Step208

Description

Rationale

Data

Source209





facility by the number of daily SE
Full LTOs for each facility in the
analysis; repeat for each day in the
analysis.





7a ii

For SE T&G

Repeat Step 7ai for SE T&G.





7a iii

For ME Full LTO

Repeat Step 7ai for ME Full LTO.





7aiv

For ME T&G

Repeat Step 7ai for ME T&G.





7b

For weekends

Repeat Steps 7ai - 7aiv using the
distribution of LTOs across
operational hours on a weekend
day.



Appendix
A & Step 6

Result: Number of hourly piston-engine LTOs that occurred on each dav of the analysis at each U.S. airport, categorized
as: 1) SE Full LTO, 2) SE T&G, 3) ME Full LTO, 4) ME T&G

Steps 8-12 Objective: Estimate how much piston-engine activity occurred on each runway end over each rolling 3-
month period.

8

Identify the
runway end at
which aircraft
activity likely
occurred for each
hour of each day
in the analysis

Use wind direction data for each
hour that an airport is open (i.e.,
operational hours)217 to identify the
runway end on which piston-engine
aircraft LTOs were conducted;
repeat for each day in the analysis.

Piston-engine aircraft take-off into the
wind, thus wind direction dictates the
runway end that is used; wind direction
can change throughout the day so we
evaluate hourly wind direction218 to
identify the runway end used
predominantly for each hour.

ASOS

wind

tower

with

shortest

distance

to airport

217	Operational hours were defined as 6 a.m. to 10 p.m. for all airport facilities in the analysis. While some airport facilities may have slightly different
operational hours (e.g., open 6 a.m. to 11 p.m.), the operational hours selected for the analysis are likely representative of most airport facilities based on
review of operational hours at numerous airports (www.airnav.com).

218	The hourly wind direction data used in this analysis is the result of 1-min wind data having been processed by EPA's AERMINUTE into hourly wind data
(see section 4.6 of AERMINUTE User's Guide for averaging method: https://www3.epa.gov/ttn/scram/7thconf/aermod/aerminute userguide.pdf

A-29

-------
Step

Step208

Description

Rationale

Data

#







Source209

8a

For each U.S.

Use latitude/longitude data and

Hourly wind direction data was available

ASOS and



airport, determine

distance formula219 to determine

at the 938 ASOS stations, most of which

FAA 5010



its closest ASOS

the closest ASOS station to each U.S.

are located at airports.221 To determine

(See



station

airport.220

runway usage based on wind direction
data, we first determined the closest
ASOS station to each U.S. airport.

Appendix
B for
details)

8b

Use the hourly
wind direction
data from an
airport's closest
ASOS station to
determine which
runway end was
used for each
hour of the
analysis

See Appendix B for details.

In order to appropriately estimate the
location of the maximum lead
concentration from piston-engine
activity, we use wind direction data to
identify where activity occurred (i.e.,
which runway end).



Result: Location (i.e., runway end) of aircraft activity at each U.S. airport during each hour of each day in the analysis

9

Determine

Assign piston-engine aircraft LTOs in

Merging information regarding the





number of LTOs

each hour (Step 7) to the runway

number of hourly LTOs (Step 7) with our





that occurred on

end that was active during each

assessment of hourly runway usage (i.e.,





each runway end

hour (Step 8); repeat for each day in

which runway end was used during each





on an hourly basis

the analysis.

hour) allows us to quantify the hourly
number of LTOs that occurred on each
runway end at each U.S. airport for each
day of the analysis.



219	See footnote 30 for distance formula.

220	The closest ASOS station to an airport with an ASOS station will be its own station.

221	ASOS & Climate Observations Fact Sheet. November 2012. U.S. NOAA

A-30

-------
Step
#

Step208

Description

Rationale

Data

Source209

9a i

ForSE Full LTO

Assign SE Full LTOs in each hour
(Step 7) to the runway end that was
active during each hour (Step 8);
repeat for each day in the analysis.



Steps 7 &
8

9a ii

For SE T&G

Repeat Step 9ai for SE T&G.





9a iii

For ME Full LTO

Repeat Step 9ai for ME Full LTO.





9a iv

For ME T&G

Repeat Step 9ai for ME T&G.





Result

at eac

: Number of piston-engine LTOs that occurred during each hour on each runway end during each day of the analysis
h U.S. airport, categorized as: 1) SE Full LTO, 2) SE T&G, 3) ME Full LTO, 4) ME T&G

10

Determine the
number of LTOs
that likely
occurred on each
runway end on a
daily basis

For each runway end at each
airport, sum the number of aircraft
LTOs that occurred during all
operational hours for a given day;
repeat for each day in the analysis.

To estimate the number and type of LTOs
that occurred at an airport on each
runway end over an entire day, we sum
the hourly LTOs, by runway end. In
subsequent steps we use this daily
information to estimate activity over 3-
month time periods, which corresponds
to the lead NAAQS averaging period.



10a i

ForSE Full LTO

For each runway end at each
airport, sum the number of SE Full
LTOs that occurred during all
operational hours for a given day;
repeat for each day in the analysis.

Summing all of the SE Full LTOs at an
airport that occurred at each runway end
during each operational hour of a day
allows us to estimate the number of SE
Full LTOs that occurred on each day of
the analysis at each runway at an airport.

Step 9

lOaii

For SE T&G

Repeat Step lOai for SE T&G.





lOaii
i

For ME Full LTO

Repeat Step lOai for ME Full LTO.





10a i

V

For ME T&G

Repeat Step lOai for ME T&G.





A-31

-------
Step
#

Step208

Description

Rationale

Data

Source209

Result: Number of piston-engine LTOs that occurred during each dav on each runwav end at each U.S. airport, categorized
as: 1) SE Full LTO, 2) SE T&G, 3) ME Full LTO, 4) ME T&G

11

Sum daily#of
LTOs estimated to
have occurred on
each runway end
by rolling 3-
month period



We estimate the number and type of
LTOs that occurred on each runway end
at each airport over a rolling 3-month
period using the daily information
generated in Step 10, since the averaging
time for the lead NAAQS is a rolling 3-
month averaging period (e.g., January -
March, February - April, March - May).222



llai

ForSE Full LTO

For each runway end at each
airport, sum the number of SE Full
LTOs that occurred during each day
of a 3-month period; repeat for each
rolling 3-month period included in
the analysis.



Step 10

llaii

For SE T&G

Repeat Step llai for SE T&G.





llaii
i

For ME Full LTO

Repeat Step llai for ME Full LTO.





llai

V

For ME T&G

Repeat Step llai for ME T&G.





Result: Number of piston-engine LTOs that occurred during each rolling 3-month period on each runwav end at each U.S.
airport, categorized as: 1) SE Full LTO, 2) SE T&G, 3) ME Full LTO, 4) ME T&G

222 At some airports available data suggest that the sum of LTOs in the 3-month period is less than one; this is predominantly due to the airport having
fewer than 5 LTOs per year, but in some cases, may be due to missing data (e.g., runway end identifiers). Low activity or a lack of data resulted in 2,095 out of
the 13,000 airports nationwide with less than one LTO in the 3-month period. Model-extrapolated concentrations at these airports are thus less than 0.0075
ug/m3 (see Section 4.1 for results). Additional analyses outside the scope of this report would be needed to evaluate airborne lead concentrations at these
individual airports.

A-32

-------
Step
#

Step208

Description

Rationale

Data

Source209

12

Identify the
runway end with
the highest
estimates of
piston-engine
aircraft activity
during any 3-
month period at
each airport



Piston-engine aircraft activity is a first-
order determinant of lead concentrations
in the maximum impact area in
monitoring and modeling studies, as
described in Section 2, and thus the
period of maximum activity is assumed to
represent the period of maximum
concentration.223

Step 11

12a

For each runway
end, sum the
number of total
piston aircraft
LTOs that
occurred during
each 3-month
period for all
engine &
operation types;
repeat for each
rolling 3-month
period included in
the analysis

Sum Steps llai - llaiv by runway
and by 3-month period for each U.S.
airport.

In addition to understanding how much
piston-engine aircraft activity of specific
engine class & cycle types occurred at
each runway end over rolling 3-month
periods (which will be used in Step 13),
we to need identify the runway end at
which the most piston aircraft activity of
any type was conducted over a rolling 3-
month period. Identifying the runway end
used most frequently by piston-engine
aircraft allows us to estimate ambient
concentrations at the location (i.e.,
runway end) with the most piston-engine
activity, and in turn the highest lead
emissions.

Step 11

12b



Review number of piston-engine
LTOs conducted at each runway end



Step 12a

223ln some instances, meteorological parameters (e.g., low mixing height) may result in maximum concentrations during relatively lower activity periods.
Uncertainty and variability in meteorological parameters is discussed further in Section 4.3.

A-33

-------
Step
#

Step208

Description

Rationale

Data

Source209





during each rolling 3-month period
included in the analysis and identify
the runway end with the most total
piston-engine LTOs during any 3-
month period; repeat for each
airport facility in the analysis.





Result: Identification of the most active runway during any 3-month period at each airport facility included in the analysis

Steps 13 - 15 Objective: Estimate maximum 3-month lead concentrations from Piston-engine aircraft at each U.S.
Airport

13

Estimate ambient
lead

concentrations
from piston-
engine aircraft
lead emissions at
the runway end
most frequently
used by piston-
engine aircraft
during the most
active rolling 3-
month period

Multiply the number of LTOs that
occurred on the runway end most
frequently used by piston-engine
aircraft during the most active 3-
month period by corresponding air
quality factors; repeat for each
facility in the analysis.

In Steps 1 - 12 we estimate piston-engine
aircraft activity (i.e., how many LTOs of
which engine class and cycle type that
occur when and where) at each airport
facility included in the analysis. We then
combine our activity estimates with
estimates of lead concentrations
associated with each type of LTO in order
to calculate total maximum 3-month lead
concentrations from piston-engine
aircraft. To do so, we use AQFs that are
specific to each engine class and cycle
type (SE Full LTO, SE T&G, ME Full LTO,
MET&G).



13a i

ForSE Full LTOs at
the most active
runway during the
most active 3-
month period

Multiply the following:
1) the number of SE Full LTOs that
occurred at the runway end most
frequently used by piston-engine
aircraft during the most active 3-
month period, by 2) the AQF for SE

As described in Section 3.1, AQFs are the
relationship of lead concentration per
unit of aircraft activity (with distinct AQFs
for each aircraft engine and operation
type) and having units of average 3-
month |ag Pb/ m3/ LTO. By multiplying

Steps

11&12;

Model

airport

(see

Section 2

A-34

-------
Step
#

Step208

Description

Rationale

Data

Source209

Full LTOs at the max impact site;
repeat for each facility in the
analysis.

each AQF by the level of activity we
estimate the lead concentration (|ag Pb/
m3) associated with the number of LTOs
we estimated in Steps 1 - 12.

Appendix
A)

13a ii

For SE T&G

Repeat Step 13ai for SE T&G.

13a ii
i

For ME Full LTO

Repeat Step 13ai for ME Full LTO.

13a i

For ME T&G

Repeat Step 13ai for ME T&G.

13av

For all piston-
engine activity

Sum Steps 13ai - 13aiv.

We need to understand total lead
concentrations from all types of piston-
engine activity, which is the sum of Steps
13ai-13aiv.

13av
i

Scaled by the lead
concentration in
avgas

First, divide the ASTM standard for
Pb concentration in avgas (2.12 g
Pb/gal) by the avgas Pb
concentration at the model airport
(2.16 g Pb/gal). Second, multiply the
ratio of 2.12/2.16 by the sum of lead
concentration from all types of
piston-engine activity (Step 13av).

The AQFs were generated at a model
airport with a concentration of Pb in
avgas that is different from the ASTM
maximum specification for this fuel. Thus,
we scale the lead concentrations at each
airport by the ratio of the ASTM standard
lead concentration to the avgas lead
concentration at the facility used to
develop AQFs.224

Result: Ambient lead concentration estimates at the max impact site at the most active runway end during the most
active 3-month period for each airport facility included in the analysis

Estimate ambient
lead

concentrations at

Repeat Step 13 with the appropriate
AQFs for the 8 locations further
downwind of the max impact site

As discussed in Section 3.1, in addition to
developing AQFs at the max impact site,
we also developed AQFs at 8 locations

Model
airport
(see

224 We examine the influence that using the ASTM standard for avgas lead concentration has on our ambient lead concentration estimates in Section 4.

A-35

-------
Step

Step208

Description

Rationale

Data

#







Source209



locations further

(50, 100, 150, 200, 250, 300, 400,

downwind of the max impact site (i.e.,

Section 2



downwind from

500 m); repeat for each facility

where piston-engine aircraft conduct run-

&



the runway end

included in the analysis.

up checks) in order to provide estimates
of how lead concentrations change with
distance. Similar to Step 13, we need to
combine each respective AQF with
activity estimates in order to estimate
concentrations of ambient lead at each
distance for each airport included in the
analysis.

Appendix
A)

Result: Ambient lead concentration estimates at 8 locations downwind of the max impact site at the most active runway

end during the most active 3-month period for each airport included in the analysis



15

Estimate wind-

Scale the model-extrapolated

As discussed in Section 3.2, wind speed

Appendix



adjusted ambient

ambient lead concentrations by the

has a consistent and well-characterized

A and



lead

ratio of the average inverse wind

impact on the near-field concentration of

ASOS



concentrations

speeds at the model airport to the

a passive tracer under dispersion.

wind



using average

average inverse wind speeds

Therefore, scaling model-extrapolated

tower



inverse wind

recorded at the nearest ASOS wind

lead concentrations to consider wind

with



speed

tower.

speed will better characterize local
concentrations at airports nationwide,
particularly those airports where wind
speeds during the maximum activity
period differ significantly from those
observed at the model airport.

shortest
distance
to airport

Result: Ambient wind-adjusted lead concentration estimates at and downwind of the max impact site at the most active

runway end during the most active 3-month period for each airport included in the analysis



A-36

-------
3.3 Evaluation of Airports for Potential Lead Concentrations Above the Lead NAAQS

The national analysis methods described in Section 3.2 provided estimates of 3-month
average model-extrapolated lead concentrations in the maximum impact area and locations
downwind out to 500-meters for 13,153 airports. Within this large set of model-extrapolated
concentrations, we identified the subset of airports where lead concentrations were estimated
to potentially approach, within 10%, or to be above the lead NAAQS.225 To do this, we first
identified airports where model-extrapolated concentrations were above the NAAQS. Next, we
ran a series of sensitivity analyses to identify any additional airports where model-extrapolated
concentrations may be above or approach the NAAQS when considering the major drivers of
airport-to-airport variability and uncertainty. For this subset of airports, we then identified
additional, airport-specific data that could refine the estimates of piston-engine aircraft activity.
Finally, for this subset of airports we considered additional airport-specific criteria, such as the
unrestricted access within 50 meters of the maximum impact location. An overview and
rationale for the approach is provided in Section 3.3.1 followed by a description of how we
adjusted activity estimates for the identified subset of airports using airport-specific data in
Section 3.3.2. The full methodology for considering concentrations using airport-specific activity
data and additional criteria is presented in Section 3.3.3.

3.3.1 Sensitivity Analysis of Airport-Specific Parameters that Influence Potential for Lead

Concentrations to be Above the NAAQS

The first step to identify airports at which model-extrapolated concentrations are potentially
above the lead NAAQS was to evaluate which airport-specific parameters may result in
uncertainty or bias that would lead to underestimates in model-extrapolated concentrations
from the national analysis methods presented in Section 3.2. There is potential uncertainty
and/or bias from using national defaults for: 1) percentages of piston aircraft at an airport, 2)
percentages of piston operations performed by single- versus multi-engine aircraft, and 3)
assigning piston operations to runway ends. To address these sources of uncertainty and to
identify airports where lead concentrations may approach or be above the NAAQs, but would
not be identified by using national defaults, we conducted a series of sensitivity analyses. These
sensitivity analyses expand the number of airports that would be within 10% of the NAAQs by
using different assumptions for each of the three parameters outlined above that used national
defaults in the national analysis.

For the first two parameters, we accounted for the possibility that the percentage of activity
conducted by piston-engine aircraft and/or the percentage of piston-engine aircraft activity
conducted by multi-engine aircraft at each airport might be underestimated by national
averages. We did so by evaluating a scenario in which all GA and half of AT activity was
conducted by piston-engine aircraft at each airport (i.e., we substituted 100% and 50% for the
national average percentages of 72% and 23% piston-engine aircraft of total GA and AT,

225 The current NAAQS for lead is 0.15 ng/m3 as a 3-month rolling average. For this analysis, "approaching" the
lead NAAQS is defined as within 10% of the current standard, or 3-month average model-extrapolated
concentrations >0.14 ng/m3.

A-37

-------
respectively; see Step 1 of Table 2).226 Because AT operations are more often conducted by ME
aircraft, this sensitivity analysis impacts both the estimates of piston-engine aircraft activity and
the predominance of ME or SE piston-engine aircraft at an airport. We then identified airports
that had 3-month average model-extrapolated lead concentrations that were within 10% of the
lead NAAQS after accounting for the possibility that national averages might be under-
representations of piston-engine activity at some airports.

An additional sensitivity analysis was performed on the percentage of operations that occur
at the most-utilized runway during the maximum activity period (Step 12 of Table 2). Two
factors contribute to this percentage: the seasonal profile of operations and the allocation of
operations to different runways based on wind direction. For the airports that were identified
as having maximum 3-month concentrations above or approaching 0.15 |-ig/m3 through the
national analysis method presented in Section 3.2, the average percentage of annual activity
occurring at the maximum period runway end is 20%. However, this percentage ranges from
<6% at some airports, up to 45% at others. Reasons why an aircraft could take-off or land on a
runway end other than the one assigned in the extrapolation, or be active during another 3-
month period, include that the airport's seasonal profile of piston operations differs from that
of the nearest ATADS airport, or the airport has two runways with similar headings, such that
the dominant wind direction bisects them. These effects could bias estimates of operations and
therefore concentrations either high or low. To better understand if some airports could have
concentrations approaching or above the NAAQs that were not identified in the initial
nationwide analysis due to a runway assignment bias, a sensitivity analysis was performed;
airports that had less than 20% of their operations occurring at their maximum utilized 3-month
period runway end were changed to having 20% of operations occur at that runway during that
period.227

Additional sources of uncertainty in operational data that could impact the national analysis
results are discussed in Section 4.4 of this report. For example, there may be uncertainty in the
annual GA operations counts that underlie the piston operations data. However, changing the
total annual GA operations count effects the resulting maximum concentrations in the same
way that changing the percentage of GA operations that are conducted by piston aircraft
effects the maximum concentration (i.e., increasing total GA operations by 10% would be
analytically equivalent to keeping GA operation counts constant and increasing the percentage
performed by piston aircraft by 10%). Thus, the sensitivity analyses performed above may be

226	The parameters presented in these sensitivity analyses, such as the 100% GA and 50% AT activity conducted
by piston-engine aircraft, were only used to identify airports for additional analysis; neither these parameters nor
the resulting maximum 3-month concentrations were used in the airport-specific activity analysis described below
and presented in Section 4.2.

227	This sensitivity analysis may not identify all airports where maximum concentrations have been under- (or
over-) estimated due to the operational profile and runway assignment methodology. For example, an airport that
the national analysis identifies as having 21% of operations occurring at the maximum runway end may in practice
have 35% of operations occurring at that runway end. However, initial analysis showed that model-extrapolated
concentrations estimated to be above the level of the lead NAAQS were mostly insensitive to operational shifts of
this scale. This suggests that the national analysis methodology is appropriate for identifying airports with the
potential for model-extrapolated concentrations to be above the lead NAAQS even considering this operational
uncertainty.

A-38

-------
interpreted to account instead, at least in part, for independent uncertainty from these other
sources.

The airports identified in the national analysis or sensitivity analyses as having maximum 3-
month concentrations above or approaching the NAAQS were the focus of a more refined
assessment of piston-engine aircraft activity, as described below.

3.3.2 Airport-Specific Activity Data

The objective of the sensitivity analyses described above was to identify additional airports
at which it would be informative to evaluate airport-specific piston-engine aircraft activity data,
rather than national average data. The above sensitivity analyses applied alternative default
assumptions for two parameters to all 13,000 airports, while the analyses in this section apply
airport-specific data to the subset of airports identified through the sensitivity analyses and the
national analysis. The objective of the analyses in this section is to account for the fact that
national average activity estimates may potentially be improved by using airport-specific
activity surrogates. As described in Section 3.2, piston-engine aircraft activity is not reported for
individual airports, thus estimates of activity specific to piston-engine aircraft were calculated
using national averages for the fraction of total GA and AT LTOs conducted by piston-engine
aircraft. Similarly, national average fractions were used to estimate piston-engine LTOs
conducted by SE versus ME aircraft. Both of these parameters (piston-engine aircraft activity
and SE versus ME activity) particularly influenced monitored and modeled lead concentrations
attributable to piston-engine aircraft in previous analyses conducted by EPA and others (Fine et.
al. 2010, Carr et. al. 2011, Heiken et. al. 2014, Feinberg et. al. 2016). In these analyses, piston-
engine aircraft activity had a direct impact on lead concentration, where more piston-engine
aircraft activity (i.e., more LTOs) generally correlated with higher lead concentrations (Figure 4
provides one example of this relationship at Palo Alto Airport (PAO), which was included in EPA
NAAQS lead surveillance monitoring network).

Additionally, sensitivity analyses conducted at two GA airports (RHV and SMO), showed that
the amount of activity conducted by multi-engine piston aircraft had a disproportionately larger
impact on lead concentrations compared with single-engine aircraft activity (see Appendix B;
(Carr et. al. 2011)).228 Based on the important influence of these two parameters in previous
analyses, additional, airport-specific information was gathered to further characterize total
piston-engine aircraft activity and the percentage of activity conducted by single- versus multi-
piston-engine aircraft at each of the airports included in this refined, airport-specific activity
analysis.229

228 Multi-engine (ME) piston aircraft have a higher fuel consumption rate compared to single-engine (SE) piston
aircraft; thus, LTOs conducted by ME aircraft result in higher lead concentrations.

229 Total landing and take-off counts, the percentage split between piston and non-piston aircraft, and the
runway assignment method may also each contribute to uncertainty in counts of piston-engine aircraft LTOs at a
given runway end. The runway assignment method and its impact on LTO counts is discussed in Appendix B.

A-39

-------
0.16

0.14

0.12

___ 0.10

ro

„E

m 0.08

.Q

a.

0.06

0.04

0.02

0.00

100	200	300	400	500	600	700	800

Number of Operations

Figure 4. Example of the relationship between monitored lead concentrations and piston-engine aircraft activity.230

Specifically, based aircraft data (i.e., the number and class of aircraft that are parked at an
airport) were collected for airports included in this airport-specific activity analysis. Data from
previous EPA studies at six airports showed agreement within 10% between the number of SE
and ME aircraft based at an airport and onsite observations of piston-engine aircraft activity at
the airport (see Appendix B for study details).231 As such, the number and class of aircraft based
at each airport included in this airport-specific activity analysis was used to refine the national
average percentages for estimating the number of LTOs specific to piston-engine aircraft, and
then SE versus ME piston aircraft.

2311 The relationship between monitored lead concentrations and piston-engine aircraft activity is impacted by
several parameters including distance of the monitor from the area where aircraft conduct run-up checks, wind
speeds, the type of aircraft (multi-engine or single-engine), and the type of operation (full landing and take-off
versus touch-and-go. This figure does not analyze each of these influencing variables but is illustrative of the
general relationship between activity and lead concentration at a general aviation airport).

231 SE and ME aircraft based at an airport were considered piston-engine aircraft. While some SE and ME
aircraft based at an airport may be turboprop or other non-piston-engine aircraft, comparisons with onsite activity
counts suggest based aircraft data provide reasonable, airport-specific data and FAA considers based aircraft data
to be a reliable indicator of activity at small airports (FAA 2015).

A-40

-------
For each airport included in this analysis, the number of aircraft based at that airport was
collected from available data sources.232 Next, for each airport, the percent of total operations
conducted by piston-engine aircraft was calculated using the number of SE and ME aircraft over
the total number of aircraft based at the airport (i.e., sum of SE and ME based aircraft over total
SE, ME, turboprop, jet, and helicopter based aircraft multiplied by 100). Similarly, the percent of
piston-engine operations conducted by SE versus ME aircraft was calculated using the numbers
of SE versus ME aircraft based at the airport (e.g., SE based aircraft over sum of SE and ME
based aircraft multiplied by 100). Table 3 presents a summary of the percent of LTOs allocated
to piston-engine aircraft, and separately SE versus ME piston aircraft, in the national analysis
compared to the allocation using data for aircraft based at the airports included in this airport-
specific activity analysis.

232 A search was conducted for airport master plans or onsite studies on piston-engine aircraft activity, and in
the absence of such information, based aircraft data were used from airport master plans or Airnav.com.

A-41

-------
Table 3. Comparison of Piston-Engine Activity Estimates Using National Averages versus Airport-Specific Data



National







Analysis
National

Airport-Specific
Based Aircraft Data233

Data Sources



Averages





% piston versus

GA: 72%

Unique to each airport

National

jet operations

AT: 23%

(%SE & ME based aircraft of total
based aircraft)

GA & AT Mean: 92%

GA & AT Range: 60 - 100%

Analysis: (FAA
2010, USEPA
2011)

Airport-
specific: Airport
Master Plans &
Airport Master
Record Forms
5010-1 & 5010-2

% single-versus

GA SE: 90%

Unique to each airport

National

multi-engine
operations

GA ME: 10%

(%SE OR ME based aircraft of SE
AND ME based aircraft)

Analysis: (FAA
2010, USEPA



AT SE: 57%

GA & AT SE:

2011)



AT ME: 43%

Mean:
89%
Range:
58-
99%
GA & AT ME:
Mean:
11%
Range:
0.02-
42%

Airport-
specific: Airport
Master Plans &
Airport Master
Record Forms
5010-1 & 5010-2

In general, for the airports evaluated here, using the number of piston-engine aircraft based
at the airports as a surrogate for activity suggests that piston-engine aircraft activity at these
airports is higher than indicated by the national average fraction (Table 3). The higher percent

233 In the national analysis, the percent of activity attributed to piston-engine vs. jet, and separately, multi- vs.
single-engine aircraft differed for GA vs. AT activity based on FAA data; however, based aircraft data do not
provide information on differences between GA and AT and thus the same percentages are used for both.

A-42

-------
of piston-engine aircraft activity at these airports is expected given that master plans and other
available information (e.g., airport websites) show that these airports are predominately GA
airports, which generally have higher levels of piston-engine aircraft activity compared to a
national average that includes activity at commercial and other larger airports with more jet
activity. For the percentage of piston-engine aircraft activity conducted by SE versus ME
aircraft, the number of SE and ME aircraft based at these airports suggest similar percentages
of aircraft activity are conducted by each aircraft class compared to the national average data
for GA activity. Conversely, the number of ME aircraft based at these airports generally suggest
ME activity is lower than the national average used to estimate ME piston aircraft activity from
total AT activity (Table 3). The airport-specific activity estimates calculated using aircraft based
at these airports were used to calculate refined model-extrapolated lead concentrations, per
the methods described in Table 2. These refined model-extrapolated concentrations are
compared with national analysis values, as well as relevant monitoring data, in Section 4.

3.3.3 Airport-Specific Criteria for Identifying Potential Lead Levels Above the NAAQS

This section summarizes the approach and rationale for selecting airports included in the
airport-specific activity analysis of lead concentrations at the maximum impact area. At a high-
level, this approach entails identifying airports where the maximum 3-month average model-
extrapolated concentrations may be above or approach the NAAQS, characterizing model-
extrapolated maximum 3-month concentrations at these airports using airport-specific, refined
estimates of aircraft activity splits, and then evaluating each airport on local criteria such as the
proximity of the maximum-impact site to unrestricted public access. The detailed methods for
this analysis are provided in Table 4.

A-43

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Table 4. Steps for Identifying Airports Where Lead Concentrations May Be Above the Lead NAAQS

Step
#

Step

Description

Rationale

Data
Source

Steps 1-3 Objective: Identify a subset of airports where, considering sources of variability and uncertainty, model-
extrapolated atmospheric lead concentrations could be above or approach the NAAQS for Lead.

1

Identify airports
with maximum
model-
extrapolated
concentrations
approaching or
above the NAAQS

Sum the contributions of single- and
multi-engine T&G and LTO
operations to atmospheric lead
concentrations at the maximum
impact site for the maximum activity
period from the national analysis
described in Section 3.2. Identify all
airports where the maximum
concentration is above or is within
10% of 0.15 |ag/m3.234

The primary and secondary National
Ambient Air Quality Standards for Lead
are 0.15 micrograms per cubic meter lead
in total suspended particles as a 3-month
average. Because the AQFs relate
operations to average atmospheric lead
concentrations over the same timescale
(3 months), the results of the national
analysis indicate whether or not model-
extrapolated lead concentrations may
approach or be above the concentrations
specified in the NAAQS for lead when the
inputs described in Section 3.2 are used.

National
Analysis
Step 14
and 40
CFR Part
50

2

Identify airports
with maximum
model-
extrapolated
concentrations
approaching or
above the NAAQS
when all GA and
half of all AT

Scale the contributions of single-
and multi-engine T&G and LTO
operations to maximum impact area
atmospheric lead concentrations to
characterize these concentrations if
100% GA operations and 50% of AT
operations were operated by piston-
engine aircraft.

As detailed in Steps la and lb in the
national analysis methods, the national
analysis assumed that 72% of GA and 23%
of AT operations are performed by
piston-engine aircraft. The current step
identifies airports where concentrations
would be above or approach the NAAQS
if piston-engine aircraft were a larger
portion of activity at each airport.

National

Analysis

Steps la

and lb

and

GAATA

Survey

234 Aircraft activity for the most recent year available was evaluated at this stage; airports where overall activity decreased such that estimated lead
concentrations were no longer within 10% of 0.15 ng/m3 were excluded.

A-44

-------
Step
#

Step

Description

Rationale

Data
Source



operations are
assumed to be
piston aircraft
operations







2a

Scale

concentration
contributions
from T&G
operations

Scale lead concentration
contributions from GA operations by
(1/0.72)

In the national analysis, all T&G
operations are assumed to be from GA
flight activity. Thus, as concentrations
scale with operations, both single- and
multi-engine concentrations can be
scaled by the proportional change in GA
piston-engine operations.



2b

Scale

concentration
contributions
from full flight
operations

Scale full flight lead concentration
contributions from AT operations by
(0.5/0.23), the ratio of new
operational cycles to old operational
cycles for both SE and ME
concentration contributions.

Both GA and AT operate SE and ME full
flight operations.

National
Analysis
(FAA

2010, EPA
2011)

3

Identify airports
with maximum
model-
extrapolated
concentrations
approaching or
above the NAAQS
when at least 20%
of operations
occur at the most-
used runway end
during the

Scale model-extrapolated lead
concentrations by the ratio (0.2/X),
where X is the airport-specific
fraction of operations occurring at
the most-used runway end during
the maximum 3-month period and X
<0.2.

For the airports that are identified as
potentially having lead concentrations
approaching or above the NAAQs for lead
at Step 1 of the airport specific analysis,
the average percentage of operations
occurring at the maximum period runway
end is 20%. This sensitivity analysis
identifies airports where operations at
the most-used runway end may have
been underestimated due to assumptions
about wind direction, runway

Airport
Specific
Analysis
Step 1

A-45

-------
Step

Step

Description

Rationale

Data

Source

maximum 3-

assignment, and local seasonal

month period

operational profile.

Result: Identification of a subset of airports as having model-extrapolated lead concentrations that could be above the

NAAQS for lead.

Steps 4-7 Objective: Refine model-extrapolated concentrations at the subset of airports identified in Steps 1-3 using

airport-specific activity data

Collect based-

Designate to each airport in the

For the national analysis, national

FAA Form

aircraft data for

airport-specific analysis counts of

average splits of piston/non-piston and

5010 Data

the subset of

jet, single-engine, and multi-engine

subsequently SE/ME operations were

airports identified

aircraft from reported based-aircraft

applied to both GA and AT operations.

in Steps 1-3

numbers at that airport.

Because individual airports may serve
different aircraft populations, an airport-
specific activity assessment may provide
a refined characterization of operational
splits by aircraft type. This assessment
uses counts of aircraft based at a
particular airport as a proxy for a
representative sample of the split of
operations by aircraft type.

Retain national

Where airports have no reported

Where based-aircraft are not reported,

National

average splits of

based-aircraft data235, retain the

the national average percentage of

Analysis

operational cycles

national average splits of

SE/ME and Full/T&G operational cycles

(FAA

for airports with

operational cycles by SE/ME and

remain the best estimates of operational

2010, EPA

no based-aircraft

Full/T&G for AT and GA.

characteristics at that individual airport.

2011)

data in Form

5010.

235 For the airport-specific analysis presented in Section 4, 5.7% of airports have no based-aircraft data.

A-46

-------
Step
#

Step

Description

Rationale

Data
Source

Retain national
average splits of
operational cycles
for airports with
low based-aircraft
counts relative to
annual
operations.

Where airports have an annual-
operations-to-based-aircraft ratio
greater than 73 0 236, retain the
national average splits of
operational cycles by SE/ME and
Full/T&G for AT and GA.

As based-aircraft numbers are self-
reported, Form 5010 Data may be
incomplete at some airports. Further, at
busy airports with significant commercial
or AT traffic, aircraft based at the airport
may not be representative of all aircraft
serving the airport. The lower the ratio of
operations-to-based-aircraft, the more
appropriate based-aircraft is expected to
be a proxy for operational splits. We
make the assumption that annual
operations-to-based aircraft greater than
730 (2 operations per based aircraft per
day), is an upper limit above which the
based aircraft data are not a suitable
proxy for activity at an individual airport.

Assign splits of GA
and AT

piston/non-piston
operations from
based-aircraft
data

Characterize the number of
operations that would be performed
by piston-engine aircraft at each
airport if the non-jet aircraft based
at the airport were representative of
the percent of GA and AT operations
performed by piston-engine aircraft
at that airport.

While several data sources provide
airport-specific aircraft activity data
(separately for General Aviation (GA) and
Air Taxi (AT) activity), none specifically
identify the number of piston-engine
aircraft LTOs that occur at each U.S.
airport. In the national analysis, a default
percentage representative of national
averages was used to determine piston-
engine aircraft operations at each airport;
this analysis uses local airport-specific

FAA Form
5010 Data

236 For the airport-specific analysis presented in Section 4,10.0% of airports have annual-operations-to-based-aircraft ratios above 730.

A-47

-------
Step

Step

Description

Rationale

Data

#







Source







information (namely based-aircraft) to









better characterize model-extrapolated









lead concentrations at those airports that









could have model-extrapolated









concentrations that approach, or be









above the NAAQS for lead as identified in









Steps 1-3.



6

Assign splits of

Characterize the percentage of

In the national analysis, default

FAA Form



ME and SE Full

piston aircraft operations that

percentages of operational splits for AT

5010 Data



and T&G

would be classified as SE Full, SE

and GA operations by aircraft class





operations from

T&G, ME Full, and ME T&G

(SE/ME) and operational cycle type





based-aircraft



(Full/T&G) representative of national





data



averages were used to characterize
piston aircraft operations at each airport;
this analysis uses local airport-specific
information (namely based-aircraft) to
better characterize model-extrapolated
lead concentrations at those airports that
could have model-extrapolated
concentrations that approach, or are
above the NAAQS for lead as identified in
Steps 1-3.



6a

Determine

The percent of AT operational cycles

All AT operations are considered to be full





operational splits

that are SE (or ME) full LTO matches

LTOs.





for AT at each

the percent of based-aircraft that







airport

are SE (or ME).





6b

Determine

The percent of GA operational

Both full LTO and T&G operational cycles





operational splits

cycles that are SE (ME) matches the
percent of based-aircraft that are SE

are performed by GA aircraft.



A-48

-------
Step
#

Step

Description

Rationale

Data
Source



for GA at each
airport

(ME). Of the GA SE operational
cycles, 24% are characterized as
T&G consistent with the national
analysis. Of the GA ME operational
cycles, 20% are characterized as
T&G consistent with the national
analysis.





Result: Characterization of a refined estimate of the number and type of operations performed by SE and ME piston-
engine aircraft for each of the airports identified in Steps 1-3.

7

Refine model-
extrapolated lead
concentrations
using updated
operational splits

For the airports identified in Steps 1-
3, estimate model-extrapolated lead
concentrations at and downwind of
the maximum impact site using the
data gathered in Steps 4-6 paired
with the methodology described in
the National Analysis (Table 2).



National
Analysis
Steps 3-14

Result: Lead concentration estimates at and downwind of the maximum impact site at the most active runway end during
the most active 3-month period for each airport identified in Steps 1-3 using airport-specific activity data.

Step 8 Objective: Identify whether there is unrestricted access to the area of maximum impact at airports identified at
Step 7

8

Identify airports
where there is
unrestricted
access to the 50
m perimeter
around a
maximum impact
site

For the airports that have model-
extrapolated lead concentrations
that are above the lead NAAQS as
identified in Step 7, estimate the
distance from the run-up area at the
most-utilized runway end to the
nearest unrestricted access using
satellite imagery.

The layout and footprint of many general
aviation airports is such that, aircraft run-
up areas and the maximum impact site
may be in close proximity to where
people have unrestricted access. We sub-
select airports where there was
unrestricted access within 50m of the
maximum impact site where lead

Satellite

and

street-

view

imagery

A-49

-------
Step
#

Step

Description

Rationale

Data
Source







concentrations were estimated as
potentially above the lead NAAQS in Step
7.



9

Identify local
airport

characteristics
that may
influence lead
concentrations at
the maximum
impact site

For the airports that have model-
extrapolated lead concentrations
that are above the lead NAAQS as
identified in Step 7, review satellite
imagery and airport documentation
to determine if there are any
airport-specific conditions or
characteristics that could influence
lead concentrations at the
maximum impact site.

As all airports are unique, any airport may
have a layout, local characteristic, or
operational pattern that may differ from
the assumptions underlying the national
analysis and may impact resulting
atmospheric lead concentrations.

Satellite

imagery,

airport

master

plans

Result: Identification of airports that have model-extrapolated lead concentrations above the NAAQS for lead considering
both airport-specific activity data and unrestricted access to the maximum impact area.

A-50

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3.4 Characterization of Uncertainty of Cross-Airport Parameters that Influence the Potential

for Lead Concentrations to Be Above the NAAQS for Lead

As discussed in Section 1, the goal of this work is to characterize lead concentrations at and
downwind of the maximum impact area at airports nationwide. The approach described in
Sections 3.2 and 3.3 was selected because of the consistent set of ground-based parameters
that are inherent to safe operation of piston-engine aircraft. Namely, that these aircraft take-off
into the wind and conduct pre-flight engine checks adjacent to the take-off runway end. These
parameters are consistent across airports, and thus constrain the uncertainty and variability
that might be associated with results based on combining information from one model airport
with activity estimates at airports nationwide. The limited set of key parameters, which
influenced maximum impact area ground-level air lead concentrations in previous modeling by
EPA and others, were: 1) the duration of run-up, where longer run-up times results in higher
concentrations, 2) the concentration of lead in the fuel, where higher avgas lead concentrations
results in higher concentrations, 3) activity, where more piston-engine aircraft activity increases
lead concentrations, 4) the percent of activity conducted by ME piston-aircraft, where more ME
activity results in higher lead concentrations due to the higher fuel consumption rates of these
aircraft relative to SE aircraft, and 5) meteorological factors and local topography (including
wind speed, wind direction, mixing height, atmospheric stability, and surface roughness)

(Section 2; Appendix A) (Carr et. al. 2011, Feinberg et. al. 2016).

Parameters 3 and 4 (activity estimates and SE/ME aircraft splits) were evaluated for a subset
of airports for which uncertainty in the extent to which national average fractions represented
the individual airport would most influence whether or not model-extrapolated concentrations
are above the lead NAAQS, as described in Section 3.3. The uncertainty from these two
parameters and the fifth parameter (meteorological and other local factors) are additionally
assessed qualitatively in Section 4.4.

The duration of run-up operations and the concentration of lead in avgas were both found to
be highly influential in ground-level 3-month average lead concentrations in air attributable to
piston-engine aircraft. Run-up emissions accounted for 82% of the 3-month average lead
concentration attributable to piston-engine aircraft in EPA air quality modeling at a model
facility, and was a primary contributor to emissions in modeling conducted by Feinberg et. al.
(Section 2, Appendix A) (Feinberg et. al. 2016). Moreover, variation between the 5th and 95th
percentiles of average run-up times observed in EPA modeling resulted in an almost 8-fold
variation in concentration attributable to only run-up emissions (Appendix C). Similarly,

Feinberg et. al. found greater variation in the duration of run-up than that of other modes of
operation in the LTO cycle (e.g., landing and take-off time in mode), and variation in run-up
time led to variation in concentrations downwind (Feinberg et. al. 2016).

Similarly, the concentration of lead in avgas has a direct impact on atmospheric lead
concentrations attributable to piston-engine aircraft activity, where higher levels of lead in fuel
result in greater lead emissions and hence concentrations of lead in air. The ASTM standard for

A-51

-------
the maximum lead concentration in 100LL was used in the national analysis; however, the
amount of lead in the fuel can vary across fuel suppliers and by batch. The concentrations of
lead in air attributable to aircraft are expected to directly scale with the concentration of lead in
avgas; thus, the lead avgas concentration was used as a scalar in the calculation of model-
extrapolated concentrations at airports nationwide (see Equation 2, Section 3.1). Based on the
important influence of these two parameters (run-up time and avgas lead concentration) in
modeling 3-month average lead concentrations attributable to piston-engine aircraft activity,
additional information was gathered to further characterize each parameter in results from
both national and airport-specific activity analyses.

Information on average run-up times was collected from a series of studies that observed
run-up operations at five airports (Appendix C) (USEPA 2010a, Heiken et. al. 20 14).237 The
average run-up time from each airport was used to develop a distribution of average run-up
times.238 This distribution of run-up times provided a way to evaluate model-extrapolated lead
concentrations based on observations at a larger number of airports compared to the run-up
times used in the national analysis, which were based on observations at the model airport. The
distribution of average run-up time across the five airports was lognormally distributed with an
average of 70 seconds, compared to the 40- or 63-seconds used for SE or ME aircraft,
respectively, in the national analysis (Table 5). The relationship between variation in run-up
time and concentrations of lead in air at and downwind of the maximum impact area was not
characterized in the additional studies used to develop the distributions of average run-up
times, and thus observations at the model airport were used to characterize how changes in
run-up time impacted changes in lead concentrations in the maximum impact area and
downwind (See Appendix C for details).

The distribution of average run-up times combined with an understanding of the
relationship between run-up time and downwind lead concentrations attributable to piston-
engine aircraft provided the necessary inputs for conducting a Monte Carlo analysis. The
objective of the Monte Carlo analysis was to characterize the impact of variation in the 3-month
average run-up time at a given airport on 3-month average model-extrapolated lead
concentrations. Conceptually, the Monte Carlo analysis entailed repeatedly selecting a run-up
time value from the distribution of average run-up times, and then adjusting the model-
extrapolated lead concentration based on the difference between the selected run-up time and
the run-up time used in the national analysis. For example, if an average run-up time of 70
seconds was selected from the distribution of average run-up times, then the national model-
extrapolated concentration for SE piston aircraft would be adjusted up to account for the 30
second difference between the time used in the national analysis (40 seconds) and the time
selected in the Monte Carlo draw. The amount of increase in concentration in this example
would be based on the relationship observed between run-up time and concentration at each

237	One airport was included in two different studies, so while four unique airports were included in the studies
referenced here, a total of five observational periods is included in the combined dataset.

238	The use of average run-up times was selected as more representative of run-up times over a 3-month
period, the time period of the model-extrapolated concentrations, than the variability observed in the raw run-up
time data. For consistency with the national analysis, the median, rather than mean, run-up time at RHV was
retained in the distribution of run-up times across the five airports included here.

A-52

-------
distance downwind at the model airport, such that the concentration of lead in air would
increase more at the maximum impact site than locations downwind (see Table C-l in Appendix
C). The resulting model-extrapolated concentration at each location, which accounted for the
change in run-up time, would then be used to adjust the model-extrapolated concentration
resulting from the national analysis. The Monte Carlo analysis used 10,000 iterations (i.e.,
10,000 average run-up times were selected from the distribution and used to adjust the model-
extrapolated concentration at each airport, at each downwind distance, which produced 10,000
adjusted concentrations that then provided a range of potential concentrations at each airport,
at each downwind distance, based on variation in run-up time).

A similar approach was used to characterize the impact of variation in avgas lead
concentrations on 3-month average model-extrapolated atmospheric lead concentrations.
Available data from FAA and EPA reporting lead concentrations in avgas samples had an
average lead concentration of 1.79 g/gal and were normally distributed within the range
specified for 100LL (i.e., 1.70 to 2.12 g/gal) (see Appendix C for details on avgas lead data and
their distribution). A Monte Carlo analysis was used to characterize variation in 3-month
average model-extrapolated lead concentrations based on variation in avgas lead
concentration. As with run-up time, a value was selected from the distribution of avgas lead
concentrations (Table 5), and then used to scale a model-extrapolated concentration. For
example, if an avgas lead concentration of 1.80 was selected from the distribution, a model-
extrapolated concentration would be scaled by 0.85 (i.e., 1.80/2.12) to decrease extrapolated
concentration and account for a lower concentration of lead in fuel. The Monte Carlo analysis
was conducted 10,000 times. Results of the avgas lead and run-up time Monte Carlo analyses
were combined per Equation 3 to provide model-extrapolated concentrations that account for
variation in each parameter at and downwind of the maximum impact area at each US airport
(see Appendix C for details).

Eq. 3:

Monte Carlo Adjusted Lead Concentration, [Pb]Mc = LMC/2.12 — (Yn X Cn)

Where:

Lmc= concentration of lead in avgas (g/gal) from Monte Carlo analysis of avgas lead
distribution

Yn= model-extrapolated concentration from national analysis at location n

Cn= %difference change in concentration at location n due to change in run-up time (see
Equation C-l)

N= location at or downwind of maximum impact (i.e., 0, 50, 100, 150, 200, 250, 300, 400,
500 meters)

A-53

-------
Table 5. Monte Carlo analysis inputs for characterizing variability in key AQF parameters

Variable

National Analysis

Monte Carlo Analysis

Assumptions

Data
Source

Value

Data Source

Mean
(SD)

Range

Distribution
Shape

Run-up Time
(seconds)

Model
facility
(Appendix
A)

SE: 40
ME: 63

(USEPA 2010a,
Carr et. al.
2011, Feinberg
and Turner
2013)(Appendi
x A) (n=5)

Model
Airport
(Appendix A)

SE& ME:
70 (21)239

Min: 49
Max: 91

Log-normal
(Time in
Mode)

Exponential
(distance)

We assume that the log-normal distribution of data from the five
airports noted in text is representative of the distribution of piston
aircraft run-up times nationwide since these are the only data in the
literature reporting this information. We assume that bounding the
distribution by one sigma above and below the logarithmic mean is
representative of average run-up times over a 3-month period.

The lead concentration attributable to run-up decreases as a
negative power law with distance from the maximum impact site. As
such, increases or decreases in run-up time compared to an average
influences lead concentration more at 0 or 50 m from run-up than at
500 m meters for run-up. Our modeling suggests an exponential
curve describes the relationship between run-up time and variability
in lead concentration estimate (see Appendix C for details).

Avgas Lead

Concentration

(g/gal)

ASTM
standard

2.12

EPA & FAAfuel

samples

(n=116)

1.79
(0.27)

Min: 1.70
Max: 2.12

Normal

We assume that the normal distribution of data from EPA and FAA
fuel samples is representative of the distribution of avgas lead
content at all US airports. The EPA fuel data were collected during
modeling studies discussed in Section 2. FAA published a study
reporting the lead concentration of avgas fuel samples which was
also used in this analysis.

We bounded the distribution based on the ASTM fuel
specifications for 100 octane Very Low Lead avgas (100VLL) which has
a lead concentration of 1.70 g/gal, and 100 Low Lead (100LL) which
has a maximum lead concentration of 2.12 g/gal.

239 As noted in the text, the average run-up times observed in four studies were used in combination with the median run-up time observed at the model
airport, and used in the national analysis, to develop a distribution of average run-up times. As such, the standard deviation here is the SD of average values.

A-54

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4. Model-Extrapolated Lead Concentrations: Results and Uncertainty Characterization

In this section we present results of the national analysis and the evaluation of individual
airports with the potential to be above the lead NAAQS described in Sections 3.2 and 3.3,
respectively, as well as the results of our methods to characterize uncertainty and variability in
model-extrapolated concentrations of lead from piston-engine aircraft operating at US airports.
Section 4.1 provides results of the national analysis; we then further evaluate of the impact of
the wind speed, and, separately, multi-engine aircraft activity on lead concentrations at the
maximum impact site. Lastly, Section 4.1 characterizes performance of the model-extrapolation
methodology through a comparison of results to monitored concentrations. Section 4.2
provides results of using airport-specific data to refine concentration estimates at airports with
the potential for lead concentrations to be above the lead NAAQS, and similarly characterizes
performance through comparisons of results with monitored concentrations. Section 4.3
discusses the results of the quantitative uncertainty analysis on variability in run-up durations
and avgas lead concentrations. Finally, Section 4.4 discusses qualitative uncertainty analyses for
results from both national and airport-specific activity analyses.

4.1 Ranges of Lead Concentrations in Air at Airports Nationwide

The national analysis methods described in Section 3.2 produced estimates of 3-month
average model-extrapolated lead concentrations at and downwind of maximum impact areas at
13,153 airports nationwide. These model-extrapolated concentrations are calculated for 3-
month periods of peak activity at each airport, and are attributable only to piston-engine
aircraft activity.240 Recall that model-extrapolated concentrations should decrease with
increasing distance from maximum impact area, based on the AQFs used in the analysis (Table
4), and that concentrations across all sites should generally correlate with estimates of piston-
engine aircraft activity given the relationship between activity and concentration described in
Section 3.3.2. Table 6 shows that indeed model-extrapolated concentrations decrease as
distance from the maximum impact area increases (left to right in table), and higher levels of
piston-engine activity (i.e., LTOs) generally correlate with higher model-extrapolated
concentrations (top to bottom in table). The decrease in model-extrapolated concentrations
with increasing distance from the maximum impact area has also been observed in lead
monitoring data near airports servicing piston-engine aircraft (Environment Canada 2000, Fine
et. al. 2010, Anchorage DHHS 2012), as well as lead modeling work conducted by others
(Feinberg et. al. 2016), and conforms to near field concentration gradients for other primary
pollutants.

240 As discussed in Section 2, since model-extrapolated lead concentrations are attributable to piston-engine
aircraft activity only, these lead concentrations may not reflect the total lead concentration (i.e., local emissions
other than aircraft as well as local background lead concentrations are not included in the estimates provided in
Table 6).

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Table 6. Ranges of Piston-Engine LTOs and 3-month model-extrapolated lead concentrations at and downwind of
maximum impact areas at airports nationwide during 3-month peak activity241,242

LTOs

Model-Extrapolated Concentrations of Lead (|ig/m3) at and Downwind of the

Maximum Impact Area

Max
Site

50 m

100 m

150 m

200 m

250 m

300 m

400 m

500 m

3,616 -
26,816

0.155-
0.475

0.038-
0.116

0.018-
0.054

0.013-
0.040

0.011-
0.032

0.009-
0.027

0.006-
0.019

0.005-
0.014

0.003-
0.010

2,579 -
8,814

0.100-
0.154

0.024-
0.038

0.011-
0.018

0.008-
0.013

0.007-
0.011

0.006-
0.009

0.004-
0.006

0.003-
0.005

0.002-
0.003

1,783 -
5,728

0.075-
0.100

0.018-
0.025

0.009-
0.012

0.006-
0.009

0.005-
0.007

0.004-
0.006

0.003-
0.004

0.002-
0.003

0.0017-
0.0023

1,275 -
4,302

0.050-
0.075

0.012-
0.018

0.006-
0.009

0.004-
0.006

0.003-
0.005

0.003-
0.004

0.002-
0.003

0.0015-
0.0023

0.0011-
0.0017

160-
2,889

0.0075-
0.050

0.002-
0.012

0.001-
0.006

0.001-
0.004

0.001-
0.004

0.0004-
0.003

0.0003-
0.002

0.0002-
0.0016

0.00002-
0.001

<1 - 446

< 0.0075

< 0.002

< 0.001

< 0.001

< 0.001

< 0.0004

< 0.0003

< 0.0002

< 0.0002

The relationship between piston-engine aircraft activity and model-extrapolated concentrations
is discussed further below; this relationship is influenced by a few key factors that include the
fraction of SE and ME piston-engine aircraft, and wind speed at a given airport. Looking
specifically at model-extrapolated concentrations at maximum impact areas, results show a
range of <0.0075 to 0.475 |-ig/m3 at airports nationwide, depending on aircraft activity levels
(Table 6). Inspecting the ranges of activity and model-extrapolated concentrations reveals that
there is a wide range of activity that could result in model-extrapolated concentrations above
the lead NAAQS. The airports with comparatively higher lead concentrations and 3-month
maximum activity levels between 3,616 and 26,816 LTOs represent a mix of airports, some of
which are dominated by SE aircraft activity and some of which have a mix of SE and ME aircraft
activity. As noted earlier, SE activity results in lower lead concentrations per LTO compared with
ME activity. Figure 5 presents a plot of the relationship between 3-month average
concentrations and activity, with the relative amount of ME depicted in shades of blue. As
indicated in Figure 5, more activity occurs at an airport dominated by SE aircraft to result in
lead concentrations similar to those at other facilities where there is a mix of ME and SE
aircraft. The mix of SE and ME activity, along with other characteristics of airports with model-
extrapolated concentrations above the lead NAAQS is explored further in Section 4.2.

241	As discussed in Section 3.2, model-extrapolated concentrations in Table 6 are attributable to piston-engine
aircraft activity and do not include local background lead concentrations.

242	In monitoring 3-month average lead concentrations at airports, concentrations in ng/m3 are typically
presented out to two decimal places. Additional decimal places and/or significant figures are shown in this table
and in select other figures either to demonstrate the trend of lead concentrations further downwind of the
maximum impact location or at airports with few operations.

A-56

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Relationship Between 3-Month Lead Concentration and 3-
Month Piston-Engine Aircraft Activity
(n = 12,932 airports)

_ 0-5
"J 0.45

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5,000 10,000 15,000 20,000 25,000 30,000

Activity at Maximum Impact Runway (LTOs per 3-Months)

Figure 5. The relationship between 3-month average lead concentration at the maximum impact site and piston-

engine aircraft activity during the same 3-months. Blue shading denotes the relative amount of multi-piston-
engine aircraft activity at each airport. Air Taxi data were used to estimate the relative ME aircraft activity at each
airport since this type of activity is generally dominated by ME and data specific to multi-piston-engine aircraft
activity is not available across US airports. Airports with zero LTOs (n = 221) were excluded from the figure for
clarity. This figure presents non-wind-adjusted concentrations using national default analysis parameters as
described in Table 2 to better highlight the impact of multi-engine activity on concentration.

As described in Section 3.2 and Table 2, wind speed at each airport relative to wind speed at
the model airport can also influence model-extrapolated lead concentrations, and thus the
maximum impact site concentrations were adjusted to reflect wind speeds at each airport.
Airports with wind speeds during the 3-months of maximum activity that are higher than wind
speeds measured at the model airport will have wind-adjusted concentrations that are lower
than the non-adjusted concentrations using national defaults. Similarly, airports with lower
wind speeds than the model airport will, in general, have higher wind-adjusted concentrations.
Results of the wind-speed adjusted lead concentrations are compared with unadjusted values
in Figure 6.

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Range of LTOs Associated with Lead Concentrations at the
Maximum Impact Site

m£ >0.15

0.10-0.15

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5,000 10,000 15,000 20,000 25,000 30,000
Number of LTOs

H Non-Adjusted ~ Wind-Adjusted

Figure 6. Average 3-month model-extrapolated concentrations versus the number of piston-engine LTOs during
the same 3-month period at the maximum impact area runway end. Concentrations are generally categorized
relative to the lead NAAQS (e.g., greater than the standard of 0.15 ng/m3, less than half the standard, 0.075 ng/m3,
less than concentrations generally detected by monitors, 0.0075 ng/m3, etc).

Across all airports, the effect of the wind adjustment ranges from a 45% decrease in
concentration to a 210% increase in concentration; however, 48% of airports have
concentrations that change by less than 10%. The impact of the wind adjustment on maximum
impact site concentrations for all airports is shown in Figure 7. In absolute difference, the 3-
month maximum concentration at the maximum impact site changes by less than 0.01 |-ig/m3 at
most airports. At airports with concentrations greater than half the lead NAAQS, the absolute
concentration change from wind adjustment tends to be higher, from -0.06 to 0.16 |-ig/m3, as
shown in Figure 8. Overall, results of adjusting for wind speed show that while this parameter is
influential at individual airports, it does not meaningfully impact the range of concentrations in
the maximum impact area at airports nationwide. In turn, individual airports with the potential
to have concentrations above the lead NAAQS are evaluated more closely in Section 4.2.

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Percent Difference in Maximum Impact Site
Concentration Between Non-Adjusted and Wind-
Adjusted Concentrations at All Airports

2500 -|
2000 -

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Figure 8. The absolute change in 3-month maximum concentration at the maximum impact site from accounting
for average inverse wind speed at airports with concentrations greater than Yi the NAAQS for Lead.

The model-extrapolated concentrations from the national analysis presented above can be
evaluated through a comparison to monitored concentrations. Such an evaluation would ideally
be informed by monitored data that corresponds spatially and temporally with the model-
extrapolated concentrations. However, as detailed below, monitored lead concentrations are
only available at a subset of airports and none of these data are spatially and temporally

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consistent with model-extrapolated data.243 Nevertheless, a coarse comparison of model-
extrapolated to monitored concentrations is feasible for a subset of airports at which monitors
were placed proximate to the maximum impact area, or downwind, as part of evaluating
attainment of the lead NAAQS.244 In evaluating these comparisons, it is noteworthy that in
addition to spatial differences, monitored and model-extrapolated concentrations differ in
temporal periods and scope. Model-extrapolated concentrations were calculated for 2011
while monitored concentrations were collected over different 1-year periods depending on the
airport.245 As described in Section 3.1, while 2011 is expected to be generally representative of
piston-engine aircraft activity during monitored periods, differences in the volume and type of
piston-engine activity (i.e., SE vs. ME, full LTO vs. T&G) and meteorological conditions would be
expected to impact the comparisons presented here. In addition, model-extrapolated
concentrations are specific to aircraft lead emissions, while monitored concentrations include
background lead from other sources. Other factors could influence lead concentrations in air
from year-to-year as well, and both monitored, and model-extrapolated concentrations also
have inherent variability and uncertainty. With the characteristics of each dataset in mind,
Figure 9 provides a coarse comparison of national model-extrapolated to monitored
concentrations at three airports with monitors placed proximate to the maximum impact area
or downwind locations.246 Each panel presents the monitored NAAQS design value (i.e.,
maximum 3-month average concentration during monitored time period) along with model-
extrapolated concentrations. Across these airports, model-extrapolated and monitored
concentrations generally align when considering both the downwind gradient, and horizontal
transport of lead emissions at the maximum impact area.

243 The monitors were in a different physical location than that for the model-extrapolated lead concentrations
and the monitoring data was collected at a different time period than that for the model-extrapolated lead
concentrations.

244 Logistical considerations (e.g., aviation safety clearance regulations for siting fixed objects near the landing
and take-off area, and availability of power in these locations) typically prevented placement of lead monitors in
the maximum impact area.

245 Monitoring agencies were required to measure the maximum lead concentration in ambient air resulting
from specific lead sources, including a subset of airports USEPA (2010b). Revisions to Lead Ambient Air Monitoring
Requirements.; these monitoring data are part of the lead surveillance network that is used to evaluate attainment
of the NAAQS for lead (https://www3.epa.gov/ttnamtil/pb-monitoring_.htmj). A summary of monitored data is
available on the EPA website USEPA. (2017a). "Airport Lead Monitoring and Modeling." 2017, from
https://www.epa,gov/regulations-emissions-vehicles-and~engines/airport-lead~inventories-air-qualitv-monitoring-
air.

246 Among the 17 airports where lead surveillance monitoring was conducted, eight NAAQS monitors were sited
in locations proximate to or downwind of the maximum impact area. Four are presented in this section with the
remaining four presented in Section 4.3. In two instances NAAQS monitors were sited particularly close to model-
extrapolated locations, which supported an extended comparison of monitored to model-extrapolated
concentrations, also in Section 4.3.

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Airport A	Airport B

Airport C

Satellite Image Source: Google Earth
Figure 9. Coarse comparison of monitored to model-extrapolated lead concentrations at airports with NAAQS
monitors sited proximate to the maximum impact area or locations downwind. Red dots represen t approximate
monitor placement, while yellow dots represent approximate locations of model-extrapolated concentrations from
national analysis methods (Section 3,2). Blue arrows denote the prevailing wind direction at each airport. As noted
above, the year in which monitored concentrations were collected varies by airport, while model-extrapolated

concentrations represent 2011. All locations are based on scientific judgment of the alignment of model-
extrapolated locations from the expected maximum impact area. The max impact concentrations represented in
the figure are not wind speed adjusted. The wind speed adjusted concentrations at max impact for airports A, B, C

are 0.36, 0.23, and 0.44 |ig/m3 respectively.

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4.2 Airports with Potential Lead Concentrations Above the Lead NAAQS with Unrestricted
Access Within 50 m of the Maximum Impact Site

As described in Section 3.3, a series of sensitivity tests were performed to identify a subset
of airports beyond those identified in the national analysis where model-extrapolated lead
concentration estimates were above the NAAQS for lead. Additional data were then identified
to calculate airport-specific activity estimates for each airport in this subset.247 Next, the
airport-specific activity estimates for each airport were used to calculate updated model-
extrapolated lead concentrations for that airport with a focus on concentrations in the
maximum impact area. In addition, for each of these airports, satellite imagery was utilized to
assess if there was unrestricted access within 50 meters of the maximum impact site. The
results of this screening analysis are presented in Table 7.

Each column in Table 7 represents the outcome of analysis steps presented in Section 3.3
and described in Table 4: the first column identifies the airport, the second column indicates
the lead concentration at the maximum impact site relative to the lead NAAQS using national
default parameters (Section 3.2); the third column adjusts the national default concentrations
based on average inverse wind speed (Section 3.2); the fourth and fifth columns present the
outcomes of airport-specific parameters that influence the potential for lead concentrations to
be above the NAAQS for lead; the sixth column shows the results of the airport-specific-activity
analysis before adjusting for average inverse wind speed; and the seventh column shows the
results using both airport-specific activity and airport-specific wind speed data. Black filled
circles indicate model-extrapolated concentrations are above the NAAQS for lead and white
unfilled circles indicate model-extrapolate concentrations that are more than 10% below the
NAAQS for lead. The potential impacts of additional local characteristics (e.g., mixing height,
local terrain) on airport-specific estimates of lead concentration are discussed qualitatively in
Section 4.4.

Among the airports in Table 7, air quality monitoring has been conducted at RHV at a
location approximately 60 m downwind from the maximum impact site. Lead concentrations at
RHV measured 60 m downwind were above half the level of the lead NAAQS.248

247	As described in Section 3.3, airport-specific data consist of the number of SE and ME piston-engine aircraft
based at an airport. Airport-specific activity estimates were calculated using the following steps. First, the number
of LTOs specific to piston-engine aircraft was estimated by summing the number of SE and ME piston-engine
aircraft based at an airport and dividing the sum by the total number of aircraft based at an airport, then
multiplying the fraction by total LTOs at the airport. Next, the fraction of piston-engine aircraft LTOs conducted by
SE piston aircraft was calculated by dividing the number of SE based aircraft by the total number of SE and ME
based aircraft at an airport. The same approach was used to calculate the fraction of piston-engine aircraft LTOs
conducted by ME piston aircraft. For airports where no based aircraft data were available or for where based
aircraft numbers represented fewer than one aircraft for every 730 operations, national default splits were used
for the airport-specific activity estimates.

248	See the program overview titled Airport Lead Monitoring:
httpsi//nepis,epa,gov/Exe/ZvPDF,cgi/P100LJDW,PDF?Dockev=P100LJDW,PDF

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Table 7. Airports with Model-Extrapolated Lead Concentrations Potentially Above the Lead NAAQS at the Maximum

Impact Area With Unrestricted Areas Within 50 Meters.

Airports249

National
Defaults

Wind % Piston Runway
Adjusted Adjusted Shift

Based
Aircraft

Based
Aircraft
Wind
Adj.

52F

¦

¦

¦

¦

¦

¦

RHV

¦

¦

¦

¦

¦

¦

ORS

¦

¦

¦

¦

¦

¦

WHP

¦

¦

¦

¦

¦

¦

For the airports identified in Table 7, model-extrapolated concentrations increase when
using airport-specific data to estimate piston-engine aircraft activity; the magnitude of the
increase varies based on the difference between the airport-specific fleet and operational
characteristics compared with the national average values used for piston-engine aircraft
activity. The percentage of piston-engine activity estimated as SE versus ME also influences the
magnitude of change between airport-specific and national analysis results. As described
previously and in greater detail in Section B.4, the use of based aircraft to estimate piston
activity, as well as SE and ME splits in activity was evaluated by comparing on-site observations
with based aircraft at a subset of airports and reasonable agreement was observed (within
10%) between based aircraft and on-site observations. Additional factors that influence model-
extrapolated concentrations (e.g., run-up time, avgas lead concentration) are discussed in
Section 4.3. Figure 10 presents the model-extrapolated lead concentrations in the maximum
impact area from both the airport-specific analysis and the national analysis at individual
airports where lead concentrations at the maximum impact site with unrestricted access may
potentially be above the lead NAAQS.

249 Airport codes are commonly used to identify airports; the name and location of airports in this table is
provided in Appendix B.

A-63

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Figure 10. Comparison of model-extrapolated lead concentrations from the wind-speed adjusted national default

parameters (orange squares; Section 3.2), and wind-speed adjusted airport-specific activity analysis (blue
diamonds; Section 3.3) at airports that have the potential for maximum impact site concentrations to be above the

NAAQS for lead with unrestricted access.

Similar to national analysis results, results of the airport-specific activity analysis can be
evaluated through a comparison to monitored data. Of the airports included in the airport-
specific activity analysis, four had NAAQS surveillance monitors located proximate to or
downwind from the maximum impact area. Figure 11 presents the comparison of monitored
and model-extrapolated concentrations at these airports. As discussed in Sections 3.4 and 4.1,
the coarse comparison presented in Figure 11 has attendant uncertainties (e.g., spatial and
temporal differences between monitor and model-extrapolated data). Despite these
uncertainties, monitored data suggest that model-extrapolated concentrations which use
airport-specific activity estimates generally align with monitored concentrations. A more in-
depth comparison of model-extrapolated to monitored concentrations is presented in the
context of additional uncertainty analysis in Section 4.4.

A-64

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Direction

Direction

• Max Impact
0.3i|ig/mi

Max Impact#
O.E>6 iig/m3

Monitor
0.12 ng/m3
•50 m
^0.14 ng/m

Monitor
0.06 (ig/m:

Airport D	Airport E

Direction

• Max Impact
0.23 |!g/m3

100m
0.02 [ig/m:

,3 • ,

Max Impact
0.20 (ig/m3

Monitor
0.10 |ig/m3

Monitor
0.17 |ig/m

Airport F	Airport G

Satellite Image Source: Google Earth

Figure 11. Coarse comparison of monitored to model-extrapolated airport-specific lead concentrations at airports
with NAAQS monitors sited proximate to the maximum impact area or locations downwind. Red dots represent
monitor location, while yellow dots represent approximate locations of model-extrapolated concentrations from
airport-specific activity analysis (Section 3.3). Blue arrows denote the prevailing wind direction at each airport.
Locations for model-extrapolated lead concentrations depicted here were based on approximated location of the
dominant run-up location. The max impact concentrations represented in the figure are not wind speed adjusted.
The wind speed adjusted concentrations at max impact for airports D, E, F, and G are 0.58, 0.31, 0.26, and 0.24

Hg/rn3 respectively.

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4.3 Quantitative Uncertainty Analysis of Concentrations of Lead in Air at Airports: The
Influence of Run-up Time and Avgas Lead Concentration

As with any analysis of this scope in which estimates of pollutant concentrations at facilities
nationwide are developed using an extrapolation approach, there is inherent uncertainty and
variability in the estimates. The focus here is on two key parameters that have been
demonstrated in previous studies to impact lead concentrations at and downwind from the
maximum impact area at airports: run-up time and avgas lead concentration. Run-up time and
avgas lead concentrations are not constrained by the functional role of a given airport, but
rather vary across airports independently of airport attributes. These two parameters were
thus the focus of a quantitative variability evaluation using a Monte Carlo analysis, which is
discussed in Section 3.4 above. Additional meteorological and local considerations may
contribute to uncertainty at individual airports; the uncertainty from these parameters is
discussed qualitatively in Section 4.4.

4.3.1 National Analysis and Airport-Specific Monte Carlo Results

Figure 12 shows the national analysis results with Monte Carlo bounds around each model-
extrapolated concentration for the airport with the highest, and, separately, the airport with
the lowest model-extrapolated concentration at the maximum impact site and downwind
locations. As the Monte Carlo bounds show, variability in run-up duration and avgas lead
concentrations add uncertainty to the exact range of model-extrapolated concentrations
nationwide (i.e., exact value of the highest and lowest model-extrapolated concentration in the
maximum impact area and downwind locations of US airports); however, the quantitative
uncertainty shown in the Monte Carlo is small enough such that it does not obscure meaningful
differences between model-extrapolated concentrations at different US airports.

Further, Monte Carlo results consistently show the potential for higher model-extrapolated
concentrations than the national analysis results (compare black or blue dots to upper error
bars in Figure 12). The potential for higher model-extrapolated concentrations is due to the
difference in observed run-up times at the model airport compared to run-up times observed at
airports included in the Monte Carlo analysis. As noted in Section 3.4 the deterministic national
analysis used 3-month median run-up times for SE and ME, separately, which were measured at
the model airport at which AQFs were developed, while the Monte Carlo analysis included
observations of longer run-up times from studies at additional airports (Table 5). The increase
in model-extrapolated concentrations due to the potential for longer durations of run-up at
airports nationwide compared to that observed at the model airport, generally aligns with a
sensitivity analysis conducted at the model airport. The sensitivity analysis showed that
increasing run-up time from the 5th (16 seconds) to 95th (121 and 160 seconds for SE and ME
respectively) percentiles resulted in approximately an order of magnitude increase in 3-month
average modeled concentrations (i.e., 5th to 95th percentiles of 3-month average modeled
concentrations increased from 0.043 to 0.322 |-ig/m3 and from 0.005 to 0.035 |-ig/m3 for SE and
ME, respectively) (Appendices A and C). The average run-up time at a given airport may be

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impacted by a number of factors (e.g., the number of pilots in training); however, the use of
average run-up time from airports with available data provides relevant information to
characterize the potential range of concentrations at airports nationwide in a manner
consistent with the approach laid out in Section 3.

While the concentration of lead in avgas is also included in the Monte Carlo analysis, this
parameter influences results less than run-up duration for two reasons. First, the range of lead
in avgas is smaller than the range of average run-up times used in the analysis (Table 5).
Second, the impact of longer run-up durations is additive, whereas the impact of lower avgas
lead concentrations is incremental (i.e., each additional second of run-up compared to the
median value used in the national analysis contributes the same amount to downwind lead
concentrations, whereas fuel with 2.10 g/gal lead rather than the 2.12 g/gal contributes
0.02 g/gal less to emissions). The difference in the influence of these parameters helps explain
why the uncertainty analysis for model-extrapolated concentrations consistently demonstrates
higher values compared with the point estimate.

Extrapolated Lead Concentration at Maximum Impact and Locations

Downwind

*o3
=5.

o
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0.1

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Max. 50 m 100 m 150 m 200 m 250 m 300 m 400 m 500 m
Impact
Site

Distance Downwind from Max. Impact

Figure 12. The range of model-extrapolated lead concentrations at and downwnind of the maximum impact area
based on national analysis results. Black diamonds represent the maximum and blue squares represent the
minimum model-extrapolated concentration at each location for the 13,153 airports included in the national
analysis. Error bars are the concentrations at the 97.5th percentile of Monte Carlo results, which account for
potential ranges in run-up time and avgas lead concentrations across airports.

Similar to the Monte Carlo bounds around national analysis results, the model-extrapolated
concentrations from the airport-specific activity analysis are consistently at or near the 2.5th
percentile of the Monte Carlo bounds while the 50th percentiles and 97.5th percentiles of the
Monte Carlo analysis are on average 38% and 91% higher than the model-extrapolated
concentrations from the airport-specific activity analysis. As discussed above, this observation is

A-67

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primarily the result of having used a shorter run-up time in developing the model-extrapolated
lead concentrations in the national analysis compared with run-up times that have been
observed at other airports, which were used in the Monte Carlo analysis (Table 5). In addition,
the greater influence of run-up time versus lead concentrations in avgas on ground-based
atmospheric lead concentrations, leads to changes in run-up time dominating the potential
range of concentrations observed in the Monte Carlo results (ICF 2014, Feinberg et. al. 2016).
The uncertainty results presented here are sensitive to the choice of input distributions for
avgas lead concentration and run-up time.

4.3.2 Comparison of Model-Extrapolated Concentrations From the Airport-Specific Activity
Analysis with Monte Carlo Bounds to Monitored Concentrations in the Maximum Impact
Area

To evaluate the approach for calculating airport-specific model-extrapolated concentrations
with Monte Carlo bounds, results from the approach were compared to relevant monitoring
data. Comparisons between model-extrapolated and monitored lead concentrations are most
informative when the model-extrapolated and monitor concentrations are in the same
approximate location. Two airports had monitors located in close proximity to the location of
the model-extrapolated concentrations; however, monitoring at each airport was conducted
during different time periods than the time period of national analysis. Thus, model-
extrapolated concentrations were adjusted to reflect activity and meteorological data from the
monitored time periods. The same national analysis data sources were used to update activity
and meteorology in model-extrapolated concentrations to monitored time periods (See Section
3.2, Table 2 for data source details). In addition, as with the airport-specific activity analysis,
onsite observational survey data or data on the number and class of aircraft based at the
airport were used to calculate piston-engine aircraft activity, as well as SE and ME activity at
each airport.250

Figure 13 compares the rolling 3-month average model-extrapolated concentrations at the
two airports with monitored data in similar locations.251 At the airport in Panel A, two lead
monitors were co-located proximate to the maximum impact area; the primary monitor is
identified with a blue dot, the co-located monitor with a black dot, and the model-extrapolated
concentrations (based on the lower run-up time estimates) are identified with green dots.
Model-extrapolated lead concentrations at this facility are consistently lower than lead
concentrations measured at the primary monitor with the difference ranging from 12% to 52%
yet the Monte Carlo bounds reflecting potential variation in model-extrapolated values due to
variability in run-up duration and avgas lead concentrations consistently include the primary
monitored value. Model-extrapolated concentrations at the airport in Panel A identified the

250	The following percentages were used to allocate total LTOs given observational survey or based aircraft
data: 70 and 86% piston-engine, 73 and 98% SE, 27% and 2% ME for each airport, respectively. See Appendix C for
details on observational survey data; based aircraft data are from Airnav.com (May 2016).

251	The time period of rolling 3-month average is used here for comparison with the lead NAAQS. Model
extrapolated values presented in Figure 13 are not wind-speed adjusted.

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majority of 3-month monitored concentrations that exceeded the lead NAAQS (noted by the
red line).

Similarly, model-extrapolated concentrations appropriately reflect attainment of the lead
NAAQS at the airport in Panel B. In this instance, both model-extrapolated (green dots) and
monitored (blue dots) concentrations are below the NAAQS. In addition to providing an
example of model-extrapolation performance below the NAAQS, Panel B, also provides an
example of a location further downwind than the maximum impact area. At this airport, the
monitor was located approximately at the 50-meter downwind model-extrapolation site, which
along with activity and other parameters discussed in previous sections, explains the lower
concentrations relative to the airport in Panel A.

A-69

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0.6

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Panel B

































































































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m 45%

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• Extrapolated 3-month Concentration

• Primary Monitor 3-month Concentration

Figure 13. Comparisons of model-extrapolated (green dots) to monitored (blue and black dots) concentrations at
the two airports with monitors placed proximate to model-extrapolated locations. The airport in Panel A had both
a primary and co-located monitor (blue and black dots, respectively) in the maximum impact area. The airport in
Panel B had a monitor approximately 50 meters downwind of the maximum impact site. The red line denotes the
NAAQS for lead (i.e., rolling 3-month average of 0.15 ng/m3).

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4.4 Qualitative Characterization of Uncertainty and Variability in Model-Extrapolated Lead
Concentrations from National and Airport-Specific Activity Analyses

As discussed in Sections 1 and 2, emissions from piston-engine aircraft during run-up is the
single largest contributor to the maximum impact area concentrations for lead from this source,
and there is consistency in how and where these run-up operations are conducted across
airports. The run-up emissions are released near the surface while the aircraft is stationary,
occur in a flat terrain that is required for landing and take-off, and predominately impact
receptor sites nearby (i.e., up to 500 meters downwind) (Carr et. al. 2011, Feinberg et. al. 2016)
(Appendix A). While the consistent nature of piston-engine aircraft run-up emissions results in a
straight-forward dispersion modeling scenario that can be used to extrapolate to other airports,
key parameters impart uncertainty on the model-extrapolated results. This section qualitatively
discusses additional sources of uncertainty that were not addressed in previous sections,
namely uncertainty from meteorological, dispersion modeling, and operational parameters.

4.4.1 Meteorological Parameters

Several meteorological parameters affect modeled concentrations that result from
dispersion modeling of pollutant emissions released at surface level. These parameters include
wind speed and direction, mixing height, atmospheric stability, and ambient temperature since
they directly relate to conditions of atmospheric turbulence, thermal buoyancy, as well as
resulting vertical and lateral dispersion.

Low wind speeds disperse emissions less rapidly compared with high wind speeds, resulting
in higher concentrations near the emissions source. Conversely, higher wind speeds result in
lower concentrations near the emissions source. Specifically, as discussed in Section 3.2 and
demonstrated in Appendix A, the near-field concentration of a non-reactive pollutant
approximately scales with , where u is wind speed and angled brackets imply a time
average (Barrett and Britter 2008). Three-month average inverse wind speeds varied -23% to +
21% from the annual average wind speed. The range of inverse wind speeds at the model
airport results in 3-month AQFs that vary +23% to -15% from the annual average.252
Approximately 51% of airports have 3-month average inverse wind speeds during the 3-month
period of maximum piston-engine aircraft activity at a single runway end that fall within the
range of 3-month average inverse wind speeds at the model airport.253 Thus, we do not expect
wind speed to be a significant source of uncertainty nationwide as sensitivity to wind speed will
be captured by the wind speed scaling technique applied, and 3-month AQFs were only
sensitive to wind speed by approximately +/-20% at the model airport. For individual airports at
the extremes of high and low wind speed, we recognize there is more uncertainty in the

252 As described in Section 3.2, the model-extrapolated lead concentrations were wind-speed adjusted to reflect
the impact of lower or higher wind speeds at each airport compared with the model airport. As such, we do not
expect variations in wind speed to impose a large uncertainty in the evaluation of the potential for airports to have
model-extrapolated lead concentrations above or below the level of the lead NAAQS.

253 Wind speed data is from the nearest ASOS station to each airport. See Appendix A for additional information
on data sources.

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extrapolated concentrations.254 However, we do not expect significant GA activity during
winds below 2.6 m/s or above 10.3 m/s as FAA safety recommendations state that these may
be conditions under which it is particularly challenging for a general aviation aircraft to fly (FAA
2006).

At both high and low wind speeds, significant variability in wind direction can result in
additional uncertainty. When wind direction shifts significantly, airport operators may or may
not initially change the runway end from which piston-engine aircraft take-off due to
considerations of cross-winds and operational consistency. As noted in Section 1, airports are
built such that one runway-end faces directly into the predominate wind direction, which limits
the likelihood of runway-end variability. Further, Section 3.3 discusses a sensitivity analysis that
evaluated the impact of shifting piston-engine aircraft operations to a specific runway-end,
which addresses instances such as when wind direction variability leads to differences between
the active runway-end and wind direction.

Mixing height is another meteorological condition that can influence atmospheric lead
concentrations both independently and in conjunction with wind conditions. When mixing
heights are very low, as is often the case overnight, then pollutants released at the surface
remain trapped in the shallow surface layer, resulting in higher concentrations. Higher mixing
heights occur when there is substantial surface mixing, which more rapidly disperses pollution
away from the surface and result in lower surface-level concentrations. An unstable
atmosphere where the mixing height is changing rapidly will also affect the concentration of
lead at the maximum impact site. Previous air quality modeling conducted by EPA at individual
airports characterized the influence of mixing height on modeled aircraft lead concentrations
(Section 2; Appendix A) (Carr et. al. 2011, Feinberg et. al. 2016). At the model airport, there is a
strong relationship between the 3-month average wind speeds and mixing heights (Figure 14),
making it difficult to separately calculate the influence of mixing height on the AQFs. However,
because run-up is the largest contributor to lead concentrations at the maximum impact site,
the AQF at the maximum impact site is not expected to be sensitive to local mixing height.
Concentrations at sites downwind may be more sensitive to mixing height and atmospheric
stability, particularly during long periods of atmospheric inversion or at airports that have
mixing height characteristics significantly different from the model airport.

254 At very low wind speeds, the inverse wind speed tends toward infinity and the wind speed scaling approach
is limited by the choice of modeled minimum wind speed and the resolution of the wind monitor data.

A-72

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£ 800

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P 550

"f 500
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450

Mixing Height vs Wind Speed

O ....

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2.6 2.8 3.0 3.2 3.4 3.6

3-Month Avg. Scalar Wind Speed (m/s)

3.8

Figure 14. 3-month average mixing height at the model airport as a function of 3-month average scalar wind speed

at the model airport over the same period.

Microclimate conditions and other meteorological parameters may contribute to some
variability in the relationship between aircraft operations and resulting atmospheric lead
concentrations. For example, near-source maximum primary pollutant concentrations have
shown some dependence on ambient air temperature, but to a lesser extent than wind speed
(Liang et. al. 2013). A preliminary analysis of 3-month AQFs at the model airport showed that
temperature was a significant variable (p-value =0.001046) when controlling for average
inverse wind speed; however, because average 3-month temperature varied by less than +/-2%
at the model airport, maximum impact and downwind concentrations were not sensitive to
ambient temperature. Thus, while results nationwide are not expected to be particularly
sensitive to microclimate conditions and other meteorological variables, there is more
uncertainty in model-extrapolated concentrations at airports that have maximum activity
periods during meteorological conditions not observed at the model airport.

4.4.2 AERMOD and AERSURFACE Parameters

Modeling parameters in AERMOD may be a source of both aleatoric and epistemic
uncertainty. 255 Near-field surface and geographic characteristics may have an impact on lead

255 Uncertainty can be classified into aleatoric uncertainty and epistemic uncertainty. Aleatoric uncertainty is
often characterized as natural randomness that is often difficult to measure. Epistemic uncertainty is typically
characterized as uncertainty due to the lack of data (e.g., data that could be collected but the methods may be
prohibitive).

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concentrations at and downwind of the maximum impact site. The calculation of AQFs included
a fixed parameterization of surface roughness, Bowen Ratio, and albedo as described in
Appendix A, but downwind surface characteristics may differ at airports nationwide. Other
research has suggested that, at certain receptors, modelled AERMOD concentrations are
sensitive to changes in surface roughness length but indifferent to albedo and Bowen Ratio
variation (Grosch and Lee 2000, Karvounis et. al. 2007). Further, the modeling approach does
not necessarily account for complex airflow around or near buildings and other obstructions.
While these factors may cause uncertainty at downwind concentrations, their impact on
variability near the maximum impact site is mitigated by requirements for on-airport
characteristics and land-use immediately downwind of runways due to landing and take-off
safety requirements, which results in some consistency nationwide. Where obstructions such as
noise barriers or fences may impact atmospheric lead concentrations near the maximum
impact site, extrapolated concentrations and their associated uncertainty should be considered
on a case-by-case basis. Finally, the aircraft were modeled as volume sources with fixed
horizontal and vertical plume extents, which may introduce uncertainty at airports with aircraft
and engines that differ significantly from those at the model airport. Details on the modeling
approach for aircraft sources, information on prior modeling work, and a comparison between
piston-engine aircraft included in the model airport modeling with those active at airports
nationwide is provided in Appendix A.

4.4.3 Operational Parameters

As discussed throughout the report, the availability, resolution, type, and detail of
operational data available at airports nationwide can contribute to uncertainty in the estimated
lead concentrations. The impact of airport-specific fleet heterogeneity (i.e., piston/turboprop
split and single-engine/multi-engine split) was explored through the use of airport-specific data
for a subset of airports in Section 4.2. However, other local fleet characteristics (e.g.,
distribution of aircraft engine types operating at the airport) are not accounted for in the
analysis and may also contribute to uncertainty at specific airports that have distinct local
characteristics. The nature of piston engines means that there is also a great deal of variability
in their emissions, even for the same pilot operating the same airplane (Yacovitch et. al. 2016);
however, the sensitivity of atmospheric lead concentrations to this variability should be
minimized by averaging concentrations over a 3-month period. Similarly, the diurnal profile of
aircraft activity may influence local lead concentrations over short timescales, but is not
expected to be a sensitive parameter in determining 3-month average concentrations as
discussed in Appendix B. Regional, local, and seasonal differences in daily operational patterns
may contribute additional uncertainty to that discussed in Appendix B. However, given the
insensitivity of average concentrations to different diurnal patterns in sensitivity analysis
modeling, these are not expected to contribute significantly to uncertainty in extrapolated
concentration estimates for airports nationwide. In modeling individual airports, national fleet
and operational data should be supplemented with local data where available and feasible.

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References

Anchorage DHHS (2012). Merrill Field Lead Monitoring Report. Municipality of Anchorage
Department of Health and Human Services. Anchorage, Alaska. December 2012.

http://www.muni.org/Departments/health/Admin/environment/AirQ/Documents/Merrill%20F
ield%20Lead%20Monitoring%20Study_2012/Merrill%20Field%20Lead%20Study%20Report%20-
%20final.pdf.

ASTM International (2016). Standard Specification for Leaded Aviation Gasolines, D910.

Barrett, S. R. H. and R. E. Britter (2008). Development of algorithms and approximations for
rapid operational air quality modelling. Atmospheric Environment, 42 (34), 8105-8111. DOI:

http://doi.Org/10.1016/j.atmosenv.2008.06.020.

Carr, E., M. Lee, K. Marin, C. Holder, M. Hoyer, M. Pedde,... J. Touma (2011). Development
and evaluation of an air quality modeling approach to assess near-field impacts of lead
emissions from piston-engine aircraft operating on leaded aviation gasoline. Atmospheric
Environment, 45 (32), 5795-5804. DOI: http://dx.doi.Org/10.1016/j.atmosenv.2011.07.017.
Chang, J. and S. Hanna (2004). Air quality model performance evaluation. Meteorology and
Atmospheric Physics, 87 (1), 167-196.

Environment Canada (2000). Airborne Particulate Matter, Lead and Manganese at Buttonville
Airport. Conor Pacific Environmental Technologies for Environmental Protection Service.
Ontario.

FAA (2010). General Aviation and Part 135 Activity Surveys - CY 2010. F. A. Administration.
FAA (2012). General Aviation Airports: A National Asset

FAA (2014). General Aviation and Part 135 Activity Surveys - CY 2014. F. A. Administration.
FAA (2015). General Aviation and Part 135 Activity Surveys - CY 2015. F. A. Administration.
FAA (2017). FAA Form 5010, Airport Master Record.

Feinberg, S. and J. Turner (2013). Dispersion Modeling of Lead Emissions from Piston Engine
Aircraft at General Aviation Facilities. Transportation Research Record: Journal of the
Transportation Research Board,(2325), 34-42.

Feinberg, S. N., J. G. Heiken, M. P. Valdez, J. M. Lyons and J. R. Turner (2016). Modeling of Lead
Concentrations and Hot Spots at General Aviation Airports. Transportation Research Record:
Journal of the Transportation Research Board, 2569, 80-87. DOI: 10.3141/2569-09.

Fine, P., A. Polidori and S. Teffera (2010). General Aviation Airport Air Monitoring Study. South
Coast Air Quality Management District.

Grosch, T. G. and R. F. Lee (2000). Sensitivity of the AERMOD air quality model to the selection
of land use parameters.

Heiken, J., J. Lyons, M. Valdez, N. Matthews, P. Sanford, J. Turner and N. Feinberg (2014).
Quantifying Aircraft Lead Emissions at Airports. ACRP Report 133.
http://www.nap.edu/catalog/22142/quantifying-aircraft-lead-emissions-at-airports.

ICF (2014). Final Report: Modeling Analysis of Air Concentrations of Lead from Piston-engine
Aircraft. ICF International.

Karvounis, G., D. Deligiorgi and K. Philippopoulos (2007). On the sensitivity of AERMOD to
surface parameters under various anemological conditions.

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Liang, M. S., T. C. Keener, M. E. Birch, R. Baldauf, J. Neal and Y. J. Yang (2013). Low-wind and
other microclimatic factors in near-road black carbon variability: A case study and assessment
implications. Atmospheric environment (Oxford, England : 1994), 80, 204-215. DOI:
10.1016/j.atmosenv.2013.07.057.

Lohr, G. W. and D. M. Williams (2008). Current practices in runway configuration management
(RCM) and arrival/departure runway balancing (ADRB). NASA/TM-2008-215557 NASA.

http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.Bov/20090010329.pdf.

Luecken, D., W. Hutzell and G. Gipson (2006). Development and analysis of air quality modeling
simulations for hazardous air pollutants. Atmospheric Environment, 40 (26), 5087-5096.
National primary and secondary ambient air quality standards for lead (40 CFR 50.12).
Amended Nov. 12, 2008.

USEPA (2010a). Development and Evaluation of an Air Quality Modeling Approach for Lead
Emissions from Piston-Engine Aircraft Operating on Leaded Aviation Gasoline. EPA-420-R-10-
007. https://nepis.epa.gov/Exe/ZyPDF.cgi/P1007H4Q.PDF?Dockey= P1007H4Q.PDF.

USEPA (2010b). Revisions to Lead Ambient Air Monitoring Requirements.

USEPA. (2011). "2011 National Emissions Inventory (NEI) Data." 2017, from
http://www.epa.gov/air-emissions-inventories/2011-national-emissions-inventory-nei-data.
USEPA. (2016a). "2014 National Emissions Inventory (NEI)", from http://www.epa.gov/air-
emissions-inventories/2014-national-emissions-inventory-nei-data.

USEPA (2016b). Review of the National Ambient Air Quality Standards for Lead EPA-HQ-OAR-
2010-0108; FRL-9952-87-OAR.

USEPA. (2017a). "Airport Lead Monitoring and Modeling." 2017, from

http://www.epa.gov/regulations-emissions-vehicles-and-engines/airport-lead-monitoring-and-
modeling.

USEPA. (2017b). "Learn About Lead Designations." 2018, from http://www.epa.gov/lead-
designations/learn-about-lead-designations#process.

Yacovitch, T., Z. Yu, S. C. Herndon, R. Miake-Lye, D. Liscinsky, W. Knighton, ... P. Pringle (2016).
Exhaust Emissions from In-Use General Aviation Aircraft (ACRP Report No. 164).

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Appendix B:

National Analysis of the Populations
Residing Near or Attending School Near
U.S. Airports

FINAL REPORT

Assessment and Standards Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency

B-l

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Table of Contents

1.0 Introduction	B-3

2.0 Data and Methods	B-4

2.1	Creation of Airport Layers	B-4

2.2	Creation of Airport Buffer Layer	B-ll

2.3	Creation of U.S. Census Block Population Layer	B-13

2.4	Creation of Education Facility Layers	B-13

2.5	Intersection Analysis	B-14

3.0 Results	B-14

4.0 Discussion	B-20

4.1	Uncertainties in Developing Runway Layers	B-20

4.2	Uncertainty Associated with the Estimate of Population Living Near a Runway	B-22

4.3	Uncertainty Associated with Census Data and School Point Data	B-24

APPENDIX	B-26

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1.0 Introduction

According to Federal Aviation Administration (FAA) records, there are approximately 20,000
airport facilities1 in the U.S.2 At the vast majority of these landing facilities, setbacks for
residential development and recreational activity can be less than 50 meters (m) from aircraft
operations.3' 4 By contrast, commercial airports (defined by FAA as those with at least 2,500
passenger boardings each year), typically have a large spatial footprint which provides greater
distance between aircraft activity and residential or recreational spaces compared with other
airport facilities. There are approximately 500 commercial airports in the U.S.5

This report focuses on estimating the number of people who live and attend school near
airports for the purposes of characterizing the magnitude of people potentially exposed to lead in
air from piston-engine aircraft operations at airports. For the purposes of this report we are
considering the population to be near an airport if they live in a census block that intersects the
500 m buffer of a runway or the 50 m buffer of a heliport. We also evaluated educational
facilities that intersects the 500 m buffer of an airport runway. These buffer distances were
selected due to results of air quality modeling and monitoring data for lead at and near airport
facilities and one study reporting a statistically significant increase in children's blood lead for
children living within 500 meters of an airport.6 EPA and local air quality management district
studies indicate that over a 3-month averaging time (the averaging time for the EPA National
Ambient Air Quality Standard for Lead), the impact of aircraft lead emissions at highly active
airports, extends to approximately 500 m downwind from the runway.7'8 These same studies
suggest that on individual days, the impact of aircraft lead emissions can extend to almost 1,000
m downwind from the runway of a highly active airport (i.e., hundreds of take-off and landing
events by piston-engine aircraft per day). The horizontal and lateral dispersion of the lead plume
from aircraft emissions depends on several variables, including: wind direction, wind speed, the
amount of aircraft activity (i.e., the number of take-off and landing operations), and the time
spent by aircraft in specific modes of operation that have been demonstrated to greatly impact
the magnitude of the ground-based lead concentrations (i.e., emissions occurring during pre-
flight engine safety checks).

1 In this paper 'airport facility' refers to airports, balloonports, seaplane bases, gliderports, heliports, STOLports,
and ultralight facilities.

2 FAA Office of Air Traffic provides a complete listing of operational airport facilities in the National Airspace
System Resources (NASR) database available at: http://www.faa.gov/airports/airport safetv/airportdata 50.1.0/.

3 U.S. FAA, 2012. General Aviation Airports: A National Asset. Available at:
http://www.faa.gov/airports/planiiing capacitv/ga studv/media/20.1.2AssetReport.pdf.

4 ASTM International (2005) ASTM F2507 - 05 Standard Specification for Recreational Airpark Design.

5 FAA National Plan of Integrated Airport Systems 2013-2017. Available at:
http://www.faa.gov/airports/plaiining capacity/npias/re ports.

6 Miranda, M., Anthopolous, R., Hastings, D. (2011) A geospatial analysis of the effects of aviation gasoline on
childhood blood lead levels. Environmental Health Perspectives 119:1513-1519.

7 Carr, E., Lee, M., Marin, K., Holder, C., Hoyer, M., Pedde, M., Cook, R., Touma, J. (2011) Development and
evaluation of an air quality modeling approach to assess near-field impacts of lead emissions from piston-engine
aircraft operating on leaded aviation gasoline. Atmos Env 45: 5795-5804.

8 South Coast Air Quality Management District (2010) General Aviation Airport Air Monitoring Study Final Report.

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Section 2.0 describes the data and methods used to quantify the number of people living near
an airport runway and/or heliport where piston-engine aircraft operate, as well as the number of
children attending school in this environment. Section 3.0 provides the resulting population
demographics for the population, by race, living near an airport runway and/or heliport. This
section also provides the results of the number of children attending school near a runway and/or
heliport by race and free or reduced-price school lunch eligibility (a proxy for socioeconomic
status of the population located in close proximity to airports) as well as the number of children
attending preschool near a runway and/or heliport. A discussion of the sources of uncertainty in
the methods applied is presented in Section 4.0.

2.0 Data and Methods

In order to quantify the population living near an airport runway and/or heliport, we first
developed layers9 to represent the location of all airport facilities (referred to here as the 'airport
layer') using ArcGIS 10.0.10 For airports with available data, the airport layer is represented by
the location of the runway(s) at the airport and is more specifically referred to as the 'runway
layer.' For airport facilities where data are not available to identify the location of the runways,
the airport facility centroid represents the facility in the airport layer and is more specifically
referred to as the 'facility layer.' The airport centroid is the approximate geometric center of all
usable runways.11 We then developed buffers around each layer element that extend out to 500
m from the airport runway and 50 m from heliport centroids. We intersected the resulting
buffers with 2010 U.S. Census data (at the block level12) and data identifying the location of
public and private schools and preschools. In this section we describe the methods used to create
airport layers, airport buffer layers, a census block population layer, education facility layers and
the intersection analysis of airport buffer layers with population and educational facility layers.
A detailed description of the data sources is described below.

2.1 Creation of Airport Layers

The availability of airport runway data that can be used to create airport layers varies among
the almost 20,000 airport facilities in the U.S. Therefore, depending on the data elements
available, different data sources and methods were used to generate the U.S. airport layers.

There are seven methods used to create the airport layers, that are focused on seven categories of
airports based on data availability as described below.

9 A layer is "the visual representation of a geographic dataset in any digital map environment. Conceptually, a
layer is a slice or stratum of the geographic reality in a particular area and is more or less equivalent to a legend item
on a paper map. On a road map, for example, roads, national parks, political boundaries, and rivers might be
considered different layers." (from: https://suPTOrt.esri.com/en/other~resoiirces/gis~dictionarv/term/bale96e7~4eae~
4714-875a-a7e3488b8bb9V

10 ESRI2011. ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research Institute.

11 U.S. Department of Transportation (2004) FAA Advisory Circular 150/5200-35, 5/20/2004, 'Submitting the
Airport Master Record in Order to Activate a New Airport.'

12 Census blocks "are statistical areas bounded by visible features, such as streets, roads, streams, and railroad
tracks, and by nonvisible boundaries, such as selected property lines and city, township, school district, and county
limits and short line-of-sight extensions of streets and roads." (from:
http://www.censns.gov/geo/reference/gtc/gtc block.htmD.

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The first method uses geospatial linear runway data produced by the FAA Research and
Innovative Technology Administration's Bureau of Transportation Statistics (RITA/BTS), which
is part of the National Transportation Atlas Databases (NTAD) 2010 data. These data are
referred to in this report as the FAA geospatial data. This geographic dataset of U.S. runways
contains information on runway geometry and is derived from the FAA's National Airspace
System Resource Aeronautical Data Product.

The remaining method categories (II-VII) were applied to airport facilities for which FAA
geospatial data were not available. The data used in these categories came from FAA's Office of
Air Traffic which provides a complete list of operational airport facilities in the National
Airspace System Resources (NASR) database, which is partly populated by airport submissions
of Airport Master Record (5010) forms. The electronic NASR data report can be generated from
the NASR database and is available for download from the FAA's website.13 Reports are
available both at the runway level (referred to here as the "5010 runway data report"), and the
airport facility level (referred to here as the "5010 airport data report"). Both reports are updated
every 56 days with any newly available information.14 For some airports, tabular runway data in
the 5010 runway data report were provided that included fields for the latitude and longitude
coordinates of the runway base end and for the runway reciprocal end (opposite to the base end)
or just one runway end. The base end of a runway is the runway end located to the west of the
north-south line and the reciprocal end is the runway end located to the east of the north-south
line. Base runway ends have a magnetic heading of 10 to 180 and reciprocal runway ends have a
magnetic heading of 190 to 360 degrees. These data from the 5010 runway data report were
used to create runway layers in methods II and III, as described below. For airports without
runway end coordinate data, data from the 5010 runway data report were supplemented with
airport centroid latitude and longitude data from the 5010 airport data report to create runway
layers in methods IV and V, as described below. For airports without relevant runway data, we
used the airport centroid latitude and longitude from the 5010 airport data report to create the
facility layers in methods VI and VII. Appendix Table A-l provides the summary of airport and
population data by method.

Methods Used to Create Airport Layers

I. Runway layers were created directly from FAA geospatial data for 6,090 runways at
4,146 facilities. This dataset was downloaded in March 201115 and contained
information for 6,159 runways, however, we excluded runways at airport facilities that
are closed16 as well as runways at facilities in U.S. territories since the U.S. Census data
used in this analysis does not provide complete coverage of the U.S. territories.17 In
total, 69 runways were excluded from this dataset.

13	"Airport Data & Contact Information" at http://www.faa.gov/airports/airport safetv/airportdata 50.1.0/.

14	This analysis used the 5010 airport and runway data reports downloaded on March 5, 2012.

15	National Transportation Atlas Databases. Washington, D.C.: U.S. Department of Transportation, 2010.
(accessed at: http://www.bts.gov/bts/sites/rita.dot.gov.bts/files/pnblications/national transportation atlas database/
20.1.3 /po ty 1 i ne. htm P).

16	Determined by comparing the geospatial data with the February 7, 2012 and September 25, 2013 versions of
the FAA 5010 facility data report, which indicates if an airport is open, closed indefinitely, or closed permanently.

17	U.S. Census Bureau (Revised 2012). 2010 Census Summary File 1 - Technical Documentation.

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II.	For 414 runways at 385 facilities, the latitude and longitude coordinates of the runway
base end and runway reciprocal end were provided in the 5010 runway data report. The
runway layer was created using the 'points to line' tool in ArcGIS to connect the
reciprocal and base end coordinates to generate a line representing the runway.

III.	For 4 runways at 4 facilities, latitude and longitude data for only one runway end - either
base or reciprocal end - were provided in the 5010 runway data report. The magnetic
heading of the runway and the runway length were also provided in the FAA database for
these facilities. The coordinates for the runway end without available longitude and
latitude data were calculated using equations 1 and 2 in Figure 1 below. Equations 1 and
2 use trigonometric functions to determine the runway location given either the base end
latitude and longitude or the reciprocal end latitude and longitude. The constants in the
denominator of both equations convert the changes from meters to degrees. The
conversion constants were calculated by dividing the circumference of the earth in meters
by 360 degrees to determine the length of one degree latitude and longitude at the
equator. Multiplying by cos Xi in the denominator of equation 2 accounts for the fact
that the distance of one degree of longitude decreases significantly as the point moves
closer to one of the earth's poles.18 The runway length (designated as 'RunwayLength'
in the FAA 5010 runway data report) was represented by I. Where the reciprocal end
coordinates were available, they were designated as Xi for the reciprocal end latitude,
and Yi for the reciprocal end longitude in equations 1 and 2. Using the information
provided for the length of the runway and the reciprocal end coordinates, the base end of
the runway was calculated. The latitude of the base end of the runway was designated as
X2, and the longitude of the base end of the runway was designated as Y2. For the
runways with available base end data (i.e., X2, Y2 coordinates in equations 1 and 2), and
the equations were used to solve for the reciprocal runway end latitude and longitude
designated as Xi, and Yi, respectively. The runway identification data (designated as
'runway ID' in the FAA 5010 runway data report) is provided by FAA in the 5010
runway data report and is defined by FAA as the whole number nearest the one-tenth of
the magnetic azimuth of the direction to which the runway is pointing (measured
clockwise, with 0° at due north). These runway IDs were used to calculate 9 as follows:
the base end runway ID was converted to an angle using Table A-2. For purposes of the
equation, 9 is measured in degrees, counterclockwise from due east, with due east having
a value of 0 degrees. A runway pointing due east has a magnetic heading of 90 as
defined by FAA (runway designation marking of 09), with the reciprocal runway end
having a magnetic heading of 270 (runway designation marking of 27). A conversion
chart in Table A-2 in the appendix links runway magnetic headings with the value of 9

18 The data for these conversion constants were obtained from
http://oceanservice.noaa.gov/ediieation/tiitorial geodesv/geo()2 hist.html and The National Center for Geographic
Information and Analysis.

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used in this equation. This value of 9 was adjusted using magnetic declination of the
closest 15-arc minute declination contour.19 The runway layer was created using the
'points to line' tool in ArcGIS to connect the reciprocal and base end coordinates to

Lat, Long of runway reciprocal end

X20,Y20

East-West

Xi°, Yi°
Lat, Long of
runway base end

generate a line, representing the runway.

. . Zsin0

*° = xf+TIUT2	(1>

n n	ICOSO

r° = r°+ HUM	(2)

Figure 1. Calculation of Runway Latitude and Longitude Coordinates for Category III

Where Xi and Yi are the latitude and longitude of the reciprocal end of the runway,
respectively; X2 and Y2 are the latitude and longitude of the base end of the runway,
respectively; theta (0) is the runway angle from the east-west line.

IV. For 8,597 runways at 8,597 airports, the airport centroid (which is the center of the runway
on the runway centerline) was used to create the runway layer.20 The coordinates for the
runway ends were calculated in a similar manner to those in category III. Both the base and

19 The magnetic declination data were obtained from the National Oceanic and Atmospheric Administration
(NOAA) National Geophysical Data Center World Magnetic Model 2010 at

http://www.ngdc.noaa.gov/geomag/data.shtml (follow links to: 'maps and shape files,' 'wmm2010,' 'shapefiles,'
and 'WMM2010_Shapefile_15min_for_NGA.zip').

211 U.S. Department of Transportation (2004) FAA Advisory Circular 150/5200-35, 5/20/2004, 'Submitting the
Airport Master Record in Order to Activate a New Airport.'

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reciprocal runway end coordinates were calculated from the reference point of the centroid
(coordinate pair X3, Y3 in Figure 2). Base end coordinates, X2, Y2, were calculated using
equations (3) and (4), which uses the distance of half the runway length (1/2) since the
centroid bisects the runway. The reciprocal end coordinates, Xi, Yi, were solved for using
equations (5) and (6), again with the distance of 1/2. In both sets of runway end calculations
the runway identification data (designated as 'runway ID' in the FAA 5010 runway data
report) were used to calculate 9 as follows: the base end runway ID was converted to an
angle using Table A-2.21 Runway IDs are based on the magnetic heading22 of each runway
end, therefore magnetic declination data from the NOAA National Geophysical Data Center
World Magnetic Model 2010 were obtained23 and the angle resulting from the use of Table
A-2 was adjusted by the magnetic declination of the closest 15-arc minute declination
contour to calculate the value of 9 used in equations (3) through (6). The runway lines for
these facilities, which comprise the runway layer, were then generated in ArcGIS using the
'points to line' tool to connect the calculated runway end latitude and longitude pairs.

21	0 is measured in degrees, counterclockwise from due east.

22	The runway designation is the whole number nearest the one-tenth of the magnetic azimuth of the direction to
which the runway is pointing (measured clockwise, with 0° at due north).

23	http://www.ngdc.noaa.gov/geomag/data.shtinl (follow links to: 'maps and shape files,' 'wmm2010,'
'shapefiles,' and 'WMM2010_Shapefile_15min_for_NGA.zip').

B-8

-------
Lat, Long of runway
reciprocal end

X2°, Y2°

Runway Length = I

East-West Axis

(3)

X30, Y30

Lat, Long of'
runway centroid

Xi°, Yi°
Lat, Long of
runway base end

%0 _ ^-0 + G/2)sinfl
2 3 111,112

,0 _ Yo ¦ (l/2)cosQ
* 111,112cosX30

*1° = *3° +

(//2)sin0
111,112

n° = ^ +

(//2)cos0
111,112cosX2°

(4)

(5)

(6)

Figure 2. Calculation of Runway Latitude and Longitude Coordinates for Category IV

V. For 41 runways at 41 facilities, the runway ID in the 5010 runway data report was
"ALL/WAY" (i.e., the runways were not identified with a runway magnetic heading
because aircraft can take off and land in many directions). An additional facility had an
ALL/WAY runway and a helipad. These facilities were all designated as seaplane bases
and ultralight24 facilities. The 5010 runway data report contained data on the length and
width of each runway. Assuming the facility latitude and longitude was located at the
center of the ALL/WAY runway and using the runway length and width data, coordinates
for the four vertices of a rectangle were calculated25: the rectangle was assumed to be

24	Ultralight facilities have activity by ultralight vehicles. The parameters defining an ultralight vehicle are set
forth in 14 CFR 1.1. Among other limitations, ultralight vehicles are used or intended for use by a single occupant,
weigh less than 155 pounds, if unpowered (254 pounds, if powered), and have a fuel capacity not exceeding 5 U.S.
gallons (http://www.ecfr.gov/current/title-14/part-103).

25	In this analysis these facilities were modeled with a rectangular runway area since the dimensions of the
runway area that were given were length and width.

B-9

-------
oriented such that the four sides ran north-south or east-west and that the two
hypotenuses of the rectangle represented /in Figure 1. The runway length was assigned
from East to West and the runway width was assigned the distance from North to South.
The method described in III above was then used to calculate the two latitude/longitude
pairs for the ends of each hypotenuse, after geometrically determining the angle, 9,
between each hypotenuse and the east-west mid-line of the rectangle (based on the given
length and width). The four latitude/longitude pairs were calculated and connected with
the 'minimum bounding geometry' tool (using the convex output type option) in ArcGIS
to generate a rectangular polygon, which represented the possible landing and take-off
paths at these facilities.26 The rectangular polygons comprised the runway layer for these
facilities.

VI. For 1,881 runways at 856 multi-runway facilities, the 5010 airport data report provided
the airport centroid coordinates, which were used to create the facility layer for these
facilities.27 These facilities had runways which were in a parallel configuration at some
airports, while others had runways that intersected at varying angles or were
perpendicular or some combination of these configurations. Additionally, some of these
facilities had one or more helipad. Therefore, the centroid coordinates could not be used
to calculate the coordinates of the runway ends as was done for category IV facilities.
Instead, the coordinate points comprised the facility layer for these facilities.

VII. There were 5,387 heliports28 with only one helipad and 202 heliports with more than one
helipad. The heliport centroid coordinates from the 5010 airport data report were the
only location data available, and this centroid location was used to create the facility
layer for heliports. For heliports with one helipad, these centroid coordinates provide a
reliable identification of the helipad location. For heliports with multiple helipads, visual
inspection of a subset of the 202 multi-helipad facilities (using Google Earth software)
suggested that there is no standard layout for the location of helipads at airfields with
multiple helipads and they were largely removed from densely populated areas by
significant setbacks or because the facility is in a rural area. The centroid provided in the
5010 report was used for this small subset of facilities as the best available data.

26 It was assumed that the runway length represented the distance from East to West and the width represented
the distance from North to South.

27 U.S. Department of Transportation (2004) FAA Advisory Circular 150/5200-35, 5/20/2004, 'Submitting the
Airport Master Record in Order to Activate a New Airport.'

28 A heliport is a facility with only helipads, so these facilities are separate from airports with runways that also
have a helipad (which we have characterized in categories IV - VI in this document). For airport facilities that also
have a helipad, we are not separately evaluating the population in a buffer around the helipad since the buffer around
the runway would include the helipad at an airport facility.

B-10

-------
2.2 Creation of Airport Buffer Layer

For runways in categories I - IV above (15,156 runways at 13,183 facilities), 500 m round-
end buffers, termed 'whole perimeter buffers' in this analysis, were created around each element
in the runway layer using the ArcGIS 'buffer' tool. As described in the air quality modeling and
monitoring studies by Carr et. al., 2011 and Feinberg, et. al., 2016,29 the maximum impact area
for ground-based lead emissions from piston-engine powered aircraft occur at a standardized
location at or near each runway end where preflight run-up checks and take-off operations occur.
In order to identify the population most highly exposed to ground-based emissions from aircraft
during preflight run-up checks and take-off operations, an end-of-runway buffer was created.
This was accomplished by first creating 500 m flat-end buffers around each runway line using
the ArcGIS buffer tool. The 'symmetrical difference' tool was then used to subtract the 500 m
flat-end buffers from the 500 m round-end buffers, creating 'end-of-runway buffers.' The end-
of-runway buffers are effectively two semicircles with a 500 m radius, with centers at each end
of a runway (Figure 3).

Whole Perimeter Buffer	End of Runway Buffer

Buffer

29 Feinberg, S., Heiken, J., Valdez, M., Lyons, J., Turner, J. (2016) Modeling of lead concentrations and hot
spots at general aviation airports. Transportation Research Record: Journal of the Transportation Research Board,
No. 2569, Transportation Research Board, Washington, D.C., 2016, pp. 80-87..

B-ll

-------
For the category V runways (42 facilities), which are the "ALL/WAY" facilities, 500 m
buffers were created around each rectangle runway shape in the runway layer using the ArcGIS
buffer tool (Figure 4). Since aircraft can take off in any direction from these runways, no 'end-
of-runway buffer' was created.

Buffer

ALL/WAY

Figure 4. Buffer around ALL/WAY Airport Facilities in Category V

For the category VI runways (856 facilities), the only data available from which to determine
the size of the buffer layer were the length of the runways. We calculated the average length of
the runways at these facilities (737 m) and chose to generate a 1,000 m radius circular buffer
around each facility centroid coordinate pair in the facility layer (Figure 530). In section 4 we
discuss the resulting uncertainties inherent in this approach.

Runway

Buffer

Runway

Airport

Centroid

Coordinates

Figure 5. Buffer for Facilities with Multiple Runways and Only Airport Centroid
Coordinate Data Available for Category VI

30 Note that the geographic location of runways in category VI are not available; the runways drawn in this figure
are hypothetical and for illustrative purposes only.

B-12

-------
For the category VII helipads at heliports (5589 facilities), 50 m buffers around the heliport
centroid coordinate pairs in the facility layer were generated using the buffer tool in ArcGIS
(Figure 6).

Figure 6. Buffer Layer for Heliports with One or More Helipad for Category VII

2.3 Creation of U.S. Census Block Population Layer

Using ArcGIS 10.0, 2010 U.S. Census Summary File l31 tabular data at the block level was
joined with the 2010 U.S. Census TIGER/Line Shapefiles32 geospatial data at the Census block
level to create the population layer used in this analysis.

2.4 Creation of Education Facility Layers

Public and private school data for grades kindergarten through twelfth grade (K-12th grade)
were obtained from the U.S. Department of Education's Institute of Education Sciences National
Center for Education Statistics.33-34 At the time this analysis was conducted, the most recent
public school data available were for the academic year 2010 - 2011 and the most recent private
school data available were for academic year 2009 - 2010. The public school and private school
databases contained latitude and longitude coordinates of the reported school physical
addresses,35-36 which were imported into ArcGIS as point data.

Data for the location of all Head Start facilities (including Head Start, Early Head Start, and
Migrant and Seasonal Head Start facilities) were obtained from the Department of Health and
Human Services, Office of Head Start. The data contained latitude and longitude coordinates for
each facility. Facility enrollment data were not available.

31	2010 Census Summary File 1 [United States]/prepared by the U.S. Census Bureau, 2011 (accessed from:
http://mcdc.missouri.edu/cgi-bin/uexplore7/pub/data/sfl2010).

32	Accessed from: http://www.census.gov/cgi-bin/geo/shapefiles2010/main.

33	http://nces.ed. gov/ccd/bat/.

34	http://nces.ed.gov/surveYs/pss/pssdata.asp.

35	https://nces.ed.gov/ccd/CCDLocaleCode.asp.

36	http://nces.ed.gov/pubs2011/2011322.pdf.

Buffei

Heliport
Centroid
Coordinates

B-13

-------
2.5 Intersection Analysis

Whole Perimeter Analysis

The 500 m 'whole perimeter' buffers for runways in categories I - IV, as well as the buffers
for the category V - VII facilities were intersected with the population and education facility
layers. Census block populations were included in the final population count if any part of a
census block intersected the airport buffer. People living in census blocks that intersected the
buffers of more than one facility or runway were included only once. The total population, by
race, and the population of children 5 and younger in census blocks that intersected the 500 m
whole perimeter buffers were calculated.

End-of-Runway Only Analysis

The 500 m 'end-of-runway' buffers for runways in categories I - IV were intersected with the
population and education facility layers. As with the whole-perimeter analysis, census block
populations were included in the final population count if any part of a census block intersected
the airport buffer; and, as with the whole perimeter analysis, people living in census blocks that
intersected more than one facility or runway buffer were included only once. The total
population, by race, and the population of children 5 and younger in census blocks that
intersected the 500 m end-of-runway buffers were calculated. End-of-runway buffers could not
be created for category V - VII facilities because the precise location of the runway at these
facilities was not known.

3.0 Results

Data comparing the population residing near an airport runway and/or heliport with the total
U.S. population are shown in Tables 1 and 2 for the entire population and those 5 years of age
and under, respectively. These data indicate that 5,179,000 people live in census blocks that
intersected the 500 m whole perimeter buffers, 363,000 of whom are children age 5 and under.

Table 1: 2010 U.S. Population, by Race, Residing in Census Blocks that Intersect 500-

meter Whole-

'erimeter

Suffers and 2010 U.S. Total Population, by Race

Total

Population

White,
alone

Black or
African
American
, alone

American
Indian or
Alaska
Native,
alone

Asian,
alone

Native

Hawaiian

or Other

Pacific

Islander,

alone

Some
Other
Race,
alone

Two or

Races

U.S. Population
Residing in
Airport 500 m
Whole-Perimeter
Buffers

5,179,000

4,134,000
(79.8%)

463,000
(8.9%)

78,000
(1.5%)

154,000
(3.0%)

8,000
(0.2%)

215,000
(4.2%)

127,000
(2.5%)

Entire U.S. 2010
Population

308,746,000

223,553,000
(72.4%)

38,929,000
(12.6%)

2,932,000
(1.0%)

14,674,000
(4.8%)

540,000
(0.2%)

19,107,000
(6.2%)

9,009,000
(2.9%)

B-14

-------
Table 2: Number of Children 5 Years and Under, by Age, Residing in Census Blocks
that Intersect 500-meter Whole-Perimeter Buffers and U.S. Total Population 5 Years and
Under, by Age								



Total

Population
5 years and
under

Under 1
year

Age 1 year

Age 2
years

Age 3
years

Age 4
years

Age 5 years

U.S. Population 5 Years and
Under Residing in Airport
500 m Whole-Perimeter
Buffers

363,000

58,000
(16.0%)

59,000
(16.3%)

61,000
(16.8%)

62,000
(17.1%)

61,000
(16.8%)

62,000
(17.1%)

Entire U.S. 2010 Population 5
Years and Under

24,258,000

3,944,000
(16.3%)

3,978,000(1
6.4%)

4,097,000
(16.9%)

4,119,000
(17.0%)

4,063,000
(16.8%)

4,057,000
(16.7%)

Data comparing those residing in census blocks that intersect the 500 m end-of-runway
buffers with those residing in census blocks that intersect the 500 m whole-perimeter buffers are
compared in Tables 3 and 4 for the entire population and those 5 years of age and under,
respectively. This analysis indicates that 3,630,000 people live in census blocks that intersected
the 500 m end-of-runway buffers (89% of the population that lives in census blocks that
intersected the 500 m whole-perimeter buffers at the same set of airports). Among this
population, 261,000 were children age 5 and under.

Table 3: 2010 U.S. Population, by Race, Residing in Census Blocks that Intersect 500-
meter End-of-Runway Buffers and Whole-Perimeter Buffers (category I - IV facilities

only)37								i	



Total

Population

White,
alone

Black or
African
American,
alone

American
Indian or
Alaska
Native,
alone

Asian,
alone

Native

Hawaiian or

Other

Pacific

Islander,

alone

Some
Other
Race,
alone

Two or

More

Races

U.S. Population Residing
in Airport 500 m End-of-
Runway Buffers

3,630,000

2,955,000
(81.4%)

302,000
(8.3%)

57,000
(1.6%)

82,000
(2.3%)

5,000
(0.1%)

143,000
(3.9%)

85,000
(2.3%)

U.S. Population Residing
in Airport 500 m Whole-
Perimeter Buffers

4,078,000

3,281,000
(80.4%)

344,000
(8.4%)

68,000
(1.7%)

107,000
(2.6%)

7,000
(0.2%)

171,000
(4.2%)

100,000
(2.5%)

3 End-of-runway buffers were not able to be generated for category V, VI, or VII airport facilities, therefore the
population which resides in census blocks that intersect the 500 m whole-perimeter buffers from only category I -
IV airport facilities is shown in row two in order to enable comparison of the results of the two buffer types across
the same set of airports.

B-15

-------
Table 4: Number of Children 5 Years and Under, by Age, Residing in Census Blocks
that Intersect 500-meter End-of-Runway Buffers and Whole-Perimeter Buffers (category I
- IV facilities only)38 							



Total

Population
5 years and
under

Under 1
year

Age 1
year

Age 2
years

Age 3
years

Age 4
years

Age 5
years

U.S. Population 5 Years and
Under Residing in Airport 500
m End-of-Runway Buffers

261,000

41,000
(15.7%)

42,000
(16.1%)

43,000
(16.5%)

45,000
(17.2%)

45,000
(17.2%)

45,000
(17.2%)

U.S. Population 5 Years and
Under Residing in Airport 500
m Whole-Perimeter Buffers

293,000

46,000
(15.7%)

48,000
(16.4%)

49,000
(16.7%)

50,000
(17.1%)

50,000
(17.1%)

51,000
(17.4%)

The total number of schools (K-12th grade) and student enrollment, by race/ethnicity, of
public and private schools that intersected the 500 m whole-perimeter buffers is shown in Table
5, below. This analysis indicates that 163,000 K-12th grade students attend the 573 public and
private schools that intersected the 500 m whole-perimeter buffers. The bottom half of the table
provides private and public school and enrollment data for the entire U.S.

38 Similar to Table 3 and as described in footnote 40, Table 4 presents data for only category I - IV airport
facilities in order to enable comparison of the two buffer types across the same set of airports.

B-16

-------
Table 5: Number of Schools (Public and Private) and Enrollment, by Race/Ethnicity, at
Schools that Intersect 500-meter Whole-Perimeter Buffers and at All U.S. Schools (Public
and Private)39								



Number
of

Schools

Total

Student

Enrollment

White
Students

Black
Students

Americ
an

Indian/
Alaska
Native
Student

s

Asian/

Native

Hawaiian/

Pacific

Islander

Students40

Hispanic
Students

Two or
More
Races
Students

Private Schools
within 500 m
Whole-
Perimeter
Buffers

115

15,000

10,000
(66.7%)

1,000
(6.7%)

Less
than 100

(0%)

1000
(6.7%)

2,000
(13.3%)

Less than

500

(2%)

Public Schools
within 500 m
Whole-
Perimeter
Buffers

458

147,000

92,000
(62.6%)

16,000
(10.9%)

5,000
(3.4%)

5,000
(3.4%)

26,000
(17.7%)

4,000
(2.7%)

TOTAL

573

163,000

101,000

17,000

5,000

6,000

28,000

4,000



















Total Private

School

Population

28,000

5,013,000

3,104,000
(61.9%)

397,000
(7.9%)

20,000
(0.4%)

249,000
(5.0%)

416,000
(8.3%)

119,000
(2.4%)

Total Public

School

Population

100,000

49,049,000

25,704,000
(52.4%)

7,812,000
(15.9%)

560,000
(1.1%)

2,442,000
(5.0%)

11,326,000
(23.1%)

1,153,000
(2.4%)

TOTAL

128,000

54,062,000

28,808,000

8,209,000

579,000

2,690,000

11,742,000

1,272,000

The total number of schools (K-12th grade) and student enrollment, by race/ethnicity, of
public and private schools that intersected the 500 m end-of-runway buffers are shown in the top
half of Table 6. This analysis indicates that 77,938 K-12th grade students attend the 254 public
and private schools that intersected the 500 m end-of-runway buffers (compared to the 120,892
K-12th grade students who attend the 383 schools that intersected the whole-perimeter buffers at
the same set of airport facilities).

39 End-of-runway buffers were not able to be generated for category V, VI, or VII airport facilities, therefore the
total number of schools (K-12th grade) and student enrollment, by race/ethnicity, of public and private schools that
intersected the 500 m whole-perimeter buffers from only category I - IV airport facilities is shown in the bottom
portion of the Table 6 in order to enable comparison of the results of the two buffer types across the same set of
airports.

411 The public school data had a race/ethnicity category labeled 'Asian and Pacific Islander Students' while the
private school data had a race/ethnicity category labeled 'Asian Students' and a separate category labeled 'Native
Hawaiian and Pacific Islander Students.' In order to combine the results of the private and public school analysis, in
this table the 'Asian/Native Hawaiian/Pacific Islander Students' column contains results from the public school data
that correspond to the 'Asian and Pacific Islander Students' category and from the private school data that
correspond to the sum of the counts from the 'Asian Students' and 'Native Hawaiian and Pacific Islander Students'
categories.

B-17

-------
Table 6: Number of Schools (Public and Private) and Enrollment, by Race/Ethnicity, at
Schools that Intersect 500-meter End-of-Runway Buffers and Whole-Perimeter Buffers
(category I - IV facilities only)41 42 					



Number
of

Schools

Total

Student

Enrollment

White
Students

Black
Student

s

American
Indian/Alaska
Native
Students

Asian/

Native

Hawaiian

/ Pacific

Islander

Students

43

Hispanic
Students

Two or
More
Races
Students

Private Schools
within 500 m
End-of-
Runway
Buffers

48

5,443

3,564
(65%)

254
(5%)

17

(<1%)

242
(4%)

480

(9%)

48
(1%)

Public Schools
within 500 m
End-of-
Runway
Buffers

206

72,495

44,656
(62%)

8,463
(12%)

973
(1%)

2,503
(3%)

14,310
(20%)

1,590
(2%)

TOTAL

254

77,938

48,220

8,717

990

2,745

14,790

1,638



















Private Schools
within 500 m
Whole-
Perimeter
Buffers

92

11,568

7,211
(62%)

812

(7%)

49

(0%)

580
(5%)

1,273
(11%)

207
(2%)

Public Schools
within 500 m
Whole-
Perimeter
Buffers

383

120,892

75,717
(63%)

12,065
(10%)

3,711
(3%)

4,517
(4%)

21,815
(18%)

3,067
(3%)

TOTAL

475

132,460

82,928

12,877

3,760

5,097

23,088

3,274

41	End-of-runway buffers were not able to be generated for category V, VI, or VII airport facilities, therefore the
total number of schools (K-12th grade) and student enrollment, by race/ethnicity, of public and private schools that
intersected the 500 m whole-perimeter buffers from only category I - IV airport facilities is shown in the bottom
portion of the Table 6 in order to enable comparison of the results of the two buffer types across the same set of
airports.

42	End-of-runway buffers were not able to be generated for category V, VI, or VII airport facilities, therefore the
total number of schools (K-12th grade) and student enrollment, by race/ethnicity, of public and private schools that
intersected the 500 m whole-perimeter buffers from only category I - IV airport facilities is shown in the bottom
portion of the Table 6 in order to enable comparison of the results of the two buffer types across the same set of
airports.

43	The public school data had a race/ethnicity category labeled 'Asian and Pacific Islander Students' while the
private school data had a race/ethnicity category labeled 'Asian Students' and a separate category labeled 'Native
Hawaiian and Pacific Islander Students.' In order to combine the results of the private and public school analysis, in
this table the 'Asian/Native Hawaiian/Pacific Islander Students' column contains results from the public school data
that correspond to the 'Asian and Pacific Islander Students' category and from the private school data that
correspond to the sum of the counts from the 'Asian Students' and 'Native Hawaiian and Pacific Islander Students'
categories.

B-18

-------
In addition to evaluating a potential in racial disparity among the children attending schools
near airports, this analysis would ideally inform whether there is a socioeconomic disparity
among the children attending schools near airports compared with the US school population
generally. There are minimal data available in the U.S. Census at the block level to evaluate this
question; data regarding free and reduced-price school lunches was used as a surrogate here for
potential socioeconomic disparity. The total number of students (K-12th grade) eligible for free
or reduced-price school lunches who attend public schools that intersected the 500 m whole-
perimeter and end-of-runway only buffers is shown in Table 7. This analysis indicates that at the
public schools that intersected the 500 m whole-perimeter buffers, 67,000 of the K-12th grade
students were eligible for free or reduced-price school lunches. The bottom half of Table 7
indicates that at the public schools that intersected the 500 m end-of-runway buffers, 34,000 of
the K-12th grade students were eligible for free or reduced-price school lunches (equal to 51% of
the K-12th grade students who were eligible for free or reduced-price school lunches at schools
that intersected the 500 m whole-perimeter buffers at the same set of airports).

Table 7: Number of Free and Reduced-Price School Lunch Eligible Students at all U.S.
Public Schools and at Public Schools that Intersect 500-meter Whole-Perimeter Buffers

,44



Number of Students
Eligible for Reduced-price
School Lunches

Number of Students
Eligible for Free School
Lunches

Total Number of
Students Eligible for Free or
Reduced- Price School
Lunches

Total U.S. Public
School Population

3,400,000
(7%)

20,082,000
(41%)

23,483,000
(48%)









Public Schools
within 500 m Whole-
Perimeter Buffers (all
airport categories)

11,000
(8%)

56,000
(38%)

67,000
(45%)









Public Schools
within 500 m End-of-
Runway Buffers (only
category I - IV
facilities)

5,000
(8%)

29,000
(40%)

34,000
(47%)

Public Schools
within 500 m Whole-
Perimeter Buffers (only
category I - IV
facilities)

9,000
(8%)

47,000
(39%)

56,000
(47%)

44 End-of-runway buffers were not able to be generated for category V, VI, or VII airport facilities, therefore the
total number of students (K - 12th grade) eligible for free or reduced-price lunches who attend public schools that
intersected the 500 m whole-perimeter buffers from only category I - IV airport facilities is shown in the bottom
portion of Table 7 in order to enable comparison of the results of the two buffer types across the same set of airports.

B-19

-------
The intersection of the Head Start preschool facilities with the 500 m whole perimeter buffers
showed that 92 out of the 16,794 Head Start Facilities (including Head Start, Early Head Start,
and Migrant and Seasonal Head Start facilities) were located within the 500 m whole-perimeter
buffers.45 The analysis of end-of-runway buffers identified 37 Head Start Facilities (compared to
84 for the whole-perimeter buffers for the same set of airport facilities) within the 500 m end-of-
runway buffers.

4.0 Discussion

This section describes data limitations and sources of uncertainty in the demographic analysis
method provided for airports in this report. We first describe the portion of the total populations
reported in Table 1 that are derived from each of the methods used to create airport layers, I-VII,
described above (this information is also summarized in Table A-l). We then discuss the
uncertainty in population included as living near a runway in urban versus rural areas and lastly,
we describe uncertainty in the precise location of educational facilities.

4.1 Uncertainties in Developing Runway Layers

Geospatial data were available for 4,146 airport facilities, which are typically the busiest
airports in the U.S.; method I was used for these facilities. The majority of these facilities are at
airports that FAA considers significant to national air transportation and are therefore listed in
the FAA National Plan of Integrated Airport System (NPIAS). These airports tend to be located
in more densely populated areas of the country compared with the other roughly 15,000 airport
facilities in the U.S. The population residing near the 4,146 facilities accounts for 35% of the
population residing near any U.S. airport facility (Table A-l), as calculated in this analysis. For
methods II, III and IV, the data provided in the 5010 airport data report and 5010 runway data
report were assumed to provide an accurate record of the data elements needed to draw the
runway line. Uncertainty in the creation of these runway layers is limited to the accuracy of the
data provided to FAA for runway length, base and/or reciprocal end coordinates, airport centroid
coordinates, and magnetic heading. The approach applied in methods II, III and IV accounts for
44% of the population reported in this analysis. Collectively, the most robust data available for
developing runway layers (i.e., methods I through IV) accounted for 79% of the population
residing near 13,132 airport facilities (approximately 68% of all U.S. airport facilities).

Facilities for which method V was used are largely seaports where aircraft are landing and
taking off from water in a near-shore environment, and we introduced uncertainty in the
population counts by assuming that the landing and take-off areas were rectangles oriented with
the reported length along the due east-west axis and the reported width along the due north-south
axis. If the landing and take-off areas were rotated around the north-south axis or if the length
and width were switched, the specific census blocks included in the population count could vary,
resulting in either an under- or over-estimate of the population. This method was used for 41

45 Enrollment data are not available for the Head Start facilities.

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facilities and accounts for 1% of the total population reported in this analysis. While alternative
assumptions could be made regarding runway orientation, it is expected that since the population
living near these facilities is limited to onshore locations, different runway orientations with the
requisite buffer would likely include the relevant census block(s). In addition, given the small
number of facilities characterized using this method, we anticipate that the assumptions made do
not impart a significant source of uncertainty in the overall results of the population analysis
presented in this report. When conducting an analysis of potentially impacted populations near a
specific seaport, data could be collected regarding dominantly used landing and take-off patterns.

The method used to create airport layers for category VI facilities creates uncertainty in the
population estimates since buffers were drawn relative to the centroid of the airport facility
instead of relative to the actual runways. The approach applied using this method accounts for
7% of the population reported in this analysis. As described above, for the method applied to
these facilities we generated a 1,000 m radius circular buffer around the facility centroid. On
average, the runway length at all of these facilities was 737 m with a minimum runway length of
61m and a maximum runway length of 3,200 m.46 Therefore, the method used and the selected
1,000 m distance led to instances when the population included in the demographic count was
from an area more distant than 500 m from the runway end and in other cases where the runway
length extended beyond the 1,000 m buffer and the relevant population was therefore not
included. Of the 856 facilities in this category, 789 (92%) are in areas defined as rural by the
U.S. Census Bureau and therefore have low population densities.47 We expect that the method
used to estimate people living near these facilities is a reasonable approach for the purpose of
conducting a national estimate of people living near airport facilities.

Beyond the specific methods used to create runway buffer layers, it is worth noting that for
category I - IV facilities, runways were treated as lines.48 In actuality, runways are rectangles
with a width element. If the buffers had been drawn relative to the edges of the runway rectangle
instead of the centerline, the buffers would have extended farther and in some instances would
have intersected additional census blocks. In the March 5, 2013 version of the FAA 5010
runway data report the runways at airports had an average width of 92 feet.49 Therefore on
average, the buffers would have extended an additional 14 m in all directions if they had been
drawn relative to the edges of runway polygons as opposed to the runway centerline.

46	As noted earlier, some of these facilities had one or more helipad and, in these cases, the "runway" length is
the width of the helicopter landing area.

47	The U.S. Census Bureau defines urban areas as densely settled core areas of census tracts with a density of
more than 1,000 persons per square mile (ppsm) as well as census tracts that are contiguous to the core area and that
have a population density of at least 500 ppsm; all remaining territory not included within an urban area is classified
as rural, (from: "Urban Area Criteria for the 2010 Census" Department of Commerce Bureau of the Census, 76 FR
53030-53043 (August 24, 2011)).

48	As described in section 2.0, buffers for category VI and VII facilities were drawn relative to the facility
centroid point, therefore this uncertainty does not apply to those facilities. Buffers for category V facilities were
drawn in a manner that incorporated the length and width elements, therefore this uncertainty does not apply to these
facilities.

49	Runways specifically at airports were analyzed by limiting the runway records to only those where the 'Site
Number' variable ended in an 'A,' which is the identifier used by the FAA for airports. Runway records where the
Site Number variable, for example, ended in an 'H' belonged to heliports and were therefore excluded.

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Uncertainty related to the category VII facilities (heliports), is attributable to the relative scale
of the buffers used around these facilities (50 m) and the much larger size of census blocks
(which vary with population density). As a result, we anticipate that in general, the analysis
conducted may overestimate populations that live within 50 m of a helipad. In contrast, the
method used to create airport layers for the 202 heliports with more than one helipad is expected
to result in an underestimate of the population in this analysis because the selection of a single
centroid may exclude relevant helipad locations and nearby populations from this analysis. This
underestimate is likely mitigated by the fact that several of these heliports have significant
setbacks between helipads and populated areas.

4.2 Uncertainty Associated with the Estimate of Population Living Near a Runway

Uncertainty is associated with the estimate of people living near a runway because census
block populations were included in the total population count if any part of a census block
intersected the 500 m airport/runway buffer. Census blocks are the smallest geographic unit that
contains demographic data such as total population by age, sex, and race.50 The U.S. Census
Bureau describes census block size as follows51: "Generally, census blocks are small in area; for
example, a block in a city bounded on all sides by streets. Census blocks in suburban and rural
areas may be large, irregular, and bounded by a variety of features, such as roads, streams, and
transmission lines. In remote areas, census blocks may encompass hundreds of square miles."

Since census block sizes differ greatly from urban to rural areas and airports are found in both
urban and rural areas, we evaluated uncertainty in the population classified as living near a
runway separately for urban and rural airports. We analyzed a subset of California airports:
those categorized in Section 2.0 as method I airports (which provides a representative sample of
airports in urban areas) and method IV airports (which represent mostly airports in rural areas).
We selected California for this evaluation because this state has the second largest number of
airport facilities among states in the US (965 airport facilities). Airports were classified as urban
or rural based on US Census Bureau urban-rural classification boundaries.52 The U.S. Census
Bureau defines urban areas as densely settled core areas of census tracts with a density of more
than 1,000 persons per square mile (ppsm) as well as census tracts that are contiguous to the core
area and that have a population density of at least 500 ppsm; all remaining territory not included
within an urban area is classified as rural.53

For this analysis we calculated the sum of the area for all census blocks intersecting each of
the 500 m buffers around the California method 1 and 4 airport runways. We made the
simplifying assumption that the total area of the census blocks intersecting the runway buffer is
equidistant from the runway.54 This simplifying assumption allows us to estimate the

50	https://www.censiis.gov/newsrooiii/blogs/random-samplings/2011/07/what-are-censiis-btocks.html.

51	http://www.censiis.gov/geo/reference/gtc/gtc block.html.

52	"Urban Area Criteria for the 2010 Census" Department of Commerce Bureau of the Census, 76 FR 53030 -
53043 (August 24, 2011).

53	"Urban Area Criteria for the 2010 Census" Department of Commerce Bureau of the Census, 76 FR 53030 -
53043 (August 24, 2011).

54	This assumption is more valid in urban areas than in rural areas.

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approximate distance that the population included in this analysis could live from a runway. The
total area of a 500 m buffer around a 1,000 m runway is 1.79 km2. If the summed census block
area for a typical airport with a 1,000 m runway is 3.57 km2, and if the area is equidistant from
the runway, then people living in these census blocks reside up to 794 m from the runway. For
the analysis presented here, actual runway lengths and their associated buffer areas were used.

Among the 103 airports in urban areas (from method 1 in California), the average summed
census block area for those census blocks intersecting 500 m runway buffers is 2.9 times larger
than the area of the 500 m buffers around the runways at these airports. Making the simplifying
assumption that the total area of the census blocks intersecting a runway buffer is distributed
equidistant around the runway, this average summed census block area suggests that people in
these census blocks live within 1,005 m of the runway. This suggests that in urban areas, the
method described in this report captures the relevant population living near airports that may
potentially experience an increase in lead concentration from aircraft emissions.

Among the 229 rural runways in California (method IV), the average summed census block
area for those census blocks intersecting 500 m runway buffers was 23.3 times larger than the
area of the 500 m buffer around these runways. Making the simplifying assumption that the total
area of the census blocks intersecting a runway buffer is distributed equidistant around the
runway, this average summed census block area suggests that people in these census blocks live
within 2,441 m of the runway. This suggests that in rural areas, the method used is including
people who live beyond the distance at which direct emissions from aircraft emissions may cause
elevated concentrations of lead. Since these rural census blocks are sparsely populated, we
expect that the misclassification of people imparts a small bias in the analysis. For example, in
California, the airport runway that intersected census blocks with the largest summed area
contributed 19 people to the analysis results (compared to an average of 1,372 to 1,675 people
per runway in the urban airports in methods I and IV, respectively).55 While there are a large
number of rural airports at which the method described in this report might include people who
live distant from an airport, comparisons with an alternative approach described below (i.e.,
dasymeteric data), indicate the approach used here appropriately estimates the number of people
who live in rural areas near a runway.

Methods exist to estimate the number of people residing only in the portion of a census block
intersecting a runway buffer. For example, one could assume that population density is constant
throughout each census block and include only the fraction of a census block population equal to
the fraction of the area of the census block that intersected the buffer. An alternative approach
for estimating the population near an airport is to include the population of a census block only if
the centroid of the block falls within the 500 m buffer. We elected not to use these methods, in
part due to the computational burden, but also in recognition that there are multiple approaches
to achieve the results desired for the purpose of conducting a national estimate of the population
residing near airports.

55 This analysis was based only on California airports in Method IV. At this particular airport the census blocks
that intersected the runway buffer had a total area 758 times larger than the airport's runway buffer; the census
blocks had an average density of 0.014 people per km2.

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A second approach for this assessment was evaluated as a sensitivity analysis; this approach
involved the use of more spatially refined population data developed by EPA's Office of
Research and Development (ORD). EPA's ORD has applied the dasymetric geospatial
population mapping technique to 2010 U.S. census block level data by distributing census block
population to 30 m square areas based on land cover, slope, and ownership data.56 These data
were created for use in EPA's EnviroAtlas57 which has been externally peer reviewed. In order
to further understand the potential uncertainty in population counts using the method described
in this report, we conducted a sensitivity analysis using dasymetric data for method I airports
(described in Section 2.0) for California. We analyzed the population near airports in urban
areas separately from those in rural areas and compared the results to the population counts using
the method described in this report.

Using the dasymetric data, we summed the population in California for method I urban
airports using a runway buffer area of approximately 700 m. This summed population of 193,000
people compares closely with the 194,000 people residing in census blocks intersecting the 500
m buffer for method 1 urban airports. However, the two methods differ somewhat in the
residences that are counted as being near a runway beyond the 500 m buffer; the analysis using
the dasymetric data estimated the population in discreet 30 meter buffer zones from a runway,
while the method described in this report includes residences throughout irregularly shaped
census blocks, some of which may occupy area that is more than 1,000 m from a runway. One
advantage of using the census block data for the purposes of this analysis is the availability of
demographic characteristics by census block. The dasymetric data do not include age or racial
characteristics of the population.

Using the dasymetric data, the summed population in a runway buffer area of approximately
950 m provided a population estimate equivalent to that from our method for rural Method I
California runways (45,484 people using dasymetric and 45,851 people using our method).

Given the analysis described above, at rural runways in California method I the total census
block areas were on average 16 times larger than the 500 m buffer area, which suggests that the
population in rural areas with a runway tend to live in the portions of census blocks that are in
closer proximity to the runway. This sensitivity analysis suggests the method described in this
paper provides a reasonable approach for estimating the rural population living near runways.

4.3 Uncertainty Associated with Census Data and School Point Data

In addition to uncertainty in the methods used in this report, there is uncertainty associated
with the input datasets. The US Census Bureau recognizes uncertainties inherent to US Census
Data reported and US Census Bureau researchers explore approaches to improve accuracy and
reduce uncertainty. Sources of error in the census total count and demographics and include
omissions, duplications, erroneous enumerations, and errors of geography and demographic

56 The method incorporated the National Land Cover Dataset (NLCD) with the assumption that individuals will
not live in areas that are classified as open water, ice/snow, or wetlands. Additionally, public lands and areas with
slopes greater than 25% were also considered uninhabitable. Other vegetated and developed areas were considered
habitable and were assigned population density probabilities based on land cover class, (from:

https://www.epa.gov/enviroatlas/dasvmetric-toolbox').

57 http://enviroatlas.epa.gov/enviroatlas/.

B-24

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characteristics. The Census Bureau employs approaches to measure error including dual-systems
estimation and demographic analysis.58 These uncertainties are not expected to have a
significant impact on the results presented in this report.

Since children are a highly susceptible population to the uptake and impacts of lead, we
included an evaluation of the proximity of schools and preschools to airport runways. U.S. public
and private K-12th grade school data and Head Start preschool data were only available as point
data (i.e., represented by a single latitude/longitude pair), which was intersected with the airport
buffer layers. However, many school campuses have multiple sports fields and/or playground
areas and can cover large areas of land. The results of the intersection analysis, therefore, are
subject to uncertainty since inclusion of a K-12th grade school or Head Start preschool is
dependent on where the school coordinates fall within the school's actual campus.

In addition, the Head Start preschool data represent only a subset of early education and care
programs that serve children and infants. There are additionally the center-based, school-based
and in-home preschool facilities for which there is no national database available for this
analysis. The absence of information regarding proximity of these facilities to aircraft lead
emissions may significantly underestimate this potentially exposed, susceptible population.

58 National Academy of Science, Engineering and Medicine (2007) Research and plans for coverage
measurement in the 2010 Census. National Academy Press, available at: www.nap.edu/download/.1. .1.94.

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APPENDIX

Table A-l: Airport and Population Data by Method of Analysis



Number
of

Runways

Number
of

Facilities

Description
of Available
Data and
Method of
Airport Facility
Layer
Generation

Description
of Buffer
Layer
Generation

Population59

Method
I

6,090

4,146

FAA GIS data.

500 m buffer
around runway line

1,809,131
(35%)

Method
II

414

385

FAA 5010
runway report had
latitude/longitude
coordinates for both
the runway base and
reciprocal ends.

500 m buffer
around runway line

98,113
(2%)

Method
III

4

4

FAA 5010
runway report had
latitude/longitude
coordinates for either
the runway base or

reciprocal end.
Runway length, the
available runway end
coordinates, and the
magnetic heading of
the runway were used
to calculate the
latitude/longitude
coordinates of the
opposite runway end.

500 m buffer
around runway line

624
(0.01%)

Method
IV

8,597

8,597

FAA 5010
runway report did not
have
latitude/longitude
coordinates for either
the runway base or
reciprocal end. These
are facilities with only
one runway so
runway length,
facility centroid
coordinates, and the
magnetic heading of
the runway were used
to calculate the
latitude/longitude

500 m buffer
around runway line

2,195,125
(42%)

59 Numbers in this column do not sum to the analysis total of 5,179,455 people from Table 1 since the population
from a census block that intersects more than one airport buffer is only included once in the Table 1 result but here,
the population from a census block that intersects more than one airport buffer is included in the total for each
method type in the column 'Population' to which it applies.

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coordinates of both











runway ends.











FAA 5010











runway data report
identified the runway
ID as "ALL/WAY."





Method
V

41

41

Centroid coordinate
along with the runway
width and length were
used to calculate the
four coordinate pairs
of the rectangle
representing this
runway area.

500 m buffer
around runway
rectangle polygon

65,124
(1%)







These facilities





Method
VI

1,881

856

are multi-runway
facilities with no
runway specific
coordinates. The
facility centroid
coordinates were used
to create this layer.

1000 m buffer
around facility
centroid

361,577
(7%)







These facilities





Method
VII

5,978

5,589

are heliports. The
heliport centroid
coordinates were used
to create this layer.

50 m buffer
around facility
centroid

740,486
(14%)

Table A-2: Conversion from Runway Designation Markings to 9 (degrees)

Runway
Designation
Marking

0 (in
degrees)

01

260

02

250

03

240

04

230

05

220

06

210

07

200

08

190

09

180

10

170

11

160

12

150

13

140

14

130

15

120

16

110

17

100

18

90

19

80

20

70

21

60

22

50

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Appendix C

Airport Lead Monitoring

T

his Program Update provides a summary of the data currently
available on concentrations of lead measured at 17 airport
facilities in the U.S.

a;
+->

as

U

O-l

P

S

<3
?-

txQ

O
(X

Concentrations of Lead at Airports

Outdoor concentrations of lead have greatly declined over the past few decades, in
large part due to regulations that removed lead from fuels used in cars and trucks.
However, lead continues to be emitted into the air from certain sources, such as ore
and metal processing and aircraft that use leaded aviation gasoline (avgas). These
aircraft are typically used for activities including business and personal travel,
instructional flying, aerial surveys, agriculture, firefighting, law enforcement, medical
emergencies, and express freight. Lead is not contained in jet fuel, which is used by
commercial aircraft.

To protect the public from harmful levels of lead in outside air, EPA has
established a National Ambient Air Quality Standard (NAAQS) for lead. In late 2008,
EPA substantially strengthened this standard, revising the level from 1.5 micrograms
per cubic meter (|ig/m3), to 0.15 |ig/m\ for a 3-month average concentration of lead
in total suspended particles. This revised standard improves health protection for at-
risk groups, especially children.

In conjunction with strengthening the lead NAAQS, EPA improved the existing
lead monitoring network by requiring monitors to be placed in areas with sources
such as industrial facilities and airports. State and local air quality agencies are now
required to monitor near industrial facilities with estimated lead emissions of 0.50
tons or more per year and at airports with estimated emissions of 1.0 ton or more per
year, as well as, on a case-by-case basis in locations where information indicates a
significant likelihood of exceeding the standard. EPA required a 1-year monitoring
study of 15 airports with estimated lead emissions between 0.50 and 1.0 ton per year
in an effort to better understand how these emissions affect the air at and near
airports. Airports for this 1-year monitoring study were selected based on factors such
as the level of piston-engine aircraft activity and the predominant use of one runway
due to wind patterns, in order to help evaluate airport characteristics that could lead to
ambient lead concentrations that approach or exceed the lead NAAQS.

£%	United States

Environmental Protection
^1	Agency

Office of Transportation and Air Quality
EPA-420-F-13-032
June 2013

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As a result of these requirements, lead monitoring has been conducted at 17 airports. As of
May 2013, states and local air authorities have collected and certified lead concentration data for
at least 3 months from the 17 airports. The certified data are available in the table below. EPA
anticipates having a full year of certified data from all 17 airports by May 2014, at which time
the airport study will be complete.

Concentrations of Lead at Airports

Airport, State

Lead Design Value,*
jig/m3

Auburn Municipal Airport, WA

0.06

Brookhaven Airport, NY

0.03

Centennial Airport, CO

0.02

Deer Valley Airport, AZ

0.04

Gillespie Field, CA

0.07

Harvey Field, WA

0.02

McClellan-Palomar Airport, CA

0.17

Merrill Field, AK

0.07

Nantucket Memorial Airport, MA

0.01

Oakland County International Airport, Ml

0.02

Palo Alto Airport, CA

0.12

Pryor Field Regional Airport, AL

0.01

Reid-Hillview Airport, CA

0.09

Republic Airport, NY

0.01

San Carlos Airport, CA

0.33

Stinson Municipal, TX

0.03

Van Nuys Airport, CA

0.06

+->
ci

Two airports have monitored lead concentrations that exceed the lead NAAQS. Fact
sheets specific to these airports have been developed and are available at the EPA Region 9
webpage provided below. Supplemental sampling is being conducted at these two airports to
evaluate lead concentrations at additional locations at and near the airport. Information from

*The design value for lead is the maximum value of three-month average concentrations
measured at that location.

C-2

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other airports that have previously been studied in greater detail indicates that air lead
concentrations decrease within short distances from aircraft emissions.

EPA's Actions Regarding Lead Emissions from Aircraft Operating on Leaded Fuel

EPA is currently conducting the analytical work, including modeling and monitoring, to
evaluate under section 231 of the Clean Air Act whether lead emissions from the use of leaded
avgas in piston-engine aircraft cause or contribute to air pollution which may reasonably be
anticipated to endanger public health or welfare. Any proposed determination with regard to
endangerment would be subject to public notice and comment, and we estimate the final
determination will be in mid-to-late 2015. Additional details regarding EPA's evaluation are
available in the Advance Notice of Proposed Rulemaking on Lead Emissions From Piston-
Engine Aircraft Using Leaded Aviation Gasoline, and the associated public docket (links
provided below).

If EPA makes a final positive endangerment finding (i.e., EPA finds that lead emissions from
general aviation cause or contribute to air pollution which may reasonably be anticipated to
endanger), the agency would initiate rulemaking to establish standards concerning lead
emissions from piston-engine aircraft. FAA would then be required to prescribe regulations to
ensure compliance with such standards, and prescribe standards for the composition of aircraft
fuel to control or eliminate certain emissions.

For Additional Information

For more information regarding monitoring at the San Carlos Airport and San Diego airports
(McClellan-Palomar and Gillespie Field), please visit:

-------
For information on the Federal Aviation Administration's actions to reduce lead
concentrations at airports, please visit:

www.faa.gov/airports/environmental/

For more information on how you can reduce your family's risk of lead exposure, please visit:

www.epa.gov/sites/default/files/2018-02/documents/epaJieadjDrochure-
posterlayout_508.pdf

For more information on lead in air, please visit:

www.epa.gov/airquality/lead/

Contact Marion Hoyer

U.S. Environmental Protection Agency
Office of Transportation and Air Quality
2000 Traverwood Drive
Ann Arbor, MI 48105
734-214-4513,

1 *

E-mail: hoyer.rriarion@epa.gov

Cy

"¦a

Qh Or: Meredith Pedde

U.S. Environmental Protection Agency
Office of Transportation and Air Quality
2000 Traverwood Drive
Ann Arbor, MI 48105

cd

L-j	734-214-4748

bJO

O
u

CU

E-mail: pedde.meredith@epa.gov

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Appendix D

Airport Lead Monitoring

T

his Program Summary provides a full year of lead
concentration data measured at 17 U.S. airport facilities
through December 2013.

2h
'>



o

a

bJO

O
(X

Concentrations of Lead at Airports

Outdoor concentrations of lead have greatly declined over the past few decades, in
large part due to regulations that removed lead from fuels used in cars and trucks.
However, lead continues to be emitted into the air from certain sources, such as ore and
metal processing and aircraft that use leaded aviation gasoline (avgas). These aircraft are
typically used for activities including business and personal travel, instructional flying,
aerial surveys, agriculture, firefighting, law enforcement, medical emergencies, and
express freight. Lead is not contained in jet fuel, which is used by commercial aircraft.

To protect the public from harmful levels of lead in outside air, the U.S.

Environmental Protection Agency (EPA) has established a National Ambient Air Quality
Standard (NAAQS) for lead. In late 2008, the EPA substantially strengthened this
standard, revising the level from 1.5 micrograms per cubic meter (|ig/m3), to 0.15 (J,g/m3,
for a 3-month average concentration of lead in total suspended particles. This revised
standard improves health protection for at-risk groups, especially children.

In conjunction with strengthening the lead NAAQS, in 2010 the EPA improved the
existing lead monitoring network by requiring monitors to be placed in areas with sources
such as industrial facilities and airports. State and local air quality agencies are now
required to monitor near industrial facilities with estimated lead emissions of 0.50 tons or
more per year and at airports with estimated emissions of 1.0 ton or more per year, as
well as, on a case-by-case basis, in locations where information indicates a significant
likelihood of exceeding the standard. The EPA required a 1-year monitoring study of 15
airports with estimated lead emissions between 0.50 and 1.0 ton per year in an effort to
better understand how these emissions affect the air at and near airports. Airports for this
1-year monitoring study were selected based on factors such as the level of piston-engine
aircraft activity and the predominant use of one runway due to wind patterns, in order to
help evaluate airport characteristics that could lead to ambient lead concentrations that
approach or exceed the lead NAAQS.

&EPA

United Slates
Eiwironmenlel Protection
Agency

Office of Transportation and Air Quality
EPA-420-F-15-003
January 2015

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As a result of these requirements and those finalized in 2008, lead monitoring has been
conducted at 17 airports, and states and local air authorities have collected and certified
lead concentration data for at least one year at the 17 airports. The certified data are
summarized in the table below. For all but one airport (the Reid-Hillview airport) the
design value is unchanged from the EPA's 2013 Program Update on Airport Lead
Monitoring, either because no more data were collected or because higher concentrations
were not measured. As a result of the concentrations measured, four airports will
continue monitoring for lead. Additional information is available at the EPA Region 9
webpage provided below.

Concentrations of Lead at Airports

Airport, State

Lead Design Value,*
jig/m3

Auburn Municipal Airport, WA

0.06

Brookhaven Airport, NY

0.03

Centennial Airport, CO

0.02

Deer Valley Airport, AZ

0.04

Gillespie Field, CA

0.07

Harvey Field, WA

0.02

McClellan-Palomar Airport, CA

0.17

Merrill Field, AK

0.07

Nantucket Memorial Airport, MA

0.01

Oakland County International Airport, Ml

0.02

Palo Alto Airport, CA

0.12

Pryor Field Regional Airport, AL

0.01

Reid-Hillview Airport, CA

0.10

Republic Airport, NY

0.01

San Carlos Airport, CA

0.33

Stinson Municipal, TX

0.03

Van Nuys Airport, CA

0.06

* Maximum three-month average concentration in the monitoring dataset

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The EPA's Actions Regarding Lead Emissions from Aircraft Operating on Leaded Fuel

The EPA is currently conducting the analytical work, including modeling and monitoring, to
evaluate under section 231 of the Clean Air Act whether lead emissions from the use of leaded
avgas in piston-engine aircraft cause or contribute to air pollution which may reasonably be
anticipated to endanger public health or welfare. Any proposed determination with regard to
endangerment would be subject to public notice and comment. Additional details regarding the
timing and next steps of the EPA's evaluation are available at: www.epa.gov/otaq/aviatioii.htin.

If the EPA makes a final positive endangerment finding (i.e., the EPA finds that lead
emissions from general aviation cause or contribute to air pollution which may reasonably be
anticipated to endanger), the agency would initiate rulemaking to establish standards concerning
lead emissions from piston-engine aircraft. The FAA would then be required to prescribe
regulations to ensure compliance with such standards, and prescribe standards for the
composition of aircraft fuel to control or eliminate certain emissions.

For Additional Information

For more information regarding monitoring at the San Carlos Airport and San Diego
airports (McClellan-Palomar and Gillespie Field), please visit:

www.epa.gov/regi on9/air/airport-! ead/

.	For more information on the EPA's actions regarding the endangerment evaluation, please

visit:

www.emeov/fdsys/pl^ t ^	_ */odf/2010-9603. t»df



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For more information on lead in air, please visit:

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www. epa. gov/airqual ity/1 ead/

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For access to the rulemaking docket containing documents relevant to the EPA's
evaluation, please visit:

www.regulations.gov and enter EPA-HQ-OAR-2007-0294

For information on the FAA's actions to eliminate leaded aviation fuels, please visit:

www.faa.gov/about/initiatives/avgas/
and www.faa.gov/news/

For information on the FAA's actions to reduce lead concentrations at airports, please
visit:

www.faa.gov/airports/environniental/

For more information on how you can reduce your family's risk of lead exposure, please
visit:

www.6pa.gov/sit6s/d6faiilt/fil6s/2018-02/dociim6nts/epaj6adj3rochiire-
post.erlayout_508.pdf

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Contact

Marion Hoyer

U.S. Environmental Protection Agency
Office of Transportation and Air Quality
2000 Traverwood Drive
Ann Arbor, MI 48105
734-214-4513 E-mail:
hoyer.marioni@epa.gov

Or:

Meredith Pedde

U.S. Environmental Protection Agency
Office of Transportation and Air Quality
2000 Traverwood Drive
Ann Arbor, MI 48105
734-214-4748

E-mail: pedde.meredith@epa.gov

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