Technical Support Document (TSD)

for the EPA's Proposed Finding that Lead Emissions from Piston Engines Used in Aircraft Cause or Contribute to Air Pollution that May Reasonably Be Anticipated to Endanger Public Health and Welfare

                      Docket ID NO. EPA-HQ-OAR-2022-0389

                     U.S. Environmental Protection Agency
                          Office of Air and Radiation
                                   July 2022

The data presented in this document supports the EPA's Proposed Finding that Lead Emissions from Piston Engines Used in Aircraft 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" and "National Analysis of the Populations Residing Near or Attending School Near U.S. Airports," and EPA's two Program Overviews reporting on airport lead monitoring.[,]

Table of Contents:
  A. Background Information Regarding 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
 EPA Report: Model-Extrapolated Estimates of Airborne Lead Concentrations at U.S. Airports.
 EPA Report: Analysis of the Populations Residing Near or Attending School Near U.S. Airports.
 Program Update:  Airport Lead Monitoring.  2013.
 EPA Program Overview:  Airport Lead Monitoring.  2015.

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 criteria for lead and the lead NAAQS.  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) as well as information summarized in previous EPA Air Quality Criteria Documents (AQCDs), including the 1977, 1986, and 2006 AQCDs.[,][,]
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.  Lead emitted in the past can remain available for environmental or human exposure for extended time in some areas.  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 surface water runoff to other areas or nearby waterbodies.  Lead in the environment today may have been airborne yesterday or emitted to the air long ago.  Over time, lead that was initially emitted to air can become less available for environmental circulation by sequestration in soil, sediment and other reservoirs.
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.  Ambient air inhalation pathways include both inhalation of air outdoors and inhalation of ambient air that has infiltrated into indoor environments.  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.[,][,][,][,][,]  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,[,] 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 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.
   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 helicopters (also referred to as rotorcraft in this document), such as the Robinson R44, and ultralight and light sport aircraft 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).  Currently, 100LL is the most commonly available and most commonly used type of avgas.  Tetraethyl lead was first used in piston-engine aircraft in 1927.  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.  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 avgas supplied in the U.S., and the Federal Aviation Administration (FAA) provides information on the volume of leaded avgas consumed in the U.S.  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.  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) provides a complete list of operational airport facilities in the U.S.  Among the approximately19,600 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.  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.  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.
As of 2019, there were 171,934 piston-engine aircraft in the U.S.  This total includes 128,926 single-engine aircraft, 12,470 twin-engine aircraft, and 3,089 rotorcraft. 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.  In 2019, 883 new piston-engine aircraft were manufactured in the U.S. some of which are exported.  For the period from 2019 through 2041, the fleet of fixed wing 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.  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.  There were approximately 664,565 pilots certified to fly general aviation aircraft in the U.S. in 2021.  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.
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.  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.
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.  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.[,]
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 (ATADS).  The ATADS 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 to 396,554, with a median of 34,365.  Of the 513 airports, 211 airports reported increased general aviation activity over the period evaluated.  The increase in the average annual number of operations by general aviation aircraft at these 211 facilities ranged from 151 to 136,871 (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.  For the 3,307 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 1,620 of the airports in the TAF, remain the same at eight airports, and increase at 1,679 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.  The annual operations estimated for the 513 airports in 2045 ranges from 113 to 427,821 with a median of 36,918.  The TAF forecasts an increase in activity at 449 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).
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.[,][,]  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.
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.  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.  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.
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.  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).  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.
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.  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 5.7x10[6] to 8.6x10[6] particles per cm[3].  The authors noted that these particle emission rates are comparable to those from a typical diesel passenger car engine without a particle filter.  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.

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 1).  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.  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 Gasoline and in the document titled "Calculating Piston-Engine Aircraft Airport Inventories for Lead for the 2008 National Emissions Inventory."  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.
               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%

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.  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).  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.  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 0.01 tons), with 677 airports estimated to have inventories above 0.1 tons in 2045.
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.
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.[,][,][,][,][,]  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."[,]
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 (concentrations for periods of approximately 18 hours to three-month averages).[,][,][,][,][,]  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.[,][,]  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).
The lead NAAQS was revised in 2008.  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.  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 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.  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.[,]
      Table 2.  Lead Concentrations Monitored at 17 Airports in the U.S.
Airport, State
                          Lead Design Value, μg/m[3]
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
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

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 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.
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.
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."  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.  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 airport was applied to piston-engine aircraft activity estimates for each U.S. airport.  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.  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.
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.  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.  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.  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 concentrations of lead were estimated above background extending almost 1,000 meters downwind from the runway.
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.  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).  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 sources, including aircraft engine emissions.  The 2017 AirToxScreen provides lead concentration estimates in air for 73,449 census tracts in the U.S.  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.
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.
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),[,] 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.  Depending on ambient conditions (e.g., ozone and hydroxyl concentrations in the atmosphere), alkyl lead may exist in the atmosphere for hours to days 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 for the smaller lead-bearing particles. 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.  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.
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.  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.
Studies summarized in the 2013 Lead ISA suggest that soil is a reservoir for contemporary and historical emissions of lead to air.  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.  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.  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.
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.  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.[,][,][,][,][,]  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).
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.[,]  In this way, air lead sources may contribute to dietary exposure pathways.  As described in Section A, piston-engine aircraft are used in the application of pesticides, 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.  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, 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.  Atmospheric deposition of lead provides a pathway for lead in vegetation as a result of contact with above-ground portions of the plant.[,][,]  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.
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).  Alkyl lead, in particular, has been identified by the EPA as a Persistent, Bioaccumulative, and Toxic (PBT) pollutant.  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.  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 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.  Leaded avgas discharged to the ground and water includes other hazardous fuel components such as ethylene dibromide.
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.[,][,]
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 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.  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.[,]
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.,,,,   The 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.  Woodburn (2017) reported on changes in communities near airports from 1970-2010, finding suggestive evidence that at many hub airports over time, the presence of marginalized groups residing in close proximity to airports increased.  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.
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."  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.[,][,]
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 Populations Residing Near or Attending School Near U.S. Airports."  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.
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.  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.  Results of this analysis are presented in the following tables.  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).

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 Within 500 Meters
                        Percent Black Within the State
                      Black Population Within 500 Meters
                         Black Population in the State
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 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%
                                                                          5,017
                                                                        335,300
Delaware
                                                                             2%
                                                                             1%
                                                                            112
                                                                          5,900
New Mexico
                                                                            21%
                                                                            10%
                                                                          2,265
                                                                        208,900
South Dakota
                                                                            22%
                                                                             9%
                                                                          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.[,]  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.
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 Disparity[a]
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
                                       7
                                       1
                                       0
People with income less than twice the Federal Poverty Level
                                      761
                                      307
                                      223
                                      180
                                      51
People of Color (all races, ethnicities and indigenous peoples)
                                      666
                                      377
                                      126
                                      123
                                      40
Non-Hispanic Black
                                      405
                                      240
                                      77
                                      67
                                      21
Hispanic
                                      551
                                      402
                                      85
                                      47
                                      17
Non-Hispanic Asian
                                      268
                                      243
                                      18
                                       4
                                       3
Non-Hispanic Native American or Alaska Native
                                      144
                                      130
                                       6
                                       7
                                       1
Non-Hispanic Hawaiian or Pacific Islander
                                      18
                                      17
                                       1
                                       0
                                       0
Non-Hispanic Other Race
                                      11
                                      11
                                       0
                                       0
                                       0
Non-Hispanic Two or More Races
                                      226
                                      226
                                       0
                                       0
                                       0

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.
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).
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.
Appendix A:
Model-extrapolated Estimates of Airborne Lead Concentrations at U.S. Airports

 Table of Contents

Abbreviations	2
Summary		3
1.	Introduction	6
1.1 Use of Leaded Avgas in Piston-Engine Aircraft	6
1.2 Lead Concentrations in Air from Leaded Avgas Use in Piston-Engine Aircraft at Individual Airports	7
1.3 Characterizing Maximum Impact Area Lead Concentrations from Piston-Engine Activity at U.S. Airports	8
2.	Air Quality Modeling of Lead from Piston-Engine Aircraft at a Model Airport	10
2.1	Overview of Air Quality Modeling at a Model Airport	10
2.2	Air Quality Model Performance at a Model Airport	11
2.3	Yearlong Air Quality Modeling to Develop AQFs at a Model Airport	13
3.	Method to Calculate Model-Extrapolated Lead Concentrations Nationwide	15
3.1 Calculation of AQFs for Piston-Engine Aircraft Activity and Lead Concentrations	15
3.2 National Analysis Methods	18
3.3 Evaluation of Airports for Potential Lead Concentrations Above the Lead NAAQS	36
3.3.1 Sensitivity Analysis of Airport-Specific Parameters that Influence Potential for Lead Concentrations to be Above the NAAQS 	36
3.3.2 Airport-Specific Activity Data	38
3.3.3 Airport-Specific Criteria for Identifying Potential Lead Levels Above the NAAQS 	41
3.4 Characterization of Uncertainty of Cross-Airport Parameters that Influence the Potential for Lead Concentratins to be Above the NAAQS for Lead	48
4. Model-Extrapolated Lead Concentrations: Results and Uncertainty Characterization	52
4.1 Ranges of Lead Concentrations in Air at Airports Nationwide	52
4.2 Airports with Potential Lead Concentrations Above the Lead NAAQS with Unrestricted Access Within 50 m of the Maximum Impact Site	59
4.3 Quantitative Uncertainty Analysis of Concentrations of Lead in Air at Airports: The Influence of Run-up Time and Avgas Lead Concentration	63
4.3.1 National Analysis and Airport-Specific Monte Carlo Results	63
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	65
4.4 Qualitative Characterization of Uncertainty and Variability in Model-Extrapolated Lead Concentrations from National and Airport-Specific Activity Analyses	68
4.4.1 Meteorological Parameters	68
4.4.2 AERMOD and AERSURFACE Parameters	70
4.4.3 Operational Parameters	71

References	72
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)

 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 ug/m[3] up to 0.475 ug/m[3] 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. 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 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.
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).
 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 2017).[,] Piston-engine aircraft conduct approximately 32 million landing and take-off operations (LTOs) annually (USEPA 2011). 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. 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 aircraft take-off and the brakes are engaged so the aircraft is stationary. 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.[,]

  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.
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 μg/m[3]) (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.

 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.
 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).[,] 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 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. 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.
 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 R[2] 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. The generally good agreement between modeled and monitored 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.

Figure 1. Comparison of modeled and monitored daily average concentrations at three sites at the model airport during a 7-day period.
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.
 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.  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 (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.
 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 runway, and 2) T&Gs, in which aircraft land and take-off without coming to a full stop. 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. For example, the SE full LTO AQF at the maximum impact site is the ratio of the 3-month average modeled lead concentration (ug/m[3]) 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 1).
   Eq. 1: SE full LTO AQF at maximum impact site = 3-month average modeled lead concentration μgm3# of full SE LTOs during 3-month period

The specific steps to calculate AQFs at and downwind of the maximum impact site are:
 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).
 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).
 Sum piston-engine activity by cycle-type and aircraft class (e.g., SE T&G, ME full LTO) in the 3-month periods.
 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).
 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).

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. 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 (ug Pb/m[3]/LTO) at and downwind of the maximum impact site
                                     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
1.5x10[-5]
                                  3.5x10[-6]
                                  1.6x10[-6]
                                  1.1x10[-6]
                                  9.2x10[-7]
                                  7.6x10[-7]
                                  5.5x10[-7]
                                  4.0x10[-7]
                                  2.9x10[-7]
SE T&G
                                  1.7x10[-7]
                                  1.6x10[-7]
                                  1.7x10[-7]
                                  1.3x10[-7]
1.2x10[-7]
                                  1.0x10[-7]
                                  8.0x10[-8]
                                  6.1x10[-8]
                                  5.5x10[-8]
ME Full
                                  9.0x10[-5]
                                  2.3x10[-5]
                                  1.1x10[-5]
                                  8.2x10[-6]
                                  6.6x10[-6]
                                  5.5x10[-6]
                                  4.0x10[-6]
                                  3.0x10[-6]
                                  2.2x10[-6]
ME T&G
                                  6.8x10[-7]
                                  5.0x10[-7]
                                  4.5x10[-7]
                                  3.3x10[-7]
                                  2.7x10[-7]
                                  2.2x10[-7]
                                  1.7x10[-7]
                                  1.3x10[-7]
                                  1.2x10[-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.

   Eq. 2[,]:

[Pb]Air=

Avgas[Pb]g Pbgal2.12 g PbgalPASE,FullxAQFSE, Full+PASE, T&GxAQFSE, T&G+PAME, FullxAQFME, Full+PAME, T&GxAQFME, T&G

  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. 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 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"). 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. 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). 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.

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.
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). 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.

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.
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 <u[-1]>, 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 <v[-1]>/<u[-1]>. 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 <v[-1]> would be less than <u[-1]> 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 11pm 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.

Table 2. Steps to Calculate Airport Facility Specific Piston-Engine Aircraft Lead Concentrations
Step #
Step
Description
Rationale
Data Source
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.
1
Estimate how much activity is conducted by piston-engine aircraft annually
Estimate the annual number of piston-engine LTOs 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 aircraft LTOs that occur at each U.S. airport facility.
2011 NEI GA and AT piston-engine annual LTOs (USEPA 2011)

1a
For GA activity
The national average percent of GA activity that was conducted by piston-engines (72%), according to the 2010 FAA GAATA report, was multiplied by total GA LTOs at each airport.
Multiplying GA LTOs at an airport by the national average of GA LTOs conducted by piston-engine aircraft was necessary to estimate the annual number of GA piston-engine LTOs that occurred at each airport.
2011 NEI GA piston-engine annual LTOs & FAA
 GAATA, 2010 (FAA 2010)
1b
For AT activity
The national average percent of AT activity that was conducted by piston-engines (23%), according to the 2010 FAA GAATA report, was multiplied by total AT LTOs at each airport.
Multiplying AT LTOs at an airport by the national average of AT LTOs conducted by piston-engine aircraft was necessary to estimate the annual number of AT piston-engine LTOs that occurred at each airport.
2011 NEI AT piston-engine annual LTOs &
(FAA 2010)
Result: Annual number of GA piston-engine LTOs and AT piston-engine LTOs at each U.S. airport
2
Estimate how much of the annual piston-engine aircraft activity is conducted by each piston-engine aircraft class, performing different cycle-types
Estimate the number of total annual piston-engine LTOs that are conducted by specific aircraft classes (i.e., SE and ME for specific cycle-types (i.e., Full LTO and T&G) at each airport.
Different aircraft classes and cycle-types have different fuel consumption rates, and therefore different quantities of lead emissions.

2a
For GA piston-engine LTOs
Multiply the annual number of GA piston-engine LTOs (from Step 1a) by the national fraction of annual GA activity conducted by each aircraft class and cycle-type (i.e., SE Full LTO, SE T&G, ME Full LTO, ME T&G).
Fractioning GA piston-engine activity into 4 combinations of aircraft and cycle-types (i.e., 68% SE Full LTO, 23% SE T&G, 8% ME Full LTO, 2% ME T&G) allows us to categorize LTOs by sub-type of GA piston-engine activity which is important since each sub-type impacts the resulting concentrations differently.
Step 1a &
(FAA 2010)(Table 1.4)
2b
For AT piston-engine LTOs
Multiply the annual number of AT piston-engine LTOs (from Step 1b) by the national fraction of annual AT activity conducted by each aircraft class and cycle-type (i.e., SE Full LTO, SE T&G, ME Full LTO, ME T&G).