Fractioning AT piston-engine activity into 4 combinations of aircraft classes and cycle-types (i.e., 57% SE Full LTO, 0% SE T&G, 43% ME Full LTO, 0% ME T&G) allows us to categorize LTOs by sub-type of AT piston-engine activity, which is important since each sub-type impacts the resulting concentrations differently.
Step 1b &
(FAA 2010) (Table 1.4)
Result: Annual number of piston-engine LTOs at each U.S. airport categorized as: 1) GA SE Full LTO, 2) GA SE T&G, 3) GA ME Full LTO, 4) GA ME T&G, 5) AT SE Full LTO, 6) AT SE T&G, 7) AT ME Full LTO, 8) AT ME T&G.
3
At the U.S. towered airports, estimate what fraction of annual activity occurred on each day of the analysis (separately for GA and AT)
Approximately 500 airports have air traffic control towers (i.e., are "towered airports") and therefore have daily activity counts (separate for GA and AT). At each of these airports we developed separate GA and AT daily activity profiles, or fractions of annual activity that occurred during each day of the analysis. These daily activity profiles will later be applied to all U.S. airports (see Step 5).
Steps 1  -  2 provide annual piston-engine activity; however, aircraft activity varies by month, day, and hour. Because of this temporal variability, identifying the maximum 3-month period of activity necessitates that we apportion the annual activity data to daily activity (this step) and subsequently (in the following steps) further apportion daily data to each hour of the day.

ATADS
3a
For GA LTOs
At each towered airport, divide daily GA LTOs for each day included in the analysis by annual GA LTOs to reach the daily fraction of GA LTOs at each towered airport.
Dividing daily by annual GA activity produces a daily GA activity profile for each towered airport.
ATADS
3b
For AT LTOs
At each towered airport, divide daily AT LTOs for each day included in the analysis by annual AT LTOs to reach the daily fraction of AT LTOs at each towered airport.
Dividing daily by annual AT activity produces a daily AT activity profile for each towered airport.
ATADS
Result: Daily Activity Profiles, separately for GA and AT activity, at each towered airport for each day in the analysis.
4
For each non-towered U.S. airport, identify its closest towered airport
Use latitude/longitude data and a distance formula to determine the closest towered airport to each non-towered U.S. airport. These data will be used in combination with the daily activity profiles calculated in step 3 to estimate daily piston activity at each U.S. airport.
Data to develop daily activity profiles are only available for airports that report daily activity data (i.e., towered airports). To apportion each airport's annual activity to individual days, we apply the daily profile from the towered airport closest in distance to the non-towered airport. To do so, we first determine the closest towered airport for each non-towered U.S. airport.
FAA 5010
Result: Identification of the closest towered airport for each non-towered airport in the U.S.
5
Estimate the number of daily piston-engine LTOs at all U.S. airports
Multiply each airport's annual activity (step 2) by the daily activity profile (step 3) for its closest towered airport. This is done separately for GA and AT.
The GA and AT daily activity profiles (step 3) allow us to apportion annual activity into daily activity.

5a
For GA LTOs
Multiply each airport's annual piston-engine GA activity (for each of the 4 types: 1) GA SE Full LTO, 2) GA SE T&G, 3) GA ME Full LTO, 4) GA ME T&G) by the GA daily activity profile for its closest towered airport.
Daily activity data are only available for the combined set of all GA aircraft engine & operation types (i.e., SE Full LTO, SE T&G, ME Full LTO, ME T&G), thus, we use the same GA daily activity profile for each of the 4 subsets of GA activity at all airports.
Steps 2a & 3a
5b
For AT LTOs
Multiply each airport's annual piston-engine AT activity (for each of the 4 types: 1) AT SE Full LTO, 2) AT SE T&G, 3) AT ME Full LTO, 4) AT ME T&G) by the AT daily activity profile for its closest towered airport.
Similar to GA, daily activity data are only available for all types of AT aircraft engine & operation types (i.e., SE Full LTO, SE T&G, ME Full LTO, ME T&G) combined, thus, we use the same AT daily activity profile for each of the 4 subsets of AT activity at all airports.
Steps 2b & 3b
Result: Number of daily piston-engine LTOs at each U.S. airport categorized as: 1) GA SE Full LTO, 2) GA SE T&G, 3) GA ME Full LTO, 4) GA ME T&G, 5) AT SE Full LTO, 6) AT SE T&G, 7) AT ME Full LTO, 8) AT ME T&G.
6
Sum the number of daily LTOs by aircraft engine type & operation mode
Sum the daily number of GA and AT LTOs across aircraft engine and operation type (i.e., SE Full LTO, SE T&G, ME Full LTO, ME T&G).
The concentration of lead emissions is related to the type of aircraft engine and operation type, thus there is no distinction in terms of emissions between a SE Full LTO conducted as GA vs. AT. Understanding levels of GA vs. AT activity was necessary to appropriately apportion annual GA and AT activity into specific piston engine and operation types.
Step 5
6a
For SE full LTO
Sum the # of GA SE full LTOs & # of AT SE full LTOs for each day at each airport.

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

6c
For ME full LT0
Same as Step 6a but for ME full LTO.

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

Result: Number of daily piston-engine LTOs at each U.S. airport categorized as: 1) SE Full LTO, 2) SE T&G, 3) ME Full LTO, 4) ME T&G
7
Estimate the number of LTOs that occurred during each hour of each day (i.e., the distribution of LTOs across facility operational hours of the day)
For each day at each U.S. airport, multiply the number of daily piston-engine LTOs (separated into 1) SE Full LTO, 2) SE T&G, 3) ME Full LTO, 4) ME T&G) by the corresponding hourly activity profile (i.e., % of daily aircraft LTOs that occurred during each operational hour) from the model airport. There are separate profiles for each engine type (1) SE Full LTO, 2) SE T&G, 3) ME Full LTO, 4) ME T&G) by weekday/weekend status.

(e.g., If 30% of SE Full LTOs occurred during Hour 5 on a weekday at the representative facility, and 10 SE Full LTOs occurred at a given facility on Day 1 (a weekday) of the analysis, then 3 SE Full LTOs would be assigned to Hour 5 of Day 1 at the given facility).
Step 6 results in daily piston-engine activity; however, aircraft activity varies by month, day, and hour. Because of this temporal variability, identifying the maximum 3-month period of activity necessitates that we apportion the daily activity data to hourly activity (this step). Subsequently (in the following step), we use wind direction data to apportion the hourly data to specific runway ends at each airport.

Model airport (see Section 2 & Appendix A) & Step 6
7a
For weekdays

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

Appendix A & Step 6
7ai
For SE Full LTO
Multiply % of SE Full LTOs that occurred in each operational hour of a weekday at a representative facility by the number of daily SE Full LTOs for each facility in the analysis; repeat for each day in the analysis.

7aii
For SE T&G
Repeat Step 7ai for SE T&G.

7aiii
For ME Full LT0
Repeat Step 7ai for ME Full LT0.

7aiv
For ME T&G
Repeat Step 7ai for ME T&G.

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

Appendix A & Step 6
Result: Number of hourly piston-engine LTOs that occurred on each day of the analysis at each U.S. airport, categorized as: 1) SE Full LTO, 2) SE T&G, 3) ME Full LTO, 4) ME T&G
Steps 8  -  12 Objective: Estimate how much piston-engine activity occurred on each runway end over each rolling 3-month period.
8
Identify the runway end at which aircraft activity likely occurred for each hour of each day in the analysis
Use wind direction data for each hour that an airport is open (i.e., operational hours) to identify the runway end on which piston-engine aircraft LTOs were conducted; repeat for each day in the analysis.
Piston-engine aircraft take-off into the wind, thus wind direction dictates the runway end that is used; wind direction can change throughout the day so we evaluate hourly wind direction to identify the runway end used predominantly for each hour.
ASOS wind tower with shortest distance to airport
8a
For each U.S. airport, determine its closest ASOS station
Use latitude/longitude data and distance formula to determine the closest ASOS station to each U.S. airport.
Hourly wind direction data was available at the 938 ASOS stations, most of which are located at airports. To determine runway usage based on wind direction data, we first determined the closest ASOS station to each U.S. airport.
ASOS and FAA 5010 (See Appendix B for details)
8b
Use the hourly wind direction data from an airport's closest ASOS station to determine which runway end was used for each hour of the analysis
See Appendix B for details.
In order to appropriately estimate the location of the maximum lead concentration from piston-engine activity, we use wind direction data to identify where activity occurred (i.e., which runway end).

Result: Location (i.e., runway end) of aircraft activity at each U.S. airport during each hour of each day in the analysis
9
Determine number of LTOs that occurred on each runway end on an hourly basis
Assign piston-engine aircraft LTOs in each hour (Step 7) to the runway end that was active during each hour (Step 8); repeat for each day in the analysis.
Merging information regarding the number of hourly LTOs (Step 7) with our assessment of hourly runway usage (i.e., which runway end was used during each hour) allows us to quantify the hourly number of LTOs that occurred on each runway end at each U.S. airport for each day of the analysis.

9ai
For SE Full LTO
Assign SE Full LTOs in each hour (Step 7) to the runway end that was active during each hour (Step 8); repeat for each day in the analysis.

Steps 7 & 8
9aii
For SE T&G
Repeat Step 9ai for SE T&G.

9aiii
For ME Full LT0
Repeat Step 9ai for ME Full LT0.

9aiv
For ME T&G
Repeat Step 9ai for ME T&G.

Result: Number of piston-engine LTOs that occurred during each hour on each runway end during each day of the analysis at each U.S. airport, categorized as: 1) SE Full LTO, 2) SE T&G, 3) ME Full LTO, 4) ME T&G
10
Determine the number of LTOs that likely occurred on each runway end on a daily basis
For each runway end at each airport, sum the number of aircraft LTOs that occurred during all operational hours for a given day; repeat for each day in the analysis.
To estimate the number and type of LTOs that occurred at an airport on each runway end over an entire day, we sum the hourly LTOs, by runway end. In subsequent steps we use this daily information to estimate activity over 3-month time periods, which corresponds to the lead NAAQS averaging period.

10ai
For SE Full LTO
For each runway end at each airport, sum the number of SE Full LTOs that occurred during all operational hours for a given day; repeat for each day in the analysis.
Summing all of the SE Full LTOs at an airport that occurred at each runway end during each operational hour of a day allows us to estimate the number of SE Full LTOs that occurred on each day of the analysis at each runway at an airport.
Step 9
10aii
For SE T&G
Repeat Step 10ai for SE T&G.

10aiii
For ME Full LT0
Repeat Step 10ai for ME Full LT0.

10aiv
For ME T&G
Repeat Step 10ai for ME T&G.

Result: Number of piston-engine LTOs that occurred during each day on each runway end at each U.S. airport, categorized as: 1) SE Full LTO, 2) SE T&G, 3) ME Full LTO, 4) ME T&G
11
Sum daily # of LTOs estimated to have occurred on each runway end by rolling 3-month period

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

11ai
For SE Full LTO
For each runway end at each airport, sum the number of SE Full LTOs that occurred during each day of a 3-month period; repeat for each rolling 3-month period included in the analysis.

Step 10
11aii
For SE T&G
Repeat Step 11ai for SE T&G.

11aiii
For ME Full LT0
Repeat Step 11ai for ME Full LT0.

11aiv
For ME T&G
Repeat Step 11ai for ME T&G.

Result: Number of piston-engine LTOs that occurred during each rolling 3-month period on each runway end at each U.S. airport, categorized as: 1) SE Full LTO, 2) SE T&G, 3) ME Full LTO, 4) ME T&G
12
Identify the runway end with the highest estimates of piston-engine aircraft activity during any 3-month period at each airport

Piston-engine aircraft activity is a first-order determinant of lead concentrations in the maximum impact area in monitoring and modeling studies, as described in Section 2, and thus the period of maximum activity is assumed to represent the period of maximum concentration.
Step 11
12a
For each runway end, sum the number of total piston aircraft LTOs that occurred during each 3-month period for all engine & operation types; repeat for each rolling 3-month period included in the analysis
Sum Steps 11ai  -  11aiv by runway and by 3-month period for each U.S. airport.
In addition to understanding how much piston-engine aircraft activity of specific engine class & cycle types occurred at each runway end over rolling 3-month periods (which will be used in Step 13), we to need identify the runway end at which the most piston aircraft activity of any type was conducted over a rolling 3-month period. Identifying the runway end used most frequently by piston-engine aircraft allows us to estimate ambient concentrations at the location (i.e., runway end) with the most piston-engine activity, and in turn the highest lead emissions.
Step 11
12b

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

Step 12a
Result: Identification of the most active runway during any 3-month period at each airport facility included in the analysis
Steps 13  -  15 Objective: Estimate maximum 3-month lead concentrations from Piston-engine aircraft at each U.S. Airport
13
Estimate ambient lead concentrations from piston-engine aircraft lead emissions at the runway end most frequently used by piston-engine aircraft during the most active rolling 3-month period
Multiply the number of LTOs that occurred on the runway end most frequently used by piston-engine aircraft during the most active 3-month period by corresponding air quality factors; repeat for each facility in the analysis.
In Steps 1  -  12 we estimate piston-engine aircraft activity (i.e., how many LTOs of which engine class and cycle type that occur when and where) at each airport facility included in the analysis. We then combine our activity estimates with estimates of lead concentrations associated with each type of LTO in order to calculate total maximum 3-month lead concentrations from piston-engine aircraft. To do so, we use AQFs that are specific to each engine class and cycle type (SE Full LTO, SE T&G, ME Full LTO, ME T&G).

13ai
For SE Full LTOs at the most active runway during the most active 3-month period
Multiply the following:
1) the number of SE Full LTOs that occurred at the runway end most frequently used by piston-engine aircraft during the most active 3-month period, by 2) the AQF for SE Full LTOs at the max impact site; repeat for each facility in the analysis.
As described in Section 3.1, AQFs are the relationship of lead concentration per unit of aircraft activity (with distinct AQFs for each aircraft engine and operation type) and having units of average 3-month ug Pb/ m[3]/ LTO. By multiplying each AQF by the level of activity we estimate the lead concentration (ug Pb/ m[3]) associated with the number of LTOs we estimated in Steps 1  -  12.
Steps 11&12; Model airport (see Section 2 & Appendix A)
13aii
For SE T&G
Repeat Step 13ai for SE T&G.

13aiii
For ME Full LT0
Repeat Step 13ai for ME Full LT0.

13aiv
For ME T&G
Repeat Step 13ai for ME T&G.

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

13avi
Scaled by the lead concentration in avgas
First, divide the ASTM standard for Pb concentration in avgas (2.12 g Pb/gal) by the avgas Pb concentration at the model airport (2.16 g Pb/gal). Second, multiply the ratio of 2.12/2.16 by the sum of lead concentration from all types of piston-engine activity (Step 13av).
The AQFs were generated at a model airport with a concentration of Pb in avgas that is different from the ASTM maximum specification for this fuel. Thus, we scale the lead concentrations at each airport by the ratio of the ASTM standard lead concentration to the avgas lead concentration at the facility used to develop AQFs.

Result: Ambient lead concentration estimates at the max impact site at the most active runway end during the most active 3-month period for each airport facility included in the analysis
14
Estimate ambient lead concentrations at locations further downwind from the runway end
Repeat Step 13 with the appropriate AQFs for the 8 locations further downwind of the max impact site (50, 100, 150, 200, 250, 300, 400, 500 m); repeat for each facility included in the analysis.
As discussed in Section 3.1, in addition to developing AQFs at the max impact site, we also developed AQFs at 8 locations downwind of the max impact site (i.e., where piston-engine aircraft conduct run-up checks) in order to provide estimates of how lead concentrations change with distance. Similar to Step 13, we need to combine each respective AQF with activity estimates in order to estimate concentrations of ambient lead at each distance for each airport included in the analysis.
Model airport (see Section 2 & Appendix A)
Result: Ambient lead concentration estimates at 8 locations downwind of the max impact site at the most active runway end during the most active 3-month period for each airport included in the analysis
15
Estimate wind-adjusted ambient lead concentrations using average inverse wind speed
Scale the model-extrapolated ambient lead concentrations by the ratio of the average inverse wind speeds at the model airport to the average inverse wind speeds recorded at the nearest ASOS wind tower.
As discussed in Section 3.2, wind speed has a consistent and well-characterized impact on the near-field concentration of a passive tracer under dispersion. Therefore, scaling model-extrapolated lead concentrations to consider wind speed will better characterize local concentrations at airports nationwide, particularly those airports where wind speeds during the maximum activity period differ significantly from those observed at the model airport.
Appendix A and ASOS wind tower with shortest distance to airport
Result: Ambient wind-adjusted lead concentration estimates at and downwind of the max impact site at the most active runway end during the most active 3-month period for each airport included in the analysis

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

   3.3.1 Sensitivity Analysis of Airport-Specific Parameters that Influence Potential for Lead Concentrations to be Above the NAAQS
The first step to identify airports at which model-extrapolated concentrations are potentially above the lead NAAQS was to evaluate which airport-specific parameters may result in uncertainty or bias that would lead to underestimates in model-extrapolated concentrations from the national analysis methods presented in Section 3.2. There is potential uncertainty and/or bias from using national defaults for: 1) percentages of piston aircraft at an airport, 2) percentages of piston operations performed by single- versus multi-engine aircraft, and 3) assigning piston operations to runway ends. To address these sources of uncertainty and to identify airports where lead concentrations may approach or be above the NAAQs, but would not be identified by using national defaults, we conducted a series of sensitivity analyses. These sensitivity analyses expand the number of airports that would be within 10% of the NAAQs by using different assumptions for each of the three parameters outlined above that used national defaults in the national analysis.
For the first two parameters, we accounted for the possibility that the percentage of activity conducted by piston-engine aircraft and/or the percentage of piston-engine aircraft activity conducted by multi-engine aircraft at each airport might be underestimated by national averages. We did so by evaluating a scenario in which all GA and half of AT activity was conducted by piston-engine aircraft at each airport (i.e., we substituted 100% and 50% for the national average percentages of 72% and 23% piston-engine aircraft of total GA and AT, respectively; see Step 1 of Table 2). Because AT operations are more often conducted by ME aircraft, this sensitivity analysis impacts both the estimates of piston-engine aircraft activity and the predominance of ME or SE piston-engine aircraft at an airport. We then identified airports that had 3-month average model-extrapolated lead concentrations that were within 10% of the lead NAAQS after accounting for the possibility that national averages might be under-representations of piston-engine activity at some airports.
An additional sensitivity analysis was performed on the percentage of operations that occur at the most-utilized runway during the maximum activity period (Step 12 of Table 2). Two factors contribute to this percentage: the seasonal profile of operations and the allocation of operations to different runways based on wind direction. For the airports that were identified as having maximum 3-month concentrations above or approaching 0.15 ug/m[3] through the national analysis method presented in Section 3.2, the average percentage of annual activity occurring at the maximum period runway end is 20%. However, this percentage ranges from <6% at some airports, up to 45% at others. Reasons why an aircraft could take-off or land on a runway end other than the one assigned in the extrapolation, or be active during another 3-month period, include that the airport's seasonal profile of piston operations differs from that of the nearest ATADS airport, or the airport has two runways with similar headings, such that the dominant wind direction bisects them. These effects could bias estimates of operations and therefore concentrations either high or low. To better understand if some airports could have concentrations approaching or above the NAAQs that were not identified in the initial nationwide analysis due to a runway assignment bias, a sensitivity analysis was performed; airports that had less than 20% of their operations occurring at their maximum utilized 3-month period runway end were changed to having 20% of operations occur at that runway during that period.
Additional sources of uncertainty in operational data that could impact the national analysis results are discussed in Section 4.4 of this report. For example, there may be uncertainty in the annual GA operations counts that underlie the piston operations data. However, changing the total annual GA operations count effects the resulting maximum concentrations in the same way that changing the percentage of GA operations that are conducted by piston aircraft effects the maximum concentration (i.e., increasing total GA operations by 10% would be analytically equivalent to keeping GA operation counts constant and increasing the percentage performed by piston aircraft by 10%). Thus, the sensitivity analyses performed above may be interpreted to account instead, at least in part, for independent uncertainty from these other sources.
The airports identified in the national analysis or sensitivity analyses as having maximum 3-month concentrations above or approaching the NAAQS were the focus of a more refined assessment of piston-engine aircraft activity, as described below.

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

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

Specifically, based aircraft data (i.e., the number and class of aircraft that are parked at an airport) were collected for airports included in this airport-specific activity analysis. Data from previous EPA studies at six airports showed agreement within 10% between the number of SE and ME aircraft based at an airport and onsite observations of piston-engine aircraft activity at the airport (see Appendix B for study details). As such, the number and class of aircraft based at each airport included in this airport-specific activity analysis was used to refine the national average percentages for estimating the number of LTOs specific to piston-engine aircraft, and then SE versus ME piston aircraft.
For each airport included in this analysis, the number of aircraft based at that airport was collected from available data sources. Next, for each airport, the percent of total operations conducted by piston-engine aircraft was calculated using the number of SE and ME aircraft over the total number of aircraft based at the airport (i.e., sum of SE and ME based aircraft over total SE, ME, turboprop, jet, and helicopter based aircraft multiplied by 100). Similarly, the percent of piston-engine operations conducted by SE versus ME aircraft was calculated using the numbers of SE versus ME aircraft based at the airport (e.g., SE based aircraft over sum of SE and ME based aircraft multiplied by 100). Table 3 presents a summary of the percent of LTOs allocated to piston-engine aircraft, and separately SE versus ME piston aircraft, in the national analysis compared to the allocation using data for aircraft based at the airports included in this airport-specific activity analysis.

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

                      National Analysis
National Averages
                     Airport-Specific
Based Aircraft Data
                                 Data Sources
% piston versus jet operations
GA: 72%
AT: 23%
Unique to each airport
(%SE & ME based aircraft of total based aircraft)
GA & AT Mean: 92%
GA & AT Range: 60  -  100%
National Analysis: (FAA 2010, USEPA 2011)
Airport-specific: Airport Master Plans & Airport Master Record Forms 5010-1 & 5010-2
% single-versus multi-engine operations
GA SE: 90%
GA ME: 10%
AT SE: 57%
AT ME: 43%
Unique to each airport
(%SE OR ME based aircraft of SE AND ME based aircraft)
 GA & AT SE:
 Mean: 89%
 Range: 58 - 99%
 GA & AT ME:
 Mean: 11%
 Range: 0.02  -  42%
National Analysis: (FAA 2010, USEPA 2011)
Airport-specific: Airport Master Plans & Airport Master Record Forms 5010-1 & 5010-2

In general, for the airports evaluated here, using the number of piston-engine aircraft based at the airports as a surrogate for activity suggests that piston-engine aircraft activity at these airports is higher than indicated by the national average fraction (Table 3). The higher percent of piston-engine aircraft activity at these airports is expected given that master plans and other available information (e.g., airport websites) show that these airports are predominately GA airports, which generally have higher levels of piston-engine aircraft activity compared to a national average that includes activity at commercial and other larger airports with more jet activity. For the percentage of piston-engine aircraft activity conducted by SE versus ME aircraft, the number of SE and ME aircraft based at these airports suggest similar percentages of aircraft activity are conducted by each aircraft class compared to the national average data for GA activity. Conversely, the number of ME aircraft based at these airports generally suggest ME activity is lower than the national average used to estimate ME piston aircraft activity from total AT activity (Table 3). The airport-specific activity estimates calculated using aircraft based at these airports were used to calculate refined model-extrapolated lead concentrations, per the methods described in Table 2. These refined model-extrapolated concentrations are compared with national analysis values, as well as relevant monitoring data, in Section 4.
   3.3.3 Airport-Specific Criteria for Identifying Potential Lead Levels Above the NAAQS
This section summarizes the approach and rationale for selecting airports included in the airport-specific activity analysis of lead concentrations at the maximum impact area. At a high-level, this approach entails identifying airports where the maximum 3-month average model-extrapolated concentrations may be above or approach the NAAQS, characterizing model-extrapolated maximum 3-month concentrations at these airports using airport-specific, refined estimates of aircraft activity splits, and then evaluating each airport on local criteria such as the proximity of the maximum-impact site to unrestricted public access. The detailed methods for this analysis are provided in Table 4.

Table 4. Steps for Identifying Airports Where Lead Concentrations May Be Above the Lead NAAQS
Step #
Step
Description
Rationale
Data Source
Steps 1  -  3 Objective: Identify a subset of airports where, considering sources of variability and uncertainty, model-extrapolated atmospheric lead concentrations could be above or approach the NAAQS for Lead.
1
Identify airports with maximum model-extrapolated concentrations approaching or above the NAAQS
Sum the contributions of single- and multi-engine T&G and LTO operations to atmospheric lead concentrations at the maximum impact site for the maximum activity period from the national analysis described in Section 3.2. Identify all airports where the maximum concentration is above or is within 10% of 0.15 ug/m[3].
The primary and secondary National Ambient Air Quality Standards for Lead are 0.15 micrograms per cubic meter lead in total suspended particles as a 3-month average. Because the AQFs relate operations to average atmospheric lead concentrations over the same timescale (3 months), the results of the national analysis indicate whether or not model-extrapolated lead concentrations may approach or be above the concentrations specified in the NAAQS for lead when the inputs described in Section 3.2 are used.
National Analysis Step 14 and 40 CFR Part 50
2
Identify airports with maximum model-extrapolated concentrations approaching or above the NAAQS when all GA and half of all AT operations are assumed to be piston aircraft operations
Scale the contributions of single- and multi-engine T&G and LTO operations to maximum impact area atmospheric lead concentrations to characterize these concentrations if 100% GA operations and 50% of AT operations were operated by piston-engine aircraft.
As detailed in Steps 1a and 1b in the national analysis methods, the national analysis assumed that 72% of GA and 23% of AT operations are performed by piston-engine aircraft. The current step identifies airports where concentrations would be above or approach the NAAQS if piston-engine aircraft were a larger portion of activity at each airport.
National Analysis Steps 1a and 1b and GAATA Survey
2a
Scale concentration contributions from T&G operations
Scale lead concentration contributions from GA operations by (1/0.72)
In the national analysis, all T&G operations are assumed to be from GA flight activity. Thus, as concentrations scale with operations, both single- and multi-engine concentrations can be scaled by the proportional change in GA piston-engine operations.

2b
Scale concentration contributions from full flight operations
Scale full flight lead concentration contributions from AT operations by (0.5/0.23), the ratio of new operational cycles to old operational cycles for both SE and ME concentration contributions.
Both GA and AT operate SE and ME full flight operations.
National Analysis (FAA 2010, EPA 2011)
3
Identify airports with maximum model-extrapolated concentrations approaching or above the NAAQS when at least 20% of operations occur at the most-used runway end during the maximum 3-month period
Scale model-extrapolated lead concentrations by the ratio (0.2/X), where X is the airport-specific fraction of operations occurring at the most-used runway end during the maximum 3-month period and X < 0.2.
For the airports that are identified as potentially having lead concentrations approaching or above the NAAQs for lead at Step 1 of the airport specific analysis, the average percentage of operations occurring at the maximum period runway end is 20%. This sensitivity analysis identifies airports where operations at the most-used runway end may have been underestimated due to assumptions about wind direction, runway assignment, and local seasonal operational profile.
Airport Specific Analysis Step 1
Result: Identification of a subset of airports as having model-extrapolated lead concentrations that could be above the NAAQS for lead.
Steps 4  -  7 Objective: Refine model-extrapolated concentrations at the subset of airports identified in Steps 1-3 using airport-specific activity data
4
Collect based-aircraft data for the subset of airports identified in Steps 1-3
Designate to each airport in the airport-specific analysis counts of jet, single-engine, and multi-engine aircraft from reported based-aircraft numbers at that airport.
For the national analysis, national average splits of piston/non-piston and subsequently SE/ME operations were applied to both GA and AT operations. Because individual airports may serve different aircraft populations, an airport-specific activity assessment may provide a refined characterization of operational splits by aircraft type. This assessment uses counts of aircraft based at a particular airport as a proxy for a representative sample of the split of operations by aircraft type.
FAA Form 5010 Data
4b
Retain national average splits of operational cycles for airports with no based-aircraft data in Form 5010.
Where airports have no reported based-aircraft data, retain the national average splits of operational cycles by SE/ME and Full/T&G for AT and GA.
Where based-aircraft are not reported, the national average percentage of SE/ME and Full/T&G operational cycles remain the best estimates of operational characteristics at that individual airport.
National Analysis (FAA 2010, EPA 2011)
4c
Retain national average splits of operational cycles for airports with low based-aircraft counts relative to annual operations.
Where airports have an annual-operations-to-based-aircraft ratio greater than 730, retain the national average splits of operational cycles by SE/ME and Full/T&G for AT and GA.
As based-aircraft numbers are self-reported, Form 5010 Data may be incomplete at some airports. Further, at busy airports with significant commercial or AT traffic, aircraft based at the airport may not be representative of all aircraft serving the airport. The lower the ratio of operations-to-based-aircraft, the more appropriate based-aircraft is expected to be a proxy for operational splits. We make the assumption that annual operations-to-based aircraft greater than 730 (2 operations per based aircraft per day), is an upper limit above which the based aircraft data are not a suitable proxy for activity at an individual airport.

5
Assign splits of GA and AT piston/non-piston operations from based-aircraft data
Characterize the number of operations that would be performed by piston-engine aircraft at each airport if the non-jet aircraft based at the airport were representative of the percent of GA and AT operations performed by piston-engine aircraft at that airport.
While several data sources provide airport-specific aircraft activity data (separately for General Aviation (GA) and Air Taxi (AT) activity), none specifically identify the number of piston-engine aircraft LTOs that occur at each U.S. airport. In the national analysis, a default percentage representative of national averages was used to determine piston-engine aircraft operations at each airport; this analysis uses local airport-specific information (namely based-aircraft) to better characterize model-extrapolated lead concentrations at those airports that could have model-extrapolated concentrations that approach, or be above the NAAQS for lead as identified in Steps 1-3.
FAA Form 5010 Data
6
Assign splits of ME and SE Full and T&G operations from based-aircraft data
Characterize the percentage of piston aircraft operations that would be classified as SE Full, SE T&G, ME Full, and ME T&G
In the national analysis, default percentages of operational splits for AT and GA operations by aircraft class (SE/ME) and operational cycle type (Full/T&G) representative of national averages were used to characterize piston aircraft operations at each airport; this analysis uses local airport-specific information (namely based-aircraft) to better characterize model-extrapolated lead concentrations at those airports that could have model-extrapolated concentrations that approach, or are above the NAAQS for lead as identified in Steps 1-3.
FAA Form 5010 Data
6a
Determine operational splits for AT at each airport
The percent of AT operational cycles that are SE (or ME) full LTO matches the percent of based-aircraft that are SE (or ME).
All AT operations are considered to be full LTOs.

6b
Determine operational splits for GA at each airport
The percent of GA operational cycles that are SE (ME) matches the percent of based-aircraft that are SE (ME). Of the GA SE operational cycles, 24% are characterized as T&G consistent with the national analysis. Of the GA ME operational cycles, 20% are characterized as T&G consistent with the national analysis.
Both full LTO and T&G operational cycles are performed by GA aircraft.

Result: Characterization of a refined estimate of the number and type of operations performed by SE and ME piston-engine aircraft for each of the airports identified in Steps 1-3.
7
Refine model-extrapolated lead concentrations using updated operational splits
For the airports identified in Steps 1-3, estimate model-extrapolated lead concentrations at and downwind of the maximum impact site using the data gathered in Steps 4  -  6 paired with the methodology described in the National Analysis (Table 2).

National Analysis Steps 3-14
Result: Lead concentration estimates at and downwind of the maximum impact site at the most active runway end during the most active 3-month period for each airport identified in Steps 1-3 using airport-specific activity data.
Step 8 Objective: Identify whether there is unrestricted access to the area of maximum impact at airports identified at Step 7
8
Identify airports where there is unrestricted access to the 50 m perimeter around a maximum impact site
For the airports that have model-extrapolated lead concentrations that are above the lead NAAQS as identified in Step 7, estimate the distance from the run-up area at the most-utilized runway end to the nearest unrestricted access using satellite imagery.

The layout and footprint of many general aviation airports is such that, aircraft run-up areas and the maximum impact site may be in close proximity to where people have unrestricted access. We sub-select airports where there was unrestricted access within 50m of the maximum impact site where lead concentrations were estimated as potentially above the lead NAAQS in Step 7.
Satellite and street-view  imagery
9
Identify local airport characteristics that may influence lead concentrations at the maximum impact site
For the airports that have model-extrapolated lead concentrations that are above the lead NAAQS as identified in Step 7, review satellite imagery and airport documentation to determine if there are any airport-specific conditions or characteristics that could influence lead concentrations at the maximum impact site.
As all airports are unique, any airport may have a layout, local characteristic, or operational pattern that may differ from the assumptions underlying the national analysis and may impact resulting atmospheric lead concentrations.
Satellite imagery, airport master plans
Result: Identification of airports that have model-extrapolated lead concentrations above the NAAQS for lead considering both airport-specific activity data and unrestricted access to the maximum impact area.

 Characterization of Uncertainty of Cross-Airport Parameters that Influence the Potential for Lead Concentrations to Be Above the NAAQS for Lead

As discussed in Section 1, the goal of this work is to characterize lead concentrations at and downwind of the maximum impact area at airports nationwide. The approach described in Sections 3.2 and 3.3 was selected because of the consistent set of ground-based parameters that are inherent to safe operation of piston-engine aircraft. Namely, that these aircraft take-off into the wind and conduct pre-flight engine checks adjacent to the take-off runway end. These parameters are consistent across airports, and thus constrain the uncertainty and variability that might be associated with results based on combining information from one model airport with activity estimates at airports nationwide. The limited set of key parameters, which influenced maximum impact area ground-level air lead concentrations in previous modeling by EPA and others, were: 1) the duration of run-up, where longer run-up times results in higher concentrations, 2) the concentration of lead in the fuel, where higher avgas lead concentrations results in higher concentrations, 3) activity, where more piston-engine aircraft activity increases lead concentrations, 4) the percent of activity conducted by ME piston-aircraft, where more ME activity results in higher lead concentrations due to the higher fuel consumption rates of these aircraft relative to SE aircraft, and 5) meteorological factors and local topography (including wind speed, wind direction, mixing height, atmospheric stability, and surface roughness) (Section 2; Appendix A) (Carr et al. 2011, Feinberg et al. 2016).
Parameters 3 and 4 (activity estimates and SE/ME aircraft splits) were evaluated for a subset of airports for which uncertainty in the extent to which national average fractions represented the individual airport would most influence whether or not model-extrapolated concentrations are above the lead NAAQS, as described in Section 3.3. The uncertainty from these two parameters and the fifth parameter (meteorological and other local factors) are additionally assessed qualitatively in Section 4.4.
The duration of run-up operations and the concentration of lead in avgas were both found to be highly influential in ground-level 3-month average lead concentrations in air attributable to piston-engine aircraft. Run-up emissions accounted for 82% of the 3-month average lead concentration attributable to piston-engine aircraft in EPA air quality modeling at a model facility, and was a primary contributor to emissions in modeling conducted by Feinberg et al. (Section 2, Appendix A) (Feinberg et al. 2016). Moreover, variation between the 5[th] and 95[th] percentiles of average run-up times observed in EPA modeling resulted in an almost 8-fold variation in concentration attributable to only run-up emissions (Appendix C). Similarly, Feinberg et al. found greater variation in the duration of run-up than that of other modes of operation in the LTO cycle (e.g., landing and take-off time in mode), and variation in run-up time led to variation in concentrations downwind (Feinberg et al. 2016).
Similarly, the concentration of lead in avgas has a direct impact on atmospheric lead concentrations attributable to piston-engine aircraft activity, where higher levels of lead in fuel result in greater lead emissions and hence concentrations of lead in air. The ASTM standard for the maximum lead concentration in 100LL was used in the national analysis; however, the amount of lead in the fuel can vary across fuel suppliers and by batch. The concentrations of lead in air attributable to aircraft are expected to directly scale with the concentration of lead in avgas; thus, the lead avgas concentration was used as a scalar in the calculation of model-extrapolated concentrations at airports nationwide (see Equation 2, Section 3.1). Based on the important influence of these two parameters (run-up time and avgas lead concentration) in modeling 3-month average lead concentrations attributable to piston-engine aircraft activity, additional information was gathered to further characterize each parameter in results from both national and airport-specific activity analyses.
Information on average run-up times was collected from a series of studies that observed run-up operations at five airports (Appendix C) (USEPA 2010a, Heiken et al. 2014). The average run-up time from each airport was used to develop a distribution of average run-up times. This distribution of run-up times provided a way to evaluate model-extrapolated lead concentrations based on observations at a larger number of airports compared to the run-up times used in the national analysis, which were based on observations at the model airport. The distribution of average run-up time across the five airports was lognormally distributed with an average of 70 seconds, compared to the 40- or 63-seconds used for SE or ME aircraft, respectively, in the national analysis (Table 5). The relationship between variation in run-up time and concentrations of lead in air at and downwind of the maximum impact area was not characterized in the additional studies used to develop the distributions of average run-up times, and thus observations at the model airport were used to characterize how changes in run-up time impacted changes in lead concentrations in the maximum impact area and downwind (See Appendix C for details).
The distribution of average run-up times combined with an understanding of the relationship between run-up time and downwind lead concentrations attributable to piston-engine aircraft provided the necessary inputs for conducting a Monte Carlo analysis. The objective of the Monte Carlo analysis was to characterize the impact of variation in the 3-month average run-up time at a given airport on 3-month average model-extrapolated lead concentrations. Conceptually, the Monte Carlo analysis entailed repeatedly selecting a run-up time value from the distribution of average run-up times, and then adjusting the model-extrapolated lead concentration based on the difference between the selected run-up time and the run-up time used in the national analysis. For example, if an average run-up time of 70 seconds was selected from the distribution of average run-up times, then the national model-extrapolated concentration for SE piston aircraft would be adjusted up to account for the 30 second difference between the time used in the national analysis (40 seconds) and the time selected in the Monte Carlo draw. The amount of increase in concentration in this example would be based on the relationship observed between run-up time and concentration at each distance downwind at the model airport, such that the concentration of lead in air would increase more at the maximum impact site than locations downwind (see Table C-1 in Appendix C). The resulting model-extrapolated concentration at each location, which accounted for the change in run-up time, would then be used to adjust the model-extrapolated concentration resulting from the national analysis. The Monte Carlo analysis used 10,000 iterations (i.e., 10,000 average run-up times were selected from the distribution and used to adjust the model-extrapolated concentration at each airport, at each downwind distance, which produced 10,000 adjusted concentrations that then provided a range of potential concentrations at each airport, at each downwind distance, based on variation in run-up time).
A similar approach was used to characterize the impact of variation in avgas lead concentrations on 3-month average model-extrapolated atmospheric lead concentrations. Available data from FAA and EPA reporting lead concentrations in avgas samples had an average lead concentration of 1.79 g/gal and were normally distributed within the range specified for 100LL (i.e., 1.70 to 2.12 g/gal) (see Appendix C for details on avgas lead data and their distribution). A Monte Carlo analysis was used to characterize variation in 3-month average model-extrapolated lead concentrations based on variation in avgas lead concentration. As with run-up time, a value was selected from the distribution of avgas lead concentrations (Table 5), and then used to scale a model-extrapolated concentration. For example, if an avgas lead concentration of 1.80 was selected from the distribution, a model-extrapolated concentration would be scaled by 0.85 (i.e., 1.80/2.12) to decrease extrapolated concentration and account for a lower concentration of lead in fuel. The Monte Carlo analysis was conducted 10,000 times. Results of the avgas lead and run-up time Monte Carlo analyses were combined per Equation 3 to provide model-extrapolated concentrations that account for variation in each parameter at and downwind of the maximum impact area at each US airport (see Appendix C for details).

   Eq. 3:
   Monte Carlo Adjusted Lead Concentration, [Pb]MC =  LMC/2.12g PbgalYnxCn

Where:
LMC= concentration of lead in avgas (g/gal) from Monte Carlo analysis of avgas lead distribution
Yn= model-extrapolated concentration from national analysis at location n
Cn= %difference change in concentration at location n due to change in run-up time (see Equation C-1)
N= location at or downwind of maximum impact (i.e., 0, 50, 100, 150, 200, 250, 300, 400, 500 meters)

Table 5. Monte Carlo analysis inputs for characterizing variability in key AQF parameters
                                   Variable
                               National Analysis
                             Monte Carlo Analysis
                                  Assumptions

                                  Data Source
                                     Value
                                  Data Source
                                   Mean (SD)
                                     Range
                              Distribution Shape

Run-up Time (seconds)
Model facility
(Appendix A)
SE: 40
ME: 63
(USEPA 2010a, Carr et al. 2011, Feinberg and Turner 2013)(Appendix A) (n=5)
SE & ME:
70 (21)

Min: 49
Max: 91

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

Model Airport (Appendix A)

Exponential
(distance)
The lead concentration attributable to run-up decreases as a negative power law with distance from the maximum impact site. As such, increases or decreases in run-up time compared to an average influences lead concentration more at 0 or 50 m from run-up than at 500 m meters for run-up. Our modeling suggests an exponential curve describes the relationship between run-up time and variability in lead concentration estimate (see Appendix C for details).
Avgas Lead Concentration
(g/gal)
ASTM standard
2.12
EPA & FAA fuel samples (n=116)
1.79 (0.27)
Min: 1.70
Max: 2.12
Normal
We assume that the normal distribution of data from EPA and FAA fuel samples is representative of the distribution of avgas lead content at all US airports. The EPA fuel data were collected during modeling studies discussed in Section 2. FAA published a study reporting the lead concentration of avgas fuel samples which was also used in this analysis.
We bounded the distribution based on the ASTM fuel specifications for 100 octane Very Low Lead avgas (100VLL) which has a lead concentration of 1.70 g/gal, and 100 Low Lead (100LL) which has a maximum lead concentration of 2.12 g/gal.

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

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

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

                                     LTOs
Model-Extrapolated Concentrations of Lead (μg/m[3]) at and Downwind of the Maximum Impact Area

                                   Max Site
                                     50 m
                                     100 m
                                     150 m
                                     200 m
                                     250 m
                                     300 m
                                     400 m
                                     500 m
3,616 - 26,816
                                  0.155-0.475
                                  0.038-0.116
                                  0.018-0.054
                                  0.013-0.040
                                  0.011-0.032
                                  0.009-0.027
                                  0.006-0.019
                                  0.005-0.014
                                  0.003-0.010
2,579 - 8,814
                                  0.100-0.154
                                  0.024-0.038
                                  0.011-0.018
                                  0.008-0.013
                                  0.007-0.011
                                  0.006-0.009
                                  0.004-0.006
                                  0.003-0.005
                                  0.002-0.003
1,783 - 5,728
                                  0.075-0.100
                                  0.018-0.025
                                  0.009-0.012
                                  0.006-0.009
                                  0.005-0.007
                                  0.004-0.006
                                  0.003-0.004
                                  0.002-0.003
                                 0.0017-0.0023
1,275 - 4,302
                                  0.050-0.075
                                  0.012-0.018
                                  0.006-0.009
                                  0.004-0.006
                                  0.003-0.005
                                  0.003-0.004
                                  0.002-0.003
                                 0.0015-0.0023
                                 0.0011-0.0017
160 - 2,889
                                 0.0075-0.050
                                  0.002-0.012
                                  0.001-0.006
                                  0.001-0.004
                                  0.001-0.004
                                 0.0004-0.003
                                 0.0003-0.002
                                 0.0002-0.0016
                                 0.00002-0.001
<1 - 446
                                  < 0.0075
                                   <= 0.002
                                   <= 0.001
                                   <= 0.001
                                   <= 0.001
                                  <= 0.0004
                                  <= 0.0003
                                  <= 0.0002
                                  <= 0.0002

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

Figure 5. The relationship between 3-month average lead concentration at the maximum impact site and piston-engine aircraft activity during the same 3-months. Blue shading denotes the relative amount of multi-piston-engine aircraft activity at each airport. Air Taxi data were used to estimate the relative ME aircraft activity at each airport since this type of activity is generally dominated by ME and data specific to multi-piston-engine aircraft activity is not available across US airports. Airports with zero LTOs (n = 221) were excluded from the figure for clarity. This figure presents non-wind-adjusted concentrations using national default analysis parameters as described in Table 2 to better highlight the impact of multi-engine activity on concentration.
As described in Section 3.2 and Table 2, wind speed at each airport relative to wind speed at the model airport can also influence model-extrapolated lead concentrations, and thus the maximum impact site concentrations were adjusted to reflect wind speeds at each airport. Airports with wind speeds during the 3-months of maximum activity that are higher than wind speeds measured at the model airport will have wind-adjusted concentrations that are lower than the non-adjusted concentrations using national defaults. Similarly, airports with lower wind speeds than the model airport will, in general, have higher wind-adjusted concentrations. Results of the wind-speed adjusted lead concentrations are compared with unadjusted values in Figure 6.

Figure 6. Average 3-month model-extrapolated concentrations versus the number of piston-engine LTOs during the same 3-month period at the maximum impact area runway end. Concentrations are generally categorized relative to the lead NAAQS (e.g., greater than the standard of 0.15 ug/m[3], less than half the standard, 0.075 ug/m[3], less than concentrations generally detected by monitors, 0.0075 ug/m[3], etc).
Across all airports, the effect of the wind adjustment ranges from a 45% decrease in concentration to a 210% increase in concentration; however, 48% of airports have concentrations that change by less than 10%. The impact of the wind adjustment on maximum impact site concentrations for all airports is shown in Figure 7. In absolute difference, the 3-month maximum concentration at the maximum impact site changes by less than 0.01 μg/m[3] at most airports. At airports with concentrations greater than half the lead NAAQS, the absolute concentration change from wind adjustment tends to be higher, from -0.06 to 0.16 μg/m[3], as shown in Figure 8. Overall, results of adjusting for wind speed show that while this parameter is influential at individual airports, it does not meaningfully impact the range of concentrations in the maximum impact area at airports nationwide. In turn, individual airports with the potential to have concentrations above the lead NAAQS are evaluated more closely in Section 4.2.

Figure 7. The percent change in 3-month maximum concentration at the maximum impact site from accounting for average inverse wind speed at all airports.

Figure 8. The absolute change in 3-month maximum concentration at the maximum impact site from accounting for average inverse wind speed at airports with concentrations greater than (1/2) the NAAQS for Lead.

The model-extrapolated concentrations from the national analysis presented above can be evaluated through a comparison to monitored concentrations. Such an evaluation would ideally be informed by monitored data that corresponds spatially and temporally with the model-extrapolated concentrations. However, as detailed below, monitored lead concentrations are only available at a subset of airports and none of these data are spatially and temporally consistent with model-extrapolated data. Nevertheless, a coarse comparison of model-extrapolated to monitored concentrations is feasible for a subset of airports at which monitors were placed proximate to the maximum impact area, or downwind, as part of evaluating attainment of the lead NAAQS. In evaluating these comparisons, it is noteworthy that in addition to spatial differences, monitored and model-extrapolated concentrations differ in temporal periods and scope. Model-extrapolated concentrations were calculated for 2011 while monitored concentrations were collected over different 1-year periods depending on the airport. As described in Section 3.1, while 2011 is expected to be generally representative of piston-engine aircraft activity during monitored periods, differences in the volume and type of piston-engine activity (i.e., SE vs. ME, full LTO vs. T&G) and meteorological conditions would be expected to impact the comparisons presented here. In addition, model-extrapolated concentrations are specific to aircraft lead emissions, while monitored concentrations include background lead from other sources. Other factors could influence lead concentrations in air from year-to-year as well, and both monitored, and model-extrapolated concentrations also have inherent variability and uncertainty. With the characteristics of each dataset in mind, Figure 9 provides a coarse comparison of national model-extrapolated to monitored concentrations at three airports with monitors placed proximate to the maximum impact area or downwind locations. Each panel presents the monitored NAAQS design value (i.e., maximum 3-month average concentration during monitored time period) along with model-extrapolated concentrations. Across these airports, model-extrapolated and monitored concentrations generally align when considering both the downwind gradient, and horizontal transport of lead emissions at the maximum impact area.

                     Satellite Image Source: Google Earth
Figure 9. Coarse comparison of monitored to model-extrapolated lead concentrations at airports with NAAQS monitors sited proximate to the maximum impact area or locations downwind. Red dots represent approximate monitor placement, while yellow dots represent approximate locations of model-extrapolated concentrations from national analysis methods (Section 3.2). Blue arrows denote the prevailing wind direction at each airport. As noted above, the year in which monitored concentrations were collected varies by airport, while model-extrapolated concentrations represent 2011. All locations are based on scientific judgment of the alignment of model-extrapolated locations from the expected maximum impact area. The max impact concentrations represented in the figure are not wind speed adjusted. The wind speed adjusted concentrations at max impact for airports A, B, C are 0.36, 0.23, and 0.44 ug/m[3] respectively.

  4.2 Airports with Potential Lead Concentrations Above the Lead NAAQS with Unrestricted Access Within 50 m of the Maximum Impact Site
As described in Section 3.3, a series of sensitivity tests were performed to identify a subset of airports beyond those identified in the national analysis where model-extrapolated lead concentration estimates were above the NAAQS for lead. Additional data were then identified to calculate airport-specific activity estimates for each airport in this subset. Next, the airport-specific activity estimates for each airport were used to calculate updated model-extrapolated lead concentrations for that airport with a focus on concentrations in the maximum impact area. In addition, for each of these airports, satellite imagery was utilized to assess if there was unrestricted access within 50 meters of the maximum impact site. The results of this screening analysis are presented in Table 7.
Each column in Table 7 represents the outcome of analysis steps presented in Section 3.3 and described in Table 4:  the first column identifies the airport, the second column indicates the lead concentration at the maximum impact site relative to the lead NAAQS using national default parameters (Section 3.2); the third column adjusts the national default concentrations based on average inverse wind speed (Section 3.2); the fourth and fifth columns present the outcomes of airport-specific parameters that influence the potential for lead concentrations to be above the NAAQS for lead; the sixth column shows the results of the airport-specific-activity analysis before adjusting for average inverse wind speed; and the seventh column shows the results using both airport-specific activity and airport-specific wind speed data. Black filled circles indicate model-extrapolated concentrations are above the NAAQS for lead and white unfilled circles indicate model-extrapolate concentrations that are more than 10% below the NAAQS for lead. The potential impacts of additional local characteristics (e.g., mixing height, local terrain) on airport-specific estimates of lead concentration are discussed qualitatively in Section 4.4.
Among the airports in Table 7, air quality monitoring has been conducted at RHV at a location approximately 60 m downwind from the maximum impact site.  Lead concentrations at RHV measured 60 m downwind were above half the level of the lead NAAQS.
Table 7. Airports with Model-Extrapolated Lead Concentrations Potentially Above the Lead NAAQS at the Maximum Impact Area With Unrestricted Areas Within 50 Meters.
                                   Airports
National Defaults
                                 Wind Adjusted
                               % Piston Adjusted
Runway Shift
                                Based Aircraft
                           Based Aircraft Wind Adj.
52F

RHV

ORS

WHP

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

Figure 10. Comparison of model-extrapolated lead concentrations from the wind-speed adjusted national default parameters (orange squares; Section 3.2), and wind-speed adjusted airport-specific activity analysis (blue diamonds; Section 3.3) at airports that have the potential for maximum impact site concentrations to be above the NAAQS for lead with unrestricted access.
Similar to national analysis results, results of the airport-specific activity analysis can be evaluated through a comparison to monitored data. Of the airports included in the airport-specific activity analysis, four had NAAQS surveillance monitors located proximate to or downwind from the maximum impact area. Figure 11 presents the comparison of monitored and model-extrapolated concentrations at these airports. As discussed in Sections 3.4 and 4.1, the coarse comparison presented in Figure 11 has attendant uncertainties (e.g., spatial and temporal differences between monitor and model-extrapolated data). Despite these uncertainties, monitored data suggest that model-extrapolated concentrations which use airport-specific activity estimates generally align with monitored concentrations. A more in-depth comparison of model-extrapolated to monitored concentrations is presented in the context of additional uncertainty analysis in Section 4.4.

      Satellite Image Source: Google Earth
Figure 11. Coarse comparison of monitored to model-extrapolated airport-specific lead concentrations at airports with NAAQS monitors sited proximate to the maximum impact area or locations downwind. Red dots represent monitor location, while yellow dots represent approximate locations of model-extrapolated concentrations from airport-specific activity analysis (Section 3.3). Blue arrows denote the prevailing wind direction at each airport. Locations for model-extrapolated lead concentrations depicted here were based on approximated location of the dominant run-up location. The max impact concentrations represented in the figure are not wind speed adjusted. The wind speed adjusted concentrations at max impact for airports D, E, F, and G are 0.58, 0.31, 0.26, and 0.24 ug/m[3] respectively.
  4.3 Quantitative Uncertainty Analysis of Concentrations of Lead in Air at Airports: The Influence of Run-up Time and Avgas Lead Concentration
As with any analysis of this scope in which estimates of pollutant concentrations at facilities nationwide are developed using an extrapolation approach, there is inherent uncertainty and variability in the estimates. The focus here is on two key parameters that have been demonstrated in previous studies to impact lead concentrations at and downwind from the maximum impact area at airports: run-up time and avgas lead concentration. Run-up time and avgas lead concentrations are not constrained by the functional role of a given airport, but rather vary across airports independently of airport attributes. These two parameters were thus the focus of a quantitative variability evaluation using a Monte Carlo analysis, which is discussed in Section 3.4 above. Additional meteorological and local considerations may contribute to uncertainty at individual airports; the uncertainty from these parameters is discussed qualitatively in Section 4.4.

   4.3.1 National Analysis and Airport-Specific Monte Carlo Results
Figure 12 shows the national analysis results with Monte Carlo bounds around each model-extrapolated concentration for the airport with the highest, and, separately, the airport with the lowest model-extrapolated concentration at the maximum impact site and downwind locations. As the Monte Carlo bounds show, variability in run-up duration and avgas lead concentrations add uncertainty to the exact range of model-extrapolated concentrations nationwide (i.e. exact value of the highest and lowest model-extrapolated concentration in the maximum impact area and downwind locations of US airports); however, the quantitative uncertainty shown in the Monte Carlo is small enough such that it does not obscure meaningful differences between model-extrapolated concentrations at different US airports.
Further, Monte Carlo results consistently show the potential for higher model-extrapolated concentrations than the national analysis results (compare black or blue dots to upper error bars in Figure 12). The potential for higher model-extrapolated concentrations is due to the difference in observed run-up times at the model airport compared to run-up times observed at airports included in the Monte Carlo analysis. As noted in Section 3.4 the deterministic national analysis used 3-month median run-up times for SE and ME, separately, which were measured at the model airport at which AQFs were developed, while the Monte Carlo analysis included observations of longer run-up times from studies at additional airports (Table 5). The increase in model-extrapolated concentrations due to the potential for longer durations of run-up at airports nationwide compared to that observed at the model airport, generally aligns with a sensitivity analysis conducted at the model airport. The sensitivity analysis showed that increasing run-up time from the 5[th] (16 seconds) to 95[th] (121 and 160 seconds for SE and ME respectively) percentiles resulted in approximately an order of magnitude increase in 3-month average modeled concentrations (i.e., 5[th] to 95[th] percentiles of 3-month average modeled concentrations increased from 0.043 to 0.322 ug/m[3] and from 0.005 to 0.035 ug/m[3] for SE and ME, respectively) (Appendices A and C). The average run-up time at a given airport may be impacted by a number of factors (e.g., the number of pilots in training); however, the use of average run-up time from airports with available data provides relevant information to characterize the potential range of concentrations at airports nationwide in a manner consistent with the approach laid out in Section 3.
While the concentration of lead in avgas is also included in the Monte Carlo analysis, this parameter influences results less than run-up duration for two reasons. First, the range of lead in avgas is smaller than the range of average run-up times used in the analysis (Table 5). Second, the impact of longer run-up durations is additive, whereas the impact of lower avgas lead concentrations is incremental (i.e., each additional second of run-up compared to the median value used in the national analysis contributes the same amount to downwind lead concentrations, whereas fuel with 2.10 g/gal lead rather than the 2.12 g/gal contributes 0.02 g/gal less to emissions). The difference in the influence of these parameters helps explain why the uncertainty analysis for model-extrapolated concentrations consistently demonstrates higher values compared with the point estimate.

Figure 12. The range of model-extrapolated lead concentrations at and downwnind of the maximum impact area based on national analysis results. Black diamonds represent the maximum and blue squares represent the minimum model-extrapolated concentration at each location for the 13,153 airports included in the national analysis. Error bars are the concentrations at the 97.5[th] percentile of Monte Carlo results, which account for potential ranges in run-up time and avgas lead concentrations across airports.
Similar to the Monte Carlo bounds around national analysis results, the model-extrapolated concentrations from the airport-specific activity analysis are consistently at or near the 2.5[th] percentile of the Monte Carlo bounds while the 50[th] percentiles and 97.5[th] percentiles of the Monte Carlo analysis are on average 38% and 91% higher than the model-extrapolated concentrations from the airport-specific activity analysis. As discussed above, this observation is primarily the result of having used a shorter run-up time in developing the model-extrapolated lead concentrations in the national analysis compared with run-up times that have been observed at other airports, which were used in the Monte Carlo analysis (Table 5). In addition, the greater influence of run-up time versus lead concentrations in avgas on ground-based atmospheric lead concentrations, leads to changes in run-up time dominating the potential range of concentrations observed in the Monte Carlo results (ICF 2014, Feinberg et al. 2016). The uncertainty results presented here are sensitive to the choice of input distributions for avgas lead concentration and run-up time.

   4.3.2 Comparison of Model-Extrapolated Concentrations From the Airport-Specific Activity Analysis with Monte Carlo Bounds to Monitored Concentrations in the Maximum Impact Area
To evaluate the approach for calculating airport-specific model-extrapolated concentrations with Monte Carlo bounds, results from the approach were compared to relevant monitoring data. Comparisons between model-extrapolated and monitored lead concentrations are most informative when the model-extrapolated and monitor concentrations are in the same approximate location. Two airports had monitors located in close proximity to the location of the model-extrapolated concentrations; however, monitoring at each airport was conducted during different time periods than the time period of national analysis. Thus, model-extrapolated concentrations were adjusted to reflect activity and meteorological data from the monitored time periods. The same national analysis data sources were used to update activity and meteorology in model-extrapolated concentrations to monitored time periods (See Section 3.2, Table 2 for data source details). In addition, as with the airport-specific activity analysis, onsite observational survey data or data on the number and class of aircraft based at the airport were used to calculate piston-engine aircraft activity, as well as SE and ME activity at each airport.
Figure 13 compares the rolling 3-month average model-extrapolated concentrations at the two airports with monitored data in similar locations. At the airport in Panel A, two lead monitors were co-located proximate to the maximum impact area; the primary monitor is identified with a blue dot, the co-located monitor with a black dot, and the model-extrapolated concentrations (based on the lower run-up time estimates) are identified with green dots. Model-extrapolated lead concentrations at this facility are consistently lower than lead concentrations measured at the primary monitor with the difference ranging from 12% to 52% yet the Monte Carlo bounds reflecting potential variation in model-extrapolated values due to variability in run-up duration and avgas lead concentrations consistently include the primary monitored value. Model-extrapolated concentrations at the airport in Panel A identified the majority of 3-month monitored concentrations that exceeded the lead NAAQS (noted by the red line).
Similarly, model-extrapolated concentrations appropriately reflect attainment of the lead NAAQS at the airport in Panel B. In this instance, both model-extrapolated (green dots) and monitored (blue dots) concentrations are below the NAAQS. In addition to providing an example of model-extrapolation performance below the NAAQS, Panel B, also provides an example of a location further downwind than the maximum impact area. At this airport, the monitor was located approximately at the 50-meter downwind model-extrapolation site, which along with activity and other parameters discussed in previous sections, explains the lower concentrations relative to the airport in Panel A.

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

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

   4.4.1 Meteorological Parameters
Several meteorological parameters affect modeled concentrations that result from dispersion modeling of pollutant emissions released at surface level. These parameters include wind speed and direction, mixing height, atmospheric stability, and ambient temperature since they directly relate to conditions of atmospheric turbulence, thermal buoyancy, as well as resulting vertical and lateral dispersion.
Low wind speeds disperse emissions less rapidly compared with high wind speeds, resulting in higher concentrations near the emissions source. Conversely, higher wind speeds result in lower concentrations near the emissions source. Specifically, as discussed in Section 3.2 and demonstrated in Appendix A, the near-field concentration of a non-reactive pollutant approximately scales with <u[-1]>, where u is wind speed and angled brackets imply a time average (Barrett and Britter 2008). Three-month average inverse wind speeds varied -23% to + 21% from the annual average wind speed. The range of inverse wind speeds at the model airport results in 3-month AQFs that vary +23% to -15% from the annual average. Approximately 51% of airports have 3-month average inverse wind speeds during the 3-month period of maximum piston-engine aircraft activity at a single runway end that fall within the range of 3-month average inverse wind speeds at the model airport. Thus, we do not expect wind speed to be a significant source of uncertainty nationwide as sensitivity to wind speed will be captured by the wind speed scaling technique applied, and 3-month AQFs were only sensitive to wind speed by approximately +/-20% at the model airport. For individual airports at the extremes of high and low wind speed, we recognize there is more uncertainty in the extrapolated concentrations.   However, we do not expect significant GA activity during winds below 2.6 m/s or above 10.3 m/s as FAA safety recommendations state that these may be conditions under which it is particularly challenging for a general aviation aircraft to fly (FAA 2006).
At both high and low wind speeds, significant variability in wind direction can result in additional uncertainty. When wind direction shifts significantly, airport operators may or may not initially change the runway end from which piston-engine aircraft take-off due to considerations of cross-winds and operational consistency. As noted in Section 1, airports are built such that one runway-end faces directly into the predominate wind direction, which limits the likelihood of runway-end variability. Further, Section 3.3 discusses a sensitivity analysis that evaluated the impact of shifting piston-engine aircraft operations to a specific runway-end, which addresses instances such as when wind direction variability leads to differences between the active runway-end and wind direction.
Mixing height is another meteorological condition that can influence atmospheric lead concentrations both independently and in conjunction with wind conditions. When mixing heights are very low, as is often the case overnight, then pollutants released at the surface remain trapped in the shallow surface layer, resulting in higher concentrations. Higher mixing heights occur when there is substantial surface mixing, which more rapidly disperses pollution away from the surface and result in lower surface-level concentrations. An unstable atmosphere where the mixing height is changing rapidly will also affect the concentration of lead at the maximum impact site. Previous air quality modeling conducted by EPA at individual airports characterized the influence of mixing height on modeled aircraft lead concentrations (Section 2; Appendix A) (Carr et al. 2011, Feinberg et al. 2016). At the model airport, there is a strong relationship between the 3-month average wind speeds and mixing heights (Figure 14), making it difficult to separately calculate the influence of mixing height on the AQFs. However, because run-up is the largest contributor to lead concentrations at the maximum impact site, the AQF at the maximum impact site is not expected to be sensitive to local mixing height. Concentrations at sites downwind may be more sensitive to mixing height and atmospheric stability, particularly during long periods of atmospheric inversion or at airports that have mixing height characteristics significantly different from the model airport.

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

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

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

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

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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

As described in Section 2, factors that relate aircraft activity to resulting air lead concentrations, referred to as Air Quality Factors (AQFs), are used to estimate atmospheric lead concentrations at airports nationwide by extrapolating the relationship between the number of piston-engine aircraft landing-and-take-off operations (LTOs) and the resulting atmospheric lead concentrations. The AQFs were developed through detailed air quality modeling at a model airport. This appendix provides details on the air quality modeling used to develop the AQFs and the model airport at which they were developed. Specifically, details on three topic areas are included in the sections below: A.1) the air quality modeling setup, input data, and parameters; A.2) characterization of the air quality model performance through model-to monitor comparisons at the model airport;  A.3 and A.4) characteristics of the model airport fleet composition and the fuel consumption rates that are incorporated into the AQFs; and A.5) details of the wind speed adjustment methodology to scale AQFs based on average inverse wind speed during.

A.1 Details Regarding Air Quality Modeling at the Model Airport
This section provides information used to conduct detailed air quality modeling at the model airport. Specifically, the subsections below present: 1) input data and methods to develop aircraft emissions inventories, 2) non-aircraft emissions inventory data, 3) meteorological inputs, 4) model receptors, and 5) the characterization of emission sources and receptors.

A.1.1 Aircraft Activity, Source Locations, and Emissions
Two aircraft emissions inventories were developed for this analysis: a seven-day inventory to facilitate model-to-monitor comparison, and an annual operations inventory used to generate the 3-month average AQFs. Both inventories were developed from a combination of published Federal Aviation Administration (FAA) Air Traffic activity data and on-site surveys. This section presents the underlying data and methods used to develop both inventories. The annual emissions inventory and its use in developing the AQFs at the model airport are further described in Section 2.3 of the report.

Aircraft Activity Surveys
Surveyors collected aircraft operations data at RHV for the following ten days in 2011 from 10 a.m. until 7 p.m. local time.
 8/14  	Sunday
 8/17  	Wednesday
 8/20  	Saturday
 8/23  	Tuesday
 8/26 	Friday
 8/28  	Sunday
 9/1  	Thursday
 9/3  	Saturday
 9/5  	Monday
 9/7  	Wednesday
The collected data included runway location, aircraft tail fin number (N-Number), LTO mode (taxi-out, run-up, take-off, climb, approach, landing, or taxi-in), duration of LTO mode (time-in-mode), and other details about the aircraft activity (e.g., touch-and-go, altitude at approach, altitude at departure). Whenever possible the surveyors visually identified the aircraft type. Where aircraft type was not visually identified, the recorded tail fin numbers were matched to the FAA tail number registry to obtain the type and number of engines. To match local typical airport flight patterns, surveyors attempted to record the timing of approaches beginning at 1,100 ft (335 m) by listening to the control tower broadcast; the surveyors stopped timing climbs at the same height. For those altitudes recorded lower or higher, adjustments were made to normalize the time-in-mode for climb and approach to 1,100 ft. 5.6% of survey entries were flagged as invalid, largely due to missing or incorrectly recorded time data.

Hourly and Daily Aircraft Activity Estimates
Hourly activity profiles for each aircraft class [single engine (SE), multi-engine (ME) and rotorcraft (R)] and each operation-cycle type [full landing and take-off (LTO) and touch-and-go (T&G)] were developed from the 10 days of survey data described above. For morning and evening hours when survey data was not available, the percentage of total daily flights occurring in each hour was taken from a prior survey of piston-aircraft operations at another airport (Carr et al. 2011). Total daily operations were estimated by adjusting the surveyed operational counts to match FAA Air Traffic Activity Data System (ATADS) operation counts for itinerant-general aviation and local-civil activity to account for operations that may have been missed or mis-categorized by surveyors. The adjustment factor equation is given below in Equation A-1. The adjustment factors for the ten days ranged from 1.02 to 1.23.

RAdj = ADaily, ATADS2-AOff HoursA On Hours, Survey						 	(Equation A-1)
Where:
   RAdj
  = the adjustment factor, used ensure estimated aircraft activity counts
                     based on surveyed data matched daily ATADS counts;
                     AOn Hours, Survey  = the hourly aircraft activity counts when surveys were conducted,  according to the survey data. This is the sum of all surveyed counts of the aircraft and operation types selected for modeling;
AOff Hours
ADaily, ATADS
 = the hourly LTO count when surveys were not conducted; and
 = the daily operations from ATADS. The ATADS data reports
                     "operations" which sums arrivals and departures. Because activity is modeled as landing and take-off cycles (LTOs), operations are divided by 2.

For fixed-wing, multi-engine aircraft the relative scarcity of data led to some hours containing very small operational counts. Thus, for these aircraft types, instead of scaling operational counts for each hour, operational counts were scaled for the entire day and the incremental increase in operations was divided evenly across all hours with non-zero survey counts. This incremental adjustment led to fractional operations being modeled for multi-engine aircraft for some hours on some days. For all aircraft types, the adjusted hourly activity counts for each day were used in three of the seven days in the emissions inventory developed for model-to monitor comparisons. These three days corresponded to those with overlapping monitor and survey data. For the remaining four days, the inventory used the average of the adjusted activity profiles across all ten survey days.

Aircraft Emission Rates
Piston engines operating on leaded fuel can emit lead in both the gaseous and particulate form. Aviation fuels containing tetraethyl lead also contain ethylene dibromide as an additive to prevent lead from depositing within the engine. Lead reacts with the ethylene dibromide to form brominated lead compounds. These brominated lead compounds are exhausted as vapors but quickly cool and condense to solid particles. In contrast, organic lead emissions remain as vapors after cooling to ambient temperatures (USEPA 2013b). A fraction of lead is retained in the engine, engine oil, and/or exhaust system, which is estimated in this work at 5% (USEPA 2013a).

Equation A-2 was used to calculate lead emission rates as a function of the mode specific fuel consumption rate.

Emissions(g·s[-1]) = Timesxfuel consumedgsxEnginesHourx(Pbggal)x(1-RegRate)(fuel weightggal)x(SecondsHour)	(Equation A-2)
   Where:
	Time(s) 	 = the time (seconds) in mode;
   fuel consumedgs = the amount (grams) of fuel consumed per second for one engine in a given LTO mode;

EnginesHour = the number of engines operating each hour;

Pb(g)gal
RetRate
fuel weight (g)gal

                                  SecondsHour
   = the concentration (g/gal) of Pb in avgas (2.16 g/gal, the average Pb concentration measured in avgas from RVH);
   = the fraction of Pb retained in the engine after fuel consumption
     (0.05);
   = the weight (g) of avgas fuel per gallon (2,730.6 g/gal); and

  = the number of seconds per hour (3,600 s);

Aircraft emissions profiles for each hour were developed by calculating hourly fuel consumption for each aircraft type by operational mode (taxi, run-up, take-off, climb, approach, landing). Total fuel consumption is a function of the time spent in each operational mode and the fuel consumption rate of the aircraft's engine(s) during that mode. Aircraft class-specific (SE, ME, R) median times-in-mode for each operational mode were developed for each hour for each of the 10 survey days. Use of median times-in-mode avoided biasing fuel consumption high for activities such as run-up which had occasional aircraft with unusually long activity durations or biasing fuel consumption low for activities such as approach where surveyors may have recorded short durations as a result of not knowing of an aircraft approach until it was on final approach and well below the 1,100 feet nominal height. For hours when surveys were not conducted, the inventory assumes the median of all recorded data for that activity mode. For the 3 days of overlapping monitor and survey data, the corresponding day-specific hourly median times-in-mode were used in the emissions inventory. For the remaining 4 days, each day was assigned the same median hourly TIM values.

Published fuel consumption data was used to develop engine- and mode-specific fuel consumption rates. Fuel consumption data by operational mode was available for 18 engines from FAA's Emission and Dispersion Modeling System and the Swiss Federal Office of Civil Aviation report on piston-engine emissions (FAA 2007, SFOCA 2007). These data spanned the range of engine technology groups (fuel injected, turbocharged, carbureted, radial) observed at the airport. Where observed aircraft had specific engine types (e.g., 4-stroke radial, 2-stroke fuel injected) not in the fuel consumption database, fuel consumption for that aircraft was modeled using the engine type with the closest rated horsepower in the same engine technology group. Further details on the mode-specific fuel consumption rates are given in Section A.4.

Aircraft Emission Inventories
The above calculation of emission rates was used to develop two separate emissions inventories. As noted above, both a seven-day and annual emissions inventory were developed for two distinct purposes but using similar methods. The seven-day emissions inventory was used to facilitate model-to-monitor comparison for the days in which survey data and on-site monitoring were conducted concurrently, and as such used the highly resolved hourly operational data from on-site survey data described above. The annual inventory was used to calculate 3-month average concentrations (and derived AQFs), and thus required emissions modeling for 14 months to understand 12 consecutive, 3-month rolling-average concentrations. Absent detailed on-site monitoring data for aircraft and engine types and hourly operations for each day of the 14 months, the annual emissions inventory used the average operational profile, median time-in modes and fuel consumption rates, and ATADs operations data as described in Section 2.3 of the main report. Aircraft emissions for months 13 and 14 were taken from months 1 and 2 respectively so that rolling-average concentrations represented concentrations from a consistent year of emissions. The total lead emissions from aircraft (in tons) are given by month and operational mode in Table A-1. (Lead emissions included in the modeling from other sources are described in Tables A-2 through A-6.)
       Table A-1. Monthly and annual lead emissions from aircraft (tons)
Activity
 Month

                                Aircraft Mode

                                 Total of all
                                   Aircraft
                                    Modes

                                   Taxi-out
                                    Run-Up
 Take-off
                                    Climb
 Approach
                                   Landing
                                   Taxi-in

ME Full LTO
                                      01
                                   1.16E-04
                                   5.85E-05
3.28E-05
 3.82E-05
                                   1.61E-05
                                   7.05E-06
                                   1.99E-05
                                   2.88E-04

                                      02
                                   9.40E-05
                                   4.52E-05
2.73E-05
 2.94E-05
                                   1.34E-05
                                   6.15E-06
                                   1.59E-05
                                   2.32E-04

                                      03
                                   1.23E-04
                                   6.00E-05
3.53E-05
 3.90E-05
                                   1.74E-05
                                   7.85E-06
                                   2.08E-05
                                   3.03E-04

Activity
 Month
                                Aircraft Mode
                                 Total of all
                                   Aircraft
                                    Modes

                                   Taxi-out
                                    Run-Up
 Take-off
                                    Climb
 Approach
                                   Landing
                                   Taxi-in

                                      04
                                   9.85E-05
                                   4.61E-05
2.87E-05
 2.99E-05
                                   1.41E-05
                                   6.55E-06
                                   1.64E-05
                                   2.40E-04

                                      05
                                   1.15E-04
                                   5.55E-05
3.31E-05
 3.62E-05
                                   1.63E-05
                                   7.40E-06
                                   1.94E-05
                                   2.83E-04

                                      06
                                   1.24E-04
                                   5.85E-05
3.57E-05
 3.80E-05
                                   1.76E-05
                                   8.10E-06
                                   2.07E-05
                                   3.02E-04

                                      07
                                   1.29E-04
                                   6.20E-05
3.71E-05
 4.02E-05
                                   1.83E-05
                                   8.30E-06
                                   2.17E-05
                                   3.16E-04

                                      08
                                   1.31E-04
                                   6.25E-05
3.77E-05
 4.05E-05
                                   1.86E-05
                                   8.50E-06
                                   2.19E-05
                                   3.20E-04

                                      09
                                   1.11E-04
                                   5.35E-05
3.18E-05
 3.48E-05
                                   1.56E-05
                                   7.10E-06
                                   1.87E-05
                                   2.72E-04

                                      10
                                   1.06E-04
                                   5.15E-05
3.04E-05
 3.36E-05
                                   1.50E-05
                                   6.75E-06
                                   1.79E-05
                                   2.61E-04

                                      11
                                   9.15E-05
                                   4.16E-05
2.69E-05
 2.68E-05
                                   1.32E-05
                                   6.30E-06
                                   1.51E-05
                                   2.21E-04

                                      12
                                   7.20E-05
                                   3.18E-05
2.13E-05
 2.04E-05
                                   1.05E-05
                                   5.05E-06
                                   1.17E-05
                                   1.73E-04

                                                                  Annual Total
                                   1.31E-03
                                   6.27E-04
3.78E-04
 4.07E-04
                                   1.86E-04
                                   8.51E-05
                                   2.20E-04
                                   3.21E-03
 ME T&G
                                      01
                                      --
                                      --
                                      --
 2.08E-05
                                   1.31E-05
                                      --
                                      --
                                   3.38E-05

                                      02
                                      --
                                      --
                                      --
 1.81E-05
                                   1.22E-05
                                      --
                                      --
                                   3.03E-05

                                      03
                                      --
                                      --
                                      --
 2.31E-05
                                   1.53E-05
                                      --
                                      --
                                   3.84E-05

                                      04
                                      --
                                      --
                                      --
 1.94E-05
                                   1.35E-05
                                      --
                                      --
                                   3.29E-05

                                      05
                                      --
                                      --
                                      --
 2.19E-05
                                   1.46E-05
                                      --
                                      --
                                   3.65E-05

                                      06
                                      --
                                      --
                                      --
 2.39E-05
                                   1.63E-05
                                      --
                                      --
                                   4.02E-05

                                      07
                                      --
                                      --
                                      --
 2.45E-05
                                   1.65E-05
                                      --
                                      --
                                   4.10E-05

                                      08
                                      --
                                      --
                                      --
 2.51E-05
                                   1.69E-05
                                      --
                                      --
                                   4.20E-05

                                      09
                                      --
                                      --
                                      --
 2.09E-05
                                   1.39E-05
                                      --
                                      --
                                   3.48E-05

                                      10
                                      --
                                      --
                                      --
 1.99E-05
                                   1.32E-05
                                      --
                                      --
                                   3.31E-05

                                      11
                                      --
                                      --
                                      --
 1.86E-05
                                   1.33E-05
                                      --
                                      --
                                   3.19E-05

                                      12
                                      --
                                      --
                                      --
 1.50E-05
                                   1.10E-05
                                      --
                                      --
                                   2.60E-05

                                                                  Annual Total
                                   0.00E+00
                                      --
                                      --
 2.51E-04
                                   1.70E-04
                                      --
                                      --
                                   4.21E-04
SE Full
LTO
                                      01
                                   8.20E-04
                                   3.62E-04
3.09E-04
 3.81E-04
                                   3.12E-04
                                   6.65E-05
                                   2.23E-04
                                   2.48E-03

                                      02
                                   7.15E-04
                                   3.16E-04
2.70E-04
 3.20E-04
                                   2.66E-04
                                   5.75E-05
                                   1.96E-04
                                   2.14E-03

                                      03
                                   9.15E-04
                                   4.03E-04
3.44E-04
 4.13E-04
                                   3.42E-04
                                   7.35E-05
                                   2.49E-04
                                   2.74E-03

                                      04
                                   7.70E-04
                                   3.38E-04
2.89E-04
 3.38E-04
                                   2.83E-04
                                   6.20E-05
                                   2.11E-04
                                   2.29E-03

                                      05
                                   8.65E-04
                                   3.81E-04
3.25E-04
 3.89E-04
                                   3.22E-04
                                   6.95E-05
                                   2.36E-04
                                   2.59E-03

                                      06
                                   9.45E-04
                                   4.17E-04
3.56E-04
 4.20E-04