3.51E-04
                                   7.60E-05
                                   2.59E-04
                                   2.83E-03
Activity
 Month

                                Aircraft Mode

                                 Total of all
                                   Aircraft
                                    Modes

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

                                      07
                                   9.70E-04
                                   4.27E-04
3.65E-04
 4.35E-04
                                   3.61E-04
                                   7.80E-05
                                   2.65E-04
                                   2.90E-03

                                      08
                                   9.90E-04
                                   4.37E-04
3.73E-04
 4.42E-04
                                   3.68E-04
                                   8.00E-05
                                   2.71E-04
                                   2.96E-03

                                      09
                                   8.25E-04
                                   3.64E-04
3.11E-04
 3.72E-04
                                   3.09E-04
                                   6.65E-05
                                   2.26E-04
                                   2.48E-03

                                      10
                                   7.90E-04
                                   3.47E-04
2.96E-04
 3.56E-04
                                   2.95E-04
                                   6.35E-05
                                   2.15E-04
                                   2.36E-03

                                      11
                                   7.35E-04
                                   3.24E-04
2.77E-04
 3.18E-04
                                   2.69E-04
                                   5.90E-05
                                   2.02E-04
                                   2.19E-03

                                      12
                                   5.95E-04
                                   2.61E-04
2.24E-04
 2.53E-04
                                   2.15E-04
                                   4.77E-05
                                   1.64E-04
                                   1.76E-03

                                                                  Annual Total
                                   9.94E-03
                                   4.37E-03
3.74E-03
 4.43E-03
                                   3.69E-03
                                   8.00E-04
                                   2.72E-03
                                   2.97E-02
SE T&G
                                      01
                                      --
                                      --
                                      --
 2.86E-04
                                   2.79E-04
                                      --
                                      --
                                   5.65E-04

                                      02
                                      --
                                      --
                                      --
 2.44E-04
                                   2.42E-04
                                      --
                                      --
                                   4.86E-04

                                      03
                                      --
                                      --
                                      --
 3.13E-04
                                   3.09E-04
                                      --
                                      --
                                   6.20E-04

                                      04
                                      --
                                      --
                                      --
 2.60E-04
                                   2.59E-04
                                      --
                                      --
                                   5.20E-04

                                      05
                                      --
                                      --
                                      --
 2.96E-04
                                   2.92E-04
                                      --
                                      --
                                   5.90E-04

                                      06
                                      --
                                      --
                                      --
 3.21E-04
                                   3.20E-04
                                      --
                                      --
                                   6.40E-04

                                      07
                                      --
                                      --
                                      --
 3.31E-04
                                   3.28E-04
                                      --
                                      --
                                   6.60E-04

                                      08
                                      --
                                      --
                                      --
 3.38E-04
                                   3.35E-04
                                      --
                                      --
                                   6.70E-04

                                      09
                                      --
                                      --
                                      --
 2.83E-04
                                   2.80E-04
                                      --
                                      --
                                   5.60E-04

                                      10
                                      --
                                      --
                                      --
 2.70E-04
                                   2.67E-04
                                      --
                                      --
                                   5.35E-04

                                      11
                                      --
                                      --
                                      --
 2.46E-04
                                   2.48E-04
                                      --
                                      --
                                   4.94E-04

                                      12
                                      --
                                      --
                                      --
 1.97E-04
                                   2.00E-04
                                      --
                                      --
                                   3.97E-04

                                                                  Annual Total
                                      --
                                      --
                                      --
                                   3.39E-03
                                   3.36E-03
                                      --
                                      --
                                   6.75E-03
                                                                  Annual Total
                                   1.20E-02
                                   5.00E-03
4.25E-03
 8.48E-03
                                   7.40E-03
                                   8.99E-04
                                   3.16E-03
                                   4.12E-02

Aircraft Source Locations
Both the seven day and annual emissions inventories are spatially allocated at the model airport to characterize resulting atmospheric concentrations. A three-dimensional representation of the modeled aircraft source locations is provided in Figure A-1 for northwest take-offs and in Figure A-2 for southeast take-offs. All ground-level release heights were set to 0.5 meter to represent the approximate aircraft exhaust height. For taxi activity, emission sources were placed approximately 50 meters apart along two taxiways  -  one directly adjacent to the terminal and hangars, and one along a separate taxiway to the west of the first taxiway. For run-up activity, two run-up locations were modeled near the terminus of both taxiways. The two run-up locations were approximately 8 meters apart at both ends of the taxiway. For modes at altitude (i.e., climb and approach), emissions sources had 50 meters horizontal spacing and ascent/descent angles of 4.7 and 3.8 degrees respectively, angles similar to those used in the SMO study (Carr et al. 2011).  Airport noise ordinances dictate that aircraft using runway 31R (northwest takeoff) make a 30-degree right turn after departing the runway and after climbing to an altitude of at least 500 feet above ground level.

                Satellite Image Source: ESRI Prime Imagery 3D
       The aircraft release heights are at each colored sphere. Symbol shadings correspond to release heights, where green values are close to the surface and dark red values are approximately 145 m above the surface. Satellite photography and terrain features are shown. Vertical terrain height is exaggerated. The point of view is elevated, approximately 2.3 km away and facing northeast.
 Figure A-1. Modeled aircraft emission source locations for northwest take-offs, in three dimensions

                Satellite Image Source: ESRI Prime Imagery 3D
       The aircraft release heights are at each colored sphere. Symbol shadings correspond to release heights, where green values are close to the surface and dark red values are approximately 145 m above the surface. Satellite photography and terrain features are shown. Vertical terrain height is exaggerated. The point of view is elevated, approximately 2.3 km away and facing northeast.
  Figure A-2. Modeled aircraft emission source locations for southeast take-offs, in three dimensions

A.1.2 Emissions from Sources Other than Aircraft
Data on emissions from sources other than aircraft operating at the model airport was collected in order to include these sources in the modeled concentrations that would be compared to monitored concentrations, the latter of which of course included atmospheric lead from any source. For purposes of modeling, these non-aircraft emissions include rotorcraft at the model airport, point sources, nearby road sources, mobile and area sources, and background lead within 20 km of the airport.

Rotorcraft
Piston-engine Rotorcraft activity estimates at the model airport were generated using the same methodology described above for fixed-wing single- and multi-engine aircraft. Daily surveys were combined with activity counts from ATADS data to develop hourly rotorcraft activity profiles. Rotorcraft parking was assumed to be near the model airport terminal, and take-offs occurred at a 4.7 degree trajectory from Runway 31R and Runway 13L, or vertically from a helicopter practice area (also called the haypatch). As with fixed-wing aircraft at the model airport, rotorcraft at the model airport were modeled as volume sources as described in section A.1.5 with 2.3 vertical meters between source points in landing and take-off as shown in Figures A-1 and A-2. Monthly and annual emissions from rotorcraft are given in Table A-2. Nearby heliports (i.e., rotorcraft landing and takeoffs not occurring at the model facility) were modeled as area sources.
      Table A-2. Monthly rotorcraft emissions at the model airport (tons)
Location
 Month
                                   Taxi-out
                                   Take-off
                                   Landing
                                   Taxi-in
                                    Total
31R/13L
                                      01
                                   3.38E-05
                                   4.88E-06
                                   1.29E-06
                                   9.10E-06
                                   4.90E-05

                                      02
                                   2.78E-05
                                   4.75E-06
                                   1.03E-06
                                   8.15E-06
                                   4.17E-05

                                      03
                                   3.61E-05
                                   5.85E-06
                                   1.35E-06
                                   1.03E-05
                                   5.35E-05

                                      04
                                   2.92E-05
                                   5.30E-06
                                   1.07E-06
                                   8.80E-06
                                   4.43E-05

                                      05
                                   3.39E-05
                                   5.65E-06
                                   1.26E-06
                                   9.80E-06
                                   5.05E-05

                                      06
                                   3.64E-05
                                   6.35E-06
                                   1.35E-06
                                   1.08E-05
                                   5.50E-05

                                      07
                                   3.79E-05
                                   6.40E-06
                                   1.41E-06
                                   1.10E-05
                                   5.65E-05

                                      08
                                   3.84E-05
                                   6.60E-06
                                   1.43E-06
                                   1.13E-05
                                   5.75E-05

                                      09
                                   3.25E-05
                                   5.35E-06
                                   1.21E-06
                                   9.35E-06
                                   4.84E-05

                                      10
                                   3.11E-05
                                   5.05E-06
                                   1.16E-06
                                   8.90E-06
                                   4.62E-05

                                      11
                                   2.72E-05
                                   5.30E-06
                                   9.85E-07
                                   8.55E-06
                                   4.20E-05

                                      12
                                   2.14E-05
                                   4.43E-06
                                   7.65E-07
                                   6.95E-06
                                   3.36E-05

                                                                  Annual Total
                                   3.85E-04
                                   6.59E-05
                                   1.43E-05
                                   1.13E-04
                                   5.78E-04

                                      01
                                   3.38E-05
                                   4.88E-06
                                      --
                                   9.10E-06
                                   4.77E-05
Location
 Month
                                   Taxi-out
                                   Take-off
                                   Landing
                                   Taxi-in
                                    Total
Haypatch
                                      02
                                   2.78E-05
                                   4.75E-06
                                      --
                                   8.15E-06
                                   4.07E-05

                                      03
                                   3.61E-05
                                   5.85E-06
                                      --
                                   1.03E-05
                                   5.20E-05

                                      04
                                   2.92E-05
                                   5.30E-06
                                      --
                                   8.80E-06
                                   4.33E-05

                                      05
                                   3.39E-05
                                   5.65E-06
                                      --
                                   9.80E-06
                                   4.93E-05

                                      06
                                   3.64E-05
                                   6.35E-06
                                      --
                                   1.08E-05
                                   5.35E-05

                                      07
                                   3.79E-05
                                   6.40E-06
                                      --
                                   1.10E-05
                                   5.50E-05

                                      08
                                   3.84E-05
                                   6.60E-06
                                      --
                                   1.13E-05
                                   5.65E-05

                                      09
                                   3.25E-05
                                   5.35E-06
                                      --
                                   9.35E-06
                                   4.72E-05

                                      10
                                   3.11E-05
                                   5.05E-06
                                      --
                                   8.90E-06
                                   4.51E-05

                                      11
                                   2.72E-05
                                   5.30E-06
                                      --
                                   8.55E-06
                                   4.10E-05

                                      12
                                   2.14E-05
                                   4.43E-06
                                      --
                                   6.95E-06
                                   3.28E-05

                                                                  Annual Total
                                   3.85E-04
                                   6.59E-05
                                      --
                                   1.13E-04
                                   5.64E-04

Point Sources
Sixteen point sources of lead emissions within approximately 20 km of the model airport were modeled based on emissions data from the 2008 National Emissions Inventory (version 1.5) (USEPA 2011). Excluded from the point source inventory were approximately 122 facilities that each emit less than 1E-05 US Tons per year (TPY) of lead (totaling 1.8E-04 TPY or 0.36 lbs/yr), which is more than a factor of 100 times smaller than the model airport aircraft emissions. The hourly emissions profiles for point sources were approximated using the temporal codes that the Bay Area Air Quality Management District (BAAQMD) used for Community Air Risk Evaluation (CARE) modeling (BAAQMD 2011). Point source facility descriptions and emissions magnitudes are given in Table A-3.
 Table A-3. Industries corresponding to the 16 modeled facilities within 20 km of the Model Airport
                              Source Description

                            Number of   Facilities
                        Modeled Pb  Emissions (TPY)[a]
                     Emissions Percentage  	of Total Point
San Jose Airport  -  Piston-engine emissions
                                      1
                                 1.82x10-1
                                    98.86%
Heliports
                                      4
                                 1.41x10-3
                                    0.77%
Nonferrous Metal (except Aluminum) Production and Processing
                                      1
                                 2.87x10-4
                                    0.16%
Aerospace Product and Parts Manufacturing
                                      1
                                 1.29x10-4
                                    0.07%
Computer Storage Device Manufacturing, Data Processing, Hosting, and Related Services
                                      2
                                 7.25x10-5
                                    0.04%
Crematorium
                                      2
                                 5.40x10-5
                                    0.03%
                                                                             I
                              Source Description

 Number of
                                  Facilities

  Modeled Pb
Emissions (TPY)a
                      Emissions Percentage of Total Point

Dry cleaning and Laundry Services
                                      1

   4.75x10-5
                                    0.03%
Commercial and Service Industry Machinery Manufacturing
                                      1

   2.70x10-5
                                    0.01%
Water, Sewage and Other Systems
                                      1

   2.70x10-5
                                    0.01%
General Medical and Surgical Hospitals
                                      1

   2.50x10-5
                                    0.01%
Recyclable Material Merchant Wholesalers
                                      1

   2.10x10-5
                                    0.01%
                                                                         TOTAL
                                      16

   1.84x10-1
                                     100%
                                                                             I
a  San Jose Airport emissions are representative of 2015. All other emissions in this Table are representative of 2008.

Nearby Road Sources
Lead emissions were modeled from three roadways in close proximity to the airport. Time-varying emission rates were calculated from a combination of diurnal traffic count data, the area of each roadway, and a mobile lead emissions per mile traveled estimate. The hot stabilized summer emission factor of 0.002 mg of Pb/mile was used for gasoline vehicles, and an emission factor of 0.00724 mg/mile was used for diesel fueled trucks and buses (USEPA 2006). The total annual average lead emissions from all three adjacent roadways was 4.3x10[-5] TPY. The location of the roadway emissions sources is shown in Figure A-3, and roadway characteristics are given in Table A-4.

Ocala
Ave.
Tully Rd.
E. Capitol
Expwy.
   Figure A-3. Explicitly modeled road sources adjacent to the model airport
Table A-4. Collected input data for explicitly modeled road sources adjacent to the model airport

                                    Street

 Length of Road (m)

Area of Road (m[2])
 Average Daily Traffic
	(ADT)
 Year of ADT Data

East Capitol Expressway
                                   1223.10

   50661.24
                                    40,700
                                     2008
Tully Road
                                    643.74

   22457.86
                                    33,676
                                     2010
Ocala Avenue
                                    740.30

   12355.21
I 	10,867
                                     2009
                                                                             I

Gridded Area, On-Road Mobile, and Non-Road Mobile Sources
Lead emissions from area,  non-road mobile,  and on-road mobile sources  within approximately 20 km of the model airport were modeled in 1x1-km grid cells using an annual year 2015 projection inventory developed previously by a local agency (BAAQMD 2011). The total modeled emissions are shown in Table A-5 (for on-road mobile) and Table A-6 (for nonroad mobile plus area).
 Table A-5. Modeled on-road mobile gridded Pb emissions within 20 km of the model airport
                              Source Description
                              Pb Emissions (TPY)
                 Emissions Percentage of Total On-Road Mobile
Highway Vehicle - Tire Wear
                                 2.27x10-5
                                     59%
Highway Vehicle - Brake Wear
                                 1.06x10-5
                                     27%
Highway Vehicle - Diesel Exhaust
                                 5.30x10-6
                                     14%
                                                                         TOTAL
                                 3.86x10-5
                                     100%

                                     Table A-6. Modeled non-road mobile and area gridded lead emissions within 20 km of the model airport
                              Source Description

Pb Emissions (TPY)

Emissions Percentage of Total Non-Road Mobile and Area
Construction: Industrial, Commercial, Institutional, Residential, Road Construction

  3.37x10-3

                                    50.96%
Paved Roads

  1.84x10-3

                                    27.85%
Food and Kindred Products: Commercial Cooking and Miscellaneous

  7.10x10-4

                                    10.73%
Agriculture Production - Livestock

  2.10x10-4

                                    3.17%
Military and Commercial Aircraft

  2.08x10-4

                                    3.14%
Geogenic Wind Erosion

  1.34x10-4

                                    2.03%
Residential Oil and Wood Burning

  5.60x10-5

                                    0.85%
Mineral Processes: Concrete, Gypsum, Plaster

  3.39x10-5

                                    0.51%
Unpaved Roads

  2.59x10-5

                                    0.39%
Forest Wildfires, Prescribed Burns, Motor Vehicle Fires

  1.24x10-5

                                    0.19%
Off-highway Vehicle Diesel (incl. Airport Ground Support Equipment)

  5.38x10-6

                                    0.08%
Agriculture Production - Crops

  3.15x10-6

                                    0.05%
Railroad Equipment

  1.82x10-6

                                    0.03%
                                                                             I

            Source Description

Pb Emissions (TPY)

Emissions Percentage of Total Non-Road Mobile and Area
Open Burning

  7.90x10-7

                                    0.01%
Agriculture Production - Crops - as nonpoint

  4.30x10-7

                                    0.01%
Pleasure Water Craft

  5.61x10[-]

                                   0.0001%

TOTAL

  6.61x10-3

                                     100%

Background Emissions
The background ambient lead concentration was set to 0.5 ng/m[3], the pristine background ambient lead concentration used by the EPA Office of Air Quality Planning and Standards Regulatory Impact Analysis for the lead National Ambient Air Quality Standard (NAAQS) (USEPA 2008b).

A.1.3 Meteorology Meteorological Data
Surface and upper-air meteorological data were processed using AERMOD's meteorological preprocessor, AERMET, to produce hourly data on mixing heights, stability, winds, temperature, and precipitation.[9,]  We used the twice-daily upper-air data from the radiosonde station at the Metropolitan Oakland International Airport (OAK), which is the nearest upper-air station (approximately 55 km north-northwest of the model airport). For the seven-day modelling used in the model-to-monitor comparison, we used on-site hourly surface wind data collected concurrently with air concentration data. As described in Section 2.3, for the annual modeling, we used the 1-minute ASOS wind data and the hourly surface meteorological data from SJC (Norman Y. Mineta San Jose International Airport; SJC; WBAN ID 23293; approximately 10 km northwest of RHV). Review of the SJC ASOS station data showed its flow directions were similar to those measured concurrently at RHV, and sensitivity analyses showed that model performance using the SJC 1-minute wind data was equivalent to using the on-site monitored data for the seven days of monitoring data.

AERSURFACE Parameterization
AERMET requires three surface characteristics for the area around the study site (RHV) in order to estimate turbulence and mixing heights. These characteristics are albedo, Bowen ratio (ratio of sensible heating to latent heating), and surface roughness length. The AERSURFACE11 preprocessor estimates these surface characteristics based on land cover and user inputs that describe the site and its climatology. Albedo and Bowen ratio values are calculated within 10 km of the study site, and 1 km is the recommended default radius for calculating surface roughness (USEPA 2008a, USEPA 2009).

While AERSURFACE only accepts version 1992 of the Multi-Resolution Land Characteristics Consortium (MRLC) and the National Land Cover Database (NLCD92), two more recent versions of the NLCD (2001 and 2006) are available. Between 1992 and the date of the most recent version, 2006, the proportion of land that was developed within 10 km of RHV increased approximately 15 percent, the developed land became more "intensely" developed,  and grasslands shrank. Reductions in grasslands and increases in development increase Bowen ratio (by up to 100-200 percent) and surface roughness (by up to two orders of magnitude), which lead to greater turbulence and higher mixing heights.

Given these important differences in land cover between 1992 and 2006 and given that the AERSURFACE only accepts NLCD92 data; Geographic Information Systems (GIS) software was used to reproduce the functionality of AERSURFACE while using NLCD06 data. For surface roughness lengths, land cover class fraction was determined within approximately 1 km of the meteorological site for each 30 degree wedge over 12 directional sectors. For simplicity, the 1km distance was determined using a 1x1-km square centered on the meteorology site, so the distance was 1 km from the site perpendicularly to each side and approximately 1.4 km to each corner. For Bowen ratio and albedo calculations, the number of each land cover class was counted within a 10x10-km area centered on the meteorology site (the default area specified in USEPA 2008a). The land cover and 10x10-km squares for the model airport and the ASOS station site (SJC) are shown in Figure A-4.

Land Cover Image Source: National Land Cover Database http://www.mrlc.gov/nlcd2006.php
  Figure A-4. The 10x10-km squares used to evaluate albedo and Bowen ratio, with NLCD06 base map
Next the surface characteristics were paired with the climate conditions (i.e., season definitions, wetness, and aridity) of each site based on 1981-2010 NCDC climate normal  data for SJC. Monthly season assignments were subjective and based on monthly average temperatures and precipitation. The aridity determination used annual average precipitation data (where the average annual rainfall at SJC is 15 inches). Wetness values were determined by comparing monthly precipitation data to the climate normal monthly precipitation data, where months receiving less than half the normal precipitation amount were considered dry and months receiving over twice the normal precipitation amount were considered wet. Monthly climate statistics at SJC are shown in Table A-7.
         Table A-7. Monthly climate statistics at the SJC ASOS Station

 Jan
 Feb
 Mar
                                     Apr
 May
                                     Jun
                                     Jul
 Aug
                                     Sep
                                     Oct
 Nov
 Dec
TEMPERATURE (F): 30-year normal
50.1
53.3
56.2
                                     58.9
 63.4
                                     67.5
 70.0
 70.1
 68.5
 63.2
55.1
 50.0
PRECIPITATION
(in.): 30-year normal
3.07
3.11
2.54
                                     1.18
 0.51
                                     0.10
 0.02
 0.02
 0.18
 0.80
1.68
 2.61
SEASON
ASSIGNMENT FOR ALL MODELING:
                                     Fall
                                     Fall
Spring
Spring
 Sum.
 Sum.
 Sum.
 Sum.
 Sum.
 Sum.
                                     Fall
                                     Fall
PRECIPITATION
(in.): 2010
4.58
2.12
1.94
                                     3.10
 0.35
                                      0
                                      0
                                      0
                                      0
 0.02
1.76
 2.58
PRECIPITATION
(in.): Ratio
2010 to 30-year normal for month
1.49
0.68
0.76
                                     2.63
 0.69
                                      0
                                      0
                                      0
                                      0
 0.03
1.05
 0.99
WETNESS
ASSIGNMENTS
AVG
 AVG
 AVG
 WET
 AVG
                                     DRY
                                     DRY
                                     DRY
                                     DRY
                                     DRY
 AVG
 AVG
PRECIPITATION
(in.): 2011
                                      --
                                      --
                                      --
                                      --
                                      --
                                      --
                                      --
                                      0
                                      --
                                      --
                                      --
                                      --
PRECIPITATION
(in.): Ratio
2011 to 30-year normal
                                      --
                                      --
                                      --
                                      --
                                      --
                                      --
                                      --
                                      0
                                      --
                                      --
                                      --
                                      --
 a  Color shading is arbitrary and is for visualization purposes only.

Finally, the above information was combined with the surface characteristic lookup tables (USEPA 2008a), to report the surface characteristics at each site by month and sector (the latter only for surface roughness length). The values of albedo and Bowen ratio are shown in Table A8 for the on-site meteorology at the model airport (only for August 2011, as used in the model-to-monitor comparison) and for the meteorology site at SJC (for 2010, as used in the annual modeling). The albedo is 0.17 at both sites, reflecting the predominantly residential land cover within 10 km of both sites. The Bowen ratio values vary by the season and wetness value determination, from 0.78 to 2.42 (with 2.07 the August 2011 value at the model airport).
 Table A-8. Albedo and Bowen ratio values within approximately 10 km of the meteorology sites
 Met. Site
I
 Month
I
 Albedo
I
 Bowen
 Ratio
I
 T&B at model airport (year 2011)
                                       8
                                     0.17
                                     2.07
 SJC (year 2010)
                                       1
                                     0.17
                                     1.24
                                                                 I
 Met. Site
I
 Month
I
 Albedo
I
 Bowen
 Ratio
I

                                       2

                                     1.24

                                       3

                                     1.12

                                       4

                                     0.78

                                       5

                                     1.15

                                       6

                                     2.42

                                       7

                                     2.42

                                       8

                                     2.42

                                       9

                                     2.42

                                      10

                                     2.42

                                      11

                                     1.24

                                      12

                                     1.24
                                                                 I

After completing the 2010 modeling results, it was discovered that the 1- and 10-km squares used at the SJC site were not oriented in a typical north-south alignment but were rotated approximately 18º counterclockwise from the north. Rotating to a north-south alignment had no effect on albedo values. On average, the Bowen ratio values were 6 percent larger than if the area was oriented north-south -- this is mostly because the north-south alignment encompassed less open water and more developed land compared to the rotated square. The effect of the rotation on surface roughness length values was mixed. Compared to the values using the north-south alignment, roughness length values were unaffected for most or all months in two sectors (the 60-90, and 150-180 degree sectors), were an average of 8 percent or 4 cm smaller for all months in six sectors (the 0-30, 90-120, 210-240, 240-270, 300-330, and 330-360-degree sectors), and were an average of 13 percent or 5 cm too large for all months in 4 sectors (the 30-60, 120-150, 180-210, and 270-330 degree sectors). The sectors that have the largest impact on the modeling results, in terms of dominant wind directions, are the 90-120, 120-150, and especially the 270-300 and 300-330 degree sectors. On average during airport operation hours, the cumulative effect of this rotation was to raise the mixing height by less than 1 percent (less than 5 m), with similarly small positive or negative effects on sensible heat flux, surface friction velocity, convective velocity scale, and stability. Test runs in AERMOD suggested that these differences would have no more than a 1-percent effect on 3-month average modeled air concentrations and depositions.

Urban Setting
Urban boundary layers were parameterized with AERMOD's urban setting option, along with the estimated 2009 population of the local area, Santa Clara County (1,785,000).

A.1.4 Receptors
To fully define the spatial extent of elevated lead concentrations within the vicinity of the airport, 2,250 receptor locations were used, as shown in Figure A-5. Also shown are the locations of the three ambient air monitoring stations. Inside the airport property and around the airport boundary, receptors were placed at 50 m intervals, with approximately 365 receptors placed across the airport on taxiways, airport hangars, access roadways, buildings, and the airport meteorology station. Beyond the airport boundary, the 50 m grid spacing continued along the northwest-southeast orientation axis which is parallel to the runways; for the other areas within 1 km of the facility boundary a 100 m grid spacing was implemented; for areas out to 2 km, 200 m grid spacing was implemented.
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             Road Image Source: ESRI U.S. Major Roads version 9.3.1
  Figure A-5. Receptor field for ambient air quality concentration analysis
A.1.5 Emission Source Characterization and Parameters
Aircraft
We modeled aircraft as volume sources within AERMOD, which is the method recommended by EPA for modeling a "line source" representing a moving object (USEPA 2004), and is the same approach used in previous air quality modeling at a general aviation airport (USEPA 2010, Carr et al. 2011). The modeled coordinates and release heights above the surface represent the center of the volume. The equations below, as shown in Table A-9, were used to calculate initial sigma-y and sigma-z values (horizontal and vertical plume extents).

(Equation A-3)
(Equation A-4)
(Equation A-5)
(Equation A-6)
Sigma − z(𝑚) = [1.8 + (0.11 x W2U)] x6030 [0.2]

Sigma − z(𝑚) = 𝐹 + 𝐺 + Eq. A-3
Sigma − y or z(𝑚) =  AB
Sigma − y(𝑚) = 𝐶 + 𝐷 + 𝐸

   Table A-9. Sigma-y and sigma-z values of modeled aircraft volume sources
                                     LTO
                                 Sigma-y (m)
                                 Sigma-z (m)
Taxi
23.26
Used Eq. A-5 where:
 = 50 m (horizontal spacing between source points);
 = 2.15
2.64
Used Eq. A-3, where:
W2 = 11.43 m (width of the wider taxiway (22.86) divided by 2);
U = 2.54 m/s (mean wind speed during the modeling period during RHV operational hours)
Run-up
6.4
Used Eq. A-6, where:
 = 4.96 m (typical wingspan (10.67 m) divided by 2.15);
 = 0.6 m (horizontal momentum of the exhaust);a
 = 0.85 m (propeller turbulence wake)b
3.48
Used Eq. A-4, where:
 = 0.5 m (release height);
 = 0.65 m (exhaust buoyancy);c  W2 = 5.34 m (typical wingspan (10.67 m) divided by 2);
U = 2.54 m/s (mean wind speed during the modeling period during RHV operational hours)
                                     LTO
                                 Sigma-y (m)
                                 Sigma-z (m)
Moving non-taxi (take-off, climb, approach, landing)
23.26
Used the same method as taxi (see above).
2.33
Used Eq. A-3, where:
W2 = 5.34 m (typical wingspan (10.67 m) divided by 2);
U = 2.54 m/s (mean wind speed during the modeling period during RHV operational hours)
      [a] The 0.6 m horizontal exhaust momentum was based on a sensitivity test with SCREEN3 with and without the typical exhaust flow of 100 ft[3]/min.
      b The 0.85 m propeller turbulence wake was calculated by dividing the typical 1.83 m propeller size by 2.15.
      [c] The 0.65 m exhaust buoyancy was based on a sensitivity test with SCREEN3 with and without an exhaust temperature of 573 K.

Point and Area Sources
SJC, a nearby airport, was modeled as an area source using the approximate airport boundary line, while all other data in the point source file were modeled as point sources. For the SJC area source, the North American Industrial Classification System (NAICS) code was used to identify the average release height from California lead emitters in the 2005 NEI v2, and this average release height was used in lieu of a sigma-z. For the point sources, the same NAICS methodology was used from the 2005 NEI v2 for release height, stack diameter, and exit gas temperature and velocity. All release heights were between 8 and 13 meters, stack diameters were less than 1 meters, exit gas temperatures were between 300 and 600 K, and exit gas velocities were between 5 and 25 m/s.

Nearby Road Sources
The lengths and widths of the area sources used to model nearby road sources were determined using aerial photos. A release height of 2 meters and an initial sigma-z of 2.15 m were applied to all roadways which accounts for the mix of light and heavy-duty vehicles as well as vehicle induced turbulence.

Gridded Area, On-Road Mobile, and Non-Road Mobile Sources
Consistent with previous air quality modeling at a General Aviation airport (USEPA 2010, Carr et al. 2011), gridded area, on-road mobile, and non-road mobile sources were modeled as area sources with a release height of 2 m and a sigma-z of 2.15 meters.

A.2 Sensitivity Analysis of Model-to-Monitor Comparison at the Model Airport
As discussed in Section 2.2 of the report, model-to-monitor comparisons at the model airport over a seven-day period showed modeled concentrations were well within a factor of two of monitored concentrations, which is a typical metric for robust model performance. The sensitivity analysis described in this section was conducted in order to evaluate key input parameters for their potential impact on modeled concentrations. Lead monitoring data also have uncertainty and variability that may contribute to differences between monitored and modeled concentrations.  This appendix presents information regarding only the modeling sensitivity analyses.

The sensitivity analyses focused on the subset of parameters that previous modeling suggested most strongly influence concentrations: 1) the location that pilots use for run-up activities, 2) the duration that pilots conduct run-up activities, and 3) whether pilots are using a SE- or ME plane.  Regarding the first parameter, run-up activities are conducted in a designated area immediately adjacent to a runway end; however, the area is generally large enough for several aircraft to park in, and thus the location of run-up relative to a monitor or model receptor can vary by several meters depending on where an aircraft is within the designated run-up area. Run-up activities that are modeled to occur closer to a model receptor than actually occurred, would be expected to result in the model over-predicting concentrations when compared with monitored concentrations. Conversely, run-up activity that is modeled to be further away from the location at which an aircraft conducted this activity would be expected to result in the model under-predicting concentrations compared with monitored data. To evaluate this distance parameter, a series of supplemental model receptors were setup in three concentric rings around the monitor adjacent to the maximum impact location on days with under- or over-prediction as shown in Figure A-6. The use of supplemental model receptors is analogous to moving the emissions source in the model (i.e., aircraft conducting run-up), but is more feasible (i.e., requires less modifications to the model runs and can be completed in a single model run).
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Figure A-6 Concentric rings of receptors around two downwind monitoring locations
Regarding the second parameter, the duration of run-up varies from pilot to pilot. The median run-up observed at RHV was 40 and 63 seconds for SE and ME aircraft, respectively. These median run-up times were used to develop modeled concentrations for SE and ME; however, the 5th and 95th percentiles of run-up times were 16 and 121 seconds for SE aircraft, or 16 and 160 seconds for ME aircraft at RHV.  If one or more pilots run-up for longer or shorter periods than the median used in modeling, then modeled concentrations could over- or under-predict monitored concentrations, respectively.

Finally, the third parameter of SE versus ME activity can influence modeled concentrations based on the fact that ME aircraft have higher fuel consumption due to the fact that ME aircraft have two, or more, engines rather than the one engine of a SE aircraft. As such, more ME activity than included in the modeling for a given day could result in model over-prediction, while fewer ME aircraft conducing run-up could result in model under-prediction.

All three parameters were examined for days during which the model over- or under-predicted monitored concentrations. On the one day of over-predication, shifting the run-up location (i.e., using supplemental model receptors) to a more southerly run-up location, which was further from the maximum impact location monitor, resulted in the median value of concentrations at supplemental model receptors falling just below the monitored concentrations, such that the monitored value was within the third quartile of supplemental receptor concentrations (Figure A-7). The remaining difference between modeled and monitored concentrations could result from less ME aircraft activity occurring on this day than was recorded by observers, or from shorter than median run-up durations. While other parameters could contribute to some of the remaining difference between monitor and modeled concentrations, this evaluation focused on those parameters shown to most strongly impact concentrations.
   Pb Concentrations from Piston-Engine Aircraft Emissions at Maximum Impact Site

Figure A-7. Range of modeled lead concentrations from piston-engine aircraft during airport operating hours at supplemental receptor sites on day that model over-predicted monitored concentrations.
Whiskers represent first and fourth quartiles. Supplemental model receptors were placed in concentric circles of 5, 10, and 20 meters from the monitor to mimic a change in run-up location.
Figure A-8 presents the range of concentrations at the supplemental model receptors on one of the days that the modeled lead concentration under-predicted the monitored concentration. The range in concentrations at supplemental model receptor sites still falls below the monitored concentration, suggesting that a shift in run-up location did not account for the difference in concentrations. Longer run-up time and higher fuel consumption on this particular day (compared with the median observed at the model airport) are possible explanations for the difference between modeled and monitored concentrations. Thirty percent of the difference in concentrations would be accounted for if 10% of pilots conducted run-up for 2 mins, as opposed to the 40 or 63 second median values for single- or multi-engine aircraft, respectively. This combined with higher levels of ME activity (i.e., higher fuel consumption) could result in a modeled concentration much closer to the monitored value, similar to model performance on the majority of model to monitor comparison days.
   Pb Concentrations from Piston-Engine Aircraft Emissions at Maximum Impact Site

Figure A-8. Range of lead concentrations from piston-engine aircraft during airport operating hours at supplemental receptor sites on one day that the model under-predicted monitored concentrations.
Whiskers represent first and fourth quartiles. Supplemental model receptors were placed in concentric circles of 5, 10, and 20 meters from the monitor to mimic a change in run-up location.
Another possible cause for the underestimation of the modeled concentration is a possible overestimate of the initial sigma-y and initial sigma-z. As described in Section A.1, we used an average wingspan of 10.67 m (35 feet) for our initial sigma-y and sigma-z calculation. If the average aircraft wingspan is smaller, then the initial lateral and vertical dispersion may be overestimated, leading to an underestimate of the peak concentration.

One additional factor of note is a challenge commonly faced when modeling mobile source emissions, namely modeling concentrations closely proximate to the emission source can result in receptors being placed within the modeling exclusion zone. The exclusion zone is the region 2.15 X sigma-y + 1 meter from the center of the volume source. When the source is parameterized as a volume source (the approach used for aircraft emissions in this work), AERMOD reports concentrations of zero for receptors that are inside the exclusion zone of that particular volume source. This results in concentrations that are biased low. In the sensitivity analysis presented here, several receptors were within the exclusion zone and, therefore, the concentration of lead reported at these receptors is biased low. Note that for the year-long modeling analysis presented in Section A.1, there were no receptors inside the exclusion zone.

A.3 Fleet Composition Between the Model Airport and the National Fleet
Section A.3 presents an evaluation of two key parameters that underlie the development of the
AQFs (as presented in Section 3 of the report) in order to understand similarities and differences between piston-engine aircraft operating at the model airport and those in the national fleet. Specifically, this appendix compares the aircraft classes and engine types active at the model airport to a national database of registered piston-engine aircraft since fuel consumption rates differ across aircraft class and engine type which in turn impacts lead concentrations. As described in Section 2 of the report, AQFs were developed using modeled concentrations at a model general aviation airport. As input to the air quality model, aircraft were observed at the model airport for a period of ten days. The counts of unique aircraft by aircraft class (SE/ME) and engine type were then compared to a national database of all US registered piston-engine aircraft. Of the 403 piston aircraft observed at the model airport, 377 (92.0%) were single-engine aircraft. In the national registered piston database, 225,697 of 245,665 (91.9%) aircraft were single engine. Thus, the differences in the aircraft class populations are not statistically significant (χ2  =1.617, p = 0.2035).

Tables A-10 and A-11 present the total number of piston aircraft organized by engine technology group (4-stroke horizontal carbureted [carb], 4-stroke horizontal fuel injected [fi], 4stroke horizontal spark turbocharged [turbo], and 2-stroke horizontal) and horsepower for the national database of registered piston aircraft and the observed aircraft at the model airport. Aircraft were categorized as `Missing' where either engine technology type or engine size data was unavailable. Radial engines are not presented in Tables A-10 and A-11 as they span a broader range of horsepower, but account for only 3.6% and 2.5% of engines in the national database and observed at the model airport, respectively.
 Table A-10. Number of piston-engine aircraft in the national fleet by technology group and horsepower

Tech. Group
                                Horsepower (hp)

hp<100
100<=hp
<150
150<=hp
<200
200<=hp
<250
250<=hp
<300
300<=hp
<350
350<=hp
<400
400<=hp
<450
450<=hp
Total
4-Stroke Carb.
                                    28,137
                                    34,110
                                    51,675
                                    17,650
                                     8,771
                                     1,116
                                      46
                                      874
                                      243
                                    142,722
4-Stroke
FI
                                      15
                                      405
                                    12,035
                                     5,502
                                    16,889
                                    12,099
                                      125
                                      152
                                       0
                                    47,225
4-Stroke Turbo
                                      17
                                      320
                                      130
                                     2,694
                                      980
                                    11,980
                                      448
                                      67
                                      185
                                    16,921
2-Stroke
                                     2,566
                                      17
                                       0
                                       0
                                      61
                                       0
                                       0
                                       0
                                       0
                                     2,644
                               Missing = 27,769

 Table A-11. Number of piston-engine aircraft in the model airport fleet by technology group and horsepower
                                 Tech. Group

                               Horsepower (hp)

hp<100
100<=hp
<150
150<=hp
<200
200<=hp
<250
250<=hp
<300
300<=hp
<350
350<=hp
<400
400<=hp
<450
450<=hp
Total
4-Stroke Carb.
                                      17
                                      51
                                      93
                                      34
                                      12
                                      2
                                      0
                                      1
                                      0
                                     210
4-Stroke
FI
                                      0
                                      0
                                      33
                                      9
                                      37
                                      26
                                      0
                                      0
                                      0
                                     105
4-Stroke
Turbo
                                      0
                                      0
                                      0
                                      6
                                      1
                                      28
                                      1
                                      0
                                      0
                                      36
2-Stroke
                                      1
                                      0
                                      0
                                      0
                                      0
                                      0
                                      0
                                      0
                                      0
                                      1

   Missing = 42

Due to the small number of observations for several horsepower groups, comparisons of engine power at the model airport and in the national fleet were difficult. Pearson's Exact Tests comparing engine powers at 50 horsepower intervals showed no statistically significant differences at the 5% significance level between the model airport and the national database. A broader comparison of engines across the five technology groups at the model airport versus the national database did show a difference in fleet composition, which is statistically significant 5% level (χ[2]  =15.83, p = 0.0012). However, in general, the distributions are similar by each technology group and for each engine horsepower rating. In particular, the most common engine category in the national fleet, 4-stroke carbureted engines between 150 and 200 hp, is also the most common engine category observed at the model airport.

Comparing across engine technology groups further identifies similarities between aircraft at the model airport and the national fleet. Table A-12 shows the total observed engine counts at the model airport for all technology groups and compares these totals to the fleet that would be expected if there was absolute parity in the engine distribution between the model airport and the national database. Table A-3 excludes missing data from the observations at the model airport and the national fleet database. The rank order and relative magnitude of technology group prevalence is the same in the observed fleet and the expected fleet from the national aircraft database. The model airport, however, has fewer observed carbureted engines than predicted and more fuel injected and turbocharged engines than predicted. Because fuel-injected engines are generally more fuel efficient and turbocharged engines are generally less fuel efficient than carbureted engines (Heiken et al. 2014), there is an expectation that any high or low bias in fuel consumption from the high prevalence of one of these engine technology types would be offset by the similar high prevalence of the other. In addition, the prevalence of fuel injected and turbocharged engines across the 10 survey days suggest that these engine technology groups may make up a slightly disproportionate percentage of total operations at the model airport compared to the national fleet. Importantly, due to the fact that not all aircraft registered in the national database may be routinely operated, differences between the observed aircraft operating at the model airport and aircraft in the registered database may not be indicative of differences between aircraft operating at the model airport and aircraft operating at other airports. Overall, results suggest that differences between the observed fleet at the model airport and the national piston-engine aircraft fleet generally balance out (i.e., turbocharged versus fuel-injected), or are not statistically significant.
          Table A-12. Fleet composition by aircraft engine technology
Engine Technology Group
Model
Airport
Composition
National
Fleet
Composition
Observed Aircraft at Model Airport
Expected Aircraft at Model Airport
Given National
Composition
4-Stroke,
Carbureted
                                    58.1%
                                    65.7%
                                     210
                                     237
4-Stroke, Fuel
Injected
                                    29.1%
                                    21.7%
                                     105
                                      79
4-Stroke, Turbocharged
                                    10.0%
                                     7.8%
                                      36
                                      28
2-Stroke
                                     0.3%
                                     1.2%
                                      1
                                      4
4-Stroke Radial
                                     2.5%
                                     3.6%
                                      9
                                      13

A.4 Mode-Specific Fuel Consumption
In the air quality modeling approach used to develop AQFs, total fuel consumption was modeled using engine-specific fuel consumption rates for 18 engine types. The 18 engine types span the five engine technology groups (4-stroke horizontal carbureted, 4-stroke horizontal fuel injected, 4-stroke horizontal turbocharged, 4-stroke radial, and 2-stroke horizontal) for piston-engine spark ignition aircraft engines and range from 64-575 horsepower. For each engine type, fuel consumption rates were prescribed for each landing-and-take-off (LTO) mode based on the FAA's Emission and Dispersion Modeling System (EDMS) Version 5.0.2 or the Swiss Federal Office of Civil Aviation (SFOCA), "Aircraft Piston Engine Emissions Summary Report" supporting data (FAA 2007, SFOCA 2007).

The approach laid out in Heiken, et al., 2014 included calculating fuel consumption by using default brake specific fuel consumption (BSFC) rates for each of the five-spark ignition piston-driven aircraft engine technology groups (Heiken et al. 2014). BSFC is the mass of fuel consumed per unit work done by the engine and is a measure of engine efficiency. BSFC data for each LTO mode for 29 unique engines were compiled from several sources including the FAA's EDMS model and the SFOCA piston engine summary report. Where BSFC data were not available for a LTO mode for an engine, mode-specific BSFC were estimated by applying a scaling factor to the takeoff BSFC for that engine. Engines were sorted by technology group, and the default mode-specific BSFCs were taken as the mean BSFC for each technology group and LTO mode.

A comparison between the resulting fuel consumption rates used to characterize lead concentrations at the model airport (labeled as MA Fuel Consumption) and in Heiken et al., 2014 (labeled as Heiken Fuel Consumption) is shown in Figure A-9. For each LTO mode, the fuel consumption rate for each of the 18 engines used in the AQF approach is plotted against the fuel consumption rate that would be assumed for an identical engine using the mode-specific default BSFC approach.
                                      (a)

                                      (b)

                                      (c)

                                      (d)

                                      (e)

  Figure A-9. Fuel consumption rates by engine technology group and LTO mode (Heiken et al. 2014)

For taxi, takeoff, climb, and approach modes, fuel consumption rates between the two studies strongly correlate (R = 0.993 for takeoff to R = 0.745 for taxi). For each of these modes, a single radial engine was an outlier with model airport fuel consumption greater than the fuel consumption rate reported by Heiken et al. Two radial engines were in the database used to determine the radial engine default BSFC in Heiken et al. one (Wright R-1820) with similarly high fuel consumption rates to the single engine value used in the model airport fuel consumption database and one radial engine with an undisclosed manufacturer and model number. Averaging the mode-specific BSFC across the two radial engines results in significantly lower predicted fuel consumption rates. Thus, using the default BSFC methodology from Heiken et al. may under-predict fuel consumption rates by not accounting for engine-to-engine variation within an engine technology group. Notably, the impact of the radial engine fuel consumption modeling assumption on resulting lead concentrations is expected to be small as radial engine aircraft account for only 3.6% of the national piston aircraft fleet.

Aircraft run-up fuel consumption rate data is sparser than data for other LTO modes. For the model airport, fuel consumption rates for the run-up mode were taken directly from EDMS and SFOCA where available. Only 4 unique run-up fuel consumption rates were identified. In the approach used in Heiken et al. run-up fuel consumption rate was defined as 52% of the maximum fuel consumption rate based on a survey of seven engine manuals that suggested individual run-up fuel consumption rates range from 43-68% of the maximum fuel consumption rate. Despite the lack of granularity in data, the models still show weak to moderate correlation (R2  = 0.28). Fuel consumption rates used at the model airport were higher than those reported by Heiken et al. for 2-stroke carbureted engines and engines less than 100 horsepower. Aircraft with these engines account for less than 3% of the national piston aircraft fleet, as seen in Table A-10. In contrast, relative to the Heiken et al. approach, the data used at the model airport generally underestimates run-up fuel consumption rates for 4-stroke horizontal engines greater than 200 horsepower, about 24% of the national piston fleet. The fuel consumption rate for the radial engine is again an outlier; however, where higher estimates of radial engine fuel consumption rates were reported for other LTO modes, the run-up fuel consumption rate used for this engine at the model airport was lower than the fuel consumption rate relative to the Heiken et al. study. Overall, while methods used to calculate fuel consumption rates differ between the two studies, a comparison of the resulting rates suggest that the values are largely similar for modes other than run-up. Within the run-up mode, the results are moderately consistent, but there is more variability in the results compared with other modes of operation. Additional data would be necessary to further characterize and evaluate fuel consumption rates during the run-up mode for piston-engine aircraft.

A.5 Wind Speed Adjustment Methodology
Meteorological, geographical, and operational parameters may vary from conditions at the model airport or from the national default parameters used in the national analysis. Wind speed is one meteorological parameter that effects local concentration profiles of atmospheric aerosols. Specifically, the near-field concentration of a non-reactive passive tracer scales with <u[-1]>, where u is wind speed and angled brackets imply a time average (Barrett and Britter 2008). Thus, the model-extrapolated concentrations at and downwind of the maximum impact site can be adjusted to better consider meteorological conditions at individual airports by using inverse wind speed data over the 3-month maximum period. The methodology for deriving and applying the wind-speed adjustment is described below.

If the wind speed at the model airport is v and the wind speed at a specific airport is u, then the wind-adjusted concentration would be the model extrapolated concentration estimated by the methodology detailed in Section 3 of the main report multiplied by the ratio of average inverse wind speeds <v[-1]>/<u[-1]>. If the wind speed at the specific airport is, in general, higher than the wind speed at the model airport where the AQFs were derived, then <v[-1]> would be less than <u[-1]> resulting in a lower concentration per unit of activity (i.e., 1 LTO) at the specific airport than the AQF.

The effect of the wind-adjusted AQF is demonstrated and validated using the model airport data. First, we evaluated the range of weighted 3-month AQFs versus the range of rolling 3month, inverse wind speeds.  As shown in Figure A-10, the average inverse wind speed for the average weighted AQF  is 0.426 s/m with 3-month average inverse wind speed ranging from 0.325 to 0.523 s/m.

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modeled, weighted AQF for each of the 3-month periods with the average AQF (or the AQF applied in the main analysis without considering wind adjustment) and the wind-adjusted AQF. Figure A-11 shows that the average AQF is always within +/-20% of the specific 3-month average modeled AQF. The wind-adjusted AQF performs even better, staying within 6% of the modeled AQF for all modeled periods.
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Utilizing the same wind data that was used to assign operations to specific runways, model-extrapolated concentrations at airports nationwide can be adjusted for wind-speed, thereby appropriately characterizing concentrations at airports with significantly higher or lower wind speeds than the model airport. For the wind speed adjustment, wind speeds from 6am to 11pm  are averaged over the entire year at the model airport and for the 3-month maximum activity period at the model airport. As the inverse of wind speed tends toward infinity as wind speed tends toward zero, 0.5 m/s is chosen as a minimum allowable wind speed to account for ASOS station wind detection limits.

References

BAAQMD (2011). 2015 Toxics Modeling to Support the Community Air Risk Evaluation (CARE) Program. 201101-008-TX. Bay Area Air Quality Management District. San Francisco. January, 2011.
Barrett, S. R. H. and R. E. Britter (2008). Development of algorithms and approximations for rapid operational air quality modelling. Atmospheric Environment, 42 (34), 8105-8111. DOI: http://doi.org/10.1016/j.atmosenv.2008.06.020.
Benson, P. E. (1979). Abridged User's Guide for CALINE-3 - A versatile dispersion model for predicting air pollutant levels near highways and arterial streets. O. o. T. Laboratory.
Carr, E., M. Lee, K. Marin, C. Holder, M. Hoyer, M. Pedde, . . . J. Touma (2011). Development and evaluation of an air quality modeling approach to assess near-field impacts of lead emissions from piston-engine aircraft operating on leaded aviation gasoline. Atmospheric Environment, 45 (32), 5795-5804. DOI: http://dx.doi.org/10.1016/j.atmosenv.2011.07.017.
FAA (2007). EDMS 5.0.2: Emission and Dispersion Modeling System Software. Washington, DC, Federal Aviation Administration. 5.0.2.
Heiken, J., J. Lyons, M. Valdez, N. Matthews, P. Sanford, J. Turner and N. Feinberg (2014).
Quantifying Aircraft Lead Emissions at Airports.  ACRP Report 133.  http://www.nap.edu/catalog/22142/quantifying-aircraft-lead-emissions-at-airports.
SFOCA (2007). Aircraft Piston Engine Emissions Summary Report. Bern, Switzerland, Swiss Federal Office of Civil Aviation (SFOCA).
USEPA (2004). User's guide for the AMS/EPA regulatory model - AERMOD. Office of Air Quality Planning and Standards. September 2004. https://www3.epa.gov/ttn/scram/models/aermod/aermod_userguide.pdf .
USEPA (2006). Air Quality Criteria for Lead - Volume 1.  EPA/600/R-05/144aF. U.S. Environmental Protection Agency. Research Triangle Park, NC. October 2006.
USEPA (2008a). AERSURFACE users' guide. EPA-454/B-08-00. Office of Air Quality Planning and Standards. January 2008.
USEPA (2008b). Regulatory impact analysis of the proposed revisions to the National Ambient
Air Quality Standards for Lead. EPA Office of Air Quality Planning and Standards. October 2008.
USEPA (2009). AERMOD implementation Guide. Office of Air Quality Planning and Standards.
March 2009.
http://www.epa.gov/ttn/scram/7thconf/aermod/aermod_implmtn_guide_19March2009.pdf.
 USEPA (2010). Development and Evaluation of an Air Quality Modeling Approach for Lead Emissions from Piston-Engine Aircraft Operating on Leaded Aviation Gasoline. EPA-420-R-10007.
USEPA. (2011). "2011 National Emissions Inventory (NEI) Data." 2017, from http://www.epa.gov/air-emissions-inventories/2011-national-emissions-inventory-nei-data.
USEPA (2013a). Calculating Piston-Engine Aircraft Airport Inventories for Lead for the 2011 National Emissions Inventory. EPA-420-B-13-040. September 2013.
USEPA (2013b). Integrated Science Assessment for Lead. EPA-600-R-10-075F. National Center for Environmental Assessment - RTP Division Office of Research and Development. June 2013.

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

This appendix provides supplemental information on data sources and their application in methods detailed in Section 3 of the main report, which characterizes 3-month average model-extrapolated lead concentrations at and downwind of the maximum impact site for the runway end with the most piston-engine aircraft activity in a 3-month period for airports nationwide. Section B.1 briefly describes the sources of airport activity data noting the extent of the data, its resolution, the data collection method, and the location of publicly available information. Section B.2 provides a detailed description of the diurnal profile used in the national and airport-specific activity analyses (Sections 3.2 and 3.3 of the main report); this profile is based on observations at the representative airport facility and is utilized in the AQFs presented in Section 3.1 of the main report. Section B.3 describes in detail the method for assigning operations to a specific runway using hourly wind data. Section B.4 provides the detailed aircraft and operational data for six airports where airport-specific data was available, which was utilized in Sections 3.3 and 4 of the main report. Section B.5 discusses piston-engine rotorcraft activity and provides information to support future analyses.

B.1 Airport Operations Data Sources
FAA provides a number of data sources related to aircraft activity at airports. The data sources relevant to this report are presented below and generally organized from the most to least detailed. As a reference, Table B-1 lists of FAA location identifiers (LID) with airport names and locations for airports identified by FAA LID in this report.

 Table B-1. List of FAA location identifiers, airport names, and location by state/territory for airports identified in this report.

         FAA ID Airport Name 				Location
 52F
ORS
RHV
WHP
Northwest Regional Airport
Orcas Island Airport
Reid-Hillview Airport
Whiteman Airport
                                                                            TX
 WA
                                                                            CA
                                                                            CA

Air Traffic Activity System (ATADS)
The Air Traffic Activity [Data] System (ATADS) contains the official National Airspace System air traffic operations data available for public release.  Approximately 500 US airports have either an FAA air traffic control tower or an FAA contract tower. ATADS provides daily operational data at the airport as reported by the control tower categorized by itinerant and local operations and separated by air carrier, air taxi, general aviation, and military. ATADS is updated monthly. While ATADS activity data is the most up-to-date and offers the finest temporal resolution of the FAA datasets, it is only available at the approximately 500 towered airports and does not specify activity by engine-type (e.g., piston- vs. jet-engine activity) within general aviation and air taxi.

National Plan of Integrated Airport Systems
The National Plan of Integrated Airport Systems (NPIAS) identifies nearly 3,400 existing and proposed airports that the FAA considers significant to national air transportation and are thus eligible to receive Federal grants under the Airport Improvement Program. The NPIAS includes all ATADS airports. Because these airports are eligible to receive Federal grants, they are subject to data reporting requirements, including reporting based aircraft by tail number.

Terminal Area Forecast
The Terminal Area Forecast (TAF) is the official FAA forecast of aviation activity for US airports. Yearly reports detail historical annual activity data and projected operations for future years at the airport level for NPIAS airports.  The TAF also provides data and future projections of based aircraft for NPIAS airports.

FAA 5010 Report (Airport Master Record)
An Airport Master Record (Form 5010) is an electronically generated file for each airport facility that details General Information, including ownership, management, and location data; Services & Facilities including available fuel types and flight services; Based Aircraft & Operations;
Runway Information; and Remarks, including special instructions and updates. A complete Form 5010 can be generated for an individual airport through the online AirportIQ DataCenter.

The FAA Office of Airport Safety & Standards (AAS-100) provides access to airport facilities and runway data from Form 5010 for all public-use and private-use facilities (including ATADS airports, non-ATADS NPIAS airports, and non-NPIAS airports) available for download through the FAA website. Based aircraft counts and annual activity levels are available through the Airport Facilities Data database. Operations data is reported for 12-month periods and are partitioned by Air Carrier, Air Taxi, General Aviation Local, General Aviation Itinerant, and Military. Operations at non-FAA airports are estimated by FAA inspectors or are based on information provided by airport managers, state aviation activity surveys, or other sources. Airport location and runway orientation are available through the Airport Runways Data database.

Airport data in the 5010 report is derived from the National Airspace System Resources Database (NASR). The NASR Database contains extensive aeronautical information on all US airports including safety critical data, navigational aid data, and airport configuration data. The NASR is updated every 56 days. The NASR Database is provided by the FAA's Aeronautical Information Services Group (AJV-5) through the National Flight Data Center (NFDC).  The NFDC is responsible for the collection, validation, and quality control of aeronautical information detailing the physical description, geographical position, and operational characteristics of airport facilities.

General Aviation and Part 135 Activity Survey/General Aviation and Air Taxi Activity (GAATA) Surveys
The General Aviation and Part 135 Activity Survey, also known as the General Aviation and Air Taxi Activity Survey (GAATA), is an annual data report produced from surveys of US pilots. The GAATA enables the FAA to monitor the general aviation fleet for purposes of anticipating and meeting demand, evaluating initiatives and regulatory changes, and measuring the safety of the GA community. The GAATA provides data as to the composition of the general aviation and air taxi fleet, including national data on fleet composition and operational hours by type of aircraft (e.g., piston-driven, turboprop, turbojet) and aircraft class (i.e., SE vs ME). The data collected are also used by other government agencies, the general aviation industry, trade associations, and private businesses to pinpoint safety problems and to form the basis for critical research and analysis of general aviation issues. Tabular data from annual GAATA reports are publicly available for download on the FAA website.

General Aviation Manufacturers Association (GAMA) Statistical Databook
GAMA is an international trade association representing more than 90 of the world's leading manufacturers of general aviation airplanes and rotorcraft, engines, avionics, components, and related services. GAMA publishes an annual statistical databook and an annual industry outlook, containing diverse data from operations at airports to sales. While the GAMA databook is not used as a primary data source for modeling in the main report, it is a useful tool for validating data assumptions, such as GA operation trends.

The trends in total piston operations over six years for a sample subset of the 50 most active GA airports, as listed in the General Aviation Statistical Databook (GAMA 2016), are taken from ATADS data and are shown in Figure B-1. The median year-on-year change in operations at the top 50 GA airports ranges from -4% to 4% for a given year, and the interquartile of airport year-on-year operational changes is between +/-10% for all years. However, individual airport year-on-year changes range from -25% to +44%.

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B.2 Diurnal profile
Section 2.2 of the main report details piston-engine aircraft emissions modeling at a GA airport facility (RHV). Part of this work addressed the fact that aircraft activity is not constant over the course of a day. Specifically, a detailed distribution of aircraft operations by hour was developed from on-site observations and survey data and applied in the modeling approach. Figure B-2 shows the distribution of operations across operational hours (i.e., diurnal profile) at RHV by aircraft class (SE vs. ME) and operational cycle-type (Full LTO vs. T&G). Distinct diurnal profiles were observed for weekdays (Figure B-2a) and weekends (Figure B-2b), with ME operations showing more variation between the two-day types.

                        RHV Diurnal Profile  -  Weekdays

                        RHV Diurnal Profile  -  Weekends

Figure B-2. Diurnal profile of aircraft operations by aircraft class, cycle-type, and day-type from RHV monitoring and survey data

Figure B-2. Diurnal profile of aircraft operations by aircraft class, cycle-type, and day-type from RHV monitoring and survey data
Detailed hourly operational data similar to that presented in Figure B-2 is unavailable for each of the 13,153 airports in the national analysis. Yet, as described in Section 3 of the main report, applying the AQFs to characterize lead concentrations at airports nationwide requires detailed operational data as an input for each airport. For each hour, aircraft activity and wind direction data are used to assign operations to specific runways at each airport. As noted in Table 2 of Section 3, we used the diurnal operational profile shown in Figure B-2 to calculate hourly operations at each airport. We recognize that the distribution of operations can vary between facilities. Figure B-3 shows the hourly operational profiles at four airports. The profiles for RHV (weekday and weekend) represent the same operational profiles shown in Figure B-2, albeit not separated out by aircraft class and cycle-type. The profile for SMO was developed by on-site observation and survey data and underlies the lead modelling development study described in Section 2.1 (Carr et al. 2011). Two additional profiles developed from on-site observation and monitoring at the Richard Lloyd Jones airport (RVS) and Centennial airport (APA) are also shown as taken from the National Academies of Sciences Airport Cooperative Research Program Report Quantifying Aircraft Lead Emissions at Airports  (Heiken et al. 2014).
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A comparison of the diurnal profiles across these four facilities shows the same basic features: a ramp-up of activity in early morning, peaks in activity in late morning and early afternoon, and decreasing operations in the evening. For the National Analysis (Section 3 of main report), we selected the RHV profile as it provides the most detailed information in terms of how activity may vary over the course of a day based on aircraft class, operation types, as well as day type (weekday vs. weekend). In modeling studies, monthly average concentration has been shown to be insensitive to diurnal profile choice while holding daily operation count and runway assignment constant (Feinberg and Turner 2013). In characterizing concentrations using AQFs in the main report, the operational diurnal profile is an important parameter because runway assignment is based on predominant hourly wind direction as described in Section B.3 of this appendix.

Because take-offs contribute more significantly to concentrations at the maximum impact location, a possible source of uncertainty is using the same diurnal profile for modeling landings and take-offs. To understand this sensitivity, the impact of using a generic "operational" diurnal profile vs. a "landing" or "take-off" was examined. For this choice of diurnal to be impactful on three-month averaged concentrations, two factors would need to occur: the difference between the diurnal profile of landings and the diurnal profile of take-off would need to be significantly different, and the average wind direction at the time of over-estimated take-offs would need to be significantly different than the average wind direction at the time of underestimated take-offs.

Given that piston aircraft do not typically operate at night and that an aircraft must first takeoff for it to land, there is an expectation that take-offs will (on average) occur earlier than landings. However, piston-engine aircraft typically perform short operational missions. Thus, while at the margins, landings should occur later than takeoffs, we do not expect the profile of landings and takeoffs to differ significantly. Airport surveys at six airport reported counts of landing and take-offs during operating hours or a subset of hours for between three and six days of operation. Operational survey data were excluded for any day that did not have survey data covering at least 80% of operational hours or for any days where both landing and take-off data were not available. Figure B-4 shows the difference in percentage points of the landing and take-off diurnal profiles at each of these airports as reported in survey data. The data confirms the expectation that, in the first (last) hour of operations monitored, take-offs were relatively more (less) prevalent than landings, but that profiles were otherwise similar over the day. The figure shows that, on average the difference between a landing diurnal profile and a takeoff diurnal profile is 2.6 percentage points. Further, 95% of examined hours show a difference of less than 6 percentage points between the landing diurnal profile and the take-off diurnal profile. Thus, using a generic "operation (LTO)" profile will, on average, over or under-predict takeoffs by 1.3% in any given hour.

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In the main report, a +/- 10% maximum 3-month concentration is considered in addressing airports where lead concentrations may approach or exceed 0.15 μg/m3 (in addition to considering variation in other more sensitive parameters such as the expected split between multi- and single-engine aircraft), which far exceeds the +/- 1.3% uncertainty from differences in landing and take-off diurnal profile.

Further, while the average difference between a generic operation diurnal profile and a take-off only diurnal profile is 1.3%, the actual uncertainty in resulting 3-month average concentration may be even smaller. Since aircraft are assigned to runway primarily based on wind direction, a modeled difference in operations would require the wind direction to change significantly from the time in which takeoffs were overestimated to the time in which they are underestimated.

Figure B-5 shows the average wind speed at 938 ASOS stations nationwide for each hour of the day. Wind direction is normalized such that the wind direction at 00:00 is 0° at all stations. Each ASOS station is plotted along a circle of different unit radius, and each hour is modeled by a dot where angle represents difference in wind direction from 00:00 and color represents time of day. The figure shows that across all ASOS stations, 86% of all hours have average wind speeds that fall within 90° of the initial recorded wind direction. Therefore, for example, at a single runway airport, even if the diurnal profile were moving 2% of operations from the morning to the afternoon, there is an expectation that, averaged over a three-month period, those operations would still generally be assigned to the same runway end given consistent wind directions.
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Given the evidence that 3-month average concentrations are insensitive to diurnal profile, the selection of the RHV diurnal profile is appropriate for the national analysis of lead concentrations.

B.3 Runway Assignment
The methodology for characterizing lead concentrations at airports nationwide presented in Section 3 of the main report requires every aircraft operation  at every airport be assigned to an active runway. Piston-engine aircraft typically take-off and land into the wind, and wind is the primary driver for selecting active runways (Lohr and Williams 2008). In the national and airport-specific analyses, hourly local wind direction data were used to identify on which runway piston-engine aircraft conduct take-off and landing operations. The active runway is determined using the minimum degree difference approach  -  identify the runway that has the smallest difference between the direction of the prevailing wind in that hour and the runway's heading in degrees and assign operations to that runway end. Where wind or runway headings are given with reference to magnetic north, directions are corrected to true north to maintain consistency across data sets.
In the national analysis presented in Section 3 of the main report, an active runway was selected for each airport for each hour of the day. The minimum degree difference approach was applied to 99.1% of airport-hour cases. In the remaining 0.9% of cases, there was no measurable wind, or wind direction data was missing. In these cases, operations were assigned assuming the most active runway configuration was consistent with the remaining hours of the day as there is an expectation that, absent other factors, an airport would maintain operational consistency.

Of the 99.1% of airport-hour cases where airport-hour pair  data existed, there is only one runway that meets the minimum degree difference requirement for the overwhelming majority of cases (98.4%). However, in the remaining 1.6% of airport-hour pairs, the minimum degree difference approach was insufficient for assigning an active runway either because there were two or more runways with identical runway headings (parallel runways) or because the wind angle bisected two or more runway headings.

Section B.3.1 details the active runway configuration selection method for these scenarios, as well as the minimum degree difference scenario.  Section B.3.2 describes the wind source data underlying the runway assignment methodology and discusses sources of uncertainty and caveats of the runway assignment method.

Section B.3.1 Runway Assignment Algorithm
This section describes the runway assignment algorithm. When hourly wind data is available and the predominant wind direction aligns with only one airport runway, the active runway end is assigned based on the minimum degree difference approach (Scenario 1). For cases where wind data is unavailable for a given hour or more than one runway aligns with the predominant wind direction, the choice of active runway end is dependent upon the layout of the airport runway system. These cases are described in Scenarios 2 through 5.

Scenario 1: Minimum Degree Difference Approach

Figure B-6. Example Scenario 1 Configurations. Black lines represent runways, green arrows represent wind direction, and red dots represent the runway-end identified as active
     Description: The minimum degree distance approach results in only one preferred available runway.
Occurrence: 98.4%
     Selection Decision: All operations for the hour are assigned to the runway identified by the minimum degree distance approach.

Scenario 2: Non-Parallel Bisection

Figure B-7. Example Scenario 2 Configurations. Black lines represent runways, green arrows represent wind direction, and red dots represent the runway-end identified as active
     Description: The prevailing wind bisects the heading of one or two non-parallel runways resulting in two options for an active runway, based on the minimum direction distance approach.
Occurrence: 0.15%
     Selection Decision: All operations are assigned to the most active runway during the remaining hours of the day, based on the minimum direction distance approach. If more than one runways have been equally active during the remainder of the day, then operations for the hour are split evenly between the runway options.

Scenario 3: Parallel Runways

Figure B-8. Example Scenario 3 Configurations. Black lines represent runways, green arrows represent wind direction, and red dots represent the runway-end identified as active.
     Description: The minimum direction distance approach identifies two or more parallel runways options for an active runway
Occurrence: 1.44%
     Selection Decision: 90% of operations are assigned to a primary runway and 10% of operations are assigned to a second runway. Any 3rd  or 4th  parallel runway is assumed to not serve piston-engine aircraft. GA airports with multiple parallel runways will often have a preferential runway for operations, while airports serving GA and commercial operations may have a designated GA runway or a preferred take-off runway for all operations. Thus, the selection decision assumes a preferred operational runway. However,
     operations can still occur on the non-preferred runway(s).  Thus, allocating 100% of operations to the preferred runway would over-estimate lead concentrations in the case where some operations occur on alternative runways.

Scenario 4: Combination Parallel and Bisection

Figure B-9. Example Scenario 4 Configurations. Black lines represent runways, green arrows represent wind direction, and red dots represent the runway-end identified as active
     Description: The minimum direction distance approach identifies both parallel and non-parallel runways
Occurrence: < 0.1%
     Selection Decision: Parallel runways are first treated as runway groups. The approach for nonparallel bisection (Scenario 2) is applied between runway groups. The approach for parallel runways is then applied to the operations assigned to each runway group.

Scenario 5: Additional Multi-Runway Airports
     Description: There are 15 airports in the national dataset with 5 or more runways. For these multi-runway airports, a subset of runways that serves piston-engine operations was identified from available operational, planning, or capacity information from the airport website or the FAA. These airports are described in more detail below.
     Selection Decision: Only runways serving piston-engine operations are considered. Operation assignment rules then follow the selection criteria in Scenarios 1-4 noted above unless otherwise noted.

(a) Runways with only one identified piston-aircraft runway.

The following multi-runway airports have a single identified runway serving as the primary runway for piston-engine aircraft operations.

     ATL: Based on the ATL Master Plan,[14] General Aviation hangars and other infrastructure are located on the north side of the airport. Therefore, piston-engine operations are assigned to runway 8L/26R following the approach laid out for Scenarios 1 and 2.
     DFW: Based on the DFW Master Plan, runway 17L/35R  is the preferred runway for General Aviation. Therefore, piston-engine operations are assigned to runway 17L/35R following approach laid out for Scenarios 1 and 2.
     IAH: Based on the IAH Master Plan,  General Aviation exclusively uses runway 15R/33L. Therefore, piston-engine operations are assigned to runway 15R/33L following the approach laid out for Scenarios 1 and 2.
     TCS: TCS has one asphalt runway (13/31) and four gravel runways. Therefore, piston-engine operations are assigned to runway 13/31 following the approach laid out for Scenarios 1 and 2.
     TX99: TX99 is a private use airport with 5 turf runways. Piston-engine operations are assigned to runway 8R/26L following the approach laid out for Scenarios 1 and 2.

(b) Airports with two non-parallel identified piston-aircraft runways.

The following multi-runway airports have two identified non-parallel runways serving as the primary runways for piston-engine aircraft operations.

     BOS: Based on the BOS Tower Standard Operating Procedures,  and information provided by Massport, the operator of the airport,[18] runway 14/32 is used exclusively for props and small jet aircraft while runway 4L/22R (purple) is not used for jets. Therefore, piston-engine operations are assigned to runways 14/32 and 4L/22R following the approach laid out for Scenarios 1 and 2.
     DEN: Two runways were identified as serving General Aviation at DEN, runway 17R/35L is the preferred GA runway and runway 8/26 was selected as the preferred runway during a crosswind due to its proximity to the GA hangars. Therefore, piston-engine operations are assigned to runways 17R/35L and 8/26 following the approach laid out for Scenarios 1 and 2.
     DTW: DTW has two non-parallel runways in close proximity to the general aviation area. Therefore, piston-engine operations are assigned to runways 3R/21L and 09/27L following the approach laid out for Scenarios 1 and 2.
     FST: FST has two asphalt runways (12/30 and 3/21) and three turf runways in poor condition. Therefore, piston-engine operations are assigned to runways 12/30 and 3/21 following the approach laid out for Scenarios 1 and 2.
     MDW: At MDW, General Aviation use runway 4L/22R during normal operations and runway 13R/31L during crosswinds as noted in the MDW Noise Compatibility Study.[19]  Therefore, piston-engine operations are assigned to runways 4L/22R and 13R/31L following the approach laid out for Scenarios 1 and 2.
     MKE: Based on the MKE Master Plan Update Study,  piston aircraft primarily use runway 1L/19R and 7R/25L. Therefore, piston-engine operations are assigned to runways 1L/19R and 7R/25L following the approach laid out for Scenarios 1 and 2.
     MWH: Based on the MWH Master Plan,  General Aviation is primarily assigned to runway 18/36 and runway14R/32L. Therefore, piston-engine operations are assigned to runways 18/36 and 14R/32L following the approach laid out for Scenarios 1 and 2.

(c) Other Runway Configurations

The following multi-runway airports have unique identified traffic patterns. Runways identified as serving piston-engine aircraft for these airports are shown in Figure B-10.

LGB: Based on the LGB Planning Capacity Profile from the FAA,  "smaller aircraft" use runways
     25R/7L and 25L/7R. In addition, an Environmental Impact Report  showed that runways 16R/34L and 16L/34R are also used for general aviation. According to the EIR the parallel runways are used evenly (i.e., when 25R/7L and 25L/7R are used, 50% of activity is on the north runway and 50% is on the south runway. The same holds for runways 16R/34L and 16L/34R). Therefore, treating the parallel runways as runway groups, piston-engine operations are assigned to a runway group following the minimum degree difference approach. Within a runway group (i.e., for the identified parallel runway pair), operations are split evenly.
     NRQ: NRQ has 4 sets of parallel runways (8 total runways). For each pair, one runway was selected as being eligible for piston-engine activity. Therefore, piston engine operations are assigned to the four non-parallel runways 04L/22R, 09L/27R, 13L/31R, and 18L/36R following the approach laid out for Scenarios 1 and 2.
     ORD: Runway 9L/27R is used primarily for GA aircraft. In addition, runways 14R/32L and 4R/22L were identified as runways that could serve piston-engine aircraft when operations were prohibited on Runway 9L/27R during a crosswind. Therefore, piston-engine operations are assigned to the three non-parallel runways 9L/27R, 14R/32L, and 4R/22L following the approach laid out for Scenarios 1 and 2.

	LGB 	NRQ 	ORD

Figure B-10. Multi-runway airports with unique operational profiles. Black lines represent runways not serving piston-engine aircraft and blue lines represent runways serving piston-engine aircraft
Section B.3.2 Wind Data Sources and Uncertainty
Wind direction data are available from Automated Surface Observing System (ASOS) meteorological stations. ASOS units are automated sensor suites that are designed to serve meteorological and aviation needs. These systems report visibility and meteorological data including wind speed and direction at approximately hourly intervals or when conditions change rapidly and cross aviation thresholds. Data is available in an online database through the National Centers for Environmental Information (NCEI).  At some airports, particularly those with higher levels of activity, an ASOS station is located on airport property, however, for other airports the nearest ASOS station may be several kilometers (km), or more, away. Of the 13,153 airports in the national analysis, 6.6% (872) have ASOS stations onsite (<1 km) (Figure B11). Among the top 5% of airports by total piston-engine aircraft traffic, 48% have ASOS stations located within 1 km. In the airport-specific activity analysis presented in Section 3.3, 37% of airports (10) have an ASOS station onsite.

     2000
Number of Airports
6000

4948

5000
4000

3634

2828

3000

872
1000

486

385

0

<1
 km
 km
50-100
 km
>100
 km
1-10
 km
10-25
 km
25-50
Distance
to ASOS Station

                                      (A)
      12

10

10

Airports
Number of
8

6

3

4
4

4

2

2
2

2
0

>1
 km
1-5
 km
5-10
 km
10-15
 km
15-20
 km
20-25
 km
>25
 km
Distance to ASOS Station

                                      (B)

Figure B-11. Distribution of the number of airports by distance to the closest ASOS station for airports in the national analysis (A), and airport-specific activity analysis (B). In both analyses, ASOS stations provided wind direction data that was used to identify the active runway end for piston-engine aircraft. Results from both analyses have greater uncertainty for airports with a longer distance to an ASOS station compared to airports closer to an ASOS station.
The relationship between ASOS distance and uncertainty in assignment of aircraft activity to a specific runway will depend on the distance to the nearest ASOS station, the number of available runways, and the geographic area in which the airport and ASOS station are located. For single runway airports, a relatively large shift in wind direction (an average shift of 90 degrees) is required for a shift in the active runway end for piston-engine aircraft, and thus such a shift would likely be detected at weather stations at even slightly longer distances (e.g., 10 km). At multi-runway airports where runway headings differ by fewer than 90°, small changes in predominant wind direction could change the active runway selection from hour to hour in the minimum-degree distance approach while the airport in practice is more likely to maintain a consistent operational pattern (Lohr 2008). Alternatively, prevailing wind direction may align with more than one runway in a given hour. In these cases, the minimum-degree distance approach is likely to underestimate maximum lead concentrations as it predicts fewer operations at the dominant runway.

In areas with relatively consistent wind direction and minimal topographic perturbations (e.g., coastal regions), wind direction is likely to remain constant between an airport and an ASOS station even when the airport and ASOS stations are separated by some distance. As such, in these areas ASOS stations that are quite distant from an airport may provide accurate wind direction data for the purposes of identifying the active runway-end, which increases confidence in the approach used to estimate activity at the maximum impact area. In areas where geographic structure (e.g., mountainous regions, river valleys) creates uncertainty in the applicability of the wind direction data from an ASOS station to a different location, there is less confidence in the approach used to identify and quantify activity at the maximum impact area. At airports with less representative ASOS station wind data, model-extrapolated concentrations may be higher or lower than actual concentrations due to more or less piston-engine aircraft activity occurring at one runway end, versus others at the airport.

While wind direction is the primary driver for determining an airport's active runway, other factors may be important including operational restrictions, airport infrastructure location, runway length, and total airport capacity. For airports with significant operational restrictions or for multi-runway commercial airports that have designated GA or piston-aircraft runways, these operational considerations are an additional source of uncertainty.

B.4 Airport-Specific Observed Aircraft
As noted previously, operations conducted by piston-engine aircraft specifically are not reported.  As described in Section 3.3 of the main report, the number and type of aircraft based at an airport (i.e., based aircraft data) were used to calculate airport-specific activity estimates for a subset of airports included in the national analysis. In the national analysis, national average fractions were used to partition activity estimates into piston-engine and non-piston engine aircraft, and separately partition piston-engine aircraft activity in single- and multiengine (SE and ME). For the airport-specific activity analysis, based aircraft data were selected as an alternative to the national average fractions for two reasons: 1) based aircraft data were available, unlike airport-specific counts of piston-engine LTOs, and 2) a comparison of observations of aircraft activity at six airports and based aircraft at those airports showed that based aircraft fractions are a reasonable proxy for activity fractions. The data used in that comparison are provided below.

Airport-specific information on piston-engine aircraft operations was collected for six airports from on-site observations and surveys. Further, four of these airports (CRQ, MRI, RHV, and VNY) had supplemental aircraft count data from noise-specific studies completed within the past ten years. For each of these six airports, data were collected on the type and operational characteristics of the observed General Aviation and Air Taxi fleet. Table B-2 shows the observed aircraft counts at each airport, as well as the comparison between the percentages of activity attributed to piston-engine aircraft, and then SE versus ME piston-engine aircraft, based on observational counts or the number of aircraft based at each airport.
 Table B-2. Observed piston-engine aircraft from noise studies and onsite observational surveys
                                   Airport
                                  Number of
Observ.
                                     Days
Number of
                                   Aircraft
                                Piston-engine
                                Single-Engine
                                 Multi-Engine

 Based
Aircraft
                                     (%)
Observ.
                                     LTOs
                                     (%)
 Based
Aircraft
                                     (%)
Observ.
                                     LTOs
                                     (%)
 Based
Aircraft
                                     (%)
Observ.
                                     LTOs
                                     (%)
CRQ
                                      14
                                    2,163
                                      63
                                      66
                                      90
                                      93
                                      10
                                      7
MRI
                                      5
                                     827
                                      98
                                      95
                                      95
                                      94
                                      5
                                      6
PAO
                                      7
                                    1,268
                                      98
                                      95
                                      90
                                      99
                                      10
                                      1
RHV
                                      7
                                    2,209
                                      99
                                      88
                                      92
                                      97
                                      8
                                      3
SQL
                                      7
                                    1,018
                                      96
                                      86
                                      90
                                      98
                                      10
                                      2
VNY
                                      30
                                    15,809
                                      59
                                      52
                                      77
                                      95
                                      23
                                      5
   Supplemental Sources:(URS 2005, Mead & Hunt 2007, LAWA 2008, LAWA 2011, HMMH 2013)

Table B-3 further summarizes the aircraft classes and operating modes at these six airports. Aircraft operational activity is broken out by jet and piston-engine aircraft, and piston-engine activity is further categorized into SE and ME aircraft activity. The share of piston-engine only activity by operational cycle-type (full LTO, T&G) is also shown.
           Table B-3. Aircraft class and operation mode survey data
Airport

General Aviation and Air Taxi Activity
                   Piston Engine Aircraft and Activity Type

                                      AT
                                     Jet
                                     (%)
                                      AT
 ME
                                     (%)
GA and
                                    AT Jet
                                     (%)
GA and
 AT ME
                                     (%)
GA and
                                    AT SE
                                     (%)
GA and
AT Heli
                                     (%)
                                      ME
                                     LTO
                                     (%)
                                      ME
 T&G
                                     (%)
                                      SE
                                     LTO
                                     (%)
SE T&G
                                     (%)
CRQ
94.9
                                     5.1
                                    12.1*
                                     4.4*
                                    65.9*
                                    17.6*
                                     97.4
                                     2.6
                                     93.7
                                     6.3
MRI

                                     2.8
                                     5.4
                                     90.0
                                     1.8
                                     93.8
                                     6.3
                                     84.1
                                     15.9
PAO

                                     5.3
                                     1.0
                                     93.7
                                     0.0
100.0
                                     0.0
                                     92.1
                                     7.9
RHV

                                     11.0
                                     4.0
                                     84.0
                                     1.1
                                     64.1
                                     35.9
                                     71.0
                                     29.0
SQL

                                     9.9
                                     1.9
                                     84.5
                                     3.7
100.0
                                     0.0
                                     95.5
                                     4.5
VNY

                                     44.9
                                     2.8
                                     49.1
                                     3.2
                                     98.9
                                     1.1
                                     88.5
                                     11.5
 * These values are for GA aircraft only.

These studies showed that the fraction of SE and ME based aircraft (i.e., sum of SE and ME based aircraft over total based aircraft) was generally within 10% of observed piston-engine LTOs (Table B-2), and that use of based aircraft can reveal airport-specific fleet and operational characteristics (Tables B-2 and B-3). The general agreement between the number of based aircraft and observed activity data suggest that the fraction of SE and ME based aircraft could be used to estimate airport-specific piston-engine aircraft activity as a refinement of national average estimates. Of course, there are inherent uncertainties in based aircraft data, including the fact that some SE and ME based aircraft may be turboprop or other non-piston-engine aircraft; however, the comparisons with onsite activity counts suggest based aircraft provide reasonable, airport-specific data and FAA considers based aircraft data to be a reliable indicator of activity at small airports (FAA 2015).

B.5 Piston-Engine Rotorcraft
Piston-Engine Rotorcraft Activity at Heliports
Piston-engine rotorcraft operate at airports and heliports and contribute a growing fraction of the activity conducted by rotorcraft (FAA 2011). Data on the activity of these aircraft is limited with activity data available from fewer than 100 of the 5,000 heliports having (FAA 2011). For the purposes of this report, fixed-wing aircraft are the focus of evaluation; however, methods similar to those applied here could be applied to estimate lead concentrations at and near heliports.

Piston-Engine Rotorcraft Activity at Airports
At airports, piston-engine aircraft include both fixed-wing airplanes and rotorcraft (i.e., helicopters). While fixed-wing aircraft take-off and land in consistent locations based on wind direction, rotorcraft may take-off from and land in multiple locations at an airport facility. As discussed in Section 1, the analysis in this report focuses on lead concentrations at and downwind of the maximum impact site attributable to fixed-wing piston-engine airplane activity at specified run-up locations adjacent to runway ends. Rotorcraft have not been included in this analysis.

If future analyses were to focus on calculating model-extrapolated concentrations for rotorcraft, then the scaling factors provided in Table B-4 below could be used with similar methods to those provided in Section 3 of the main report. For additional details on methods to develop the scaling factors or the underlying air quality modeling, see Sections 2.1 and 3.1 of the main report.
 	 	Table B-4. Rotorcraft air quality factor data
                                Operation Mode
Air Quality Factor
                             (ug/m[3]  per LTO)
                             Distance Description
Rotorcraft All Operational Modes
                                3.57 x 10[-5]
Approximately 433 meters north of haypatch, alongside hangers near terminal
Rotorcraft Climb & Landing Only
                                6.07 x 10[-7]
Approximately 18 meters southeast of haypatch

References

Carr, E., M. Lee, K. Marin, C. Holder, M. Hoyer, M. Pedde, . . . J. Touma (2011). Development and evaluation of an air quality modeling approach to assess near-field impacts of lead emissions from piston-engine aircraft operating on leaded aviation gasoline. Atmospheric Environment, 45 (32), 5795-5804. DOI: http://dx.doi.org/10.1016/j.atmosenv.2011.07.017.
FAA (2011). FAA Aerospace Forecast Fiscal Years 2011 - 2031.
FAA (2015). Evaluating the Formulation of the National Plan of Integrated Airport Systems (NPIAS). US Department of Transportation.  https://www.faa.gov/airports/planning_capacity/npias/media/evaluating-formulation-npiasreport-to-congress.pdf.
Feinberg, S. and J. Turner (2013). Dispersion Modeling of Lead Emissions from Piston Engine Aircraft at General Aviation Facilities. Transportation Research Record: Journal of the Transportation Research Board, (2325), 34-42.
GAMA (2016). 2016 General Aviation Statistical Databook & 2017 Industry Outlook.
Heiken, J., J. Lyons, M. Valdez, N. Matthews, P. Sanford, J. Turner and N. Feinberg (2014).
Quantifying Aircraft Lead Emissions at Airports.  ACRP Report 133.  http://www.nap.edu/catalog/22142/quantifying-aircraft-lead-emissions-at-airports.
HMMH (2013). Noise Exposure Map Update: Merrill Field Airport. Municipality of Anchorage.
LAWA (2008). Van Nuys Airport Noisier Aircraft Phaseout Draft Environmental Impact Report Los Angeles World Airports. September 2008. http://www.lawa.org/welcome_VNY.aspx?id=1076.
LAWA (2011). Van Nuys Airport Updated 14 C.F.R. Part 150 Noise Exposure Maps. Los Angeles World Airports.  http://www.lawa.org/welcome_VNY.aspx?id=6645.
Lohr, G. W. and D. M. Williams (2008). Current practices in runway configuration management (RCM) and arrival/departure runway balancing (ADRB).  NASA/TM-2008-215557 NASA.

Mead & Hunt (2007). Reid-Hillview Airport Master Plan Update. County of Santa Clara.
URS (2005). McClellan-Palomar Airport FAR Part 150 Study Update.  1 & 2. McClellan-Palomar Airport. San Diego, CA.

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

Section 4 of the report presents a quantitative and qualitative characterization of variability and uncertainty in lead concentration at and downwind of the maximum impact site at airports nationwide. This analysis focused on parameters identified in previous sections as particularly influential on the atmospheric concentration of lead from piston-aircraft operations. This appendix provides details on the data underlying this uncertainty analysis. Section C.1 describes the distribution of avgas lead concentrations used in the Monte Carlo analysis. Section C.2 describes the data underlying the evaluation of variation in run-up time on atmospheric lead concentration, as a function of downwind distance from the maximum impact site, which was also evaluated in the Monte Carlo analysis.