2010 Emissions Modeling Platform Spatial Surrogate 

Documentation

Contract No: EP-D-12-044

Work Assignment: 1-05

Version No: 1.0

Prepared for: Rich Mason

U.S. EPA OAQPS

C339-02 USEPA Mailroom

Research Triangle Park, NC 27711

Prepared by: Zachariah Adelman

Institute for the Environment

The University of North Carolina at Chapel Hill

137 E. Franklin St., CB 6116

Chapel Hill, NC 27599-6116

Date due: September 30, 2013
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Contents

Acknowledgements ....................................................................................................................... 4

1 Introduction............................................................................................................................. 4

2 Shapefile Database .................................................................................................................. 7

2.1 U.S. Census-based Surrogates........................................................................................ 7

2.1.1 Population and Housing ..................................................................................... 7

2.1.2 Home Heating Fuels........................................................................................... 8

2.2 Transportation Surrogates............................................................................................... 9

2.2.1 Roadways ........................................................................................................... 9

2.2.2 Rail Length ....................................................................................................... 10

2.2.3 Rail Freight Density ......................................................................................... 10

2.2.4 Rail Yards......................................................................................................... 11

2.2.5 Highway Exits.................................................................................................. 11

2.2.6 Major Roads..................................................................................................... 12

2.2.7 Transportation Terminals................................................................................. 12

2.3 Shipping and Port Surrogates ....................................................................................... 13

2.3.1 Waterways........................................................................................................ 13

2.3.2 Marine Ports Point Locations........................................................................... 13

2.3.3 Marine Port Area Emissions............................................................................. 14

2.3.4 Great Lakes Tugboat Zones.............................................................................. 15

2.3.5 Gulf of Mexico Platforms................................................................................. 16

2.3.6 Offshore Commercial Shipping........................................................................ 16

2.3.7 Midwest Shipping and Inland Waterways........................................................ 17

2.4 FEMA Building Footprints........................................................................................... 18

2.5 Landcover/Landuse Surrogates .................................................................................... 19

2.5.1 Agriculture........................................................................................................ 19

2.5.2 Water and Land ................................................................................................ 19

2.5.3 Developed Areas .............................................................................................. 20

2.5.4 Forest Land....................................................................................................... 20

2.6 Oil and Gas Production................................................................................................. 21

2.7 Other Industrial and Commercial Activities................................................................. 25
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2.7.1 Mines................................................................................................................ 25

2.7.2 Quarries............................................................................................................ 25

2.7.3 Dry Cleaners..................................................................................................... 26

2.7.4 Gas Stations...................................................................................................... 26

3 Known Issues and Future Work.......................................................................................... 27

4 Surrogate Revision History.................................................................................................. 28

5 REFERENCES...................................................................................................................... 29
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Acknowledgements

ENVIRON International Corporation was a collaborator on this Work Assignment and worked 

extensively on the development and documentation of the oil and gas production surrogates. 

They provided all of the content of Section 2.6 of this memo. ERG, Inc. contributed to this work 

through the development of the Gulf of Mexico platform and support vessel density Shapefile. 

The Lake Michigan Air Directors Consortium (LADCO) provided the Midwest shipping lane 

and Inland Waterways Shapefile. The Georgia Department of Natural Resources provided the 

rail yard point inventory used for generating the rail yard Shapefile and surrogate. Computer 

Sciences Corporation (CSC) developed the Great Lakes tugboat zone Shapefile and provided 

support in the testing and QA/QC of the final surrogate data. 

1 Introduction

This memo describes the collection, processing, and development of updated geospatial data for 

calculating spatial surrogates to be used in the EPA 2008 and 2011 emissions modeling 

platforms. Spatial surrogates are used to allocate county level emission inventories into the 

rectangular grid cells used by urban and regional scale air quality models. Spatial surrogates are

based on data at resolutions different from county-level, such as census tracts or road locations, 

and can therefore be used to allocate the emissions more specifically than assuming they are 

uniformly distributed through out the county. For example, motor vehicle emissions from 

interstate highways can be placed into the grid cells that intersect the highways themselves, and 

dry cleaning emissions can be allocated to parts of the county that have higher population, or the 

parts that have more dry cleaners – if data are available on specific locations of dry cleaners. 

Spatial surrogates consist of values between 0 and 1.0 that specify the fraction of the county 

emissions that should be allocated to each grid cell that intersects the county. The spatial 

surrogate fractions for each county sum to 1.0, except the sum may be less than 1 for some 

counties that intersect the edge and are not completely contained within the modeling domain. 

The emissions for a specific county within a grid cell are computed by multiplying the surrogate 

fraction for that county and grid cell by the total emissions for the county (Eyth et al., 2007). A 

diagram illustrating how spatial surrogates impact the emissions levels within the grid cells of a 

county is shown in Figure 1.

Figure 1. Spatial surrogate schematic
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Spatial surrogates have three components:

1. Weight Shapefile – geospatial dataset used for allocating (or weighting) county-level 

emission inventory sources to a modeling grid. Examples include roadways, population 

density, or the point locations of marine ports. This memo describes the weight Shapefile 

updates completed under this Work Assignment.

2. Data Shapefile – geospatial dataset that corresponds to the administrative unit boundaries 

of the emission inventory. For this Work Assignment, the data Shapefile defines U.S. 

county boundaries. EPA specified that the year 2000 county boundaries used for the 

previous version of the national spatial surrogates be used as the data Shapefile for this 

Work Assignment.

3. Output grid or polygon – definition of the modeling grid(s) or polygon(s) upon which the 

spatial surrogates map emission inventories. For this Work Assignment, the three output 

grids include continental U.S. modeling grids at resolutions of 36-km, 12-km, and 4-km.

For air quality modeling studies prior to 2012, EPA used geospatial data primarily from the year 

2000 and earlier for the weight Shapefiles that characterize the spatial distribution of non-point 

anthropogenic emissions sources. In November 2011 the University of North Carolina Institute 

for the Environment (UNC-IE) initiated updates to the spatial surrogate and Shapefile database 

used by the EPA Office of Air Quality Planning and Standards for emissions modeling. By July 

2012 many of the original surrogates were updated with weight Shapefiles that reflected North 

American geospatial data collected since the year 2006. Incremental updates through September 

2013 targeted specific inventory sectors, such as offshore commercial shipping and on-shore oil 

and gas production. The data and configurations used to update these surrogates are described 

here. All of the surrogates described in this memo were generated with the Surrogate Tool1 of the 

Spatial Allocator (SA)2. 

The Fall 2011 release of the 2010 U.S. Census population and housing database provided the 

basis for several updated surrogates3. Year 2010 roadway data from TIGER and rail/port data 

from the National Transportation Atlas Database (NTAD)4 also supported several surrogate 

updates. Three entirely new surrogates were created to support the processing of MOVES offnetwork sources. The Off-Network HD/MD surrogate (251) is designed for MOVES rate per 

profile (RPP) and rate per vehicle (RPV) medium and heavy-duty vehicles. It’s built from a 

combination of industrial building square footage data and highway exit ramps. The rationale 

behind this surrogate is that idling and starting/stopping medium and heavy-duty vehicles occur 

mainly while loading freight in industrial areas and at rest areas and along highway exits ramps. 

As we were not able to locate a national Shapefile of highway rest areas, we used exit ramps to 

represent the locations of idling tractor-trailers. The Off-Network LD surrogate (252) is 

designed for MOVES RPP and RPV light-duty vehicles, including motorcycles. It’s built from a 

combination of commercial building square footage data, residential building square footage 

data, and local roads. The rationale behind this surrogate is that idling and starting/stopping light 

 

1 http://www.cmascenter.org/sa-tools/documentation/4.0/html/srgtool/SurrogateToolUserGuide_4_0.htm

2 http://www.cmascenter.org/sa-tools/ 3 http://www.census.gov/geo/www/tiger/tgrshp2010/tgrshp2010.html

4 http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/index.html
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duty vehicles and motorcycles occur mostly in driveways/residential parking areas, parking lots

of shopping centers, and along non-residential roadways with street parking spots. The OffNetwork Buses surrogate (253) is designed for MOVES RPP and RPV buses. It’s built from a 

combination of bus terminal locations and local roads, with the concept behind this surrogate 

being that bus terminals and local roads represent the majority of locations that buses are 

loading/unloading passengers. 

Additional surrogate updates included home heating, building square footage, land-used based 

surrogates, oil and gas production, commercial businesses, and offshore shipping and ports. The 

5-year American Community Survey (ACS) release in 2010 included updated data on home 

heating distributions by Census block5. The Federal Emergency Management Administrations 

(FEMA) HAZUS-MH v2.0 was released in September 2011 and contains updated building 

square footage data that are applicable to all of the 500-series EPA surrogates6. The 30-m 

resolution, year 2006 National Landcover Database (NLCD) was used to update the land-use 

based surrogates, such as total land area, forest land, and crops7. Year 2008 and 2011 oil and gas 

production data from IHS, Inc. were aggregated to the continental U.S. (CONUS) modeling grid 

for the Rocky Mountain and Northeast U.S. states to create twelve new surrogates for different 

types of oil and gas production. Census block group counts of gas stations and dry cleaners from 

2009 were used to create new surrogates for these commercial business categories. Several new 

datasets of commercial shipping lane activity, tugboat activity, and rail freight density were used 

to update the surrogates for the aircraft/locomotive/marine inventory sector. Table 1 lists the 

surrogate classes currently in the EPA emissions modeling platform that have been updated as 

part of this effort. Table 1 includes the years of the previous and updated geospatial data used to 

create the different classes of surrogates.

Table 1. Summary of updates to the EPA surrogate database

Shapefile Surrogates

Current 

Year Previous Year

Population and Housing 100-140 2010 2000

Home Heating 150-165 2005-2010 2000

Road and Rail 200-280 2010 2000

Land-use/Land Cover 300-350 2006 1992

Building Square Footage 500-595 2000-2006 1990

Gas Stations and oil & gas 600-699 2008-2011 2000

Shipping 800-820 2010 1999-2003

Other industrial and 

commercial 850-890 2010 2000

UNC-IE generated spatial surrogates on the CONUS domain at resolutions of 36-km, 12-km, and 

4-km. All of the surrogates were generated on a Lambert Conformal Conic projection (lat_0 = 

40° north, lon_0 = 97° west, true_lat1 = 33° north, true_lat2 = 45° north) assuming a spherical 

earth with a radius of 6,370,000 m. The grid definitions of the three surrogate domains are 

provided in Table 2.

 

5 http://www.census.gov/acs/www

6 http://www.fema.gov/plan/prevent/hazus

7 http://www.mrlc.gov/nlcd06_data.php
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Table 2. Surrogate domain definitions

Parameter 36km 12km 4km

X-orig -2,736,000 m -2,736,000 m -2,736,000 m

Y-orig -2,088,000 m -2,088,000 m -2,088,000 m

columns 148 444 1332

rows 112 336 1008

UNC-IE used the gapfilling, normalization, and QA tools of the SA to ensure completeness and 

quality of the surrogates. Normalizing the surrogates eliminates surrogate values that do not 

equal 1.0, which can result from rounding errors in the surrogate calculations. The QA steps 

check for surrogates that do not equal 1.0, missing surrogates, and surrogates that are gapfilled. 

In addition to these tabulated QA summaries, UNC-IE produced spatial plots of the surrogates on 

each of the three CONUS domains. The QA tables, spatial plots, and a README file describing 

the surrogates are packaged in a “QA” directory in the surrogate Linux tar archives. The 

surrogates and accompanying QA data are available from the U.S. EPA CHIEF8 website as part 

of the OAQPS 2008 and 2011 emissions modeling platforms and from the CMAS Center Data 

Clearinghouse9. 

The list of GIS Shapefiles used for the updated spatial surrogates and details of how these data 

were preprocessed for generating surrogates are provided in Section 2. An Excel Spreadsheet 

(US_SpatialSurrogate_Workbook_v093013.xlsx) that accompanies this document includes the 

detailed specifications of the Shapefiles and surrogates that were collected and updated under 

this Work Assignment.

2 Shapefile Database

2.1 U.S. Census-based Surrogates

2.1.1 Population and Housing

The 2010 TIGER/Line database contains U.S. Census population and housing unit counts at the 

Census block level for each state. UNC-IE downloaded the entire database, merged the statelevel data into a national Shapefile, and projected the data to a U.S. national Lambert Conformal 

projection on a normal sphere. UNC-IE calculated urban and rural areas using Census block 

groups. Urban was defined as Census block groups that have a population density of at least 

1,000 people per square mile and everything else was defined as rural. In addition, Census block 

groups are classified based on the number of housing units per square kilometers (housing unit 

density [HUD]) and the attribute AREA_CODE is assigned to census block groups with the 

following conditions:

1. Urban: HUD >= 1000

2. Suburban: HUD >= 125 and HUD < 1000

3. Exurban: HUD >= 6 and HUD < 125

 

8 http://www.epa.gov/ttnchie1/

9 http://www.cmascenter.org/download/data.cfm
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4. Rural: HUD < 6

The attributes in the file used to compute surrogates include:

• POP2010 – 2010 population

• HU2010 – 2010 housing units

• URBAN – urban population in 2010

• RURAL – rural population in 2010

• HUCH1000 – housing unit changes from 2000 to 2010

• AREA_CODE – census block group codes; 1=urban, 2=suburban, 3=exurban, 4=rural

Shapefile Name: pophu_bg2010

Shapefile Type: Polygon

Year: 2010

Attributes: STFID, FIRST_STAT, FIRST_COUN, FIRST_TRAC, FIRST_BLOC, POP2010, 

HU2010, POP2000, HU2000, HUCH1000, POPCH1000, BG_AREA, URBAN10, POPD10, 

URBAN, RURAL, AREA_CODE

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters

Derived Surrogates: 100, 110, 120, 130, 131, 132, 133, 134, 137, 140, 165, 400

2.1.2 Home Heating Fuels

The American Community Survey (ACS) is a U.S. Census project that collects yearly 

demographic and housing information from randomly selected households throughout the U.S. 

The data are aggregated in 5 and 10-year increments to provide Census tract estimates for the 

statistics collected during the survey. Home heating fuel type from the ACS was used to develop 

spatial surrogates for home heating sources (i.e. residential wood combustion) in the nonpoint 

inventory. The ACS 5-year 2010 survey results that were released in 2011 represent data 

collected from 2006-2010. These data were used to create home heating surrogates.

As a review of the 5-year ACS data for 2010 showed that the number of housing units per 

Census tract in the ACS are always lower than the number of housing units reported in the 2010 

Census. This trend indicates that the ACS only estimates demographic and housing statistics for 

a random sampling of households in each Census tract and does not represent the entire 

distribution of households. In order to represent the spatial distribution of home heating sources 

accurately, the ACS was used to calculate a distribution of heating sources for each Census tract 

and then these distributions where applied to the 2010 Census Housing Unit Shapefile to 

construct a home heating Shapefile that is consistent with the number of census tract housing 

units in the 2010 Census database. For example, if the ACS reports that in Census tract X 75% 

of the households use coal for heating and 25% of the households use heating fuel, the 2010 U.S. 

Census estimate of the total number of households in tract X were multiplied by 0.75 and 0.25 to 

estimate the distribution of coal and heating fuel use, respectively, for tract X.

The attributes in the file used to compute surrogates include:

• UTIL_GAS - number of housing units using Utility Gas for primary heating
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• WOOD - number of housing units using Wood for primary heating

• FUEL_OIL - number of housing units using Fuel Oil for primary heating

• COAL - number of housing units using Coal for primary heating

• LP_GAS - number of housing units using Bottled Gas for primary heating

Shapefile Name: heating_fuels_acs0510_c2010

Shapefil Type: Polygon

Year: 2011

Attributes: UTIL_GAS, WOOD, FUEL_OIL, LP_GAS

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters

Derived Surrogates: 150, 160, 165, 170, 180, 190

2.2 Transportation Surrogates

2.2.1 Roadways

UNC-IE merged state-level 2010 TIGER/Line Shapefile data with population density data to 

create a national file with urban and rural roadways. UNC-IE projected the data to a U.S. 

national Lambert Conformal Conic projection on a normal sphere. The TIGER/Line MTFCC 

codes S1100 and S1200 were used to define primary and secondary roads, respectively. Urban 

and rural roadway classifications were calculated by UNC-IE using Census block group 

population densities (see Section 2.1.1) overlaid onto TIGER roads.

The attributes in the file used to compute surrogates include:

• RDTYPE = road types; 1=urban primary, 2=rural primary, 3=urban secondary, 4=rural 

secondary

Shapefile Name: rd_ps_tiger2010

Shapefile Type: Line

Year: 2010

Attributes: STATEFP, LINEARID, FULLNAME, RTTYP, MTFCC, STFID, URBAN10, 

RDTYPE

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters 

Derived Surrogates: 200, 210, 220, 230, 240, 250
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2.2.2 Rail Length

UNC-IE merged state-level 2010 TIGER/Line Shapefile data to create a national railway 

network file. The TIGER/Line MTFCC codes R1052, R1051, and R1011 were used to define the 

different classes of rail lines. UNC projected the data to a U.S. national Lambert Conformal 

Conic projection on a normal sphere.

The attributes in the file used to compute surrogates include:

• RRTYPE – rail classes; 1=class 1; 2=class 2; 3=class 3

Shapefile Name: rail_tiger2010

Shapefile Type: Line

Year: 2010

Attributes: LINEARID, FULLNAME, MTFCC, RRTYPE

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters

Derived Surrogates: 260, 270, 280

2.2.3 Rail Freight Density

UNC-IE used the 2012 National Transportation Atlas Database (NTAD) freight densities to 

weight the line Shapefile of rail activity locations. The NTAD RAILTYPE codes were used to 

define the different classes of rail lines. UNC-IE projected the data to a U.S. national Lambert 

Conformal Conic projection on a normal sphere.

The attributes in the file used to compute surrogates include:

• RAILTYPE – rail classes; 1=class 1,2,3 rail; 2=Amtrak; 3=Commuter rail

Shapefile Name: rail_lines_ntad2012

Shapefile Type: Line

Year: 2012

Attributes FRAARCID, MILES, STATEAB, STATEFIPS, CNTYFIPS, STCNTYFIPS,

FRAREGIONS, RROWNER1, RROWNER2, RROWNER3, TRKRGHTS1, TRKRGHTS2,

TRKRGHTS3, TRKRGHTS4, TRKRGHTS5, TRKRGHTS6, TRKRGHTS7, TRKRGHTS8,

TRKRGHTS9, STRACNET, SIGSYS, TRACKS, SUBDIV, FRFRANODE, TOFRANODE,

NET, PASSNGR, YARDS, INT_TYPE, DEN09CODE, VERSION, MEDDENS, RAILTYPE

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters

Derived Surrogates: 261, 271, 272, 273
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2.2.4 Rail Yards

Michelle Bergin and Byeong Kim of GA DNR provided a spreadsheet of rail yard locations and 

emissions collected as part of the Eastern Regional Technical Advisory Committee (ERTAC) 

railway inventory development and improvement effort (Bergin et al., 2009). UNC-IE converted 

this spreadsheet to a point Shapefile for generating a rail yard surrogate for the U.S. The 

surrogate is based on the sum of the NOx and PM2.5 emissions at the rail yard locations in the 

ERTAC rail yard inventory.

The attributes in the file used to compute surrogates include:

• NOXPMTOT – total of NOx and PM2.5 emissions at each rail yard

Shapefile Name: ERTAC_railyard_WRF

Shapefile Type: Point

Year: 2012

Attributes OBJECTID, OWNER1, OWNER2, OWNER3, STATE, FIPS, YARDNAME, LON,

LAT, NOX, PM25, NOXPMTOT,

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters

Derived Surrogates: 275

2.2.5 Highway Exits

This point Shapefile of highway exit ramp locations was obtained from the ESRI Data and Maps 

2010 database. UNC projected the data to a U.S. national Lambert Conformal Conic projection 

on a normal sphere. As these data are proprietary, this Shapefile cannot be redistributed.

The attributes in the file used to compute surrogates include:

• NONE – default to point locations

Shapefile Name: exits_lcc

Shapefile Type: Point

Year: 2010

Attributes: EXIT_ID, FROM_NAME, EXIT_NUM, TO_NAME, COUNTRY

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters

Derived Surrogates: 201, 251
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2.2.6 Major Roads

2010 U.S. Major roads, including interstates, freeways, U.S. and state highways, major streets 

and roads, primary, secondary, and local roads, access ramps, ferry crossing, and other major 

thoroughfares within the U.S. This Shapefile was used to define the locations of local and minor 

roadways for allocating off-network light-duty vehicle MOVES emissions to modeling grids. 

This line Shapefile of major roads was obtained from the ESRI Data and Maps 2010 database. 

UNC projected the data to a U.S. national Lambert Conformal Conic projection on a normal 

sphere. As these data are proprietary, this Shapefile cannot be redistributed

The attributes in the file used to compute surrogates include:

• FDC – road classes; 4=secondary road; 5=local connecting road

Shapefile Name: mjrrds_lcc

Shapefile Type: Line

Year: 2010

Attributes: PREFIX, NAME, TYPE, SUFFIX, FDC, SHIELD, HWY_NUM, OID

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters

Derived Surrogates: 252

2.2.7 Transportation Terminals

2010 U.S. and Canada locations for transportation terminals such as bus terminals, train stations, 

marine and ferry terminals, and other significant transportation nodes. This Shapefile was used to 

define the locations of bus stations for allocating off-network MOVES emissions to modeling 

grids. This point Shapefile of transportation terminals was obtained from the ESRI Data and 

Maps 2010 database. UNC projected the data to a U.S. national Lambert Conformal Conic

projection on a normal sphere. As these data are proprietary, this Shapefile cannot be 

redistributed.

The attributes in the file used to compute surrogates include:

• FCC – feature class codes; D53=bus terminals

Shapefile Name: transterm

Shapfile Type: Point

Year: 2010

Attributes: NAME, FCC

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters
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Derived Surrogates: 203, 253

2.3 Shipping and Port Surrogates

2.3.1 Waterways

2011 National Transportation Atlas Database (NTAD) data for waterway lengths. UNC projected 

the data to a U.S. national Lambert Conformal Conic projection on a normal sphere.

The attributes in the file used to compute surrogates include:

• LENGTH – navigable waterway lengths

Shapefile Name: waterway_ntad2011

Shapefile Type: Line

Year: 2011

Attributes: LENGTH, FEATUREID, ANODE, BNODE, LINKNAME, RIVERNAME, AMILE, 

BMILE, LENGTH1, LENTH_SRC, LINKTYPE, CTRL_DEPTH, WATERWAY, 

GEO_CLASS, FUNC_CLASS, WTWY_TYPE, CHART_ID, NUM_PAIRS, WHO_MOD, 

DATE_MOD, HEADING, STATE, FIPS, FIPS2, NONUS, VERSION

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters

Derived Surrogates: 807

2.3.2 Marine Ports Point Locations

2010 NTAD data for marine ports. UNC projected the data to a U.S. national Lambert 

Conformal Conic projection on a normal sphere.

The attributes in the file used to compute surrogates include:

• NONE – default to point locations

Shapefile Name: ports_ntad2010

Shapefile Type: point

Year: 2010

Attributes: NAME, LOCATION, ADDRESS, COUNTY, COUNTYFIPS, TOWN, STATE, 

WATERWAY, PORT_NAME, MILE, BANK, LATDEC, LONG_DEC, OPERATOR1, 

OWNER, PURPOSE, RWY_CONN, PORTSERIES, SEQNO, LOC_CD, PWDNO, OLDPWD, 

DOCKCD, NDCCODE, CNGDST, COMMODITY1, COMMODITY2, COMMODITY3, 

COMMODITY4, REMARKS, DATUM, DEPTH1, DEPTH1A, DEPTH2, DEPTH2A, 

DEPTH3, DEPTH3A, TOTBERTH1, TOTBERTH2, TOTBERTH3, YEAR, MAPNO, 

FIRSTNAME, LASTNAME, PHONE, FAX, STFIPS, VERSION

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)
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Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters

Derived Surrogates: 800

2.3.3 Marine Port Area Emissions

2012 marine port areas weighted by NEI2011 NOx emissions. UNC-IE joined a Shapefile of 

marine port areas with NEI2011 NOx emissions for SCC 2280002100. UNC projected the data 

to a U.S. national Lambert Conformal Conic projection on a normal sphere.

The attributes in the file used to compute surrogates include:

• ann_value – annual NOx emissions for port vessels from the NEI2011

Shapefile Name: Ports_081412_NEI2011_NOx_WRF

Shapefile Type: polygon

Year: 2012

Attributes: NAME,FIPS_1,Area_sqmi,ShapeID,PortID,OID_,country_cd,region_cd,tribal_cod,

census_tra,shape_id,scc,emis_type,poll,ann_value,ann_pct_re,control_id,control_me,current_co,

cumulative,projection,reg_codes,calc_metho,calc_year,date_updat,data_set_i,jan_value,feb_valu

e,mar_value,apr_value,may_value,jun_value,jul_value,aug_value,sep_value,oct_value,nov_valu

e,dec_value,jan_pctred,feb_pctred,mar_pctred,apr_pctred,may_pctred,jun_pctred,jul_pctred,aug

_pctred,sep_pctred,oct_pctred,nov_pctred,dec_pctred,comment,SHPID

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters

Derived Surrogates: 820
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2.3.4 Great Lakes Tugboat Zones

Figure 2. 2011 NEI Great Lakes tugboat zones

U.S. EPA defined a tugboat operating zone in the Great Lakes as 0.5-2.0 miles offshore (Figure 

2). A Shapefile that defines the tugboat zone by county was developed for use in creating a 

spatial surrogate for emissions in the Great Lakes. 

The attributes in the file used to compute surrogates include:

• AREA – area of tugboat zone by county

Shapefile Name: TUGvessel_SURROGATE_GREATLAKES

Shapefile Type: Polygon

Year: 2012

Attributes: FIPS, STATE, COUNTY, NAME, ST, STATECTY, AREA,

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters
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Derived Surrogates: 808

2.3.5 Gulf of Mexico Platforms

Figure 3. Gulf of Mexico oil and gas platform support vessel densities

The locations of class 1 and 2 commercial shipping vessels in the Gulf of Mexico were tied to 

the density of shipping activity surrounding oil and gas platforms (Figure 3). A Shapefile that 

defines the fraction of total Gulf activity density around the energy development platforms was 

used to create a Gulf Shipping Lanes surrogate. 

The attributes in the file used to compute surrogates include:

• Fraction – fraction of Gulf-wide activity associated with individual oil and gas platforms

Shapefile Name: GulfofMexico_SupportVessels_Density_WRF

Shapefile Type: Polygon

Year: 2010

Attributes: GRIDCODE, Area, Fraction

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters

Derived Surrogates: 803, 802

2.3.6 Offshore Commercial Shipping

Offshore shipping lane areas for the Pacific, Atlantic, Gulf of Mexico, and Great Lakes weighted 

by NEI2011 NOx emissions. UNC-IE joined a Shapefile of offshore commercial shipping lane 
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areas with NEI2011 NOx emissions for SCC 2280002200. UNC projected the data to a U.S. 

national Lambert Conformal projection on a normal sphere

The attributes in the file used to compute surrogates include:

• ann_value– annual NOx emissions for C1/C2 vessels from the NEI2011

Shapefile Name: ShippingLanes_112812_NEI2011_NOx_WRF

Shapefile Type: Polygon

Year: 2012

Attributes: FIPS,Area_sqmi,ShapeID,OID_,country_cd,region_cd,tribal_cod,census_tra,

shape_id, scc,emis_type,poll,ann_value,ann_pct_re,control_id,control_me,current_co,

cumulative, projection,reg_codes,calc_metho,calc_year,date_updat,data_set_i,jan_value,

feb_value,mar_value,apr_value,may_value,jun_value,jul_value,aug_value,sep_value,

oct_value,nov_value,dec_value,jan_pctred,feb_pctred,mar_pctred,apr_pctred,may_pctred,

jun_pctred,jul_pctred,aug_pctred,sep_pctred,oct_pctred,nov_pctred,dec_pctred,comment,SHPID

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters

Derived Surrogates: 805, 806, 802

2.3.7 Midwest Shipping and Inland Waterways

Figure 4. Great Lakes shipping lane and inland waterway inventory
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LADCO provided a 2011 shipping lane and inland waterway emission inventory for the Great 

Lakes region (Figure 4). UNC projected the data to a U.S. national Lambert Conformal Conic 

projection on a normal sphere and used these data to generate a surrogate for the regional 

shipping lane emissions.

The attributes in the file used to compute surrogates include:

• G_nonCO2– annual non-CO2 emissions for C1/C2 vessels from the LADCO 2011 

inventory

Shapefile Name: GreatLakes_IWW_EERA_2011_WRF

Shapefile Type: Line

Year: 2011

Attributes: NWN_ID,G_CO2GAL,G_NOXGAL,G_SOXGAL,G_HCGAL,G_PMGAL,

G_BCGAL, G_POMGAL,G_COGAL,seg_km,G_nonCO2,GROUP,Seg_Miles,G_CO2,

G_NOX,G_SOX,G_HC,G_PM,G_BC,G_POM,G_CO,

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters

Derived Surrogates: 812, 802

2.4 FEMA Building Footprints

HAZUS is a nationally standardized methodology that contains models for estimating potential 

losses from natural disasters. A key component of the HAZUS database is information on 

building locations, sizes, and classifications. These geospatial data on buildings are useful for 

allocating inventory data to modeling grids because they contain details on the locations and 

sizes of different building types. The Federal Emergency Management Agency HAZUS-MH 

version 2 was released in September 2011 and contains square footage data for different types of 

buildings throughout the U.S. The building square footage data are used to identify building 

classifications (i.e. commercial, residential, industrial, institutional) for allocating non-point 

inventory sources to modeling grids. UNC merged Census-block level data in state Shapefiles 

into a national Shapefile for select database attributes. UNC used 2002 Census block boundaries 

when merging these data to maintain consistency with the previous spatial surrogates used by 

EPA.

The attributes in the file used to compute surrogates include:

• COM# - commercial building square footage

• IND# - industrial building square footage

• RES# - residential building square footage

• EDU# - educational building square footage
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• REL# - religious building square footage

• GOV# - government building square footage

Shapefile Name: fema_bsf_2002bnd

Shapefile Type: Polygon

Year: 2011

Attributes: COM#, IND#, RES#, EDU#, REL#, GOV#

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters

Derived Surrogates: 500, 505, 510, 512, 515, 520, 525, 527, 530, 535, 540, 545, 550, 555, 560, 

565, 570, 575, 580, 585, 590, 595, 596

2.5 Landcover/Landuse Surrogates

2.5.1 Agriculture

2006 National Land Cover Database (NLCD) land cover data was used to create this Shapefile. 

UNC-IE re-gridded the original 30-meter resolution data to 250-meter resolution using the 

dominant land use class. UNC-IE extracted agricultural classes 81 and 82 into a 250-meter raster 

file and then converted the raster file to a polygon Shapefile. 

The attributes in the file used to compute surrogates include:

• GRID_CODE – 81 for Pasture or Hay class and 82 for Crop class

Shapefile Name: nlcd2006_80s_agri

Shapefile Type: Polygon

Year: 2006

Attributes: GRID_CODE

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters

Derived Surrogates: 310, 318, 319

2.5.2 Water and Land

2006 NLCD land cover data was used to create this Shapefile. UNC-IE re-gridded the original 

30-meter resolution data to 250-meter resolution using the dominant land use class. UNC-IE

extracted water (class 11) and land (class 0) into a 250-meter raster file and then converted the 

raster file to a polygon Shapefile. 

The attributes in the file used to compute surrogates include:
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• GRID_CODE – 11=water; 0=land

Shapefile Name: nlcd2006_10s_waterland

Shapefile Type: Polygon

Year: 2006

Attributes: GRID_CODE

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters

Derived Surrogates: 340, 346, 350

2.5.3 Developed Areas

2006 NLCD land cover data was used to create this Shapefile. UNC-IE re-gridded the original 

30-meter resolution data to 250-meter resolution using the dominant land use class. UNC-IE

extracted developed land use classes (21 to 24) into a 250-meter raster file and then converted 

the raster file to a polygon Shapefile. 

The attributes in the file used to compute surrogates include:

• GRID_CODE – 21=Open Space; 22=Developed, Low Intensity; 23=Developed, Medium 

Intensity; 24=Developed, High Intensity

Shapefile Name: nlcd2006_20s_developed

Type: Polygon

Year: 2006

Attributes: GRID_CODE

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters

Derived Surrogates: 300, 301, 302, 303

2.5.4 Forest Land

2006 NLCD land cover data was used to create this Shapefile. UNC-IE re-gridded the original 

30-meter resolution data to 250-meter resolution using the dominant land use class. UNC-IE

extracted the forest land use classes (41,42,43, and 90) into a 250-meter raster file and then 

converted the raster file to a polygon Shapefile. 

The attributes in the file used to compute surrogates include:

• GRID_CODE – 40=Forest land

Shapefile Name: nlcd2006_40_forest
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Type: Polygon

Year: 2006

Attributes: GRID_CODE

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters

Derived Surrogates: 320

2.6 Oil and Gas Production

Figure 5. IHS database regions

2008 O&G activities Shapefile for the Rocky Mountain Region 

ENVIRON developed a region-wide oil and gas (O&G) emissions inventory for 2008 in the 

western US for the West-wide Jump Start Air Quality Modeling Study (WestJumpAQMS) 

managed by the Western Governors’ Association. For the WestJumpAQMS, ENVIRON also 

developed gridding surrogates based on 2008 O&G activities (production and drilling) obtained 

from the IHS database for use in the SMOKE modeling system. The Rocky Mountain region

(Figure 5) Shapefile was based on this existing O&G activities dataset. 

ENVIRON developed the Shapefile by aggregating 2008 O&G activities to a 2-km grid. A 

threshold of three or more wells per grid-cell was applied (i.e. grid-cells having three or more 

wells were kept in the Shapefile and the remaining cells were dropped). The aggregation was 

performed to mask any proprietary information contained in the original Shapefiles. As a 
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consequence, approximately 6% of oil production and 8% of gas production were dropped from 

the Shapefiles. Figure 6 presents a display of O&G activities dropped due to the threshold (red 

grid-cells) and O&G activities in the Shapefile (green grid-cells). 

Figure 6. 2008 O&G activities dropped due to threshold for the Rocky Mountain region

2011 O&G activities shapefile for the Northeastern Region

A similar Shapefile was prepared for the Northeastern Region as defined by the IHS database in 

Figure 5. The Shapefile format is identical to the Rocky Mountain region Shapefile described 

above. The Shapefile is based on the 2011 O&G activity data from the IHS database. This was 

the first use by ENVIRON of the Northeastern IHS dataset. The quality of data in the 

Northeastern IHS dataset differed in some respects from the Rocky Mountain region:

• Gas production data was not available for the following states due to state reporting 

requirements: IL, IN, KY.

• Oil production data for KY is based on an incomplete dataset. IHS must obtain data from 

refiners due to lack of state reporting requirements. Comparison of IHS to the US 

Department of Energy Energy Information Administration (EIA) data indicated that the 

IHS data included only approximately 40% of the total KY oil production in 2011.

• Except for the above notes, comparisons of the compiled 2011 state-level IHS data to 

EIA state-level estimates indicated gas production estimates within 7% of EIA estimates 

for gas production and 15% for oil production. These differences may be due to IHS 

incorporation of more recent reported volumes than the EIA data.
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• ENVIRON found that a number of wells in the IHS database are missing lat-long 

coordinates. Gap-filling of lat-long coordinates for some wells was possible based on 

their Township, Range and Section (TRS) (Public Land Survey System) information. 

• A small number of IHS records have no available location information and so cannot be 

translated into surrogates. The following is a listing of the fraction of wells for which 

location is not included in the Northeastern region:

o Oil production: 12.8%

o Gas production: 0.3%

o Active well count: 1.2%

An analysis was conducted of the aggregation of production data to 2 km grid cells, applying the 

same threshold of three or more wells per grid cell. The results indicated that approximately 

24% of oil production and approximately 13% of gas production would be dropped if this 

threshold were used. This does not represent a reasonably small fraction of the production that 

would be dropped; therefore a similar analysis for 3 km and 4 km grid resolutions was 

performed. Table 4 below shows the results of this analysis. 

Table 3. Analysis results for 2011 O&G activities dropped by grid resolution for the 

Northeastern region 

All Wells with location Retained Wells * Difference % Difference

Grid 

resolution

OIL 

(BBL) GAS (MCF)

OIL 

(BBL) GAS (MCF)

OIL 

(BBL)

GAS 

(MCF)

OIL 

(BBL)

GAS 

(MCF)

2km Resolution 25,873,260

2,003,377,27

4 19,567,620

1,745,387,93

9 6,305,640 257,989,335 24.37% 12.88%

3km Resolution 25,873,260

2,003,377,27

4 22,062,519

1,845,365,22

4 3,810,741 158,012,050 14.73% 7.89%

4km Resolution 25,873,260

2,003,377,27

4 22,981,769

1,914,400,99

6 2,891,491 88,976,278 11.18% 4.44%

In consultation with EPA, it was decided to develop the Shapefile by aggregating 2011 O&G 

activities to the 4 km grid-cell. The 4 km grid was defined such that it aligns with the CONUS 4 

km domain. A threshold of three or more wells per grid-cell was used, similar to the Rocky 

Mountain region. Using these criteria, approximately 11% of oil production and approximately 

5% of gas production were dropped. Figure 7 shows Northeastern region O&G activities 

dropped due to the threshold (red grid-cells) and O&G activities in the Shapefile (green grid

cells).
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Figure 7. 2011 O&G activities dropped due to threshold for the Northeastern region

The attributes in the file used to compute surrogates include:

• oil – oil production in barrels

• gas – gas production in millions of cubic feet

• wells – number of wells

• spuds – number of spuds

• SHPID – filter for well type: A = all wells, O = oil wells, G = gas wells, C = CBM wells, 

S = spuds (drilling)

Shapefile Name: US_Oil_Gas_2008West2km_2011East4km
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Type: Polygon

Year: 2008 and 2011

Attributes: oil, gas, wells, shp_id, spuds, SHPID

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters

Derived Surrogates: 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699

2.7 Other Industrial and Commercial Activities

2.7.1 Mines

2003 USGS point locations of mineral operations and mines for the following: agricultural uses, 

construction uses, ferrous metal mines, ferrous metals processing plants, miscellaneous industrial 

uses, nonferrous metal mines, nonferrous metal processing plants, refractory, abrasive, and other 

industrial uses, sand and gravel, and crushed stone operations; NOTE: this Shapefile does not 

include energy minerals (i.e., coal is not included here).

The attributes in the file used to compute surrogates include:

• NONE – point locations of the mining operations are used to derive the surrogate

Shapefile Name: mines_usgs2003_wrf

Shapefile Type: Point

Year: 2003

Attributes: SNDGRVX020,COMP_NAME,OPER_NAME,COUNTY,STATE,FIPS,

STATE_FIPS,LATITUDE,LONGITUDE,OPER_TYPE,COMMODITY

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters

Derived Surrogates: 860, 861

2.7.2 Quarries

2003 USGS point locations of mineral operations and mines for the following: sand and gravel, 

and crushed stone operations

The attributes in the file used to compute surrogates include:

NONE – point locations of the mining operations are used to derive the surrogate

Shapefile Name: quarries_usgs2003_wrf

Shapefile Type: Point

Year: 2003
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Attributes: SNDGRVX020,COMP_NAME,OPER_NAME,COUNTY,STATE,FIPS,

STATE_FIPS,LATITUDE,LONGITUDE,OPER_TYPE,COMMODITY

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters

Derived Surrogates: 862

2.7.3 Dry Cleaners

Counts of dry cleaners by Census Block Group in operation during calendar year 2009 were used 

to update the dry cleaner location surrogate. These data were purchased from the Spatial Insights, 

Inc. Business Counts Database10. As these data are proprietary, this Shapefile cannot be

redistributed. 

The attributes in the file used to compute surrogates include:

• DRY – number of dry cleaner operations in each Census Block Group

Shapefile Name: drycleaner_surrogate

Shapefile Type: Polygon

Year: 2009

Attributes: STFID, FIRST_STAT, FIRST_COUN, FIRST_TRAC, FIRST_BLOC, GEOID,

DRY, FIPSSTCO

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters

Derived Surrogates: 880

2.7.4 Gas Stations

Counts of gas stations (with and without convenience stores) by Census Block Group in 

operation during calendar year 2009 were used to update the gas station location surrogate. 

These data were purchased from the Spatial Insights, Inc. Business Counts Database9. As these 

data are proprietary, this Shapefile cannot be redistributed. 

The attributes in the file used to compute surrogates include:

• GAS – number of gas station operations in each Census Block Group

Shapefile Name: gas_station_surrogate

Shapefile Type: Polygon

Year: 2009

 

10 http://www.spatialinsights.com/catalog/product.aspx?product=126&content=1606
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Attributes: STFID, FIRST_STAT, FIRST_COUN, FIRST_TRAC, FIRST_BLOC, GEOID,

GAS, FIPSSTCO

Projection: Lambert Conformal Conic (X0=-97.0, Y1=33.0, Y2=45.0, Y0=40.0)

Datum: NAD83

Spheroid: Normal Sphere with radius 6,370,000 meters

Derived Surrogates: 600

3 Known Issues and Future Work

While the Shapefiles and surrogates documented here and in the accompanying spreadsheet 

represent the best available information for allocating non-point emissions to modeling grids, 

there are some caveats to these data and issues that should be addressed in the future.

• Some of the surrogates are derived from proprietary Shapefiles that cannot be distributed. 

These data include:

o The off-network mobile surrogates (major roads, bus stops, and highway exits) are 

generated from Shapefiles contained in the 2010 ESRI Data and Maps CD.

o The dry cleaners and gas stations surrogates are generated from data purchased from 

Spatial Insights, Inc.

o The oil and gas surrogates (689-699) are generated from data purchased from IHS, 

Inc. These data were aggregated from points to polygons (grid cells) to obscure the 

proprietary nature of the data. As described in Section 2.6, this aggregation process 

results in the loss of some of the original data.

• The off-network mobile surrogates (251-253) were generated using data that 

approximated the activity locations. As the off-network, heavy duty diesel truck rate per 

profile (RPP) emissions from MOVES simulates idling at truck stops and travel plazas, 

the surrogate for these sources should be derived from a point Shapefile that characterizes 

these locations. Although we were not able to find a national Shapefile of truck 

stops/travel plazas, these data are available for some states. An improvement of surrogate 

251 (Off-Network MD/HD) will be to integrate a truck stop/travel plaza Shapefile into 

this surrogate.

• The total agriculture surrogate (310) is not specific enough for livestock sources and 

could be improved using a point Shapefile of confined animal feeding operations 

(CAFOs). Although we were not able to find a national Shapefile of CAFO locations, 

these data are available from some states. State departments of water quality track CAFO 

locations for monitoring runoff and water permitting purposes. It will be worthwhile to 

collect data from as many states as possible for developing a national CAFO surrogate to 

use for allocating non-point livestock inventories to model grids.

• The high resolution, national NCLD Shapefile breaks the Surrogate Tool by causing 

memory allocation failures while processing the data (SRGCREATE_ERROR>gpc 

malloc failure). To avoid this error it is necessary to split the NLCD Shapefiles into 

subregions, generate surrogates for each subregion, and then paste the data back together 
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to create a national surrogate. We needed to split the NLCD Shapefiles into eight 

subregions to avoid the memory violations in the Surrogate Tool. The Surrogate Tool 

needs to be updated to take advantage of the larger memory and multi-processing 

capabilities of modern computing hardware.

4 Surrogate Revision History 

• Version 4 (09/2013) Updates

o Shipping lanes surrogate (802): Built as a combination of Great Lakes shipping 

lanes/Inland waterways, Gulf of Mexico platform densities (803), and offshore 

shipping locations weighted by NEI2011 NOX emissions from SCC 2280002200 

(806), and water (350).

o Ports weighted by NEI2011 NOx emissions (820): Port areas joined with 

NEI2011 NOX emissions from SCC 2280002100

o Oil and gas development surrogates for the following basins: Greater Green River 

(WY), Uintah (UT), Piceance (CO), Denver-Julesberg (CO), San Juan (NM), 

Raton (NM), Paradox (CO/NM), Permian (TX/NM), Marcellus 

(PA/NY/OH/WV/KY), Illinois (IL/IN); surrogates include Gas production at all 

wells (689), Oil production at all wells (690), Well count of CBM wells (691), 

Spud count (692), Well count of all wells (693), Oil production at oil wells (694), 

Well count of all oil wells (694), Gas production at gas wells (696), Oil 

production at gas wells (697), Well count of gas wells (698), Gas production at 

CBM wells (699).

o Gas stations (600) - 2012 gas station point locations weighted by the number of 

locations in each Census Block Group

o Dry cleaners (880) - 2012 dry cleaner point locations weighted by the number of 

locations in each Census Block Group

o Great Lakes Tug Zone Area (808) - Tug zone from 0.5 - 2.0 miles offshore in the 

Great Lakes

• Version 3 (10/2012) Updates

o All NLCD-based (3**) surrogates updated with NLCD 2006 30-m raster files 

aggregated to 250-m Shapefiles.

§ Includes new med. (301) and high intensity residential (302) surrogates

§ Includes new open space surrogate (303)

§ Total agriculture with orchards/vineyards and orchards/vineyards no 

longer available

§ Includes new pasture land (318) and crop land (319) surrogates

o Four new housing surrogates (131-134) from the 2010 US Census data.

o Three new rail density surrogates (271-273) from 2012 NTAD freight density line 

Shapefiles
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o New rail yard surrogate (275) from ERTAC rail yard point Shapefile (provided by 

Georgia DNR)

o Construction and mining surrogate (861) added as a combination of 0.5 housing 

change/population + 0.5 mines

o Quarries surrogate (862) from point locations of mineral operations and mines for 

sand, gravel, and crushed stone

• Version 2 (06/2012) Updates

o All FEMA-based (5**) surrogates re-generated with the correct data Shapefile 

(cty_pophu2k_revised). One artifact of using the incorrect data Shapefile was the 

inclusion of defunct counties/FIPs or the exclusion of new counties/FIPs in the 

new surrogates. Dade County, FL and Yellowstone National Park, MT were 

removed in the new data. Broomfield County, CO, Miami-Dade County, FL, and 

Boyd County, NE now exist.

o All surrogates use new gapfilling definitions, including being gapfilled with 

surrogates that used the correct data Shapefile; some of the previous surrogates 

were gapfilled using FEMA data that used an incorrect data Shapefile

o Two WY counties had surrogates that didn't normalize to 1.0. The Normalization 

Tool was used to force these two counties to have surrogates that add to 1.0.

5 REFERENCES

Bergin, M. et al (2009) ERTAC Rail: A collaborative effort in building a railroad-related 

emission inventory between Eastern states’ air protection agencies and participation with 

the railroad industry, presented at the 18th Annual Emission Inventory Conference, U.S. 

EPA, Baltimore, MD, http://www.epa.gov/ttnchie1/conference/ei18/session6/bergin.pdf.

Eyth, A. et al. (2007) Accounting for Land Use Changes in Projecting Future-Year Emissions 

Scenarios, presented at the 16th Annual Emission Inventory Conference, U.S. EPA, 

Raleigh, NC, http://www.epa.gov/ttnchie1/conference/ei16/session7/eyth.pdf.