Light-Duty Vehicle Greenhouse Gas 

Emissions Inventory for Air Quality 

Modeling Technical Support Document 
EPA-420-R-10-011

April 2010

Assessment and Standards Division

Office of Transportation and Air Quality

U.S. Environmental Protection Agency

Light-Duty Vehicle Greenhouse Gas 

Emissions Inventory for Air Quality 

Modeling Technical Support Document 
TABLE OF CONTENTS 

ACRONYMS ...................................................................................................................................................iii
 

LIST OF TABLES...........................................................................................................................................iv
 

LIST OF APPENDICES.................................................................................................................................iv
 

1 Introduction .............................................................................................................................................. 1
 

2 2005 Emission inventories and their preparation ................................................................................. 2
 

2.1 Custom configuration for emissions modeling for LDGHG..............................................................3 

2.2 Point sources.......................................................................................................................................6 

2.3 2005 Nonpoint sources.....................................................................................................................10 

2.4 2005 Mobile sources.........................................................................................................................10 

3 VOC speciation changes that represent fuel changes ......................................................................... 11
 

4 2030 Reference Case............................................................................................................................... 13
 

4.1 2030 Reference Case Point sources..................................................................................................13 

4.1.1 Part 1: Projecting 2005 to 2030 Reference for ptnonipm........................................................14 

4.1.2 Part 2: Additional OTAQ-supplied emissions data for ptnonipm............................................17 

4.2 2030 Reference Case Nonpoint sources...........................................................................................18 

4.2.1 Part 1: Projecting 2005 to 2030 Reference case for nonpt .......................................................19 

4.2.2 Part 2: Additional OTAQ-supplied emissions data for nonpt ..................................................19 

4.3 Mobile sources..................................................................................................................................20 

4.3.1 US Aircraft, locomotive, and non-c3 commercial marine (alm_no_c3)..................................20 

4.3.2 Canada and Mexico onroad mobile sources (othon)................................................................20 

4.3.3 C3 commercial marine sources from all waters (seca_c3).......................................................20 

4.3.4 US nonroad mobile sources (nonroad).....................................................................................22 

4.3.5 Onroad mobile sources (on_moves_runpm, on_moves_startpm, and on_noadj) ....................23 

5 2030 Control Case .................................................................................................................................. 28
 

5.1 2030 Control Case Point sources......................................................................................................28 

5.2 2030 Control Case Nonpoint sources...............................................................................................29 

5.3 2030 Control Case Mobile sources...................................................................................................29 

ii 
 

ACRONYMS 

AEO Annual Energy Outlook 

BEIS Biogenic Emission Inventory System 

btp Bulk plant terminal-to-pump 

C3 Category 3 (commercial marine vessels) 

CAMD EPA’s Clean Air Markets Division 

CAP Criteria Air Pollutant 

CARB California Air Resources Board 

CEM Continuous Emissions Monitoring 

CMAQ Community Multiscale Air Quality 

DOE Department of Energy 

E0 0% Ethanol gasoline 

E10 10% Ethanol gasoline 

E85 85% Ethanol gasoline 

EISA Energy Independence and Security Act of 2007 

EGU Electric Generating Utility 

FAA Federal Aviation Administration 

FIPS Federal Information Processing Standard 

HAP Hazardous Air Pollutant 

HDGV Heavy-duty Gasoline Vehicles 

IPM Integrated Planning Model 

LDGHG Light Duty Greenhouse Gas 

LDGT1 Light-duty Gasoline Trucks, 0-6000 pounds gross vehicle weight 

LDGT2 Light-duty Gasoline Trucks, 6000-8500 pounds gross vehicle weight 

LDGV Light-duty Gasoline Vehicles 

MOBILE6 Mobile Source Emission Factor Model, version 6 

MOVES Motor Vehicle Emissions Simulator 

NEEDS National Electric Energy Database System 

NEI National Emission Inventory 

NMIM National Mobile Inventory Model 

OAQPS EPA’s Office of Air Quality Planning and Standards 

ORL One Record per Line (a SMOKE input format) 

ORNL Oak Ridge National Laboratory 

MP Multipollutant 

PFC Portable Fuel Container 

rtb Refinery-to-bulk terminal 

RFS1 Renewable Fuel Standard program 

RFS2 Revised annual renewable fuel standard 

SMOKE Sparse Matrix Operator Kernel Emissions 

SCC Source Category Code 

TAF Terminal Area Forecast 

VOC Volatile Organic Compound 

WRAP Western Regional Air Partnership 

iii 
LIST OF TABLES 

Table 1. List of cases run in support of the LDGHG air quality modeling 

Table 2. Sectors Used in Emissions Modeling for the LDGHG Platform 

Table 3. Comparison of model species in the 2005 v4 platform and the LDGHG platform 

Table 4. Description of differences in ancillary data between the LDGHG 2005 case and the 2005 v4 

platform 

Table 5. Emissions from ethanol plants added to 2005 v1 point inventory (tons/year) 

Table 6. Quantifying MOVES Diesel Exhaust PM Error for 2005 Base Case 

Table 7. Summary of VOC speciation profile approaches by sector across cases 

Table 8. Explanation of VOC profile codes listed in Table 7. 

Table 9a. Impact on the ptnonipm Sector of Not Applying Base Case Controls to the LDGHG 2030 

Reference Case. 

Table 9b. Impact on Total Anthropogenic Emissions of Not Applying Base Case Controls to the LDGHG 

2030 Reference Case. 

Table 10. HAP emission ratios for generation of HAP emissions from criteria emissions for C3 commercial 

marine vessels. 

Table 11. Components of 2030 Nonroad Sector for Reference and Control Cases. 

Table 12. MOVES and NMIM Inventory Components of 2030 Onroad Sectors for Reference and Control 

Cases. 

LIST OF APPENDICES 

APPENDIX A: Equations to adapt pre-speciated diesel emissions from MOVES to air quality modeling 

species needed for CMAQ. 

APPENDIX B: Inventory Data Files Used for Each LDGHG Modeling Case – SMOKE Input Inventory 

Datasets 

APPENDIX C: Ancillary Data Files Used for LDGHG 2005 Case Compared to 2005 v4 Platform Data Files 

APPENDIX D: Growth and Control Assumptions and Affected Pollutants for the 2030 Reference Case 

iv 
1 Introduction 

This document provides the details of emissions data processing done in support of the Environmental 

Protection Agency (EPA) and National Highway Traffic Safety Administration (NHTSA) joint rulemaking 

effort to establish Light Duty Vehicle Greenhouse Gas (LDGHG) Emissions Standards and Corporate 

Average Fuel Economy (CAFÉ) Standards. The emissions and modeling effort is hereafter referred to as 

LDGHG and consists of three emissions cases. Table 1 provides of list of the emissions cases created for 

this modeling effort. 

Table 1. List of cases run in support of the LDGHG air quality modeling 

Case Name 

Internal EPA 

Abbreviation Description 

2005 basecase 2005cp_tox 2005 case done with average year fires data and an average 

year temporal allocation approach for Electrical Generating 

Units (EGUs), to use for computing relative response factors 

with 2030 scenarios 

2030 Reference case 2030cp_tox 2030 “baseline” scenario representing the best estimate for the 

future year without implementation of national CO2 emissions 

standards. 

2030 Control case 2030cp_tox_ldghg 2030 “control” case scenario representing implementation of 

national CO2 emissions standards for light-duty vehicles. 

These standards will require these vehicles to meet an 

estimated combined average emissions level of 250 

grams/mile of CO2 in model year 2016. 

The data used in the 2005 emissions cases are often the same as those described in the 2005-based, v4 

platform document (http://www.epa.gov/ttn/chief/emch/index.html#2005). The LDGHG cases use some 

different emissions data than the official v4 platform for two reasons. First, the LDGHG Standard was 

evaluated in comparison to the modeling performed for the Revised annual Renewable Fuel Standard 

(RFS2); therefore, RFS2-specific inputs were retained for LDGHG. The 2005 RFS2 modeling was 

performed from December 2008 through March of 2009, prior to the completion of the 2005 v4 platform. 

Second, the LDGHG modeling used data intended only for the rule development and not for general use. All 

of the documentation provided here describes what was done differently and specifically for the LDGHG 

effort in contrast to what is used in the v4 platform. 

In LDGHG, we used a 2005 base case approach for the year 2005 emissions scenario. While there is not 

documentation on the approach specific to 2005, this approach is similar to that in OAQPS’s 2002-based v3 

platform (http://www.epa.gov/ttn/chief/emch/index.html#2002). A base case approach uses average year 

fires and EGU temporal profiles from 3 years of EGU data. We use a base case approach because we want 

to reduce year-specific variability in these components of the inventory: for example large fires vary in 

location and day of the year each year, and EGU shutdowns and high use on high energy demand days also 

vary by year. By using a base case approach, these two aspects of the inventory are maintained in future 

year modeling and therefore do not introduce potentially spurious year-specific artifacts in air quality 

modeling estimates. In addition, the same biogenic emissions data as the v4 platform was used not only in 

the 2005 case for LDGHG, but also in both future-year cases run for LDGHG. For LDGHG, the only 

significant data changes between the 2005 and future-year cases are emission inventories and speciation 

approaches. 

1
 
 

For this effort, we have created and provided state, county, and source category code (SCC) emission 

summaries for the nonpoint and mobile sectors. For point sources, we have posted the actual inventory 

datasets that we used for emissions modeling. These data have been provided to the EPA docket for this 

rule. In addition, the data can be found associated with the “LDGHG 2005 and 2030 emissions data” link on 

the Clearinghouse for Inventories and Emissions Factors (CHIEF) website at 

http://www.epa.gov/ttn/chief/emch/index.html#2005. 

In the remainder of this document, we provide a description of the approaches taken for the emissions in 

support of air quality modeling for LDGHG. In Section 2, we describe the ancillary data and 2005 inventory 

differences from the v4 platform. In Section 3, we describe the speciation differences among each of the 

cases run for LDGHG. In Section 4, we describe the 2030 Reference case as compared to the 2005 base 

case, and in Section 5, we describe the 2030 Control Case in comparison to the 2030 Reference case. 

2 2005 Emission inventories and their preparation 

As mentioned previously, the 2005 emissions modeling approach for LDGHG used much of the same data 

and approaches as the 2005 v4 platform. In this section, we identify the differences between the data used 

for LDGHG and that used for the 2005 v4 platform. Section 2.1 provides ancillary data differences that 

impact multiple sectors and Sections 2.2 through 2.4 provides differences for the point, area, and mobile 

sectors. 

Table 2 below lists the platform sectors used for the LDGHG modeling platform. It also indicates which 

platform sectors include HAP emissions and the associated sectors from the National Emission Inventory 

(NEI). Subsequent sections refer to these platform sectors for identifying the emissions differences between 

the v4 platform and the LDGHG platform. 

Table 2. Sectors Used in Emissions Modeling for the LDGHG Platform 

Platform Sector 

2005 NEI 

Sector Description 

Contains HAP 

emissions? 

IPM sector: ptipm Point NEI EGU units at facilities mapped to the IPM model using the 

National Electric Energy Database System (NEEDS) database. 

Yes 

Non-IPM sector: 

ptnonipm 

Point+ All NEI point source units not matched to the ptipm sector. Yes 

Average-fire sector: 

avefire 

N/A Average-year wildfire and prescribed fire emissions derived from 

the 2001 Platform avefire sector, county and annual resolution. 

Yes 

Agricultural sector: 

ag 

Nonpoint NH3 emissions from NEI nonpoint livestock and fertilizer 

application. 

No 

Area fugitive dust 

sector: afdust 

Nonpoint PM10 and PM2.5 emissions from fugitive dust sources in the NEI 

nonpoint inventory. 

No 

Remaining nonpoint 

sector: nonpt 

Nonpoint+ All nonpoint sources not otherwise included in other emissions 

modeling sectors. 

Yes 

Nonroad sector: 

nonroad 

Mobile: 

Nonroad 

Nonroad emissions from National Mobile Inventory Model 

(NMIM) using NONROAD2005, other than for California, for 

which emissions submitted by the California Air Resources Board 

(CARB) were used. CARB data used for HAPs are annual, 

allocated to monthly using NMIM, while other data are monthly. 

Yes 

Aircraft, 

locomotive, marine: 

alm_no_c3 

Mobile: 

Nonroad 

Aircraft, locomotive, commercial marine except for category 3 

(C3) commercial marine vessels 

Yes 

C3 commercial 

marine: seca_c3 

Mobile: 

nonroad 

C3 commercial marine vessels Yes 

2
 
 

 

 

 

 

 

 

 

 

 

Platform Sector 

2005 NEI 

Sector Description 

Contains HAP 

emissions? 

Onroad, except 

gasoline PM: 

on_noadj 

Mobile: 

onroad+ 

A combination of onroad mobile sources from MOBILE6, 

MOVES, and 2005 NEI v2 data from California. MOVES-based 

data used for: 

1) onroad diesel exhaust PM, CO, NOx, VOC, some VOC HAPs 

2) onroad gasoline exhaust CO and NOx 

3) onroad gasoline evaporative and exhaust VOC, and some VOC 

HAPs. 

More details are provided in the 2005 v4 platform documentation. 

Yes 

Onroad starting 

exhaust PM: 

on_moves_startpm 

Mobile: 

onroad+ 

MOVES-based onroad mobile start gasoline exhaust PM data. 

More details provided in the 2005 v4 platform documentation. 

No 

Onroad running 

exhaust PM 

on_moves_runpm 

Mobile: 

onroad+ 

MOVES-based onroad mobile running gasoline exhaust PM data. 

More details provided in the 2005 v4 platform documentation. 

No 

Biogenic: biog N/A Hour-specific emissions generated from the Biogenic Emission 

Inventory System (BEIS), version 3.14 model (includes emissions 

in Canada and Mexico) run with the Sparse Matrix Operator 

Kernel Emissions (SMOKE) modeling system. 

No 

Other point sources 

not from the NEI: 

othpt 

N/A Point sources from Canada’s 2000 inventory, Mexico’s 1999 

inventory, and off-shore point sources from the 2001 platform 

No 

Other nonpoint and 

nonroad not from 

the NEI: othar 

N/A Canada 2000 and Mexico 1999 nonpoint and nonroad mobile 

inventories 

No 

Other onroad 

sources not from the 

NEI: othon 

N/A Canada 2000 and Mexico 1999 onroad mobile inventories No 

+ Some data included in modeling sector has been revised beyond what is included in the 2005 NEI v1 or v2. 

As with the 2005 v4 platform, we processed all emissions data with a custom version of the Sparse Matrix 

Operator Kernel Emissions (SMOKE) modeling system, version 2.5. Users seeking to replicate modeling 

done for this effort can use version 2.6 of SMOKE. More details about SMOKE including user 

documentation are available at its website (http://www.smoke-model.org). 

2.1 Custom configuration for emissions modeling for LDGHG 

Unlike the 2005 v4 platform, the configuration for LDGHG modeling included additional hazardous air 

pollutants (HAPs) and used slightly older ancillary data. Both of these differences are described in this 

section. 

Table 3 lists the additional HAP pollutants processed for the LDGHG platform, which were not included in 

the 2005 v4 platform. However, since using the full multipollutant HAP version of the Community 

Multiscale Air Quality (CMAQ) model would have taken longer than the time available for our project, we 

used a “lite” version of the multipollutant CMAQ that required emissions only for the species flagged in the 

third column of Table 3. Additional model species that appear in model-ready data files are listed in the 

right two columns of the table, but we did not run these additional HAPs through CMAQ for this effort. 

3
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 3. Comparison of model species in the 2005 v4 platform and the LDGHG platform 

Description 

2005 v4 

platform 

species 

LDGHG platform 

Species in CMAQ 

MP lite 

Additional LDGHG 

platform HAP 

Species* Description 

Carbon Monoxide CO CO ACRYLONITRILE Acrylonitrile 

Nitrogen Oxide NO NO BR2_C2_12 1,2 Dibromoethane 

Nitrogen Dioxide NO2 NO2 CARBONTET Carbontet 

Nitrous acid HONO HONO CHCL3 Chloroform 

Ammonia NH3 NH3 CL_ETHE Vinyl Chloride 

Sulfur dioxide SO2 SO2 CL2_C2_12 1,2 Dichloroethane 

Sulfuric acid vapor SULF SULF CL2_ME Methylene Chloride 

PM2.5 Elemental carbon PEC PEC CL3_ETHE Trichloroethylene 

PM2.5 Organic carbon POC POC CL4_ETHANE1122 1,1,2,2 Tetrachloroethane 

PM2.5 primary nitrate PNO3 PNO3 CL4_ETHE Perchloroethylene 

PM2.5 primary sulfate 

PSO4 PSO4 DICHLOROBENZE 

NE 

Dichlorobenzene 

PM2.5 other 

PMFINE PMFINE DICHLOROPROPE 

NE 

Dichloropropene 

PM Coarse (PM10 – PM2.5) PMC PMC ETOX Ethylene Oxide 

Acetaldehyde 

ALD2 ALD2 HEXAMETHY_DIIS Hexamethylene 1,6­

Diisocyanate 

Higher aldehydes ALDX ALDX HYDRAZINE Hydrazine 

Ethene ETH ETH MAL_ANHYDRIDE Maleic Anhydride 

Ethane ETHA ETHA PROPDICHLORIDE Propdichloride 

Ethanol ETOH ETOH QUINOLINE Quinoline 

Formaldehyde FORM FORM TOL_DIIS 2,4-Toluene Diisocyanate 

Internal olefin carbon bond IOLE IOLE TRIETHYLAMINE Triethylamine 

Isoprene ISOP ISOP DIESEL_PEC Diesel PM2.5 elemental carbon 

Methanol MEOH MEOH DIESEL_POC Diesel PM2.5 organic carbon 

Nonreactive VOC NR NR DIESEL_PMFINE Diesel PM2.5 primary nitrate 

Nonvolatile (from VOC mass) NVOL NVOL DIESEL_PNO3 Diesel PM2.5 primary sulfate 

Terminal olefin carbon bond OLE OLE DIESEL_PMC Diesel PM2.5 other 

Parrafin carbon bond PAR PAR DIESEL_PSO4 Diesel coarse PM 

Toluene and other monoalkyl 

aromatics 

TOL TOL BERYLLIUM_C Coarse Beryllium 

Unknown VOC UNK UNK BERYLLIUM_F Fine Beryllium 

Unreactive VOC UNR UNR CADMIUM_C Coarse Cadmium 

Xylene and other polyalkyl 

aromatics 

XYL XYL CADMIUM_F Fine Cadmium 

Sequiterpenes SESQ SESQ CHROMHEX_C Coarse Hexavalent Chromium 

Terpene TERP TERP CHROMHEX_F Fine Hexavalent Chromium 

Benzene BENZENE BENZENE CHROMTRI_C Coarse Trivalent Chromium 

Chlorine CL2 CL2 CHROMTRI_F Fine Trivalent Chromium 

Hydrochloric acid HCL HCL LEAD_C Coarse Lead 

Divalent gaseous mercury HGIIGAS HGIIGAS LEAD_F Fine Lead 

Elemental mercury HGNRVA HGNRVA MANGANESE_C Coarse Manganese 

Particulate mercury PHGI PHGI MANGANESE_F Fine Manganese 

Naphthalene from the HAP 

inventory

 NAPHTHALENE NICKEL_C Coarse Nickel 

Acrolein from the HAP 

inventory

 ACROLEIN NICKEL_F Fine Nickel 

Acetaldehyde from the HAP 

inventory

 ALD2_PRIMARY 

1,3 Butadiene from the HAP 

inventory

 BUTADIENE13 

4
 
 

 

 

Description 

2005 v4 

platform 

species 

LDGHG platform 

Species in CMAQ 

MP lite 

Additional LDGHG 

platform HAP 

Species* Description 

Methane from the HAP 

inventory

 CH4 

Formaldehyde from the HAP 

inventory

 FORM_PRIMARY 

M-isomer of Xylene from the 

HAP inventory 

MXYL 

O-isomer of Xylene from the 

HAP inventory 

OXYL 

P-isomer of Xylene from the 

HAP inventory 

PXYL 

Toluene from the HAP 

inventory 

TOLU 

* These species are created by the emissions configuration, but were not modeled. 

In addition to the model species differences, the LDGHG platform had a few additional custom aspects in the 

2005 cases. Table 4 lists the datasets used by the LDGHG platform that are different from the v4 platform, 

including a description of the impact of the differences. These differences stem from the 2005 v4 platform 

having been done after the RFS2 platform, which was used as the basis for the LDGHG platform, resulting 

in newer inventory data used for the v4 platform. These inventory differences are described more in later 

sections of this document. In addition, Appendix B provides a more detailed comparison of the ancillary 

datasets for the 2005 v4 platform versus the LDGHG platform. 

Another consideration is the speciation across the LDGHG future-year cases as compared to 2005. Section 3 

provides a detailed account of these differences. Otherwise, the future-year ancillary data were largely the 

same as those in 2005, with no substantial differences. All ancillary data files can be found at the 2005­

based platform website (http://www.epa.gov/ttn/chief/emch/index.html#2005). 

5
 
Table 4. Description of differences in ancillary data between the LDGHG 2005 case 

and the 2005 v4 platform 

Ancillary Data Type Difference between 2005 v4 platform and LDGHG platform 

Spatial cross references The 2005 v4 platform data are updated to support the newer (2006) Canadian 

inventories. The LDGHG files contain references to diesel exhaust start 

emissions from MOVES, that were not available for the v4 platform. 

Temporal crossreferences The 2005 v4 platform data are updated to support the 2006 Canadian data, 

additional source category codes (SCCs) found in the 2005 v2 NEI point 

inventory, and a revised oil and gas inventory. The LDGHG files contain 

references to diesel exhaust start emissions from MOVES. 

Temporal profiles The 2005 v4 platform dataset adds additional profiles from Environment 

Canada to support processing of the 2006 Canadian inventory. 

Speciation crossreferences and 

Speciation profiles 

The LDGHG data files are configured to support the multi-pollutant (MP) 

version of CMAQ, whereas the 2005 v4 platform data file is configured to 

support only the non-MP version. Therefore, the LDGHG data files include 

profiles for additional HAP species, including HAP VOCs, HAP metals, 

chromium, and diesel PM. The 2005 v4 platform data files include profiles for 

passing through the pre-speciated VOCs for the 2006 Canadian inventory. 

Furthermore, new headspace vapor VOC speciation profiles were used based on 

new test data for E0 and E10 fuels for LD GHG. 

Inventory tables The LDGHG data file is configured to support the MP version of CMAQ, 

whereas the 2005 v4 platform data file is configured to support only the nonMP version. 

NonHAP exclusions 

data 

The 2005 v4 platform data has been updated with new oil and gas SCCs not 

used for the LDGHG platform. 

2.2 Point sources 

The 2005 emissions from the U.S. point source sectors (ptipm and ptnonipm) used for LDGHG differ from 

the v4 platform primarily because the emissions are based on the 2005 NEI version 1, rather than the 2005 

NEI version 2. While the NEI version 2 was available in time for this work, we intentionally remained 

consistent with the RFS2 modeling approach that had just been completed. The original emissions were 

created from the NEI database on June 10, 2008. These emissions were further modified, further changing 

the 2005 NEI version 1, as follows: 

1) Inventory split into ptipm and ptnonipm sectors using the IPM column of the SMOKE-ready datasets 

2) Applied fugitive dust transport fractions to the appropriate SCCs (SCC list available at 

http://www.epa.gov/ttn/chief/emch/dustfractions) 

3)	 Further analyzed HCl emissions for missing ptipm sector HCl sources and moved emissions originally 

labeled as ptnonipm to correctly be placed with their associated criteria air pollutants (CAP) emissions 

in the ptipm sector 

4)	 Removed emissions of benzene, acetaldehyde, formaldehyde, methanol, to support the “no hap use” 

approach for the ptipm and ptnonipm sectors. This approach creates emissions of these HAPs from 

VOC speciation profiles rather than the HAP emissions inventory, because the CAP and HAP emissions 

were not consistent enough to be able to use the HAP inventory for VOC speciation. 

5)	 Added an additional 47 ethanol plants. Unless otherwise noted in Table 5 (a list of ethanol facilities and 

their emissions), we adjusted the original VOC emissions provided by a factor of 0.65 (a reduction) to 

offset the ethanol-heavy speciation profile (99.6% ethanol) that we had available for speciation of 

ethanol plants. This prevented the overstating of ethanol emissions. In addition, we multiplied all 

6
 
emissions by 1/(453.6  2000)  1,000,000 = 1.1023113109 (converting from grams/yr per million 

gallons to tons/yr) to correct the units of the emissions (from grams per million gallon per year to 

tons/year). These changes resulted in roughly a 10.23% increase in the emissions values initially 

provided by OTAQ. The list of these facilities and their emissions is available in Table 5. 

6)	 Removed facilities that closed between 2002 and 2005, since much of the ptnonipm portion of the 2005 

v1 NEI was carried forward from the 2002 v3 NEI. This change was also made in creating the v4 

platform. The list of removed sources is available in the Excel® file 

“Closures_applied_to_2002v3_point_for_2005af.xls” provided with the 2005 v4 platform 

documentation. 

7) Removed Minnesota airport ground support equipment (SCCs 2265008005 and 2270008005), to prevent 

double counting with the nonroad sector. 

8) Other minor adjustments: 

o	 Removed exclamation marks, asterisks, and embedded double quotes in facility names, and 

other key text fields, changed “PM25” and “PM25-PRI” to “PM2_5”, and changed the 

state/county Federal Information Processing Standard (FIPS) code field for tribal records from 

00000 to 88TTT, where TTT is the tribal code. 

o	 Removed two SCC=201002 records because this is an invalid SCC; both records had zero 

emissions 

To implement the inventory processing, we split the 2005 ptnonipm CAP and HAP inventories into five 

separate datasets to facilitate replacement and projection for the 2030 reference and control scenarios. These 

datasets are: 

1) A dataset with one ethanol plant (Chippewa) CAP and HAP emissions for which the emissions are 

held at 2005 values for the 2030 LDGHG reference and control scenarios; 

2) A dataset with three ethanol plants’ CAP and HAP emissions that are replaced in both the 2030 

LDGHG reference and control scenarios; 

3) A dataset with CAP and HAP emissions for 43 additional ethanol plants not available in the 2005 

v1 NEI, which also have different emissions in the 2030 reference and control scenarios; 

4) A dataset with CAP emissions from the all other nonEGU sources from the 2005 NEI v1 

5) A dataset with HAP emissions from the all other nonEGU sources from the 2005 NEI v1 

In addition to differences in the U.S. point sources, further differences exist from the v4 platform for the 

Canadian point emissions. The Mexico point emissions are identical to those documented for the 2002 v3 

platform and the 2005 v4 platform. The LD GHG modeling inventories included year 2000 Canadian 

emissions from the 2002 v3 platform and did not include the updated 2006 Canadian emissions, to be 

consistent with the modeling done for RFS2. We did not model mercury and therefore did not use Canadian 

mercury emissions data. The offshore sources were different from what was used in the 2005 v4 platform 

because they were not updated with the new offshore inventory available in the 2005 v2 NEI. For more 

information on the Canadian and Mexican emissions used for this effort, please refer to the 2002 v3 platform 

documentation at http://www.epa.gov/ttn/chief/emch/index.html#2002. 

In addition, we processed emissions for LDGHG using the 3-d emissions option for all point source sectors 

rather than the “inline” point source option that we used for the 2005 v4 platform. This approach has 

essentially no effect on the modeling results. Using the inline approach makes the CMAQ emissions data 

files smaller, but that option was not available for multi-pollutant CMAQ in time for use on this effort. 

7
 
8
 

Table 5. Emissions from ethanol plants added to 2005 v1 point inventory (tons/year) 

County Name State Name 

St/Co FIPS PLANT CO NOX VOC SO2 P

Swift Co Minnesota 27151 Chippewa Valley Ethanol Co LLLP (*) 29.7 40.4 155.1 0.6 

Chippewa Co Minnesota 27023 Granite Falls WWTP (+) 0.0 0.0 0.7 0.0 

Dodge Co Minnesota 27039 Al-Corn Clean Fuel (+) 28.5 45.0 207.6 0.5 

Sibley Co Minnesota 27143 Heartland Corn Products (+) 51.4 62.9 378.3 0.4 

Dawson Co Nebraska 31047 Cornhusker Energy Lexington (CEL) 10.5 30.3 14.3 38.6 

Stevens Co Minnesota 27149 Diversified Energy Company (DENCO), 

LLC 

52.4 151.6 71.6 192.9 

Wichita Co Kansas 20203 ESE Alcohol Inc. 3.1 9.1 4.3 11.6 

Sedgwick Co Kansas 20173 Abengoa Bioenergy Corporation 52.4 151.6 71.6 192.9 

Roosevelt Co New Mexico 35041 Abengoa Bioenergy Corporation 62.8 181.9 86.0 231.5 

Linn Co Iowa 19113 Archer Daniels Midland (ADM) 964.7 1,338.4 418.1 1,266.3 5

Clinton Co Iowa 19045 Archer Daniels Midland (ADM) 733.2 1,017.2 317.7 962.4 4

Platte Co Nebraska 31141 Archer Daniels Midland (ADM) 366.6 508.6 158.9 481.2 2

Lyon Co Minnesota 27083 Archer Daniels Midland (ADM) 154.4 214.1 66.9 202.6 

Peoria Co Illinois 17143 Archer Daniels Midland (ADM) 439.8 1,273.1 601.9 1,620.4 3

Pembina Co North 

Dakota 

38067 Archer Daniels Midland (ADM) 52.4 151.6 71.6 192.9 

Tazewell Co Illinois 17179 Aventine Renewable Energy, Inc. (formerl 385.9 535.4 167.2 506.5 2

Monroe Co Wisconsin 55081 Badger State Ethanol, LLC 108.9 315.3 149.0 401.2 

Des Moines Co Iowa 19057 Big River Resources, LLC 108.9 315.3 149.0 401.2 

Washington Co Nebraska 31177 Cargill (was PGLA-1CO) 328.0 455.1 142.1 430.5 1

Christian Co Kentucky 21047 Commonwealth Agri-Energy, LLC 41.9 121.3 57.3 154.3 Codington Co South 

Dakota 

46029 Glacial Lakes Energy, LLC (GLE) 104.7 303.1 143.3 385.8 

Kossuth Co Iowa 19109 Global Ethanol 209.4 606.3 286.6 771.6 

Riverside Co California 06065 Golden Cheese Company of CA 10.5 30.3 14.3 38.6 

Cerro Gordo Co 

Iowa 19033 Golden Grain Energy LLC 146.6 424.4 200.6 540.1 

Hardin Co Iowa 19083 Hawkeye Renewables, LLC 115.2 333.4 157.6 424.4 

Brown Co South 

Dakota 

46013 Heartland Grain Fuels, LP 20.9 60.6 28.7 77.2 

Beadle Co South 

Dakota 

46005 Heartland Grain Fuels, LP 29.3 84.9 40.1 108.0 

Pierce Co Nebraska 31139 Husker Ag, LLC 54.5 157.6 74.5 200.6 

Kearney Co Nebraska 31099 KAAPA Ethanol, LLC 125.7 363.8 172.0 463.0 Stearns Co Minnesota 27145 Land O' Lakes / Melrose Dairy Proteins 5.4 15.8 7.5 20.1 

Crawford Co Illinois 17033 Lincolnland Agri-Energy 58.6 169.8 80.2 216.0 
9
 

County Name State Name 

St/Co FIPS PLANT CO NOX VOC SO2 P

Cherokee Co Iowa 19035 Little Sioux Corn Processors 184.3 533.5 252.2 679.0 

Jefferson Co Colorado 08059 Merrick & Company (Coors Brewery) 4.2 12.1 5.7 15.4 

Tazewell Co Illinois 17179 MGP Ingredients, Inc. 188.5 545.6 257.9 694.4 Lincoln Co Nebraska 31111 Midwest Renewable Energy, LLC (MRE) 41.9 121.3 57.3 154.3 Hamilton Co Nebraska 31081 Nebraska Energy 104.7 303.1 143.3 385.8 

Roberts Co South 

Dakota 

46109 North Country Ethanol (NCE) 52.4 151.6 71.6 192.9 

Delaware Co Iowa 19055 Permeate Refining 3.1 9.1 4.3 11.6 

Ida Co Iowa 19093 Quad-County Corn Processors 56.5 163.7 77.4 208.3 

Finney Co Kansas 20055 Reeve Agri-Energy 25.1 72.8 34.4 92.6 

Sioux Co Iowa 19167 Siouxland Energy & Livestock Coop 

(SELC) 

46.1 133.4 63.1 169.8 

Red Willow Co Nebraska 31145 SW Energy, LLC. 0.2 0.6 0.3 0.8 

Loudon Co Tennessee 47105 Tate & Lyle 254.7 353.3 110.4 334.3 1

Hitchcock Co Nebraska 31087 Trenton Agri-Products, LLC. (TAP) 83.8 242.5 114.6 308.6 

Winnebago Co Wisconsin 55139 Utica Energy, LLC 108.9 315.3 149.0 401.2 

Gove Co Kansas 20063 Western Plains Energy, LLC 62.8 181.9 86.0 231.5 

Mitchell Co Georgia 13205 Wind Gap Farms (Anheuser/Miller 

Brewery) 

0.8 2.4 1.1 3.1 

Total 6,074.3 12,610.4 5,923.2 14,417.5 2,5

(*) Data taken from the 2002 NEI rather than provided by OTAQ for 2005. Units conversion not performed on this facility and no adjustme(+) Data taken from the 2005 NEI v1, rather than provided by OTAQ for 2005. Units conversion not performance on these facilities and no 

data by 0.65. 
2.3 2005 Nonpoint sources 

The emissions from the agricultural ammonia (ag) and nonpoint fugitive dust (afdust) sectors are 

the same as the v4 platform. For the “other” nonpoint (nonpt) sector, the only difference from 

the v4 platform is that these emissions do not include the oil and gas extraction emissions (SCCs 

matching 23100XXXXX) provided by the Western Regional Air Partnership (WRAP) for the 

western states. These updated oil and gas extraction emissions were provided after the modeling 

platform development for RFS2 and LDGHG. 

For the Canadian and Mexican nonpoint sector (othar), we used the same inventories as the 2002 

v3 platform, and did not yet have the updated 2006 Canadian inventory used in the v4 platform. 

The avefire emissions are the same as those in the 2005ci_tox_05b case mentioned above, with 

the exception that we added 1-3-butadiene, acrolein, and xylenes, and toluene using ratios to 

PM2.5 available in the 2005 platform documentation. These factors were also used to create 

emissions for benzene, acetaldehyde, and formaldehyde in the 2005 v4 platform and were 

unchanged for LDGHG; however, this sector was processed as “no-integrate”; therefore, 

emissions for benzene, acetaldehyde, and formaldehyde were obtained from speciated VOC. 

2.4 2005 Mobile sources 

Mobile sources include three US onroad sectors (on_noadj, on_moves_startpm, 

on_moves_runpm) and three US nonroad sectors (nonroad, alm_no_c3, and seca_c3). In 

addition, it includes Canadian and Mexican emissions in a separate onroad sector (othon) and 

nonroad/nonpoint sector (othar). 

For onroad mobile, the on_moves_startpm and on_moves_runpm emissions inventory data are 

from an updated draft version of the Motor Vehicle Emission Simulator (MOVES) compared to 

the 2005 v4 platform. The LDGHG emissions also keep additional pollutants as described in 

Section 2.1. In addition for these MOVES sectors, the temperature adjustment calculations 

applied to PM2.5 species were the same as the v4 platform, since these adjustments had not 

changed between draft MOVES versions. 

The on_noadj sector for LDGHG also uses an updated version of MOVES, and this updated 

version also provides onroad diesel exhaust emissions for CO, NOx, PM, and VOC HAPs. Note 

that PM emissions from these diesel sources are not subject to temperature adjustments like the 

on_moves_startpm and on_moves_runpm sectors. These MOVES-based onroad diesel 

emissions replace the MOBILE6-based NMIM emissions in the 2005v4 platform. 

For onroad gasoline exhaust PM emissions, the allocation of MOVES PM2.5 emissions to 

SMOKE-ready format PM species is documented in Appendix B of the 2005v4 Platform 

Documentation. However, LDGHG MOVES data also provides diesel exhaust PM2.5 

emissions, and these equations are provided in this document in Appendix A of this document. 

A small processing error impacted the MOVES diesel PM exhaust emissions used for this 

modeling. The fraction of metals in the speciation profile (Fmetal) in equation 4 in Appendix A 

10
 
 

 

 

 

 

 

 

 

 

 

was inadvertently squared, resulting in an underestimate in metals, which caused a very small 

underestimate in inventory organic carbon (POC) and a corresponding equal overestimate in 

inventory PMFINE (other PM species not already classified). Total PM2.5 was not impacted. 

This error impacts all LDGHG scenarios, and the small impact of this error is shown in the 

national totals provided in Table 6. 

Table 6. National summary of MOVES Diesel Exhaust PM Error for 2005 Base Case 

Pollutant HDDV LDDV/LDDT HDDV 

Error 

LDDV/LDDT 

Error 

PEC (Elemental Carbon) 90,734 3,107 

PSO4 (PM Sulfate) 6,812 154 

PNO3 (PM Nitrate) 134 6 

metal_bad (Eq 4 in Appendix A) 1 0 

metal (Eq 4 in Appendix A) 313 14 

POC_bad (Organic Carbon) 45,315 1,300 260 12 

POC (Organic Carbon) 45,054 1,288 

PMFINE_bad (Other PM) 11,661 319 -260 -12 

PMFINE (Other PM) 11,921 331 

PMC (PM-Coarse, or PM10 minus PM2.5) 13,300 420 

PM2_5_total_bad 154,656 4,886 

PM2_5_total 154,656 4,886 

The nonroad emissions inventory data are the same as the v4 platform, with additional HAPs 

being kept for LDGHG as well. The alm_no_c3 emission sector does use different data from 

that of the v4 platform. Specifically, the aircraft emissions remain in this sector and are older 

data from that of the v4 platform. In the v4 platform, the aircraft emissions had been revised to 

be consistent with the 2005 NEI v2 and included in the ptnonipm sector. The airport emissions 

used in LDGHG were from the 2002 NEI, version 3, acquired March 27, 2007 and used in the 

2002 v3.1 platform. 

Additionally, the onroad emissions for Canada and Mexico for LDGHG (othon sector) differ 

from the v4 platform because we used the older Canadian data. The data we used reflect 2000 

emissions and are the same data used in the v3 and v3.1 2002-based platforms. The 2005 v4 

platform uses 2006 Canadian inventory data. 

3 VOC speciation changes that represent fuel changes 

A significant detail that changes for each of the LDGHG modeling cases is the VOC speciation 

profiles used to split total VOC emissions into the various VOC model species needed for 

CMAQ. In this section, we summarize the various speciation profile information used in 

configuring the various cases, and we include Table 7 to provide a summary of the VOC 

speciation approach for each of the future-year cases. 

The approaches taken in the LD GHG 2005 case below are not the same as the 2005 v4 platform, 

and so the first 2005 column shown represents the 2005 v4 platform and the second 2005 column 

shown is for LD GHG. Two new headspace vapor profiles were created for LD GHG modeling 

that were not available for the 2005 v4 platform development. The approaches used for each of 

11
 
 

 

 

 

 

 

 

the future-year cases are customized for those cases, and they include the impact of fuel changes 

for each of the future-year cases on emissions from the on_noadj sector, the nonroad sector, and 

parts of the nonpt and ptnonipm sectors. The speciation changes from fuels in the nonpt sector 

include changes for portable fuel containers (PFCs) and some parts of the bulk-plant-to-pump 

(btp) and refinery-to-bulk terminal (rbt) emissions. The speciation changes from fuels in the 

ptnonipm sector include the remainder of the emissions for the btp and rbt emissions. Mapping 

of fuel distribution SCCs to btp and rbt emissions categories can be found in Appendix A of the 

RFS2 Emissions Inventory for Air Quality Modeling Technical Support Document (EPA Report 

No. 420-R-10-005, January 2010, http://www.epa.gov/otaq/renewablefuels/420r10005.pdf). 

A general indication of the VOC speciation approach is provided in Table 7. 

Table 7. Summary of VOC speciation profile approaches by sector across cases 

Inventory 

type and 

mode 

VOC speciation approach 

for fuels 

VOC 

Profile 

Codes 

2005 v4 

platform 

2005 

LD GHG 

case 

2030 

Reference 

Case 

2030 

Control 

Case 

Mobile 

Exhaust 

Tier 1 E0 and E10 combinations 8750 

8751 

on_noadj 

nonroad 

on_noadj 

nonroad 

Tier 1 E0 or E10 by county 8750 

8751 nonroad 

Tier 2 E0 or E10 by county 8756 

8767 on_noadj 

Tier 1 E10 8751 nonroad 

Tier 2 E10 8757 on_noadj 

Mobile 

Evaporative 

E0 and E10 combinations 8753 

8754 

on_noadj 

nonroad 

on_noadj 

nonroad 

E0 or E10 by county 8753 

8754 

on_noadj 

nonroad 

E10 8754 on_no_adj 

nonroad 

Other 

sources: 

nonroad 

refueling, 

PFCs, 

btp, 

rbt 

E0 headspace (old) 8737 All listed 

E0 headspace (new) 8762 All listed 

E0 headspace (new) or E10 

headspace (new) by county 

8762 

8763 All listed 

E10 headspace (new) 8763 All listed 

Table 8 provides the purpose of the VOC speciation profile codes used in the table: 

12
 
Table 8. Explanation of VOC profile codes listed in Table 7 

Exhaust Evaporative Refueling 

8750 

8751 

8756 

8757 

Tier1 E0 

Tier1 E10 

Tier2 E0 

Tier2 E10 

8753 E0 

8754 E10 

8737 E0 headspace vapor (old) 

8762 E0 headspace vapor 

8763 E10 headspace vapor 

Appendix C summarizes the data file names used for all of the data files that are updated from 

the v4 platform. All ancillary data files are available on the 2005-based platform website 

previously referenced. 

4 2030 Reference Case 

The 2030 Reference case is intended to represent the emissions associated with use of the most 

likely volume of ethanol in the absence of the LDGHG CO2 reductions and RFS2 rule and 

Energy Independence and Security Act of 2007 (EISA) renewable fuel requirements. For this 

case, the ethanol volume was projected for 2030 using the Department of Energy, Energy 

Information Administration in the 2007 Annual Energy Outlook (AEO) report. That year’s AEO 

projections were used because Department of Energy (DOE) started accounting for EISA in their 

2008 AEO projections. Therefore, the 2030 LDGHG Reference scenario shares several of the 

same emissions (especially for stationary sources) as the 2022 RFS-2 AEO case (please see the 

RFS-2 TSD, available at http://www.epa.gov/otaq/renewablefuels/420r10005.pdf). A list of 

inventory datasets used for this and all cases is provided in Appendix C. A list of all growth and 

control assumptions for this Case is provided in Appendix D. Section 5 describes the projection 

differences between the 2030 Reference and 2030 Control cases. 

4.1 2030 Reference Case Point sources 

The point sources for the 2030 Reference case included US EGU point sources (ptipm), US 

nonEGU point sources (ptnonipm) and sources from Mexico, Canada, and the Gulf of Mexico 

(othpt). The US EGU point sources for both the reference and control 2030 cases use an 

Integrated Planning Model (IPM) run for criteria pollutants, HCl, and mercury in 2020 (though 

Hg was not modeled). We used 2020 because it was the closest readily available year to the 

2030 year used for LD GHG modeling. With very few exceptions, we have used year 2020 as 

the furthest “out year” for stationary source projections. Also, by using many of the same 

emissions inputs as RFS-2 that are not impacted by the LDGHG control strategy, we could better 

isolate the impacts of LDGHG-related controls versus RFS-2 controls and obtain a more ‘apples 

to apples’ comparisons of air quality and emissions-related impacts between RFS-2 and 

LDGHG. 

The code number used by EPA’s Clean Air Markets Division (CAMD) to denote the run is 

EPA30Draft_BC_42l. While these IPM emissions are different from those used in the v4 

platform, they are consistent with the 2020 emissions used in the v3 platform. OAQPS postprocessed these data in the same way as described in the 2005 v4 platform documentation for the 

“base case” to create daily emissions that include temporal allocation information from three 

years of Continuous Emissions Monitoring (CEM) data. The temporal allocation approach is the 

13
 
same as for the LDGHG 2005 base case (2005cp_tox_05b), to eliminate artificial differences in 

temporal allocation between the base and future years. 

For the Mexican and offshore point source emissions, we held the data constant with the 2005 

base case. This means that the Mexican emissions were based on year 1999, since Mexico has 

not provided emissions projections to date. We used 2020 emissions projections for Canadian 

emissions, provided by Environment Canada, which are consistent with the base year Canadian 

data. The Canadian data are the same as those used for the 2002-based v3 platform. The data 

used for LDGHG future year reference and control cases are different from our v4 platform. 

For the US nonEGU emissions (sector ptnonipm), there were two main pieces for the 2030 

LDGHG ptnonipm inventories: data projected from 2005 values and data that were replaced by 

OTAQ-generated data that are intended to represent emissions estimates for any year beyond 

2016. Referring to the five parts listed in Section 2.2, datasets (4) and (5) were projected from 

2005 to 2030 values, datasets (2) and (3) were replaced, and dataset (1) was unchanged. 

4.1.1 Part 1: Projecting 2005 to 2030 Reference for ptnonipm 

We applied both control and growth factors to a subset of the 2005 ptnonipm to create the 2030 

Reference data. We started with 2020 projection factors from the 2002 v3.1 platform for most of 

the LDGHG year 2030 projections. Given the uncertainty of emissions projections and the lack 

of any additional controls coming into effect between 2020 and 2030, we decided that using 

2020 as the year for most of our non-EGU point and nonpoint projections would be sufficient; 

exceptions to this are described below. This approach matched the year used for the ptipm 

sector. Furthermore, we did not have to adjust the factors for a 2005-base year in most cases, 

because most of the 2005 v1 nonEGU point emissions data were populated with 2002 emissions 

values. 

The 2002 v3.1 platform growth and control factors had been revised from the 2002v3.0 platform 

projection factors that are documented in the 2002 v3 emissions modeling platform 

documentation (see http://www.epa.gov/ttn/chief/emch/index.html#2002). These updates 

included Hazardous Waste Incineration adjustments and Small and Large Municipal Waste 

Combustor closures and adjustments. The following describes how we further modified the 

2002v3.1 projection factors for the 2030 Reference case. 

We intended to use a SMOKE “control” packet (data file) to apply control factors that implement 

known emissions reductions from point sources for national rules. For the Reference case, the 

control packet was intended to be the same as the revised control packet from the 2002 v3.1 

factors to give key VOC HAPs the same control factors as those for VOC. The VOC HAPs (and 

CAS numbers) of interest for this effort were: 1,3-butadiene (CAS=106990), acrolein 

(CAS=107028), formaldehyde (CAS=50000), methanol (CAS=67561), benzene (CAS=71432), 

acetaldehyde (CAS=75070), and naphthalene (CAS=91203). However, the “control” packet 

inadvertently was not applied for either the 2030 Reference or 2030 Control cases. This 

emissions processing error results in the same amount of missed reductions from both the 2030 

Reference and Control Cases. These “missed” reductions, and the percent of the ptnonipm 

inventory that is under-controlled are provided in Table 9a. Appendix D documents which 

14
 
control and projection programs were erroneously not included in the 2030 Reference and 

Control cases. No other sectors were impacted by this error. 

15
 
Table 9a. Impact on the ptnonipm Sector of Not Applying Base Case Controls to the LDGHG 

2030 Reference Case 

Pollutant 

2030 

Reference 

Case: 

Corrected 

2030 

Reference 

Case: 

Modeled 

Missing 

Reductions 

Percent of 

ptnonipm Inventory 

Erroneously Not 

Reduced 

CO 3,194,382 3,206,284 11,902 0.4% 

NH3 153,039 157,947 4,907 3.1% 

NOX 2,205,555 2,408,024 202,469 8.4% 

PM10 586,770 613,850 27,080 4.4% 

PM2_5 356,113 372,081 15,969 4.3% 

SO2 2,102,054 2,290,193 188,139 8.2% 

VOC 1,165,031 1,414,238 249,208 17.6% 

While these fractions are somewhat large for only the ptnonipm sector, the fractions when 

compared to the entire anthropogenic inventory are much smaller. Table 9b shows the impact 

across all anthropogenic sectors, which is closer to what the air quality model uses. The VOC 

impact is further lessened in the air quality model because of the biogenic VOC emissions, which 

are much larger than the anthropogenic in most cases. In addition, since the discrepancy 

occurred consistently in both the 2030 Reference and Control cases, the impact on the affect of 

the control strategy is further limited. 

Table 9b. Impact on Total Anthropogenic Emissions of Not Applying Base Case Controls to the 

LDGHG 2030 Reference Case 

Pollutant 

2030 

Reference 

Case: 

Corrected 

2030 

Reference 

Case: 

Modeled 

Missing 

Reductions 

Percent of 

Total Anthropogenic 

Inventory 

Erroneously Not 

Reduced 

CO 53,960,990 53,972,892 11,902 0.0% 

NH3 4,258,901 4,263,808 4,907 0.1% 

NOX 11,516,138 11,718,607 202,469 1.7% 

PM10 12,720,907 12,747,988 27,080 0.2% 

PM2_5 4,031,794 4,047,763 15,969 0.4% 

SO2 9,170,404 9,358,543 188,139 2.0% 

VOC 12,093,860 12,343,067 249,208 2.0% 

We also used SMOKE with a “projection” packet to apply growth adjustments, some control 

adjustments, and plant closures. For LDGHG, the projection packet for ptnonipm consisted of 

several components. First, we modified the data from the 2002v3.1 platform packet to remove 

entries for which we had new factors for this effort, such as onroad refueling and aircraft. In 

addition, we added the same list of VOC HAPs to the projection factors as we had added to the 

control packet for the VOC-specific entries already present (e.g., landfills in nonpt sector). Next, 

16
 
we added the LDGHG Reference case-specific projection factors from several OTAQ sources, as 

follows: 

1.	 Onroad refueling: We retained the same list of refueling ptnonipm and nonpt 

state/county FIPS codes and SCCs from the 2002 v3 emissions modeling platform. For 

California, we used the 2005v2 California-submitted data and 2005 NMIM and 2030 

NMIM reference case VOC and VOC-HAP refueling emissions to obtain annual 

adjustment ratios by pollutant and county. The formula for the projection factors was 

Factor 2030 = NMIM Emis2030 /NMIM Emis2005 (note: we applied different refueling 

adjustment factors to the Reference and Control cases because of different onroad 

gasoline refueling inventories in the 2 cases. These factors were applied to the refueling 

SCCs from the 2005 base case. 

2.	 Year 2025 aircraft Federal Aviation Administration (FAA) takeoff and landing data from 

the Terminal Area Forecast (TAF) data: This is the furthest out-year of FAA TAF data 

activity and replaces the 2020 factors for both LDGHG 2030 scenarios. 

3.	 We applied adjustments to refinery emissions by state and SCC, using the same factor for 

all pollutants to represent activity adjustments (note: different adjustments were made to 

the control case). The state-level adjustments and the list of SCCs affected were 

provided by Rich Cook on 11/25/2008 in the Excel® workbook 

“RFS2_Refinery_Adjust.xls”; this is the same set of factors used in the RFS-2 AEO 

(baseline) scenario. The LDGHG control scenario applies an adjustment on top of these 

LDGHG reference case emissions. 

4.	 For gasoline distribution SCCs (both ptnonipm and nonpt SCCs), we additionally applied 

VOC and VOC HAP adjustments to SCCs representing emissions from bulk-plant-topump (btp) and refinery-to-bulk terminal (rtb) processes. These SCC-level adjustments 

impact VOC and VOC HAPs in both the ptnonipm and nonpt sectors. The adjustments 

were provided by OTAQ on 12/16/2008 in the Excel® workbook 

“2005ai_tox_SCC_50state_CAPHAP-20081216.xls”. Different adjustments were 

applied to the LDGHG control scenario, based on assumed changes in the ratios of E0 to 

E10. The LDGHG control scenario applies an adjustment on top of these LDGHG 

reference case emissions. 

We applied the control and projection factors to the ptnonipm 2005 inventory described in 

Section 2.2, resulting in a “2030” ptnonipm inventory. To this inventory, we added additional 

OTAQ-supplied inventory data as described in Step 2 below. The configuration of the 

processing was designed specifically to prevent inadvertent double counting of emissions. 

4.1.2 Part 2: Additional OTAQ-supplied emissions data for ptnonipm 

In addition to the data preparation described above, we added the following data supplied by 

OTAQ: 

17
 
1.	 Ethanol plant additions: These emissions data completely replace all of the ethanol 

plants listed in Table 5 except the first one. OTAQ provided the emissions, stack 

parameters, locations and the same SCC assignments were made as described for 2005: 

PM2.5 (30299999) and VOC and all other pollutants (30125010). The original VOC 

emissions were supplied by OTAQ in Excel® and OAQPS further adjusted the data to 

correct for units conversions and to adjust VOC downward to account for too much 

ethanol in the speciation profile. These were the same adjustments made to the 2005 data 

and are described in Section 2.2. Further, we changed the original data to use the same 

Stack IDs for both PM2.5 and PM10, to ensure SMOKE could properly calculate PMC as 

PM10-PM2.5 after matching emissions sources. Lastly, we removed the benzene, 

formaldehyde, acetaldehyde, and methanol inventory emissions from these sources 

because they are “no integrate” sources for VOC HAPs (we do not use the VOC HAPs to 

augment speciation information for this sector), and so these model species were to be 

calculated from VOC speciation. 

2.	 In addition, we retained the Chippewa plant from 2005 (the emissions at this plant were 

only changed for an emissions scenario not related to LDGHG modeling). 

4.2 2030 Reference Case Nonpoint sources 

The nonpoint sources are sources aggregated to the county (or Canadian Province) and process 

level and included US fugitive dust sources (afdust sector), US agricultural NH3 (ag sector), US 

average fires (avefire sector), other US nonpoint sources (nonpt sector), and emissions for 

Canada and Mexico (othar and othar_hg). Of these, the nonpt sector contained the most detailed 

changes for the RFS2 modeling, as explained further below. 

The emissions used for this case for the afdust and ag sectors are the same as those used in 2020 

in the 2002-based v3 platform, previously referenced. Additionally, the avefire emissions are the 

same as those in the 2005cp_tox_05b case mentioned above, and were intentionally held 

constant between the 2005 base case and the 2030 Reference and Control cases. 

The emissions for Canada and Mexico were the same as the 2002 v3 platform referenced earlier, 

with one minor exception: we modified the Canada nonroad sources to remove C3 commercial 

marine SCC (2280003010), which was just one record in British Columbia and which prevented 

double-counting with the seca_c3 sector. This means that the LDGHG platform differs from the 

v4 platform in that older estimates of 2020 emissions were used for nonpoint sources in Canada, 

rather than 2006 estimates retained between the base and future years in the v4 platform. This 

sector includes both nonroad mobile and nonpoint sources. 

The nonpt sector is comprised of several different pieces. Like the ptnonipm sector, the nonpt 

sector required two steps to compile the 2030 Reference case inventories: data projected from 

2005 values and data that are replaced by OTAQ-generated data in 2030. 

We discovered a very minor error in our creation of 2030 Reference and Control case nonpt 

emissions. We inadvertently dropped NEI nonpoint inventory oil and gasoline production 

emissions in WRAP states (excluding California) from both the 2030 Reference and Control 

18
 
cases. This error resulted in 1 ton of CO and 31 tons of NOx dropped in New Mexico, and a 

cumulative 2,184 tons of VOC dropped in Arizona, Montana, Nevada, New Mexico, North 

Dakota, South Dakota, and Wyoming. 

4.2.1 Part 1: Projecting 2005 to 2030 Reference case for nonpt 

The same steps taken and described for the ptnonipm sector (Section 4.1.1) were also taken for 

the nonpt sector. In fact, all of the information that we added to the SMOKE projection and 

control packets has already been described in that section and the parts that also applied to the 

nonpt sector have already been identified in that section. Unlike the ptnonipm sector, we did not 

miss applying controls to this sector because all of the nonpoint sector reductions are done using 

a different packet than the missed control packet described for ptnonipm. 

In addition to the documentation provided previously for ptnonipm, the 2020 projection factors 

for Residential Wood Combustion SCCs in this nonpt sector were not updated for 2030 because 

the difference would have been very small and these emissions are highly uncertain. 

The refueling and gasoline distribution projection approaches described in the point source 

section also apply to the nonpoint sector, since those approaches affect SCCs in both sectors. 

4.2.2 Part 2: Additional OTAQ-supplied emissions data for nonpt 

In addition to the nonpt emissions projected from 2005 to 2030, several additional OTAQprovided emission inventories were created to complete the emissions needed for the 2030 

Reference case nonpt sector: 

1.	 Ethanol plant additions: These are conceptually the same as the ptnonipm plant additions 

but are for plants with unknown coordinates, and are therefore aggregated to the countySCC level. SCC assignments determine spatial allocation using spatial surrogate 

ancillary data. The nonpoint ethanol plants were created from an Excel® spreadsheet 

provided by OTAQ on 11/17/2008: Corn_EtOH_Plant_Inv_2022-aeo.xls. The same 

VOC adjustments and SCC assignments detailed for ptnonipm were made here as well. 

These additional emissions are expected to be implemented by year 2022 and are held at 

the same level for year 2030 LDGHG modeling. 

2.	 Ethanol transfer additions: These new VOC emissions account for vapor loss during 

transport and loading by truck and rail are assigned three SCCs (30205031, 30205052, 

and 30205053). Ethanol transfer emissions were provided for an RFS-2 AEO scenario on 

12/11/2008 by OTAQ in an Excel® workbook called “EtOH_transport_vapor_AEO.xls”. 

These additional emissions are expected to be implemented by year 2022 and are held at 

the same level for year 2030 LDGHG modeling. 

3.	 Biodiesel plant additions: These new emissions are assigned SCC 2102006001 and also 

contain an units correction (grams/year to tons/year) of 1.102311309E-06, applied by 

OAQPS after receiving the data from OTAQ. These data were created from an Excel® 

spreadsheet provided by OTAQ on 11/21/2008: Biodsl_Plant_Inv_2022-aeo-prelim.xls. 

We selected the SCC 2102006001 (Stationary Source Fuel Combustion;Industrial;Natural 

19
 
Gas;All Boiler Types) to represent these sources in consultation with Ron Ryan. The 

VOC was not adjusted by 65% like it was for ethanol and cellulosic plants. OAQPS 

converted emissions from grams to tons. These additional emissions are expected to be 

implemented by year 2022 and are held at the same level for year 2030 LDGHG 

modeling. 

4.	 Portable Fuel Containers: These MSAT-based emissions were provided by OTAQ for 

the year 2030 for the 2002v3 platform and are unchanged for the 2030 LDGHG 

Reference case. We applied adjustment factors (provided by OTAQ) to the LDGHG 

control case. 

4.3 Mobile sources 

The mobile sources included many different approaches, depending on the modeling sector. 

Each of these is described in a separate subsection below. 

4.3.1 US Aircraft, locomotive, and non-c3 commercial marine 

(alm_no_c3) 

This sector was projected from the base year (2005) to the LDGHG future year (2030); the base 

year emissions are actually year 2002 and the same as those used in the 2002v3 platform. There 

are two components for projecting these emissions to year 2030: 

1.	 FAA TAF data for year 2025. This data was updated in December 2007 and, as 

discussed in Section 4.1.1, the furthest out year of 2025 is used to approximate year 2030 

emissions. 

2.	 Updated locomotives and C1/C2 CMV factors. OTAQ provided updated loco-marine 

Final Rulemaking (FRM) national-based adjustment factors for locomotive-marine 

emissions on 12/17/2009: “alm_no_c3_2030LDGHG_vs_2005_pcQA_locofix.xls”. 

We applied HAP factors to VOC to obtain 1,3-butadiene, acetaldehyde, acrolein, benzene, and 

formaldehyde. The remaining HAPs -metals and other non-VOC HAPs not already providedare held at base year levels (the 2002 emissions estimates used in the 2005 basecase). 

The alm_no_c3 emissions are the same for the 2030 Reference and Control cases. 

4.3.2 Canada and Mexico onroad mobile sources (othon) 

The data in this sector are the same as 2020 data released in 2002-based v3 platform, previously 

referenced. These are different from 2005 v4 platform because the 2006 Canadian data were not 

available for use in this project. 

4.3.3 C3 commercial marine sources from all waters (seca_c3) 

The seca_c3 sector emissions data were provided by OTAQ in an ASCII raster format used since 

the ECA-IMO project began in 2005. The (S)ECA C3 year 2030 base case from the ECA/IMO 

project was unchanged and used for both the 2030 LDGHG Reference and 2030 LDGHG 

20
 
 

 

Control cases. The 2030 base seca_c3 inventory was provided by OTAQ 03/27/2008: 

task11_2030base.zip. OTAQ also provided factors to compute HAP emission (based on 

emissions ratios) on 2/28/2008, which OAQPS applied to either VOC or PM2.5 to obtain HAP 

emissions values. Table 10 below shows these factors and whether they were applied to VOC or 

PM2.5. As with the 2005 case, this sector uses CAP-HAP VOC integration. 

Table 10. HAP emission ratios for generation of HAP emissions from 

criteria emissions for C3 commercial marine vessels 

Pollutant Apply to Pollutant Code Factor 

Acetaldehyde VOC 75070 0.0002286 

Benzene VOC 71432 9.795E-06 

Formaldehyde VOC 50000 0.0015672 

Benz[a]Anthracene PM2_5 56553 5.674E-07 

Benzo[a]Pyrene PM2_5 50328 1.844E-07 

Benzo[b]Fluoranthene PM2_5 205992 1.56E-07 

Benzo[k]Fluoranthene PM2_5 207089 1.56E-07 

Chrysene PM2_5 218019 9.929E-08 

Indeno[1,2,3-c,d]Pyrene PM2_5 193395 1.418E-08 

Acenaphthene PM2_5 83329 3.404E-07 

Acenaphthylene PM2_5 208968 5.248E-07 

Anthracene PM2_5 120127 5.248E-07 

Benzo[g,h,i,]Perylene PM2_5 191242 1.277E-07 

Fluoranthene PM2_5 206440 3.12E-07 

Fluorene PM2_5 86737 6.95E-07 

Naphthalene PM2_5 91203 1.987E-05 

Phenanthrene PM2_5 85018 7.943E-07 

Pyrene PM2_5 129000 5.532E-07 

Beryllium PM10 7440417 5.459E-07 

Cadmium PM10 7440439 7.642E-06 

Chromium VI PM10 18540299 2.948E-06 

Chromium III PM10 16065831 1.343E-05 

Lead PM10 7439921 3.002E-05 

Manganese PM10 7439965 5.732E-05 

Nickel PM10 7440020 0.0016377 

Selenium PM10 7782492 1.337E-05 

OAQPS converted emissions to SMOKE point source ORL format allowing for emissions to be 

allocated to modeling layers above the surface layer. OAQPS corrected emissions for one 

state/county FIPS code fix in Rhode Island. All non-US emissions (i.e., in waters considered 

outside of US territory) are simply assigned a dummy state/county FIPS code=98001. Due the 

huge size of these data, the CAP emissions are in one ORL file and the HAP emissions for 

benzene, acetaldehyde, and formaldehyde are in a separate ORL file; all other HAPs are not 

provided for LDGHG as they are uninvolved in air quality model chemistry and are not of 

interest for this rulemaking. The emissions spatial extent includes waters off of the coasts of the 

US, Canada, and Mexico, as well as emissions in major waterways and the Great Lakes. The 

21
 
 

 

SMOKE-ready data have also been cropped from the original data provided by OTAQ to cover 

only the 36km CMAQ domain, which is the largest domain used for this effort. 

4.3.4 US nonroad mobile sources (nonroad) 

All states except California: 

OTAQ provided several runs of NMIM emissions that were blended together to create the 2030 

Reference and Control case nonroad sector emissions. Table 11 shows how the various NMIM 

runs were combined to create the reference and control case non-California nonroad mobile 

inventories. The first component “2002v3-based 2030 Base Case” is from the 2030 Base case in 

our 2002v3 platform for the SCCs listed in Table 11, and these emissions are the same in the 

reference and control cases. OTAQ also provided diesel recreational marine (pleasure craft) 

emissions in November 2009 that are used in both the reference and control cases. An NMIM 

run specific to the Reference case “LdGhgN2030Aeo.txt” was provided in November 2009 for 

the remainder of the nonroad mobile sector: gasoline engine and recreational marine sources. 

The only difference in the control case is a substitution of the gasoline engine and recreational 

marine emissions with the “E10” NMIM run: “LdGhgN2030e10.txt”. 

Table 11. Components of 2030 Nonroad Sector for Reference and Control Cases 

NMIM file SCCs Description of Nonroad 

SCCs 

Use 

2002v3-based 2030 Base Case 2267x LPG equipment Both Reference and Control 

2268x CNG equipment Cases

2270x Diesel engines 

2285002015, 

2285006015 

Railway maintenance 

LdGhgN2030e0_nponzseg11dies.txt 22820200x Diesel recreationalmarine Both Reference and Control 

Cases 

LdGhgN2030Aeo.txt 2260x 2-stroke gasoline engines Reference Case only 

2265x 4-stroke gasoline engines 

228200x, 

228201x 

Gasoline recreational 

marine 

LdGhgN2030e10.txt 2260x 2-stroke gasoline engines Control Case only 

2265x 4-stroke gasoline engines 

228200x, 

228201x 

Gasoline recreational 

marine 

All NMIM data are based on AEO2007 fuels and NMIM county database NCD20080727. We 

also reassigned NMIM evaporative and refueling xylene (compound XYL or 

CAS=EVP__1330207, RFL__1330207) into MXYL (CAS=EVP__108383, RFL__108383) and 

OXYL (CAS=EVP__95476, RFL__95476) using a 68% and 32% ratio to both evaporative and 

refueling XYL, respectively. We also split NMIM exhaust xylene (CAS=EXH__1330207) into 

MXYL (CAS=EXH__108383) and OXYL (CAS=EXH__95476) using a 74% and 26% ratio to 

XYL, respectively. We converted emissions from monthly totals to monthly average-day values 

based the on number of days in each month. CO2 and all of California emissions were removed 

prior to creating SMOKE ORL files. 

22
 
California nonroad: 

California monthly nonroad emissions are year 2030 and are based on March 2007 California Air 

Resources Board (CARB) data (Martin Johnson: mjohnson@arb.ca.gov). NH3 emissions are 

from NMIM runs for California (same data as were used in 2030 from the 2002 v3 platform). 

We allocated refueling emissions to the gasoline equipment types based on evaporative mode 

VOC emissions from the 2002 v3 platform 2030 NMIM data, and the refueling emissions were 

computed by multiplying SCC 2505000120 emissions by 0.61, to adjust to remove double 

counting with Portable Fuel Container inventory for California. We estimated HAP emissions 

by applying HAP-to-CAP ratios computed from the California data provided for the 2005 NEI 

v2, collected by EPA on 12/2007. This was done because the CARB submittal from March 2007 

did not include estimates for HAPs. We retained only those HAPs that are also estimated by 

NMIM for nonroad mobile sources; all other HAPs were dropped. 

4.3.5 Onroad mobile sources (on_moves_runpm, on_moves_startpm, 

and on_noadj) 

As in 2005, the on_moves_runpm and on_moves_startpm sectors include emissions from onroad 

gasoline sources for PM, which need temperature adjustment factors. The temperature 

adjustment factors were specific to 2030 (different from those used in 2005) and we used the 

same adjustment factors in both of the 2030 cases. The temperature adjustments have the 

limitation that they were based on the use of MOVES default inputs rather than county-specific 

inputs, because a county-specific database for input to MOVES was not available at the time this 

approach was needed. Further, the version of MOVES used for all runs was a draft version and 

has since changed. 

Also like 2005, the on_noadj sector includes non-PM MOVES data for gasoline and diesel 

vehicles for some pollutants, NMIM-based data for motorcycles and the remaining pollutants for 

onroad gasoline and diesel, as well as all California onroad mobile emissions. The detailed 

approaches described here are the same as those for 2005 (except for the NMIM and MOVES 

data used), but are included here for convenience. 

Table 12 shows the various pieces of onroad mobile NMIM and MOVES-based emissions 

inventories that were used to create the 2030 Reference and Control case onroad mobile sectors: 

on_moves_startpm, on_moves_runpm, and on_noadj. Unless otherwise noted, the emissions are 

part of the “no-adjust” on_noadj sector. As Table 12 shows, several pieces of NMIM and the 

state-level MOVES data used in this project were different between the 2030 Reference and 

Control cases; the only exceptions are the NMIM onroad diesel vehicles and the adjustment 

factors (used in the on_moves_startpm and on_moves_runpm) that were the same for each case. 

23
 
 

Table 12. MOVES and NMIM Inventory Components of 2030 Onroad Sectors for Reference 

and Control Cases 

MOVES and NMIM files SCCs Description of 

Onroad SCCs 

Use 

LdGhgO2030e10.txt 

(NMIM) 

223x All diesel vehicles Reference Case: Used only to 

1) apportion diesel vehicle emissions 

to county-level for MOVES 

pollutants, 

2) as-is for all non-MOVES pollutants 

ALL All Onroad Emissions Control Case: Used to 

1) apportion emissions to county-level 

for all MOVES pollutants, 

2) as-is for all non-MOVES pollutants 

LdGhgO2030Aeo.txt 

(NMIM) 

220x All gasoline vehicles 

and trucks 

Reference Case only: Used for 

1) all motorcycle emissions, 

2) as-is for all non-MOVES pollutants, 

3) apportioning MOVES gasoline 

vehicle emissions to county-level 

LgrDiesExh2030_812_Ref.txt 

(MOVES exhaust) 

223007x Diesel vehicles classes 

8-12 (HDDV) 

Both Reference and Control Cases: PM, 

CO, NOx, VOC, acetaldehyde, benzene, 

formaldehyde, acrolein, 1,3-butadiene 

LgrDiesExh2030_67_Ref.txt 

(MOVES exhaust) 

2230001x Diesel vehicles class 6 

(LDDV) 

Reference Case only: PM, CO, NOx, 

VOC, acetaldehyde, benzene, 

223006x Diesel vehicles class 7 formaldehyde, acrolein, 1,3-butadiene 

(LDDT) 

LgrDiesExh2030_67_Ctltxt 

(MOVES exhaust) 

2230001x Diesel vehicles class 6 

(LDDV) 

Control Case only: PM, CO, NOx, 

VOC, acetaldehyde, benzene, 

223006x Diesel vehicles class 7 formaldehyde, acrolein, 1,3-butadiene 

(LDDT) 

LgrGasExh2030aeoR2_Ref.txt 

(MOVES exhaust) 

22010x 

except 

220108x 

Non-motorcycle 

gasoline vehicles and 

trucks 

Reference Case only: CO, NOx, VOC, 

acetaldehyde, benzene, formaldehyde, 

acrolein, 1,3-butadiene 

LgrGasExh2030e10R2_Ctl.txt 

(MOVES exhaust) 

22010x 

except 

220108x 

Non-motorcycle 

gasoline vehicles and 

trucks 

Control Case only: CO, NOx, VOC, 

acetaldehyde, benzene, formaldehyde, 

acrolein, 1,3-butadiene 

LgrGasPm2030aeo_Ref.txt 

(MOVES exhaust) 

22010x 

except 

220108x 

Non-motorcycle 

gasoline vehicles and 

trucks 

Reference Case only: PM-only, subject 

to temperature adjustments 

(on_moves_startpm and 

on_moves_runpm sectors) 

LgrGasPm2030e10_Ctl.txt 

(MOVES exhaust) 

22010x 

except 

220108x 

Non-motorcycle 

gasoline vehicles and 

trucks 

Control Case only: PM–only, subject to 

temperature adjustments 

(on_moves_startpm and 

on_moves_runpm sectors) 

LgrEvapAeoR2_Ref.txt 

(MOVES evaporative) 

22010x 

except 

220108x 

Non-motorcycle 

gasoline vehicles and 

trucks 

Reference Case only: VOC and benzene 

LgrEvapE10R3_Ctl.txt 

(MOVES evaporative) 

22010x 

except 

220108x 

Non-motorcycle 

gasoline vehicles and 

trucks 

Control Case only: VOC and benzene 

on_moves_startpm and on_moves_runpm 

For the on_moves_runpm and on_moves_startpm sectors, the same preprocessing as was done in 

2005 was done here, but using a 2030 Reference case NMIM runs to create the monthly countyto-state ratios by state and SCC and using the 2030 PM adjustment factors. 

24 
OTAQ supplied four input files for the reference and control cases that contain state-level 

MOVES-based onroad gasoline and diesel emissions by month for the following pollutants (NEI 

pollutant IDs are provided in parentheses): 

1.	 Onroad Gasoline Exhaust: VOC, NOX, CO, 1,3-butadiene (106990), acetaldehyde 

(75070), acrolein (107028), benzene (71432), and formaldehyde (50000); 

2.	 Onroad Diesel Exhaust: VOC, NOX, CO, 1,3-butadiene (106990), acetaldehyde (75070), 

acrolein (107028), benzene (71432), formaldehyde (50000), and MOVES-defined PM 

species OC, EC, and SO4, components of PM2.5 –PM25OC, PM25EC, and PM25SO4 

respectively; 

3.	 Evaporative: Non-refueling VOC and benzene (71432) –gasoline vehicles only; 

4.	 Onroad Gasoline MOVES-speciated PM at 72oF: MOVES-defined PM species OC, EC, 

and SO4, components of PM2.5 –PM25OC, PM25EC, and PM25SO4 respectively. 

Emissions are computed at 72oF and we used SAS® to convert the MOVES-based PM2.5 

species into the following SMOKE-ready pollutants: 

	 PEC_72: unchanged from MOVES-based PM25EC, subject to temperature 

adjustment below 72oF. 

	 POC_72: modified MOVES-based PM25OC to remove metals, PNO3 (computed 

from MOVES-based PM25EC), NH4 (computed from MOVES-based PM25SO4 

and PNO3), and MOVES-based PM25SO4. Subject to temperature adjustment 

below 72oF. 

 PSO4: unchanged from MOVES-based PM25SO4, not subject to temperature 

adjustment. 

 PNO3: computed from MOVES-based PM25EC, not subject to temperature 

adjustment. 

 OTHER: sum of computed metals (fraction of MOVES-based PM25EC) and 

NH4 (computed from PNO3 and PSO4), not subject to temperature adjustment. 

 PMFINE_72: Computed from OTHER and fraction of POC_72. Subject to 

temperature adjustment below 72oF. 

 PMC_72: Computed as fraction of sum of PMFINE_72, PEC_72, POC_72, 

PSO4, and PNO3. Subject to temperature adjustment below 72oF. 

The first three inputs listed above are processed in the on_noadj sector because temperature 

adjustments are not needed for these inventories. The fourth input listed above is further broken 

into the on_moves_runpm and on_moves_startpm sectors; these emissions require separate 

temperature adjustments after some intermediate emissions processing. 

MOVES gasoline emissions were used for light-duty gasoline vehicles (LGDV), light-duty 

gasoline trucks 0-6000 pounds gross vehicle weight (LDGT1), light-duty gasoline trucks 6000­

8500 pounds gross vehicle weight (LDGT2), and heavy-duty gasoline trucks (HDGV). 

Motorcycle emissions were not available from MOVES at the time of this project and so 

emissions from that vehicle class came from the case-specific NMIM runs. 

25
 
MOVES-based, monthly state-level emissions were first allocated to counties using county-SCC 

specific state-to-county ratios that we created from the 2030 Reference case NMIM run. 

California MOVES-based emissions are discarded; they do not replace the existing California 

inventories (discussed in on_noadj sector). MOVES data were provided by OTAQ in October 

2009. 

In each MOVES file, “start” emissions are represented by SCCs with the SCC characters 8-9 

equal to “00”: 2201001000 (LDGV), 2201020000 (LDGT1), 2201040000 (LDGT2), 

2201070000 (HDGV), 2230001000 (LDDV), 22300601000 (LDDT), and 223007X000 

(HDDV). These start emissions are assigned to urban and rural SCCs based on the county-level 

ratio of NMIM emissions from urban versus local roads. For example, LDGV start emissions 

(2201001000) were split into urban (2201001370) and rural (2201001350) based on the ratio of 

LDGV emissions from urban (2201001330) and rural (2201001210) local roads. 

Finally, the set of emissions are broken into 3 sets that are processed in 3 separate sectors for 

LDGHG: 

1.	 on_moves_startpm: monthly MOVES-based onroad gasoline “start” PM emissions 

subject to temperature adjustments, and onroad gasoline PM species not subject to 

temperature adjustments (e.g., PNO3 and PSO4). These are limited to 8 SCCs 

(urban/rural and 4 vehicle types) representing parking areas for the following pollutants: 

PEC_72, POC_72, PNO3, PSO4, OTHER, PMFINE_72, and PMC_72. 

2.	 on_moves_runpm: monthly MOVES-based onroad gasoline “running” PM emissions 

subject to a different set of temperature adjustments compared to “start” emissions; 

similar to the on_moves_startpm sector, this sector includes all onroad gasoline PM 

species, not just those subject to temperature adjustments. The same pollutants are 

provided as on_moves_startpm. 

3.	 on_noadj MOVES-based emissions: The remaining monthly diesel exhaust and non-PM 

gasoline MOVES-based emissions that are also not subject to temperature adjustments ­

see inputs (1) and (2) above. These emissions are modeled in the on_noadj sector 

discussed below and include both start and running emissions for all diesel 

(MOVES_based) pollutants and gasoline non-PM pollutants. 

on_noadj 

The on_noadj sector contains all US onroad mobile emissions that do not get a PM temperature 

adjustment. There are three sources of data that are pre-processed to create three sets of monthly 

inventories for this sector. 

1.	 MOVES-based diesel exhaust and non-PM gasoline: These are the monthly non-PM 

MOVES-based emissions discussed in item #3 in “Outputs” in the on_moves_runpm and 

on_moves_startpm sector discussion. In short, these are non-California, select pollutants 

exhaust and evaporative (non-refueling) onroad gasoline LDGV, LDGT1, LDGT2, 

HDGV, and onroad diesel (HDDV –all classes) emissions. 

2.	 California onroad inventory: California year 2030 complete CAP/HAP onroad inventory. 

California monthly onroad emissions are year 2030 and are based on March 2007 

California Air Resources Board (CARB) data (Martin Johnson: mjohnson@arb.ca.gov). 

26
 
NH3 emissions are from NMIM runs for California (same data as were used in 2030v3). 

We estimated HAP emissions by applying HAP-to-CAP ratios computed from California 

2005 NEI submittal provided by EPA in 12/2007. This was done because the CARB 

submittal from March 2007 did not include estimates for HAPs. We retained only those 

HAPs that were also estimated by NMIM for nonroad mobile sources; all other HAPs 

were dropped. 

3.	 Remaining onroad NMIM-based onroad inventory: The remainder of the non-California 

onroad inventory not replaced by MOVES. This includes monthly emissions for all 

onroad diesel, all motorcycles, all refueling, and onroad LDGV, LDGT1, LDGT2, 

HDGV, and HDDV emissions for pollutants not covered by MOVES (e.g., SO2, NH3). 

All NMIM onroad data are based on a composite Reference case inventory from Table 

10, which uses AEO2007 fuels and NMIM county database NCD20080727. 

The remainder of this section discusses the pre-processing required to create monthly ORL files 

for the remainder of the on_noadj sector (#3 above). 

Table 10 shows how two NMIM inventories were combined to create the Reference case NMIM 

emissions. OTAQ provided a “reference and control” set (LdGhgO2030e10.txt) in October 2009 

of NMIM E10-penetration emissions to be used for both the Reference and Control case. This is 

the complete NMIM scenario for the control case, but is used only for diesel vehicle emissions in 

the Reference case scenario. This reference and control set of monthly emissions includes all 50 

states plus DC and all CAPs and HAPs of interest. A Reference case-specific 

(LdGhgO2030Aeo.txt) set of NMIM monthly emissions was also provided in October 2009 by 

OTAQ for onroad gasoline emissions. Onroad gasoline emissions in the control case use the 

“reference and control” case E10-based inventory. 

Similar to nonroad pre-processing, we also reassigned NMIM evaporative and refueling xylene 

(compound XYL or CAS=EVP__1330207, RFL__1330207) into MXYL (CAS=EVP__108383, 

RFL__108383) and OXYL (CAS=EVP__95476, RFL__95476) using a 68% and32% ratio to 

both evaporative and refueling XYL, respectively. We also split NMIM exhaust xylene 

(CAS=EXH__1330207) into MXYL (CAS=EXH__108383) and OXYL (CAS=EXH__95476) 

using a 74% and 26% ratio to XYL, respectively. 

Emissions were converted from monthly to average-day based the on number of days in each 

month. CO2 and all of California emissions were removed prior to creating the NMIM-only 

parts of the final inventory files. 

We also removed refueling emissions from the raw NMIM data prior to creating the SMOKE 

inputs, since these emissions were included in the nonpt and ptnonipm sectors. The NMIM 

refueling data were used with existing 2005 refueling emissions to create projection ratios for 

ptnonipm and nonpt refueling, as previously described in Section 4.1.1. 

27
 
5 2030 Control Case 

The 2030 LDGHG Control Case was intended to represent the implementation of national CO2 

emissions standards for light-duty vehicles. A list of inventory datasets used for this and the 

2030 reference and 2005 cases is provided in Appendix A. 

5.1 2030 Control Case Point sources 

The point sources for the 2030 Control Case include the same emissions as the 2030 Reference 

Case for the following point source sectors: US EGU point source (ptipm), sources from Mexico, 

Canada, and the Gulf of Mexico (othpt). 

As described in Section 4.1.1, the base case controls were erroneously not applied to both the 

2030 Reference, and by extension, the 2030 Control case ptnonipm inventories. No other sectors 

are impacted by this error. 

For the nonEGU point sources (ptnonipm), some of the 2030 Reference case emissions were 

adjusted to reflect the 2030 Control case. These differences from the 2030 Reference case are: 

	 Gasoline Distribution: These SCC- adjustments were supplied by OTAQ on 11/09/2009 

in the Excel® workbook “LDGHG_SCC_GasDistrib_Adjust2.xls”. These adjustments 

reduce all pollutant emissions from the Reference to the Control case for Refinery-ToBulk-Terminal (rbt) and Bulk-Terminal-To-Pump (btp) by approximately 18.3% to 

18.8%, respectively. On 12/28/2009, OTAQ also provided an additional 25 rbt-related 

SCCs that were subject to the same projection factors. These adjustments impact both 

the ptnonipm and nonpt sectors. 

	 Oil Refining, Crude Oil Transport, and Crude Oil Production: These SCC-level 

adjustments were supplied by OTAQ on 11/09/2009 in the Excel® workbook 

“Oil_Prod_Transp_Refine_2030_adjust.xls”. These adjustments reduce all pollutant 

emissions from the Reference to the Control case for refinery-related SCCs by 

approximately 6.1% and for transport and production-related SCCs by approximately 

0.6%. These adjustments impact both the ptnonipm and nonpt sectors. 

	 Onroad refueling: We used the same overall projection approach for onroad refueling as 

was used in the 2030 Reference case for the ptnonipm and nonpt sectors. Instead of 

using the 2030 Reference case NMIM run, we used the Control case-specific NMIM run 

for the 2030 refueling ratio calculation. We applied these factors to the refueling SCCs 

from the 2030 Reference case using this formula: Factor2030control = NMIM Emis2030Control 

/NMIM Emis2030Refernce. These adjustments impact both the ptnonipm and nonpt sectors. 

	 The headspace vapor VOC speciation profile changed to reflect the assumption of 100% 

E10 in the 2030 control case fpr rbt and btp emissions, the specifics of which are 

provided in Section 3. 

. 

28
 
 

5.2 2030 Control Case Nonpoint sources 

The nonpoint sources for the 2030 Control Case include the same emissions as the 2030 

Reference Case for the following sectors: US fugitive dust sources (afdust sector), US 

agricultural NH3 (ag sector), the US average fires (avefire sector), and the nonpoint emissions 

for Canada and Mexico (othar). 

The 2030 Control Case emissions for the “other nonpoint” sector (nonpt) emissions differed 

from the 2030 Reference case in the following ways: 

	 Created revised PFC emissions by applying adjustment factors by county and pollutant, 

based on an Excel® spreadsheet provided by OTAQ on 1/14/2009: “aeo_to_eisa.xls”. 

These adjustments reduced PFC emissions as compared to the 2030 Reference case. 

	 As previously mentioned for the point sources, we changed refueling, rbt/btp, and oil 

refining, transport, and production emissions projections, which also affected the nonpt 

sector. 

	 The headspace vapor VOC speciation profile changed to reflect the assumption of 100% 

E10 in the 2030 control case fpr rbt and btp emissions, the specifics of which are 

provided in Section 3. 

5.3 2030 Control Case Mobile sources 

Compared to the 2030 Reference case, the mobile source emissions included changes for all 

onroad mobile (on_noadj, on_moves_startpm, and on_moves_runpm), and non-California 

nonroad mobile sources (nonroad). The aircraft, locomotive, and non-C3 commercial marine 

(alm_no_c3), C3 commercial marine (seca_c3) and the Canada and Mexico emissions (othon) 

were unchanged. 

For the nonroad sector, as discussed in Section 4.3.4, OAQPS substituted 2030 Control case 

emissions for nonroad gasoline 2-stroke and 4-stroke equipment and gasoline recreational marine 

sources. California emissions were not changed, but emissions in all other states were updated. 

Otherwise the steps taken were the same as described for the 2030 Reference case. 

For the US onroad mobile sectors, as discussed in Section 4.3.5, OAQPS substituted NMIM 

2030 Control case emissions for onroad gasoline vehicles and motorcycles. In addition, the 

MOVES-based emissions for the control case completely replaced the reference case MOVES 

data with the exception of MOVES-based vehicle class 8-12 diesel (HDDV) that are shared by 

both the reference and control cases. 

We allocated the state-SCC MOVES data to county-SCC using ratios developed from the 

Control case-specific NMIM county-SCC data. Other than the different data used for creating 

the monthly county-SCC SMOKE-ready inventories, we used the same processing steps as 

described for the 2005 base and 2030 Control cases. 

Lastly, VOC speciation profile changes affected this sector, as described in Section 3. 

29
 
 

 

APPENDIX A 

Equations to adapt pre-speciated diesel emissions from MOVES to air quality modeling species needed 

for CMAQ 

As shown in equation (1) below, MOVES provides total PM2.5, PEC and PSO4. A remainder term, R, makes 

up the difference between the two species and the total PM2.5. 

MOVES total PM2.5 = PEC + PSO4 + R (1) 

The R term includes POM, which consists of POC and the hydrogen and oxygen atoms attached to the 

carbon as part of the organic matter, PNO3, soil oxides and metals (also known as “crustal” and called 

METAL here), ammonium, and water, and thus can be also written as: 

R = POM + PNO3 + METAL + NH4 + H20 (2) 

To correctly calculate the five PM2.5 species needed for CMAQ, we first needed to break out the POC, 

PNO3, and PMFINE from R. Different calculations are used for light-duty diesel vehicles and heavy-duty 

diesel vehicles, since the speciation profiles for these are different. The speciation profiles used for these 

calculations are: 

For both light duty diesel vehicles and heavy duty diesel vehicles, the SPECIATE 4.0 PM2.5 speciation 

profiles “3914” (HHDV) and “92042” (LDDV) will be used to help calculate the other species. At the time, 

OTAQ did not provide a justification for choosing this profile, but the fractions of metals and PNO3 are 

small and so presumably the choice does not matter too much as long as the smallest of those fractions is 

representative. 

We computed the primary nitrate based on speciation profile 92011 from the SPECIATE4.1 database (Hsu et 

al., 2006) using equation (3) shown below. 

PNO3 = PEC × FNO3 / FEC (3) 

where, 

FEC = Fraction of elemental carbon in speciation profile: 

- LDDV: 57.4805% (based on profile 92042)1 

- HDDV: 77.1241% (based on profile 3914) 

FNO3 = Fraction of nitrate in speciation profile 

- LDDV: 0.1141% (based on profile 3914, intentionally inconsistent) 

- HDDV: 0.1141% (based on profile 3914) 

To identify which sources should get the LDDV and which should get the HDDV approach, see Table 1, 

below. 

Since CMAQ’s PMFINE species is the sum of soil oxides, metals, ammonium, and water, we needed to 

calculate all of its components. First, the metals and ammonium are computed using equations (4) and (5). 

Equation (5) is based on stoichiometric calculations. 

METAL = PEC × Fmetal / FEC (4) 

1

 All profile fractions provided in email from Catherine Yanca on 11/6/2009, 1:49pm in attachment “Equations for diesel MOVES 

speciation use in CMAQ 110609.doc” 

30
 
 

 

NH4 = (PNO3/MWNO3 +2 × PSO4/MWSO4) × MWNH4 (5) 

where, 

Fmetal = Fraction of metals in speciation profile (0.0026632

) 

MWSO4 = Molecular weight of sulfate (96.0576) 

MWNO3 = Molecular weight of nitrate (62.0049) 

MWNH4 = Molecular weight of ammonium (18.0383) 

The final component of PMFINE is the non-carbon mass of organic carbon. To calculate the non-carbon 

mass, we first needed to compute organic carbon from the remainder term, R. 

A key assumption is that POM is a factor of 1.2 greater than the mass of primary organic carbon, which is 

also used in the CMAQ postprocessing software at EPA. 

POM = 1.2 × POC (6) 

Using this assumption and assuming that the H20 is negligible, the equation needed for the calculation of 

POC is shown in equation (7) below. 

POC = 5/6 × (R – METAL – NH4 – PNO3) (7) 

From equation (6), the non-carbon portion of the organic carbon matter is 20%, of the POC. By definition, 

PMFINE is the sum of the non-carbon portion of the mass, METAL and NH4. Thus, we computed 

PMFINE_72 using equation (8) shown below. 

PMFINE_72 = METAL + NH4 + 0.2 × POC_72 (8) 

For mobile sources, we assumed that PMC is 8.6% of the PM2.5 mass. Equation (9) shows how we 

calculated it. 

PMC = 0.086 × (PMFINE + PEC + POC + PSO4 + PNO3) 

Table A-1. List of SCC groups for application of LDDV or HDDV approach 

Approach SCC list 

LDDV 2230001000 through 2230060334 

HDDV 2230071110 through 2230075330 

2

 Value provided by Catherine Yanca and Joe Somers to OAQPS in email provided 11/5/2009 

31
 
APPENDIX B 

Inventory Data Files Used for Each LDGHG Modeling Case – SMOKE Input Inventory Datasets 

In any of the following dataset names where the placeholder <mon> has been provided, this is intended to 

mean 12 separate files with the <mon> placeholder replaced with either jan, feb, mar, apr, may, jun, jul, aug, 

sep, oct, nov, or dec, each associated with a particular month of the year. 

32
 
Table B-1. List of inventory data associated with LDGHG modeling cases. 

Case Sector SMOKE Input Files 

2005 Base 

(2005cp_tox_05b) 

ptipm Annual: ptinv_ptipm_cap2005nei_26aug2008_v2_orl.txt 

Annual: ptinv_ptipm_hap2005agtox_v1_noBAFM_28aug2008_v0_orl.txt 

Daily: ptday_ptipm_caphap_noncem_2005ag_<mon>_ida.txt 

Daily: ptday_ptipm_caphap_cem_2005ci_<mon>_ida.txt 

ptnonipm ptinv_ptnonipm_xportfrac_2005cap_remainder_24nov2008_v0_orl.txt 

ptinv_ptnonipm_2005hap_noBAFM_remainder_24nov2008_v0_orl.txt 

ptinv_ptnonipm_2005caphap_chippewa_delete_for_2022EISA_19nov2008_v0_orl.txt 

ptinv_ptnonipm_caphap_ethanol_plant_additions_2005_17dec2008_v1_orl.txt 

ptinv_ptnonipm_2005caphap_3ethanol_plants_delete_for_all2022_19nov2008_v0_orl.txt 

afdust arinv_afdust_2002ad_xportfrac_26sep2007_v0_orl.txt 

ag arinv_ag_cap2002nei_06nov2006_v0_orl.txt 

alm_no_c3 arinv_alm_no_c3_cap2002v3_30jul2008_v1_orl.txt 

arinv_alm_no_c3_hap2002v4_12sep2008_v0_orl.txt 

nonpt arinv_nonpt_caphap_ethanol_plant_additions_2005_17dec2008_v0_orl.txt 

arinv_nonpt_pf4_cap_nopfc_08sep2008_v0_orl.txt 

arinv_nonpt_pf4_hap_nopfc_11sep2008_v0_orl.txt 

arinv_pfc_2002_caphap_27dec2007_v0_orl.txt 

nonroad arinv_nonroad_caps_2005v2_<mon>_revised_08sep2008_v0_orl.txt 

arinv_nonroad_calif_caphap_2005v2_<mon>_02apr2008_v0_orl.txt 

arinv_nonroad_haps_2005v2_<mon>_revised_05sep2008_v0_orl.txt 

on_noadj mbinv_onroad_calif_caphap_2005v2_<mon>_02apr2008_v0_orl.txt 

mbinv_noadj_capshaps_2005v2_nmim_not2moves4LDGHG_<mon>_12NOV09_12nov2009_v0_orl.txt 

mbinv_on_noadj_MOVES_LDGHG_2005_<mon>_17NOV09_17nov2009_v0_orl.txt 

on_moves_runpm mbinv_on_moves_runpm_LDGHG_2005_<mon>_12NOV09_12nov2009_v0_orl.txt 

on_moves_startpm mbinv_on_moves_startpm_LDGHG_2005_<mon>_12NOV09_12nov2009_v0_orl.txt 

seca_c3 ptinv_seca_c3_caps2005pf4_31jul2008_v0_orl.txt 

ptinv_seca_c3_haps_east2005pf4_31jul2008_v0_orl.txt 

ptinv_seca_c3_haps_central2005pf4_31jul2008_v0_orl.txt 

ptinv_seca_c3_haps_west2005pf4_31jul2008_v0_orl.txt 

ptinv_seca_c3_haps_NonUS_east2005pf4_09sep2008_v0_orl.txt 

ptinv_seca_c3_haps_NonUS_central2005pf4_09sep2008_v0_orl.txt 

ptinv_seca_c3_haps_NonUS_west2005pf4_09sep2008_v0_orl.txt 

All Cases avefire arinv_avefire_2002ce_21dec2007_v0_ida.txt 

arinv_avefire_2002_hap_18nov2008_v0_orl.txt 

othar arinv_nonroad_mexico_interior1999_21dec2006_v0_ida.txt 

arinv_nonroad_mexico_border1999_21dec2006_v0_ida.txt 

arinv_nonroad_Canada_2000_inventory_11feb2008_v2_ida.txt 

arinv_nonpoint_Canada2000_21dec2006_v0_ida.txt 

arinv_nonpt_mexico_interior1999_21dec2006_v0_ida.txt 

arinv_nonpt_mexico_border1999_21dec2006_v0_ida.txt 

othon mbinv_onroad_Canada2000_07nov2006_v0_ida.txt 

mbinv_onroad_mexico_border1999_21dec2006_v0_ida.txt 

mbinv_onroad_mexico_interior1999_21dec2006_v0_ida.txt 

othpt ptinv_2000_canada_egu_07nov2006_v0_ida.txt 

ptinv_2000_canada_nonegu_05dec2006_v0_ida.txt 

ptinv_mexico_border99_03mar2008_v1_ida.txt 

ptinv_mexico_interior99_05feb2007_v0_ida.txt 

ptinv_offshore_point_from_2001_platform_07nov2006_v0_ida.txt 

2030 Reference 

 and Control 

ptipm Annual: ptinv_egu_summer_2020_pf31capsHgHCl_19feb2008_v0_orl.txt 

Annual: ptinv_ptipm_hap2005agtox_v1_noBAFM_noHgHCL_07jan2009_v2_orl.txt 

Daily: ptday_ptipm_caphap_cem_2022ci_<mon>_ida.txt 

Daily: ptday_ptipm_caphap_noncem_2022ci_<mon>_ida.txt 

Daily: ptday_ptipm_hap_cem_2005ci_<mon>_ida.txt 

33 
Case Sector SMOKE Input Files 

Daily: ptday_ptipm_hap_noncem_2005ag_<mon>_ida.txt 

afdust arinv_afdust_2020ce_02b_BASE_07apr2008_v0_orl.txt 

ag arinv_ag_2020ce_02b_BASE_08apr2008_v0_orl.txt 

alm_no_c3 arinv_alm_no_c3_cap2030cp_21dec2009_v0_orl.txt 

arinv_alm_no_c3_hap2030cp_21dec2009_v0_orl.txt 

seca_c3 ptinv_seca_c3_BAF_HAPs2030pf31_17dec2009_v0_orl.txt 

ptinv_seca_c3_caps2030pf31_10jun2008_v0_orl.txt 

2030 Reference Only 

(2030cp_tox_05b) 

ptnonipm ptinv_ptnonipm_xportfrac_2030cp_tox_cap_remainder_17dec2009_v0_orl.txt 

ptinv_ptnonipm_2005caphap_chippewa_delete_for_2022EISA_19nov2008_v0_orl.txt 

ptinv_ptnonipm_2030cp_tox_hap_noBAFM_remainder_17dec2009_v0_orl.txt 

ptinv_ptnonipm_caphap_ethanol_plant_additions_2022AEO_05jan2009_v1_orl.txt 

nonpt arinv_nonpt_caphap_biodiesel_plant_additions_2022AEO_18dec2008_v0_orl.txt 

arinv_nonpt_caphap_ethanol_plant_additions_2022AEO_15dec2008_v0_orl.txt 

arinv_nonpt_pf4_2030cp_tox_cap_nopfc_17dec2009_v0_orl.txt 

arinv_nonpt_pf4_2030cp_tox_hap_nopfc_fixed_with_wrap_oilgas_17dec2009_v0_orl.txt 

arinv_nonpt_voc_ethanol_transfer_additions_2022AEO_15dec2008_v0_orl.txt 

arinv_pfc_reference_caphap2030LDGHG_14dec2009_v0_orl.txt 

nonroad arinv_nonroad_capshaps_LDGHG_2030_REFERENCE_<mon>_15DEC09_15dec2009_v0_orl.txt 

arinv_nonroad_calif_caphap_2030v31_<mon>_17apr2008_v0_orl.txt 

on_noadj mbinv_onroad_calif_caphap_2030v31_<mon>_30apr2008_v0_orl.txt 

mbinv_on_noadj_nmim_not2moves_2030LDGHG_REFERENCE_<mon>_10DEC09_10dec2009_v0_orl.txt 

mbinv_on_noadj_MOVES_LDGHG_2030_REFERENCE_<mon>_11DEC09_11dec2009_v0_orl.txt 

on_moves_runpm mbinv_on_moves_runpm_LDGHG_2030_REFERENCE_<mon>_11DEC09_11dec2009_v0_orl.txt 

on_moves_startpm mbinv_on_moves_startpm_LDGHG_2030_REFERENCE_<mon>_11DEC09_11dec2009_v0_orl.txt 

ptnonipm ptinv_ptnonipm_2005caphap_chippewa_delete_for_2022EISA_19nov2008_v0_orl.txt 

ptinv_ptnonipm_2030cp_tox_ldghg_hap_noBAFM_remainder_28dec2009_v0_orl.txt 

ptinv_ptnonipm_caphap_ethanol_plant_additions_2022AEO_05jan2009_v1_orl.txt 

ptinv_ptnonipm_xportfrac_2030cp_tox_ldghg_cap_remainder_28dec2009_v0_orl.txt 

arinv_nonpt_caphap_biodiesel_plant_additions_2022AEO_18dec2008_v0_orl.txt 

arinv_nonpt_caphap_ethanol_plant_additions_2022AEO_15dec2008_v0_orl.txt 

arinv_nonpt_pf4_2030cp_tox_ldghg_cap_nopfc_28dec2009_v0_orl.txt 

arinv_nonpt_pf4_2030cp_tox_ldghg_hap_nopfc_fixed_with_wrap_oilgas_28dec2009_v0_orl.txt 

arinv_nonpt_voc_ethanol_transfer_additions_2022AEO_15dec2008_v0_orl.txt 

arinv_pfc_control_caphap2030LDGHG_14dec2009_v0_orl.txt 

nonroad arinv_nonroad_calif_caphap_2030v31_<mon>_17apr2008_v0_orl.txt 

arinv_nonroad_capshaps_LDGHG_2030_CONTROL_<mon>_15DEC09_15dec2009_v0_orl.txt 

on_noadj mbinv_onroad_calif_caphap_2030v31_<mon>_30apr2008_v0_orl.txt 

mbinv_on_noadj_nmim_not2moves_2030LDGHG_CONTROL_<mon>_10DEC09_10dec2009_v0_orl.txt 

mbinv_on_noadj_MOVES_LDGHG_2030_CONTROL_<mon>_11DEC09_11dec2009_v0_orl.txt 

on_moves_runpm mbinv_on_moves_runpm_LDGHG_2030_CONTROL_feb_11DEC09_11dec2009_v0_orl.txt 

on_moves_startpm mbinv_on_moves_startpm_LDGHG_2030_CONTROL_<mon>_11DEC09_11dec2009_v0_orl.txt 

34 
 

APPENDIX C 

Ancillary Data Files Used for LDGHG 2005 Case Compared to 2005 v4 Platform Data Files 

To match the Datasets and Versions listed in this table to actual data files, combine the Dataset name and the 

version number in the following pattern: <Dataset Name>_<Date>_<Version number>.txt, where <Date> is 

the last date of change for that version and will have a unique value for the combination of Dataset Name 

and Version number. 

35
 
36
 

Table C-1. Detailed list of ancillary data differences between the LDGHG 2005 and the 2005 v4 p

Description 

Environment Variable Sectors 

2005 v4 platform 2005 LDGHG platform 

Dataset Vsn Dataset Vsn Comment and Impact 

Area-source spatial cross-reference AGREF All sectors amgref_us_can_mex_revised 5 amgref_us_can_allmex3 12 

For v4 platform, revised for nspatial surrogates. LDGHG diesel parking areas 

Onroad spatial cross-reference MGREF All sectors amgref_us_can_mex_revised 7 amgref_us_can_allmex3 12 

For v4 platform, revised for nspatial surrogates. LDGHG diesel parking areas 

Area and onroad temporal profiles 

ATPRO, MTPRO, PTPRO 

All sectors amptpro_2005_us_can_revised 0 amptpro_2005_us_can 3 For v4 platform, revised for n

temporal profile. LDGHG in

Area temporal cross-reference 

ATREF, PTREF All sectors amptref_v3_3_revised 1 amptref_v3_3 9 

For v4 platform, added SCCinventory and for WRAP oil aincludes MOVES-based dies

Onroad temporal cross-reference MTREF All sectors amptref_v3_3_revised 0 amptref_v3_3 9 

For v4 platform, added SCCinventory (but not for WRAP Superceeded by v1 of the sa

Grid descriptions GRIDDESC All sectors griddesc_lambertonly 24 griddesc_lambertonly 23 An older file used for LDGHG 

defined. 

Inventory table INVTABLE All sectors invtable_hapcapintegate_cb05s oa_nomp 4 invtable_hapcap_cb05soa 9 

LDGHG used a full toxics apemissions and 2005 v4 platfmost toxics. Impacts only thquality modeling. LDGHG aMOVES diesel sources. 

Inventory table INVTABLE 

avefire, ptnonipm, ptipm 

invtable_hapcapnohapuse_cb05 soa_nomp 

4 n/a 

Approach for implementing "these sectors was different ithe same. 

Non-HAP Exclusions for nonpt sector 

NHAP EXCLUDE nonpt nhapexclude_nonpt_pf4 3 nhapexclude_nonpt_pf4 2 

v3 includes WRAP oil and ghave HAP VOCs either. v3 nnot LDGHG as LDGHG doeSCCs. 

Elevated configuration file for seca_c3 sector 

PELV CONFIG seca_c3 pelvconfig_seca_c3 0 n/a v4 platform used inline point 

file needed only in v4 platfor

Speciation profiles for TOG GSPRO All sectors gspro_tog_cb05_soa_pf4_pretie r2 1 gspro_tog_nohapuse_cb05_tx_ pf4_pretier2 1 Excluded ald2_primary and 

since not needed 

Speciation profiles speciated VOC GSPRO othpt gspro_speciated_voc 0 n/a 

For the v4 platform, this spethrough the pre-speciated Vprovided by Environment Ca
Description 

Environment Variable Sectors 

2005 v4 platform 2005 LDGHG platform 

Dataset Vsn Dataset Vsn Comment and Impact 

Speciation profiles Canada PM GSPRO othpt gspro_pm25_canada_2006_poi nt 0 n/a 

For the v4 platform, this spethe Canadia-specific PM2.5 s

by Environment Canada for PEC, 12% PSO4, 85% PMF

Speciation profiles for biogenic emissions 

GSPRO biog gspro_biogenics 0 n/a 

For the v4 platform, this dataspeciation profiles previously 

v7 for the RFS2 effort 

Speciation profiles Other VOC HAP GSPRO All sectors n/a gspro_other_hapvoc_nobenz- benz 0 For LDGHG, this dataset ha

get passed through from invinputs created for LDGHG. 

Speciation profiles CHROMIUM GSPRO All sectors n/a gspro_chromium 0 Chromium speciation profile

polltutant approach used in 

Speciation profiles METALS GSPRO All sectors n/a gspro_hapmetals 0 HAP metal pass-through pro

pollutant approach used in L

Speciation profiles MOVES PM GSPRO All sectors n/a gspro_speciated _pm 3 Speicated PM (non-72 degre

exhaust in LDGHG. 

Speciation profile 8762/8763 for TOG to BAF 

GSPRO All sectors 

n/a gspro_cmaq_cb05_hspace_BA 

F 1 For LDGHG, benzene from T

8762 and 8763 

Speciation profile 8762/8763 for nontoxics 

GSPRO All sectors 

n/a gspro_cmaq_cb05_hspace_non 

toxic 0 For LDGHG, toxics from TO

and 8763 

Speciation profile 8762/8763 for toxics GSPRO All sectors n/a gspro_cmaq_cb05_hspace_toxi c 0 For LDGHG, toxics from NO

profiles 8762 and 8763 

Speciation xref for PM2.5 diesel SCCs but do not produce diesel 

GSREF All sectors gsref_no_dieselpm 1 n/a 

Both the LDGHG and v4 plaDIESEL_PM species needeof CMAQ, the data set used SCC cross-references for diDIESEL_PM species. 

Speciation xref NO DIESEL PM, othar GSREF othar gsref_no_dieselpm 1 gsref_no_dieselpm 2 Prevents creation of diesel P

LDGHG 

Speciation xref for non-diesel PM2.5 GSREF All sectors gsref_pm25_pf4_nondiesel 8 gsref_pm25_pf4_nondiesel 1 

For the v4 platform, this datav2 point sources as well as WNote that future years of LD

than "v1" of this dataset, and for replicating LDGHG runs. 

37
 
Description 

Environment Variable Sectors 

2005 v4 platform 2005 LDGHG platform 

Dataset Vsn Dataset Vsn Comment and Impact 

Speciation xref for VOC, not yearspecific GSREF All sectors gsref_voc_general 19 gsref_voc_general_ldghg 0 

LDGHG copied V20 file from headspace profile 8737 with includes updates for 2005 v2 gas SCCs, changes to gasoSCCs from HAP inventory. 

Speciation xref for NONHAPVOC, not year-specific 

GSREF All sectors gsref_nonhapvoc_general_upda te 3 gsref_nonhapvoc_general 1 For the v4 platform, includes 

removed duplicates. LDGHheadspace profile 8762. 

Speciation xref for VOC, year-specific GSREF All sectors gsref_voc_2005 2 gsref_voc_2005_ldghg 0 The LDGHG file is the v4 file 

headspace profiles 8762. 

Speciation xref for NONHAPVOC, year-specific 

GSREF All sectors gsref_nonhapvoc_2005 1 gsref_nonhapvoc_2005_ldghg 0 LDGHG replaces headspace 

Speciation xref static NOX -- HONO for mobile sources 

GSREF All sectors gsref_static_nox_hono_pf4 3 gsref_static_nox_hono_pf4 2 The v4 platform dataset add

needed for processing the 2

Speciation xref for Integrate-HAPs static 

GSREF All sectors gsref_static_integratehap_emv4 2 n/a 

For the v4 platform, this spepasses through the pre-specspecies provided by Environ

Speciation xref for Canada PM GSREF othpt gsref_pm25_canada_2006_poin t 2 n/a 

For the v4 platform, this speassigns the single Canadia-

recommended by Environmesources. 

Speciation xref HAP chromium nonroad sectors 

GSREF 

alm_no_c3 nonroad seca_c3 

n/a 

gsref_chromium_nonroad 1 Enables creation of chromiu

Speciation xref HAP chromium on_noadj sector 

GSREF on_noadj 

n/a 

gsref_chromium_onroad 1 Enables creation of chromiu

Speciation xref HAP chromium stationary 

sectors 

GSREF 

nonpt ptipm ptnonipm 

n/a 

gsref_chromium_stationary 0 Enables creation of chromiu

Speciation xref HAP metals nonroad sectors 

GSREF 

alm_no_c3 nonroad seca_c3 

n/a 

gsref_metals_nonroad 0 Enables creation of HAP me

38
 
Description 

Environment Variable Sectors 

2005 v4 platform 2005 LDGHG platform 

Dataset Vsn Dataset Vsn Comment and Impact 

Speciation xref HAP metals stationary sector 

GSREF 

nonpt ptipm ptnonipm 

n/a 

gsref_metals_stationary 1 Enables creation of HAP me

Speciation xref HAP metals nonroad sectors 

GSREF nonroad 

n/a 

gsref_metals_nonroad 0 Enables creation of HAP me

Speciation xref HAP metals on_noadj sector 

GSREF on_noadj 

n/a 

gsref_metals_onroad 0 Enables creation of HAP me

SCC Descriptions SCCDESC All sectors sccdesc_pf31 5 sccdesc_pf31 8 LDGHG updated to include 

Canada 2006 added (late fo

39 
 

 

 

 

APPENDIX D 

Growth and Control Assumptions and Affected Pollutants for the 2030 LDGHG Reference Case 

For nonEGU point and stationary area sources, the “2005” inventory data used 2002 emissions. As a result, 

we used our 2002-based approaches for these sectors to project to 2030. Many of these controls have 

effective dates between 2002 and 2005, and therefore would not be applied to a true 2005 inventory value. 

As mentioned in Section 4.1.1, these controls were inadvertently not applied to the 2030 reference and 

control cases. 

Table D-1. Control Strategies and Projection Assumptions in the 2030 LDGHG Reference Case 

Emissions Inventories 

Control Strategies 

(Grouped by Affected Pollutants or Standard and Approach Used to 

Apply to the Inventory) 

Pollutants 

Affected 

Approach or 

Reference: Applied? 

Non-EGU Point Controls 

NOX SIP Call (Phase II): 

Cement Manufacturing 

Large Boiler/Turbine Units 

Large IC Engines 

NOx 1 No 

DOJ Settlements: plant SCC controls 

Alcoa, TX 

MOTIVA, DE 

NOx, SO2 2 No 

Refinery Consent Decrees: plant/SCC controls NOx, PM, SO2 3 No 

Closures, pre-2007: plant control of 100% 

Auto plants 

Pulp and Paper 

Large Municipal Waste Combustors 

Small Municipal Waste Combustors 

Plants closed in preparation for 2005 inventory 

all 4 No 

Industrial Boiler/Process Heater plant/SCC controls for PM PM 5 No 

Large Municipal Waste Combustors (LMWC) 

PM, Hg, and 

metals 6 Partially 

Small Municipal Waste Combustors (SMWC) 

PM, Hg, metals, 

NOx, SO2 6 Partially 

MACT rules, national, VOC: national applied by SCC, MACT 

Boat Manufacturing 

Polymers and Resins III (Phenolic Resins) 

Polymers and Resins IV (Phenolic Resins) 

Wood Building Products Surface Coating 

Generic MACT II: Spandex Production, Ethylene manufacture 

Large Appliances 

Miscellaneous Organic NESHAP (MON): Alkyd Resins, Chelating Agents, 

Explosives, Phthalate Plasicizers, Polyester Resins, Polymerized Vinylidene 

Chloride 

Manufacturing Nutritional Yeast 

Oil and Natural Gas 

Petroleum Refineries -Catalytic Cracking, Catalytic Reforming, & Sulfur 

Plant Units 

Pesticide Active Ingredient Production 

Publicly Owned Treatment Works 

Reinforced Plastics 

Rubber Tire Manufacturing 

Asphalt Processing & Roofing 

Combustion Sources at Kraft, Soda, and Sulfite Paper Mills 

Fabric Printing, Coating and Dyeing 

VOC EPA, 2007 

Only 

Municipal 

Solid Waste 

Landfills 

were 

applied 

40
 
 

 

 

 

 

Control Strategies 

(Grouped by Affected Pollutants or Standard and Approach Used to 

Apply to the Inventory) 

Pollutants 

Affected 

Approach or 

Reference: Applied? 

Iron & Steel Foundries 

Metal: Can, Coil 

Metal Furniture 

Miscellaneous Metal Parts & Products 

Municipal Solid Waste Landfills 

Paper and Other Web 

Plastic Parts 

Plywood and Composite Wood Products 

Wet Formed Fiberglass Production 

Wood Building Products Surface Coating 

Carbon Black Production 

Cellulose Products Manufacturing 

Cyanide Chemical Manufacturing 

Friction Products Manufacturing 

Leather Finishing Operations 

Miscellaneous Coating Manufacturing 

Organic Liquids Distribution (Non-Gasoline) 

Refractory Products Manufacturing 

Sites Remediation 

Solid Waste Rules (Section 129d/111d) 

Hospital/Medical/Infectious Waste Incinerator Regulations NOx, PM, SO2 EPA, 2005 Partially 

MACT rules, national, PM: 

Portland Cement Manufacturing 

Secondary Aluminum 

PM 7 No 

MACT rules, plant-level, VOC: 

Auto Plants VOC 8 No 

MACT rules, plant-level, PM & SO2: 

Lime Manufacturing PM, SO2 9 No 

MACT rules, plant-level, PM: 

Taconite Ore PM 10 No 

Stationary Area Assumptions 

Municipal Waste Landfills: project factor of 0.25 applied, VOC EPA, 2007 Yes 

Livestock Emissions Growth to year 2020 NH3 11 Yes 

Residential Wood Combustion Growth and Changeouts to year 2020 all 12 Yes 

Gasoline Stage II growth and control to 2030 Reference 

VOC 13 Yes 

Portable Fuel Container growth and control to year 2030 VOC 14 Yes 

EGU Point Controls 

CAIR/CAMR/CAVR 

IPM Model 3.0 NOx, SO2, PM 15 Yes 

Onroad Mobile and Nonroad Mobile Controls (list includes all key 

mobile control strategies but is not exhaustive) 

National Onroad Rules: 

Tier 2 Rule 

2007 Onroad Heavy-Duty Rule 

Final Mobile Source Air Toxics Rule (MSAT2) 

Renewable Fuel Standard 

all Yes 

41
 
 

 

 

 

 

Control Strategies 

(Grouped by Affected Pollutants or Standard and Approach Used to 

Apply to the Inventory) 

Pollutants 

Affected 

Approach or 

Reference: Applied? 

Local Onroad Programs: 

National Low Emission Vehicle Program (NLEV) 

Ozone Transport Commission (OTC) LEV Program 

VOC 16 Yes 

National Nonroad Controls: 

Clean Air Nonroad Diesel Final Rule – Tier 4 

Control of Emissions from Nonroad Large-Spark Ignition Engines and 

Recreational Engines (Marine and Land Based): “Pentathalon Rule” 

Clean Bus USA Program 

Control of Emissions of Air Pollution from Locomotives and Marine 

Compression-Ignition Engines Less than 30 Liters per Cylinder 

Exhaust emission standards for marine spark-ignition engines and small 

land-based nonroad engines 

all 17, 18, 19 Yes 

Aircraft, Locomotives, and Commercial Marine Assumptions 

Aircraft: 

Itinerant (ITN) operations at airports to year 2022 all 20 Yes 

Locomotives: 

Energy Information Administration (EIA) fuel consumption projections for 

freight rail 

Clean Air Nonroad Diesel Final Rule – Tier 4 

Locomotive Emissions Final Rulemaking, December 17, 1997 

Control of Emissions of Air Pollution from Locomotives and Marine 

all 18, 21, (EPA, 

2009) Yes 

Commercial Marine: 

EIA fuel consumption projections for diesel-fueled vessels 

OTAQ ECA C3 Base 2022 inventory for residual-fueled vessels 

Clean Air Nonroad Diesel Final Rule – Tier 4 

Emissions Standards for Commercial Marine Diesel Engines, December 29, 

1999 

Tier 1 Marine Diesel Engines, February 28, 2003 

all 21, (EPA, 2009) Yes 

Reference Case-specific inventory adjustments 

Nonpt and ptnonipm: 

Gasoline distribution by SCC: bulk-plant-to-pump (btp) and refinery-tobulk terminal (rtb) processes 

Ethanol plant additions 

VOC 

all 

EPA, 2010 Yes 

ptnonipm: 

Refinery adjustments by state and SCC 

Ethanol plant additions 

all 

all 

EPA, 2010 Yes 

nonpt: 

Ethanol transfer additions 

Biodiesel Plant additions 

VOC 

all 

EPA, 2010 Yes 

Nonroad (not including California): 

AEO-specific gasoline equipment and gasoline rec-marine 

E0-based emissions for diesel rec-marine 

all 

EPA, 2010 

Yes 

Onroad (not including California): 

NMIM and MOVES AEO-specific emissions for onroad gasoline and diesel all EPA, 2010 Yes 

APPROACHES: 

1.	 Used Emission Budget Inventories report (EPA, 1999) for list of SCCs for application of controls, and for percent reductions 

(except IC Engines). Used Federal Register on Response to Court decisions (Federal Register, 2004) for IC Engine percent 

reductions and geographic applicability 

2.	 For ALCOA consent decree, used http:// cfpub.epa.gov/compliance/cases/index.cfm; for MOTIVA: used information sent 

by State of Delaware 

3.	 Used data provided by Brenda Shine, EPA, OAQPS 

4.	 Closures obtained from EPA sector leads; most verified using the world wide web. 

5.	 Used data list of plants provided by project lead from 2001-based platform; required mapping the 2001 plants to 2002 NEI 

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plants due to plant id changes across inventory years 

6.	 Used data provided by Walt Stevenson, EPA, OAQPS 

7.	 Same as used in CAIR, except added SCCs appeared to be covered by the rule: both reductions based on preamble to final 

rule. (Portland Cement used a weighted average across two processes ) 

8.	 Percent reductions recommended and plants to apply to reduction to were based on recommendations by rule lead engineer, 

and are consistent with the reference: EPA, 2007e 

9.	 Percent reductions recommended are determined from the existing plant estimated baselines and estimated reductions as 

shown in the Federal Register Notice for the rule. SO2 % reduction will therefore be 6147/30,783 = 20% and PM10 and 

PM2.5 reductions will both be 3786/13588 = 28% 

10.	 Same approach used in CAIR: FR notice estimates reductions of "PM emissions by 10,538 tpy, a reduction of about 62%." 

Used same list of plants as were identified based on tonnage and SCC from CAIR. 

11.	 Except for dairy cows and turkeys (no growth), based in animal population growth estimates from USDA and Food and
 

Agriculture Policy and Research Institute. 

12.	 Expected benefits of woodstoves change-out program: http://www.epa.gov/woodstoves/index.html 

13.	 VOC emission ratios of year 2022 AEO-specific from year 2005 from the National Mobile Inventory Model (NMIM) results 

for onroad refueling including activity growth from VMT, Stage II control programs at gasoline stations, and phase in of 

newer vehicles with onboard Stage II vehicle controls. 

14.	 VOC and benzene emissions for year 2020 from year 2002 from MSAT rule (EPA, 2007c, EPA, 2007d) 

15.	 http://www.epa.gov/airmarkets/progsregs/epa-ipm/docs/summary2006.pdf 

16.	 Only for states submitting these inputs: http://www.epa.gov/otaq/lev-nlev.htm 

17.	 http://www.epa.gov/nonroad-diesel/2004fr.htm 

18.	 http://www.epa.gov/cleanschoolbus/ 

19.	 http://www.epa.gov/otaq/marinesi.htm 

20.	 Federal Aviation Administration (FAA) Terminal Area Forecast (TAF) System, December 2007: 

http://www.apo.data.faa.gov/main/taf.asp 

21.	 http://www.epa.gov/nonroad-diesel/2004fr.htm 

Appendix D References 

EPA, 2005. Clean Air Interstate Rule Emissions Inventory Technical Support Document, U.S. 

Environmental Protection Agency, Office of Air Quality Planning and Standards, March 2005. Available at 

http://www.epa.gov/cair/pdfs/finaltech01.pdf 

EPA, 2007. Guidance for Estimating VOC and NOx Emission Changes from MACT Rules, U.S. 

Environmental Protection Agency Office of Air Quality Planning and Standards, Air Quality Policy 

Division, Research Triangle Park, NC 27711, EPA-457/B-07-001, May 2007. Available at 

http://www.epa.gov/ttn/naaqs/ozone/o3imp8hr/documents/guidance/200705_epa457_b-07­

001_emission_changes_mact_rules.pdf. 

EPA, 2009. Regulatory Impact Analysis: Control of Emissions of Air Pollution from Locomotive Engines 

and Marine Compression Ignition Engines Less than 30 Liters Per Cylinder. U.S. Environmental Protection 

Agency Office of Transportation and Air Quality, Assessment and Standards Division, Ann Arbor, MI 48105, 

EPA420-R-08-001a, May 2009. Available at: http://www.epa.gov/otaq/regs/nonroad/420r08001a.pdf 

EPA, 2010 : RFS2 Emissions Inventory for Air Quality Modeling Technical Support Document, U.S. 

Environmental Protection Agency, Assessment and Standards Division, Office of Transportation and Air 

Quality, and Emissions Inventory Analysis Group, Office of Air Quality Planning and Standards, January 

2010. Available at http://www.epa.gov/otaq/renewablefuels/420r10005.pdf 

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