# 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-supplie d 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-supp lied 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_r unpm, 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 Appl ying Base Case Contro ls to the LDGHG 2030

Reference Case.

Table 9b. Impact on Total Anthropogenic Emissions of No t Applying Base Case C ontrols to the LDGHG

2030 Reference Case.

Table 10. HAP emission ratios for generation of HAP emissi ons 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 em issions from MOVES to air quality modeling species needed for CMAQ.

APPENDIX B: Inventory Data Files Used for Each L DGHG Modeling Case – SM OKE Input Inventory

Datasets

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

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

iv

# 1 Introduction

This document provides the details of emissions da ta processing done in suppor t of the Environmental Protection Agency (EPA) and National Highway Traffi c Safety Administration (NHTSA) joint rulemaking effort to establish Light Duty Vehicle Greenhous e 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 av erage 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 CO 2 

### emissions

standards.

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

national CO 2 

### 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 CO 2 

### in model year 2016.

The data used in the 2005 emissions cases are ofte n 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, pr ior to the completion of the 2005 v4 platform. Second, the LDGHG modeling used data intended only for the rule developmen t and not for general use. All of the documentation provided here describes what was done differently and sp ecifically 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 a pproach is similar to th at 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 da ta. We use a base case approach because we want to reduce year-specific vari ability in these components of the inventory: for example large fires vary in location and day of the year each year, and EGU s hutdowns and high use on high energy demand days also vary by year. By using a base case approach, these tw o 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 emi ssions data as the v4 platfo rm was used not only in the 2005 case for LDGHG, but also in both future-y ear cases run for LDGHG. For LDGHG, the only significant data changes between the 2005 and future-y ear cases are emission i nventories and speciation approaches.

1

### For this effort, we have created and provided st ate, county, and source cate gory code (SCC) emission summaries for the nonpoint and mobile sectors. For point sources, we have pos ted the actual inventory

datasets that we used for emissions modeling. These data have been pr ovided 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 Invent ories 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 desc ription 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 em issions modeling approach for LDG HG used much of the same data and approaches as the 2005 v4 platform. In this secti on, we identify the differences between the data used for LDGHG and that used for the 2005 v4 platform. Section 2.1 provides ancillary da ta differences that impact multiple sectors and Sections 2.2 through 2.4 provides differences for th e point, area, and mobile

sectors.

Table 2 below lists the platform sectors used for the L DGHG modeling platform. It also indicates which platform sectors include HAP emissi ons and the associated sectors fr om the National Emission Inventory (NEI). Subsequent sections refer to these platform sectors for identifyi ng 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 prescr ibed 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 PM 10 and PM 2.5 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, commercia l marine except for category 3 (C3) commercial marine vessels Yes

C3 commercial

marine: seca_c3 Mobile:

nonroad C3 commercial marine vessels Yes



### 2Platform 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 SM OKE. More details about SMOKE including user

documentation are availa ble 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 addi tional 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 fu ll multipollutant HAP version of the Community Multiscale Air Quality (CMAQ) model would have taken longer than the tim e available for our project, we used a “lite” version of the multipollutant CMAQ that re quired 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 additiona l 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 ACRYLONITRI LE 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

PM 2.5 Elemental carbon PEC PEC CL3_ETHE Trichloroethylene

PM 2.5 Organic carbon POC POC CL4_ETHANE1122 1,1,2,2 Tetrachloroethane

PM 2.5 primary nitrate PNO3 PNO3 CL4_ETHE Perchloroethylene

PM 2.5 primary sulfate PSO4 PSO4 DICHLOROBENZE

NE Dichlorobenzene

PM 2.5 other PMFINE PMFINE DICHLOROPROPE

NE Dichloropropene

PM Coarse (PM 10 – PM 2.5 ) 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 PM 2.5 elemental carbon

Methanol MEOH MEOH DIESEL_POC Diesel PM 2.5 organic carbon

Nonreactive VOC NR NR DIESEL_PMFINE Diesel PM 2.5 primary nitrate
Nonvolatile (from VOC mass) NVOL NVOL DIESEL_PNO3 Diesel PM 2.5 primary sulfate

Terminal olefin carbon bond OLE OLE DIESEL_PMC Diesel PM 2.5 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



### 4Description 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 emissi ons 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 us ed as the basis for the LDGHG platform, resulting in newer inventory data used for the v4 platform. Th ese 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 specia tion across the LDGHG future-year ca ses as compared to 2005. Section 3 provides a detailed account of these differences. Otherwise, the future-y ear 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 anci llary 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 th e 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 plat form data file is configured to support only the non-MP version. Theref ore, 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 sp eciation profiles were used based on

new test data for E0 and E10 fuels for LD GHG. Inventory tables The LDGHG data file is c onfigured 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 upda ted 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 sector s (ptipm and ptnonipm) used for LDGHG differ from the v4 platform primarily because the emissions ar e based on the 2005 NEI version 1, rather than the 2005

NEI version 2. While the NEI versio n 2 was available in time for this work, we intentionally remained consistent with the RFS2 modeling a pproach that had just been comple ted. 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 usi ng the IPM column of th e 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 s ector HCl sources and moved emissions originally labeled as ptnonipm to correctly be placed with thei r associated criteria air pollutants (CAP) emissions in the ptipm sector 4) Removed emissions of benzene, acetaldehyde, fo rmaldehyde, methanol, to support the “no hap use” approach for the ptipm and ptnonipm sectors. This approach creates emissi ons of these HAPs from VOC speciation profiles rather than the HAP emi ssions 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 speciati on 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 gram s 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 th ese facilities and their emissions is available in Table 5. 6) Removed facilities that closed between 2002 and 2005, since much of the pt nonipm 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 s ources 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 eq uipment (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” a nd “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 facil itate 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 HA P emissions that are replaced in both the 2030 LDGHG reference and control scenarios; 3) A dataset with CAP and HAP emissions for 43 add itional 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 emi ssions are identical to th ose documented for the 2002 v3 platform and the 2005 v4 platform. The LD GHG modeling inventories incl uded year 2000 Canadian emissions from the 2002 v3 platform and did not in clude the updated 2006 Cana dian 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 di fferent from what was used in the 2005 v4 platform because they were not updated with the new offshor e 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 th e 3-d emissions option for all point source sectors rather than the “inline” point sour ce 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 fo r multi-pollutant CMAQ in time for use on this effort.

7

### 8Table 5.Emissions from ethanol plants added to 2005 v1 point inventory (tons/year)County NameState NameSt/CoFIPSPLANTCONOXVOCSO2PM2.5PM10ACROLEINSwift CoMinnesota27151Chippewa Valley Ethanol Co LLLP (*)29.740.4155.10.619.728.40.0001Chippewa CoMinnesota27023Granite Falls WWTP (+)0.00.00.70.00.00.00.0000Dodge CoMinnesota27039Al-Corn Clean Fuel (+)28.545.0207.60.50.024.00.0618Sibley CoMinnesota27143Heartland Corn Products (+)51.462.9378.30.436.761.60.0000Dawson CoNebraska31047Cornhusker Energy Lexington (CEL)10.530.314.338.61.112.10.0557Stevens CoMinnesota27149Diversified Energy Company (DENCO),LLC52.4151.671.6192.96.560.60.2783Wichita CoKansas20203ESE Alcohol Inc.3.19.14.311.60.43.60.0166Sedgwick CoKansas20173Abengoa Bioenergy Corporation52.4151.671.6192.95.560.60.2783Roosevelt CoNew Mexico35041Abengoa Bioenergy Corporation62.8181.986.0231.56.672.80.3339Linn CoIowa19113Archer Daniels Midland (ADM)964.71,338.4418.11,266.3546.71,248.92.7827Clinton CoIowa19045Archer Daniels Midland (ADM)733.21,017.2317.7962.4415.5949.22.1148Platte CoNebraska31141Archer Daniels Midland (ADM)366.6508.6158.9481.2207.8474.61.0574Lyon CoMinnesota27083Archer Daniels Midland (ADM)154.4214.166.9202.687.5199.80.4452Peoria CoIllinois17143Archer Daniels Midland (ADM)439.81,273.1601.91,620.4365.5509.32.3375Pembina CoNorthDakota38067Archer Daniels Midland (ADM)52.4151.671.6192.943.560.60.2783Tazewell CoIllinois17179Aventine Renewable Energy, Inc. (formerl385.9535.4167.2506.5218.7499.61.1131Monroe CoWisconsin55081Badger State Ethanol, LLC108.9315.3149.0401.213.4126.10.5788Des Moines CoIowa19057Big River Resources, LLC108.9315.3149.0401.213.4126.10.5788Washington CoNebraska31177Cargill (was PGLA-1CO)328.0455.1142.1430.5185.9424.60.9458Christian CoKentucky21047Commonwealth Agri-Energy, LLC41.9121.357.3154.35.248.50.2226Codington CoSouthDakota46029Glacial Lakes Energy, LLC (GLE)104.7303.1143.3385.812.9121.30.5567Kossuth CoIowa19109Global Ethanol209.4606.3286.6771.625.8242.51.1131Riverside CoCalifornia06065Golden Cheese Company of CA10.530.314.338.61.312.10.0557Cerro GordoCoIowa19033Golden Grain Energy LLC146.6424.4200.6540.115.3169.80.7793Hardin CoIowa19083Hawkeye Renewables, LLC115.2333.4157.6424.414.2133.40.6122Brown CoSouthDakota46013Heartland Grain Fuels, LP20.960.628.777.22.224.30.1113Beadle CoSouthDakota46005Heartland Grain Fuels, LP29.384.940.1108.03.134.00.1558Pierce CoNebraska31139Husker Ag, LLC54.5157.674.5200.65.763.10.2894Kearney CoNebraska31099KAAPA Ethanol, LLC125.7363.8172.0463.015.5145.50.6678Stearns CoMinnesota27145Land O' Lakes / Melrose Dairy Proteins5.415.87.520.10.76.30.0290Crawford CoIllinois17033Lincolnland Agri-Energy58.6169.880.2216.07.267.90.3119

### 9County NameState NameSt/CoFIPSPLANTCONOXVOCSO2PM2.5PM10ACROLEINCherokee CoIowa19035Little Sioux Corn Processors184.3533.5252.2679.019.3213.40.9795Jefferson CoColorado08059Merrick & Company (Coors Brewery)4.212.15.715.40.54.90.0223Tazewell CoIllinois17179MGP Ingredients, Inc.188.5545.6257.9694.423.2218.31.0020Lincoln CoNebraska31111Midwest Renewable Energy, LLC (MRE)41.9121.357.3154.35.248.50.2226Hamilton CoNebraska31081Nebraska Energy104.7303.1143.3385.810.9121.30.5565Roberts CoSouthDakota46109North Country Ethanol (NCE)52.4151.671.6192.96.560.60.2783Delaware CoIowa19055Permeate Refining3.19.14.311.60.43.60.0166Ida CoIowa19093Quad-County Corn Processors56.5163.777.4208.37.065.50.3005Finney CoKansas20055Reeve Agri-Energy25.172.834.492.63.129.10.1336Sioux CoIowa19167Siouxland Energy & Livestock Coop(SELC)46.1133.463.1169.84.853.40.2449Red Willow CoNebraska31145SW Energy, LLC.0.20.60.30.80.00.20.0011Loudon CoTennessee47105Tate & Lyle254.7353.3110.4334.3144.3329.70.7346Hitchcock CoNebraska31087Trenton Agri-Products, LLC. (TAP)83.8242.5114.6308.610.397.00.4452Winnebago CoWisconsin55139Utica Energy, LLC108.9315.3149.0401.211.4126.10.5788Gove CoKansas20063Western Plains Energy, LLC62.8181.986.0231.56.672.80.3339Mitchell CoGeorgia13205Wind Gap Farms (Anheuser/MillerBrewery)0.82.41.13.10.11.00.0045Total6,074.312,610.45,923.214,417.52,537.17,456.624.0(*)Data taken from the 2002 NEI rather than provided by OTAQ for2005. Units conversion not performed on this facility and no adjustment of the emissions by 0.65.(+)Data taken from the 2005 NEI v1, rather than provided by OTAQ for 2005. Units conversion not performance on these facilities and no adjustments of the emissionsdata by 0.65.

## 2.3 2005 Nonpoint sources

The emissions from the agricultura l ammonia (ag) and nonpoint fugi tive 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 extr action emissions were provided after the modeling platform development for RFS2 and LDGHG.

For the Canadian and Mexican nonpoi nt sector (othar), we used the same inventories as the 2002 v3 platform, and did not yet have the updated 2006 Canadian invent ory used in the v4 platform.

The avefire emissions are the same as thos e 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

PM 2.5 

### available in the 2005 platform documentation. These factors were also used to create emissions for benzene, acet aldehyde, and formaldehyde in the 2005 v4 platform and were unchanged for LDGHG; however, this sector was processed as “no-inte grate”; therefore, emissions for benzene, acetal dehyde, 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 sect ors (nonroad, alm_no_c3, and seca_c3). In addition, it includes Canadian and Mexican emissi ons in a separate onroa d 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 adju stment calculations applied to PM 2.5 

### species were the same as the v4 plat form, since these adjustments had not changed between draft MOVES versions.

The on_noadj sector for LDGHG also uses an up dated version of MOVES, and this updated version also provides onroad di esel exhaust emissions for CO, NOx, PM, and VOC HAPs. Note

that PM emissions from these diesel sources ar e not subject to temperature adjustments like the on_moves_startpm and on_moves_runpm sector s. These MOVES-based onroad diesel emissions replace the MOBILE6-based NM IM emissions in the 2005v4 platform.

For onroad gasoline exhaust PM emissions, th e 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 di esel PM exhaust emissions used for this modeling. The fraction of metals in the speciation profile ( F metal 

### ) in equation 4 in Appendix A

10

### was inadvertently squared, resulting in an undere stimate in metals, which caused a very small underestimate in inventory organic carbon (P OC) 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 sa me as the v4 platform, with additional HAPs being kept for LDGHG as well. The alm_no_c3 em ission 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 plat form, 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 change s for each of the LDGHG modeli ng cases is the VOC speciation profiles used to split total VOC emissions in to the various VOC model species needed for CMAQ. In this section, we summarize the va rious 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 be low 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 va por profiles were created for LD GHG modeling that were not available for the 2005 v4 platform de velopment. The approaches used for each of

11

### the future-year cases are customized for those cas es, 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 spec iation changes from fuel s 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) emissi ons. The speciation changes from fuels in the ptnonipm sector include the remainder of the em issions for the btp and rbt emissions. Mapping of fuel distribution SCCs to btp and rbt emissi ons 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 speciati on 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 speci ation 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 ar e available on the 2005-based platform website previously referenced.



# 4 2030 Reference Case

The 2030 Reference case is intended to represent th e emissions associated with use of the most likely volume of ethanol in the absence of the LDGHG CO 2 

### reductions and RFS2 rule and Energy Independence and Security Act of 2007 (EIS A) 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 En ergy Outlook (AEO) report. That year’s AEO projections were used because Department of En ergy (DOE) started accounting for EISA in their 2008 AEO projections. Therefore, the 2030 LDGHG Re ference 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/r enewablefuels/420r10005.pdf ). A list of inventory datasets used for this and all cases is provided in Appendi x C. A list of all growth and control assumptions for this Case is provided in Appendix D. S ection 5 describes the projection

differences between the 2030 Refe rence 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 st ationary source projections. Al so, 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-rela ted controls versus RFS-2 cont rols and obtain a more ‘apples to apples’ comparisons of air quality a nd emissions-related impacts between RFS-2 and

LDGHG.

The code number used by EPA’s Clean Air Ma rkets 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 em issions used in the v3 platform. OAQPS postprocessed these data in the same way as descri bed in the 2005 v4 platform documentation for the “base case” to create daily emissions that incl ude temporal allocation information from three years of Continuous Emissions Monitoring (CEM) da ta. The temporal allo cation approach is the

13

### same as for the LDGHG 2005 base case (2005cp_t ox_05b), to eliminate artifi cial differences in temporal allocation between the base and future years.

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

base case. This means that the Mexican emi ssions were based on year 1999, since Mexico has not provided emissions projections to date. We used 2020 emissi ons projections for Canadian emissions, provided by Environment Canada, which ar e consistent with the base year Canadian

data. The Canadian data are the same as thos e used for the 2002-based v3 platform. The data used for LDGHG future year reference and cont rol 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: da ta projected from 2005 values a nd data that were replaced by OTAQ-generated data that are intended to re present 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 eff ect 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 emi ssions data were populated with 2002 emissions

values.

The 2002 v3.1 platform growth and control factor s had been revised from the 2002v3.0 platform projection factors that are documented in the 2002 v3 emissions modeling platform documentation (see http://www.ep a.gov/ttn/chief/emch/index.html#2002 )

. These updates included Hazardous Waste Incineration adjustme nts and Small and Large Municipal Waste Combustor closures and adjustments. The fo llowing describes how we further modified the 2002v3.1 projection factors for the 2030 Reference case.

We intended to use a SMOKE “control” packet (dat a file) to apply control factors that implement known emissions reductions from point sources fo r 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 contro l 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), meth anol (CAS=67561), benzene (CAS=71432), acetaldehyde (CAS=75070), and naphthalene (CAS=91203). However, the “control” packet inadvertently was not applied for either th e 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 ar e provided in Table 9a. Appendix D documents which

14

### control and projection programs were erroneous ly not included in the 2030 Reference and

Control cases. No other sector s were impacted by this error.

15

### Table 9a. Impact on the ptnonipm Sector of Not A pplying Base Case Cont rols 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 m odel 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 effo rt, 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-specifi c entries already present (e.g., landf ills in nonpt sector). Next,

16

### we added the LDGHG Reference case-specific proj ection 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 th e 2002 v3 emissions modeling platform. For California, we used the 2005v2 Californ ia-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 Emis 2030 

### /NMIM Emis 2005 

### (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 appl ied to the refueling

SCCs from the 2005 base case.

2. Year 2025 aircraft Federal Av iation Administration (FAA) ta keoff and landing data from the Terminal Area Forecast (TAF) data: This is the furthest out-ye ar 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 adjustme nts 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 scen ario 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 re presenting 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 applie s an adjustment on top of these LDGHG

reference case emissions.

We applied the control and pr ojection 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 prev ent inadvertent double c ounting of emissions.



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

In addition to the data prepara tion described above, we added the following data supplied by OTAQ:

17

### 1. Ethanol plant additions: These emissions da ta completely replace all of the ethanol plants listed in Table 5 except the firs t one. OTAQ provided the emissions, stack parameters, locations and the same SCC assi gnments were made as described for 2005:

PM 2.5 

### (30299999) and VOC and all other pollu tants (30125010). The original VOC emissions were supplied by OTAQ in Excel ® 

### and OAQPS further adjusted the data to correct for units conversions and to adju st 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 PM 2.5 

### and PM 10 

### , to ensure SMOKE could properly calculate PMC as

PM 10 

### -PM 2.5 

### after matching emissions sources. Lastly, we removed the benzene, formaldehyde, acetaldehyde, and methanol inventory emissions from these sources because they are “no integrate” sources fo r VOC HAPs (we do not use the VOC HAPs to augment speciation information for this secto r), 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 scenar io 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 ot har_hg). Of these, the nonpt sect or contained the most detailed changes for the RFS2 modeling, as explained further below.

The emissions used for this case for the afdust an d ag sectors are the same as those used in 2020 in the 2002-based v3 platform, previously referen ced. Additionally, the avefire emissions are the same as those in the 2005cp_tox_05b case men tioned above, and were intentionally held

constant between the 2005 base case a nd 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 Ca nada 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 m eans that the LDGHG platform differs from the v4 platform in that older estimates of 2020 emi ssions were used for nonpoint sources in Canada,

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

sector includes both nonroad mobile and nonpoint sources.

The nonpt sector is comprised of several differe nt pieces. Like the ptnonipm sector, the nonpt sector required two steps to compile the 2030 Re ference case inventories: data projected from 2005 values and data that are repl aced by OTAQ-generated data in 2030.

We discovered a very minor error in our cr eation of 2030 Reference and Control case nonpt emissions. We inadvertently dropped NEI nonpoi nt inventory oil and gasoline production emissions in WRAP states (excluding Califor nia) 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 Arizon a, 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 informati on 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 iden tified 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 c ontrol packet described for ptnonipm.

In addition to the documentation provided prev iously for ptnonipm, th e 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 dist ribution 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-supp lied emissions da ta for nonpt

In addition to the nonpt emissions project ed 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 sp atial allocation using spatial surrogate ancillary data. The nonpoint ethanol plants were created from an Excel ® 

### spreadsheet provided by OTAQ on 11/17/2008: Corn_EtO H_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_tran sport_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 em issions 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 So urce Fuel Combustion;Industrial;Natural

19

### Gas;All Boiler Types) to represent these s ources 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. Th ese 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-b ased 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 (2 005) to the LDGHG future year (2030); the base year emissions are actually year 2002 and the sa me 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 ye ar 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 adju stment factors for locomotive-marine emissions on 12/17/2009: “alm_no_c 3_2030LDGHG_vs_2005_pcQA _locofix.xls”.

We applied HAP factors to VOC to obtain 1,3-b utadiene, acetaldehyde, acrolein, benzene, and formaldehyde. The remaining HAPs -metals a nd 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 fo r 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 plat form 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)E CA C3 year 2030 base case from the ECA/IMO project was unchanged and used for bot h the 2030 LDGHG Reference and 2030 LDGHG

20

### Control cases. The 2030 base seca_c3 inventory was provide d by OTAQ 03/27/2008: task11_2030base.zip. OTAQ also provided fact ors to compute HAP emission (based on emissions ratios) on 2/28/2008, which OAQPS applied to either VOC or PM 2.5 

### to obtain HAP

emissions values. Table 10 below shows these fact ors and whether they we re applied to VOC or

PM 2.5 

### . As with the 2005 case, this sector uses CA P-HAP VOC integration.

Table 10. HAP emission ratios for gene ration 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 surf ace 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 emi ssions. Table 11 shows how the various NMIM

runs were combined to create the reference and control case non-Ca lifornia nonroad mobile inventories. The first compone nt “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 bo th the reference and control cases. An NMIM run specific to the Reference case “LdGhgN2030 Aeo.txt” was provided in November 2009 for

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

The only difference in the control case is a subs titution 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

Cases 2268x CNG equipment

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 spli t 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. CO 2 

### 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 Johns on: 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 gasoli ne 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 inventor y for California. We estimated HAP emissions by applying HAP-to-CAP ratios com puted 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 retain ed 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_mo ves_runpm, on_moves_startpm, and on_noadj)

As in 2005, the on_moves_runpm and on_moves_star tpm sectors include emissions from onroad gasoline sources for PM, which need temperat ure 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 Cali fornia 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 includ ed 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 Contro l case onroad mobile sectors: on_moves_startpm, on_moves_runpm, and on_noadj. Unless otherwise note d, 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 projec t 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_st artpm and on_moves_runpm) that were the same for each case.

23

### Table 12. MOVES and NMIM Inventory Component s 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 Refe rence 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, formaldehyde, acrolein, 1,3-butadiene 223006x Diesel vehicles class 7 (LDDT) LgrDiesExh2030_67_Ctltxt (MOVES exhaust) 2230001x Diesel vehicles class 6 (LDDV) Control Case only: PM, CO, NOx, VOC, acetaldehyde, benzene, formaldehyde, acrolein, 1,3-butadiene 223006x Diesel vehicles class 7 (LDDT) LgrGasExh2030aeoR2_Ref.txt (MOVES exhaust) 22010x
except
220108x Non-motorcycle gasoline vehicles and

trucks Reference Case only: CO, NOx, VOC, acetaldehyde, benzen e, 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, benzen e, 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 sect ors, 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 usi ng the 2030 PM adjustment factors.

24

### OTAQ supplied four input files for the referen ce 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), benzen e (71432), and formaldehyde (50000);

2. Onroad Diesel Exhaust: VOC, NOX, CO, 1,3-butadiene ( 106990), acetaldehyde (75070), acrolein (107028), benzene (71432), formal dehyde (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 72 oF: 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 PM 2.5 

### species into the following SMOKE-ready pollutants:

 PEC_72: unchanged from MOVES-base d 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 PM 25SO4, not subject to temperature adjustment.

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

 OTHER: sum of computed metals (f raction of MOVES-based PM25EC) and NH4 (computed from PNO3 and PSO4), not subject to temperature adjustment.

 PMFINE_72: Computed from OTHER a nd 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 temp erature adjustment below 72oF.

The first three inputs listed a bove are processed in the on_noad j sector because temperature adjustments are not needed for th ese inventories. The fourth i nput 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 vehicl es (LGDV), light-duty gasoline trucks 0-6000 pounds gross vehicle wei ght (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 co unties 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). M OVES data were provided by OTAQ in October

2009.

In each MOVES file, “start” emissions are repr esented by SCCs with the SCC characters 8-9 equal to “00”: 2201001 00 0 (LDGV), 2201020 00 0 (LDGT1), 2201040 00 0 (LDGT2),

2201070 00 0 (HDGV), 2230001 00 0 (LDDV), 22300601 00 0 (LDDT), and 223007X 00 0 (HDDV). These start emissions ar e assigned to urban and rural SCCs based on the county-level

ratio of NMIM emissions from urban versus lo cal roads. For example, LDGV start emissions (2201001000) were split into urban (2201001370) a nd 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 se ts 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 type s) 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 ga soline “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 temperat ure adjustments. The same pollutants are provided as on_moves_startpm.

3. on_noadj MOVES-based emissions: The remaini ng 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 shor t, these are non-Californi a, 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 ye ar 2030 complete CAP/HAP onroad inventory. California monthly onroad emissions ar e year 2030 and are based on March 2007 California Air Resources Board (CARB) da ta (Martin Johnson: mjohnson@arb.ca.gov ).

26

### NH3 emissions are from NMIM runs for Califor nia (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 fo r nonroad mobile sources; all other HAPs were dropped.

3. Remaining onroad NMIM-based onroad invent ory: The remainder of the non-California onroad inventory not replaced by MOVES. Th is includes monthly emissions for all onroad diesel, all motorcycles, all re fueling, 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 compos ite 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 co mbined to create the Reference case NMIM emissions. OTAQ provided a “reference and co ntrol” set (LdGhgO2030e10.txt) in October 2009 of NMIM E10-penetration emissions to be used for both the Refere nce 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 em issions 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 ga soline emissions in the control case use the

“reference and control” case E10-based inventory.

Similar to nonroad pre-processing, we also re assigned NMIM evaporative and refueling xylene (compound XYL or CAS=EVP__1330207, RF L__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 aver age-day based the on number of days in each

month. CO 2 

### 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 th e 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 refu eling emissions to create projection ratios for ptnonipm and nonpt refueling, as pr eviously described in Section 4.1.1.

27

# 5 2030 Control Case

The 2030 LDGHG Control Case was intended to represent the implemen tation of national CO 2 

### 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 incl ude 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 errone ously not applied to both the 2030 Reference, and by extension, the 2030 Control cas e 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. Th ese differences from the 2030 Reference case are:

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

in the Excel ® 

### workbook “LDGHG_SCC_GasDistrib_Adj ust2.xls”. These adjustments reduce all pollutant emissions from the Refe rence 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 projec tion 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 C ontrol 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 fo r onroad refueling as was used in the 2030 Reference case for the pt nonipm and nonpt sect ors. Instead of using the 2030 Reference case NMIM run, we used the Control case- specific NMIM run for the 2030 refueling ratio calc ulation. We applied these fa ctors to the refueling SCCs from the 2030 Reference case using this formula: Factor 2030control 

### = NMIM Emis 2030Control 

### /NMIM Emis 2030Refernce 

### . 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, th e 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), th e US average fires (avefire se ctor), 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 ad justment 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 sour ces, we changed refueling, rbt/btp, and oil refining, transport, and produc tion emissions projections, wh ich 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, th e specifics of which are provided in Section 3.



## 5.3 2030 Control Case Mobile sources

Compared to the 2030 Reference case, the mob ile 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 aircra ft, locomotive, and non-C3 commercial marine (alm_no_c3), C3 commercial marine (seca_c3) an d the Canada and Mexico emissions (othon) were unchanged.

For the nonroad sector, as discussed in S ection 4.3.4, OAQPS substituted 2030 Control case emissions for nonroad gasoline 2-stroke and 4-str oke equipment and gasolin e 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 disc ussed in Section 4.3.5, OAQPS substituted NMIM 2030 Control case emissions for onroad gasoline ve hicles and motorcycles. In addition, the

MOVES-based emissions for the control case co mpletely replaced the reference case MOVES data with the exception of MOVES-based vehicl e 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. Ot her 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 affect ed 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 PM 2.5 

### , PEC and PSO4. A remainder term, R , makes up the difference between the two species and the total PM 2.5



### .

MOVES total PM 2.5 

### = PEC + PSO4 + R (1)

The R term includes POM, which consists of POC a nd the hydrogen and oxygen atoms attached to the carbon as part of the organic matter, PNO3, soil oxi des 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 + H 2 

### 0 (2)

To correctly calculate the five PM 2.5 

### 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. Th e speciation profiles used for these

calculations are:

For both light duty diesel vehicles and heavy duty diesel vehicles, the SPE CIATE 4.0 PM2.5 speciation profiles “3914” (HHDV) and “92042” (L DDV) will be used to help calculat e 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 smalle st 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 × F NO3 

### / F EC 

### (3)

where,

F

EC 

### = Fraction of elemental carbon in speciation profile:

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



### - HDDV: 77.1241% (based on profile 3914)

F

NO3 

### = Fraction of nitrat e in speciation profile

- LDDV: 0.1141% (based on profile 3 914, intentionally inconsistent)

- HDDV: 0.1141% (based on profile 3914)

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

below.

Since CMAQ’s PMFINE species is the sum of soil oxide s, metals, ammonium, and water, we needed to calculate all of its components. First, the metals and ammonium are computed usi ng equations (4) and (5). Equation (5) is based on stoi chiometric calculations.

METAL = PEC × F metal 

### / F EC 

### (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/ MW NO3 

### +2 × PSO4/ MW SO4 

### ) × MW NH4 

### (5)

where,

F

metal 

### = Fraction of metals in speciation profile (0.002663 2 

### )

MW SO4 

### = Molecular weight of sulfate (96.0576)

MW NO3 

### = Molecular weight of nitrate (62.0049)

MW NH4 

### = Molecular weight of ammonium (18.0383)

The final component of PMFINE is the non-carbon mass of organic ca rbon. To calculate the non-carbon mass, we first needed to compute or ganic carbon from the remainder term, R

.

A key assumption is that POM is a f actor 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 H 2 

### 0 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 orga nic 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 PM 2.5 

### mass. Equation (9) shows how we

calculated it.

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

Table A-1. List of SCC groups for applic ation 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 Mo deling 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 associat ed 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_capha p_noncem_2005ag_<mon>_ida.txt

Daily: ptday_ptipm_capha p_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_de lete_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_20 05v2_<mon>_02apr2008_v0_orl.txt

mbinv_noadj_capshaps_2005v2_nmim_not2m oves4LDGHG_<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_startp m_LDGHG_2005_<mon>_12NOV09_12nov2009_v0_orl.txt

seca_c3 ptinv_seca_c3_caps2005pf4_31jul2008_v0_orl.txt

ptinv_seca_c3_haps_east200 5pf4_31jul2008_v0_orl.txt

ptinv_seca_c3_haps_central2005p f4_31jul2008_v0_orl.txt

ptinv_seca_c3_haps_west2005 pf4_31jul2008_v0_orl.txt

ptinv_seca_c3_haps_NonUS_east 2005pf4_09sep2008_v0_orl.txt

ptinv_seca_c3_haps_NonUS_centr al2005pf4_09sep2008_v0_orl.txt

ptinv_seca_c3_haps_NonUS_west 2005pf4_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_invent ory_11feb2008_v2_ida.txt

arinv_nonpoint_Canada2000_21dec2006_v0_ida.txt

arinv_nonpt_mexico_interior 1999_21dec2006_v0_ida.txt

arinv_nonpt_mexico_border1999_21dec2006_v0_ida.txt

othon mbinv_onroad_Canada2000_07nov2006_v0_ida.txt

mbinv_onroad_mexico_border1 999_21dec2006_v0_ida.txt

mbinv_onroad_mexico_interio r1999_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_pf31 capsHgHCl_19feb2008_v0_orl.txt

Annual: ptinv_ptipm_hap2005agtox_v1_n oBAFM_noHgHCL_07jan2009_v2_orl.txt

Daily: ptday_ptipm_capha p_cem_2022ci_<mon>_ida.txt

Daily: ptday_ptipm_capha p_noncem_2022ci_<mon>_ida.txt

Daily: ptday_ptipm_ha p_cem_2005ci_<mon>_ida.txt33Case Sector SMOKE Input Files

Daily: ptday_ptipm_hap_nonc em_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_de lete_for_2022EISA_19nov2008_v0_orl.txt

ptinv_ptnonipm_2030cp_tox_hap_noBA FM_remainder_17dec2009_v0_orl.txt

ptinv_ptnonipm_caphap_ethanol_plant_ additions_2022AEO _05jan2009_v1_orl.txt

nonpt arinv_nonpt_caphap_biodiesel_plant_a dditions_2022AEO_18dec2008_v0_orl.txt

arinv_nonpt_caphap_ethanol_plant_a dditions_2022AEO_15dec2008_v0_orl.txt

arinv_nonpt_pf4_2030cp_tox_cap_nopfc_17dec2009_v0_orl.txt

arinv_nonpt_pf4_2030cp_tox_hap_nopfc_fi xed_with_wrap_oilgas_17dec2009_v0_orl.txt

arinv_nonpt_voc_ethanol_transfer_a dditions_2022AEO_15dec2008_v0_orl.txt

arinv_pfc_reference_capha p2030LDGHG_14dec2009_v0_orl.txt

nonroad arinv_nonroad_capshaps_LDGHG_2030_REFEREN CE_<mon>_15DEC09_15dec2009_v0_orl.txt

arinv_nonroad_calif_caphap_2030v31_<mon>_17apr2008_v0_orl.txt

on_noadj mbinv_onroad_calif_caphap_2030 v31_<mon>_30apr2008_v0_orl.txt

mbinv_on_noadj_nmim_not2moves_203 0LDGHG_REFERENCE_<mon>_10D EC09_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_R EFERENCE_<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_de lete_for_2022EISA_19nov2008_v0_orl.txt

ptinv_ptnonipm_2030cp_tox_ldghg_hap_noB AFM_remainder_28dec2009_v0_orl.txt

ptinv_ptnonipm_caphap_ethanol_plant_ additions_2022AEO _05jan2009_v1_orl.txt

ptinv_ptnonipm_xportfrac_2030cp_tox_ld ghg_cap_remainder_28dec2009_v0_orl.txt

arinv_nonpt_caphap_biodiesel_plant_a dditions_2022AEO_18dec2008_v0_orl.txt

arinv_nonpt_caphap_ethanol_plant_a dditions_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_fixe d_with_wrap_oilgas_28dec2009_v0_orl.txt

arinv_nonpt_voc_ethanol_transfer_a dditions_2022AEO_15dec2008_v0_orl.txt

arinv_pfc_control_caphap203 0LDGHG_14dec2009_v0_orl.txt

nonroad arinv_nonroad_calif_caphap_2030v31_<mon>_17apr2008_v0_orl.txt

arinv_nonroad_capshaps_LDGHG_2030_CONTROL_<mon>_15DEC 09_15dec2009_v0_orl.txt

on_noadj mbinv_onroad_calif_caphap_2030 v31_<mon>_30apr2008_v0_orl.txt

mbinv_on_noadj_nmim_not2moves_2030LDGHG_CONT ROL_<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_11 dec2009_v0_orl.txt34

### 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 Na me>_<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

### 36Table C-1.Detailed list of ancillarydata differences between theLDGHG 2005 and the 2005 v4 platformDescriptionEnvironmentVariableSectors2005 v4 platform2005 LDGHG platformComment and ImpactImpact?DatasetVsnDatasetVsnArea-source spatialcross-referenceAGREFAll sectorsamgref_us_can_mex_revised5amgref_us_can_allmex312For v4 platform, revised for new Canadian inventory andspatial surrogates. LDGHG includes MOVES SCCs fordiesel parking areasYesOnroad spatialcross-referenceMGREFAll sectorsamgref_us_can_mex_revised7amgref_us_can_allmex312For v4 platform, revised for new Canadian inventory andspatial surrogates. LDGHG includes MOVES SCCs fordiesel parking areasYesArea and onroadtemporal profilesATPRO,MTPRO,PTPROAll sectorsamptpro_2005_us_can_revised0amptpro_2005_us_can3For v4 platform, revised for new Canadian inventory andtemporal profile. LDGHG includes HDD idling profilesYesArea temporalcross-referenceATREF,PTREFAll sectorsamptref_v3_3_revised1amptref_v3_39For v4 platform, added SCCs needed for 2005 v2 pointinventory and for WRAP oil and gas inventory. LDGHGincludes MOVES-based diesel parking SCCsYesOnroad temporalcross-referenceMTREFAll sectorsamptref_v3_3_revised0amptref_v3_39For v4 platform, added SCCs needed for 2005 v2 pointinventory (but not for WRAP oil and gas inventory).Superceeded by v1 of the same dataset.YesGrid descriptionsGRIDDESCAllsectorsgriddesc_lambertonly24griddesc_lambertonly23An older file used for LDGHG with an unrelated grid notdefined.NoInventory tableINVTABLEAll sectorsinvtable_hapcapintegate_cb05soa_nomp4invtable_hapcap_cb05soa9LDGHG used a full toxics approach for processing theemissions and 2005 v4 platform used an approach withoutmost toxics. Impacts only the species included in the airquality modeling. LDGHG also includes speciated PM fromMOVES diesel sources.NoInventory tableINVTABLEavefire,ptnonipm,ptipminvtable_hapcapnohapuse_cb05soa_nomp4n/aApproach for implementing "no HAP use" approach forthese sectors was different in LDGHG, but the result wasthe same.NoNon-HAPExclusions for nonptsectorNHAPEXCLUDEnonptnhapexclude_nonpt_pf43nhapexclude_nonpt_pf42v3 includes WRAP oil and gas SCCs since these do nothave HAP VOCs either. v3 needed for the v4 platform butnot LDGHG as LDGHG does not include WRAP oil and gasSCCs.YesElevatedconfiguration file forseca_c3 sectorPELVCONFIGseca_c3pelvconfig_seca_c30n/av4 platform used inline point sources, and so PELVCONFIGfile needed only in v4 platform.YesSpeciation profilesfor TOGGSPROAll sectorsgspro_tog_cb05_soa_pf4_pretier21gspro_tog_nohapuse_cb05_tx_pf4_pretier21Excluded ald2_primary and form_primary from v4 platformsince not neededNoSpeciation profilesspeciated VOCGSPROothptgspro_speciated_voc0n/aFor the v4 platform, this speciation profile dataset passesthrough the pre-speciated VOC point source speciesprovided by Environment Canada 2006 data.YesDescriptionEnvironmentVariableSectors2005 v4 platform2005 LDGHG platformComment and ImpactImpact?DatasetVsnDatasetVsnSpeciation profilesCanada PMGSPROothptgspro_pm25_canada_2006_point0n/aFor the v4 platform, this speciation profile dataset providesthe Canadia-specific PM2.5speciation profile recommendedby Environment Canada for point sources (1% POC, 2%PEC, 12% PSO4, 85% PMFINE)YesSpeciation profilesfor biogenicemissionsGSPRObioggspro_biogenics0n/aFor the v4 platform, this dataset contains the biogenic VOCspeciation profiles previouslyincluded ingspro_static_cmaqv7 for the RFS2 effortNoSpeciation profilesOther VOC HAPGSPROAll sectorsn/agspro_other_hapvoc_nobenz-benz0For LDGHG, this dataset has the HAP VOC species thatget passed through from inventory to the multipollutantinputs created for LDGHG.YesSpeciation profilesCHROMIUMGSPROAll sectorsn/agspro_chromium0Chromium speciation profiles needed only for multi-polltutant approach used in LDGHGYesSpeciation profilesMETALSGSPROAll sectorsn/agspro_hapmetals0HAP metal pass-through profiles needed only for multi-pollutant approach used in LDGHG.YesSpeciation profilesMOVES PMGSPROAll sectorsn/agspro_speciated _pm3Speicated PM (non-72 degrees) added for MOVES dieselexhaust in LDGHG.YesSpeciation profile8762/8763 for TOGto BAFGSPROAll sectorsn/agspro_cmaq_cb05_hspace_BAF1For LDGHG, benzene from TOG for headspace profiles8762 and 8763YesSpeciation profile8762/8763 fornontoxicsGSPROAll sectorsn/agspro_cmaq_cb05_hspace_nontoxic0For LDGHG, toxics from TOG for headspace profiles 8762and 8763YesSpeciation profile8762/8763 for toxicsGSPROAll sectorsn/agspro_cmaq_cb05_hspace_toxic0For LDGHG, toxics from NONHAPTOG for headspaceprofiles 8762 and 8763YesSpeciation xref forPM2.5 diesel SCCsbut do not producedieselGSREFAll sectorsgsref_no_dieselpm1n/aBoth the LDGHG and v4 platform did not create theDIESEL_PM species needed for the multipollutant versionof CMAQ, the data set used for the v4 platform contains theSCC cross-references fordiesel SCCs without theDIESEL_PM species.NoSpeciation xref NODIESEL PM, otharGSREFothargsref_no_dieselpm1gsref_no_dieselpm2Prevents creation of diesel PM species in Canada forLDGHGNoSpeciation xref fornon-diesel PM2.5GSREFAll sectorsgsref_pm25_pf4_nondiesel8gsref_pm25_pf4_nondiesel1For the v4 platform, this dataset includes updates for 2005v2 point sources as well as WRAP oil and gas inventory.Note that future years of LDGHGused more current versionthan "v1" of this dataset, and the v8 dataset could be usedfor replicating LDGHG runs.No



### 37DescriptionEnvironmentVariableSectors2005 v4 platform2005 LDGHG platformComment and ImpactImpact?DatasetVsnDatasetVsnSpeciation xref forVOC, not yearspecificGSREFAll sectorsgsref_voc_general19gsref_voc_general_ldghg0LDGHG copied V20 file from v4 platform and replacedheadspace profile 8737 with 8762. For v4 platform,includes updates for 2005 v2 point inventory, WRAP oil andgas SCCs, changes to gasoline distribution SCCs and allSCCs from HAP inventory.YesSpeciation xref forNONHAPVOC, notyear-specificGSREFAll sectorsgsref_nonhapvoc_general_update3gsref_nonhapvoc_general1For the v4 platform, includes WRAP oil and gas SCCs andremoved duplicates. LDGHG replaces profile 8737 withheadspace profile 8762.YesSpeciation xref forVOC, year-specificGSREFAll sectorsgsref_voc_20052gsref_voc_2005_ldghg0The LDGHG file is the v4 file updated to include newheadspace profiles 8762.Speciation xref forNONHAPVOC,year-specificGSREFAll sectorsgsref_nonhapvoc_20051gsref_nonhapvoc_2005_ldghg0LDGHG replaces headspace profile 8737 with 8762YesSpeciation xrefstatic NOX --HONO for mobilesourcesGSREFAll sectorsgsref_static_nox_hono_pf43gsref_static_nox_hono_pf42The v4 platform dataset adds four Canadian SCCs that areneeded for processing the 2006 Canadian inventory.Speciation xref forIntegrate-HAPsstaticGSREFAll sectorsgsref_static_integratehap_emv42n/aFor the v4 platform, this speciation cross-reference datasetpasses through the pre-speciated VOC point sourcespecies provided by Environment Canada 2006 data.YesSpeciation xref forCanada PMGSREFothptgsref_pm25_canada_2006_point2n/aFor the v4 platform, this speciation cross-reference datasetassigns the single Canadia-specific PM2.5speciation profilerecommended by Environment Canada for all pointsources.YesSpeciation xref HAPchromium nonroadsectorsGSREFalm_no_c3nonroadseca_c3n/agsref_chromium_nonroad1Enables creation of chromium speciesYesSpeciation xref HAPchromium on_noadjsectorGSREFon_noadjn/agsref_chromium_onroad1Enables creation of chromium speciesYesSpeciation xref HAPchromium stationarysectorsGSREFnonptptipmptnonipmn/agsref_chromium_stationary0Enables creation of chromium speciesYesSpeciation xref HAPmetals nonroadsectorsGSREFalm_no_c3nonroadseca_c3n/agsref_metals_nonroad0Enables creation of HAP metal speciesYes



### 38DescriptionEnvironmentVariableSectors2005 v4 platform2005 LDGHG platformComment and ImpactImpact?DatasetVsnDatasetVsnSpeciation xref HAPmetals stationarysectorGSREFnonptptipmptnonipmn/agsref_metals_stationary1Enables creation of HAP metal speciesYesSpeciation xref HAPmetals nonroadsectorsGSREFnonroadn/agsref_metals_nonroad0Enables creation of HAP metal speciesYesSpeciation xref HAPmetals on_noadjsectorGSREFon_noadjn/agsref_metals_onroad0Enables creation of HAP metal speciesYesSCC DescriptionsSCCDESCAll sectorssccdesc_pf315sccdesc_pf318LDGHG updated to include MOVES diesel start SCCs andCanada 2006 added (late for v4)Yes



### 39

### APPENDIX D

Growth and Control Assumptions and Affected Pollutants for the 2030 LDGHG Reference Case

For nonEGU point and stationary area s ources, 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 203 0 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

NO X 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: nati onal applied by SCC, MACT Boat Manufacturing Polymers and Resins III (Phenolic Resins) Polymers and Resins IV (Phenolic Resins) Wood Building Produ cts Surface Coating Generic MACT II: Spandex Production, Ethylene manufacture Large Appliances Miscellaneous Organic NESHAP (MON): Alkyd Resins, Chelating Agents, Explosives, Phthalate Plasicizers, Po lyester Resins, Polymerized Vinylidene

Chloride Manufacturing Nutritional Yeast

Oil and Natural Gas Petroleum Refineries -Catalytic Crack ing, 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



### 40Control 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 Produ cts 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 & SO 2 : 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 H eavy-Duty Rule Final Mobile Source Air Toxics Rule (MSAT2)

Renewable Fuel Standard all Yes



### 41Control 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 La nd 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 Co mmercial Marine Assumptions

Aircraft: Itinerant (ITN) oper ations 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 emission s 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/complia nce/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



### 42plants 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 weight ed average across two processes )

8. Percent reductions recommended and plants to apply to reduc tion to were based on recommendations by rule lead engineer,

and are consistent with th e reference: EPA, 2007e

9. Percent reductions recommended are determined from the exis ting 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 20 05 from the National Mobile Inventory Model (NMIM) results for onroad refueling including activity growth from VMT, Stag e II control programs at gasolin e 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.fa a.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 Qu ality 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/o3i mp8hr/documents/guidance/200705_epa457_b-07­ 001_emission_changes_mact_rules.pdf

.

EPA, 2009. Regulatory Impact Analys is: Control of Emissions of Air Pollution from Locomotive Engines and Marine Compression Igni tion Engines Less than 30 Liters Per Cylin der. U.S. Environmental Protection Agency Office of Transp ortation and Air Quality, Assessment and Standards Division, 

### Ann Arbor, MI 48105,

EPA420-R-08-001 a 

### , May 2009. Available at: http://www.epa.gov/otaq/regs/nonroad/420r08001a.pdf

EPA, 2010 : RFS2 Emissions Inventory for Air Qu ality Modeling Technical Support Document, U.S. Environmental Protection Agency, Assessment and St andards 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

43