# RFS2 Emissions Inventory for Air Quality Modeling Technical Support

DocumentEPA-420-R-10-005 January 2010 Assessment and Standards Division Office of Transportation and Air Quality

and

Emissions Inventory and Analysis Group Office of Air Quality Planning and Standards

U.S. Environmental Protection Agency 

# RFS2 Emissions Inventory for Air Quality Modeling Technical Support

Documentii 

## 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 RFS2...................................................................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 ......................................................................... 10

4 2022 AEO Case ....................................................................................................................................... 12

4.1 2022 AEO Point sources ..................................................................................................................12 4.1.1 Part 1: Projecting 2005 to 2022 AEO for ptnonipm................................................................13 4.1.2 Part 2: Additional OTAQ-supplied emissions data for ptnonipm............................................14 4.2 2022 AEO Nonpoint sources.................................................................................................. ..........15 4.2.1 Part 1: Projecting 2005 to 2022 AEO for nonpt .......................................................................15 4.2.2 Part 2: Additional OTAQ-supp lied emissions data for nonpt ..................................................15

4.3 Mobile sources............................................................................................................. .....................16 4.3.1 US Aircraft, locomotive, and non- c3 commercial marine (alm_no_c3) ..................................16 4.3.2 Canada and Mexico onroad mobile sources (othon) ................................................................17

4.3.3 C3 commercial marine sources from all waters (seca_c3).......................................................17 4.3.4 US nonroad mobile sources (nonroad) .....................................................................................18 4.3.5 Onroad mobile sources (on_moves_r unpm, on_moves_startpm, and on_noadj) ....................19

5 2022 RFS1 Case ................................................................................................................. ..................... 21

5.1 2022 RFS1 Point sources.................................................................................................... ..............22 5.2 2022 RFS1 Nonpoint sources................................................................................................. ..........22

5.3 2022 RFS1 Mobile sources ................................................................................................... ...........23

6 2022 RFS1 headspace prof ile sensitivity case ...................................................................................... 24

7 2022 RFS2 Case ................................................................................................................. ..................... 26

7.1 2022 RFS2 Point sources.................................................................................................... ..............26 7.2 2022 RFS2 Nonpoint sources................................................................................................. ..........26

7.3 2022 RFS2 Mobile sources ................................................................................................... ...........27

8 2022 RFS2 nonroad speciation sensitivity case.................................................................................. .. 28iii 

## ACRONYMS

AEO Annual Energy Outlook

BEIS Biogenic Emission Inventory System btp Bulk plant-to-pump

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

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 Partnershipiv 

## LIST OF TABLES

Table 1: List of cases run in support of RFS2 air quality modeling

Table 2: Sectors Used in Emissions M odeling for the RFS2 Platform

Table 3 : Comparison of model species in 2005 v4 platform and RFS2 platform

Table 4: Description of differences in ancillary data between the R FS2 2005 case and the 2005 v4 platform

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

Table 6: Summary of VOC speciation profile approaches by sector across cases

Table 7: Explanation of VOC profile codes listed in Table 6.

Table 8 : HAP emission ratios for generation of HAP emi ssions from criteria emissions for C3 commercial

marine vessels.

Table 9: Comparison of exhaust speciation profil es used for RFS1 headspace profile sensitivity case

Table 10 : SCCs affected by revised headspace pr ofile in the 2022 RFS1 headspace profile sensitivity case



## LIST OF APPENDICES

APPENDIX A: Approach for Allocating Emissi on Reductions and Speciating Emissions from Finished Fuel Transp ort and Distribution

APPENDIX B: Implementation Of Speciation Profile U pdate For Future Year “Bulk Terminal To Pump”

SCCs

APPENDIX C: Inventory Data Files Used for E ach RFS2 Modeling Case – SMOKE Input Inventory

Datasets

APPENDIX D: Ancillary Data Files Used for RFS2 2005 Case Compared to 2005 v4 Platform Data Files

APPENDIX E: Growth and Control Assumptions and Affected Pollutants for the 2022 AEO Case1 

## 1 Introduction

This document provides the details of emissions da ta processing done in suppor t of the revised annual renewable fuel standard (RFS2) air quality modeling. The modeling effort had five cases and two of these cases had additional sensitivities performed to evaluate the impact of improved approaches, for a total of 7 different emissions cases. Table 1 pr ovides of list of the emissions cases created for this modeling effort.

Table 1: List of cases run in support of RFS2 air quality modeling

Case Name Internal EPA

Abbreviation Description

2005 model evaluation 2005ai_tox 2005 case done w ith year-specific data for fires and hourly electric generating utilities (EGUs), to use for model performance evaluation

2005 basecase 2005ci_tox 2005 case done w ith average year fires data and average year temporal allocation approach, to use for computing relative response factors with 2022

scenarios

2022 AEO case 2022ci_tox_aeo 2022 scenario representing Annual Energy Outlook (AEO) 2007 projected ethanol volume (13.2 billion gallons)

2022 RFS1 case 2022ci_tox_rfs1 2022 scenario re presenting the original reformulated fuel standard (RFS1) ma ndated ethanol volume (6.7 billion gallons) 2022 RFS1 headspace profile sensitivity case 2022ci_tox_rfs1v2_sens1 Revised RFS1 cas e with adjusted nonroad exhaust speciation and headspace vapor for fuel with zero percent ethanol (E0) speciation profile

2022 RFS2 case 2022ci_tox_eisa_rr 2022 scenario representing proposed ethanol volume of 34 billion gallons to meet RFS2 renewable fuel requirement

2022 RFS2 nonroad speciation sensitivity

case 2022ci_tox_eisa2 Revised RFS2 case with volatile organic compound (VOC) speciation profiles a ll nonroad and Portable Fuel Container (PFC) emissions changed to 100% fuel with 10% ethanol (E10). Also does not include

cellulosic ethanol plant emissions.

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.ep a.gov/ttn/chief/emch/index.html#2005). The RFS2 cases use some

different emissions data than th e official v4 platform for two reasons: (1) the 2005 RFS2 modeling performed from December 2008 through March of 2009 wa s completed prior to the completion of the 2005 v4 platform and (2) the RFS2 modeling used data intended only for the rule development and not for general use. All of the documentation provided here describe s what was done differently and specifically for the

RFS2 effort in contrast to wh at is used in the v4 platform.

In RFS2, the same emission inventories were used for both the 2005 model performance case and the 2005 base case, with the exception of the fires data and EGU temporal allocati on approach. These differences are documented with the v4 platform documentation as well and use the same approaches previously documented with OAQPS’s 2002-based v3 platform (h ttp://www.epa.gov/ttn/chief /emch/index.html#2002). In addition, the same biogenic emissions data as th e v4 platform was used not only in the 2005 cases for2 RFS2, but also in all future-year cases run for RFS2. For RFS2, the only significant data changes between the 2005 and future-year cases are emission inventories and speciation approaches.

For this effort, we have created and provided state, county, and source category code (SCC) summaries for external review for the nonpoint and mobile sectors. Fo r point sources, we have posted the actual inventory

datasets that we used for emissions modeling. The data have been provided to the EPA docket for this rule. In addition, the data can be found associated w ith the “RFS2 2005 and 2022 emissions data” link on the CHIEF website at http://www.ep a.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 RFS2. In Section 2 , we describe the ancill ary data and 2005 inventory differences from the v4 platform. In Section 3 , we describe the speciation differences among each of the

cases run for RFS2. In Section 4 , we describe the 2022AEO case as compared to the 2005 base case, and in

Sections 5 through 8 , we describe each of the additional future-year cases in comparison to a previously described future-year ca se.



## 2 2005 Emission inventories and their preparation

The 2005 emissions modeling approach for RFS2 used mu ch of the same data and approaches as the v4 platform. In this section, we iden tify the differences between the data us ed for RFS2 and that used for the 2005 v4 platform. Section 2.1 provides ancillary data differences that impact multiple sectors and Sections

2.2 through 2.4 provides differences for the poi nt, area, and mobile sectors.

Table 2 below lists the platform sectors used for the R FS2 m odeling 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 RFS2 platform.

Table 2: Sectors Used in Emissions M odeling for the RFS2 Platform

Platform Sector 2005 NEI

Sector Description Contains HAP

emissions?

IPM sector: ptipm Point NEI EGU processes 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 records not matched to the ptipm sector. Yes

Point source fire

sector: ptfire Fires Point source fire inventory - contains wildfires and prescribed burning. Used only for th e 2005 model performance case. 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. Used for the 2005 base year and the future base model runs, but

not for the model evaluation case. 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 from fugitive dust sources from 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, in which emissions submitted by the California Air Resources Board (CARB) were used. CARB data used for HAPs are annual, other data are monthly. Yes3 Platform Sector 2005 NEI Sector Description Contains HAP

emissions? Aircraft, locomotive, marine: alm_no_c3 Mobile:

Nonroad Aircraft, locomotive, commercial marine except for c3

commercial marine vessels Yes

C3 commercial

marine: seca_c3 Mobile:

nonroad C3 commercial marine vessels Yes

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 only for onroad gasoline CO, NOx, VOC, some VOC HAPs, Exhaust PM2.5, and Exhaust PM10. More details provided in the 2005 v4 platform documentation. Yes

Onroad starting

exhaust PM:
on_moves_startpm Mobile:

onroad + MOVES-based onroad mobile start exhaust PM and naphthalene data. More details provided in the 2005 v4 platform

documentation. Yes, naphthalene only Onroad running

exhaust PM

on_moves_runpm Mobile:

onroad + MOVES-based onroad mobile running exhaust PM and naphthalene data. More details provided in the 2005 v4 platform

documentation. Yes, naphthalene only Biogenic: biog N/A Hour-specific emissions generated from the Biogenic Emission Inventory System (BEIS), version 3.13 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 and Mexico nonpoint and nonroad mobile inventories No

Other onroad

sources not from the

NEI: othon N/A Canada and Mexico onroad mobile inventories No

Other point

mercury: othpt_hg N/A Canada point Hg Yes, Hg only

Other nonpoint

mercury: othar_hg N/A Canada nonpoint Hg Yes, Hg only

+ Some data used for footnoted sectors are not contained 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 available at its website (http://www.smoke-model.org).



### 2.1 Custom configuration for emissions modeling for RFS2

Unlike the 2005 v4 platform, the configuration for RFS2 modeling included additional hazardous air pollutants (HAPs) and used slightly older ancillary data. Both of these differences are described in this

section.

Table 3 lists the additional HAP pollutants processed for the RFS2 platform, which were not included in the 2005 v4 platform. However, since using the full multi pollutant HAP version of the Community Multiscale Air Quality (CMAQ) model would have taken longer than the time available for our project, we used a “lite” version of the multipollutant CMAQ th at required emissions only for the sp ecies flagged in the third column of Table 3. Additional model species that will appear in model-ready data files are listed in the right two columns of the table, but we did not run these additional HAPs through CMAQ for this effort.4 Table 3 : Comparison of model species in 2005 v4 platform and RFS2 platform

Description 2005 v4 platform

species RFS2 platform Species in CMAQ

MP lite Additional RFS2 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 BUTADIENE135 Description 2005 v4 platform

species RFS2 platform Species in CMAQ

MP lite Additional RFS2 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 species changes, the RFS2 platform had a few additional custom aspects in the 2005 cases. Table 4 lists the datasets used by the RFS2 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, resulting in newer invent ory data used for the v4 platform. These inventory

differences are described more in la ter sections of this document. In addition, Appendix D provides a more detailed comparison of the ancillary datasets for the 2005 v4 platform versus the RFS2 platform.

Another consideration is the specia tion across the RFS2 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 substa ntial differences. Are ancillary data files can be found at the 2005- based platform website (http://www. epa.gov/ttn/chief/emch/index.html#2005).

Table 4: Description of differences in ancillary data between the RFS2 2005 case and the 2005 v4 platform. Ancillary Data Type Difference between 2005 v4 platform and RFS2 platform Spatial cross references The 2005 v4 platform data are updated to support th e newer (2006) Canadian

inventories that were not avai lable in time for RFS2 modeling.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. 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 RFS2 data files are configured to support the multi-pollutant (MP) version of CMAQ, whereas the 2005 v4 platform data file is configured to support only

the non-MP version. Theref ore, the RFS2 data f iles 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. Inventory tables The RFS2 data file is configured to support the MP vers ion of CMAQ, whereas the 2005 v4 platform data file is c onfigured to support only the non-MP

version.

NonHAP exclusions

data The 2005 v4 platform data has been upda ted with new oil and gas SCCs not used for the RFS2 platform.6 

### 2.2 Point sources

The 2005 emissions from the U.S. point source sectors (ptipm and ptnonipm) used for RFS2 differ from the v4 platform primarily because the emissions are ba sed on the 2005 NEI version 1, rather than the 2005 NEI version 2. The original emissions were created fro m 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 indicator of the SMOKE-ready datasets 2) Applied fugitive dust transport fracti ons to the appropriate SCCs (see 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 5) Added an additional 47 ethanol plants based on direc tion from staff at the Office of Transportation and Air Quality (OTAQ). Unless otherwise note in Table 5 (a list of ethanol facili ties and their emissions), we adjusted the original VOC emi ssions provided by a factor of 0.65 (a reduction) to offset the ethanolheavy speciation profile (99.6% ethanol ) that we had available for speci ation of ethanol plants. This prevented the overstating of ethanol emissions. In addition, we multiplied all emissions by 1/(453.6  2000)  1,000,000 = 1.1023113109 (converting from grams/yr pe r million gallons to tons/yr) at the direction of OTAQ to correct the units of the emissions (from grams per million gallon per year to tons/year). These changes resulted in roughly a 10.23% increase in the emissions values initially provided by OTAQ. The list of 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 faci litate replacement and projection for th e 2022 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 AEO and RFS1 cases and changed in the RFS2 case 2) A dataset with three ethanol plants’ CAP and HAP emissions that are replaced in all 2022 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 n all 2022 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 v17 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 RFS2 modeling inventories included year 2000 Canadian emissions from the 2002 v3 platform and did not include th e updated 2006 Canadian emissions, because these

emissions were not available in tim e to support the development of the model-ready emissions. There was no Canadian mercury emissions data, and so these are the same as the 2002 v3 platform. The offshore sources were different from what was used in the 2005 v4 platform because they were not updated with the new offshore inventory available in the 2005 v2 NEI. For more inform ation 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 RFS2 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 av ailable in time for use on this effort.Table 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

89County Name State NameSt/CoFIPSPLANT CO NOXVOC SO2PM2.5PM10ACROLEINCherokee 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 0.65.10 

### 2.3 2005 Nonpoint sources

The emissions from the agricultural a mmonia (ag) and nonpoint fugitive dust (afdust) sectors are the same as the v4 platform, documented elsewhere. For the “oth er” 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 Re gional Air Partnership (WRAP) for the western states. These updated oil and gas extraction emissi ons were provided after the modeling platform development for RFS2.

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

The 2005ci_tox_05b case uses the average fi res (avefire) sector rather than the point source fires (ptfire)

sector. 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-but adiene, acrolein, and xylenes, a nd toluene using ratios to PM 2.5 available in

the Excel ® file “Wildfire HAP ratios to PM2.5 08oct2008.xls” compiled by Madeleine Strum on 10/8/2008.

The factors in this Excel ® file also were used to create em issions for benzene, acetaldehyde, and formaldehyde in the 2005 v4 platform and were unchanged for RFS2. References for these data are provided in the spreadsheet.



### 2.4 2005 Mobile sources

Mobile sources include three US onroad sectors (on_noadj, on_moves_startpm, on_moves_runpm) and two US nonroad sectors (nonroad and alm_no_c3). In addi tion, it includes Canadian a nd Mexican emissions in a separate onroad sector (othon) and nonroad/nonpoint sector (othar).

For onroad mobile, the on_noadj sector is the same as the v4 platform , with the exception of keeping additional pollutants as described in Section 2.1 . The on_moves_startpm and on_moves_runpm emissions inventory data are also the same as the v4 platform, with the only additio nal pollutant for these sectors being naphthalene. In addition for the Motor Vehicle Em ission Simulator (MOVES) sectors, the temperature adjustment calculations applied to PM 2.5 species were also applied to naph thalene, but the methods we used were the same as the v4 platform.

The nonroad emissions inventory data are the same as the v4 platform, with additional HAPs being kept for
RFS2 as well. The alm_no_c3 emission sector does use different data from that of the v4 platform. Specifically, the aircraft emissions remain in this sector and are older data from th at of the v4 platform. In the v4 platform, the aircraft emissions had been revise d and included in the ptnonipm sector as part of the 2005 NEI v2. The airport emissions used in RFS2 we re from the 2002 NEI, version 3, acquired March 27, 2007 and used in the Office of Air Quality Pla nning and Standards’ ( OAQPS’s) 2002 v3.1 platform.

Additionally, the othon sector for RFS2 differs 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 in ventory data, not availabl e at the time that we performed RFS2 modeling.



## 3 VOC speciation changes that represent fuel changes

A significant detail that changes for each of the RFS2 modelingcases is th e VOC speciation profiles used to split total VOC emissions into the various VOC model species needed for CMAQ. In this section, we11 summarize the various speciation profile information used in configuring the various cases, and we include Table 6 to provide a summary of the VOC speciati on approach for each of the future-year cases.

The approaches taken in the 2005 cases below are the same as the 2005 v4 platform , but are included here for completeness. The approaches used for each of the future year cases are customized for those cases, and they include the impact of fuel changes for each of the future-year cases on emissions from the on_noadj sector, the nonroad sector, and parts of the nonpt and ptnonipm sectors. Th e speciation changes from fuels in the nonpt sector include changes for portable fuel containers (PFCs) a nd some parts of the bulk-plant-topump (btp) and refinery-to-bulk term inal (rbt) emissions. The speciation changes from fuels in the ptnonipm

sector include the remainder of the emissions for th e btp and rbt emissions. Appendices A and B provide additional details on the origin of the speciation cha nges based on fuel assumptions among the three futureyear cases.

In Table 6 below, the VOC speciation approach gives a general indication of the approached used, but the details of the implementation are found in the ancillar y data provided and in A ppendices A and B. Although a sector might take the same general approach between two cases, differences may exist in the details of the approach. For example, the nonroad sector uses combin ations of E0 and E10 Tier 1 profiles in the 2005 and 2022 RFS1 sensitivity cases, but different fractions of these two profiles were used for each case.

Table 6: Summary of VOC speciation profile approaches by sector across cases

Inventory type and

mode VOC speciation approach

for fuels VOC

Profile

Codes 2005

cases 2022

AEO 2022

RFS1 2022

RFS1 sensitivity 2022

RFS2 2022

RFS2 sensitivity

Tier 1 E0 and

E10

combinations 8750

8751 on_noadj

nonroad nonroad

Tier 1 and

Tier 2 E0 and

E10

combinations 8750

8751

8756

8767 on_noadj

nonroad on_noadj

nonroad on_noadj

Tier 2 E10

and E85

combinations 8752

8757 on_noadj

nonroad on_noadj Mobile

Exhaust

Tier 1 E10 8751 nonroad

E0 and E10

combinations 8753

8754 on_noadj

nonroad

E0 or E10 (by county) 8753

8754 on_noadj

nonroad

E0 or E10 or combinations 8753

8754 on_noadj

nonroad on_noadj

nonroad

E10 and E85

combinations 8754

8755 on_noadj

nonroad on_noadj Mobile Evaporative

E10 8754 nonroad12 Inventory type and

mode VOC speciation approach

for fuels VOC

Profile

Codes 2005

cases 2022

AEO 2022

RFS1 2022

RFS1 sensitivity 2022

RFS2 2022

RFS2 sensitivity

E0 8737 All listed

E0 and E10

combination 8736

8737 All

listed + All listed

E0 (revised)

and E10

combinations 8736

8737B All listed

E0 (revised) 8737B rbt

E10 and E85

combinations 8736

8755 PFCs btp

nonroad btp

rbt Other

sources: nonroad refueling, PFCs, btp,

rbt

E10 8736 PFCs

nonroad

+ A negligible fraction of E85 evaporative emissions (profile 8755, fraction 0.00075) was assumed in this combination.

Table 7 provides the purpose of the VOC speci ation profile codes used in the table:

Table 7: Explanation of VOC profile codes listed in Table 6.

Exhaust Evaporative Refueling

8750 Tier1 E0

8751 Tier1 E10

8752 E85

8756 Tier2 E0

8757 Tier2 E10 8753 E0

8754 E10

8755 E85 8736 E10 headspace vapor 8737 E0 headspace vapor 8737B E0 headspace vapor

with reduced olefins.

Appendix D summarizes the data file names used for all of the data files that are updated from the v4 platform. All ancillary data files are available on the 2005-based platform website previously referenced.



## 4 2022 AEO Case

The 2022 AEO case is intended to represent the emissions associated with use of the most likely volume of

ethanol in the absence of the RFS2 rule and En ergy Independence and Security Act of 2007 (EISA) renewable fuel requirements. For this case, the ethanol volume was projected for 2022 using the Department of Energy, Energy Information Administration in the 2007 Annual Energy Outlook (AEO) report. That year’s AEO projections were used because Departme nt of Energy (DOE) started accounting for EISA in their 2008 AEO projections. A list of i nventory datasets used for this an d all cases is provided in Appendix A. A list of all growth and contro l assumptions for this Case is provi ded in Appendix E. Other Sections describe the projection differences am oung the 2022 AEO, RFS1, and RFS2 cases.



### 4.1 2022 AEO Point sources

The point sources for the 2022 AE O case included US EGU point s ources (ptipm), US nonEGU point sources (ptnonipm) and sources from Mexico, Canada, and the Gulf of Me xico (othpt and othpt_hg). The US EGU point sources for all 2022 case s use an Integrated Planning Model (IPM) run for criteria pollutants, HCl, and mercury in 2020. We used 2020 because it was the year closest to the 2022 modeling year supported by the IPM model. The code number used by EPA’s Clean Air Markets Division (CAMD) to13 denote the run is EPA30Draft_BC_42l. While these emissions are different fr om those used in the v4 platform, they are consistent with the 2020 emissions used in the v3 platform. OAQPS post-processed these data in the same way as described in the 2005 v4 platform documentation for the “base case” to create daily emissions that include temporal al location information from three year s of Continuous Emissions Monitoring (CEM) data. The temporal allocation approach is the same as for the RFS2 2005 base case (2005ci_tox_05b), to eliminate artificial di fferences in temporal allocation be tween the base and future years. In addition to the IPM emissions, we held the remaini ng HAPs at the same values used in the 2005 cases, except mercury was removed from the original 2005 data set (i.e., no future-year merc ury data are included in the RFS2 platform).

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

This means that the Mexican emissions were based on year 1999, since Mexico has not provided emissions projections to date. We used 2020 emissions project ions for Canadian emissi ons, provided by Environment Canada, which are consistent with the 2000 base year. Th e Canadian data are the same as those used for the 2002-based v3 platform. The data used for RFS2 future years are different from our v4 platform, which was

not available for this work and which used 2006 Cana dian emissions provided by Environment Canada.

For the US nonEGU emissions (sector ptnonipm) ther e were two main pieces for the 2022 AEO ptnonipm inventories: data projected from 2005 values and da ta that were replaced by OTAQ-generated data in 2022. Referring to the five parts listed in Section 2.2 , datasets (4) and (5) were pr ojected from 2005 to 2022 values, datasets (2) and (3) were repla ced, and dataset (1) was unchanged.



### 4.1.1 Part 1: Projecting 2005 to 2022 AEO for ptnonipm

We applied both control and growth factors to a subset of the 2005 ptnonipm to create 2022 AEO. We started with 2020 projection factors from the 2002 v3.1 plat form for most of the R FS2 year 2022 projections. Given the uncertainty of emissions projections and the lack of any additional cont rols coming into effect between 2020 and 2022, we decided that using 2020 as the year for our non-EGU point and nonpoint projections would be sufficient. Th is approach matched the ptipm approach since IPM results were available for 2020 and not 2022. 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 factors had been revised from the 2002v3.0 platform projection

factors that are documented in the 2002 v3 emissions modeling platform documentation (see http://www.epa.gov/ttn/chief/emch/index.html#2002)

. These updates included Hazardous Waste Incineration adjustments and Small a nd Large Municipal Waste Combustor closures and adjustments. The following describes how we further modified th e 2002v3.1 projection factors for the 2022 AEO case.

We used SMOKE with a “control” packet to appl y control factors that imp lement known emissions reductions from point and nonpoint sour ces for national rules. For RFS2 , the control packet was revised from the 2002 v3.1 factors to give key VOC HAPs the sa me control factors as those for VOC. The VOC HAPs (and CAS numbers) of interest for this ef fort were: 1,3-butadiene (CAS=106990), acrolein (CAS=107028), formaldehyde (CAS=50000), methanol (CAS=67561), benzene (CAS=71432), acetaldehyde (CAS=75070), and naphthalene (CAS=91203) . The “control” packet (data f ile) header lists all the various components and the SAS ® programs used to create it.

We also used SMOKE with a “projection” packet to apply growth adjustments, some control adjustments, and plant closures. For RFS2, the proj ection packet for ptnonipm consisted of several components. First, we modified the data from the 2002v3.1 platform packet to re move entries for which we had new factors for this14 effort, such as onroad refueling a nd aircraft. In additi on, we added the same list of VOC HAPs to the projection factors as we had added to the control packet for the VOC-specific entries already present (e.g., landfills in nonpt sector). Next, we added the AEO- specific projection factors fr om several OTAQ sources,

as follows:

1. Onroad refueling: We retained the same list of refueling ptnonipm and nonpt state/county FIPS

codes and SCCs from the 2002 v3 emissions modeli ng platform. For California, we used the 2005v2 California and NMIM data and 2022 NMIM reference case VOC and VOC-HAP refueling emissions to obtain annual adjustment ratios by pollutant a nd county. Next, we applied an additional OTAQprovided ethanol adjustment factor of 1.0306 to each ratio for each of the RFS2 scenarios, provided by David Brzezinski on 12/12/2008. The final formula for the projection fa ctors was Factor 2022 =

NMIM Emis 2022 * 1.0306 /NMIM Emis 2005 (note: we applied different ethanol adjustment factors to the RFS1 and RFS2 cases. These factors were applie d to the refueling SCCs from the 2005 base case.

2. Year 2022 aircraft Federal Avia tion Administration (FAA) take off and landing data from the Terminal Area Forecast (TAF) data: This activit y data replaces the 2020 factors for all RFS2 2022

scenarios.

3. We applied adjustments to refinery emissions by state and SCC, using the same factor for all pollutants to represent activity adju stments (note: different adjustments were made to the RFS2 case, and no adjustments were made to the RFS1 case). The state-level adjustment s and the list of SCCs affected were provided by Rich Cook on 11/25/2008 in Excel ® workbook “RFS2_Refinery_Adjust.xls”.

4. For gasoline distribution SCCs ( both ptnonipm and nonpt SCCs), we additionally applied VOC and VOC HAP adjustments to SCCs representing emissi ons from bulk-plant-to-pump (btp) and refineryto-bulk terminal (rtb) processes. These SCC-level adjustments impact VOC and VOC HAPs in both the ptnonipm and nonpt sectors. The adjustment s were provided by Craig Harvey on 12/16/2008 in

the Excel ® workbook “2005ai_tox_SCC_50state_CAPHAP- 20081216.xls”. (Note: these adjustments were not applied to RFS1 and different adjustment s were applied to RFS2, based on assumed changes in the ratios of E0 to E10 and E85).

We applied the control and proj ection factors to the ptnonipm 2005 inventory described in Section 2.2

, resulting in a “2022” ptnonipm inve ntory. To this inventory, we adde d additional OTAQ-supplied inventory data as described in Step 2 below. The configuration of the processi ng was designed specifically to prevent inadvertent double counting of emissions.



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

In addition to the data preparati on described above, we added the following data supplied by OTAQ:

1. Ethanol plant additions: Th ese emissions data completely replace all of the ethanol plants listed in Table 5 except the first one. OTAQ provided the em issions, stack parameters, locations and the same SCC assignments were made as described for 2005: PM 2.5 (30299999) and VOC and all other pollutants (30125010). The original VOC emis sions were supplied by Rich Cook in Excel ® and OAQPS further adjusted the data to correct for un its conversions and to adjust VOC downward to

account for too much ethanol in the speciation profile. These were the same adjustments made to the

2005 data and are described in Section 2.2 . Further, we changed the original data to use the same

Stack IDs for both PM 2.5 and PM 10 , to ensure SMOKE could properly calculate PMC as PM 10 -PM 2.515 after matching emissions sources. Lastly, we rem oved the benzene, formaldehyde, acetaldehyde, and methanol inventory emissions from these sources because they are “no integrate” sources for VOC HAPs (we do not use the VOC HAPs to augment specia tion information for this sector), and so these model species were to be cal culated from VOC speciation.

2. In addition, we retained the Chippewa plant from 2005 (the emissions at this plant were only changed for the RFS2 case).



### 4.2 2022 AEO Nonpoint sources

The nonpoint sources are sources aggregated to the c ounty (or Canadian Province) and process level and included US fugitive dust sources (afdust sector), US agricultural NH3 (ag sector), US average fires (avefire sector), other US nonpoint sources (nonpt sector), and emissions for Canada and Mexico (othar and othar_hg). Of these, the nonpt se ctor contained the most detailed changes for the RFS2 modeling, as explained further below.

The emissions used for this case for the afdust and ag sectors are the same as those used in 2020 in the 2002- based v3 platform, previously referen ced. Additionally, the avefire emissi ons are the same as those in the 2005ci_tox_05b case mentioned above, and were intentiona lly held constant between the 2005 base case and

the 2022 AEO case and all other 2022 cases.

The emissions for Canada and Mexico were the same as the 2002 v3 platform referenced earlier, with one minor exception: we modified the Canada nonroad sources to remove C3 commercial marine SCC (2280003010), which was just one record in British Colu mbia and which prevented double-counting with the seca_c3 sector. This means that the RFS2 platform diffe rs from the v4 platform in that older estimates of 2020 emissions were used for nonpoint sources in Canada , rather than 2006 estimat es retained between the base and future years in the v4 platform. This sector includes both nonroad mobile and nonpoint sources.

The nonpt sector is comprised of se veral different pieces. Like th e ptnonipm sector, the nonpt sector required two steps to compile the 20 22 AEO inventories: data projected from 2005 values and data that are replaced by OTAQ-generated data in 2022.



### 4.2.1 Part 1: Projecting 2005 to 2022 AEO 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 informa tion that we added to the SMOKE proj ection and control packets h as already been described in that section and the parts that also applied to the nonpt sector have already been identified

in that section.

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

Residential Wood Combustion SCCs in this nonpt sector were not upda ted for 2022 because the difference would have been very small and these emissions are highly uncertain.

The refueling and gasoline distribution projection approaches described in th e point source section also apply to the nonpoint sector, sin ce 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 projected from 2005 to 2022, several additional OTAQ-provided emission inventories were created to complete the emissions needed for the 2022 AEO nonpt sector:16 1. Ethanol plant additions: These are conceptually the same as th e ptnonipm plant additions but are for plants with unknown coordinates, and are therefore aggregated to the county-SCC level. SCC assignments determine spatial allocation using spatia l surrogate ancillary data . The nonpoint ethanol plants were created from an Excel ® spreadsheet provided by Cr aig Harvey on 11/17/2008:
Corn_EtOH_Plant_Inv_2022-aeo.xls. The same VOC adjustments and SCC assignments detailed for ptnonipm were made here as well.

2. Ethanol transfer additions: Th ese new VOC emissions account fo r vapor loss during transport and loading by truck and rail are assigned three SCCs (30205031, 30205052, and 30205053). Ethanol transfer emissions were pr ovided for AEO on 12/11/2008 by Craig Harvey in an Excel ® workbook called “EtOH_transport_vapor_AEO.xls”.

3. Biodiesel plant additions: These new emissions are assigned SCC 2102006001 and also contain an units correction (grams/year to tons/year) of 1.102311309E-06, applie d by OAQPS after receiving the data from OTAQ. These da ta were created from an Excel ® spreadsheet provided by Craig Harvey

on 11/21/2008 : Biodsl_Plant_Inv_2022-aeo-prelim.xl s. We selected the SCC 2102006001 (Stationary Source Fuel Combusti on;Industrial;Natural Gas;All Boil er Types) to represent these sources in consultation with Ron Ryan. The VO C was not adjusted by 65% like it was for ethanol and cellulosic plants. OAQPS converte d emissions from grams to tons.

4. Portable Fuel Containers: These MSAT-based emissions were provided by OTAQ for the year 2020 for the 2002v3 platform and are unchanged for the 2022 AEO case. We applied adjustment factors (provided by OTAQ) to the RFS1 and RFS2 cases.



### 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 has traditionally been projected from a base year (e.g., 2005) to a future year (e.g., 2022); however, so many elements of these data were devel oped specifically for RFS2 that we created a new program to combine all the pieces (ALM_no_C3_create _2022.sas), which includes some leftover projection steps normally done in SMOKE. This program creates three inventories in SMOKE’s One Record per Line (ORL) nonroad format:

1. C1 and C2 CMV : Class 1 and class 2 Commerci al Marine Vehicle SCCs (2280002100, 2280002200, and 2280004000) complete inventory created by OTAQ for each of the 2022 RFS2 scenarios. For this case, data were provided by Penny Carey on 12/10/2008 in Excel ® file:
CMV_C1C2_2022RFS2cases.xls. We applied HAP factors to VOC to obtain 1,3-butadiene, acetaldehyde, acrolein, benzene, and formaldehyde, and applied factors to PM 10 to compute naphthalene. The remaining HAPs -metals and ot her non-VOC HAPs not already provided- are held at base year levels (the 2002 emissions estimates used in the 2005 basecase).

2. Class 1 Locomotives : This group includes the class 1 locomotives SCC (2285002006), though a very

small amount of the class 2 and 3 emissions were also captured by this SCC. Emissions data were provided by OTAQ (Craig Harvey) 11/17/2008 in the data file: Class_I_II_III_locomotives_alm_2020ce_cty_scc_aeo.xl s. These data included CAP emissions as well as HAP emissions for 1,3-butadiene, acet aldehyde, acrolein, benzene, formaldehyde, and17 naphthalene. Tribal data (S MOKE region code=88206) from the 2005 NEI were retained. The remaining HAPs -metals and other non-VOC HAPs not pr ovided- are held at ba se year levels (the 2002 emissions estimates used in the 2005 basecase).

3. Remaining alm_no_c3 emissions : These emissions are the leftover alm_no_c3 emissions after removing the data provided and computed in (1) a nd (2) above. These em issions are limited to

aircraft and non-class 1 locomotives. For aircraft , all CAPs and VOC HAPs are projected to year 2022 using FAA TAF data. This is same approach and FAA TAF output as used for year 2020 projections in 2002v3 and 2002v3.1-based platforms, ex cept we used 2022 activity data instead of

2020. Metals and non-VOC HAP aircraft emissions were held at base year levels (2002 emissions estimates used for 2005). We projected the other component of this file , non-class 1 locomotive emissions (SCCs=228500200##, where # = 07, 08, 09, and 10) using the final Locomotive-Marine Rule projection factors for year 2020 for CAPs a nd VOC HAPs. Non-VOC HAPs are also held at 2005 base year levels. For California, we applied lo comotive/marine controls to diesel pleasure craft SCCs (2282020005, 2282020010) to project from 2005 to 2022.



### 4.3.2 Canada and Mexico onroad mobile sources (othon)

The data in this sector are the same as 2020 data releas ed in 2002-based v3 platform, previously referenced. These are different from 2005 v4 platform because the 2006 Canadian data were not available for use in this project.



### 4.3.3 C3 commercial marine sources from all waters (seca_c3)

The seca_c3 sector emissions data were provided by OTAQ in an ASCII raster format used since the ECAIMO project began in 2005. (S)ECA C3 year 2022 reference (AEO and RFS1) and control (RFS2) inventories were provided by OTAQ (Penny Care y) 11/20/2008: 2022base_re do.zip and 2022rfs2.zip. Penny Carey also provided factors 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 8 below shows these factors and whether they were applied to VOC or PM 2.5 . As with the 2005 case, this sector uses CAP-HAP VOC integration. The “base” dataset is used for both AEO and RFS1 cases and different data are used for

the RFS2 case.

Table 8 : HAP emission ratios for generation of HAP emissions from criteria

emissions for C3 commercial marine vessels.

Pollutant Apply to Pollutant Code Factor

Acetaldehyde VOC 75070 0.0002286

Benzene VOC 71432 9.795E-06
Formaldehyde VOC 50000 0.0015672
Benz[a]Anthracene PM2_5 56553 5.674E-07
Benzo[a]Pyrene PM2_5 50328 1.844E-07

Benzo[b]Fluoranthene PM2_5 205992 1.56E-07

Benzo[k]Fluoranthene PM2_5 207089 1.56E-07
Chrysene PM2_5 218019 9.929E-08
Indeno[1,2,3-c,d]Pyrene PM2_5 193395 1.418E-08
Acenaphthene PM2_5 83329 3.404E-07
Acenaphthylene PM2_5 208968 5.248E-07

Anthracene PM2_5 120127 5.248E-07
Benzo[g,h,i,]Perylene PM2_5 191242 1.277E-0718 Pollutant Apply to Pollutant Code Factor

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 poi nt source ORL format allowing fo r emissions to be allocated to modeling layers above the surface layer. OAQPS corrected emissions for one state/ county FIPS code fix in Rhode Island. All non-US emissions (i .e., in waters considered outside of US territory) are simply assigned a dummy state/county FIPS code=98001. Due the huge si ze of these data, the CAP emissions are in one ORL file and the HAP emissions are split into 6 separate ORL files. The emissions spatial extent includes waters off of the coasts of the US, Canada, and Mexi co, as well as emissions in major waterways and the Great Lakes. The SMOKE-ready data have also been cropped from the original data provided by OTAQ to cover only the 36km CMAQ domain, which is th e largest domain used for this effort.



### 4.3.4 US nonroad mobile sources (nonroad)

All states except California: OTAQ provided a reference set (NMI M case “Rfs2Ref2022Nr”) of NMIM emissions to be used for all 2022 RFS2 scenarios. This reference set of monthly emissi ons includes all 50 states plus DC and all CAPs and HAPs of interest. An AEO-specific set of NMIM monthly emissions was also provided for gasoline equipment that was used to replace gasoline equipment em issions in the reference set. All NMIM data are based on AEO2007 fuels and NMIM county data base NCD20080727. We also reassigned NMIM evaporative and refueling xylene (compound XY L 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, resp ectively. 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 emi ssions 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.

California nonroad: California monthly nonroad emissions are year 2020 and are based on March 2007 California Air Resources Board (CARB) data (Martin Johnson: mjohnson@arb. ca.gov). NH3 emissions are from NMIM runs for California (same data as were used in 2020 from the 2002 v3 platform). We allocated refueling emissions to the gasoline equipment types based on evaporative mode VOC emissions from the v3 platform 2020 NMIM data, and the refueling emissions were com puted by multiplying SCC 2505000120 emissions by 0.61, to adjust to remove double counting with Portable Fuel Container inventory for California. We estimated HAP emissions by applying HAP-to-CAP ratio s computed from the California data provided for the 2005 NEI v2, collected by EPA (Laurel Driver) 12/2007. This wa s done because the CARB submittal from March 200719 did not include estimates for HAPs. We retained onl y those HAPs that are also estimated by NMIM for nonroad mobile sources; all other HAPs were dropped.



### 4.3.5 Onroad mobile sources (on_move s_runpm, on_moves_startpm, and on_noadj )

As in 2005, the on_moves_runpm and on_moves_startpm sectors include emissions from onroad gasoline sources for PM and naphthalene, whic h need temperature adjustment fact ors. The temperature adjustment factors were specific to 2022 (different from those used in 2005) and we used the same adjustment factors in all of the 2022 cases. The temperature adjustments ha ve the limitation that they were based on the use of MOVES default inputs rather than c ounty-specific inputs, because a coun ty-specific database for input to

MOVES was not available at the time this approach was needed. Furthe r, the version of MOVES used for all runs was a preliminary ve rsion and has since changed.

Also like 2005, the on_noadj sector in cludes non-PM MOVES data for gasolin e vehicles for some pollutants, NMIM-based data for motorcycles, diesel vehicles, a nd the remaining pollutants for onroad gasoline, as well

as all California onroad mobile emissi ons. The detailed approaches described here are the same as those for 2005 (except for the NMIM and MOVES data used), but are included here for convenience.

on_moves_startpm and on_moves_runpm For the on_moves_runpm and on_moves_startpm sector s, the same preprocessi ng as was done in 2005 was done here, but using the 2022 AEO NMIM runs to create the monthly county-to-state ratios by state and SCC and using the 2022 PM adjustment factors. The MOVES data used in this projec t were unique for each of the three main 2022 cases (AEO, RFS1, and RFS2), except for the adjustment factors that were the same for

each case.

OTAQ supplied three input files cont aining state-level MOVES-based onro ad gasoline emissions by month for the following pollutants:

1. Exhaust: VOC, NOX, CO, 1,3-butadiene (106 990), acetaldehyde (75070), acrolein (107028), benzene (71432), and formaldehyde (50000);

2. Evaporative: Non-refueling VOC, benzene, and naphthalene (91203);

3. MOVES-speciated PM at 72 degree s: Naphthalene, and what MOVE S labels as the OC, EC, and SO4 components of PM2.5 –PM25OC, PM25EC, and PM25SO4 respectively. Emissions are computed at 72F and we used SAS ® and existing technical directi on to convert the MOVES-based

PM 2.5 species into the followi ng SMOKE-ready pollutants:

 NAPHTH_72: unchanged from MOVES-based file, subject to temperature adjustment below

72F.

 PEC_72: unchanged from MOVES- based PM25EC, subject to te mperature adjustment below

72F.

 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 temper ature adjustment below 72 degrees F.

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

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

 OTHER: sum of computed metals (fracti on of MOVES-based PM25EC) and NH4 (computed from PNO3 and PSO4), not subjec t to temperature adjustment.20  PMFINE_72: Computed from OTHER and frac tion of POC_72. Subj ect to temperature adjustment below 72 degrees F.

 PMC_72: Computed as fraction of sum of PMFINE_72, PEC_72, POC_72, PSO4, and PNO3. Subject to temperature adjustment below 72 degrees F.

MOVES gasoline emissions were used for light-duty ga soline vehicles (LGDV), li ght-duty gasoline trucks 0- 6000 pounds gross vehicle weight (LDGT1), light-dut y gasoline trucks 6000-8500 pounds gross vehicle weight (LDGT2), and heavy-duty gaso line trucks (HDGV). Motorcycle emi ssions 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.

MOVES-based, monthly state-level emi ssions were first allocated to co unty based emissions using countySCC specific state to county ratios that we crea ted from the 2022 AEO NMIM run. California MOVESbased emissions are discarded; they do not replace the existing Californi a inventories (discussed in on_noadj sector). MOVES data were provided by OTAQ (Harvey Michaels) on 01/16/2009.

In each MOVES file, “start” emissions are represented by SCCs with the SCC characters 8-9 equal to “00”:

2201001 00 0 (LDGV), 2201020 00 0 (LDGT1), 2201040 00 0 (LDGT2), and 2201070 00 0 (HDGV). These start emissions are assigned to ur ban and rural SCCs based on the count y-level ratio of NMIM emissions from urban versus local roads. For example, LD GV start emissions (2201001000) were split into urban (2201001370) and rural (2201001350) based on the ratio of LDGV emissions from urban (2201001330) and rural (2201001210) local roads.

Finally, the set of emissions are broken into 3 sets:

1. on_moves_startpm: monthly MOVES-based “sta rt” PM emissions subject to temperature adjustments, and PM species not subject to temp erature adjustments (e.g., PNO3 and PSO4). These are limited to 8 SCCs (urban/rural and 4 vehicl e types) for the following pollutants: PEC_72, POC_72, PNO3, PSO4, OTHER, PMFINE_72, PMC_72, NAPHTH_72.

2. on_moves_runpm: monthly MOVES- based “running” PM emissions s ubject to a different set of temperature adjustments compared to “start” emissi ons; similar to the on_moves_startpm sector, this sector includes all PM species, not just those subject to temperature adjustments. The same pollutants are provided as on_moves_startpm.

3. on_noadj MOVES-based emissions: The remaining monthly non-PM MOVES-based emissions that are also not subject to temperature adjustments -see inputs (1) a nd (2) above. These emissions are modeled in the on_noadj sector discussed below a nd include both start and running emissions for non-PM pollutants.

on_noadj The on_noadj sector contains all US onroad mobile emissions not repl aced by MOVES. There are four sources of data that are pre-pro cessed to create three sets of m onthly inventories for this sector.

1. MOVES-based non-PM: These are the monthly non-PM, non-naphthalene MOVES-based emissions discussed in item #3 in “Outputs” in the on_moves_runpm and on_move s_startpm sector discussion. In short, these are non-California, select pollutants exhaust and ev aporative (non-refueling) onroad gasoline LDGV, LDGT1, LDGT2, and HDGV emissions.

2. California onroad inventory: California year 202 0 complete CAP/HAP onroad inventory. California monthly onroad emissions are year 2020 and ar e based on March 2007 California Air Resources Board (CARB) data (Martin Johnson: mjohnson@arb.ca.gov). NH3 emissions are from NMIM runs21for California (same data as were used in 2020v3). We estimated HAP emissions by applying HAPto-CAP ratios computed from California 2005 NEI submittal provided by EPA (Laurel Driver)

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 al so estimated by NMIM for nonroad mobile sources; all other HAPs were dropped.

3. Remaining onroad NMIM-based onroad inventory: The remainder of the non-California onroad inventory not replaced by MOVES. This include s monthly emissions for all onroad diesel, all motorcycles, all refueling, and onroad LDGV, LDGT1, LDGT2, and HDGV emissions for pollutants not covered by MOVES (e.g., SO2, NH3). All NMIM onroad data are based on AEO2007 fuels and NMIM county database NCD20080727.

The remainder of this section discusses the pre-pro cessing required to create monthly ORL files for the remainder of the on_noadj sector (#3 above).

OTAQ (Harvey Michaels) provided a reference set (Rfs2Ref2022Or) 12/12/2008 of NMIM emissions to be

used for all 2022 RFS2 scenarios. This reference set of monthly emissions includes all 50 states plus DC and all CAPs and HAPs of interest. An AEO-specific (Rfs2ref2022orVc11aeo) set of NMIM monthly emissions was also provided 12/15/2008 by OTAQ (Harvey Mich aels) for 816 counties th at Oak Ridge National Laboratory (ORNL) determined would have enhanced tr uck traffic for the distri bution of ethanol in 2022. The only trucks affected are part5vc lass=11; hence the “Vc11” designation. (These have been referred to previously as “8b trucks,” but Vc11 is more accurate, since it refers to both 8a and 8b.) The adjustments were determined by annual VMT in the affected counties. We assumed that trucks made a return trip. The adjustments often average a fraction of a percent, but the largest on a county basis is about 6%. These “Vc11” emissions replaced the reference case emissi ons for AEO; similar “Vc11” emissions are also

substituted for the RFS2 and RFS1 cases.

Similar to nonroad pre-processing, we also reassigned NMIM evaporative and refueling xylene (compound XYL or CAS=EVP__1330207, RFL__1330207) into MXYL (CAS=EVP__108383, RFL__108383) and OXYL (CAS=EVP__95476, RFL__95476) using a 68% and32% ratio to both evaporative and refueling XYL, respectively. We also split NMIM exhaust xylene (CAS=EXH__1330207) into MXYL (CAS=EXH__108383) and OXYL (CAS=EXH__95476) using a 74% and 26% ratio to XYL, respectively.

Emissions were converted from monthly to average- day based the on number of days in each month. 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 the raw NM IM data prior to creati ng the SMOKE inputs, since these emissions were included in the nonpt and ptnonipm s ectors. The NMIM refueling data were used with existing 2005 refueling emissions to create projection ratios for ptnonipm and nonpt refueling, as previously

described in Section 4.1.1

.



## 5 2022 RFS1 Case

The 2022 RFS1 reference case was intended to represent the emissions associated with only the volume of ethanol required by the RFS1 rule. RFS1 required 7.5 billion gallons of renewable fuel in 2012 and later, and 6.7 billion gallons of that was considered to be ethanol. A list of inventory datasets used for this and all cases is provided in Appendix A.22 

### 5.1 2022 RFS1 Point sources

The point sources for the 2022 RFS1 Case include the same emissions as the 2022 AEO Case for the following sectors: US EGU point source (ptipm), sources from Mexico, Canada, and the Gulf of Mexico (othpt and othpt_hg).

For the nonEGU point sources (ptnonipm), some of th e data are replaced for the 2022 RFS1 case. These

differences from the 2022 AEO case are:

 46 ethanol plants were revised from the 2002 AE O case with new emissions values. Only the Chippewa ethanol plant is unchanged in this case. These data were pr ovided by Craig Harvey on

11/17/2008 in the Excel ® dataset “Corn_EtOH_Plant_Inv_2022-rfs1.xls”.

 Onroad refueling: We used the same overall proj ection approach for onroad re fueling as was used in the 2022 AEO case for the ptnonipm and nonpt sector s. Instead of using the 2022 AEO NMIM run, we used the RFS1-specific NMIM run for the 2022 refueling ratio calcula tion, with an additional ethanol adjustment factor of 1.0153, provided by David Brzezinski on 12/12/2008. The final formula for the projection factors wa s Factor 2022 = Emis2022 * 1.0153 /Emis2005. We applied these factors to the refueling S CCs from the 2005 base case.

Note that we did not adjust emissions from refineries, bulk-plant-to-pump (btp), or refinery-to-bulk terminal (rtb) processes (these adjustments were applied in the AEO and RFS2 cases ). Lastly, VOC speciation profile changes affected this sector, as described in Section 3

.



### 5.2 2022 RFS1 Nonpoint sources

The nonpoint sources for the 2022 RFS1 Case include th e same emissions as the 2022 AEO Case for the following sectors: US fugitive dust sources (afdust sector), US agricultural NH3 (ag sector), the US average fires (avefire sector), and the nonpoint emissions for Canada and Mexico (o thar and othar_hg).

The 2022 RFS1 Case emissions for the “other nonpoint ” sector (nonpt) emissi ons differed from the 2022 AEO case in the following ways:

 Revised the ethanol plant emissions . OAQPS converted these data to SMOKE input format using the same approaches as in the 2022 AEO case from an Excel ® workbook provided by Craig Harvey
11/17/2008: “Corn_EtOH_Plant_Inv_2022-rfs1.xls”.

 Revised the ethanol transfer emi ssions. OAQPS converted these data to SMOKE input format using the same approaches as in the 2022 AEO case from an Excel ® workbook provided by Craig Harvey on 12/15/2008: “EtOH_transport_vapor_RFS1.xls”.

 Revised biodiesel plant emissions . OAQPS converted these data to SMOKE input format using the same approaches as in the 2022 AEO case from an Excel ® workbook provided by Craig Harvey on 11/25/2008: “Biodsl_Plant_Inv_2022-rfs1-prelim.xls”.

 Created revised PFC emissions by applying adjustment factors by c ounty and pollutant, based on an

Excel ® spreadsheet provided by Ari Kahan on 1/14/ 2009: “aeo_to_rfs1.xls”. These adjustments reduced PFC emissions as compared to the AEO case.

 As previously mentioned for the point sources, we changed refueling emissions projections, which also affected the nonpt sector.23 The gasoline distribution adjustments that affected AEO and RFS2 were not relevant to this Case. Lastly, VOC speciation profile changes affected this sector, as described in Section 3

.



### 5.3 2022 RFS1 Mobile sources

Compared to the 2022 AEO case, the mobile source emissi ons included changes in all of the mobile source sectors except for the C3 commerc ial marine (seca_c3) and the Cana da and Mexico emissions (othon).

For the aircraft, locomotive, and non-C3 commerci al marine (alm_no_c3) emissions, the “remaining alm_no_c3 emissions” (item 3 in Section 4.3.1 ) were unchanged from the 2022 AEO case, but the C1 and C2

commercial marine emissions and th e locomotive class 1 emissions both changed. Otherwise, the processing

for this sector was the same as for 2005 and 2022 AEO. For the C1/C2 Co mmercial Marine (SCCs 2280002100, 2280002200, and 2280004000), OAQPS prepared the SMOKE-ready inventory from an

Excel ® worksheet provided by Penny Carey on 12/10/ 2008: “CMV_C1C2_2022RFS2cases.xls”. OAQPS applied factors also provided by Penny Carey on 12/11/2008 to compute Benzene, Acetaldeyde, Formaldehyde, Acrolein, 1,3-butad iene, and naphthalene.

For the locomotive class 1 emissions, OAQPS cr eate the SMOKE-ready inventory from an Excel ®
worksheet provided by Crai g Harvey on 11/17/2008: “Class_I_II_III_locomotives_alm_2020ce_cty_scc_rfs1.xls” . This CAP-only dataset was updated by Craig

on 12/10/2008 to include HAP emissi ons of the key HAPs for RFS2.

For the nonroad sector, OAQPS made adjustments to the 2022 AEO inventory to crea te the RFS1 inventory. California emissions were not changed, but emissions in all other states were updated. OAQPS used data provided by OTAQ for gasoline equipment SCCs to re place those emissions from the reference case

“Rfs2Ref2022Nr” mentioned in Section 4.3.4 . The RFS1 case “Rfs2AqmRFS12022Nr” replaced the gasoline equipment SCCs: 2260*, 2265*, 2282005010, 2282005015, 2282010005, and 2285004015. Otherwise the steps taken were the sam e as described for the 2022 AEO case.

For the US onroad mobile sectors, revised NMIM data were provided by Harvey Mi chaels in late September 2008. OAQPS adjusted the reference case emissi ons “Rfs2Ref2022Or” (mentioned in Section 4.3.5 ) using RFS1-specific NMIM data from case “Rfs2ref2022orVc11r fs1”, which contains adju sted inventories for 497

counties that ORNL determ ined would have enhanced truck traffic for the distri bution of ethanol in 2022. The only trucks affected are part5vcla ss=11 (these have been referred to pr eviously ad “8b trucks,” but Vc11 is more accurate, since it refers to both 8a and 8b). The adjustments were determined by annual VMT in the

affected counties. We assumed that trucks made a return trip. All county-SCC, pollutant-code, emissiontype, month combinations in rfs2ref2022orVc11rfs1 re placed those combinations from the Rfs2Ref2022Or NMIM run. These changes affected th e emissions in the on_noadj sector di rectly, and were also used in the

MOVES emissions allocati on step described next.

In addition to the changes in the on_noadj sector, th e monthly onroad gasoline emissions were changed using a MOVES run specifically for the RFS1 scenario. We allocated the state-SCC MOVES data to county-SCC using ratios developed from the RFS1-specific NMIM count y-SCC data. Other than the different data used for creating the monthly county-SCC SMOKE-ready inventories, we used the same processing steps as

described for 2005 and the 2022 AEO cases.

Lastly, VOC speciation profile changes affect ed this sector, as described in Section 3

.24 

## 6 2022 RFS1 headspace profile sensitivity case

This case made two updates: (1) revised VOC speci ation for the nonroad sector from the 2022 RFS1 reference case and (2) replaced th e E0 headspace vapor VOC speciation profile 8737 profile with profile 8737B. This second change affect s all categories that use this E0 headspace vapor profile.

In the RFS1 Reference case, the nonroad sector used the same VOC speciation profiles as onroad mobile for the exhaust mode. Specifically for e xhaust, the RFS1 Reference case used combinations of Tier 1 and Tier 2 E0 profiles, combinations of Tier 1 and Tier2 E10 profiles, or combinatio ns of all four profiles, depending on the county. In other words, some counties got E0, so me got E10, some got both E0 and E10, and the mix of

Tier 1 and Tier2 onroad vehi cles was considered in creating composite profiles.

Since nonroad mobile is not affected by Tier 2 standards, the improved approach that we implemented in this case used only Tier 1 E0, Tier 1 E10, or a mixtur e of E0 and E10, depending on the county. Table 9 below shows the different exhaust specia tion profiles used for each case:

Table 9: Comparison of exhaust speciation profiles used for RFS1 headspace profile sensitivity case

RFS1 Reference exhaust VOC speciation codes for

nonroad mobile RFS1 Sensitivity exhaust VOC speciation for

nonroad mobile 8750 (Tier 1 E0) and 8756 (Tier 2 E0)

OR 8751 (Tier 1 E10) and 8757 (Tier 2 E10)

OR (8750 and 8756 and 8751 and 8757) Depending on the county, using fractions of Tier 1 and Tier 2 and fractions of E10 penetration 8750 (Tier 1 E0)

OR 8751 (Tier 1 E10)

OR

8750 and 8751 Depending on the county, using fractions of E10 penetration

The improved exhaust speciation information was provided by Harvey Michaels on 6/11/2009 in Excel ® file “Gspro_Combo_Rfs2Aqm2022Rfs1NR.zip” . OAQPS further post-processed th ese data to reduce the size and reformatted into SMOKE-ready format.

In addition to the exhaust speciation changes, we also replaced the E0 headspace vapor VOC speciation profile 8737 profile with profile 8737B. This affects all categories that use this E0 headspace vapor profile. This headspace profile part of the sensitivity analysis was done because EPA staff noted that the headspace profiles used in the RFS1 and AEO reference case s cenarios exhibited a reducti on in alkene levels going

from E0 to E10 that was not consistent with what one w ould expect as a result of increased ethanol use. In these cases, the E0 gasoline headsp ace profile has 13% of the VOC as alkenes and the E10 profile has an

alkene content of 4%. To address this inconsistenc y, EPA adjusted the E0 headspace profile 8737, assuming the emissions have an alkene content of 4%, consistent with the percent alkene content of the E10 headspace profile. This adjusted profile was designated 8737B. Development of the adjusted profile is described in EPA Report No. EPA-420-D-10-001, “Hydrocarbon Co mposition of Gasoline Vapor Emissions from Enclosed Fuel Tanks,” which is included in the docket for this rule.

This sensitivity affected em issions in the nonroad, nonpt, a nd ptnonipm sectors. Table 10 below shows the affected SCCs with their descriptions (the first 3 leve ls of the SCC descriptions). For the SCCs categorized as “Bulk plant to pump”, the VOC emissions used a co mbination profile that consistent of 53.78% profile 8737B and 46.22% profile 8736 (E10 headspace vapor)

. The SCCs categorized as “Refinery to bulk terminal” used the 8737B profile directly.25Table 10: SCCs affected by revised headspace profile in the 2022 RFS1 headspace profile sensitivity case.SCCSectorDescriptionSCCTier1SCC Tier2SCC Tier340600233Petroleum and Solvent EvaporationTransportation and Marketing of Petroleum ProductsMarine Vessels40600235Petroleum and Solvent EvaporationTransportation and Marketing of Petroleum ProductsMarine Vessels40600241Petroleum and Solvent EvaporationTransportation and Marketing of Petroleum ProductsMarine Vessels40688805Petroleum and Solvent EvaporationTransportation and Marketing of Petroleum ProductsFugitive Emissions404002**Petroleum and Solvent EvaporationPetroleum Liquids Storage (non-Refinery)Bulk Plants404004**Petroleum and Solvent EvaporationPetroleum Liquids Storage (non-Refinery)Petroleum Products - Underground Tanks406001**Petroleum and Solvent EvaporationTransportation andMarketing of Petroleum ProductsTank Cars and Trucks406003**Petroleum and Solvent EvaporationTransportation and Marketing of Petroleum ProductsGasoline Retail Operations - Stage I406004**Petroleum and Solvent EvaporationTransportation and Marketing of Petroleum ProductsFilling Vehicle Gas Tanks - StageII406006**Petroleum and Solvent EvaporationTransportation and Marketing of Petroleum ProductsConsumer (Corporate) Fleet Refueling - Stage II406007**ptnonipmPetroleum and Solvent EvaporationTransportation and Marketing of Petroleum ProductsConsumer (Corporate) Fleet Refueling - Stage I2501011***Storage and TransportPetroleum and PetroleumProduct StorageResidential Portable Gas Cans2501012***Storage and TransportPetroleum and PetroleumProduct StorageCommercial Portable Gas Cans2501055***Storage and TransportPetroleum and Petroleum Product StorageBulk Plants: All Evaporative Losses2501060***Storage and TransportPetroleum and PetroleumProduct StorageGasoline Service Stations2505030***Storage and TransportPetroleum and Petroleum Product TransportTruck2660000***nonptBulk plant topump(note: for SCCsstarting with404002, allSCCs includedas bulk plant topump exceptfor the 3 SCCsstarting with404002 listed inthe refinery-tobulk sectionbelow)Waste Disposal, Treatment, & RecovLeaking Underground Storage TanksLeaking Underground Storage Tanks40400240Petroleum and Solvent EvaporationPetroleum Liquids Storage (non-Refinery)Bulk Plants40400249Petroleum and Solvent EvaporationPetroleum Liquids Storage (non-Refinery)Bulk Plants40400260Petroleum and Solvent EvaporationPetroleum Liquids Storage (non-Refinery)Bulk Plants40600231Petroleum and Solvent EvaporationTransportation and Marketing of Petroleum ProductsMarine Vessels40600232Petroleum and Solvent EvaporationTransportation and Marketing of Petroleum ProductsMarine Vessels40600234Petroleum and Solvent EvaporationTransportation and Marketing of Petroleum ProductsMarine Vessels40600236Petroleum and Solvent EvaporationTransportation and arketing of Petroleum ProductsMarine Vessels40600237Petroleum and Solvent EvaporationTransportation and Marketing of Petroleum ProductsMarine Vessels40600238Petroleum and Solvent EvaporationTransportation and Marketing of Petroleum ProductsMarine Vessels40600239Petroleum and Solvent EvaporationTransportation and Marketing of Petroleum ProductsMarine Vessels40600240Petroleum and Solvent EvaporationTransportation and Marketing of Petroleum ProductsMarine Vessels40600298Petroleum and Solvent EvaporationTransportation and Marketing of Petroleum ProductsMarine Vessels40600299Petroleum and Solvent EvaporationTransportation and Marketing of Petroleum ProductsMarine Vessels40688803Petroleum and Solvent EvaporationTransportation and Marketing of Petroleum ProductsFugitive Emissions403001**Petroleum and Solvent EvaporationPetroleum Product Storage at Refineries403002**Petroleum and Solvent EvaporationPetroleum Product Storage at Refineries403003**Petroleum and Solvent EvaporationPetroleum Product Storage at Refineries403010**Petroleum and Solvent EvaporationPetroleum Product Storage at RefineriesFixed Roof Tanks (Varying Sizes)403011**Petroleum and Solvent EvaporationPetroleum Product Storage at RefineriesFloating Roof Tanks (Varying Sizes)403012**Petroleum and Solvent EvaporationPetroleum Product Storage at RefineriesVariable Vapor Space404001**Petroleum and Solvent EvaporationPetroleum Liquids Storage (non-Refinery)Bulk Terminals406005**ptnonipmPetroleum and Solvent EvaporationTransportation and Marketing of Petroleum ProductsPipeline Petroleum Transpt - General - AllProducts2501050***Storage and TransportPetroleum and Petroleum Product StorageBulk Terminals: All Evaporative Losses2505000***Storage and TransportPetroleum and Petroleum Product TransportAll Transport Types2505020***Storage and TransportPetroleum and Petroleum Product TransportMarine Vessel2505040***nonptRefinery to bulkterminalStorage and TransportPetroleum and Petroleum Product TransportPipeline26 

## 7 2022 RFS2 Case

This case represented the emissions associated with EPA’s best estimate at the time of proposal of the

renewable fuel volumes that would be used to meet the EISA requirements. This included 34.14 billion gallons of ethanol and 0.81 billion gall ons of biodiesel. A list of inventory datasets used for this and all cases is provided in Appendix A.



### 7.1 2022 RFS2 Point sources

The point sources for the 2022 RFS2 Case include the same emissions as the 2022 AEO Case for the following sectors: US EGU point source (ptipm) and sour ces from Mexico, Canada, and the Gulf of Mexico (othpt and othpt_hg).

For the nonEGU point sources (ptnonipm), some of th e data is replaced for the 2022 RFS2 case. These

differences from the 2022 AEO case are:

 47 ethanol plants revised from the 2002 AEO case w ith new emissions values. The only additional plant changed in this case that was not changed in the RFS1 Case was the Chippewa ethanol plant These data were provided by Craig Harvey on 11/17/2008: in the Excel ® dataset
“Corn_EtOH_Plant_Inv_2022-eisa-fix.xls”.

 We applied adjustments to refinery emissions by state and SCC, using the same factor for all pollutants to represent activity adju stments (note: different adjustments were made to the AEO case, and no adjustments were made to the RFS1 case). The state-level adjustment s and the list of SCCs affected were provided by Rich Cook on 11/25/2008 in Excel ® workbook “RFS2_Refinery_Adjust.xls”.

 Onroad refueling: We used the same overall proj ection approach for onroad re fueling as was used in the 2022 AEO case for the ptnonipm and nonpt sector s. Instead of using the 2022 AEO NMIM run, we used the RFS2-specific NMIM run for the 2022 refueling ratio calcula tion, with an additional ethanol adjustment factor of 1.0153, provided by David Brzezinski on 12/12/2008. The final formula for the projection factors was Factor 2022 = Emis2022 * 1.0780 /Emis2005. These factors were applied to the refueling SCCs from the 2005 base case.

 For gasoline distribution SCCs ( both ptnonipm and nonpt SCCs), we additionally applied VOC and VOC HAP adjustments to SCCs representing emissi ons from bulk-plant-to-pump (btp) and refineryto-bulk terminal (rtb) processes. These SCC-level adjustments only impact VOC and VOC HAPs in both the ptnonipm and nonpt sectors. The adjustments were provided by Craig Harvey on

12/16/2008 in the Excel ® workbook “2005ai_tox_SCC_50state_CAPHAP-20081216.xls”. (Note: these adjustments were not applied to RFS1 and di fferent adjustments were applied to AEO, based on assumed changes in the ratios of E0 to E10 and E85)

 Upstream ag-related adjustments described below for the “ag” and “afdust” sectors also affected some ptnonipm SCCs.

Lastly, VOC speciation profile changes affect ed this sector, as described in Section 3 . Emissions were

otherwise the same as the 2022 AEO case.



### 7.2 2022 RFS2 Nonpoint sources

The nonpoint sources for the 2022 RFS2 Case include th e same emissions as the 2022 AEO Case for the following sectors: the US average fires (avefire sect or), and the nonpoint emissions for Canada and Mexico (othar and othar_hg). Changes were made to the ag, afdust, and nonpt sectors.27 For the ag and afdust sectors, we adjusted emissions by SCC or SCC-pol lutant combinations based on data provided by Craig Harvey on 11/7/2008 in Excel ® workbook“otaq_upstream_2020cc_2020ce_2005ag_toxcmp.xls”. These adjustments were different for the following different groups of SCCs: “Fertilizer Application”, “Pesticide Applicatio n”, “Fertilizer Production, mixing/bl ending”, “Pesticide Production”, “Ag Tilling/loading dust”, “Ag Burning”, “Liv estock dust”, and “Livestock waste”.

The emissions for the “other nonpoin t” emissions (nonpt) changed for th e 2022 RFS2 Case in the following

ways:

 Upstream ag-related adjustments just described for the “ag” and “afdust” sectors also affected some nonpt SCCs.

 Revised the ethanol plant emissions . OAQPS converted these data to SMOKE input format using the same approaches as in the 2022 AEO case from an Excel ® workbook provided by Craig Harvey
11/17/2008: “Corn_EtOH_Plan t_Inv_2022-eisa-fix.xls”.

 Revised the ethanol transfer emi ssions. OAQPS converted these data to SMOKE input format using the same approaches as in the 2022 AEO case from an Excel ® workbook provided by Craig Harvey on 12/15/2008: “EtOH_transport_vapor_EISA.xls”.

 Revised biodiesel plant emissions . OAQPS converted these data to SMOKE input format using the same approaches as in the 2022 AEO case from an Excel ® workbook provided by Craig Harvey on
11/25/2008: “Biodsl_Plant_ Inv_2022-eisa-prelim.xls”.

 Added cellulosic ethanol plants. OAQPS converted emissions estimates for cellulosic ethanol plants to run in SMOKE, based on an Excel ® file provided by Craig Harvey on 10/21/2009:
“Cellulosic_Plant_Inv_2022- eisa-fixC-RevCell.xls”.

 Created revised portable fuel c ontainer (PFC) emissions by applyi ng adjustment factors by county and pollutant, based on an Excel ® spreadsheet provided by Ari Kahan on 1/14/2009: “aeo_to_eisa.xls”. These adjustments reduced PFC emissions as compared to the AEO case.

 As previously mentioned for the point sources in Section 7.1 , we changed refueling emissions projections, which also a ffected the nonpt sector.

 As previously mentioned fo r point sources in Section 7.1 , we adjusted the gasoline distribution emissions, which also affected the nonpt sector.

 Speciation changes also affected this case for th e nonpt and ptnonipm sectors as noted in Section 3 by using the 8737B profile for E0 fuels for re finery to bulk (rtb) emissions sources.

Lastly, VOC speciation pr ofile changes affect ed this sector, as described in Section 3

.



### 7.3 2022 RFS2 Mobile sources

Compared to the 2022 AEO case, the mobile source emissi ons included changes in all of the mobile source sectors except for the Canada and Mexico emissions (othon).

For the aircraft, locomotive, and non-C3 commerci al marine (alm_no_c3) emissions, the “remaining alm_no_c3 emissions” (item 3 in Section 4.3.1 ) were unchanged from the 2022 AEO case, but the C1 and C2

commercial marine emissions and th e locomotive class I emissions both changed. Otherwise, the processing

for this sector was the same as for 2005 and 2022 AEO. For the C1/C2 Co mmercial Marine (SCCs 2280002100, 2280002200, and 2280004000), OAQPS prepared the SMOKE-ready inventory from an

Excel ® worksheet provided by Penny Carey on 12/ 10/2008: “CMV_C1C2_2022RFS2 cases.xls”. OAQPS28 applied factors also provided by Penny Carey on 12/11/2008 to compute benzene, acetaldehyde, formaldehyde, acrolein, 1,3-butadiene , and naphthalene. Although the process was the same, these

emissions differed from those in the AEO and RFS1 cases.

For the locomotive class I emissions, OAQPS create the SMOKE-ready inventory from an Excel ® worksheet provided by Craig Harvey on 11/17/2008: “Class_I _II_III_locomotives_alm_2020ce_cty_scc_eisa.xls”. This CAP-only dataset was updated by Craig on 12/10/2008 to include HAP emissions of the key HAPs for

RFS2.

For the C3 commercial marine sources , new inventory data were provided in the same pre-gridded formats as for 2005 and 2022 AEO by Penny Carey on 11/20/2008: “ 2022base_redo.zip and 2022rfs2.zip”. OAQPS pre-processed these data using the same approaches described previ ously to create SMOKE-ready point source inventories (with each “point” representing a grid cell). Otherwise, the processing for this sector was

the same as 2005 and 2022 AEO.

For the US nonroad sector, OAQPS made adjustments to the 2022 AEO inventory to create the RFS2 inventory. California emissi ons were not changed, but emissions in all other states were updated. OAQPS used data provided by OTAQ for gasoline equipment SCCs to replace those emissions from the reference

case “Rfs2Ref2022Nr” mentioned in section 4.3.4 . The RFS2 case “Rfs2AqmE102022Nr” replaced the gasoline equipment SCCs: 2260*, 2265*, 2282005010, 2282005015, 2282010005, and 2285004015. Otherwise the steps taken were the sam e as described for the 2022 AEO case.

For the US onroad mobile sectors, revised NMIM data were provided by Harvey Mi chaels in late September 2008. OAQPS adjusted the reference case emissi ons “Rfs2Ref2022Or” (mentioned in Section 4.3.5 ) using RFS2-specific NMIM data from case “Rfs2ref2022orVc 11eisa”, which contains ad justed inventories for

1023 counties that ORNL determined would have enhanced truck traffic for the di stribution of ethanol in 2022. The only trucks affected are part5vclass=11 (these have been referred to prev iously ad “8b trucks,” but Vc11 is more accurate, sin ce it refers to both 8a and 8b). The adjustments were determined by annual

VMT in the affected counties. We assumed that trucks made a return trip. All county-SCC, pollutant-code, emission-type, month combinations in rfs2re f2022orVc11eisa replaced those combinations in Rfs2Ref2022Or. These changes affected the emissions in the on_noadj sector directly , and were also used in

the MOVES emissions alloca tion step described next.

In addition to the changes in the on_noadj sector, th e monthly onroad gasoline emissions were changed using a MOVES run specifically for the RFS2 scenario. We allocated the state-SCC MOVES data to county-SCC using ratios developed from the RFS2-specific NMIM count y-SCC data. Other than the different data used for creating the monthly county-SCC SMOKE-ready inventories, we used the same processing steps as

described for 2005 and the 2022 AEO cases.

Lastly, VOC speciation profile changes affect ed this sector, as described in Section 3

.



## 8 2022 RFS2 nonroad speciation sensitivity case

The purpose of this case was to test the impact of improving the nonroad speciation approach from the approach used in the 2022 RFS2 case. In the 2022 R FS2 case, the same mixtures of fuels as onroad were assumed for the nonroad exhaust and evaporative VOC emissions, and for refueling VOC speciation we assumed a mixture of 81% E10 (prof ile 8736) and 19% E85 (profile 8755). For this sensitivity, we revised our approach to assume 100% E10 fuel usage for nonroad mobile, which meant profile 8751 for exhaust VOC, profile 8754 for evaporative VOC, and profile 8736 for refueling VOC. Thus, this sensitivity was able to help quantify the modest magnitude of the im pact of the original VOC speciation assumptions.29 Given the timing of this sensitivity run, it did not include the revised E0 h eadspace vapor VOC speciation profile 8737B, but rather used the original 8737 profile that was also used in the 2022 RFS1 and 2022 AEO

cases.

In addition, this sensitivity did not include the cellulosic etha nol plant emissions that were included in the 2022 RFS2 case, because we ran this sens itivity prior to adding those emissi ons to the RFS2 case. Since this run was compared primarily to the RFS1 case for the pur poses of the nonroad mobile speciation impacts, this sensitivity was still useful for our analysis. Compar ison of this case with the RFS2 case will be confounded in areas that have significant emissions from cellulosic ethanol emissions sources.30 APPENDIX A

Approach for Allocating Emission Reduc tions and Speciating Emissions from Finished Fuel Transport and Distribution

Allocation of Emission Reductions

1) Emission Reductions were estima ted for RFS2 at the National-scal e as follows for each scenario by OTAQ using a spreadsheet tool. References for this spreadsheet tool are:

 EPA Docket number EPA-HQ-OAR-2005-0161-0537; Renewable Fuels Standard (RFS) 2 Production/Distribution Emission Impacts Calcul ation Spreadsheet - AEO Reference Case

 EPA Docket number EPA-HQ-OAR-2005-0161-0538; RFS2 Production/distribution Emission Impacts Calculation Spreadsheet - RFS1 Mandate reference case

E0 – Refinery to Bulk Terminal E0 – Bulk Terminal to Pump E10 – Bulk Terminal to Pump E85 – Bulk Terminal to Pump

2) Definitions:

Refinery to Bulk Terminal = Emissions a ssociated with pipelin es and bulk terminals

Bulk Terminal to Pump = Emissions associated with bulk plants, tank trucks in transit, service station unloading, and gasoline serv ice stations, underground tank: breathing and emptying

3) Assignment of reduction estimates to SCCs:

There are over 90 SCCs associated with finished fuel transport and distribution. In order to assign reductions to SCCs, each SCC must be identifie d as “refinery to bulk terminal” and “bulk terminal to pump.” Table A-1 provides this mapping.

4) Allocation of emissi ons to fuel streams:

In order to calculate percen t reduction estimates to apply to each fu el transport and distribution SCC in the NEI, emission reductions for each scenario in the categories in Step 1 must be applied to total emissions in the platform for those categories. That means total em issions from the platform must first be allocated to “refinery to bulk terminal” and “bulk te rminal to pump” using the allocation in Step 3. Then “bulk terminal to pump” emissions must be assigned to E0, E10 and E 85 using national fuel volumes for the scenarios. The national fuel volumes are given in Table A-1. Due to the complexity with determining differences in volumes for each fuel type across geographic areas for different SCCs, we will not account for local differences. Once the national emissions in the platfo rm are allocated, and emission reductions from the spreadsheet tool can be used to ca lculate percent change estimates, and these percent change estimates applied to each SCC.31 Table A-1. E0, E10 and E85 fuel volumes for RFS2 scenarios in 2022 (billions of gallons).

Scenario

RFS1 AEO RFS2

E0 77.95 16.03 0

E10 67 131 124.6

E85 0 0.1097 29.3

Application of Sp eciation Profiles

The most straightforward approach for speciating emi ssions is to apply the following speciation profiles:

E0 – Profile 8737: Composite profile - Non-oxygena ted gasoline headspac e vapor (except 8734 nonoxygenated gasoline composite profile for splash filling). For an RFS1 sensitivity case and for the RFS2 case, profile 8737B was used instead. See EP A document number EPA-420-D-10-001 “Hydrocarbon Composition of Gasoline Vapor Emissions from Enclos ed Fuel Tanks” for more information about this profile.

E10 -- 8736: Composite profile - ethanol blended ga soline headspace vapor (except 8733 Composite ProfileEthanol blended gasoline)

E85 – Profile for light-duty gasoline evaporative emissions (attached).

The E0 profile would be applied to all SCCs represen ting refinery to bulk terminal emissions. For the remaining SCCs, E0, E10 and E85 profiles are be weighted by national fuel sales volumes to come up with composite profiles for ea ch control scenario.32 Table A-2. Mapping of Fuel Distribution SCCs to “Refin ery to Bulk Terminal” and “Bulk Terminal to Pump” Categories.

Assignment

NONPOINT

GASOLINE DISTRIBUTION: STAGE I SCCs: 2501050120 (Bulk Terminals) Refinery to Bulk Terminal 2501055120 (Bulk Plants) Bulk Terminal to Pump 2505030120 (Tank Trucks in Transit) Bulk Terminal to Pump 2505040120 (Pipelines) Refinery to Bulk Terminal 2501060051 (Gasoline Service Station Unloading: Submerged Fill) Bulk Terminal to Pump 2501060052 (Gasoline Service Station Unloading: Splash Fill) Bulk Terminal to Pump 2501060053 (Gasoline Service Station Unloading: Balanced Submerged Fill) Bulk Terminal to Pump 2501060201 (Gasoline Service Stations, Underground Tank: Breathing and Emptying) Bulk Terminal to Pump

GASOLINE DISTRIBUTION: STAGE II SCC: 2501060100 (Gasoline Service Stations, Stage 2: Total) Bulk Terminal to Pump

POINT

40400103 Bulk Terminals, Gasoline Reid vapor pressure (RVP) 7: Breathing Loss (67000 Bbl. Capacity) - Fixed R Refinery to Bulk T erminal 40400104 Bulk Terminals, Gasoline RVP 13: Breathing Loss (250000 Bbl Capacity)-Fixed Roof Tank Refinery to Bulk Terminal 40400105 Bulk Terminals, Gasoline RVP 10: Breathing Loss (250000 Bbl Capacity)-Fixed Roof Tank Refinery to Bulk Terminal 40400106 Bulk Terminals, Gasoline RVP 7: Breathing Loss (250000 Bbl Capacity) - Fixed Roof Tank Refinery to Bulk Terminal 40400107 Bulk Terminals, Gasoline RVP 13: Working Loss (Diam. Independent) - Fixed Roof Tank Refinery to Bulk Terminal 40400108 Bulk Terminals, Gasoline RVP 10: Working Loss (Diam. Independent) - Fixed Roof Tank Refinery to Bulk Terminal 40400109 Bulk Terminals, Gasoline RVP 7: W orking Loss (Diam. Independent) - Fixed Roof Tank Refinery to Bulk Terminal 40400110 Bulk Terminals, Gasoline RVP 13: Standing Loss (67000 Bbl Capacity)-Float. Roof Tank Refinery to Bulk Terminal 40400111 Bulk Terminals, Gasoline RVP 10: Standing Loss (67000 Bbl Capacity)-Float. Roof Tank Refinery to Bulk Terminal 40400112 Bulk Terminals, Gasoline RVP 7: Standing Loss (67000 Bbl Capacity)- Floating Roof Tank Refinery to Bulk Terminal 40400113 Bulk Terminals, Gasoline RVP 13: Standing Loss (250000 Bbl Capacity) - Floating Roof Tank Refinery to Bulk Terminal 40400114 Bulk Terminals, Gasoline RVP 10: Standing Loss (250000 Bbl Capacity) - Floating Roof Tank Refinery to Bulk Terminal 40400115 Bulk Terminals, Gasoline RVP 7: Standing Loss (250000 Bbl Capacity) - Floating Roof Tank Refinery to Bulk Terminal 40400116 Bulk Terminals, Gasoline RVP 13/10/7: Withdrawal Loss (67000 Bbl Capacity) - Floating Roof Tank Refinery to Bulk Termin al 40400117 Bulk Terminals, Gasoline RVP 13/10/7: Withdrawal Loss (250000 Bbl Capacity) - Floating Roof Tank Refinery to Bulk Termi nal 40400118 Bulk Terminals, Gasoline RVP 13: Filling Loss (10500 Bbl Capacity) - Variable Vapor Space Refinery to Bulk Terminal 40400119 Bulk Terminals, Gasoline RVP 10: Filling Loss (10500 Bbl Capacity) - Variable Vapor Space Refinery to Bulk Terminal 40400120 Bulk Terminals, Gasoline RVP 7: Filling Loss (10500 Bbl Capacity) - Variable Vapor Space Refinery to Bulk Terminal 40400131 Bulk Terminals, Gasoline RVP 13: Standing Loss - External Floating Roof w/ Primary Seal Refinery to Bulk Terminal 40400132 Bulk Terminals, Gasoline RVP 10: Standing Loss - External Floating Roof w/ Primary Seal Refinery to Bulk Terminal 40400133 Bulk Terminals, Gasoline RVP 7: Standing Loss - External Floating Roof w/ Primary Seal Refinery to Bulk Terminal 40400141 Bulk Terminals, Gasoline RVP 13: Standing Loss - External Floating Roof w/ Secondary Seal Refinery to Bulk Terminal 40400142 Bulk Terminals, Gasoline RVP 10: Standing Loss - External Floating Roof w/ Secondary Seal Refinery to Bulk Terminal 40400143 Bulk Terminals, Gasoline RVP 7: Standing Loss - External Floating Roof w/ Secondary Seal Refinery to Bulk Terminal 40400148 Bulk Terminals, Gasoline RVP 13/10/7: Withdrawal Loss - External Floating Roof (Primary/Secondary S Refinery to Bulk Terminal 40400150 Bulk Terminals, Miscellaneous Losses/Leaks: Loading Racks Refinery to Bulk Terminal 40400151 Bulk Terminals, Valves, Flanges, and Pumps Refinery to Bulk Terminal 40400152 Bulk Terminals, Vapor Collection Losses Refinery to Bulk Terminal 40400153 Bulk Terminals, Vapor Control Unit Losses Refinery to Bulk Terminal 40400161 Bulk Terminals, Gasoline RVP 13: Standing Loss - Internal Floating Roof w/ Primary Seal Refinery to Bulk Terminal 40400162 Bulk Terminals, Gasoline RVP 10: Standing Loss - Internal Floating Roof w/ Primary Seal Refinery to Bulk Terminal 40400163 Bulk Terminals, Gasoline RVP 7: Standing Loss - Internal Floating Roof w/ Primary Seal Refinery to Bulk Terminal 40400171 Bulk Terminals, Gasoline RVP 13: Standing Loss - Internal Floating Roof w/ Secondary Seal Refinery to Bulk Terminal 40400172 Bulk Terminals, Gasoline RVP 10: Standing Loss - Internal Floating Roof w/ Secondary Seal Refinery to Bulk Terminal 40400173 Bulk Terminals, Gasoline RVP 7: Standing Loss - Internal Floating Roof w/ Secondary Seal Refinery to Bulk Terminal 40400178 Bulk Terminals, Gasoline RVP 13/10/7: Withdrawal Loss - Internal Float Roof (Primary/Secondary Seal ) Refinery to Bulk Terminal

SCC SCC Description 40400201 Bulk Plants, Gasoline RVP 13: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank Bulk Terminal to Pump 40400202 Bulk Plants, Gasoline RVP 10: Breathing Loss (67000 Bbl Capacity) - Fixed Roof Tank Bulk Terminal to Pump 40400203 Bulk Plants, Gasoline RVP 7: Breathing Loss (67000 Bbl. Capacity) - Fixed Roof Tank Bulk Terminal to Pump 40400204 Bulk Plants, Gasoline RVP 13: Working Loss (67000 Bbl. Capacity) - Fixed Roof Tank Bulk Terminal to Pump 40400205 Bulk Plants, Gasoline RVP 10: Working Loss (67000 Bbl. Capacity) - Fixed Roof Tank Bulk Terminal to Pump 40400206 Bulk Plants, Gasoline RVP 7: Working Loss (67000 Bbl. Capacity) - Fixed Roof Tank Bulk Terminal to Pump 40400207 Bulk Plants, Gasoline RVP 13: Standing Loss (67000 Bbl Cap.) - Floating Roof Tank Bulk Terminal to Pump 40400208 Bulk Plants, Gasoline RVP 10: Standing Loss (67000 Bbl Cap.) - Floating Roof Tank Bulk Terminal to Pump 40400209 Bulk Plants, Gasoline RVP 7: Standing Loss (67000 Bbl Cap.) - Floating Roof Tank Bulk Terminal to Pump 40400210 Bulk Plants, Gasoline RVP 13/10/7: Withdrawal Loss (67000 Bbl Cap.) - Floating Roof Tank Bulk Terminal to Pump 40400211 Bulk Plants, Gasoline RVP 13: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space Bulk Terminal to Pump 40400212 Bulk Plants, Gasoline RVP 10: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space Bulk Terminal to Pump 40400213 Bulk Plants, Gasoline RVP 7: Filling Loss (10500 Bbl Cap.) - Variable Vapor Space Bulk Terminal to Pump 40400231 Bulk Plants, Gasoline RVP 13: Standing Loss - External Floating Roof w/ Primary Seal Bulk Terminal to Pump 40400232 Bulk Plants, Gasoline RVP 10: Standing Loss - External Floating Roof w/ Primary Seal Bulk Terminal to Pump 40400233 Bulk Plants, Gasoline RVP 7: Standing Loss - External Floating Roof w/ Primary Seal Bulk Terminal to Pump 40400241 Bulk Plants, Gasoline RVP 13: Standing Loss - External Floating Roof w/ Secondary Seal Bulk Terminal to Pump 40400242 Bulk Plants, Gasoline RVP 10: Standing Loss - External Floating Roof w/ Secondary Seal Bulk Terminal to Pump 40400243 Bulk Plants, Gasoline RVP 7: Standing Loss - External Floating Roof w/ Secondary Seal Bulk Terminal to Pump33 APPENDIX B

IMPLEMENTATION of SPECIA TION PROFILE UPDATE FOR

FUTURE YEAR “bulk term inal to pump” SCCs

Speciation approach is provided in : Rich Cook’s writeup (email sent 11/18/2008, file called “Transport AQ Inv.doc”) along with the profiles to use via the “GSPRO_COMBO” approach and the fuels data needed to compute the linear weights to use for each profile for each future year scenario. It should be noted that the bulk terminal to pump SCCs are in both the nonpt and ptnonipm modeling sectors.

See below table for how the weights are computed from the fuels data.

All state/county FIPS codes and all months use the same speciation profile combinations based on the table from Rich Cook’s writeup (email sent 11/18/2008, file called “Transport AQ Inv.doc”), as follows:

RFS1

billions of gallons RFS2-

fraction AEO

billions of gallons AEOfraction RFS2

billions of

gallons RFS2-

fraction

E0 77.95 0.5378 16.03 0.1089 0 0.0000

E10 67 0.4622 131 0.8903 124.6 0.8096

E85 0 0.1097 0.0007 29.3 0.1904

Total 144.95 147.14 153.9

E0 – Profile 8737: Composite profile - Non-oxygena ted gasoline headspac e vapor (except 8734 nonoxygenated gasoline composite profile for splash fillin g): use only headspace vapor because VOC emissions from SCCs assigned to headspace vapor in the nonpoint inventory are three times higher than the emissions from SCCs assigned to gasoline spillage (= 933,818/295,259)

E10 -- 8736: Composite profile - ethanol blended ga soline headspace vapor (except 8733 Composite ProfileEthanol blended gasoline): use only headspace vapor since approximating that more emissions assigned to headspace than spillage (as discussed above)

E85 – Profile for light-duty gasoline evaporative emissions: use pre-tier2 since it is the only evaporative E85 profile available: 875534 Steps taken to represent the approaches above in the modeling cases:

Step 1 :

 Copy gsref file that is used for future y ear VOC to the following name: “gsref_rfs2_voc_2022”

 Change “bulk terminal to pump” sccs in the gsref file to xref to “combo”

 Change all “refinery to bulk terminal SCCs” to the E0 profile. This is prof ile 8737 (they are probably currently 8736). If there are any 8733 (don’t think there are any), then they should be changed to 8734 (E0 profile for spillage)

 Import to Emissions Modeling Framework (data system we use to manage modeling cases)

 List of “bulk terminal to pump ” and refinery to bulk terminal SCCs” provided in Appendix A.

Step 2 : Make 3 new GSPRO_COMBO files

1. AEO Case: gspro_combo_aeo_stationary all coun ties/months use the following:

0.1089  Profile 8737 + 0.8903  Profile 8736 + 0.0007  Profile 8755

2. gspro_combo_rfs1_stationary all counties/months use the following

0.5378  Profile 8737 + 0.4622  Profile 8736

3. gspro_combo_eisa_stationary: all counties/months use the following

0.8096  Profile 8736 + 0.1904  Profile 8755

Note: even though some stationary source SCCs use gasoline profile spillage , the ratio of nonpoint VOC emissions from SCCs assigned to the headsp ace speciation –to- SCCs assigned to spillage speciation is 3 ( 933818/295259) so we will use only headspace for E10. There are no point source SCCs assigned to spillage, so headspace is applicable to all point source sector SCCs.

Step 3 : Assign the appropriate gspro_combo stationary data sets to ptnonipm and nonpt sectors in the future year cases. No need (and not possible) to co ncatenate with GSPRO combo from other sectors.35 APPENDIX C

Inventory Data Files Used for Each RFS2 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.36Table C-1:List of inventory data associated with RFS2 modeling cases.CaseSectorSMOKE input filesptipmAnnual:ptinv_ptipm_cap2005nei_26aug2008_v2_orl.txtptinv_ptipm_hap2005agtox_v1_noBAFM_28aug2008_v0_orl.txtDaily: ptday_ptipm_caphap_noncem_2005ag_<mon>_ida.txtHourly: HOUR_UNIT_2005_*.txtptnonipmptinv_ptnonipm_xportfrac_2005cap_remainder_24nov2008_v0_orl.txtptinv_ptnonipm_2005caphap_3ethanol_plants_delete_for_all2022_19nov2008_v0_orl.txtptinv_ptnonipm_2005caphap_chippewa_delete_for_2022EISA_19nov2008_v0_orl.txtptinv_ptnonipm_2005hap_noBAFM_remainder_24nov2008_v0_orl.txtptinv_ptnonipm_caphap_ethanol_plant_additions_2005_17dec2008_v1_orl.txtnonptarinv_nonpt_pf4_cap_nopfc_08sep2008_v0_orl.txtarinv_nonpt_pf4_hap_nopfc_11sep2008_v0_orl.txtarinv_pfc_2002_caphap_27dec2007_v0_orl.txtarinv_nonpt_caphap_ethanol_plant_additions_2005_17dec2008_v0_orl.txtafdustarinv_afdust_2002ad_xportfrac_26sep2007_v0_orl.txtagarinv_ag_cap2002nei_06nov2006_v0_orl.txtalm_no_c3arinv_alm_no_c3_cap2002v3_30jul2008_v1_orl.txtarinv_alm_no_c3_hap2002v4_12sep2008_v0_orl.txtnonroadarinv_nonroad_caps_2005v2_<mon>_revised_08sep2008_v0_orl.txtarinv_nonroad_haps_2005v2_<mon>_revised_05sep2008_v0_orl.txtarinv_nonroad_calif_caphap_2005v2_<mon>_02apr2008_v0_orl.txtseca_c3ptinv_seca_c3_caps2005pf4_31jul2008_v0_orl.txtptinv_seca_c3_haps_east2005pf4_31jul2008_v0_orl.txtptinv_seca_c3_haps_central2005pf4_31jul2008_v0_orl.txtptinv_seca_c3_haps_west2005pf4_31jul2008_v0_orl.txtptinv_seca_c3_haps_NonUS_east2005pf4_09sep2008_v0_orl.txtptinv_seca_c3_haps_NonUS_central2005pf4_09sep2008_v0_orl.txtptinv_seca_c3_haps_NonUS_west2005pf4_09sep2008_v0_orl.txton_noadjmbinv_onroad_capshaps_2005v2_nmim_not2moves_<mon>_08sep2008_v0_orl.txtmbinv_on_noadj_moves_<mon>_14NOV08_14nov2008_v0_orl.txtmbinv_onroad_calif_caphap_2005v2_<mon>_02apr2008_v0_orl.txton_moves_startpmmbinv_onroad_moves_startpm_<mon>_20oct2008_v0_orl.txton_moves_runpmmbinv_onroad_moves_runpm_<mon>_20oct2008_v0_orl.txt2005 AllptfireMaster: ptinv_ptfire_2005rfs2_18nov2008_v1_orl.txtDaily: ptday_ptfire_<mon>_*2005rfs2_*_v0.orl.txt37Case SectorSMOKE input filesothararinv_nonroad_mexico_interior1999_21dec2006_v0_ida.txtarinv_nonroad_mexico_border1999_21dec2006_v0_ida.txtarinv_nonroad_Canada_2000_inventory_11feb2008_v2_ida.txtarinv_nonpoint_Canada2000_21dec2006_v0_ida.txtarinv_nonpt_mexico_interior1999_21dec2006_v0_ida.txtarinv_nonpt_mexico_border1999_21dec2006_v0_ida.txtothar_hgarinv_area_canada_hg_2000_noduplicates_23jul2008_v0_ida.txtothonmbinv_onroad_Canada2000_07nov2006_v0_ida.txtmbinv_onroad_mexico_border1999_21dec2006_v0_ida.txtmbinv_onroad_mexico_interior1999_21dec2006_v0_ida.txtothptCanadian point sources from the 2002 platform are proprietaryptinv_mexico_border99_03mar2008_v1_ida.txtptinv_mexico_interior99_05feb2007_v0_ida.txtptinv_offshore_point_from_2001_platform_07nov2006_v0_ida.txtothpt_hgCanadian point sources from the 2002 platform are proprietaryptipmAnnual: Same as 2005ai_tox_05bDaily: ptday_ptipm_caphap_noncem_2005ag_<mon>_ida.txtptday_ptipm_caphap_cem_2005ci_<mon>_ida.txt2005 “ci”caseavefirearinv_avefire_2002ce_21dec2007_v0_ida.txtarinv_avefire_2002_hap_18nov2008_v0_orl.txtptipmAnnual/seasonal:ptinv_egu_summer_2020_pf31capsHgHCl_19feb2008_v0_orl.txtptinv_egu_winter_2020_pf31capsHgHCl_19feb2008_v0_orl.txtptinv_ptipm_hap2005agtox_v1_noBAFM_noHgHCL_07jan2009_v2_orl.txtDaily: ptday_ptipm_caphap_noncem_2022ci_31mar_ida.txtptday_ptipm_caphap_noncem_2022ci_apr_ida.txtptday_ptipm_caphap_cem_2022ci_31mar_ida.txtptday_ptipm_caphap_cem_2022ci_apr_ida.txtptday_ptipm_hap_cem_2005ci_31mar_ida.txtptday_ptipm_hap_cem_2005ci_apr_ida.txtptday_ptipm_hap_noncem_2005ag_31mar_ida.txtptday_ptipm_hap_noncem_2005ag_apr_ida.txtavefireSame as data inbase case (2005ci_tox_05b)afdustarinv_afdust_2020ce_02b_BASE_07apr2008_v0_orl.txt2022 – Allothararinv_nonroad_mexico_interior1999_21dec2006_v0_ida.txtarinv_nonroad_mexico_border1999_21dec2006_v0_ida.txt38Case SectorSMOKE input filesarinv_2020nr_canada_province_17apr2008_v1_ida.txtarinv_2020as_canada_province_05oct2007_v0_ida.txtarinv_nonpt_mexico_interior1999_21dec2006_v0_ida.txtarinv_nonpt_mexico_border1999_21dec2006_v0_ida.txtothonmbinv_2020ms_canada_province_05jun2007_v0_ida.txtmbinv_onroad_mexico_border1999_21dec2006_v0_ida.txtmbinv_onroad_mexico_interior1999_21dec2006_v0_ida.txtothptCanadian point sources from the 2002 platform are proprietaryptinv_mexico_border99_03mar2008_v1_ida.txtptinv_mexico_interior99_05feb2007_v0_ida.txtptinv_offshore_point_from_2001_platform_07nov2006_v0_ida.txtothar_hg andothpt_hgSame as 2005ptnonipmptinv_ptnonipm_2022ci_tox_aeo_05b_AEO_06jan2009_v0_orl.txtptinv_ptnonipm_caphap_ethanol_plant_additions_2022AEO_05jan2009_v1_orl.txtptinv_ptnonipm_2005caphap_chippewa_delete_for_2022EISA_19nov2008_v0_orl.txtnonptarinv_nonpt_2022ci_tox_aeo_05b_AEO_29dec2008_v0_orl.txtarinv_pfc_caphap2020_02apr2008_v0_orl.txtarinv_nonpt_caphap_biodiesel_plant_additions_2022AEO_18dec2008_v0_orl.txtarinv_nonpt_caphap_ethanol_plant_additions_2022AEO_15dec2008_v0_orl.txtarinv_nonpt_voc_ethanol_transfer_additions_2022AEO_15dec2008_v0_orl.txtagarinv_ag_2020ce_02b_BASE_08apr2008_v0_orl.txton_noadjmbinv_on_noadj_MOVES_2022rfs_aeo_<mon>_21JAN2009_21jan2009_v0_orl.txtmbinv_on_noadj_rfs2_aeo_caphap2022_<mon>_12jan2009_v0_orl.txtmbinv_onroad_calif_caphap_2020v31_<mon>_17apr2008_v0_orl.txton_moves_startpmmbinv_onroad_moves_startpm_2022rfs_aeo_<mon>_18dec2008_v0_orl.txton_moves_runpmmbinv_onroad_moves_runpm_2022rfs_aeo_<mon>_18dec2008_v0_orl.txt2022AEOseca_c3ptinv_eca_c3_caps2022BASE_10dec2008_v0_orl.txtptinv_eca_c3_haps_NonUS_central2022BASE_10dec2008_v0_orl.txtptinv_eca_c3_haps_NonUS_east2022BASE_10dec2008_v0_orl.txtptinv_eca_c3_haps_NonUS_west2022BASE_10dec2008_v0_orl.txtptinv_eca_c3_haps_central2022BASE_10dec2008_v0_orl.txtptinv_eca_c3_haps_east2022BASE_10dec2008_v0_orl.txtptinv_eca_c3_haps_west2022BASE_10dec2008_v0_orl.txt39Case SectorSMOKE input filesnonroadarinv_nonroad_rfs2_aeo_caphap2022_<mon>_09jan2009_v0_orl.txtarinv_nonroad_calif_caphap_lm_2020v31_<mon>_17apr2008_v0_orl.txtalm_no_c3arinv_alm_no_c3_LOCOMOTIVES_Class1_rfs2_aeo_caphap2022_14dec2008_v0_orl.txtarinv_alm_no_c3_C1C2_rfs2_aeo_caphap2022_14dec2008_v0_orl.txtarinv_alm_no_c3_AIRCRAFT_nonClass1_LOCO_rfs2_aeo_caphap2022_14dec2008_v0_orl.txtptnonipmptinv_ptnonipm_2022ci_tox_rfs1_05b_RFS1_22jan2009_v0_orl.txtptinv_ptnonipm_caphap_ethanol_plant_additions_2022RFS1_26jan2009_v1_orl.txtptinv_ptnonipm_2005caphap_chippewa_delete_for_2022EISA_19nov2008_v0_orl.txtnonptarinv_nonpt_2022ci_tox_rfs1_05b_RFS1_22jan2009_v0_orl.txtarinv_pfc_rfs2_rfs1_caphap2020_15jan2009_v0_orl.txtarinv_nonpt_caphap_biodiesel_plant_additions_2022RFS1_18dec2008_v0_orl.txtarinv_nonpt_caphap_ethanol_plant_additions_2022RFS1_15dec2008_v0_orl.txtarinv_nonpt_voc_ethanol_transfer_additions_2022RFS1_15dec2008_v0_orl.txtagSame as 2022 AEOon_noadjmbinv_on_noadj_MOVES_2022rfs_RFS1_apr_09FEB2009_10feb2009_v0_orl.txtmbinv_on_noadj_rfs2_rfs1_caphap2022_apr_12jan2009_v0_orl.txtmbinv_onroad_calif_caphap_2020v31_apr_17apr2008_v0_orl.txton_moves_startpmmbinv_onroad_moves_startpm_2022rfs_RFS1_<mon>_09FEB2009_10feb2009_v0_orl.txton_moves_runpmmbinv_onroad_moves_runpm_2022rfs_RFS1_<mon>_09FEB2009_10feb2009_v0_orl.txtseca_c3Same as 2022 AEOnonroadarinv_nonroad_rfs2_rfs1_caphap2022_<mon>_09jan2009_v0_orl.txtarinv_nonroad_calif_caphap_lm_2020v31_<mon>_17apr2008_v0_orl.txt2022RFS1 andRFS1sensitivityalm_no_c3arinv_alm_no_c3_AIRCRAFT_nonClass1_LOCO_rfs2_all_RFS2_scenarios_caphap2022_14dec2008_v0_orl.txtarinv_alm_no_c3_C1C2_rfs2_rfs1_caphap2022_14jan2009_v0_orl.txtarinv_alm_no_c3_LOCOMOTIVES_Class1_rfs2_rfs1_caphap2022_14jan2009_v0ptnonipmptinv_ptnonipm_2022ci_tox_eisa_05b_EISA_23jan2009_v0_orl.txtptinv_ptnonipm_caphap_ethanol_plant_additions_2022EISA_27jan2009_v2_orl.txtnonptarinv_nonpt_2022ci_tox_eisa_05b_EISA_26jan2009_v0_orl.txtarinv_nonpt_caphap_biodiesel_plant_additions_2022EISA_18dec2008_v0_orl.txtarinv_nonpt_caphap_ethanol_plant_additions_2022EISA_15dec2008_v0_orl.txtarinv_nonpt_voc_ethanol_transfer_additions_2022EISA_15dec2008_v0_orl.txtarinv_pfc_rfs2_eisa_caphap2020_15jan2009_v0_orl.txtarinv_nonpt_caphap_cellolosic_plant_additions_2022eisa_21OCT2009_orl.txt2022RFS2agarinv_ag_2022ci_tox_eisa_05b_EISA_27jan2009_v0_orl.txt40Case SectorSMOKE input filesafdustarinv_afdust_2022ci_tox_eisa_05b_EISA_27jan2009_v0_orl.txton_noadjmbinv_on_noadj_MOVES_2022rfs_EISA_<mon>_26FEB2009_26feb2009_v0_orl.txtmbinv_on_noadj_rfs2_eisa_caphap2022_<mon>_14jan2009_v0_orl.txtmbinv_onroad_calif_caphap_2020v31_<mon>_17apr2008_v0_orl.txton_moves_startpmmbinv_onroad_moves_startpm_2022rfs_EISA_<mon>_26FEB2009_26feb2009_v0_orl.txton_moves_runpmmbinv_onroad_moves_runpm_2022rfs_EISA_<mon>_26FEB2009_26feb2009_v0_orl.txtseca_c3ptinv_eca_c3_caps2022EISA_10dec2008_v0_orl.txtptinv_eca_c3_haps_east2022EISA_10dec2008_v0_orl.txtptinv_eca_c3_haps_central2022EISA_10dec2008_v0_orl.txtptinv_eca_c3_haps_west2022EISA_10dec2008_v0_orl.txtptinv_eca_c3_haps_NonUS_east2022EISA_10dec2008_v0_orl.txtptinv_eca_c3_haps_NonUS_central2022EISA_10dec2008_v0_orl.txtptinv_eca_c3_haps_NonUS_west2022EISA_10dec2008_v0_orl.txtnonroadarinv_nonroad_rfs2_eisa_caphap2022_<mon>_09jan2009_v0_orl.txtarinv_nonroad_calif_caphap_lm_2020v31_<mon>_17apr2008_v0_orl.txt2022RFS2,SensitivitynonptSame as 2022 RFS2 case, but didnotinclude dataset:arinv_nonpt_caphap_biodiesel_plant_additions_2022EISA_18dec2008_v0_orl.txt41 APPENDIX D

Ancillary Data Files Used for RFS2 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.42Table D-1:Detailed list of ancillary data differences betweenRFS2 2005 and 2005 v4 platform2005 v4 platform2005 RFS2 platformDescriptionEnvironmentVariableSectorsDatasetVsnDatasetVsnComment and ImpactImpact?Area-source spatialcross-referenceAGREFAll sectorsamgref_us_can_mex_revised5amgref_us_can_allmex35For v4 platform, revised for new Canadian inventory andspatial surrogatesYesOnroad spatialcross-referenceMGREFAll sectorsamgref_us_can_mex_revised7amgref_us_can_allmex35For v4 platform, revised for new Canadian inventory andspatial surrogatesYesArea and onroadtemporal profilesATPRO,MTPRO,PTPROAll sectorsamptpro_2005_us_can_revised0amptpro_2005_us_can1For v4 platform, revised for new Canadian inventory andtemporal profilesYesArea temporalcross-referenceATREF,PTREFAll sectorsamptref_v3_3_revised1amptref_v3_35For v4 platform, added SCCs needed for 2005 v2 pointinventory and for WRAP oil and gas inventoryYesOnroad temporalcross-referenceMTREFAll sectorsamptref_v3_3_revised0amptref_v3_35For 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_lambertonly13An older file used for RFS2 with fewer grids defined.NoInventory tableINVTABLEAll sectorsinvtable_hapcapintegate_cb05soa_nomp4invtable_hapcap_cb05soa6RFS2 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.NoInventory tableINVTABLEavefire,ptnonipm,ptipminvtable_hapcapnohapuse_cb05soa_nomp4n/aApproach for implementing "no HAP use" approach forthese sectors was different in RFS2, but the result was thesame.NoNon-HAPExclusions for nonptsectorNHAPEXCLUDEnonptnhapexclude_nonpt_pf43nhapexclude_nonpt_pf40v1 file contains updates for ethanol 8-digit SCCs with notHAPs associated with the VOC. v3 includes WRAP oil andgas SCCs since these do not have HAP VOCs either. v1needed for future year RFS2 and v3 needed for the v4platform.YesElevatedconfiguration file forseca_c3 sectorPELVCONFIGseca_c3pelvconfig_seca_c30n/av4 platform used inline point sources, and so PELVCONFIGfile needed only in v4 platform.YesSpeciation profilesfor INTEGRATEHAPSGSPROAll sectorsgspro_integratehaps_cb05_tx_pf41gspro_integratehaps_cb05_tx_pf40For v4 platform: Added benzene-to-benzene record: movethis record from the "other HAP VOC" gspro since it is usedin SOANoSpeciation profilesfor TOGGSPROAll sectorsgspro_tog_cb05_soa_pf4_pretier21gspro_tog_nohapuse_cb05_tx_pf4_pretier21Excluded ald2_primary and form_primary from v4 platformsince not neededNoSpeciation profilesstaticGSPROAll sectorsgspro_static_cmaq9gspro_static_cmaq7For the v4 platform, this dataset modified to removebiogenic profiles to put them into a separate dataset.NoSpeciation 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.Yes432005 v4 platform 2005 RFS2 platformDescriptionEnvironmentVariableSectors Dataset VsnDataset VsnComment and ImpactImpact?Speciation 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)YesSpeciagtino profilesfor biogenicemissionsGSPRObioggspro_biogenics0n/aFor the v4 platform, this dataset contains the biogenic VOCspeciation profiles previously included ingspro_static_cmaqv7 for the RFS2 effortNoSpeciation profilesOther VOC HAPGSPROAll sectorsn/agspro_other_hapvoc4For RFS2, this dataset has the HAP VOC species that getpassed through from inventory to the multipollutant inputscreated for RFS2.YesSpeciation profilesCHROMIUMGSPROAll sectorsn/agspro_chromium0Chromium speciation profiles needed only for multi-polltutant approach used in RFS2YesSpeciation profilesDIESEL PMGSPROAll sectorsn/agspro_dieselpm1DIESEL_PM speciation profiles needed only for multi-pollutant approach used in RFS2.YesSpeciation profilesMETALSGSPROAll sectorsn/agspro_hapmetals0HAP metal pass-through profiles needed only for multi-pollutant approach used in RFS2.YesSpeciation xref forPM2.5 diesel SCCsbut do not producedieselGSREFAll sectorsgsref_no_dieselpm1n/aSince the v4 platform did not create the DIESEL_PMspecies needed for the multipollutant version of CMAQ, thedata set used for the v4 platform contains the SCC crossreferences for diesel SCCs without the DIESEL_PMspecies. The RFS2 platform cross-references to theDIESEL_PM profiles.YesSpeciation 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 RFS2 used more current versionthan "v1" of this dataset, and the v8 dataset could be usedfor replicating RFS2 runs.YesSpeciation xref forVOC, not yearspecificGSREFAll sectorsgsref_voc_general19gsref_voc_general9For v4 platform, includes updates for 2005 v2 pointinventory, WRAP oil and gas SCCs, changes to gasolinedistribution SCCs.YesSpeciation xref forNONHAPVOC, notyear-specificGSREFAll sectorsgsref_nonhapvoc_general_update3gsref_nonhapvoc_general0Matches differences between "voc" filesYesSpeciation xref forVOC, year-specificGSREFAll sectorsgsref_voc_20052gsref_voc_20022The v4 platform includes gasoline distribution SCCsdeemed not to be year specific (refinery to bulk terminal)and updates for 2006 Canadian point sources.Speciation xref forNONHAPVOC,year-specificGSREFAll sectorsgsref_nonhapvoc_20051gsref_nonhapvoc_20021Matches differences between "voc" filesYes442005 v4 platform 2005 RFS2 platformDescriptionEnvironmentVariableSectors Dataset VsnDataset VsnComment and ImpactImpact?Speciation 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.GSREFAllsectorsgsref_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 xrefDIESEL PM,alm_no_c3GSREFalm_no_c3nonroadon_noadjn/agsref_dieselpm0Enables creation of diesel PM speciesYesSpeciation xref NODIESEL PM, otharGSREFotharn/agsref_no_dieselpm0Prevents creation ofdiesel PM species in CanadaNoSpeciation 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 speciesYesSpeciation 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 speciesYes45 APPENDIX E

Growth and Control Assumptions and Aff ected Pollutants for the 2022 AEO 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 2022. Many of these controls have effective dates between 2002 and 2005, and therefore woul d not be applied to a true 2005 inventory value.

Table E-1: Control Strategies and Projectio n Assumptions in the 202 2 RFS2 AEO Emissions

Inventories.
Control Strategies (Grouped by Affected Pollutants or Standard and Approach Used to Apply to the Inventory) Pollutants

Affected Approach or

Reference:

Non-EGU Point Controls

NO X SIP Call (Phase II): Cement Manufacturing Large Boiler/Turbine Units Large IC Engines NOx 1

DOJ Settlements: plant SCC controls Alcoa, TX MOTIVA, DE NOx, SO2 2

Refinery Consent Decrees: plant/SCC controls NOx, PM, SO2 3 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

Industrial Boiler/Process Heater plant/SCC controls for PM PM 5

Large Municipal Waste Combustors (LMWC) PM, Hg, and

metals 6

Small Municipal Waste Combustors (SMWC) PM, Hg, metals, NOx, SO2 6

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

Iron & Steel Foundries Metal: Can, Coil

Metal Furniture VOC EPA, 2007f46 Control Strategies (Grouped by Affected Pollutants or Standard and Approach Used to Apply to the Inventory) Pollutants

Affected Approach or

Reference:

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

MACT rules, national, PM: Portland Cement Manufacturing Secondary Aluminum PM 7

MACT rules, plant-level, VOC:

Auto Plants VOC 8

MACT rules, plant-level, PM & SO 2 : Lime Manufacturing PM, SO2 9

MACT rules, plant-level, PM:

Taconite Ore PM 10

Stationary Area Assumptions

Municipal Waste Landfills: project factor of 0.25 applied, VOC EPA, 2007f

Livestock Emissions Growth to year 2020 NH3 11

Residential Wood Combustion Growth and Changeouts to year 2020 all 12

Gasoline Stage II growth and control to 2022 AEO VOC 13

Portable Fuel Container growth and control to year 2020 VOC 14

EGU Point Controls

CAIR/CAMR/CAVR

IPM Model 3.0 NOx, SO2, PM 15

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

Local Onroad Programs: National Low Emission Vehicle Program (NLEV) Ozone Transport Commission (OTC) LEV Program VOC 1647 Control Strategies (Grouped by Affected Pollutants or Standard and Approach Used to Apply to the Inventory) Pollutants

Affected Approach or

Reference:

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

Aircraft, Locomotives, and Co mmercial Marine Assumptions

Aircraft: Itinerant (ITN) oper ations at airports to year 2022 all 20

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 EPA, 2007e, 21,

18

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, 2007e)

AEO-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

ptnonipm: Refinery adjustments by state and SCC Ethanol plant additions all

all EPA, 2010

nonpt:

Ethanol transfer additions

Biodiesel Plant additions VOC

all EPA, 2010

C1 and C2 Commercial Marine: Complete OTAQ inventory replacement w ith AEO adjustments to year 2022 all EPA, 2010

Class 1 Locomotives: Complete OTAQ inventory replacement w ith AEO adjustments to year 2022 all EPA, 2010

ECA C3: Complete OTAQ inventory replacement with AEO adjustments for year

2022 all EPA, 2010

Nonroad (not including California): AEO-specific gasoline equipment all EPA, 2010

Onroad (not including California): MOVES AEO-specific emissions for onroad gasoline AEO-specific Class 8a and 8B truck emissions for 816 counties determined

to have enhanced truck traffic from ethanol distribution all

all EPA, 2010

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 (Federal48 Register, 2004) for IC Engine percent reductions and geographic applicability

2. For ALCOA consent decree, used http:// cfpub.epa.gov /compliance/cases/index.cfm; for MOTIVA: used information sent by State of Delaware

3. Used data provided by Brenda Shine, EPA, OAQPS

4. Closures obtained from EPA sector leads; most verified using the world wide web.

5. Used data list of plants provided by project lead from 2001-based platform; required mapping the 2001 plants to 2002 NEI plants due to plant id changes across inventory years

6. Used data provided by Walt Stevenson, EPA, OAQPS

7. Same as used in CAIR, except added SCCs appeared to be covered by the rule: both reductions based on preamble to final rule. (Portland Cement us ed a weighted average across two processes )

8. Percent reductions recommended and plants to apply to reduction to were based on recommendations by rule lead engineer, and are consistent with the reference: EPA, 2007e

9. Percent reductions recommended are determined from the existing plant estimated baselines and estimated reductions as shown in the Federal Register Noti ce for the rule. SO2 % reduction will therefore be 6147/30,783 = 20% and PM10 and PM2.5 reductions will both be 3786/13588 = 28%

10. Same approach used in CAIR: FR notice estimates reductions of "PM emissions by 10,538 tpy, a reduction of about 62%." Used same list of plants as were identified based on tonnage and SCC from CAIR.

11. Except for dairy cows and turkeys (no growth), based in animal population growth estimates from USDA and Food and Agriculture Policy and Research Institute.

12. Expected benefits of woodstoves change-out program: http://www.epa.gov/woodstoves/index.html

13. VOC emission ratios of year 2022 AEO-specific from year 2005 from the National Mobile Inventory Model (NMIM) results for onroad refueling including activity growth from VMT, Stage II control programs at gasoline stations, and phase in of newer vehicles with onboard Stage II vehicle controls.

14. VOC and benzene emissions for year 2020 from year 2002 from MSAT rule (EPA, 2007c, EPA, 2007d)

15. http://www.epa.gov/airmarkets/progsregs/epa-ipm/docs/summary2006.pdf

16. Only for states submitting these inputs: http://www.epa.gov/otaq/lev-nlev.htm

17. http://www.epa.gov/nonroad-diesel/2004fr.htm

18. http://www.epa.gov/cleanschoolbus/

19. http://www.epa.gov/otaq/marinesi.htm

20. Federal Aviation Administration (FAA) Terminal Area Forecast (TAF) System, December 2007: http://www.apo.data.fa a.gov /main /taf.asp

21. http://www.epa.gov/nonroad-diesel/2004fr.htm