Appendix 8.1 

Emissions Modeling (TSD Chapter 2) 
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2.0 EMISSIONS MODELING 

2.1 Emissions Modeling Overview 

For the emissions modeling work conducted in support of CENRAP air quality modeling, we 

used updated 2002 emissions data for the U.S., 1999 emissions data for Mexico, and 2000 

emissions data for Canada to generate a final base 2002 Base G Typical (Typ02G) annual 

emissions database. Numerous iterations of the emissions modeling were conducted using 

interim databases before arriving at the final Base G emission inventories. The 2002 and 2018 

emissions inventories and ancillary modeling data were provided by CENRAP emissions 

inventory contractors (Pechan and CEP, 2005c,e; Reid et al., 2004a,b; Coe and Reid, 2003), 

other Regional Planning Organizations (RPOs) and EPA. Building from the CENRAP 

preliminary 2002 database (Pechan and CEP. 2005e) and 2018 projections (Pechan, 2005d), we 

integrated several updates to the inventories and ancillary data to create final emissions input 

files; the final simulations are referred to as 2002 Typical and 2018 Base G, or Typ02G and 

Base18G. We used the Sparse Matrix Operator Kernel Emissions (SMOKE) version 2.1 

processing system (CEP, 2004) to prepare the inventories for input to the air quality modeling 

systems. The SMOKE simulations documented in this report include emissions generated for 

annual CMAQ and CAMx simulations at a 36-km model grid resolution, and a short-term 

CMAQ test simulation at a 12-km model grid resolution. We performed the modeling and 

quality assurance (QA) work based on the CENRAP modeling Quality Assurance Project Plan 

(QAPP; Morris and Tonnesen, 2004) and Modeling Protocol (Morris et al., 2004a). 

The Typ02G and Base18G emission inventories represent significant improvements to the 

preliminary emissions modeling performed by CENRAP (Morris et al., 2005). While the 

preliminary 2002 modeling served its purpose to develop the infrastructure for modeling large 

emissions data sets and producing annual emissions simulations, much of the input data (both as 

inventories and ancillary data) were placeholders for actual 2002 data that were being prepared 

through calendar year 2005. As these actual 2002 data sets became available, they were 

integrated into the SMOKE modeling and QA system that was developed during the preliminary 

modeling, to produce a high-quality emissions data set for use in the final CMAQ and CAMx 

modeling. The addition of entirely new inventory categories, like marine shipping, added 

complexity to the modeling. By the end of the emissions data collection phase, there were 23 

separate emissions processing streams covering a variety of sources categories necessary to 

general model-ready emission inputs for the 2002 calendar year. 

2.1.1 SMOKE Emissions Modeling System Background 

The purpose of SMOKE (or any emissions processor) is to process the raw emissions reported by 

states and EPA into gridded hourly speciated emissions required by the air quality model. 

Emission inventories are typically available as an annual total emissions value for each 

emissions source, or perhaps with an average-day emissions value. The air quality models, 

however, typically require emissions data on an hourly basis, for each model grid cell (and 

perhaps model layer), and for each model species. Consequently, emissions processing involves 

(at a minimum) transformation of emission inventory data by temporal allocation, chemical 

speciation, spatial allocation, and perhaps layer assignment, to achieve the input requirements of 

the air quality model. For the CENRAP modeling effort, all of these steps were needed. In 
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addition, CENRAP processing requires special MOBILE6 processing and growth and control of 

emissions for the future-year inventories. Finally, the biogenic emission processing using BEIS2 

includes additional processing steps. SMOKE formulates emissions modeling in terms of sparse 

matrix operations. Figure 2-1 shows an example of how the matrix approach organizes the 

emissions processing steps for anthropogenic emissions, with the final step that creates the 

model-ready emissions being the merging of all the different processing streams of emissions 

into a total emissions input file for the air quality model. Figure 2-1 does not include all the 

potential processing steps, which can be different for each source category in SMOKE, but does 

include the major processing steps listed in the previous paragraph, except the layer assignment. 

Specifically, the inventory emissions are arranged as a vector of emissions, with associated 

vectors that include characteristics about the sources such as its state and county or source 

classification code (SCC). SMOKE also creates matrices that will apply the gridding, speciation, 

and temporal factors to the vector of emissions. In many cases, these matrices are independent 

from one another, and can therefore be generated in parallel. The processing approach ends with 

the merge step, which combines the inventory emissions vector (now an hourly inventory file) 

with the control, speciation, and gridding matrices to create model-ready emissions. 

Figure 2-1. Flow diagram of major SMOKE processing steps needed by all source categories.

Temporal processing includes both seasonal or monthly adjustments and day-of-week 

adjustments. Emissions are known to be quite different for a typical weekday versus a typical 

Saturday or Sunday. For the day-of-week temporal processing step, emissions may be processed 

using representative Monday, weekday, Saturday, and Sunday for each month; we refer to this 

type of processing here as MWSS processing (note that because SMOKE operates in Greenwich 

Mean Time [GMT] then Monday would include some of local time Sunday so needs to be 

processed separately from the typical weekday). This approach significantly reduces the number 

of times the temporal processing step must be run. In the sections below, we have identified the 

cases in which we have used the MWSS processing approach. Figure 2-2 provides a schematic 

diagram of SMOKE/BEIS2 processing steps used in this project to generate biogenic emissions 

rates for Volatile Organic Compounds (VOCs) and oxides of nitrogen (NOx). Because biogenic 

emissions are temperature sensitive, they are generated for each day of 2002 using day-specific 

meteorological conditions from the MM5 meteorological model. 
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Figure 2-2. Flow diagram of SMOKE/BEIS2 processing steps.

2.1.2 SMOKE Scripts 

The scripts are the interface that emissions modelers use to run SMOKE and define the set up 

and databases used in the emissions modeling so are important for anyone wishing to reproduce 

the CENRAP SMOKE emissions modeling. Many iterations of the CENRAP SMOKE 

emissions modeling were performed using updated and corrected emissions data and 

assumptions resulting in the creation of numerous SMOKE modeling scripts during the course of 

the study. For the CENRAP annual 2002 SMOKE emissions modeling, the default SMOKE 

script set up, which is based on source categories, was used to configure the scripts. We made 

several modifications to the default SMOKE scripts to modularize them, add error checking 

loops, and break up the report and logs directories by source category. The result is one script for 

each major source category being modeled that calls all of the SMOKE programs required for 

simulating that source category. 16 major source categories were modeled by SMOKE for 

CENRAP. An addition seven SMOKE scripts were also run to set up the emissions modeling. 

Table 2-1 lists all of the SMOKE scripts used for the 2002 base year modeling and the SMOKE 

programs called by each script. In addition to the source-specific scripts listed in Table 2-1, we 

also listed the SMOKE utility scripts that actually call executables, manage the log files, and 

manage the configuration of the SMOKE simulations.
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Table 2-1. Summary of SMOKE scripts 

Source Category Script Name SMOKE 

Programs/Functions 

Area /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_ar_base02f.csh 

smkinev, grdmat, spcmat, 

temporal, smkmerge, smkreport 

Area fire /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_arf_base02f.csh 

smkinev, grdmat, spcmat, 

temporal, smkmerge, smkreport 

Offshore Area /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_ofsar_base02f.csh 

smkinev, grdmat, spcmat, 

temporal, smkmerge, smkreport 

Non-road* 

Mobile 

/home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_nr_base02f.csh 

smkinev, grdmat, spcmat, 

temporal, smkmerge, smkreport 

Fugitive dust /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_fd_base02f.csh 

smkinev, grdmat, spcmat, 

temporal, smkmerge, smkreport 

Road dust /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_rd_base02f.csh 

smkinev, grdmat, spcmat, 

temporal, smkmerge, smkreport 

Ammonia* /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_nh3_base02f.csh 

smkinev, grdmat, spcmat, 

temporal, smkmerge, smkreport 

On-road 

Mobile (non-VMT-based) 

/home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_mb_base02f.csh 

smkinev, grdmat, spcmat, 

temporal, smkmerge, smkreport 

On-road non-US Mobile 

(non-VMT-based) 

/home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_nusm_base02f.csh 

smkinev, grdmat, spcmat, 

temporal, smkmerge, smkreport 

On-road Mobile (VMTbased) /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_mbv_base02f.csh 

smkinev, mbsetup, grdmat, 

spcmat, premobl, emisfac, 

temporal, smkmerge, smkreport 

WRAP Oil and Gas /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_wog_base02f.csh 

smkinev, grdmat, spcmat, 

temporal, smkmerge, smkreport 

Point /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_pt_base02f.csh 

smkinev, grdmat, spcmat, laypoint, 

temporal, smkmerge, smkreport 

Offshore point /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_ofs_base02f.csh 

smkinev, grdmat, spcmat, laypoint, 

temporal, smkmerge, smkreport 

Canadian Point fires /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_bsf_base02f.csh 

smkinev, grdmat, spcmat, laypoint, 

temporal, smkmerge, smkreport 

All point fires /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_alf_base02f.csh 

smkinev, grdmat, spcmat, laypoint, 

temporal, smkmerge, smkreport 

Biogenec /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smk_bg_base02f.csh 

Normbies3, tmpbies3, smkmerge 

n/a /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/make_invdir.csh 

builds output file names and 

directories 

n/a /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/smk_run.csh 

Calls SMOKE executables for 

everything but projection, controls, 

and QA 

n/a /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/qa_run.csh 

Calls the SMOKE executables for 

running QA program & names the 

input/output directories for reports 

n/a /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

scripts/run/36km/smoke_calls.csh 

Calls smk_run.csh, qa_run.csh, 

configuration and management 

n/a /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

Assignes/ASSIGNES.cenrap_base02f.cmaq.cb4

p25 

Sets up the environment variables 

for use of SMOKE 

n/a /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

Assignes/smk_mkdir 

Creates the input/output 

directories 

n/a /home/aqm2/edss2/cenrap02f/subsys/smoke/ 

Assignes/setmerge_files.scr 

Sets up the output environment 

variables for the smkmerge 

program 

* The nr and nh3 where farther divided to nrm and nry and nh3m and nh3y for the monthly/seasonal and yearly inventories
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2.1.3 SMOKE Directory Structures 

The SMOKE directories can be divided into three broad categories: 

1. Program Directories: These directories contain the model source code, assigns files, 

scripts and executables needed to run SMOKE. 

2. Input Directors: These directories contain the raw emissions inventories, the 

meteorological data and the ancillary input files. 

3. Output Directories: These directories contain all of the output from the model. Also, the 

output directories contain the MOBILE6 input files. 

The directories are described in the Table 2-2. The final pre-merged emission file names and 

sources of the data re provided in Appendix B.

Table 2-2. Summary of SMOKE directories. 

Category Directory Location Directory Contents 

/home/aqm2/edss2/ cenrap02f/subsys/smoke/src SMOKE source code 

/home/aqm2/edss2/ 

cenrap02f/subsys/smoke/assigns 

SMOKE assigns files 

/home/aqm2/edss2/ cenrap02f/subsys/smoke/scripts SMOKE make and run 

scripts 

Program 

/home/aqm2/edss2/ 

cenrap02f/subsys/smoke/Linux2_x86pg 

SMOKE executables 

/home/aqm2/edss2/ cenrap02f/data/met MCIP out metrology files 

/home/aqm2/edss2/ cenrap02f/data/ge_dat SMOKE ancillary input files 

Input 

/home/aqm2/edss2/ 

cenrap02f/data/inventory/cenrap2002 

Raw emissions inventory 

files 

/home/aqm2/edss2/ 

cenrap02f/data/run_base02f/static 

Non-time dependent SMOKE 

intermediate outputs and 

MOBILE6 inputs 

/home/aqm2/edss2/ cenrap02f/ 

data/run_base02f/scenario 

Time dependent SMOKE 

intermediate outputs 

/home/aqm2/edss2/ 

cenrap02f/data/run_base02f/outputs 

Model-ready SMOKE 

outputs 

Output 

/home/aqm2/edss2/ cenrap02f/data/reports SMOKE QA reports 

2.1.3 SMOKE Configuration 

SMOKE was configured to generate emissions for all months of 2002 on the 36-km unified RPO 

modeling domain (Figure 1-2). For the anthropogenic emissions sources that use hourly 

meteorology and daily or hourly data (i.e., on-road mobile sources, point sources with CEM data, 

point source fires and biogenic sources) we configured SMOKE to represent the daily emissions 

explicitly. For the non-meteorology dependent emissions, we used a representative Saturday, 

Sunday, Monday, and weekday for each month as surrogate days for the entire month’s 

emissions (we refer to this as the MWSS processing approach). For these non-meteorology 

dependent emissions sources we explicitly represented the holidays as Sundays. Table 2-3 lists 

the days that we modeled as representative days in the months that we simulated for the 2002 

base year modeling. Table 2-4 lists the holidays in 2002 that were modeled as Sundays.
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We used the designations in Table 2-5 to determine which months fell into each season when 

temporally allocating the seasonal emissions inventories. Some of the inventories for the 

Electrical Generating Units (EGUs) were received for Winter and Summer. Table 2-6 

determines which months fell into each season 
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Table 2-5: Assignments of months to four seasons for use of 

seasonal inventory files in SMOKE.

Table 2-6: Assignments of months to two seasons for use of 

seasonal inventory files in SMOKE. 

2.1.5 SMOKE Processing Categories 

Emissions inventories are typically divided into area, on-road mobile, non-road mobile, point, 

and biogenic source categories. These divisions arise from differing methods for preparing the 

inventories, different characteristics and attributes of the categories, and how the emissions are 

processed through models. Generally, emissions inventories are divided into the following 

source categories, which we refer to later as “SMOKE processing categories.” 

• Stationary Area Sources: Sources that are treated as being spread over a spatial extent 

(usually a county or air district) and that are not movable (as compared to non-road 

mobile and on-road mobile sources). Because it is not possible to collect the emissions at 

each point of emission, they are estimated over larger regions. Examples of stationary 

Month Season 

January Winter 

February Winter 

March Winter 

April Winter 

May Summer 

June Summer 

July Summer 

August Summer 

September Summer 

October Winter 

November Winter 

December Winter 
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area sources are residential heating and architectural coatings. Numerous sources, such as 

dry cleaning facilities, may be treated either as stationary area sources or as point sources. 

• On-Road Mobile Sources: Vehicular sources that travel on roadways. These sources can 

be computed either as being spread over a spatial extent or as being assigned to a line 

location (called a link). Data in on-road inventories can be either emissions or activity 

data. Activity data consist of vehicle miles traveled (VMT) and, optionally, vehicle 

speed. Activity data are used when SMOKE will be computing emission factors via 

another model, such as MOBILE6 (U.S. EPA, 2005). Examples of on-road mobile 

sources include light-duty gasoline vehicles and heavy-duty diesel vehicles. 

• Non-Road Mobile Sources: These sources are engines that do not always travel on 

roadways. They encompass a wide variety of source types from lawn and garden 

equipment to locomotives and airplanes. Emission estimates for most non-road sources 

come from EPA’s NONROAD model (OFFROAD in California). The exceptions are 

emissions for locomotives, airplanes, pleasure craft and commercial marine vessels. 

• Point Sources: These are sources that are identified by point locations, typically because 

they are regulated and their locations are available in regulatory reports. In addition, 

elevated point sources will have their emissions allocated vertically through the model 

layers, as opposed to being emitted into only the first model layer. Point sources are often 

further subdivided into electric generating unit (EGU) sources and non-EGU sources, 

particularly in criteria inventories in which EGUs are a primary source of NOx and SO2. 

Examples of non-EGU point sources include chemical manufacturers and furniture 

refinishers. Point sources are included in both criteria and toxics inventories. 

• Biogenic Land Use Data: Biogenic land use data characterize the types of vegetation that 

exist in either county-total or grid cell values. The biogenic land use data in North 

America are available using two different sets of land use categories: the Biogenic 

Emissions Landcover Database (BELD) version 2 (BELD2), and the BELD version 3 

(BELD3) (CEP, 2004b). 

In addition to these standard SMOKE processing categories, we have added other categories 

either to represent specific emissions processes more accurately or to integrate emissions data 

that are not compatible with SMOKE. Examples of emissions sectors that fall outside of the 

SMOKE processing categories include emissions generated from process-based models for 

representing windblown dust and agricultural ammonia (NH3) sources. An emissions category 

with data that are not compatible with SMOKE is one with gridded emissions data sets, such as 

commercial marine sources. Another nonstandard emissions category that we modeled was 

emissions from fires. All of the emissions categories that we used to build CENRAP simulations 

are described in detail in the following sections. 

Continuing the enhancement of the emissions source categories that we initiated during the 

preliminary 2002 modeling, we further refined the categories from the standard definitions listed 

above to include more explicit emissions sectors. The advantage of using more detailed 

definitions of the source categories is that it leads to more flexibility in designing control 

strategies, substituting new inventory or profile data into the modeling, managing the input and 

output data from SMOKE and conducting QA of the SMOKE outputs. The major drawback to 

defining more emissions source categories is the increased level of complexity and 

computational requirements (run times and disk space) that results from having a larger number 

of input data sets. Another motivation behind separating the various emissions categories is 
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related to the size and flexibility of the input data. Some data sets, like the CENRAP on-road 

mobile inventory, were so large that we had to process them separately from the rest of the 

sources in the on-road sector due to computational constraints. We also separated the non-road 

mobile and ammonia sectors into yearly and monthly inventories to facilitate the application of 

uniform monthly temporal profiles to the monthly data. Additional details about how we 

prepared the emissions inventories and ancillary data for modeling are described in Sections 2.2 

through 2.16. Table 2-7 summarizes the entire group of source sectors that composed simulation 

Typ02G. Each emissions sector listed in the table represents an explicit SMOKE simulation. As 

discussed in Section 2.1.2 below, after finishing all of the source-specific simulations, we used 

SMOKE to combine all of the data into a single file for each day for input to the air quality 

modeling systems. Each subsection on the emissions sectors describes each sector in terms of the 

SMOKE processing category, the year covered by the inventory, and the source(s) of the data. 

Additional details about the inventories are also provided, including any modifications that we 

made to prepare them for input into SMOKE. 

Table 2-7. CENRAP Typ02G emissions categories. 

Emissions Sector Abbreviation* 

Fires as Point Sources (WRAP, CENRAP, VISTAS) Alf 

Area Sources (All domain) ar 

CENRAP area fires arf 

Area fires, Anthropogenic (All domain, excluding 

WRAP and CENRAP) 

arfa 

Area fires, Wild (All domain, excluding WRAP) arfw 

Biogenic b3 

Ontario, Canada, point-source fires bsf 

Fugitive dust fd 

WRAP on-road mobile mb 

CENRAP on-road mobile mbv_CENRAP 

Other US on-road mobile mbv 

Monthly CENRAP/MRPO anthropogenic NH3 nh3m 

Ammonia from annual inventory (CENRAP) nh3y 

WRAP anthropogenic NH3 nh3 

Seasonal/Monthly non-road mobile (WRAP, CENRAP, 

MW) 

nrm 

Annual non-road mobile nry 

On-road Mobile (Non-US) nusm 

Offshore shipping (Gulf, Atlantic) ofs 

Offshore area (Gulf) ofsar 

Stationary point (All domain, including offshore) pt 

Road dust rd 

Windblown dust (All domain) wb_dust 

WRAP oil and gas wog 

*These abbreviations are used in the file naming of the SMOKE output 

files for each sector. 
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Emissions models such as SMOKE are computer programs that convert annual or daily 

estimates of emissions at the state or county level to hourly emissions fluxes on a uniform spatial 

grid that are formatted for input to an air quality model. For the Typ02G and Base18G emission 

inventories we prepared emissions for CMAQ version 4.5 using SMOKE version 2.1 on the 

UCR Linux computing cluster. SMOKE integrates annual county-level emissions inventories 

with source-based temporal, spatial, and chemical allocation profiles to create hourly emissions 

fluxes on a predefined model grid. For elevated sources that require allocation of the emissions 

to the vertical model layers, SMOKE integrates meteorology data to derive dynamic vertical 

profiles. In addition to its capacity to represent the standard emissions processing categories, 

SMOKE is also instrumented with the Biogenic Emissions Inventory System, version 3 (BEIS3) 

model for estimating biogenic emissions fluxes (U.S. EPA, 2004) and the MOBILE6 model for 

estimating on-road mobile emissions fluxes from county-level vehicle activity data (U.S. EPA, 

2005a). 

SMOKE uses C-Shell scripts as user interfaces to set configuration options and call executables. 

SMOKE is designed with flexible QA capabilities to generate standard and custom reports for 

checking the emissions modeling process. After modeling all of the source categories individually, including those categories generated outside of SMOKE, we used SMOKE to merge all of 

the categories together to create a single CMAQ input file per simulation day. Also, for use in 

the CAMx modeling, we converted the CMAQ-ready emissions estimates to CAMx-ready files 

using the CMAQ2CAMx converter. Additional technical details about the version of SMOKE 

used for final simulations are available from CEP (2004b). All scripts, data, and executables used 

to generate the Typ02G and Base18G emissions for CMAQ and CAMx are archived on the 

CENRAP computing cluster. 

2.1.6 2002 and 2018 Data Sources 

This section describes the procedures that the CENRAP followed to collect and prepare all 

emissions data for Typ02G and Base18G simulations. We discuss the sources of all inventory 

and ancillary data used for simulations. CENRAP worked with emissions inventory contractors, 

other RPOs, and EPA to collect all of the data that constitute the simulation. Table 2-8 lists all of 

the contacts for the various U.S. anthropogenic emission inventories we used. For the CENRAP 

inventories, this table lists the contacts for the contractors who prepared the inventories; for the 

non-CENRAP inventories it lists the contacts at the RPOs who provided us inventory data. We 

obtained the emissions inventories for Canada and Mexico from the U.S. EPA Emissions Factors 

and Inventory Group (EFIG) via the Clearinghouse for Inventories and Emissions Factors 

(CHIEF) website (http://www.epa.gov/ttn/chief/index.html). 
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 Table 2-8. CENRAP anthropogenic emissions inventory contacts.

Source Category Emissions Data Contact 

WRAP 

All Tom Moore, Western Governors' Association 

Phone: (970) 491-8837 

Email: mooret@cira.colostate.edu

CENRAP 

2002 Consolidated Inventory Randy Strait, E.H. Pechan & Assoc., Inc. 

Phone: 919-493-3144 

Email: rstrait@pechan.com 

NH3 Inventory, Prescribed and 

Agricultural Fires, and On-road 

mobile emissions 

Dana Sullivan, Sonoma Technology, Inc. 

Phone: 707-665-9900 

Email: dana@sonomatech.com

Gulf Off-shore platform and support 

vessel emissions 

Holly Ensz, Minerals Management Service 

Phone: (504) 736-2536 

Email: holli.ensz@mms.gov

VISTAS 

All Greg Stella, Alpine Geophysics, LLC, 

Phone: 828-675-9045 

Email: gms@alpinegeophysics.com 

MANE-VU 

All Megan Schuster, MARAMA, 

Baltimore, MD USA 

Phone: 410-467-0170 

Email: mschuster@marama.org 

MRPO 

All Mark Janssen, LADCO, 

Des Plaines, IL, USA 

Phone: 847-296-2181 

Email:janssen@ladco.org 

As mentioned above, the refinement of these inventories involved splitting some of the inventory 

files into more specific source sectors. As the stationary-area-source emissions sector has 

traditionally been a catch-all for many types of sources, this is the inventory sector that required 

the greatest amount of preparation. Upon receiving all stationary-area-source inventories we 

extracted fugitive dust, road dust, anthropogenic NH3, and for the non-WRAP U.S. inventories, 

stage II refueling sources. We retained the dust sources as separate categories that we would 

further refine with the application of transport factors (see Section 2.8). 

We collected the ancillary data used for SMOKE modeling from several sources. SMOKE 

ancillary modeling data include: 

• Temporal and chemical allocation factors by state, county, and source classification code 

(SCC); 

• Spatial surrogates and cross-reference files for allocating county-level emissions to the 

model grid; 

• Hourly gridded meteorology data; 

• Stack defaults for elevated point sources; 

• MOBILE6 configuration files; 
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• A Federal Implementation Standards (FIPS) codes (i.e., country/state/county codes) 

definition file; 

• A Source Category Classification (SCC) codes definition file; 

• A pollutant definition file; and 

• Biogenic emission factors. 

Except for the meteorology data and the MOBILE6 configuration files, we used default data sets 

provided by EPA as the basis for all of the ancillary data except for temporal profiles used for 

Electric Generating Units (EGUs). These profiles were developed based on CEM data from 2000 

through 2003 (Pechan and CEP, 2005c). CENRAP provided the meteorology data for the 

simulations at 36-km and 12-km grid resolutions (Johnson, 2007). The inventory contractor who 

prepared the MOBILE6 inventories provided the MOBILE6 configuration files either directly or 

via an RPO representative; details about the sources of the MOBILE6 inputs are provided in 

Section 2.4. We made minor modifications to the chemical allocation, pollutant definition, and 

country/state/county codes files for new sources, pollutants, or counties contained in the 

inventories that we had not previously modeled. We made major modifications to the temporal 

and spatial allocation inputs, as described below. 

2.1.7 Temporal Allocation 

Temporally allocating annual, daily, or hourly emissions inventories in SMOKE involves 

combining a temporal cross-reference file and a temporal profiles file. 

• Temporal cross-reference files associate monthly, weekly, and diurnal temporal profile 

codes with specific inventory sources, through a combination of a FIPS 

(country/state/county) code, an SCC, and sometimes for point sources, facility and unit 

identification codes. 

• Temporal profiles files contain coded monthly, weekly, and diurnal profiles in terms of a 

percentage of emissions allocated to each temporal unit (e.g., percentage of emissions per 

month, weekday, or hour). 

As a starting point for the temporal allocation data for simulations, we used the files generated 

by emission inventory contractors (Pechan and CEP, 2005c). Based on guidance from the 

developers of some of the inventory files, we enhanced the temporal profiles and assignments for 

some source categories (Pechan, 2005b). 

We modified the temporal allocation data for the simulations to improve the representation of 

temporal emissions patterns for certain source categories. We implemented the adjusted profiles 

in SMOKE by modifying the temporal cross-reference file for the applicable FIPS and SCC 

combinations. 

Updated temporal profiles for EGUs were made available for MRPO in the MRPO Base K 

inventory. Since the non-road emissions for IA and MN were monthly emissions developed by 

MRPO, new temporal profiles were created for all the SCCs in these emissions files for these 

two states only. The monthly profile was uniform and the weekly and diurnal profiles were kept 

the same as were modeled for the rest of the country. 
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An updated temporal profile, profile 485, based on NOAA 1971-2000 population weighted 

average heating degree days for home heating area source emissions was obtained from 

VISTAS. This profile provided state specific updates for home heating emissions and was 

applied to the full inventory in place of profile 17XX. 

Other additions to the Base02G temporal allocation data included updates that made by other 

RPOs that are applicable to their inventories. These other updates to the temporal allocation files 

included 

• VISTAS continuous emissions monitoring (CEM)-specific profiles for EGUs in the 

VISTAS states; 

• VISTAS agricultural burning profiles; 

• Wildfire and prescribed fire profiles developed by VISTAS for the entire U.S.; 

• MANE-VU on-road mobile profiles; 

• WRAP weekly and diurnal road dust profiles; 

• WRAP diurnal wildfire, agricultural fire, and prescribed fire profiles; and 

• WRAP on-road mobile weekly and diurnal profiles. 

Finally, for all of the monthly and seasonal emissions inventories, we modified the temporal 

cross-reference files to apply uniform monthly profiles to the sources contained in these 

inventories. The monthly variability is inherent in monthly and seasonal inventories and does not 

need to be reapplied through the temporal allocation process in SMOKE. The inventories to 

which we applied uniform monthly temporal profiles included: 

• WRAP, CENRAP, and MRPO non-road mobile sources; 

• WRAP on-road mobile sources; 

• WRAP road dust; and 

• CENRAP anthropogenic ammonia. 

2.1.8 Spatial Allocation 

SMOKE uses spatial surrogates and SCC cross-reference files to allocate county-level emissions 

inventories to model grid cells. Geographic information system (GIS)-calculated fractional land 

use values define the percentage of a grid cell that is covered by standard sets of land use 

categories. For example, spatial surrogates can define a grid cell as being 50% urban, 10% forest, 

and 40% agricultural. In addition to land use categories, spatial surrogates can also be defined by 

demographic or industrial units, such as population or commercial area. Similar to the temporal 

allocation data, an accompanying spatial cross-reference file associates the spatial surrogates 

(indexed with a numeric code) to SCCs. Spatial allocation with surrogates is applicable only to 

area and mobile sources that are provided on a county level basis. Point sources are located in the 

model grid cells by SMOKE based on the latitude-longitude coordinates of each source. 

Biogenic emissions are estimated based on 1-km2

 gridded land use information that is mapped to 

the model grid using a processing program such as the Multimedia Integrated Modeling System 

(MIMS) Spatial Allocator (CEP, 2004). 
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We used various sources of spatial surrogate information for the U.S., Canada, and Mexico 

inventories in the simulations. For the U.S. and Canadian sources, we used the EPA unified 

surrogates available through the EFIG web site (EPA, 2005c). For the 36-km grid, EPA provides 

these data already formatted for SMOKE on the RPO Unified 36-km domain that we used for the 

simulations. We modified the spatial surrogates for Canada on the RPO Unified 36-km domain 

by adopting several surrogate categories that were enhanced by the WRAP. Table 2-9 provides 

details about the new Canadian spatial surrogates that were developed by the WRAP and used 

for CENRAP simulations. For modeling Mexico, we used Shapefiles developed for the Big Bend 

Regional Aerosol and Visibility Observations Study (BRAVO) modeling to create surrogates for 

Mexico on the RPO Unified 36-km domain (EPA, 2005c). 

 Table 2-9. New Canadian spatial surrogates.

Attribute Base02a Code Shapefile Reference 

Land area 950 can_land93_land Natural Resources Canada (1993) 

AVHRR land cover data 

Water area 951 can_land93_water Natural Resources Canada (1993) 

AVHRR land cover data 

Forest land area 952 can_land93_forest Natural Resources Canada (1993) 

AVHRR land cover data 

Agricultural land area 953 can_land93_agri Natural Resources Canada (1993) 

AVHRR land cover data 

Urban land area 954 can_land93_urban Natural Resources Canada (1993) 

AVHRR land cover data 

Rural land area 955 can_land93_rural Natural Resources Canada (1993) 

AVHRR land cover data 

Airports 956 can_airport U.S. DOT Bureau of Transportation Statistics (2005) NORTAD 

1:1,000,000 scale data 

Ports 957 can_port U.S. DOT Bureau of Transportation Statistics (2005) NORTAD 

1:1,000,000 scale data 

Roads 958 can_road1m Natural Resources Canada (2001) 

National Scale Frameworks data 

Rail 959 can_rail1m Natural Resources Canada (1999)

National Scale Frameworks data 

2.2 Stationary Point Source Emissions 

Stationary-point-source emissions data for SMOKE consist of (1) Inventory Data Analyzer 

(IDA)-formatted inventory files; (2) ancillary data for allocating the inventories in space, time, 

and to the Carbon Bond-IV chemistry mechanism used in CMAQ and CAMx; and 

(3) meteorology data for calculating plume rise from the elevated point sources. This section 

describes where CENRAP obtained these data, how we modeled them, and the types of QA that 

we performed to ensure that SMOKE processed the data as expected. 
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2.2.1 Data Sources 

For the stationary-point-source inventories in Typ02G and Base18G, we used actual 2002 data 

developed by the RPOs for the U.S., version 2 of the year 2000 Canadian inventory, and the 

BRAVO 1999 Mexican inventory. The BRAVO inventory was updated with entirely new 

inventories for the six northern states of Mexico for stationary area, as well as stationary point, 

on-road mobile, and off-road mobile sources. Emissions for the southern states of Mexico were 

included for the first time in CENRAP simulations Typ02G and Base18G. These data were 

provided by ERG, Inc., who completed an updated 1999 emissions inventory for northern 

Mexico (ERG, 2006b) and delivered these data to the WRAP. The CENRAP stationary-point 

inventory consisted of annual county-level and tribal data provided in August of 2005 (Pechan 

and CEP, 2005e). The WRAP (ERG, 2006a) and VISTAS Base G (MACTEC, 2006) stationarypoint inventories consisted of an annual data set and monthly CEM data for selected EGUs. The 

WRAP and VISTAS provided these data directly to CENRAP. We downloaded the MANE-VU 

stationary-point inventories from the MANE-VU web sites. MRPO base K data was 

downloaded and processed for SMOKE modeling by Alpine Geophysics under contract from 

MARAMA. UCR entered into a nondisclosure agreement with Environment Canada to obtain 

version 2 of the 2000 Canadian point-source inventory. This inventory represented a major 

improvement over the version of the data that we had used in the preliminary 2002 modeling. 

Reductions anticipated from BART controls for electric generating units (EGU) in Oklahoma, 

Arkansas, Kansas, and Nebraska were included in projections of 2018 emissions. These 

anticipated reductions were based on actual operating conditions and estimated control 

efficiencies from utilities. 

Newly permitted coal-fired utilities were included in 2018 projections. Conservatively, no IPM 

projected new units were removed from the simulation with the addition of the permitted 

facilities. 

Due to missing or clearly erroneous stack parameters, several facilities in CENRAP states were 

relegated to default stack profiles based on SCC in the NEI QA process. Prioritizing for the 

largest emissions sources, these default parameters were corrected by CENRAP States and 

updated files were provided to modeling contractors. Final IDA input files Typ02G and 

Base18G for point sources reflect State corrections. 

For coal-fired point and area sources, The EPA Office of Air Quality and Planning Standards 

(OAQPS) determined that the organic carbon fraction in the speciation profile code "NCOAL" 

was not representative of most coal combustion occurring in the U.S. This profile has an organic 

carbon fraction of 20%, which includes an adjustment factor of 1.2 to account for other atoms 

(like oxygen) attached to the carbon. OAQPS has reverted back to the profile code "22001" for 

coal combustion, which has an organic carbon fraction of 1.07% (again including the 1.2 factor 

adjustment). This is the same profile that EPA used for previous rulemaking efforts including 

the Heavy Duty Diesel Rule and Non-Road Rule, which were proposed (and publicly reviewed) 

prior to the introduction of the NCOAL profile. 

The consensus in OAQPS is that the NCOAL profile has a high organic carbon percentage 

because it is based on measurements of combustion of lignite coal. With the exception of Texas, 

lignite is not widely used in the U.S.. Thus, OAQPS staff stopped relying on this profile as a 

national default profile. A new coal speciation profile developed based on Eastern bituminous 
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coal combustion (since much of the coal burned in the U.S. is of this type) is being developed by 

EPA's Office of Research and Development but was not completed for this study. 

The profile recently developed for MRPO by Carnegie Mellon was provided to CENRAP and is 

representative of combustion of eastern bituminous coal. This profile is a more appropriate 

profile for most facilities in the U.S. than the default NCOAL profile. 

Additionally, the "22001" profile has been flagged as problematic because of the apparent 

inadvertent switching of the organic carbon and elemental carbon fractions, which are 1.07% and 

1.83% respectively. The report discovering the discrepancy in the profile did not offer a clear 

alternative to correct the problem (MACTEC, 2003). 

CENRAP has continued to use the NCOAL factor for facilities burning lignite in North Dakota 

and Texas. For the remainder of the U.S., the MRPO profile, CMU, was used. The NCOAL 

factor was modified reducing the organic carbon by half and assigning the remainder to PM2.5. 

The modification was at the request of Texas and was reflective of the original study for the 

NCOAL factor conducted in Texas (Chow, 2005). Table 2-10 summarizes the PM2.5 speciation 

profiles for the NCOAL, 2201 and CMU speciation profiles for coal burning sources. 

Table 2-10. PM 2.5 speciation profiles for coal-burning sources.

Profile POC PEC PNO3 PSO4 PM2.5 

NCOAL 0.1000 0.0100 0.0050 0.1600 0.7250 

22001 0.0107 0.0183 0.0000 0.1190 0.8520 

CMU 0.0263 0.0315 0.0036 0.0447 0.8938 

Final simulations used improved temporal allocation and speciation information relative to the 

preliminary 2002 modeling; the rest of the ancillary data for modeling stationary point sources 

stayed the same (Mansell et al., 2005). 

2.2.2 Emissions Processing 

For Typ02G and Base18G simulations we configured SMOKE to process the annual inventories 

for the U.S., Canada, and Mexico and process hourly CEM data for the VISTAS. We configured 

SMOKE to allocate these emissions up to model layer 15 (approximately 2,500 m AGL), which 

roughly corresponds to the maximum planetary boundary layer (PBL) heights across the entire 

domain throughout the year. As coarse particulate matter (PMC) is not an inventory pollutant but 

is required by the air quality models as input species, we used SMOKE to calculate PMC during 

the processing as (PM10 - PM2.5). With the SMOKE option WKDAY_NORMALIZE set to “No,” 

we treated the annual inventories based on the assumption that they represent average-day data 

based on a seven-day week, rather than average weekday data. We also assumed that all of the 

volatile organic compound (VOC) emissions in the inventories are reactive organic gas (ROG), 

and thus used SMOKE to convert the VOC to total organic gas (TOG) before converting the 

emissions into CB-IV speciation for the air quality models. To capture the differences in diurnal 

patterns that are contained in the CEM temporal profiles for VISTAS and CENRAP states 

(Base02F), we configured SMOKE to generate daily temporal matrices, as opposed to using a 

Monday-weekday-Saturday-Sunday (MWSS) temporal allocation approach. 
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To QA the stationary-point emissions, we used the procedures in the CENRAP emissions 

modeling QA protocol (Morris and Tonnesen, 2004) and a suite of graphical summaries. We 

used tabulated summaries of the input data and SMOKE script settings to document the data and 

configuration of SMOKE for all simulations. These QA graphics are available on the web site 

at: http://pah.cert.ucr.edu/aqm/cenrap/emissions.shtml 

2.2.3 Uncertainties and Recommendations 

There were issues with the stationary-point emissions that we left unresolved at the completion 

of the Typ02G and Base18G emissions modeling either because we did not feel they would have 

a major impact on the modeling results in CENRAP states or because we did not have alternative 

approaches and they represented the best available information. Canadian emissions for 2000 

were found to have a significant number of missing stack parameters. These stacks when 

modeled with default parameters frequently resulted in lower plume heights. Stack parameters 

for 2000 were corrected based on cross referencing sources with the 2005 Canadian inventory for 

the largest emitting points. Stack parameters for many of the sources with lower emissions 

remain incorrect, but are assumed to have a less significant impact on CENRAP Class I areas. 

The 2020 projected emissions for Canada were obtained as air quality model-ready files from 

EPA. EPA has not confirmed that missing stack parameters were corrected for the projected 

inventory. It is assumed that they were not corrected and default parameters were used instead. 

Given confidentiality issues that surround Canadian inventories, EPA processed emissions 

represent the best available data. 

2.3 Stationary Area Sources 

Stationary-area-source emissions data for SMOKE consist of IDA-formatted inventory files and 

ancillary data for allocating the inventories in space, time, and to the Carbon Bond-IV chemistry 

mechanism used in CMAQ and CAMx. This section describes where we obtained these data, 

how we modeled them, and the types of QA that we performed to ensure that SMOKE processed 

the data as expected. 

2.3.1 Data Sources 

For the stationary area source inventories in the Typ02G and Base18G simulations, we used 

actual 2002 data developed by the RPOs for the U.S., version 2 of the year 2000 Canadian 

inventory, and the updated Mexican inventory, http://www.epa.gov/ttn/chief/net/mexico.html. 

The BRAVO inventory was updated with entirely new inventories for the six northern states of 

Mexico for stationary area, as well as stationary point, on-road mobile, and off-road mobile 

sources. Emissions for the southern states of Mexico were included for the first time in 

CENRAP simulations Typ02G and Base18G. The CENRAP stationary-area inventory consisted 

of annual county-level and tribal data provided by in August of 2005 (Pechan and CEP, 2005e). 

The WRAP (ERG, 2006a) and VISTAS Base G (MACTEC, 2006) stationary-area inventories 

consisted of an annual data set. We downloaded the MANE-VU stationary-area inventories from 

the MANE-VU web sites. MRPO base K data was downloaded and processed for SMOKE 

modeling by Alpine Geophysics under contract from MARAMA. 
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To prepare the stationary-area inventories for modeling, we made several modifications to the 

files by removing selected sources either to model them as separate source categories or to omit 

them from simulations completely. Using guidance provided by EPA (EPA, 2004b), we 

extracted fugitive and road dust sources from all stationary-area inventories for adjustment by 

transport factors and modeling as separate source categories (see Section 2.8). We also extracted 

and discarded the stage II refueling sources (Table 2-11) from the U.S. inventories; we modeled 

these sources with MOBILE6 as part of the on-road mobile-source emissions. We left the stage 

II refueling emissions in the WRAP stationary-area inventory because the on-road mobile 

inventory that we received for this region did not contain these emissions. 

Table 2-11. Refueling SCCs removed from the non-WRAP U.S. stationary-area inventory. 

SCC Description 

2501060100 Storage and Transport Petroleum and Petroleum Product Storage Gasoline Service 

Stations Stage 2: Total 

2501060101 Storage and Transport Petroleum and Petroleum Product Storage Gasoline Service 

Stations Stage 2: Displacement Loss/Uncontrolled 

2501060102 Storage and Transport Petroleum and Petroleum Product Storage Gasoline Service 

Stations Stage 2: Displacement Loss/Controlled 

2501060103 Storage and Transport Petroleum and Petroleum Product Storage Gasoline Service 

Stations Stage 2: Spillage 

2501070100 Storage and Transport Petroleum and Petroleum Product Storage Diesel Service 

Stations Stage 2: Total 

2501070101 Storage and Transport Petroleum and Petroleum Product Storage Diesel Service 

Stations Stage 2: Displacement Loss/Uncontrolled 

2501070102 Storage and Transport Petroleum and Petroleum Product Storage Diesel Service 

Stations Stage 2: Displacement Loss/Controlled 

2501070103 Storage and Transport Petroleum and Petroleum Product Storage Diesel Service 

Stations Stage 2: Spillage 

Other steps that we took to prepare the stationary-area inventories included confirming that there 

is no overlap between the anthropogenic NH3 inventory (Section 2.9) and stationary area 

sources, and moving area-source fires in each regional inventory to separate files. In addition to 

these inventory modifications we made a few changes to the ancillary data files for simulation 

Typ02G, as described next. 

Simulation Typ02G used improved temporal and spatial allocation information relative to the 

preliminary 2002 modeling; the rest of the ancillary data for modeling stationary area sources 

stayed the same as in the preliminary 2002 modeling (Mansell et al., 2005). We adopted 

enhanced spatial allocation data with additional area-based surrogates for Canada (Table 2-9), 

and added surrogates for a missing county in Colorado (Broomfield) from WRAP modeling and 

QA work. The WRAP had noticed when looking at the Canadian data for the preliminary 2002 

modeling that forest fire emissions from the Canadian area-source inventory, which are relatively 

large sources of CO, NOx, and PM2.5, were being allocated to a surrogate for logging activities. 

They found similar discrepancies for other area and non-road SCCs in Canada. To improve the 

representation of the Canadian emissions, we adopted several land-area-based surrogates 

developed by the WRAP, such as forested land area, urban land area, and rural land area, and 

made the accompanying additions to the spatial cross-reference file to associate inventory SCCs 

with these surrogates. We also added spatial surrogates for Broomfield County, CO; this county 

was included in the inventory but was not included in the base EPA surrogates (this county was 

recently created from portions of other counties). 
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Improvements to the temporal allocation data for simulation Typ02G included the addition of 

several FIPS-specific profiles provided by VISTAS and CENRAP contractors (Pechan 2005b). 

These temporal profiles listed in Table 2-12 targeted mainly fire and agricultural NH3 sources, 

such as open burning and livestock operations, respectively. 

Table 2-12. New Temporal Profile Assignments for CENRAP Area Source SCCs.

SCC Description Month Week Diurnal 

Recommend

ation Based 

on Profile 

Data for SCC 

Description of Similar 

SCC used to 

Recommend Profiles 

2310001000 Industrial Processes; Oil and 

Gas Production: SIC 13;All 

Processes : On-shore; Total: All 

Processes 

262 7 26 2310000000 Industrial Processes;Oil 

and Gas Production: SIC 

13;All Processes;Total: All 

Processes 

2310002000 Industrial Processes;Oil and 

Gas Production: SIC 13;All 

Processes : Off-shore;Total: All 

Processes 

262 7 26 2310000000 Industrial Processes;Oil 

and Gas Production: SIC 

13;All Processes;Total: All 

Processes 

2461870999 Solvent 

Utilization;Miscellaneous Nonindustrial: Commercial;Pesticide 

Application: NonAgricultural;Not Elsewhere 

Classified 

258 7 26 

2461800000 

Solvent 

Utilization;Miscellaneous 

Non-industrial: 

Commercial;Pesticide 

Application: All 

Processes;Total: All 

Solvent Types 

2805009200 Miscellaneous Area 

Sources;Agriculture Production 

- Livestock;Poultry production - 

broilers;Manure handling and 

storage 

1500 7 26 2805009300 Miscellaneous Area 

Sources;Agriculture 

Production - 

Livestock;Poultry 

production - broilers;Land 

application of manure 

2805021100 Miscellaneous Area 

Sources;Agriculture Production 

- Livestock;Dairy cattle - scrape 

dairy;Confinement 

1500 7 26 2805021300 Miscellaneous Area 

Sources;Agriculture 

Production - 

Livestock;Dairy cattle - 

scrape dairy;Land 

application of manure 

2805021200 Miscellaneous Area 

Sources;Agriculture Production 

- Livestock;Dairy cattle - scrape 

dairy;Manure handling and 

storage 

1500 7 26 2805021300 Miscellaneous Area 

Sources;Agriculture 

Production - 

Livestock;Dairy cattle - 

scrape dairy;Land 

application of manure 

2805023100 Miscellaneous Area 

Sources;Agriculture Production 

- Livestock;Dairy cattle - 

drylot/pasture 

dairy;Confinement 

1500 7 26 2805023300 Miscellaneous Area 

Sources;Agriculture 

Production - 

Livestock;Dairy cattle - 

drylot/pasture dairy;Land 

application of manure 

2805023200 Miscellaneous Area 

Sources;Agriculture Production 

- Livestock;Dairy cattle - 

drylot/pasture dairy;Manure 

handling and storage 

1500 7 26 2805023300 Miscellaneous Area 

Sources;Agriculture 

Production - 

Livestock;Dairy cattle - 

drylot/pasture dairy;Land 

application of manure 

2810020000 Miscellaneous Area 

Sources;Other 

Combustion;Prescribed Burning 

of Rangeland;Total 

3 11 13 2810015000 Miscellaneous Area 

Sources;Other 

Combustion;Prescribed 

Burning for Forest 

Management;Total 
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2.3.2 Emissions Processing 

For simulations Typ02G and Base18G we configured SMOKE to process the annual stationaryarea-source inventories for the U.S., Canada, and Mexico. As PMC is not an inventory pollutant 

but is required by the air quality models as input species, we used SMOKE to calculate PMC 

during the processing as (PM10 - PM2.5). With the SMOKE option WKDAY_NORMALIZE set 

to “Yes,” we treated the annual stationary-area inventories based on the assumption that they 

represent average weekday data, causing SMOKE to renormalize the data to a seven-day 

estimate before applying any temporal adjustments. We also assumed that all of the VOC 

emissions in the inventories are ROG and thus used SMOKE to convert the VOC to TOG before 

converting the emissions into CB-IV speciation for the air quality models. We configured 

SMOKE to use a MWSS temporal allocation approach, as opposed to a daily temporal approach. 

To QA the stationary-area emissions, we used the procedures in the CENRAP modeling QAPP 

and Modeling Protocol (Morris and Tonnesen, 2004; Morris et al., 2004a) and a suite of 

graphical summaries. We used tabulated summaries of the input data and SMOKE script settings 

to document the data and configuration of SMOKE for all simulations. The graphical QA 

summaries include, for all emissions output species, daily spatial plots summed across all model 

layers, daily time-series plots, and annual time-series plots. These QA graphics are available on 

the UCR/CENRAP web site at http://pah.cert.ucr.edu/aqm/cenrap/emissions.shtml . 

2.3.3 Uncertainties and Recommendations 

Most of the issues that we encountered with the stationary area sources related to the removal of 

certain SCCs from the base inventories for inclusion as other source categories or complete 

omission from simulations. We spent considerable effort on ensuring that we did not have 

overlap between the area inventory and the other sectors that explicitly represent sources 

traditionally contained in the area inventory, such as NH3 and dust. 

Both the Canadian and Mexican inventories presented minor problems that we resolved for 

simulation Typ02G but that can be addressed more thoroughly in future simulations. The 

Canadian inventory we used contained data only at the province level, essentially equivalent to a 

statewide rather than county-level inventory. A higher resolution inventory would have allowed 

us to use higher-resolution and more accurate spatial allocation data. Future modeling that uses 

Canadian data should move to the newly released municipality-level year 2000 inventories for 

Canada. 

There was a discrepancy between the state and county coding in the Mexican inventory and the 

SMOKE file that defines acceptable FIPS codes. Differences in the ordering of the Mexican state 

names between these two data sets led to some of the Mexican inventory sources being 

mislabeled in the SMOKE QA reports. The state codes in the inventory and spatial surrogate 

files for two Mexican states were changed to be consistent with the SMOKE 

country/state/county codes file. 
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2.4 On-Road Mobile Sources 

On-road mobile-source emissions data for SMOKE consist of IDA-formatted emissions and 

vehicle activity inventory files, and ancillary data for allocating the inventories in space, time, 

and to the Carbon Bond-IV chemistry mechanism used in CMAQ and CAMx. This section 

describes where we obtained these data, how we modeled them, and the types of QA that we 

performed to ensure that SMOKE processed the data as expected. 

2.4.1 Data Sources 

The SMOKE processing for CENRAP included two approaches for processing on-road mobile 

sources depending on the source of the data provided. The first approach was to compute mobile 

emissions values prior to providing them to SMOKE; we call this the pre-computed emissions 

approach. The second approach was to provide SMOKE with VMT data, meteorology data, and 

MOBILE6 inputs, and let the SMOKE/MOBILE6 module compute the mobile emissions based 

on these data; we call this the VMT approach. These approaches are not mutually exclusive for a 

single SMOKE run; therefore, we performed single SMOKE runs in which both approaches were 

used as follows: 

• Annual VMT for computing CO, NOx, VOC, SO2, NH3 and PM using MOBILE6 for all 

CENRAP States. 

• Pre-computed, seasonal MOBILE6-based emissions of all pollutants for the 13 WRAP 

states that included pre-speciated PM2.5 data. 

• Annual VMT for computing CO, NOx, VOC, SO2, NH3 and PM using MOBILE6 for the 

rest of the United States (VISTAS, MRPO and MANE-VU). 

• Pre-computed, annual 1999 emissions of all pollutants for Mexico. 

• Pre-computed, annual 2000 emissions of all pollutants for Canada. 

For the CENRAP states, STI provided VMT data and MOBILE6 input files for all counties in 

the CENRAP region (Reid et al., 2004a). MOBILE6 input files were provided only for the 

months of January and July for 2002. MOBILE6 input files for the remaining months of 2002 

had to be generated. These data were then processed within SMOKE. Using one set of 

MOBILE6 input files for each county in the CENRAP states resulted in compute memory 

requirements that were to large to process all CENRAP states together. Therefore the on-road 

mobile processing for the CENRAP states was split into two groups for SMOKE processing. The 

resulting gridded emissions data files were then merged together to obtain an on-road mobile 

source emissions file for the entire CENRAP region.

For the WRAP states we used actual 2002 data split into California and non-California seasonal 

inventories that were provided by the WRAP (Pollack et al., 2006). In addition to the standard 

criteria pollutants, these files contained pre-speciated PM2.5 emissions. For the rest of the U.S. 

we used annual county-level activity and speed inventories with monthly, county-level 

MOBILE6 inputs, and hourly meteorology to estimate the hourly emissions with the 

SMOKE/MOBILE6 module. For the non-U.S. inventories, we used version 2 of the year 2000 

Canadian inventory and the updated 1999 Mexican inventory pre-computed mobile source 

emissions. 
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2.4.2 Emissions Processing 

For the Typ02G emissions modeling we configured SMOKE to process the annual on-road 

mobile emissions inventory data for the WRAP, Canada, and Mexico as pre-computed 

inventories. For the non-WRAP states, we used the SMOKE/MOBILE6 integration to process 

the annual activity inventories and monthly, county-based roadway information. The WRAP 

inventories contained pre-computed speciated PM emissions (Pollack et al, 2006) so the SMOKE 

PM speciation module was not used. The WRAP on-road mobile inventories were developed to 

represent seven-day (weekly) average emissions (as compared to the area source inventory, 

which represented average weekday emissions). As actual weekly average emissions, we 

configured SMOKE to process the WRAP on-road mobile source emissions by setting 

WKDAY_NORMALIZE to “No” in which case the emissions are adjusted to represent weekday 

and Saturday and Sunday emissions (as in contrast to the area sources where the emissions are 

just adjusted for Saturday and Sunday). We also assumed that all of the VOC emissions in the 

inventories are ROG and used SMOKE to convert the VOC to TOG before converting the 

emissions into CB-IV speciation for the air quality models. We configured SMOKE to create 

day-of-week specific rather than MWSS, temporal profiles because the WRAP on-road mobile 

temporal profiles contain weekly profiles that vary across the weekdays. 

As noted previously, the large number of county roadway inputs for MOBILE6 processed for the 

non-WRAP portion of the U.S. required us to split the states mobile-source processing into three 

subsets because of computer memory limitations. Separate MOBILE6 input files were used for 

each separate county for CENRAP states, where as one MOBILE6 input file was used for several 

counties outside of the CENRAP region. The three subsets consisted of two sets of 

SMOKE/MOBILE6 simulations for the CENRAP and a simulation that computed on-road 

mobile emissions for the MRPO, VISTAS, and MANE-VU states. We configured MOBILE6 to 

use weekly temperature averaging for computing these emissions within SMOKE. 

To QA the on-road mobile emissions, we used the CENRAP emissions modeling QA protocol 

(Morris and Tonnesen, 2004; Morris et al., 2004a) and a suite of graphical summaries. We used 

tabulated summaries of the input data and SMOKE script settings to document the data and 

configuration of SMOKE for simulations Typ02G and Base18G. The graphical QA summaries 

include, for all emissions output species, daily spatial plots, daily time-series plots, and annual 

time-series plots. These graphics are available at 

http://pah.cert.ucr.edu/aqm/cenrap/qa_base02b36.shtml#mb

2.4.3 Uncertainties and Recommendations 

We approached the on-road mobile emissions preparation for simulation Typ02G from three 

different directions, which were based on the form of the input inventories and ancillary 

emissions data for different regions of the modeling domain: 

• The WRAP region used emissions estimates pre-computed with EMFAC for California 

and MOBILE6 for the rest of WRAP states and processed like area sources with SMOKE 

adjusted from weekly to day-of-week emissions. 

• The CENRAP, VISTAS, MRPO, and MANE-VU states used county-level activity data to 

compute emissions with the SMOKE/MOBILE6 module. 
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• The non-U.S. parts of the domain also had pre-computer on-road mobile source 

emissions so used an area-source approach for processing with SMOKE. 

Different approaches for modeling a single emissions sector adds complexity and additional 

sources of error and inconsistencies to the modeling because of the different assumptions that 

went into the preparation of the input data. For example, refueling emissions from the on-road 

mobile sector are represented in the WRAP area-source sector but are computed with MOBILE6 

for the rest of the U.S. Not using MOBILE6-based emissions for the non-U.S. portion of the 

domain neglects the effects of the actual 2002 meteorology on these emissions. Applying 

MOBILE6 outside of the U.S. is currently not possible because MOBILE6 is instrumented only 

for calculating emissions for the U.S. automotive fleet. The result of using MOBILE6 to 

calculate U.S. emissions and not using it to calculate the non-U.S. on-road mobile emissions 

estimates is that the non-U.S. emissions are not specific to this modeling year and the 2002 

meteorological conditions, whereas the U.S. emissions are 2002-specific. 

While we used the best available information to compute the on-road mobile emissions for the 

various portions of the modeling domain, inconsistent approaches for representing these 

emissions may lead to unnatural emissions gradients along political boundaries. We recommend 

for future work a unified approach for at least the U.S. inventories, where either we use 

MOBILE6 in SMOKE for the entire domain (or alternative emissions model such as 

CONCEPT), or we calculate the emissions with MOBILE6 outside of SMOKE and then use the 

resulting county-based emissions inventories. 

2.5 Non-Road Mobile Sources 

Non-road mobile source emissions data for SMOKE consist of annual, seasonal, and monthly 

IDA-formatted emission inventory files and ancillary data for allocating the inventories in space, 

time, and to the Carbon Bond-IV chemistry mechanism used in CMAQ and CAMx. This section 

describes where we obtained these data, how we modeled them, and the types of QA that we 

performed to ensure that SMOKE processed the data as expected. 

2.5.1 Data Sources 

The non-road mobile-source inventories in the Typ02G and Base18G emissions modeling used 

actual 2002 data developed by the RPOs for the U.S., version 2 of the year 2000 Canadian 

inventory and the improved 1999 Mexican inventory. The U.S. inventories consisted of annual, 

seasonal, and monthly inventories; the non-U.S. inventories were annual data. Pechan provided 

the CENRAP inventories divided between annual data for aircraft, locomotive, and commercial 

marine and annual files for all other non-road sources (Pechan and CEP, 2005e). Minnesota 

substituted the monthly MRPO Base K non-road inventory for the CENRAP inventory in their 

state. Iowa substituted the monthly estimates for non-road agricultural sources from the MRPO 

base K inventory for the CENRAP inventory. Texas provided estimates for 2002 non-road 

emissions in lieu of the CENRAP prepared inventory. WRAP provided non-road inventories 

divided between California and non-California seasonal inventories, further subdivided into 

aircraft, locomotives, shipping, and all other non-road mobile sources (Pollack et al., 2006). Note 

that the California Air Resources Board uses their own OFFROAD model for California nonroad emissions, whereas the EPA NONROAD model is used for the rest of the states (with the 
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exception of locomotives, aircraft and shipping). With these data WRAP also provided temporal 

adjustments to apply to the inventories to split them between weekday and weekend emissions. 

We used these weekday/weekend splits to derive new weekly temporal profiles for the WRAP 

sources. The MRPO base K monthly non-road inventories were obtained from MRPO in NIF 

format and were converted to SMOKE format by Wendy Vit of the Missouri DNR. The VISTAS 

Base G and MANE-VU non-road mobile inventories consisted of annual county-level data 

(Pechan and CEP, 2005c). We received these inventories directly from the respective RPO 

inventory representatives. We received the Canadian 2000 inventory version 2 from the U.S. 

EPA EFIG (EPA, 2005d). For Mexico we used the improved 1999 inventory available at 

http://www.epa.gov/ttn/chief/net/mexico.html. 

Along with adding the WRAP weekday/weekend emissions splits to the temporal allocation 

files, we also created temporal input files that apply a flat, uniform monthly profile to the 

monthly and seasonal non-road inventories. With the monthly and seasonal variability inherent 

in these inventories, we avoided applying redundant monthly profiles by splitting the inventories 

into seasonal/monthly and annual data. We applied the uniform monthly temporal profiles to the 

seasonal/monthly inventories and non-uniform monthly temporal profiles to the annual 

inventories. How the non-road emissions inventory data were split into those with 

monthly/seasonal emission and those with annual emissions is provided in Table 2-13. 

Table 2-13. Non-road mobile-source inventory temporal configuration. 

Region Source Temporal Coverage 

WRAP (non-CA) Non-road mobile Seasonal 

WRAP (CA) Non-road mobile Seasonal 

WRAP Aircraft Seasonal 

WRAP Locomotive Annual 

WRAP In-port and near-shore shipping Annual 

CENRAP All non-road Annual 

CENRAP, IA Non road Ag. Monthly 

VISTAS All non-road Annual 

MRPO and MN All non-road Monthly 

MANE-VU All non-road Annual 

Canada All non-road Annual 

Mexico All non-road Annual 

Iowa elected to use the CENRAP-sponsored inventory for all of the non-road categories except 

for the agricultural equipment categories provided in Table 2-14. For these agricultural 

equipment categories, Iowa elected to use the Midwest RPO Base K inventory because this 

inventory provided improvements to the temporal allocation of emissions for the agricultural 

sector. The Base K inventory includes monthly emissions. The monthly emissions are used in 

the SMOKE IDA files for modeling. 

Table 2-14. Non-road agricultural emissions categories where the MRPO Base K inventory was 

used instead of the CENRAP inventory in Iowa. 

 SCC SCC Description 

22600050xx Off-highway Vehicle Gasoline, 2-Stroke: Agricultural Equipment (2 SCCs); 

22650050xx Off-highway Vehicle Gasoline, 4-Stroke: Agricultural Equipment (11 SCCs); 

22670050xx LPG : Agricultural Equipment (3 SCCs); 

22680050xx CNG : Agricultural Equipment (3 SCCs); and 

22700050xx Off-highway Vehicle Diesel : Agricultural Equipment (11 SCCs). 
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Texas provided annual and daily emissions for CO, CO2, NOx, VOC, SO2, PM10-FIL, and 

PM25-FIL for several oil and gas field equipment non-road categories (Table 2-15). Texas 

provided authorization to change the pollutant codes from PM10-FIL to PM10-PRI and PM25-

FIL to PM25-PRI. 

Table 2-15. Non-road oil and gas development equipment categories that Texas provided 

emissions to be used instead of the CENRAP inventory. 

SCC SCC Description 

2265010010 Off-highway Vehicle Gasoline, 4-Stroke : Industrial Equipment: Other Oil Field Equipment; 

2268010010 CNG : Industrial Equipment : Other Oil Field Equipment; and 

2270010010 Off-highway Vehicle Diesel : Industrial Equipment : Other Oil Field Equipment 

Lancaster County Nebraska provided its own non-road inventory for SCC 2260000000 (Offhighway Vehicle Gasoline, 2-Stroke : 2-Stroke Gasoline except Rail and Marine: All). The 

CENRAP-sponsored inventories for SCCs starting with 226 in Lancaster County were removed 

to correct double-counting of emissions. This adjustment was made by Pechan for Base02b 

modeling. 

2.5.2 Emissions Processing 

We configured SMOKE to process all of the non-road mobile emissions inventory data as arealike inventories using spatial surrogates to grid the county-level emissions. As the WRAP 

inventories contained pre-computed PM emissions, we did not have to use SMOKE to compute 

coarse mass PM (PMC). The WRAP non-road mobile inventories represented seven-day average 

emissions (different from the area inventory, which represented weekday average emissions). As 

actual weekly average emissions, we configured SMOKE to process them by setting 

WKDAY_NORMALIZE to “No.” For the rest of the non-road mobile inventories we processed 

the data as weekday average data by setting WKDAY_NORMALIZE to “Yes.” We also 

assumed that all of the VOC emissions in the inventories are ROG and used SMOKE to convert 

the VOC to TOG before converting the emissions into CB-IV speciation for the air quality 

models. We configured SMOKE to create MWSS temporal intermediates rather than daily 

temporal files because the non-road mobile sources do not use weekly temporal profiles that vary 

across the weekdays, but do have very different emissions on weekdays versus weekend days. 

We divided the non-road mobile emissions modeling based on whether the data were annual or 

seasonal/monthly inventories. This split facilitated the application of uniform monthly temporal 

profiles to the seasonal/monthly inventories. After processing the non-road emissions as two 

separate categories, non-road yearly and non-road monthly, we combined them with the rest of 

the emissions sectors to create model-ready emissions for CMAQ and CAMx. 

To QA the non-road mobile emissions we used the procedures in the CENRAP emissions 

modeling QAPP (Morris and Tonnesen, 2004) and Modeling Protocol (Morris et al., 2004a) and 

a suite of graphical summaries. We used tabulated summaries of the input data and SMOKE 

script settings to document the data and configuration of SMOKE for simulations. The graphical 

QA summaries include, for all emissions output species, daily spatial plots, daily time-series 

plots, and annual time-series plots. These QA graphics are available at 

http://pah.cert.ucr.edu/aqm/cenrap/qa_base02f36.shtml#nr
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2.5.3 Uncertainties and Recommendations 

We prepared non-road mobile emissions using a combination of inventories having different 

temporal resolutions and various forms of ancillary data. These different combinations of 

information may lead to inconsistencies in how these emissions are represented across the 

modeling domain. In addition, the Canadian inventories contain only province-level information 

and thus have low-resolution spatial and temporal profiles applied to them. The Mexican nonroad emissions are deficient in the number of different SCCs contained in the inventory and the 

availability of spatial surrogates that are applicable to non-road mobile sources. Improvements to 

the temporal profiles and spatial surrogates could provide a more consistent approach to 

representing the non-road emissions across the entire modeling domain. 

2.6 Biogenic Sources 

Biogenic emissions data for SMOKE consist of input files to the BEIS3 model (EPA, 2004a). 

BEIS3 is a system integrated into SMOKE for deriving emissions estimates of biogenic gasphase pollutants from land use information, emissions factors for different plant species, and 

hourly, gridded meteorology data. The results of BEIS3 modeling are hourly, gridded emissions 

fluxes formatted for input to CMAQ or CAMx. This section describes the sources of the BEIS3 

input data that we used for the Typ02G and Base18G emissions, how we modeled these data and 

the types of QA that were performed to ensure that SMOKE processed the data as expected. 

2.6.1 Data Sources 

The BELD3 land use data and biogenic emissions factors that were developed during the WRAP 

preliminary 2002 modeling were used for the CENRAP biogenic emissions modeling (Tonnesen 

et al., 2005). These data included BELD3 1-km resolution land use estimates and version 0.98 of 

the BELD emissions factors. Since the WRAP and CENRAP use the same 36 km Inter-RPO 

continental U.S. modeling domain, CENRAP was able to leverage of the WRAP work performed 

previously. 

2.6.2 Emissions Processing 

We used BEIS3.12 integrated in SMOKE to prepare emissions for the simulations. Most of the 

preparation for the biogenic emissions processing was completed during the preliminary 2002 

modeling (Morris et al., 2005). As the modeling domains did not change from the preliminary 

2002 to the final modeling, we re-used the gridded land use data and vegetation emissions factors 

that we prepared for the preliminary simulations. 

To QA the biogenic emissions, we used the CENRAP emissions modeling QAPP (Morris and 

Tonnesen, 2004) and Modeling Protocol (Morris et al., 2004a) and a suite of graphical 

summaries. We used tabulated summaries of the input data and SMOKE script settings to 

document the data and configuration of SMOKE for simulation Base02b. The graphical QA 

summaries include, for all emissions output species, daily spatial plots, daily time-series plots, 

and annual time-series plots. These QA graphics are available at 

http://pah.cert.ucr.edu/aqm/cenrap/qa_base02b36.shtml#b3
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2.6.3 Uncertainties and Recommendations 

The use of newer versions of BEIS (BEIS3.13) and the new MEGAN biogenic emissions models 

should be considered in future modeling. 

2.7 Fire Emissions 

Fire emissions data for SMOKE have traditionally been represented as county-level area-source 

inventories that were placed in only the first vertical model layer. We advanced the 

representation of fire emissions for air quality modeling by preparing portions of the inventory 

data as point sources with specific latitude-longitude coordinates for each fire centroid and precomputed plume rise parameters that were derived from individual fire characteristics. These 

new inventories were based on the fire data products prepared by a CENRAP emission 

contractor (Reid et al., 2004b) and modified by the project team to be properly modeled as point 

sources. These data consist of annual, daily, and hourly IDA-formatted emissions inventory files 

and ancillary data for allocating the inventories in space, time, and to the Carbon Bond-IV 

chemistry mechanism used in CMAQ and CAMx. This section describes where we obtained 

these data, how we modeled them, and the types of QA performed to ensure that SMOKE 

processed the fire emissions data as expected. 

2.7.1 Data Sources 

The fire inventories in the Typ02G emissions inventory were held constant through Base18G. 

We used actual 2002 fire data developed by the RPOs for the U.S., version 2 of the year 2000 

Canadian inventory fire data, and actual 2002 fire data for Ontario, Canada. The inventories used 

consisted of both area and point source data for the U.S., Canada, and Mexico. Sonoma 

Technology, Inc. provided the fire emissions for the CENRAP states (Reid et al., 2004b). Air 

Sciences provided us with the WRAP inventories divided among six different fire categories: 

wildfires, agricultural fires, wildland fire use, natural prescribed, anthropogenic prescribed, and 

non-Federal rangeland fires (Air Sciences, 2007a). These inventories consisted of annual, daily, 

and hourly IDA-formatted files with information on daily emissions totals and hourly plume 

characteristics for each fire. We received similar fire emission inventories for the other RPOS 

(Air Sciences, 2007b). We modeled these sources with the rest of the stationary-area-source 

sector. 

CENRAP received data for 54 fires that occurred in Ontario during the year 2002. Information 

on the data code abbreviations, data definitions, and data units used in the raw data files was 

obtained from Mr. Rob Luik (Data Management Specialist) at the Ontario Ministry of Natural 

Resources (Rob.Luik@MNR. gov.on.ca). Emissions for each fire were estimated using the 

Emission Production Model (EPM)/CONSUME within the BlueSky framework. A fire 

identification code is needed to track individual fires throughout the processing. The unique fire 

identification code was created for each fire by concatenating the FIRE_NUMBER and 

CUR_DIST fields of the original data. The fire identification code also contains the FIPS code 

of the fire; this information is not used by BlueSky but is needed by BlueSky2Inv, the utility 

program that converts the BlueSky output to the SMOKE inventory format. The FIPS code 

135000 was used for all fires with longitudes east of –90°, and FIPS code 135059 was used for 
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fires west of –90°. These FIPS codes were used to ensure that the fires would be assigned the 

correct time zones in later SMOKE processing. Some of the dates provided in the original data 

included hourly information. In all cases, the hourly information was not used leaving all data at 

a daily resolution. 

2.7.2 Emissions Processing 

SMOKE is instrumented to distribute point-source-formatted fire inventories to the vertical 

model layers either by using a pre-computed plume rise approach or by computing the plume rise 

dynamically using actual 2002 meteorology. We applied both approaches for modeling pointsource fire emissions in simulation Typ02G. For the pre-computed plume rise approach, 

SMOKE reads an annual inventory file with information on fire locations, a daily inventory file 

with daily emission totals for each fire, and an hourly inventory file with hourly plume bottom, 

plume top, and layer 1 fractions for each fire. SMOKE uses this information to locate the fires on 

the horizontal model grid and to distribute the plume of each fire vertically to the model layers. 

Because some of these fires have plumes that reach the model top, we set the number of 

emissions layers for processing these inventories to the full 19 layers of the meteorology. We 

applied this approach to the point-source fires for the WRAP, CENRAP and VISTAS regions. 

The alternative plume rise approach uses information on fuel loading and the heat flux of the 

fires to distribute the fires vertically to the model layers. The data are provided to SMOKE in the 

form of an annual inventory with information on fire locations and a daily inventory with daily 

emission totals for each fire, daily heat flux, and daily fuel loading. We applied this approach to 

the point-source fires for Ontario, Canada. 

All of the point-source fires used diurnal temporal profiles and speciation profiles for VOC and 

PM2.5 developed by Air Sciences (2007a) during the preliminary 2002 modeling (Morris et al., 

2005). 

We modeled the area-source fires for U.S. and Canada as standard stationary area sources. We 

applied monthly temporal profiles provided by RPOs, flat weekly temporal profiles, and the 

diurnal profiles developed by Air Sciences for WRAP fires (Air Sciences, 2007a), and for the 

rest of the RPOs we used diurnal profiles that were provided by them (Air Sciences, 2007b). We 

used the forestland area surrogate to distribute these emissions from the county or province level 

in the inventories to the model grid cells. 

To QA the fire emissions, we used the procedure in the CENRAP emissions modeling QA 

protocol (Environ, 2004) and a suite of graphical summaries. We used tabulated summaries of 

the input data and SMOKE script settings to document the data and configuration of SMOKE for 

simulation Typ02G. The graphical QA summaries include, for all emissions output species, daily 

spatial plots, daily time-series plots, annual time-series plots, and vertical profiles. These QA 

graphics are available at: http://pah.cert.ucr.edu/aqm/cenrap/qa_typ02g36.shtml. 

2.7.3 Uncertainties and Recommendations 

We used forestland spatial surrogates to distribute these county level (province level for Canada) 

data to the model grid. Using spatial surrogates to locate fires is a crude approach that results in 

the artificial smearing of the emissions over too large an area. This issue can be remedied by 
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moving to a point-source approach for representing these fires, similar to the approach used by 

Air Sciences for preparing the WRAP fire inventories.

2.8 Dust Emissions 

Dust emissions data for SMOKE have traditionally taken the form of county-level stationaryarea-source inventories. As these emissions are correlated to meteorology, land use, and 

vegetative cover, we made several changes to how dust emissions are simulated by SMOKE to 

take these parameters into consideration. This section describes where we obtained data for 

windblown, fugitive, and road dust sources, how we modeled them, and the types of QA 

performed to ensure that SMOKE processed the data as expected. 

2.8.1 Data Sources 

For the fugitive dust and road dust inventories in the Typ02G emission scenario, we used actual 

2002 data developed by the RPOs for the U.S., version 2 of the year 2000 Canadian inventory, 

and the BRAVO 1999 Mexican inventory. We extracted the fugitive dust inventories from the 

stationary-area inventories for each of the RPOs, Mexico, and Canada. Before modeling these 

data we further divided them into construction/mining sources and agricultural sources. We 

defined the fugitive dust sources in the Base02f modeling based on guidance provided by EPA 

(2004b). WRAP provide road dust emission inventories (Pollack et al., 2006). For the rest of the 

RPOs and Canada, we extracted the road dust SCCs from the stationary-area-source inventories. 

The BRAVO 1999 Mexico inventory did not contain any road dust SCCs. Table 2-16 lists the 

SCCs for the various fugitive and road dust sources that we modeled in the Base02f and Typ02G 

inventories. We applied near-source capture transport factors that are based on county-level 

vegetative cover to the fugitive and road dust inventories to prepare them for input to the air 

quality models. 

For windblown dust, we used gridded emissions prepared outside of SMOKE using a land use 

and meteorology-based model developed under funding from the WRAP by ENVIRON and UCRiverside (Mansell, 2005; Mansell et al., 2005). 

Table 2-16. Fugitive and road dust SCCs. 

Dust Category SCCs 

Fugitive dust (construction and mining) 2275085000, 2311000000, 2311010000, 2311010070, 

2311020000, 2311030000, 2325000000, 2305070000, 

2530000020, 2530000100, 2530000120 

Fugitive dust (agricultural) 2801000003, 2801000005, 2801000008, 2805001000 

Road dust 2294000000, 2296000000 

2.8.2 Emissions Processing 

We modeled the fugitive and road dust inventories through SMOKE using an area-source 

approach. We modeled these data on the assumption that they represented weekday, rather than 

seven-day week, emissions and thus used the SMOKE setting WKDAY_NORMALIZE to 

convert the data to a seven-day average. We configured SMOKE to compute PMC during the 

processing as (PM10 - PM2.5). Usually the records with dust do not include any other pollutants 

such as VOC, and NOx. For the few records that did include pollutants other than the PM we 
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split the records where the PMs processed with dust and the non PMs processed with the area. 

We configured SMOKE to create MWSS temporal intermediates rather than daily temporal files 

because the dust sources do not use weekly temporal profiles that vary across the weekdays. 

As noted above, we used SMOKE to apply near-source transport factors to the raw fugitive and 

road dust inventories to prepare them for input to the air quality models. We used U.S. transport 

factors from work done by Pace (2005) and a 2001 land use/land cover database to develop a 

SMOKE input file of county and SCC-based transport factors for the U.S., Canada, and Mexico. 

We applied these factors to create a new set of inventories adjusted for these transport factors for 

all regions except VISTAS; the VISTAS dust sources that we received already had the transport 

factors applied to them. 

We calculated the windblown dust emissions outside of SMOKE using an internally developed, 

process-based model. By “process-based” we refer to an emissions model that integrates 

information about the processes that lead to the emissions of interest, in this case windblown 

dust. The process-based windblown dust model developed by the WRAP considers wind speeds, 

precipitation history, and soil types to derive gridded dust fluxes resulting from wind 

disturbances for the modeling domain. More information on this model, its modes of operation, 

and the configuration used for simulation Base02a are available in Mansell et al. (2005). 

To QA the fire emissions, we used the procedures in the CENRAP emissions modeling QAPP 

(Morris and Tonnesen, 2004) and Modeling Protocol (Morris et al., 2004a) and a suite of 

graphical summaries. We used tabulated summaries of the input data and SMOKE script settings 

to document the data and configuration of SMOKE for Base02f emissions. The graphical QA 

summaries include, for all emissions output species, daily spatial plots, daily time-series plots, 

and annual time-series plots. These QA graphics are available at 

http://pah.cert.ucr.edu/aqm/cenrap/qa_base02f36.shtml#fd for fugitive dust, 

http://pah.cert.ucr.edu/aqm/cenrap/qa_base02f36.shtml#rd for road dust, and 

http://pah.cert.ucr.edu/aqm/cenrap/qa_base02b36.shtml#wbd for windblown dust. 

2.8.3 Uncertainties and Recommendations 

There are several improvements that should be made to the dust emissions modeling in future 

simulations. We will expand the list of fugitive dust SCCs that we extract from the stationaryarea-source inventories for application of transport factors. This expanded list is based on recent 

work by EPA (2004b). We will also explore improvements to the assumptions that we used for 

generating emissions with the WRAP windblown dust model. Areas of improvement in the 

windblown dust model include refinements to the land use data and soil characteristics, 

additional information about agricultural activities in the WRAP and CENRAP regions, detailed 

model evaluation on targeted windblown dust case studies, and the application of snow-cover 

and vegetative transport factors to these emissions (Mansell et al., 2005). 

2.9 Ammonia Emissions 

Ammonia (NH3) emissions from agricultural activities are a major source of ammonia and are 

dependent on many different environmental parameters, such as meteorology, crop and soil 

types, and land use. CENRAP developed NH3 emissions for the CENRAP states (Pechan and 

CEP, 2005e). Ammonia emissions were estimated for 13 source categories using the Carnegie 
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Mellon University (CMU) model and supplemental technical work; 80% of technical work was 

dedicated to improving emissions estimates for two source categories—livestock production and 

fertilizer use. For these two categories, as well as biogenic sources, improvements were made to 

the activity data and/or emission factors used by the CMU model. For four other source 

categories (industrial point sources, landfills, ammonia refrigeration, and non-road mobile 

sources), emissions estimates were prepared independently of the CMU model, and for the 

remaining six source categories (publicly owned treatment works, wildfires, domestic animals, 

wild animals, human respiration, and on-road mobile sources), emissions estimates were derived 

by running the CMU model with no alterations.

CENRAP NH3 model emissions estimates were combined with data provided by the other RPOs 

to represent agricultural NH3 emissions in simulations Typ02G and Base18G. 

2.9.1 Data Sources 

The WRAP provided NH3 emissions using the WRAP NH3 model (Mansell et al, 2005) that 

generated emissions for the following sectors: domestic sources, wild animals, fertilizers, soils, 

and livestock. MWRPO provided monthly IDA-formatted inventories reflective of base K to 

CENRAP that they produced from process-based models of their own, along with temporal 

profiles and spatial cross-reference information for these sources. Iowa elected to use the 

MWRPO estimates of NH3 emissions for fertilizer application, livestock, and wastewater 

treatment or SCC 28017XXXXX, 28050XXXXX, and 2630020000 respectively. Minnesota 

reviewed the MWRPO inventory and chose to move forward with the CENRAP developed data 

set. The rest of the U.S., Canada, and Mexico had agricultural NH3 emissions contained within 

their annual stationary-area-source inventories. 

2.9.2 Emissions Processing 

The WRAP NH3 emissions were processed outside of SMOKE using the WRAP NH3 model and 

provided to CENRAP as gridded, hourly emissions in network common data form (NetCDF) 

files. CENRAP and MWRPO provided monthly IDA-formatted, county-level NH3 inventories 

that were developed separately with process-based models. We modeled these emissions like 

area sources with SMOKE, applying the temporal profiles and the spatial cross-referencing 

developed for CENRAP that we received from the MWRPO. The agricultural NH3 emissions 

for the rest of the RPOs, Canada, and Mexico are contained within their stationary-area 

inventories. We applied the SMOKE default temporal profiles and spatial surrogates to all nonprocess-based NH3 emissions. 

To QA the NH3 emissions, we used the procedures in the CENRAP modeling QAPP (Morris and 

Tonnesen, 2004) and Modeling Protocol (Morris et al., 2004a) and a suite of graphical 

summaries. We used tabulated summaries of the input data and SMOKE script settings to 

document the data and configuration of SMOKE for simulations Typ02G and Base18G. The 

graphical QA summaries include, for all emissions output species, daily spatial plots, daily timeseries plots, and annual time-series plots. These QA graphics are available at 

http://pah.cert.ucr.edu/aqm/cenrap/index.shtml
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2.9.3 Uncertainties and Recommendations 

Like the other emissions categories that have traditionally been represented as stationary area 

sources, the agricultural NH3 emissions sector is affected by interregional inconsistencies in the 

way these emissions are represented. 

During the QA of the Base02a emissions, the WRAP discovered a problem with their soil NH3

estimates. The emission factor for soil NH3 that were used in developing these data produced too 

high an emission estimate from this sector. For simulations Base02B through Typ02G, we 

therefore removed the soil NH3 sector completely from the WRAP domain. In future simulations 

we will include these emissions with a revised emission factor for NH3 emissions from soils. 

2.10 Oil and Gas Emissions 

Emissions from oil and gas development activities have been poorly characterized in the past. 

Simulations These emissions have been sporadically reported by some states in their stationaryarea-source inventories, but for the most part were missing from our preliminary modeling. In 

the Typ02G and Base18G simulations, significant effort was made to better represent oil and gas 

production emissions explicitly as both area and point sources. 

2.10.1 Data Sources 

Emissions from oil and gas production activities for the CENRAP states were included with the 

other CENRAP state emission source categories (Pechan and CEP, 2005e). We received oil and 

gas production emissions inventories for the WRAP states and for tribal lands in the WRAP 

region as stationary-area-source and stationary-point-source IDA-formatted inventories. ERG, 

Inc. provided the point-source inventories with the rest of the stationary-point data (ERG, 

2006a). ENVIRON provided the area-source oil and gas inventories for non-CA WRAP states 

and for tribal lands in the WRAP region, along with spatial surrogates for allocating these data to 

the model grid (Russell and Pollack. 2005). Oil and gas production emissions data for outside of 

the WRAP region are contained in the stationary-area inventories. 

2.10.2 Emissions Processing 

We modeled the WRAP point-source oil and gas production emissions in combination with the 

rest of the stationary-point-source emissions. We modeled the WRAP area-source oil and gas 

production emissions explicitly as a separate category that included WRAP and tribal 

inventories. These data represent weekly average emissions and did not require any 

renormalization within SMOKE. We used spatial surrogates generated by ENVIRON to allocate 

these annual county-level emissions to the model grid. For all oil and gas emissions, we applied 

flat temporal profiles to create hourly inputs to CMAQ and CAMx. 

2.10.3 Uncertainties and Recommendations 

In future 2002 modeling California oil and gas production emissions should be replaced with 

revised data provided by the California Air Resources Board (CARB). In addition, WRAP has 
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updated their oil and gas production inventory for the base and future years in a Phase II work 

effort that substantially improved the emissions inventory estimates (Bar-Ilan et al., 2007). 

2.11 MMS Off-shore Gulf of Mexico Emissions 

Offshore area point source emissions include emissions in the Gulf of Mexico and off the coast 

of California that are associated with oil and gas drilling platforms. 

2.11.1 Data Sources 

We obtained year 2000 IDA-formatted point-source inventories for oil and gas platforms in the 

Gulf of Mexico from the Minerals Management Service (MMS) web site: 

http://www.gomr.mms.gov/homepg/regulate/environ/airquality/gulfwide_emission_inventory/20

00GulfwideEmissionInventory.html

We combined these with point-source data for coastal California provided to us by CARB during 

the preliminary 2002 modeling. We also obtained gridded area source emissions for platforms in 

the Gulf of Mexico from the MMS that we converted to the CENRAP 36-km model grid. 

The 2000 MMS Gulf wide Emission Inventory was updated as of June 2006 to account for a 

change in vessel emissions in the non-point source (non-platform) database file. The point 

source (platform) emission inventory database file has not changed from the original version. 

Area source emissions from offshore activities in the Gulf of Mexico were developed from the 

latest estimates provided by the Minerals Management Service (MMS). The MMS inventory 

includes both platform and non-platform sources. The non-platform area source emissions 

estimates are spatially allocated to lease blocks and protraction units throughout the Gulf of 

Mexico. Temporal and spatial allocation cross-reference data were developed from the MMS 

inventory data and formatted for input to the SMOKE emissions model by Carolina 

Environmental Programs. These data were provided to the CENRAP emissions modeling team 

for implementation within SMOKE. The spatial allocation surrogates were provided for 4-km 

grid cells. The UCR team used these surrogates and developed surrogates for 36-km grid cells. 

Because these data are references to lease blocks/protraction units, rather than counties, this 

source category was processed separately form all other emissions using a customized reference 

data and SMOKE run scripts.

We modeled the offshore point and area sources as separate categories in the simulations. We 

used SMOKE to locate the offshore point sources on the model grid and to vertically allocate 

them into 15 model layers. 

To QA the offshore platform emissions, we used the procedures in the CENRAP modeling 

QAPP (Morris and Tonnesen, 2004) and Modeling Protocol (Morris et al., 20042) and a suite of 

graphical summaries. We used tabulated summaries of the input data and SMOKE script settings 

to document the data and configuration of SMOKE for simulation Base02a. The graphical QA 

summaries include, for all emissions output species, daily spatial plots, daily time-series plots, 

and annual time-series plots. These QA graphics are available at 

http://pah.cert.ucr.edu/aqm/cenrap/index.shtml for the point and area sources. 
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2.11.3 Uncertainties and Recommendations 

While the MMS data that we used were an improvement over previously modeled Gulf of 

Mexico platform inventories, the data were developed for a different modeling application that 

covered only the extreme northwestern portion of the Gulf, so they are missing large areas of the 

region of the Gulf that contain drilling platforms. The California offshore inventory represents an 

initial attempt at compiling an emission inventory for this area and contains very few sources. 

Future simulations will focus on improving these emissions by expanding the coverage of the 

offshore platform inventories for both the Gulf of Mexico and the Pacific Coast. 

2.12 Off-shore Shipping Emissions 

Emission inventory development for regional- and continental-scale air quality modeling has 

historically neglected offshore emissions sources beyond 25 miles offshore. Concern over the 

environmental effects of commercial shipping emissions in the Pacific on the coastal states in the 

WRAP region led to the development of a commercial marine shipping inventory for the Pacific. 

This inventory of off-shore marine vessels emissions made a substantial difference in some of 

the coastal western PM estimates (e.g., SO4). VISTAS developed an off-shore marine vessels 

inventory for the entire modeling domain that included the Pacific and Atlantic Oceans and the 

Gulf Of Mexico. For Typ02G and Base18G emission inventories CENRAP adopted the offshore 

shipping inventories developed by VISTAS. 

2.12.1 Data Sources 

Initially we obtained gridded annual commercial marine shipping emissions for the Pacific on 

the 36-km model grid from WRAP for inclusion in CENRAP simulations in the Base F modeling 

(Pollack et al., 2006). The commercial marine inventory contains all of the criteria pollutants 

contained in the non-road mobile-source inventory: CO, NOx, VOC, NH3, SO2, PM10, and PM2.5. 

This inventory was subsequently updated in the Typ02G and Base18G modeling with the 

VISTAS off-shore commercial marine emissions inventory that covered the Gulf of Mexico and 

the Atlantic and Pacific Oceans and was based on the EPA/ARB SOx Emissions Control Area 

(SECA) program. Dr. James Corbett (University of Delaware) analyzed off-shore marine vessel 

data and worked with ENVIRON/ICF to convert to gridded emissions for the SECA grid. 

ENVIRON then provided SO2, NOX, PM and VOC emissions for the RPO 36-km grid. 

2.12.2 Emissions Processing 

The commercial marine shipping inventory was not processed through SMOKE. VISTAS 

provided the data to the as gridded text files on the 36-km model grid. These data were 

reformatted to the NetCDF CMAQ input format with a utility developed by UCR. The VOC 

inventory was converted to CB-IV speciation and the NOx and PM2.5 inventory pollutants to 

CMAQ input species with SMOKE chemical profiles for commercial shipping sources. No 

temporal adjustments were applied to these emissions; they use uniform monthly, daily, and 

diurnal profiles. An SCC for commercial marine vessels within the MMS inventory (SCC 

CM80002200) was accounted for in the commercial marine inventory developed for VISTAS. 
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The duplicate emissions were removed from the MMS inventory prior to processing emissions 

for Base G simulations. The duplicated emissions amounted to 19,000 TPY of NOX and 3,184 

TPY of SO2. For simulation Typ02G and Base18G we received binary netCDF file from 

ENVIRON for one day and that day was used for every day of the year. 

To QA the commercial marine shipping emissions, we used the procedures in the CENRAP 

modeling QAPP (Morris and Tonnesen, 2004) and Modeling Protocol (Morris et al., 2004a) and 

a suite of graphical summaries. The graphical QA summaries include, for all emissions output 

species, daily spatial plots, daily time-series plots, and annual time-series plots. These QA 

graphics are available at http://pah.cert.ucr.edu/aqm/cenrap/index.shtml. 

2.12.3 Uncertainties and Recommendations 

As a first attempt at representing shipping emissions in the Pacific in international waters, the 

WRAP and VISTAS 2002 commercial shipping inventory is a breakthrough in a historically 

neglected emissions category. As the RPOs evaluate the effects of these emissions on the air 

quality modeling, we anticipate that there will be refinements to the temporal profiles and to the 

vertical allocation of the emissions. Many of the stacks of large commercial ships contained in 

this inventory extend vertically above the first model layer. Future versions of this inventory 

should use higher-resolution temporal adjustments and should allocate the emissions to the 

appropriate model layers. Off-shore marine shipping activity is projected to increase. However, 

there are also the potential for emission controls on this source category (e.g., SECA program). 

Given these two off setting activities, the 2002 off-shore marine shipping emissions were 

assumed to be unchanged going from 2002 to 2018. Better estimates of 2018 marine emissions 

are being developed that should be considered in future modeling activities. 

2.13 2018 Growth and Control 

Base18G was based on grown inventories assuming on-the-books control strategies. CENRAP 

contracted with Pechan to deliver growth and control data for CENRAP and to consolidate 

growth and control information for other RPOs where available (Pechan, 2005d). The data are 

applicable to all source categories and pollutants included in the CENRAP 2002 emission 

inventory. This includes the following pollutants: sulfur oxides (SOx), oxides of nitrogen (NOx), 

volatile organic compounds (VOC), carbon monoxide (CO), ammonia (NH3), and primary PM10 

and PM2.5. Some source categories were held constant between 2002 and 2018 because either 

stagnant growth was deemed appropriate or insufficient data was available to adequately project 

future growth or controls. These source categories include the following: 

• Wind Blown Dust from non-agricultural land use categories. 

• Emissions from wildfires. 

• Emissions from Mexico. 

• Global transport sources (i.e., the 2002 GEOS-CHEM boundary conditions). 

2.13.1 Data Sources 

CENRAP contracted with Pechan to provide growth and control factors to be applied with 

SMOKE for the CENRAP region (Pechan, 2005d). These growth and control parameters were 
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based on growth estimates derived from EGAS 5.0 and control estimates assumed for 

implementation of federal regulations and on-the-books state and local control programs. 

Emissions projections for electric generating units were developed for the RPOs with the 

Integrated Planning Model (IPM). The RPO 2.1.9 IPM results were subsequently modified by 

VISTAS, MRPO and CENRAP to reflect planned new construction and controls. The WRAP 

provided 2018 EGU estimates developed in coordination with State and Industry stakeholders. 

VISTAS, MWRPO and the WRAP provided emissions for 2018, having applied growth and 

control factors outside of SMOKE processing. EPA provided SMOKE processed emissions, 

applying both growth and controls, for Canada for the year 2020. These emissions were 

provided on the RPO 36-km grid. However, emissions were inexplicably processed for an 

alternative vertical structure. Alpine Geophysics, under contract to VISTAS reallocated the 

emissions through the vertical layers to more accurately reflect the vertical structure applied 

uniformly by the RPOs. The modified data was obtained directly from Alpine Geophysics. 

Emissions from Mexico were held constant between the inventory year 1999 and modeled 2002 

and 2018. Improvements to the Mexican inventory have been continuously made between 

generation of the original BRAVO inventory and the present improved 1999 inventory. 

However, given the continued uncertainties in the improved inventory, no future year projections 

where attempted by CENRAP. 

2.13.2 Emissions Processing 

Growth and control factors developed by Pechan (2005d) for Arkansas did not match the final 

delivered inventory for Arkansas. Arkansas underwent major revisions to point and facility IDs 

in mid-2005. These updates were not available by the delivery date of the growth and control 

parameters. In coordination with Arkansas, a cross-walk was developed to correct the point and 

facility IDs. 

The assumptions that went into the development of controls for engines covered under the RICE 

MACT were not consistent with the final rule. Rule penetration values for CENRAP states were 

adjusted to more accurately reflect the impact of the final rule. 

The impact of the refinery global settlements was not incorporated into CENRAP modeling until 

the base G simulations. Control assumptions provided by EPA and referenced in EPA CAIR 

modeling were applied to the 2018 inventory. These reductions primarily impacted SO2

emissions; however, NOX reductions were applied in Oklahoma, Louisiana, and Minnesota. 

2.13.3 Uncertainties and Recommendations 

The impact of control programs is an area of uncertainty that will need continued review as the 

programs are implemented. Development of growth and control assumptions for Mexico will be 

necessary for continued refinement of the impact of international transport. CENRAP obtained 

estimates of increased prescribed burn activity for the Forest Service after processing of the base 

G simulations was underway. These estimates of increased activity should be reviewed for 

inclusion in future simulations. EPA developed 2020 estimates of Canadian emissions are 

assumed to include erroneous stack parameters previously addressed in the 2000 emissions 

processing. Further review of this data set is recommended. 
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2.14 Emissions Summaries 

Appendix B provides details on the source of the emission files used in the CENRAP Typ02G 

and Base18G modeling. Also in Appendix B are sample emission summary plots, additional 

plots are available on the CENREAP modeling website: 

http://pah.cert.ucr.edu/aqm/cenrap/emissions.shtml. 

CENRAP has contracted with E.H. Pechan and Associates to provide emissions summaries used 

in the final Typ02G and Base18G modeling in Excel spreadsheets and in an Access database that 

are available on the CENRAP website (http://www.cenrap.org/projects.asp#). Figures 2-3 

through 2-9 display the, respectively, SO2, NOx, VOC, PM2.5, PM10, NH3 and CO 

anthropogenic emissions for the CENRAP states and the Typ02G and Base18G emission 

scenarios. Emissions are broken down by major source sector. For the state of Texas the 

emissions are broken by three groups, northeast Texas, southeast Texas and remainder of Texas 

(west Texas). 

For most states, EGUs are the largest contributor to SO2 emissions (Figure 2-3). As EGU SO2 

emissions are generally projected to be reduced in the future, most states show a reduction in 

total SO2 emissions from 2002 to 2018. One exception to this is Louisiana for which non-EGU 

point source SO2 emissions are greater than for EGU and are projected to increase from 2002 to 

2018. The reasons for these increases are unclear, but the growth factors for non-EGU points 

should be examined more carefully. 

NOx emissions are fairly evenly distributed across non-EGU point, EGU point, non-road mobile, 

on-road mobile and area sources for the 2002 Typ02G emissions scenario (Figure 2-4). In 2018, 

the contributions of on-road mobile source NOx emissions is reduced dramatically, with some 

states also showing reductions in EGU NOx emissions as well, resulting in all states exhibiting 

lower NOx emissions in 2018 than 2002. 

VOC emissions are dominated by area, non-road mobile, on-road mobile and non-EGU point 

sources in both 2002 and 2018 (Figure 2-5). VOC emissions from on-road and non-road mobile 

source are projected to go down in the future, whereas VOC emissions from non-EGU point and, 

especially, area sources are projected to increase. Thus, whether a state’s total VOC emissions 

increase or decrease depends on the relative contributions of mobile versus area sources and the 

level of increase in area source VOC emissions. Note that the VOC emissions listed in Figure 

2-5 do not include biogenic VOC emissions that would be greater than the anthropogenic VOC 

emissions shown in Figure 2-5. Note that because biogenic VOC emissions are processed using 

the SMOKE/BEIS module on the 36 km grid, state-wide biogenic VOC emissions summaries are 

not readily available. 

Primary PM2.5 emissions are primarily from road dust and fugitive dust, and for some states fires 

(Figure 2-6). Kansas, Oklahoma, Louisiana and Texas all have large contributions from fires not 

seen in the other states. Road dust and fugitive dust are the most dominate source categories for 

coarse particulate as well (Figure 2-7). 

CENRAP developed a separate ammonia emissions for 13 categories using the CMU model 

including livestock and fertilizer that dominates the ammonia emissions across the CENRAP 
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states (Figure 2-8). Several states also have significant ammonia contributions from non-EGU 

point sources, whereas others do not. 

CO emissions are dominated by the on-road and non-road mobile source sectors (Figure 2-9). 

However, states with fires also see large CO contributions from them as well. On-road mobile 

source CO emissions are projected to go down substantially from 2002 to 2018, whereas the 

other source categories are flat. 

Annual SO2 Emissions by Source Sector

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

Arkansas-02Arkansas-18

Iowa-02Iowa-18

Kansas-02

Kansas-18

Louisiana-02

Louisiana-18

Minnesota-02

Minnesota-18

Missouri-02Missouri-18

Nebraska-02Nebraska-18

Oklahoma-02

Oklahoma-18

Texas-02-NE

Texas-18-NE

Texas-02-SE

Texas-18-SE

Texas-02-REST

Texas-18-REST

Tons per year

roaddust

point_fire

point_negu

point_egu

onroad

offroad

fug_dust

area_fire

area

ammonia

Figure 2-3. Summary of Typ02G and Base18G SO2 emissions by CENRAP state and major 

source sector (tons per year). 
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Annual NOX Emissions by Source Sector (tons)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

Arkansas-02

Arkansas-18

Iowa-02

Iowa-18

Kansas-02Kansas-18

Louisiana-02Louisiana-18

Minnesota-02Minnesota-18

Missouri-02

Missouri-18

Nebraska-02

Nebraska-18

Oklahoma-02Oklahoma-18

Texas-02-NE

Texas-18-NE

Texas-02-SE

Texas-18-SE

Texas-02-RESTTexas-18-REST

Tons per year

roaddust

point_fire

point_negu

point_egu

onroad

offroad

fug_dust

area_fire

area

ammonia

Figure 2-4. Summary of Typ02G and Base18G NOx emissions by CENRAP state and major 

source sector (tons per year).

Annual VOC Emissions by Source Sector (tons)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

Arkansas-02

Arkansas-18

Iowa-02

Iowa-18

Kansas-02

Kansas-18

Louisiana-02Louisiana-18

Minnesota-02Minnesota-18

Missouri-02

Missouri-18

Nebraska-02

Nebraska-18

Oklahoma-02

Oklahoma-18

Texas-02-NE

Texas-18-NE

Texas-02-SETexas-18-SE

Texas-02-REST

Texas-18-REST

Tons per year

roaddust

point_fire

point_negu

point_egu

onroad

offroad

fug_dust

area_fire

area

Figure 2-5. Summary of Typ02G and Base18G VOC emissions by CENRAP state and major 

source sector (tons per year).
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Annual PM25 Emissions by Source Sector (tons)

0

50,000

100,000

150,000

200,000

250,000

Arkansas-02Arkansas-18

Iowa-02Iowa-18

Kansas-02Kansas-18

Louisiana-02Louisiana-18

Minnesota-02Minnesota-18

Missouri-02Missouri-18

Nebraska-02

Nebraska-18

Oklahoma-02Oklahoma-18

Texas-02-NE

Texas-18-NE

Texas-02-SE

Texas-18-SE

Texas-02-REST

Texas-18-REST

Tons per year

roaddust

point_fire

point_negu

point_egu

onroad

offroad

fug_dust

area_fire

area

ammonia

Figure 2-6. Summary of Typ02G and Base18G PM2.5 emissions by CENRAP state and major 

source sector (tons per year).

Annual PM10 Emissions by Source Sector (tons)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

Arkansas-02

Arkansas-18

Iowa-02Iowa-18

Kansas-02

Kansas-18

Louisiana-02

Louisiana-18

Minnesota-02

Minnesota-18

Missouri-02

Missouri-18

Nebraska-02

Nebraska-18

Oklahoma-02

Oklahoma-18

Texas-02-NE

Texas-18-NE

Texas-02-SE

Texas-18-SE

Texas-02-RESTTexas-18-REST

Tons per year

roaddust

point_fire

point_negu

point_egu

onroad

offroad

fug_dust

area_fire

area

ammonia

Figure 2-7. Summary of Typ02G and Base18G PM10 emissions by CENRAP state and major 

source sector (tons per year).
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Annual NH3 Emissions by Source Sector (tons)

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

Arkansas-02

Arkansas-18

Iowa-02Iowa-18

Kansas-02

Kansas-18

Louisiana-02Louisiana-18

Minnesota-02Minnesota-18

Missouri-02

Missouri-18

Nebraska-02

Nebraska-18

Oklahoma-02

Oklahoma-18

Texas-02-NETexas-18-NE

Texas-02-SE

Texas-18-SE

Texas-02-REST

Texas-18-REST

Tons per year

roaddust

point_fire

point_negu

point_egu

onroad

offroad

fug_dust

area_fire

area

ammonia

Figure 2-8. Summary of Typ02G and Base18G NH3 emissions by CENRAP state and major 

source sector (tons per year).

Annual CO Emissions by Source Sector (tons)

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

Arkansas-02Arkansas-18

Iowa-02Iowa-18

Kansas-02Kansas-18

Louisiana-02Louisiana-18

Minnesota-02Minnesota-18

Missouri-02Missouri-18

Nebraska-02Nebraska-18

Oklahoma-02Oklahoma-18

Texas-02-NETexas-18-NE

Texas-02-SE

Texas-18-SE

Texas-02-RESTTexas-18-REST

Tons per year

roaddust

point_fire

point_negu

point_egu

onroad

offroad

fug_dust

area_fire

area

ammonia

Figure 2-9. Summary of Typ02G and Base18G CO emissions by CENRAP state and major 

source sector (tons per year).