# Appendix 8.1

Emissions Modeling (TSD Chapter 2)August 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-1 2.0 EMISSIONS MODELING

2.1 Emissions Modeling Overview

For the emissions modeling work conducted in suppor t 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 emi ssions 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 w ere provided by CENRAP emissions inventory contractors (Pechan and CEP, 2005c,e; Rei d et al., 2004a,b; Coe and Reid, 2003), other Regional Planning Organizations (RPOs) and EP A. Building from the CENRAP preliminary 2002 database (Pechan and CEP. 2005e) a nd 2018 projections (Pechan, 2005d), we integrated several updates to the inventories and a ncillary data to create final emissions input files; the final simulations are referred to as 200 2 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 invent ories for input to the air quality modeling systems. The SMOKE simulations documented in this r eport include emissions generated for annual CMAQ and CAMx simulations at a 36-km model g rid resolution, and a short-term CMAQ test simulation at a 12-km model grid resoluti on. We performed the modeling and quality assurance (QA) work based on the CENRAP mod eling Quality Assurance Project Plan (QAPP; Morris and Tonnesen, 2004) and Modeling Prot ocol (Morris et al., 2004a).

The Typ02G and Base18G emission inventories represe nt significant improvements to the preliminary emissions modeling performed by CENRAP (Morris et al., 2005). While the preliminary 2002 modeling served its purpose to dev elop 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 f or actual 2002 data that were being prepared through calendar year 2005. As these actual 2002 da ta sets became available, they were integrated into the SMOKE modeling and QA system th at 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 ca tegories, like marine shipping, added complexity to the modeling. By the end of the emiss ions data collection phase, there were 23 separate emissions processing streams covering a va riety of sources categories necessary to general model-ready emission inputs for the 2002 ca lendar year.

2.1.1 SMOKE Emissions Modeling System Background

The purpose of SMOKE (or any emissions processor) i s to process the raw emissions reported by states and EPA into gridded hourly speciated emissi ons 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 em issions value. The air quality models, however, typically require emissions data on an hou rly basis, for each model grid cell (and perhaps model layer), and for each model species. C onsequently, emissions processing involves (at a minimum) transformation of emission inventory data by temporal allocation, chemical speciation, spatial allocation, and perhaps layer a ssignment, to achieve the input requirements of the air quality model. For the CENRAP modeling effo rt, all of these steps were needed. InAugust 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-2 addition, CENRAP processing requires special MOBILE 6 processing and growth and control of emissions for the future-year inventories. Finally, the biogenic emission processing using BEIS2 includes additional processing steps. SMOKE formul ates emissions modeling in terms of sparse matrix operations. Figure 2-1 shows an example of h ow the matrix approach organizes the emissions processing steps for anthropogenic emissi ons, 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 quali ty 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 pr evious paragraph, except the layer assignment. Specifically, the inventory emissions are arranged as a vector of emissions, with associated
vectors that include characteristics about the sour ces such as its state and county or source classification code (SCC). SMOKE also creates matri ces 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 emissi ons 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 month ly adjustments and day-of-week adjustments. Emissions are known to be quite diffe rent for a typical weekday versus a typical Saturday or Sunday. For the day-of-week temporal p rocessing 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 th at because SMOKE operates in Greenwich Mean Time [GMT] then Monday would include some of l ocal time Sunday so needs to be processed separately from the typical weekday). Thi s 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 app roach. Figure 2-2 provides a schematic diagram of SMOKE/BEIS2 processing steps used in thi s project to generate biogenic emissions rates for Volatile Organic Compounds (VOCs) and oxi des of nitrogen (NOx). Because biogenic emissions are temperature sensitive, they are gener ated for each day of 2002 using day-specific meteorological conditions from the MM5 meteorologic al model.August 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-3 Figure 2-2. **Flow diagram of SMOKE/BEIS2 processing steps.**

2.1.2 SMOKE Scripts

The scripts are the interface that emissions modele rs 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 iteratio ns of the CENRAP SMOKE emissions modeling were performed using updated and corrected emissions data and assumptions resulting in the creation of numerous S MOKE modeling scripts during the course of the study. For the CENRAP annual 2002 SMOKE emissi ons 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 ca tegories 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 t he 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.August 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-4 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/smo ke/**  **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/smo**  **ke/ 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 (VMT based)**  **/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/sm oke/**  **scripts/run/36km/smk_ofs_base02f.csh smkinev, grdmat, spcmat, laypoint,**  **temporal, smkmerge, smkreport Canadian Point fires /home/aqm2/edss2/cenrap02f/sub**  **sys/smoke/ scripts/run/36km/smk_bsf_base02f.csh**  **smkinev, grdmat, spcmat, laypoint, temporal, smkmerge, smkreport**  **All point fires /home/aqm2/edss2/cenrap02f/subsys/s moke/**  **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 yea rly inventoriesAugust 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-5 2.1.3 SMOKE Directory Structures

The SMOKE directories can be divided into three bro ad 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 inven tories, 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 SM OKE make and run**  **scripts Program** 

**/home/aqm2/edss2/**

**cenrap02f/subsys/smoke/Linux2_x86pg SMOKE executables** 

**/home/aqm2/edss2/ cenrap02f/data/met MCIP out metro logy files**  **/home/aqm2/edss2/ cenrap02f/data/ge_dat SMOKE ancil lary 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 r eports** 

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 configu red SMOKE to represent the daily emissions explicitly. For the non-meteorology dependent emiss ions, we used a representative Saturday, Sunday, Monday, and weekday for each month as surro gate 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 represent ed 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.August 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-6 We used the designations in Table 2-5 to determine which months fell into each season when temporally allocating the seasonal emissions invent ories. Some of the inventories for the Electrical Generating Units (EGUs) were received fo r Winter and Summer. Table 2-6
determines which months fell into each seasonAugust 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-7 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 ar ea, on-road mobile, non-road mobile, point, and biogenic source categories. These divisions ari se from differing methods for preparing the inventories, different characteristics and attribut es of the categories, and how the emissions are processed through models. Generally, emissions inve ntories are divided into the following source categories, which we refer to later as “SMOK E processing categories.”

• Stationary Area Sources : Sources that are treated as being spread over a s patial 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 n ot possible to collect the emissions at each point of emission, they are estimated over lar ger 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**August 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-8 area sources are residential heating and architectu ral coatings. Numerous sources, such as dry cleaning facilities, may be treated either as s tationary 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 e xtent or as being assigned to a line location (called a link). Data in on-road inventori es can be either emissions or activity data. Activity data consist of vehicle miles travel ed (VMT) and, optionally, vehicle speed. Activity data are used when SMOKE will be co mputing emission factors via another model, such as MOBILE6 (U.S. EPA, 2005). Ex amples of on-road mobile sources include light-duty gasoline vehicles and he avy-duty diesel vehicles.

• Non-Road Mobile Sources : These sources are engines that do not always trav el on roadways. They encompass a wide variety of source types from lawn and garden equipment to locomotives and airplanes. Emission es timates for most non-road sources come from EPA’s NONROAD model (OFFROAD in Californi a). The exceptions are emissions for locomotives, airplanes, pleasure craf t and commercial marine vessels.

• Point Sources : These are sources that are identified by point lo cations, typically because they are regulated and their locations are availabl e in regulatory reports. In addition, elevated point sources will have their emissions al located vertically through the model layers, as opposed to being emitted into only the f irst model layer. Point sources are often further subdivided into electric generating unit (E GU) sources and non-EGU sources, particularly in criteria inventories in which EGUs are a primary source of NO

x and SO 2

. Examples of non-EGU point sources include chemical manufacturers and furniture
refinishers. Point sources are included in both cri teria 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. T he biogenic land use data in North America are available using two different sets of l and use categories: the Biogenic Emissions Landcover Database (BELD) version 2 (BELD 2), and the BELD version 3 (BELD3) (CEP, 2004b).

In addition to these standard SMOKE processing cate gories, we have added other categories either to represent specific emissions processes mo re accurately or to integrate emissions data that are not compatible with SMOKE. Examples of emi ssions sectors that fall outside of the SMOKE processing categories include emissions gener ated from process-based models for representing windblown dust and agricultural ammoni a (NH 3 ) 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 emis sions category that we modeled was emissions from fires. All of the emissions categori es 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 c ategories from the standard definitions listed above to include more explicit emissions sectors. T he advantage of using more detailed definitions of the source categories is that it lea ds to more flexibility in designing control strategies, substituting new inventory or profile d ata into the modeling, managing the input and output data from SMOKE and conducting QA of the SMO KE outputs. The major drawback to defining more emissions source categories is the in creased level of complexity and computational requirements (run times and disk spac e) that results from having a larger number of input data sets. Another motivation behind separ ating the various emissions categories isAugust 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-9 related to the size and flexibility of the input da ta. Some data sets, like the CENRAP on-road mobile inventory, were so large that we had to proc ess 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 da ta. Additional details about how we prepared the emissions inventories and ancillary da ta 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 r epresents an explicit SMOKE simulation. As discussed in Section 2.1.2 below, after finishing a ll 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 p rovided, 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 NH 3 nh3m

Ammonia from annual inventory (CENRAP) nh3y

WRAP anthropogenic NH 3 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) p t

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.August 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-10 Emissions models such as SMOKE are computer progra ms 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 annua l county-level emissions inventories with source-based temporal, spatial, and chemical a llocation profiles to create hourly emissions fluxes on a predefined model grid. For elevated sou rces that require allocation of the emissions to the vertical model layers, SMOKE integrates mete orology 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 Emissi ons 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 cou nty-level vehicle activity data (U.S. EPA, 2005a).

SMOKE uses C-Shell scripts as user interfaces to se t 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 mode ling 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 inp ut file per simulation day. Also, for use in the CAMx modeling, we converted the CMAQ-ready emis sions 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 CM AQ and CAMx are archived on the CENRAP computing cluster.

2.1.6 2002 and 2018 Data Sources

This section describes the procedures that the CENR AP 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 wo rked 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 emi ssion 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 M exico from the U.S. EPA Emissions Factors and Inventory Group (EFIG) via the Clearinghouse fo r Inventories and Emissions Factors (CHIEF) website ( http://www.epa.gov/ttn/chief/index.html ).August 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-11 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. Pech**  **an & 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 invento ries involved splitting some of the inventory files into more specific source sectors. As the sta tionary-area-source emissions sector has traditionally been a catch-all for many types of so urces, 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 N H 3 , and for the non-WRAP U.S. inventories, stage II refueling sources. We retained the dust so urces as separate categories that we would further refine with the application of transport fa ctors (see Section 2.8).

We collected the ancillary data used for SMOKE mode ling 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 al locating county-level emissions to the model grid;

• Hourly gridded meteorology data;

• Stack defaults for elevated point sources;

• MOBILE6 configuration files;August 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-12 • A Federal Implementation Standards (FIPS) codes (i. e., country/state/county codes) definition file;

• A Source Category Classification (SCC) codes defin ition file;

• A pollutant definition file; and

• Biogenic emission factors.

Except for the meteorology data and the MOBILE6 con figuration files, we used default data sets provided by EPA as the basis for all of the ancilla ry data except for temporal profiles used for Electric Generating Units (EGUs). These profiles we re developed based on CEM data from 2000 through 2003 (Pechan and CEP, 2005c). CENRAP provi ded the meteorology data for the simulations at 36-km and 12-km grid resolutions (Jo hnson, 2007). The inventory contractor who prepared the MOBILE6 inventories provided the MOBIL E6 configuration files either directly or via an RPO representative; details about the source s of the MOBILE6 inputs are provided in
Section 2.4. We made minor modifications to the che mical allocation, pollutant definition, and country/state/county codes files for new sources, p ollutants, 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 emis sions inventories in SMOKE involves combining a temporal cross-reference file and a tem poral profiles file.

• Temporal cross-reference files associate monthly, w eekly, and diurnal temporal profile codes with specific inventory sources, through a co mbination 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, week ly, 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 dat a 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 enhan ced the temporal profiles and assignments for some source categories (Pechan, 2005b).

We modified the temporal allocation data for the si mulations to improve the representation of temporal emissions patterns for certain source cate gories. 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 availa ble 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 th e SCCs in these emissions files for these two states only. The monthly profile was uniform an d the weekly and diurnal profiles were kept
the same as were modeled for the rest of the countr y.August 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-13 An updated temporal profile, profile 485, based on NOAA 1971-2000 population weighted average heating degree days for home heating area s ource emissions was obtained from VISTAS. This profile provided state specific updat es for home heating emissions and was applied to the full inventory in place of profile 1 7XX.

Other additions to the Base02G temporal allocation data included updates that made by other RPOs that are applicable to their inventories. Thes e other updates to the temporal allocation files
included

• VISTAS continuous emissions monitoring (CEM)-specif ic 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 presc ribed fire profiles; and

• WRAP on-road mobile weekly and diurnal profiles.

Finally, for all of the monthly and seasonal emissi ons inventories, we modified the temporal cross-reference files to apply uniform monthly prof iles 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 allocatio n 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-referen ce files to allocate county-level emissions inventories to model grid cells. Geographic informa tion system (GIS)-calculated fractional land use values define the percentage of a grid cell tha t is covered by standard sets of land use categories. For example, spatial surrogates can def ine a grid cell as being 50% urban, 10% forest, and 40% agricultural. In addition to land use categ ories, 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-refe rence file associates the spatial surrogates (indexed with a numeric code) to SCCs. Spatial allo cation with surrogates is applicable only to area and mobile sources that are provided on a coun ty level basis. Point sources are located in the model grid cells by SMOKE based on the latitude-lon gitude coordinates of each source. Biogenic emissions are estimated based on 1-km 2 gridded land use information that is mapped to the model grid using a processing program such as t he Multimedia Integrated Modeling System (MIMS) Spatial Allocator (CEP, 2004).August 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-14 We used various sources of spatial surrogate inform ation for the U.S., Canada, and Mexico
inventories in the simulations. For the U.S. and Ca nadian 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 U nified 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 t hat were developed by the WRAP and used for CENRAP simulations. For modeling Mexico, we use d 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 Can ada (1993)** 
**AVHRR land cover data**

**Water area 951 can_land93_water Natural Resources C anada (1993)** 
**AVHRR land cover data**

**Forest land area 952 can_land93_forest Natural Reso urces 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 Resour ces Canada (1993)** 
**AVHRR land cover data**

**Rural land area 955 can_land93_rural Natural Resour ces Canada (1993)** 
**AVHRR land cover data**

**Airports 956 can_airport U.S. DOT Bureau of Transpo rta**  **tion Statistics (2005) NORTAD 1:1,000,000 scale data** 

**Ports 957 can_port U.S. DOT Bureau of Transporta tion 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 co nsist 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 fro m 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 dat a as expected.August 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-15 2.2.1 Data Sources

For the stationary-point-source inventories in Typ0 2G and Base18G, we used actual 2002 data developed by the RPOs for the U.S., version 2 of th e year 2000 Canadian inventory, and the BRAVO 1999 Mexican inventory. The BRAVO inventory w as updated with entirely new
inventories for the six northern states of Mexico f or stationary area, as well as stationary point, on-road mobile, and off-road mobile sources. Emiss ions for the southern states of Mexico were
included for the first time in CENRAP simulations T yp02G and Base18G. These data were provided by ERG, Inc., who completed an updated 199 9 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 trib al data provided in August of 2005 (Pechan and CEP, 2005e). The WRAP (ERG, 2006a) and VISTAS B ase G (MACTEC, 2006) stationarypoint inventories consisted of an annual data set a nd monthly CEM data for selected EGUs. The WRAP and VISTAS provided these data directly to CEN RAP. We downloaded the MANE-VU stationary-point inventories from the MANE-VU web s ites. MRPO base K data was downloaded and processed for SMOKE modeling by Alpi ne Geophysics under contract from MARAMA. UCR entered into a nondisclosure agreement with Environment Canada to obtain version 2 of the 2000 Canadian point-source invento ry. This inventory represented a major improvement over the version of the data that we ha d used in the preliminary 2002 modeling.

Reductions anticipated from BART controls for elect ric generating units (EGU) in Oklahoma, Arkansas, Kansas, and Nebraska were included in pro jections of 2018 emissions. These anticipated reductions were based on actual operati ng 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 simulatio n with the addition of the permitted
facilities.

Due to missing or clearly erroneous stack parameter s, 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 Offi ce 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 occu rring in the U.S. This profile has an organic carbon fraction of 20%, which includes an adjustmen t factor of 1.2 to account for other atoms (like oxygen) attached to the carbon. OAQPS has re verted back to the profile code "22001" for coal combustion, which has an organic carbon fracti on of 1.07% (again including the 1.2 factor adjustment). This is the same profile that EPA use d 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 ha s a high organic carbon percentage
because it is based on measurements of combustion o f lignite coal. With the exception of Texas, lignite is not widely used in the U.S.. Thus, OAQP S staff stopped relying on this profile as a national default profile. A new coal speciation pr ofile developed based on Eastern bituminousAugust 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-16 coal combustion (since much of the coal burned in t he U.S. is of this type) is being developed by EPA's Office of Research and Development but was no t 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 de fault NCOAL profile.

Additionally, the "22001" profile has been flagged as problematic because of the apparent inadvertent switching of the organic carbon and ele mental carbon fractions, which are 1.07% and 1.83% respectively. The report discovering the dis crepancy in the profile did not offer a clear alternative to correct the problem (MACTEC, 2003).

CENRAP has continued to use the NCOAL factor for fa cilities 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 PM 2.5

. The modification was at the request of Texas and wa s reflective of the original study for the NCOAL factor conducted in Texas (Chow, 2005). Tabl e 2-10 summarizes the PM 2.5 speciation profiles for the NCOAL, 2201 and CMU speciation pro files 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 ancillar y 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 SM OKE 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 bounda ry layer (PBL) heights across the entire domain throughout the year. As coarse particulate m atter (PMC) is not an inventory pollutant but is required by the air quality models as input spec ies, we used SMOKE to calculate PMC during the processing as (PM 10 - PM 2.5 ). With the SMOKE option WKDAY_NORMALIZE set to “No ,”
we treated the annual inventories based on the assu mption that they represent average-day data based on a seven-day week, rather than average week day data. We also assumed that all of the volatile organic compound (VOC) emissions in the in ventories are reactive organic gas (ROG), and thus used SMOKE to convert the VOC to total org anic 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 pro files for VISTAS and CENRAP states (Base02F), we configured SMOKE to generate daily te mporal matrices, as opposed to using a Monday-weekday-Saturday-Sunday (MWSS) temporal allo cation approach.August 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-17 To QA the stationary-point emissions, we used the p rocedures in the CENRAP emissions modeling QA protocol (Morris and Tonnesen, 2004) an d a suite of graphical summaries. We used tabulated summaries of the input data and SMOK E 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.s html

2.2.3 Uncertainties and Recommendations

There were issues with the stationary-point emissio ns 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 st ates 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 si gnificant impact on CENRAP Class I areas. The 2020 projected emissions for Canada were obtain ed as air quality model-ready files from EPA. EPA has not confirmed that missing stack para meters were corrected for the projected inventory. It is assumed that they were not correc ted 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 con sist of IDA-formatted inventory files and ancillary data for allocating the inventories in sp ace, time, and to the Carbon Bond-IV chemistry mechanism used in CMAQ and CAMx. This section descr ibes where we obtained these data, how we modeled them, and the types of QA that we pe rformed to ensure that SMOKE processed the data as expected.

2.3.1 Data Sources

For the stationary area source inventories in the T yp02G 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 i nventories for the six northern states of Mexico for stationary area, as well as stationary p oint, on-road mobile, and off-road mobile
sources. Emissions for the southern states of Mexi co 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, 20 06) 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 downlo aded and processed for SMOKE modeling by Alpine Geophysics under contract from M ARAMA.August 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-18 To prepare the stationary-area inventories for mode ling, 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 pr ovided by EPA (EPA, 2004b), we extracted fugitive and road dust sources from all s tationary-area inventories for adjustment by transport factors and modeling as separate source c ategories (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 m obile-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. stat ionary-area inventory.** 
SCC Description **2501060100 Storage and Transport Petroleum and Petr oleum Product Storage Gasoline Service** 

**Stations Stage 2: Total 2501060101 Storage and Transport Petroleum and Petr**  **oleum Product Storage Gasoline Service**

**Stations Stage 2: Displacement Loss/Uncontrolled 2501060102 Storage and Transport Petroleum and Petr**  **oleum Product Storage Gasoline Service**

**Stations Stage 2: Displacement Loss/Controlled 2501060103 Storage and Transport Petroleum and Petr**  **oleum Product Storage Gasoline Service**

**Stations Stage 2: Spillage 2501070100 Storage and Transport Petroleum and Petr**  **oleum Product Storage Diesel Service**

**Stations Stage 2: Total 2501070101 Storage and Transport Petroleum and Petr**  **oleum Product Storage Diesel Service**

**Stations Stage 2: Displacement Loss/Uncontrolled 2501070102 Storage and Transport Petroleum and Petr**  **oleum Product Storage Diesel Service**

**Stations Stage 2: Displacement Loss/Controlled 2501070103 Storage and Transport Petroleum and Petr**  **oleum 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 NH 3 inventory (Section 2.9) and stationary area sources, and moving area-source fires in each regio nal 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 spatia l allocation information relative to the preliminary 2002 modeling; the rest of the ancillar y 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 ar ea-based surrogates for Canada (Table 2-9), and added surrogates for a missing county in Colora do (Broomfield) from WRAP modeling and QA work. The WRAP had noticed when looking at the C anadian data for the preliminary 2002 modeling that forest fire emissions from the Canadi an area-source inventory, which are relatively large sources of CO, NO

x , and PM 2.5 , were being allocated to a surrogate for logging a ctivities. They found similar discrepancies for other area and non-road SCCs in Canada. To improve the representation of the Canadian emissions, we adopte d 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 cros s-reference file to associate inventory SCCs with these surrogates. We also added spatial surrog ates 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).August 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-19 Improvements to the temporal allocation data for si mulation Typ02G included the addition of several FIPS-specific profiles provided by VISTAS a nd CENRAP contractors (Pechan 2005b). These temporal profiles listed in Table 2-12 target ed mainly fire and agricultural NH 3 sources, such as open burning and livestock operations, resp ectively.

Table 2-12. **New Temporal Profile Assignments for CENRAP Area So urce 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 Non industrial: Commercial;Pesticide** **Application: Non Agricultural;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** August 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-20 2.3.2 Emissions Processing

For simulations Typ02G and Base18G we configured SM OKE to process the annual stationaryarea-source inventories for the U.S., Canada, and M exico. 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 (PM 10 - PM 2.5 ). With the SMOKE option WKDAY_NORMALIZE set to “Yes,” we treated the annual stationary-area inv entories based on the assumption that they represent average weekday data, causing SMOKE to re normalize 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, a s opposed to a daily temporal approach.

To QA the stationary-area emissions, we used the pr ocedures in the CENRAP modeling QAPP and Modeling Protocol (Morris and Tonnesen, 2004; M orris 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-se ries 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 sta tionary area sources related to the removal of
certain SCCs from the base inventories for inclusio n as other source categories or complete omission from simulations. We spent considerable ef fort on ensuring that we did not have overlap between the area inventory and the other se ctors that explicitly represent sources traditionally contained in the area inventory, such as NH 3 and dust.

Both the Canadian and Mexican inventories presented minor problems that we resolved for simulation Typ02G but that can be addressed more th oroughly in future simulations. The Canadian inventory we used contained data only at t he province level, essentially equivalent to a statewide rather than county-level inventory. A hig her resolution inventory would have allowed us to use higher-resolution and more accurate spati al allocation data. Future modeling that uses Canadian data should move to the newly released mun icipality-level year 2000 inventories for
Canada.

There was a discrepancy between the state and count y coding in the Mexican inventory and the SMOKE file that defines acceptable FIPS codes. Diff erences in the ordering of the Mexican state
names between these two data sets led to some of th e Mexican inventory sources being mislabeled in the SMOKE QA reports. The state code s in the inventory and spatial surrogate files for two Mexican states were changed to be con sistent with the SMOKE country/state/county codes file.August 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-21 2.4 On-Road Mobile Sources

On-road mobile-source emissions data for SMOKE cons ist of IDA-formatted emissions and vehicle activity inventory files, and ancillary dat a 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 mode led them, and the types of QA that we performed to ensure that SMOKE processed the data a s expected.

2.4.1 Data Sources

The SMOKE processing for CENRAP included two approa ches for processing on-road mobile sources depending on the source of the data provide d. 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 co mpute 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 SM OKE runs in which both approaches were
used as follows:

• Annual VMT for computing CO, NO x , VOC, SO2, NH3 and PM using MOBILE6 for all

CENRAP States.

• Pre-computed, seasonal MOBILE6-based emissions of a ll pollutants for the 13 WRAP states that included pre-speciated PM 2.5 data.

• Annual VMT for computing CO, NO x , 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 pollutan ts for Mexico.

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

For the CENRAP states, STI provided VMT data and MO BILE6 input files for all counties in the CENRAP region (Reid et al., 2004a). MOBILE6 in put 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 s tates resulted in compute memory requirements that were to large to process all CENR AP states together. Therefore the on-road mobile processing for the CENRAP states was split i nto two groups for SMOKE processing. The resulting gridded emissions data files were then me rged 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-spec iated PM 2.5 emissions. For the rest of the U.S. we used annual county-level activity and speed inve ntories 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 inv entory pre-computed mobile source
emissions.August 2007

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For the Typ02G emissions modeling we configured SMO KE to process the annual on-road mobile emissions inventory data for the WRAP, Canad a, and Mexico as pre-computed inventories. For the non-WRAP states, we used the S MOKE/MOBILE6 integration to process the annual activity inventories and monthly, county -based roadway information. The WRAP inventories contained pre-computed speciated PM emi ssions (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 a ctual 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 t o the area sources where the emissions are just adjusted for Saturday and Sunday). We also ass umed that all of the VOC emissions in the
inventories are ROG and used SMOKE to convert the V OC 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 pro files because the WRAP on-road mobile temporal profiles contain weekly profiles that vary across the weekdays.

As noted previously, the large number of county roa dway inputs for MOBILE6 processed for the non-WRAP portion of the U.S. required us to split t he states mobile-source processing into three subsets because of computer memory limitations. Sep arate MOBILE6 input files were used for each separate county for CENRAP states, where as on e MOBILE6 input file was used for several counties outside of the CENRAP region. The three s ubsets consisted of two sets of
SMOKE/MOBILE6 simulations for the CENRAP and a simu lation that computed on-road mobile emissions for the MRPO, VISTAS, and MANE-VU states. We configured MOBILE6 to use weekly temperature averaging for computing thes e emissions within SMOKE.

To QA the on-road mobile emissions, we used the CEN RAP emissions modeling QA protocol (Morris and Tonnesen, 2004; Morris et al., 2004a) a nd a suite of graphical summaries. We used tabulated summaries of the input data and SMOKE scr ipt settings to document the data and configuration of SMOKE for simulations Typ02G and B ase18G. The graphical QA summaries include, for all emissions output species, daily sp atial plots, daily time-series plots, and annual time-series plots. These graphics are available at http://pah.cert.ucr.edu/aqm/cenrap/qa_base02b36.sht ml#mb

2.4.3 Uncertainties and Recommendations

We approached the on-road mobile emissions preparat ion 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 modelin g domain:

• The WRAP region used emissions estimates pre-comput ed with EMFAC for California and MOBILE6 for the rest of WRAP states and process ed like area sources with SMOKE adjusted from weekly to day-of-week emissions.

• The CENRAP, VISTAS, MRPO, and MANE-VU states used c ounty-level activity data to compute emissions with the SMOKE/MOBILE6 module.August 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-23 • The non-U.S. parts of the domain also had pre-compu ter on-road mobile source emissions so used an area-source approach for proce ssing with SMOKE.

Different approaches for modeling a single emission s sector adds complexity and additional
sources of error and inconsistencies to the modelin g because of the different assumptions that went into the preparation of the input data. For ex ample, refueling emissions from the on-road mobile sector are represented in the WRAP area-sour ce sector but are computed with MOBILE6 for the rest of the U.S. Not using MOBILE6-based em issions for the non-U.S. portion of the domain neglects the effects of the actual 2002 mete orology on these emissions. Applying MOBILE6 outside of the U.S. is currently not possib le because MOBILE6 is instrumented only for calculating emissions for the U.S. automotive f leet. The result of using MOBILE6 to calculate U.S. emissions and not using it to calcul ate the non-U.S. on-road mobile emissions estimates is that the non-U.S. emissions are not sp ecific to this modeling year and the 2002 meteorological conditions, whereas the U.S. emissio ns are 2002-specific.

While we used the best available information to com pute the on-road mobile emissions for the various portions of the modeling domain, inconsiste nt 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 alternat ive emissions model such as CONCEPT), or we calculate the emissions with MOBILE 6 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 con sist of annual, seasonal, and monthly IDA-formatted emission inventory files and ancillar y 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 mode led them, and the types of QA that we performed to ensure that SMOKE processed the data a s expected.

2.5.1 Data Sources The non-road mobile-source inventories in the Typ02 G 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. inv entories 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 sour ces (Pechan and CEP, 2005e). Minnesota substituted the monthly MRPO Base K non-road invent ory 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 p rovided estimates for 2002 non-road emissions in lieu of the CENRAP prepared inventory. WRAP provided non-road inventories
divided between California and non-California seaso nal 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 us ed for the rest of the states (with theAugust 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-24 exception of locomotives, aircraft and shipping). With these data WRAP also provided temporal adjustments to apply to the inventories to split th em 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 inventor ies 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 cons isted of annual county-level data (Pechan and CEP, 2005c). We received these inventor ies 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 impro ved 1999 inventory available at http://www.epa.gov/ttn/chief/net/mexico.html

.

Along with adding the WRAP weekday/weekend emission s splits to the temporal allocation files, we also created temporal input files that ap ply 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 t he uniform monthly temporal profiles to the seasonal/monthly inventories and non-uniform monthl y 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 emi ssions is provided in Table 2-13.

Table 2-13. **Non-road mobile-source inventory temporal configur ation.**  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 Midwe st RPO Base K inventory because this inventory provided improvements to the temporal all ocation of emissions for the agricultural sector. The Base K inventory includes monthly emis sions. 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); a**  **nd 22700050xx Off-highway Vehicle Diesel : Agricultura**  **l Equipment (11 SCCs).**August 2007

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x , VOC, SO 2 , PM10-FIL, and PM25-FIL for several oil and gas field equipment no n-road categories (Table 2-15). Texas provided authorization to change the pollutant code s from PM10-FIL to PM10-PRI and PM25-

FIL to PM25-PRI.

Table 2-15. **Non-road oil and gas development equipment catego ries that Texas provided** 
**emissions to be used instead of the CENRAP inventor y.**  SCC SCC Description **2265010010 Off-highway Vehicle Gasoline, 4-Stroke : Industrial Equipment: Other Oil Field Equipment;**  **2268010010 CNG : Industrial Equipment : Other Oil F ield Equipment; and**  **2270010010 Off-highway Vehicle Diesel : Industrial Equipment : Other Oil Field Equipment** 

Lancaster County Nebraska provided its own non-roadinventory for SCC 2260000000 (Offhighway Vehicle Gasoline, 2-Stroke : 2-Stroke Gasol ine 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 adju stment 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 t he 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 inve ntories represented seven-day average emissions (different from the area inventory, which represented weekday average emissions). As actual weekly average emissions, we configured SMOK E to process them by setting WKDAY_NORMALIZE to “No.” For the rest of the non-ro ad mobile inventories we processed the data as weekday average data by setting WKDAY_N ORMALIZE to “Yes.” We also
assumed that all of the VOC emissions in the invent ories 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 emi ssions on weekdays versus weekend days.

We divided the non-road mobile emissions modeling b ased on whether the data were annual or seasonal/monthly inventories. This split facilitate d 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 m onthly, we combined them with the rest of the emissions sectors to create model-ready emissio ns for CMAQ and CAMx. To QA the non-road mobile emissions we used the pro cedures in the CENRAP emissions modeling QAPP (Morris and Tonnesen, 2004) and Model ing Protocol (Morris et al., 2004a) and a suite of graphical summaries. We used tabulated s ummaries of the input data and SMOKE script settings to document the data and configurat ion of SMOKE for simulations. The graphical QA summaries include, for all emissions output spec ies, daily spatial plots, daily time-series plots, and annual time-series plots. These QA graph ics are available at http://pah.cert.ucr.edu/aqm/cenrap/qa_base02f36.sht ml#nrAugust 2007

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We prepared non-road mobile emissions using a combi nation of inventories having different temporal resolutions and various forms of ancillary data. These different combinations of information may lead to inconsistencies in how thes e emissions are represented across the modeling domain. In addition, the Canadian invento ries contain only province-level information and thus have low-resolution spatial and temporal p rofiles applied to them. The Mexican nonroad emissions are deficient in the number of diffe rent SCCs contained in the inventory and the availability of spatial surrogates that are applica ble 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 enti re 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 derivin g emissions estimates of biogenic gasphase pollutants from land use information, emissio ns factors for different plant species, and hourly, gridded meteorology data. The results of BE IS3 modeling are hourly, gridded emissions fluxes formatted for input to CMAQ or CAMx. This se ction 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 fact ors 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 resol ution land use estimates and version 0.98 of
the BELD emissions factors. Since the WRAP and CEN RAP use the same 36 km Inter-RPO continental U.S. modeling domain, CENRAP was able t o leverage of the WRAP work performed previously.

2.6.2 Emissions Processing

We used BEIS3.12 integrated in SMOKE to prepare emi ssions for the simulations. Most of the preparation for the biogenic emissions processing w as completed during the preliminary 2002 modeling (Morris et al., 2005). As the modeling dom ains 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 em issions 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 si mulation 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.sht ml#b3August 2007

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The use of newer versions of BEIS (BEIS3.13) and th e new MEGAN biogenic emissions models should be considered in future modeling.

2.7 Fire Emissions

Fire emissions data for SMOKE have traditionally be en represented as county-level area-source inventories that were placed in only the first vert ical model layer. We advanced the representation of fire emissions for air quality mo deling by preparing portions of the inventory data as point sources with specific latitude-longit ude coordinates for each fire centroid and precomputed plume rise parameters that were derived fr om individual fire characteristics. These new inventories were based on the fire data product s 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 i n space, time, and to the Carbon Bond-IV chemistry mechanism used in CMAQ and CAMx. This sec tion describes where we obtained these data, how we modeled them, and the types of Q A performed to ensure that SMOKE processed the fire emissions data as expected.

2.7.1 Data Sources

The fire inventories in the Typ02G emissions invent ory 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 th e U.S., Canada, and Mexico. Sonoma Technology, Inc. provided the fire emissions for th e CENRAP states (Reid et al., 2004b). Air Sciences provided us with the WRAP inventories divi ded among six different fire categories: wildfires, agricultural fires, wildland fire use, n atural 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 wit h 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, a nd data units used in the raw data files was obtained from Mr. Rob Luik (Data Management Special ist) at the Ontario Ministry of Natural

Resources ( Rob.Luik@MNR. gov.on.ca ). Emissions for each fire were estimated using th e Emission Production Model (EPM)/CONSUME within the BlueSky framework. A fire
identification code is needed to track individual f ires throughout the processing. The unique fire identification code was created for each fire by co ncatenating the FIRE_NUMBER and CUR_DIST fields of the original data. The fire ide ntification code also contains the FIPS code of the fire; this information is not used by BlueSk y but is needed by BlueSky2Inv, the utility program that converts the BlueSky output to the SMO KE inventory format. The FIPS code 135000 was used for all fires with longitudes east of –90°, and FIPS code 135059 was used forAugust 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-29 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 hou rly information was not used leaving all data at a daily resolution.

2.7.2 Emissions Processing

SMOKE is instrumented to distribute point-source-fo rmatted fire inventories to the vertical model layers either by using a pre-computed plume r ise approach or by computing the plume rise dynamically using actual 2002 meteorology. We appli ed both approaches for modeling pointsource fire emissions in simulation Typ02G. For th e pre-computed plume rise approach, SMOKE reads an annual inventory file with informati on on fire locations, a daily inventory file with daily emission totals for each fire, and an ho urly inventory file with hourly plume bottom, plume top, and layer 1 fractions for each fire. SMO KE uses this information to locate the fires on the horizontal model grid and to distribute the plu me 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 t o 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 informatio n on fuel loading and the heat flux of the fires to distribute the fires vertically to the mod el layers. The data are provided to SMOKE in the form of an annual inventory with information on fir e 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

PM 2.5 developed by Air Sciences (2007a) during the preli minary 2002 modeling (Morris et al., 2005).

We modeled the area-source fires for U.S. and Canad a 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 th ese 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 s ummaries. We used tabulated summaries of the input data and SMOKE script settings to documen t the data and configuration of SMOKE for simulation Typ02G. The graphical QA summaries inclu de, 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.shtm l

.

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 l arge an area. This issue can be remedied byAugust 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-30 moving to a point-source approach for representing these fires, similar to the approach used by Air Sciences for preparing the WRAP fire inventorie s.

2.8 Dust Emissions

Dust emissions data for SMOKE have traditionally ta ken the form of county-level stationaryarea-source inventories. As these emissions are cor related to meteorology, land use, and vegetative cover, we made several changes to how du st emissions are simulated by SMOKE to take these parameters into consideration. This sect ion 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 a s 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., versi on 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, M exico, and Canada. Before modeling these
data we further divided them into construction/mini ng sources and agricultural sources. We defined the fugitive dust sources in the Base02f mo deling based on guidance provided by EPA (2004b). WRAP provide road dust emission inventorie s (Pollack et al., 2006). For the rest of the RPOs and Canada, we extracted the road dust SCCs fr om 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 transpo rt factors that are based on county-level vegetative cover to the fugitive and road dust inve ntories to prepare them for input to the air quality models.

For windblown dust, we used gridded emissions prepa red 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 t hrough SMOKE using an area-source approach. We modeled these data on the assumption t hat they represented weekday, rather than seven-day week, emissions and thus used the SMOKE s etting WKDAY_NORMALIZE to convert the data to a seven-day average. We configu red SMOKE to compute PMC during the processing as (PM 10 - PM 2.5 ). 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 weAugust 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-31 split the records where the PMs processed with dust and the non PMs processed with the area. We configured SMOKE to create MWSS temporal interme diates 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 la nd 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 inv entories 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 em issions of interest, in this case windblown dust. The process-based windblown dust model develo ped by the WRAP considers wind speeds, precipitation history, and soil types to derive gri dded dust fluxes resulting from wind disturbances for the modeling domain. More informat ion on this model, its modes of operation, and the configuration used for simulation Base02a a re 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.sht ml#fd for fugitive dust, http://pah.cert.ucr.edu/aqm/cenrap/qa_base02f36.sht ml#rd for road dust, and http://pah.cert.ucr.edu/aqm/cenrap/qa_base02b36.sht ml#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 du st SCCs that we extract from the stationaryarea-source inventories for application of transpor t factors. This expanded list is based on recent work by EPA (2004b). We will also explore improveme nts to the assumptions that we used for generating emissions with the WRAP windblown dust m odel. Areas of improvement in the
windblown dust model include refinements to the lan d use data and soil characteristics, additional information about agricultural activitie s in the WRAP and CENRAP regions, detailed model evaluation on targeted windblown dust case st udies, and the application of snow-cover and vegetative transport factors to these emissions (Mansell et al., 2005).

2.9 Ammonia Emissions

Ammonia (NH 3 ) emissions from agricultural activities are a majo r source of ammonia and are dependent on many different environmental parameter s, such as meteorology, crop and soil types, and land use. CENRAP developed NH 3 emissions for the CENRAP states (Pechan and CEP, 2005e). Ammonia emissions were estimated for 13 source categories using the CarnegieAugust 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-32 Mellon University (CMU) model and supplemental tech nical 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 a s biogenic sources, improvements were made to the activity data and/or emission factors used by t he CMU model. For four other source categories (industrial point sources, landfills, am monia refrigeration, and non-road mobile sources), emissions estimates were prepared indepen dently of the CMU model, and for the remaining six source categories (publicly owned tre atment 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 NH 3 model emissions estimates were combined with data provided by the other RPOs to represent agricultural NH 3 emissions in simulations Typ02G and Base18G.

2.9.1 Data Sources

The WRAP provided NH 3 emissions using the WRAP NH 3 model (Mansell et al, 2005) that generated emissions for the following sectors: dome stic sources, wild animals, fertilizers, soils, and livestock. MWRPO provided monthly IDA-formatte d 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 fo r these sources. Iowa elected to use the
MWRPO estimates of NH 3 emissions for fertilizer application, livestock, a nd wastewater treatment or SCC 28017XXXXX, 28050XXXXX, and 263002 0000 respectively. Minnesota reviewed the MWRPO inventory and chose to move forw ard with the CENRAP developed data set. The rest of the U.S., Canada, and Mexico had agricultural NH 3 emissions contained within their annual stationary-area-source inventories.

2.9.2 Emissions Processing

The WRAP NH 3 emissions were processed outside of SMOKE using th e WRAP NH 3 model and provided to CENRAP as gridded, hourly emissions in network common data form (NetCDF) files. CENRAP and MWRPO provided monthly IDA-forma tted, county-level NH 3 inventories that were developed separately with process-based m odels. We modeled these emissions like area sources with SMOKE, applying the temporal prof iles and the spatial cross-referencing developed for CENRAP that we received from the MWRP O. The agricultural NH 3 emissions for the rest of the RPOs, Canada, and Mexico are co ntained within their stationary-area inventories. We applied the SMOKE default temporal profiles and spatial surrogates to all nonprocess-based NH 3 emissions.

To QA the NH 3 emissions, we used the procedures in the CENRAP mo deling 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 si mulations Typ02G and Base18G. The graphical QA summaries include, for all emissions o utput species, daily spatial plots, daily timeseries plots, and annual time-series plots. These Q A graphics are available at http://pah.cert.ucr.edu/aqm/cenrap/index.shtmlAugust 2007

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Like the other emissions categories that have tradi tionally been represented as stationary area sources, the agricultural NH 3 emissions sector is affected by interregional inco nsistencies in the way these emissions are represented.

During the QA of the Base02a emissions, the WRAP di scovered a problem with their soil NH 3 estimates. The emission factor for soil NH 3 that were used in developing these data produced t oo high an emission estimate from this sector. For si mulations Base02B through Typ02G, we
therefore removed the soil NH 3 sector completely from the WRAP domain. In future simulations
we will include these emissions with a revised emis sion factor for NH 3 emissions from soils.

2.10 Oil and Gas Emissions

Emissions from oil and gas development activities h ave 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 eff ort was made to better represent oil and gas production emissions explicitly as both area and po int sources.

2.10.1 Data Sources

Emissions from oil and gas production activities fo r the CENRAP states were included with the other CENRAP state emission source categories (Pech an and CEP, 2005e). We received oil and gas production emissions inventories for the WRAP s tates and for tribal lands in the WRAP region as stationary-area-source and stationary-poi nt-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 ga s 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-are a inventories.

2.10.2 Emissions Processing

We modeled the WRAP point-source oil and gas produc tion 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 categ ory that included WRAP and tribal inventories. These data represent weekly average em issions and did not require any renormalization within SMOKE. We used spatial surro gates generated by ENVIRON to allocate these annual county-level emissions to the model gr id. For all oil and gas emissions, we applied flat temporal profiles to create hourly inputs to C MAQ and CAMx.

2.10.3 Uncertainties and Recommendations

In future 2002 modeling California oil and gas prod uction emissions should be replaced with revised data provided by the California Air Resourc es Board (CARB). In addition, WRAP hasAugust 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-34 updated their oil and gas production inventory for the base and future years in a Phase II work effort that substantially improved the emissions in ventory estimates (Bar-Ilan et al., 2007).

2.11 MMS Off-shore Gulf of Mexico Emissions

Offshore area point source emissions include emissi ons 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 in ventories 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/air quality/gulfwide_emission_inventory/20 00GulfwideEmissionInventory.html

We combined these with point-source data for coasta l California provided to us by CARB during the preliminary 2002 modeling. We also obtained gri dded area source emissions for platforms in
the Gulf of Mexico from the MMS that we converted t o the CENRAP 36-km model grid.

The 2000 MMS Gulf wide Emission Inventory was updat ed 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 t he Gulf of Mexico were developed from the latest estimates provided by the Minerals Managemen t Service (MMS). The MMS inventory includes both platform and non-platform sources. Th e non-platform area source emissions estimates are spatially allocated to lease blocks a nd protraction units throughout the Gulf of Mexico. Temporal and spatial allocation cross-refer ence 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 alloca tion 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/p rotraction units, rather than counties, this source category was processed separately form all o ther emissions using a customized reference data and SMOKE run scripts.

We modeled the offshore point and area sources as s eparate 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 Proto col (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.August 2007

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While the MMS data that we used were an improvement over previously modeled Gulf of Mexico platform inventories, the data were develope d for a different modeling application that covered only the extreme northwestern portion of th e 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 em issions 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 co ntinental-scale air quality modeling has historically neglected offshore emissions sources b eyond 25 miles offshore. Concern over the environmental effects of commercial shipping emissi ons 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 emission s made a substantial difference in some of the coastal western PM estimates (e.g., SO4). VIST AS developed an off-shore marine vessels inventory for the entire modeling domain that inclu ded the Pacific and Atlantic Oceans and the Gulf Of Mexico. For Typ02G and Base18G emission in ventories CENRAP adopted the offshore shipping inventories developed by VISTAS.

2.12.1 Data Sources

Initially we obtained gridded annual commercial mar ine shipping emissions for the Pacific on the 36-km model grid from WRAP for inclusion in CEN RAP simulations in the Base F modeling (Pollack et al., 2006). The commercial marine inven tory contains all of the criteria pollutants contained in the non-road mobile-source inventory: CO, NO

x , VOC, NH 3 , SO 2 , PM 10 , and PM 2.5

. This inventory was subsequently updated in the Typ0 2G and Base18G modeling with the
VISTAS off-shore commercial marine emissions invent ory that covered the Gulf of Mexico and
the Atlantic and Pacific Oceans and was based on th e EPA/ARB SOx Emissions Control Area (SECA) program. Dr. James Corbett (University of D elaware) analyzed off-shore marine vessel data and worked with ENVIRON/ICF to convert to grid ded emissions for the SECA grid. ENVIRON then provided SO 2 , NO X , PM and VOC emissions for the RPO 36-km grid.

2.12.2 Emissions Processing

The commercial marine shipping inventory was not pr ocessed through SMOKE. VISTAS provided the data to the as gridded text files on t he 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 NO

x and PM 2.5 inventory pollutants to CMAQ input species with SMOKE chemical profiles for commercial shipping sources. No temporal adjustments were applied to these emission s; they use uniform monthly, daily, and diurnal profiles. An SCC for commercial marine ves sels within the MMS inventory (SCC CM80002200) was accounted for in the commercial mar ine inventory developed for VISTAS.August 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-36 The duplicate emissions were removed from the MMS i nventory prior to processing emissions for Base G simulations. The duplicated emissions a mounted to 19,000 TPY of NO X and 3,184
TPY of SO 2 . For simulation Typ02G and Base18G we received bin ary 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 Model ing Protocol (Morris et al., 2004a) and a suite of graphical summaries. The graphical QA su mmaries include, for all emissions output species, daily spatial plots, daily time-series plo ts, 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 emissio ns 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 s tacks of large commercial ships contained in this inventory extend vertically above the first mo del layer. Future versions of this inventory should use higher-resolution temporal adjustments a nd should allocate the emissions to the appropriate model layers. Off-shore marine shippin g 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 of f-shore marine shipping emissions were assumed to be unchanged going from 2002 to 2018. B etter estimates of 2018 marine emissions are being developed that should be considered in fu ture 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 contro l 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 (SO x ), oxides of nitrogen (NO x ), volatile organic compounds (VOC), carbon monoxide ( CO), ammonia (NH 3 ), and primary PM 10 and PM 2.5 . Some source categories were held constant betwee n 2002 and 2018 because either stagnant growth was deemed appropriate or insuffici ent data was available to adequately project future growth or controls. These source categories include the following:

• Wind Blown Dust from non-agricultural land use cate gories.

• 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 wereAugust 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-37 based on growth estimates derived from EGAS 5.0 and control estimates assumed for implementation of federal regulations and on-the-bo oks 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 cons truction and controls. The WRAP provided 2018 EGU estimates developed in coordinati on with State and Industry stakeholders. VISTAS, MWRPO and the WRAP provided emissions for 2 018, having applied growth and control factors outside of SMOKE processing. EPA p rovided SMOKE processed emissions, applying both growth and controls, for Canada for t he 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 accur ately reflect the vertical structure applied uniformly by the RPOs. The modified data was obtai ned directly from Alpine Geophysics.
Emissions from Mexico were held constant between th e inventory year 1999 and modeled 2002 and 2018. Improvements to the Mexican inventory ha ve been continuously made between generation of the original BRAVO inventory and the present improved 1999 inventory. However, given the continued uncertainties in the i mproved inventory, no future year projections where attempted by CENRAP.

2.13.2 Emissions Processing

Growth and control factors developed by Pechan (200 5d) for Arkansas did not match the final delivered inventory for Arkansas. Arkansas underwe nt major revisions to point and facility IDs in mid-2005. These updates were not available by t he 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 c ontrols 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 t he final rule.

The impact of the refinery global settlements was n ot incorporated into CENRAP modeling until the base G simulations. Control assumptions provid ed by EPA and referenced in EPA CAIR modeling were applied to the 2018 inventory. These reductions primarily impacted SO 2 emissions; however, NO X reductions were applied in Oklahoma, Louisiana, an d Minnesota.

2.13.3 Uncertainties and Recommendations

The impact of control programs is an area of uncert ainty that will need continued review as the programs are implemented. Development of growth an d 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 inc reased activity should be reviewed for inclusion in future simulations. EPA developed 202 0 estimates of Canadian emissions are assumed to include erroneous stack parameters previ ously addressed in the 2000 emissions processing. Further review of this data set is rec ommended.August 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-38 2.14 Emissions Summaries

Appendix B provides details on the source of the em ission files used in the CENRAP Typ02G and Base18G modeling. Also in Appendix B are sampl e 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 Associat es to provide emissions summaries used in the final Typ02G and Base18G modeling in Excel s preadsheets 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, VO C, PM2.5, PM10, NH3 and CO anthropogenic emissions for the CENRAP states and t he Typ02G and Base18G emission scenarios. Emissions are broken down by major sour ce sector. For the state of Texas the emissions are broken by three groups, northeast Tex as, southeast Texas and remainder of Texas (west Texas).

For most states, EGUs are the largest contributor t o 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 excepti on 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 emis sions 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 mobil e, on-road mobile and non-EGU point sources in both 2002 and 2018 (Figure 2-5). VOC em issions from on-road and non-road mobile source are projected to go down in the future, wher eas 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 contri butions of mobile versus area sources and the
level of increase in area source VOC emissions. No te that the VOC emissions listed in Figure 2-5 do not include biogenic VOC emissions that woul d be greater than the anthropogenic VOC emissions shown in Figure 2-5. Note that because b iogenic 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 PM 2.5 emissions are primarily from road dust and fugitive dust, and for some states fires (Figure 2-6). Kansas, Oklahoma, Louisiana and Texa s all have large contributions from fires not seen in the other states. Road dust and fugitive d ust are the most dominate source categories for coarse particulate as well (Figure 2-7).

CENRAP developed a separate ammonia emissions for 1 3 categories using the CMU model including livestock and fertilizer that dominates t he ammonia emissions across the CENRAPAugust 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-39 states (Figure 2-8). Several states also have sign ificant ammonia contributions from non-EGU point sources, whereas others do not.

CO emissions are dominated by the on-road and non-r oad mobile source sectors (Figure 2-9). However, states with fires also see large CO contri butions from them as well. On-road mobile source CO emissions are projected to go down substa ntially 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

**Ar k**  **a ns**  **a s**  **-0 2**  **Arkansa s**  **-18**

**I ow**  **a-02**

**I ow**  **a-18 Kansa**  **s -02**  **Kansa s**  **- 1**  **8**

**L ou**  **i s**  **i a**  **na-0 2**  **Louisia n**  **a- 1**  **8 Minn**  **es o**  **ta-02**

**M innesota-18**  **Miss ou**  **r i**  **-02**

**M i**  **ssouri-18**

**Ne braska-02**  **Nebrask a**  **- 1**  **8 Oklahoma-02** 

**Ok l**  **a hom**  **a -**  **1 8**  **Texas-02-NE**

**Te x**  **as-18-NE**

**T exas-02**  **- SE**  **Texas-18-SE**

**T e**  **x as**  **-02-RES T**  **Texas-18-RES**

**T**

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 CE NRAP state and major**  **source sector (tons per year).**August 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-40 

### Annual NOX Emissions by Source Sector (tons)

0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000

**Arkan sa**  **s-02 Arkan**  **sa s-18**  **Iowa-02**

**I owa-**  **18 Kans**  **a s-02**  **Kan sa**  **s-1 8** 
**Loui s**  **ian a**  **-0 2** 

**L ou**  **is i**  **ana-18**

**Mi n**  **ne so**  **t a**  **-0 2**  **Minnesota-18**
**Mis s**  **ouri-02 Missouri**  **- 18** 

**Ne b**  **raska-02**

**N e**  **bra s**  **k a**  **-18 Oklahoma-02** 

**O klah**  **oma -1**  **8**

**T e**  **xas-0 2**  **-NE**

**Te x**  **as-18 -**  **NE Texa**  **s-0 2-**  **S E**  **Texas -**  **18-SE Texas**  **-0 2-RE**  **ST Texas-18-RES** 

**T**

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 CE NRAP 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- 0**  **2 Arkansa**  **s -1**  **8**

**I ow**  **a- 02** 

**Io w**  **a-1 8** 

**K a**  **n s**  **as- 02** 

**K a**  **nsas-1 8**  **Louisiana-02 Louisi**  **a na-18**  **Minnesota-02**

**M i**  **nne sot**  **a-18**

**Mi s**  **souri-02**

**M issour**  **i-1 8** 

**Ne br**  **a s**  **ka-0 2**  **Nebraska-18**

**Okl aho**  **ma-02 Oklahoma-18**  **Texas-02-N E** 

**T e**  **x a**  **s-18 -**  **N E**  **Texas-02-SE**

**Tex a**  **s-18-SE Texas-02-REST** 

**Tex a**  **s-18-R E**  **ST**

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 CE NRAP state and major**  **source sector (tons per year).**August 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-41 Annual PM25 Emissions by Source Sector (tons)

0 50,000 100,000 150,000 200,000 250,000

**A r**  **ka n**  **s as**  **- 0**  **2 Arkansas-18** 

**Io w**  **a -**  **0 2** 

**Io wa-**  **1 8**  **Kansas-02**

**K an**  **sa s**  **- 1**  **8**

**Lo ui**  **sia n**  **a -**  **0 2**  **Louisiana-18**

**M i**  **nnesot a-**  **02**

**M i**  **n n**  **es o**  **t a-1**  **8 Missouri-02**  **Missouri-18**

**N eb**  **ras k**  **a-02**

**N ebrask**  **a -18**  **Oklahoma- 0**  **2**

**O kl**  **a h**  **om a**  **- 18** 

**Te x**  **as-02- NE**  **Texas-18-NE**

**T exas**  **-0 2-S**  **E**

**Te x**  **as-1 8**  **- S**  **E Texas**  **- 02-REST** 

**Te xas-**  **1 8-RES**  **T**

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

**A rk**  **an s**  **as-0 2** 

**A rk**  **ansas-1 8**  **Iowa-02 Iowa-**  **1 8** 

**K a**  **n s**  **as-0 2** 

**K a**  **n s**  **as-1 8** 

**Lou i**  **si a**  **na -0**  **2**

**L o**  **ui si**  **a n**  **a-1 8** 

**Mi n**  **n e**  **sota -**  **0 2** 

**Mi n**  **nesota -**  **18 Missouri-02**  **Miss o**  **uri-1 8** 

**Ne brask**  **a -02** 

**Ne b**  **ra sk**  **a -1**  **8 Oklaho**  **ma -02**  **Oklah oma**  **-1 8** 

**T e**  **xas -**  **0 2**  **-NE Texas**  **- 18-NE**  **Texas-02-SE**

**T e**  **xas-1 8**  **-SE**

**Te xa**  **s -**  **02- R**  **ES T** 

**T e**  **xa s**  **-1 8**  **-R ES**  **T**

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 C ENRAP state and major**  **source sector (tons per year).**August 2007

T:\BAR\Planning\Regional Haze SIP\Volume I\Kansas R egional Haze SIP and Appendices Oct 2009\Chapter 8 Appendices\Appendix 8.1 Emissions Modeling (TSD Cha pter 2).doc 2-42 Annual NH3 Emissions by Source Sector (tons)

0 50,000 100,000 150,000 200,000 250,000 300,000 350,000

**Arkan s**  **a s**  **-02**

**Ark a**  **n s**  **a s**  **-18 Iowa-02** 

**Io w**  **a -**  **18**

**K ansas-02** 

**Ka nsas-18** 

**Lo uisi**  **a na**  **-02 Louisi**  **a na**  **- 1**  **8 Minne**  **sota -**  **0 2**  **Minnesota-18 Misso**  **uri -**  **0 2**  **Misso u**  **r i**  **- 1**  **8**
**Neb r**  **a ska-**  **0 2** 
**Neb r**  **a ska-**  **1 8** 

**Okl a**  **h oma-02**  **Oklahoma-18 Texas-02-**  **N E**  **Texas -**  **1 8-N**  **E**

**T exa**  **s- 0**  **2-SE**

**T exa**  **s-18-SE**

**Texa s**  **- 0**  **2-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 CE NRAP 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

**Arka n**  **sas- 0**  **2**

**A rkansa**  **s -1**  **8 Iowa-02**  **Iowa-18**

**K ans**  **as-0 2** 

**K a**  **n s**  **as-18 Louisiana-02** 

**Lo u**  **i s**  **iana-1 8** 

**Mi n**  **neso ta-**  **02**

**Mi n**  **nesot a**  **-18 Missouri-02** 

**Mi s**  **souri-18**

**N e**  **b r**  **a sk**  **a -02**  **Nebra sk**  **a- 18** 

**Ok la**  **hom a**  **- 0**  **2 Oklah**  **o ma-18** 

**T exas-02-NE** 

**T exas-18-NE** 

**T ex**  **a s**  **- 02-**  **S E** 

**T e**  **xa s**  **- 18-**  **SE**

**T e**  **xas-02-REST**

**T ex**  **a s**  **-1 8**  **-R E**  **S T** 

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 CEN RAP state and major**  **source sector (tons per year).**