### Kentucky Energy and Environment Cabinet Kentucky Division for Air Quality



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Appendix D.4 Emissions Modeling Documentation

### Kentucky Energy and Environment Cabinet Kentucky Division for Air Quality



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Appendix D.4.a Smoke - Emissions Modeling Documentation[This page intentionally left blank.]

### Background

The Sparse Matrix Operator Kernel Emissions ( SMOKE ) modeling system is an

em issions modeling system that generates ho urly gridded speciated emission inputs of mobile, nonroad mobile, area, point, fi re and biogenic emission sources for photochemical grid models. SMOKE is the fastest emissions processing tool currently available to the air quality modeling community. The sparse matrix approach utilized throughout SMOKE permits both rapid and flexib le processing of emissions data. The processing is rapid because SMOKE utilizes a series of matrix calculations instead of less efficient algorithms used in previous systems. The processing is flexible because the processing steps of temporal projection, controls, chemical speciation, temporal allocation, and spatial allocation have b een separated into i ndependent operations wherever possible. The results from these st eps are merged together at a final stage of processing. Each of these emissions processing steps are detailed below.



### Temporal Allocation

VISTAS 2002, 2009 and 2018 annual emissions m odeling was configured to generate point, area, nonroad mobile, on-road mobile, and biogenic source emissions. In addition, certain subcategories, such as fires and EGUs were maintained in separate source category files in order to allow maximum flex ibility in producing alternate strategies. With the exception of biogenic and on-road m obile source emissions that are generated using the BEIS and MOBILE6 modules in SMOKE, pre-computed annual emissions were processed using the month, day, and hou r specific temporal profiles of the SMOKE model. Point and biogenic sources were modeled for each day of the annual period while area and nonroad sources were modeled as a block of Thursday, Friday, Saturday, Sunday, Monday, one per month (total of 60 days modeled).

VISTAS based its temporal profiles and s ource catego ry cross-reference files on the USEPA CAIR/CAMR/CAVR modeling platform with files located on USEPA’s CAIR file transfer website ( ftp://www.airmodelingftp.com/ ). Modifications were made to reflect State specific profiles or updated state of knowledge application of these profiles. Some of these changes included the reallocation of North Carolina NONROAD generated emission categories to a regional set of temporal profiles more consistent with the operation of these source types in the St ate. Additionally, EGU CEM-based temporal profiles and onroad emissions modeling were prepared in manners deviating from USEPA’s original CAIR platform.

New temporal profiles used by VISTAS during modeling of the 2002, 2009 and 2018 EGU e missions took the place of USEPA provi ded default temporal profiles that are generally accepted as not accurately depicti ng temporal distribution of emissions from EGUs in the U.S. (see Attachment 1 by Stella et. al.). VISTAS EG U temporal profiles were developed using hourly CEM data as reported to USEPA’s Clean Air Market’s Division (CAMD) for the Acid Rain Program.The work conducted in this process had th e main objective of developing temporal profiles for VISTAS EGUs necessary to apply in the generation of SMOKE PTHOUR formatted emissions. Additionally, State- level monthly, day-of-week, and diurnal profiles were developed for application to non-CEM matched units in the VISTAS emissions inventory. These temporal distri butions represent a significant improvement over the USEPA defaults.

On-road mobile modeling in SMOKE was done fo r selected weeks (sev en days) of each month - using these days as a “representative week” of th e entire month. This selection allows for the representation of day-of-the-week variability in the on-road motor vehicles, and models a representation of the meteorological variability in each month. The modeled weeks were selected from mid-month, avoiding in clusion of major holidays. Holidays were modeled as the Sunday of the representative week, while the day after a holiday was modeled as a Monday. VISTAS executed sensitivity tests to examine this “representative week” methodology versus an everyday on-road mobile modeling method. VISTAS determined that the use of representative week on-road mobile emissions produced ozone and particulate matte r concentrations (and thus regional haze) that were nearly indisti nguishable from the “everyday” mobile method. VISTAS determined that the difference in the mode led air quality - resulting from the on-road mobile modeling methods - was insignificant. For more information on this study see Attachment 2 by Abraczinskas et. al.

On-road mobile emissions are represented by the following weeks per month: January 15-21 February 12-18 March 12-18 April16-22 May 14-20 June 11-17 July 16-22 August 13-19 September 17-23 October 15-21 November 12-18 December 17-23



### Speciation

Speciation is the process of disaggregating in ventory pollutants into individual chem ical species components or groups of species. The need for speciation is determined by the inventory purpose. Inventory applications that require detailed speciation include photochemical modeling, air toxics inventor ies, chemical mass balance modeling, and visibility modeling.

Depending on the purpose of a particular emi ssions inventory, the inventory m ay include TOG, NOx, sulfur oxides (SOx), CO, to tal suspended particulate matter (TSP),particulate matter less than 10 micromet ers in aerodynamic diameter (PM10), or ammonia (NH3). However, modeling inventor ies may require these emissions to be expressed in terms of other pollutants. Additionally, for some models, NOx emissions may need to be specified as NO and NO2. Also, PM may need to be separated into various fractions, such as PM10 and PM less than 2.5 micrometers in aerodynamic diameter (PM2.5).

SMOKE was configured to speciate the emissions estimates according to the requirem ents of the Carbon Bond Mechanism version four (CBM-IV, CB-IV or CB4). The SMOKE model reformats the emissions estimates for use in CMAQ modeling based on source category code (SCC) and speciati on profile cross-reference files. The speciation profiles and source category cross-references us e in VISTAS modeling are based on USEPA’s CAIR/CAVR/CAMR modeli ng platform with files located on USEPA’s CAIR file transfer website ( ftp://www.airmodelingftp.com/ ). Minor modifications were made to reflect State sp ecific profiles or updated state of knowledge application of these profiles. One major ch ange made in the VISTAS modeling was the modification of coal combustion cross-refe rence from speciation profile “NCOAL” to profile “22001.”



### Spatial Allocation

Because air quality modeling strives to re plicate the actual physical and chemical processes that occur in an i nventory domain, it is important that the physical location of emissions be determined as accurately as po ssible. In an ideal situation, the physical location of all emissions would be known ex actly. In reality, however, the spatial allocation of emissions in a modeling inventor y only approximates th e actual location of emissions.

Gridding surrogates are used to spatially allocate emission sources from a coarse geographic area to finer grid cells used fo r m odeling. There can be hundreds of unique source categories in an emissions inventory, which is typically de veloped for counties, states, or other areas. The exact location of most major emission sources is known and their geographic coordinates ar e usually contained in the i nventory. These usually are referred to as major point sources and incl ude electric utilitie s and major industrial facilities. However, other emission sources are estimated for the entire c ounty or other area as an aggregate since the exact locati ons of each source are not included in the modeling inventory. Surrogates are human ac tivities or land use information that are used to represent a more precise location of emission source category groups. A gridded surrogate ratio is the ratio of the amount of a surrogate in a modeling grid cell to the total amount of that surrogate in a county. Grid cell emissions are calculated by multiplying the cell’s gridded surrogate ratio by the county emissions.

These surrogates and their associated SCC cro ss-referen ces were orig inally developed by USEPA ( http://www.epa.gov/ttn/chief/emch/spatial/newsurrogate.html ) and converted to the gridded domain definitions of the VISTAS model requirements.

### Development of Gri dded Surrogate Files

The general process for creating the SMOKE -ready gridded surrogate files from the ArcGIS shape files is as follows:

1. Overlay the grid on the surrogates. Gene rate the grid polygons (36/12km ) with the specifications of the VISTAS domain and spat ially overlay (interse ct) the grid onto the surrogate area polygons or points. The resulting geodatabase contains, for each surrogate, the county FIPS code, the grid colu mn and row number, and the amount (area, miles or count) of the county’s portion of the surrogate in that cell.

2. Extract and convert each geodata base table to a useful dataset. Each table contains the gridded area, m iles or count in each count y for a specific surrogate. The variables include FIPS code, column number, row number and area, miles or count.

3. Calculate surrogate ratios. Surrogate ratio s are calculated for each surrogate using a series of program files. The programs sum the surrogates for each county and calculate each the ratio by dividing the county cell surr ogate value by the total county surrogate value. Combination surrogates where both ar e of the same type (i.e., Heavy and High Tech Industrial are both area) were summed prior to calculating the ratio. Combination surrogates with unlike data (i.e., 3/4 Roadway Miles plus 1/4 Popula tion are line and area data) were summed after calculating the ratios and then normalized. The surrogate crossreference code was also assigned here.

4. Gap-fill surrogates for counties missing data. There will be many instances where inventory em issions exist for a particular coun ty but there is no data, for that county, for the surrogate assigned. For example, a count y with class 1 locomo tive emissions may not have data for the class 1 railroa d surrogate. In this case we have selected to incorporate, within the assigned surrogate, a different source of data (a different surrogate) for that particular county. We incor porate secondary surrogates ev en if there is no emission source that requires it for that particular county. We denote this process as “gap-filling.” All surrogates resulting from the gap-fill ing process have ratios for all counties.

For each surrogate, we assign a secondary or tertiary su rrogate where needed for gap filling. For the class 1 railroad surrogate example mentioned above, we chose total railroads as the secondary surrogate. The s econdary or tertiary surrogate chosen would be the same across all counties and apply to all SCCs that use the particular primary surrogate. We pull in and substitute the secondary surrogate for counties where the primary surrogate is missing. Tertiary surroga tes will then be assi gned to those counties that are still without surrogates.

For identified counties having no values for each surrogate, we assign the data based on the appropriate secondary or te rtiary surrogate to these coun ties. A check to see that surrogate ratios for each county sum to approxim ately 1.00 is also performed in our surrogate development. Ratios will not al ways sum exactly to 1.00 due to rounding. However, SMOKE will normalize surrogates greater than 1.00.5. Create SMOKE-formatted spatial surrogate files. The resulting data from the previous steps is then reconfigured into SMOKE-ready format and used in the spatial allocation

process.



### Treatment of Large Fire Plume Rise

Wildfire, agricultural, and prescribed burn em issions were handled s eparately from the standard area source input f iles. We used day specific or monthly estimates of fire emissions from VISTAS, which include bur n acreage and biomass loading information for the VISTAS states. Depending on the completeness and quality of the data received, VISTAS-specific calculations were made to calculate spatial and temporal distributions of the fire emissions, rather than relying on st andard distribution prof iles. We calculated vertical distribution of the fire emissions, based on fire size and biomass involvement. SMOKE v2.1 can model fire plume rise when provided with the following variables:

PTOP – Top of the fire plume profile (meters above ground level) PBOT – Bottom of the fire plume pr ofile (m eters above ground level) Lay1 – The percent of the emissions en tra ined in the first modeling layer

For those fires as having the necessary data elem ents to s ite these files with distinct time and space coordinates, these variables were prepared and included in the modeling files used to process this emission source type.

The WRAP Fire Emissions Joint Forum Emissions Inventory Report (see http://www.wrapair.org/foru ms/fejf/doc uments/WRAP_2002_PhII_EI_Report_20050722 .pdf) has documented an approach for calcula ting these plume descriptors. In this method, the fires are assigned to one of 5 size categories, based on the total burn acreage, and the biomass fuel loading. These categories are then used to calc ulate representative hourly plume profiles. These profiles are then used by SMOKE 2.1 to distribute the vertical emissions for the fires. To successfu lly model fires as elevat ed point sources, the data included both the day or days on which th e fire occurs, and a spat ial identifier of the fire location.



### Quality Assurance

The Quality Assurance (QA) is one of the m ost important steps in performing an air quality modeling study. Because emissions inventory development is tedious, time consuming and involves complex manipulation of many different types of large data sets, errors are frequently made and, if rigorous QA measures are not in place, these errors may remain undetected.

A number of QA files were prepared and used to check for gross erro rs in the emissions inputs. Importing the model-ready emissions into PAVE and looking at both the spatial and temporal distribution of the emission prov ides insight into the quality and accuracyof the emissions inputs. So me of the additional steps for checking the emissions are summarized in the bulleted list below.

• Visualizing the m odel-ready emissions with the scale of the plots set to a very low value, we can determine whether there are areas omitted from the raw inventory or if emissions sources are err oneously located in water cells.

• Spot-check the holiday emi ssions files to confirm that they are temporally allocated like Sundays.

• Producing pie charts em ission summaries th at highlight the contribution of each emissions source component (e.g. nonroad mobile).

• Nor malizing the emissions by population for each state will illustrate where the inventories may be deficient and provide a reality check of the inventories.

• Spot check vertical allo cation of point sources using PAVE.

State inventory summaries prepared prior to th e emissions processing were compared against SMOKE output report totals generated after each major step of the emissions generation process.

For speciation, a comparison of the inventory stat e totals versus the sa me state totals with the spec iation matrix applied was completed.

For checking the vertical allo cation of the e missions, reports by source, hour, and layer for randomly selected states in the domain were created. Th ese reports were created for a representative weekday in each of the ep isodes for each of these selected states.

The quantitative QA analyses ofte n reveal significant deficiencies in the input data or the model setup. It m ay become necessary to tailor these procedures to track down the source of each major problem. As such, we will only outline the basic quantitative QA steps that were performed in an attempt to reveal the underlying problems with the inventories or processing.

Following are some of the reports that were generated to review the processed em issions:

• State and co unty totals from invent ory for each source category

o (exam ple provided in Table 1 below for Area sources)

• State and co unty totals after spatia l allocation for each source category

• State and county totals by day after tem poral allocation for each source category for represen tative days

• State and county totals by m odel species af ter chemical speciation for each source category

• State and county m odel-ready totals (after spatial allocation, temporal allocation, and chemical speciation) for each source category and for all source categories combined

• If elevated s ource selection is chosen by user, the report indi cating which sources have been selected as elevated and plume-in-grid will be included.• Totals by source category code (SCC) fr om the inventory for area, mobile, and point sources

• Totals by state and SCC from the inventory for area, mobile, and point sources

• Totals by county and SCC from the invent ory for area, mobile, and point sources

• Totals by S CC and spatial surrogates code for area and mobile sources

• Totals by speciation prof ile code fo r area, mobile, and point sources

• Totals by speciation prof ile code and S CC for area, mobile, and point sources

• Totals by m onthly temporal profile code for area, mobile, and point sources

• Totals by monthly temporal profile code and SCC for area, m obile, and point

sources

• Totals by w eekly temporal profile code for area, mobile, and point sources

• Totals by weekly temporal profile c ode and SCC for area, mobile, and point

sources

• Totals by diurnal tem poral profile code for area, mobile, and point sources

• Totals by diurnal tem poral profile c ode and SCC for area, mobile, and point

sources

• PAVE plots of gridded inventory pollutants for all pollutants for area, mobile, and point sourcesTable 1. Example of a State to tals report for Area Sources

Stationary area Processed as Area sources Base inventory year 2018 No gridding matrix applied No speciation matrix applied Temporal factors applied for episode from Thursday July 18, 2002 at 000000 to Thursday July 18, 2002 at 230000 Annual total data basis in report Date Region State CO NOX VOC NH3 SO2 PM10 PM2_5 PMC [tons/day] [tons/day] [tons/day] [tons/day] [tons/day] [tons/day] [tons/day] [tons/day]

------------------------------------------------------------------------------------------------------------------------------------------------------------------ 07/18/200 2 1000 Alabama 5.21E+01 2.19E+01 3.88E+02 2.16E+02 9.34E+01 3.66E+01 2.52E+01 1.14E+01 07/18/200 2 4000 Arizona 4.34E+02 1.94E+02 3.55E+02 1.22E+02 1.01E+01 5.17E+01 4.84E+01 3.24E+00 07/18/200 2 5000 Arkansas 4.48E+02 6.06E+01 2.14E+02 4.51E+02 7.90E+01 7.08E+01 6.25E+01 8.30E+00 07/18/200 2 6000 California 8.67E+02 3.73E+02 1.48E+03 9.55E+02 2.65E+01 2.71E+02 2.18E+02 5.26E+01 07/18/200 2 8000 Colorado 9.01E+00 1.01E+01 1.83E+02 2.16E+02 2.80E+00 2.39E+00 2.30E+00 9.67E-02 07/18/200 2 9000 Connectic u 6.37E+00 1.05E+01 1.18E+02 1.99E+01 5.39E-01 3.81E+00 3.54E+00 2.69E-01 07/18/200 2 10000 Delaware 6.56E+00 4.74E+00 2.36E+01 6.12E+01 1.48E+00 7.90E+00 2.75E+00 5.15E+00 07/18/200 2 11000 District of C 1.74E+00 1.99E+00 1.33E+01 2.68E-02 2.32E+00 9.36E-01 8.92E-01 4.37E-02 07/18/200 2 12000 Florida 1.14E+02 8.36E+01 1.35E+03 1.08E+02 1.36E+02 3.07E+01 2.56E+01 5.03E+00 07/18/200 2 13000 Georgia 1.63E+02 8.88E+01 8.71E+02 3.11E+02 1.68E+02 6.60E+01 5.05E+01 1.55E+01 07/18/200 2 16000 Idaho 1.06E+02 1.32E+02 7.85E+02 2.00E+02 2.93E+00 9.50E+01 6.70E+01 2.81E+01 07/18/200 2 17000 Illinois 8.25E+01 6.82E+01 5.73E+02 2.14E+02 1.50E+01 2.69E+01 2.40E+01 4.38E+00 07/18/200 2 18000 Indiana 1.27E+02 6.27E+01 5.45E+02 2.69E+02 1.58E+02 1.56E+02 8.07E+01 7.51E+01 07/18/200 2 19000 Iowa 4.81E+01 6.41E+00 3.07E+02 7.24E+02 2.23E+00 1.86E+01 1.65E+01 2.19E+00 07/18/200 2 20000 Kansas 2.26E+03 1.09E+02 3.71E+02 2.32E+02 3.86E+01 2.86E+02 2.19E+02 6.72E+01 07/18/200 2 21000 Kentucky 8.95E+01 1.21E+02 2.70E+02 1.24E+02 1.02E+02 3.00E+01 2.55E+01 4.50E+00 07/18/200 2 22000 Louisiana 1.66E+03 3.31E+02 3.21E+02 2.24E+02 2.51E+02 1.93E+02 1.65E+02 2.81E+01 07/18/200 2 23000 Maine 2.74E+01 7.23E+00 1.21E+02 3.06E+01 1.53E+01 1.19E+01 1.09E+01 1.04E+00 07/18/200 2 24000 Maryland 2.49E+02 2.66E+01 2.06E+02 9.56E+01 2.21E+01 3.43E+01 3.31E+01 1.14E+00 07/18/200 2 25000 Massachu s 9.95E+01 6.14E+01 2.80E+02 3.87E+01 3.46E+01 5.01E+01 4.36E+01 6.46E+00 07/18/200 2 26000 Michigan 4.64E+01 5.89E+01 5.22E+02 1.39E+02 1.06E+02 1.98E+01 8.99E+00 1.08E+01 07/18/200 2 27000 Minnesota 1.80E+02 1.41E+02 4.52E+02 3.66E+02 3.55E+01 4.87E+01 4.19E+01 6.78E+00 07/18/200 2 28000 Mississippi 4.91E+00 3.25E+00 3.41E+02 1.76E+02 7.73E-01 5.19E+00 1.50E+00 3.69E+00 07/18/200 2 29000 Missouri 2.32E+02 7.43E+01 4.40E+02 3.90E+02 1.24E+02 6.70E+01 5.39E+01 1.31E+01 07/18/200 2 30000 Montana 4.60E+01 3.25E+01 1.02E+02 9.16E+01 2.63E+00 8.46E+00 7.97E+00 4.88E-01 07/18/200 2 31000 Nebraska 1.38E+02 4.25E+01 2.07E+02 3.43E+02 2.68E+01 2.20E+01 1.80E+01 4.06E+00 07/18/200 2 32000 Nevada 1.44E+02 1.95E+01 1.35E+02 2.41E+01 8.17E+00 1.77E+01 1.71E+01 6.02E-01 07/18/200 2 33000 New Ham p 3.82E+01 2.61E+01 8.11E+01 7.21E+00 7.62E+00 2.30E+01 2.06E+01 2.40E+00 07/18/200 2 34000 New Jerse y 1.45E+01 2.24E+01 2.70E+02 5.55E+01 8.55E+00 1.94E+01 9.83E+00 9.72E+00 07/18/200 2 35000 New Mexi c 8.55E+01 7.68E+01 1.48E+02 1.37E+02 1.78E+01 1.52E+01 1.37E+01 1.55E+00 07/18/200 2 36000 New York 7.14E+01 1.15E+02 7.02E+02 2.64E+02 2.28E+02 9.92E+01 4.78E+01 5.14E+01 07/18/200 2 37000 North Car o 1.08E+03 1.12E+02 4.50E+02 5.32E+02 1.63E+01 1.97E+01 1.53E+01 4.51E+00 07/18/200 2 38000 North Dak o 1.80E+01 5.18E+01 1.28E+02 1.25E+02 1.43E+02 7.04E+00 3.74E+00 3.30E+00 07/18/200 2 39000 Ohio 4.02E+01 5.19E+01 6.51E+02 2.81E+02 3.48E+01 5.26E+00 4.70E+00 5.61E-01 07/18/200 2 40000 Oklahoma 1.00E+03 3.38E+02 5.98E+02 3.30E+02 3.23E+01 1.48E+02 1.18E+02 2.97E+01 07/18/200 2 41000 Oregon 1.77E+02 5.42E+01 5.19E+02 9.80E+01 5.96E+01 7.30E+01 6.81E+01 4.93E+00 07/18/200 2 42000 Pennsylva n 3.47E+02 5.72E+01 5.80E+02 3.18E+02 7.39E+01 7.30E+01 5.08E+01 2.22E+01 07/18/200 2 44000 Rhode Isla 1.11E+01 5.57E+00 1.29E+02 2.85E+00 5.43E+00 2.17E+00 2.00E+00 1.65E-01 07/18/200 2 45000 South Car o 1.23E+02 5.25E+01 3.95E+02 9.36E+01 3.12E+01 2.27E+01 1.95E+01 3.20E+00 07/18/200 2 46000 South Dak o 1.73E+01 1.43E+01 8.98E+01 2.69E+02 5.49E+01 5.92E+00 4.44E+00 1.48E+00 07/18/200 2 47000 Tennesse e 8.91E+01 3.53E+01 4.42E+02 8.82E+01 7.95E+01 3.51E+01 2.80E+01 7.16E+00 07/18/200 2 48000 Texas 1.71E+03 2.24E+02 1.18E+03 8.96E+02 1.87E+01 1.96E+02 1.65E+02 3.32E+01 07/18/200 2 49000 Utah 1.99E+02 5.54E+01 1.62E+02 9.43E+01 2.39E+01 2.68E+01 2.54E+01 1.41E+00 07/18/200 2 50000 Vermont 3.95E+01 4.55E+00 3.79E+01 4.14E+01 1.05E+01 6.09E+00 5.48E+00 6.09E-01 07/18/200 2 51000 Virginia 1.98E+02 1.26E+02 3.34E+02 1.40E+02 2.87E+01 5.71E+01 2.31E+01 3.40E+01 07/18/200 2 53000 Washingto 3.64E+02 4.41E+01 3.26E+02 1.21E+02 4.90E+00 6.00E+01 5.81E+01 1.92E+00 07/18/200 2 54000 West Virgi n 3.84E+01 3.07E+01 1.57E+02 3.65E+01 3.15E+01 1.56E+01 1.36E+01 2.03E+00 07/18/200 2 55000 Wisconsin 8.57E+01 3.00E+01 4.35E+02 3.56E+02 9.08E+00 1.62E+01 1.49E+01 1.30E+00 07/18/200 2 56000 Wyoming 5.24E+01 2.14E+02 4.57E+01 6.93E+01 3.83E+01 7.36E+00 6.09E+00 1.27E+00