# Air Quality Modeling Technical Support Document: Boiler Source Sector RulesEPA-454/R-10-006 December 2010



# Air Quality Modeling Technical Support Document: Boiler Source Sector Rules

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Air Quality Assessment Division Research Triangle Park, NC 277111 I Introduction

This document describes the air quality modelin g performed by EPA in support of the final boiler MACT rule. A national scale air quality modeling analysis was performed to estimate the impact of the sector emissions change s on future year: annual and 24-hour PM2.5 concentrations, 8-hr maximum ozone, total mercur y deposition, as well as visibility impairment. Air quality benefits are estimated with the Comprehensive Air Quality Model with Extensions (CAMx) model. CAMx simulates the numerous physic al and chemical processes involved in the formation, transport, and destruction of ozone, pa rticulate matter and air toxics. In addition to the CAMx model, the modeling platform incl udes the emissions, meteorology, and initial and boundary condition data which are inputs to this model.

Emissions and air quality modeling decisions are made early in the analytical process. For this reason, it is important to note that the inventories used in the air quality modeling and the benefits modeling are slightly different than the final boiler sector invent ories presented in the RIA. However, the air quality i nventories and the final rule inve ntories are generally consistent, so the air quality modeling adequately reflects the effects of the rule.

II. Photochemical Model Versio n, Inputs and Configuration

Photochemical grid models use state of the sc ience numerical algorithms to estimate pollutant formation, transport, and deposition over a variety of spatial scales that range from urban to continental. Emissions of precursor species are in jected into the model where they react to form secondary species such as ozone and then transport around the modeling domain before ultimately being removed by deposition or chemical reaction. Photochemical model source apportionment tracks the formation and transport of primarily and secondarily formed pollutants from emissions sources and allows the estimation of contributions at rece ptors. This type of emissions apportionment is useful to understa nd what types of sour ces or regions are contributing to pollutants estimat ed by photochemical grid models.

The 2005-based CAMx modeling platform was used as the basis for the air quality modeling for this rule. This platform represents a structur ed system of connected modeling-related tools and data that provide a consistent and transparent basis for assessing the air quality response to projected changes in emissions. The base year of data used to construct this platform includes emissions and meteorology for 2005. The platform is intended to support a variety of regulatory and research model applications and analyses. Th is modeling platform and analysis is described below.

As part of the analysis for this rulemaking, the modeling system was used to calculate daily and annual PM2.5 concentrations, 8-hr maximum ozon e, annual total mercury deposition levels and visibility impairment. Model predictions are us ed in a relative sense to estimate scenariospecific, future-year design values of PM 2.5 and ozone. Specifically, we compare a 20162 reference scenario, a scenario wi thout the boiler sector controls, to a 2016 c ontrol scenario which includes the adjustments to the boiler sector. This is done by calcula ting the simulated air quality ratios between any pa rticular future year simu lation and the 2005 base.

These predicted ratios are then applied to ambien t base year design values. The design value projection methodology used here followed EPA guidance for such analyses (USEPA, 2007). Additionally, the raw model outputs ar e also used in a relative sens e as inputs to the health and welfare impact functions of the benefits analysis . Only model predictions for mercury deposition were analyzed using absolute model changes, although percent changes between the control case and two future baselines are also estimated.

A. Model version

CAMx version 5.20 is a freely available computer m odel that simulates the formation and fate of photochemical oxidants, ozone, primary and seconda ry PM concentrations, and air toxics, over regional and urban spatial scales for given input sets of meteorological conditions and emissions. CAMx includes numerous science modules th at simulate the emission, production, decay, deposition and transport of organi c and inorganic gas-phase and pa rticle-phase pollutants in the atmosphere (Baker and Scheff, 2007; Nobel et al., 2001; Russell, 2008). CAMx is applied with ISORROPIA inorganic chemistry (N enes et al., 1999), a semi-volatile equilibrium scheme to partition condensable organic gases between gas and particle phase (Strader et al., 1999), Regional Acid Deposition Model (RADM) aqueous phase chemistry (Chang et al., 1987), and Carbon Bond 05 (CB05) gas-phase chemistry module (ENVIRON, 2008; Gery et al., 1989).

CAMx contains a variety of ozone source apport ionment tools, including the original ozone source apportionment tool (OSAT) and the an thropogenic pre-cursor culpability assessment (APCA) tool (ENVIRON, 2008). Ozone source apportionment tracers are treated using the standard model algorithms for vertical advection, vertical diffusion, and hori zontal diffusion. Horizontal advective fluxes for each of the regul ar model species that make up nitrogen oxides (NO X ) and volatile organic compounds (VOC) are combined and normalized by a concentration based weighted mean. Separate ozone tracers are used in CAMx to track ozone formation that happens under NO X and VOC limited conditions.

Particulate matter source appor tionment technology (PSAT) implemented in CAMx estimates the contribution from specific emissions source groups to PM2.5 and all forms of mercury using reactive tracers (ENVIRON, 2008; Wagstrom et al., 2008). The tracer species are estimated with source apportionment algorithms rather than by the host model routines. PSAT tracks contribution to PM2.5 sulfate, nitrate, ammonium , secondary organic aerosol, and inert primarily emitted species. Non-linear processes like gas and aqueous phase chemistry are solved for bulk species and then apportioned to the tagged sp ecies. Emissions of nitr ogen oxides are tracked through all intermediate nitrogen species to particulate nitrate ion. Ammonia emissions are tracked to particulate ammonium ion. This mo deling assessment used the PSAT approach to estimate source contribution to PM2.5 species and mercury and the AP CA method to estimate source contribution to modeled ozone.3 B. Model domain and grid resolution

The modeling analyses were performed for a domai n covering the continental United States, as shown in Figure II-1. This domain has a parent horizontal grid of 36 km with two finer-scale 12 km grids over portions of the eastern and wester n U.S. The model extends vertically from the surface to 100 millibars (approximately 15 km) usi ng a sigma-pressure coordinate system. Air quality conditions at the outer boundary of the 36 km domain were taken from a global model and vary in time and space. The 36 km grid was only used to establish the incoming air quality concentrations along the boundaries of the 12 km grids. Only the finer grid data were used in determining the impacts of the emission standa rd program changes. Table II-1 provides some basic geographic information regard ing the photochemical model domains.

Table II-1. Geographic elements of domain s used in photochemical modeling. Photochemical Modeling Configuration

National Grid Western U.S. Fine Grid Eastern U.S. Fine Grid

Map Projection Lambert Conformal Projection

Grid Resolution 36 km 12 km 12 km

Coordinate Center 97 deg W, 40 deg N

True Latitudes 33 deg N and 45 deg N

Dimensions 148 x 112 x 14 213 x 192 x 14 279 x 240 x 14

Vertical extent 14 Layers: Surface to 100 mil libar level (see Table II-3)

Figure II-1. Map of the photochemical modeling domains. The black outer box denotes the 36 km national modeling domain; the red inner box is the 12 km west ern U.S. grid; and the blue inner box is the 12 km ea stern U.S. grid.4 C. Modeling Time-period

The 36 km and both 12 km modeling domains were modeled for the entire year of 2005. Data from the entire year were utilized when looking at the estimation of PM2.5, total mercury deposition, and visibility impact s from the regulation. Data from April through October is used to estimate ozone impacts.

D. Model Inputs: Emissions, Meteorology and Boundary Conditions

The 2005-based modeling platform was used for the air quality modeling of future emissions scenarios. In addition to the photochemical model, the modeling platform also consists of the base- and future-year emissions estimates, meteorological fields, as well as initial and boundary condition data which are all i nputs to the air quality model.

1. Emissions Input Data

2005 baseline emissions

The emissions data used in the base year and fu ture reference and future emissions adjustment case are based on the 2005 v4.1 platform. Emissi ons are processed to photochemical model inputs with the SMOKE emissions modeling syst em (Houyoux et al., 2000). The emissions cases use different emissions data for mercury than the official v4 platform to use data intended only for the rule development and not for general us e. Unlike the 2005 v4 platform, the configuration for this modeling application included merc ury emissions from the National Air Toxics Assessment Inventory and some a dditional boiler sector mercury em issions more consistent with the engineering analysis of potential control options.

The ICR emissions are not explicitly used in the modeling assessment si nce the ICR database lacks important emissions releas e point information necessary for photochemical grid modeling such as stack coordinates, stack release height, exit temperature, exit velocity, etc. Additionally, plant ID information is not always consiste nt between ICR and NEI which makes removing boiler emissions, necessary to prevent double coun ting emissions in the photochemical model, from the NEI very problematic.

It is important to note that the NEI is broken up into general sect ors such as EGU point (PTIPM), non-EGU point (PTNONIPM), and stationary em issions not included in the point source inventories (NONPT). This non- point category is generally re ferred to as the “area” source inventory and is not a direct repr esentation of sources classified as area sources for the purposes of NESHAP/MACT standards.

2005 ICR emissions data in tegration with NEI

For the non-EGU point sector (PTNONIPM), a cro ss-walk was used to remove boiler mercury emissions in the 2005 NEI at facili ties with identification informa tion that matched facilities in the ICR database. Boiler mercury emissions were removed from facilities in the EGU point5 sector (PTIPM) that matched facilities in th e ICR database and that had NEEDS unit design capacity less than 25 MW. If de sign capacity was not available th en units with a boiler MACT code were removed from the inventory. All ar ea (NONPT) mercury em issions were removed from the NEI inventory to prevent double counting of emissions.

The specific procedure used to replace the Hg emissions in the NATA in ventory with the boiler MACT ICR data is detailed below.

NON-EGU point (PT NONIPM) sector:

• Apply a crosswalk containing Maximum Achievable Control Technology (MACT), Standard Industrial Classification (SIC) a nd Source Classification Codes (SCC) and North American Industry Classsi fication (NAICS) codes that map to the boiler source category to the NATA inventory. This crossw alk contains nearly 4500 combinations of the above codes.

• Remove only facilities that match faciliti es in the ICR database (based on the NEI_UNIQUE_ID field)

• Add all Hg emissions from the ICR databa se that include an NEI_UNIQUE_ID (this procedure added all but 0.177 t ons (3.7% of the total) Hg in the ICR database.

EGU Point (PTIPM) sector:

• Remove units from facilities that matched to the ICR database whereby

o Design capacity of the unit is less than 25 Megawatts (MW)

o Or, if no design capacity was available, removed units that had a MACT code representing the boi ler MACT category

Non-point/area (NONPT) sector:

• Remove nonpoint boiler Hg emissions to prevent double counting.

• Boiler emissions in the nonpoint inventory ut ilized a crosswalk of MACT and SCC codes shown in Table II-5.

The resulting emissions for boiler MACT sources in the modeling inventory is 4.7 tons and all were included in the PTIPM sector.

Future Year Baseline Emissions

The 2016 reference case is intende d to represent the emissions associated with growth and controls in that year projected from the 2005 simulation year. The Un ited States EGU point source emissions estimates for the future year reference and control case are based on an Integrated Planning Model (IPM) run for criteria pollutants, hydrochloric acid, and mercury in 2016. Both control and growth factors were appl ied to a subset of the 2005 non-EGU point and nonpoint to create the 2016 reference case. Th e 2005 v4 platform 2014 projection factors were the starting point for most of the 2016 SMOKE -based projections. The projected inventory incorporates emissions projections for the propo sed transport rule, cement kiln NESHAP, RICE NESHAP, gold mine NESHAP, known changes to boiler emissions based on the ICR database,6 and known consent decrees. A complete list of rule s included in the future point source baseline inventory is shown in Table II-2.

Table II-2. Control Strategies and/or Growth Assump tions Applied to 2005 emissions for the 2016 projection Projections Unchanged from v4 platform Description Species MACT rules, national, VOC: national applied by SCC, MACT VOC Consent Decrees and Setttlements, including re finery consent decrees, and settlements for: 1) Alcoa, TX and 2) Premcor (formerly MOTIVA), DE ALL

Municipal Waste Combustor Reductions –plant level PM Hazardous Waste Combustion PM Hospital/Medical/Infectious Waste Incinerator Regulations under Section 129d/11 1d NOX, PM, SO2 Large Municipal Waste Combustors – growth applied to specific plants ALL MACT rules, plant-level, VOC: Auto Plants VOC MACT rules, plant-level, PM & SO2: Lime Manufacturing PM, SO2 MACT rules, plant-level, PM: Taconite Ore PM Municipal Waste Landfills: project factor of 0.25 applied ALL Livestock Emissions Growth from year 2002 to years 2012 and year 2015 NH3, PM Residential Wood Combustion Growth and Changeouts from year 2005 to years 2012 and 2015 ALL

Gasoline Stage II growth and control from year 2005 to years 2012 and 2015 VOC Portable Fuel Container MSAT2 inventory growth and control from year 2005 to years 2012 and 2015 VOC

Additional or Modified Projections Used In Utility MACT modeling NESHAP: Portland Cement (09/09/10) – plant level based on Industrial Sector Integrated Solutions (ISIS policy emissions in 2013. The ISIS results are from the ISIS-Cement model runs for the NESHAP and NSPS analysis of July 28, 2010. HG, NOX, SO2, PM, HCL NESHAP: Gold Mine Ore Processing and Production Area Source Category (based on proposed rule 04-15-10) – finalized 12/2010 HG

NewYork SIP VOC, HAP VOC, NOX Additional Plant and Unit closures ALL NESHAP: Reciprocating Internal Combustion Engines 1 NOX, CO, PM

The future onroad emissions reflect control programs including the Light-Duty Vehicle Tier 2 Rule, the Onroad Heavy-Duty Rule, and the Mobi le Source Air Toxics (MSAT) final rule and the category 3 marine diesel engines Clean Ai r Act and International Maritime Organization standards which includes the esta blishment of emission control ar eas for these ships. Emission reductions and increases from the Renewable Fu el Standard version 2 (RFS2) are not included. The future case nonroad mobile emissions redu ctions for these years include reductions to locomotives, various nonroad engines including dies el engines and various marine engine types,

1 Note that SO2 reductions are expected to occur to due fuel sulfur limits but were excluded from the projection. They were expected to reduce SO2 by 27,000 tons, nationwide7 fuel sulfur content, and eva porative emissions standards. A summary of the mobile source control programs included in the projected futu re year baseline is shown in Table II-3.

The 2016 onroad emissions were based on MOVES 2010. Future year VMT was projected from the 2005 NEI v2 VMT using growth rates from AEO2009. The emissions from onroad gasoline sources were augmented with emissions based on the same preliminary version of MOVES as was used for 2005. The same MOVES-based PM2.5 temperature adjustment factors were also applied as in 2005 for running mode emissions; howe ver, cold start emissions used year-specific temperature adjustment factors.

Nonroad mobile emissions were created with NMIM using a consistent approach as was used for 2005, but emissions were calculat ed using NMIM future-year equipment population estimates and control programs for 2014. Emissions for lo comotives and category 1 and 2 (C1 and C2) commercial marine vessels were derived for 2016 based on emissions published in the Locomotive Marine Rule, Regulatory Impact Assessment, Chapter 3 (see http://www.epa.gov/otaq/locomotives.htm#2008final).

Table II-3. Summary of Mobile Source Control Programs Included in 2016 Base Case (signature date in parenthesis) National Onroad Rules: Tier 2 Rule (February 28, 2000) Onroad Heavy-Duty Rule (February 24, 2009) Final Mobile Source Air Toxics Rule (MSAT2) (February 9, 2007) Renewable Fuel Standard (March 26, 2010) Local Onroad Programs: National Low Emission Vehicle Pr ogram (NLEV) (March 2, 1998) Ozone Transport Commission (OTC) LEV Program (January, 1995) National Nonroad Controls: Tier 1 nonroad diesel rule (June 17, 2004) Phase 1 nonroad SI rule (July 3, 1995) Marine SI rule (October 4, 1996) Nonroad diesel rule (October 23, 1998) Phase 2 nonroad nonhandheld SI rule (March 30, 1999) Phase 2 nonroad handheld SI rule (April 25, 2000) Nonroad large SI and recreationa l engine rule (November 8, 2002) Clean Air Nonroad Diesel Rule - Tier 4 (June 29, 2004) Locomotive and marine rule (May 6, 2008) Nonroad SI rule (October 8, 2008) Aircraft: Itinerant (ITN) operations at ai rports adjusted to year 2014 Locomotives: Clean Air Nonroad Diesel Final Rule – Tier 4 (June 29, 2004) Locomotive rule (April 16, 2008) Locomotive and marine rule (May 6, 2008) Commercial Marine: Locomotive and marine rule (May 6, 2008)8 Future Year Sector Contribution Approach

The 2016 reference scenario for th e boiler sector incl udes growth and control for the industry from the 2005 sector emissions estimates. The le ngth of time required to conduct emissions and photochemical modeling preclude the use of the fina l facility specific emissions estimates based on controls implemented for this rule. A 2016 “control” or emissions adjustment case was developed by tracking the total c ontribution from potentially contro llable boiler sector emissions from the 2016 baseline inventory. This total contri bution estimate of the sector creates a policy space where potential control impacts would be maximized at all locations. Since this is unrealistic, the boiler sector air quality contribution estimates from the 2016 source apportionment model simulation are adjusted based on nation-wide estimates of control percentages by pollutant to create a final 2016 “control” emissions scenario. The 2016 estimated controllable emissions for the boiler sector are sh own in Table II-4. Boiler sector emissions are contained in several different general classes of emissions used for emissions modeling: nonpoint or area (nonpt), point EGU (pti pm), and point non-EGU (ptnonipm).

Table II-4. Estimated future year controllable boiler sector emissions

Sector VOC NO X SO2 PM2_5 NH3 HG0 HG2 PM25 HG Total (TPY) Area - Boiler (nonpt) 14,107 394,459 1,149,402 108,386 8,489 0 0 0 Total (TPY) EGU - Boiler (ptipm) 57 0 54,337 826 0 0 0 0 Total (TPY) Non-EGU Point - Boiler (ptnonipm) 15,451 215,809 492,676 28,838 706 1 2 1

Pct of Sector Area - Boiler (nonpt) 0 19 82 9 5 Pct of Sector EGU - Boiler (ptipm) 0 0 1 0 0 Pct of Secto r Non-EGU Point - Boiler (ptnonipm) 1 9 26 6 0 10 7 12

Boiler emissions in the non-point/area (nonpt) modeli ng sector are used as the basis of estimating air quality impacts and health be nefits for the area source rule. These emissions are based on SCC codes shown in Table II-5. Boiler emissi ons in the point non-EGU (ptnonipm) and point EGU (ptipm) are used to estimate impacts of the major source rule. Facilities were identified for the major source rule based on meeting several criteri a: 1) NEI facility ID and process level fuel type matched to a facility in the boiler ICR database, 2) NEI source ha d a boiler SCC code, and 3) unit design capacity was less than 25 MW. Ma jor source mercury emissions were identified by matching facilities in the NE I/NATA inventory with facilities in the boiler ICR database. A percent emissions reduction was estimated based on the rule proposal unit -specific facility-fuel combination in the ICR database. Each facility/process matching the facility-fuel combinations that had emission reductions larger than 1% ba sed on the proposed rule were tracked for source contribution.9 Table II-5. SCC codes used to identify area source sector emissions

MACT Code MACT Source Category SCC
2101002000 2101004000 2101005000 107 Industrial/Commercial/ Institutional Boilers & Process Heaters 2102011000 107 Industrial/Commercial/ Institutional Boilers & Process Heaters 2103011000 107 Industrial/Commercial/ Institutional Boilers & Process Heaters 2103011005 107 Industrial/Commercial/ Institutional Boilers & Process Heaters 2199011000 0107-1 Industrial/Commercial/ Institutional Boilers & Process Heaters - coal 2102001000 0107-1 Industrial/Commercial/ Institutional Boilers & Process Heaters - coal 2102002000 0107-1 Industrial/Commercial/ Institutional Boilers & Process Heaters - coal 2103001000 0107-1 Industrial/Commercial/ Institutional Boilers & Process Heaters - coal 2103002000 0107-1 Industrial/Commercial/ Institutional Boilers & Process Heaters - coal 2199001000 0107-3 Industrial/Commercial/ Institutional Boilers & Process Heaters - oil 2102004000 0107-3 Industrial/Commercial/ Institutional Boilers & Process Heaters - oil 2102005000 0107-3 Industrial/Commercial/ Institutional Boilers & Process Heaters - oil 2102012000 0107-3 Industrial/Commercial/ Institutional Boilers & Process Heaters - oil 2103004000 0107-3 Industrial/Commercial/ Institutional Boilers & Process Heaters - oil 2103005000 0107-3 Industrial/Commercial/ Institutional Boilers & Process Heaters - oil 2199004000 0107-3 Industrial/Commercial/ Institutional Boilers & Process Heaters - oil 2199004001 0107-3 Industrial/Commercial/ Institutional Boilers & Process Heaters - oil 2199005000 0107-4 Industrial/Commercial/ Institutional Boilers & Process Heaters - wood or waste 2102008000 0107-4 Industrial/Commercial/ Institutional Boilers & Process Heaters - wood or waste 2103008000

SCC SCC Description 2101002000 Stationary Source Fuel Combustion, Electric Ut ility, Bituminous/S ubbituminous Coal, Total: All Boiler Types 2101004000 Stationary Source Fuel Combustion, Electric Ut ility, Distillate Oil, Total: Boilers and IC Engines 2101005000 Stationary Source Fuel Combustion, Electric Ut ility, Residual Oil, Total: All Boiler Types 2102011000 Stationary Source Fuel Combustion, Industrial, Kerosene, Total: All Boiler Types 2103011000 Stationary Source Fuel Combustion, Commercial/Institutional, Kerosene, Total: All Combustor Types 2103011005 Stationary Source Fuel Combustion, Commercial/Institutional, Kerosene, All Boiler Types 2199011000 Stationary Source Fuel Combustion, Total Area Source Fuel Combustion, Kerosene, Total: All Heater Types 2102001000 Stationary Source Fuel Combustion, Industrial, Anthracite Coal, Total: All Boiler Types 2102002000 Stationary Source Fuel Combustion, Industrial, Bituminous/Subbituminous Coal, Total: All Boiler Types 2103001000 Stationary Source Fuel Combustion, Commercial/Institutional, Anthracite Coal, Total: All Boiler Types 2103002000 Stationary Source Fuel Combustion, Commercial/Institutional, Bituminous/Subbituminous Coal, Total: All Boiler Types 2199001000 Stationary Source Fuel Combustion, Total Area Source Fuel Combustion, Anthracite Coal, Total: All Boiler Types 2102004000 Stationary Source Fuel Combustion, Industrial, Distillate Oil, Total: Boilers and IC Engines 2102005000 Stationary Source Fuel Combustion, Industrial, Residual Oil, Total: All Boiler Types 2102012000 Stationary Source Fuel CombustionIndustrialWasteOil Total: All Boiler Types 2103004000 Stationary Source Fuel Combustion, Commercial/Institutional, Distillate Oil, Total: Boilers and IC Engines 2103005000 Stationary Source Fuel Combustion, Commercial/Institutional, Residual Oil, Total: All Boiler Types 2199004000 Stationary Source Fuel Combustion, Total Area Source Fuel Combustion, Distillate Oil, Total: Boilers and IC Engines 2199004001 Stationary Source Fuel Combustion, Total Area Source Fuel Combustion, Distillate Oil, All Boiler Types 2199005000 Stationary Source Fuel Combustion, Total Area Source Fuel Combustion, Residual Oil, Total: All Boiler Types 2102008000 Stationary Source Fuel Combustion, Industrial, Wood, Total: All Boiler Types 2103008000 Stationary Source Fuel Combustion, Commercial/Institutional, Wood, Total: All Boiler Types10 2. Meteorological Input Data

The gridded meteorological input data for the en tire year of 2005 were derived from simulations of the Pennsylvania State University / Nationa l Center for Atmospheric Research Mesoscale Model. This model, commonly referred to as MM5, is a limited-area, nonhydrostatic, terrainfollowing system that solves for the full set of physical and thermodynamic equations which govern atmospheric motions. Meteorological model input fields were prepared separately for each of the three domains shown in Figure II-1 using MM5 version 3.7.4. The MM5 simulations were run on the same map projec tion as shown in Figure II-1.

All three meteorological model r uns were configured similarly. The selections for key MM5 physics options are shown below:

• Pleim-Xiu PBL and land surface schemes • Kain-Fritsh 2 cumulus parameterization • Reisner 2 mixed phase moisture scheme • RRTM longwave radiation scheme • Dudhia shortwave radiation scheme

Three dimensional analysis nudging for temper ature and moisture was applied above the boundary layer only. Analysis nudging for the wind field was applied above and below the boundary layer. The 36 km domain nudging weighting factors were 3.0 x 10 4 for wind fields and temperatures and 1.0 x 10 5 for moisture fields. The 12 km domain nudging weighting factors were 1.0 x 10 4 for wind fields and te mperatures and 1.0 x 10 5 for moisture fields. All three sets of model runs we re conducted in 5.5 day segments with 12 hours of overlap for spin-up purposes. All three domains contained 34 vertical layers with an approximately 38 m deep surface layer and a 100 millib ar top. The MM5 and CAMx vert ical structures are shown in Table II-6 and do not vary by horizontal grid re solution. The meteorologic al outputs from all three MM5 sets were processed to create mode l-ready inputs for CAMx using the MM5CAMx
processor.

Before initiating the air quality simulations, it is important to identify the biases and errors associated with the meteorological modeli ng inputs. The 2005 MM5 model performance evaluations used an approach which include d a combination of qualitative and quantitative analyses to assess the adequacy of the MM5 si mulated fields. The qualitative aspects involved comparisons of the model-estimated synoptic patterns against obser ved patterns from historical weather chart archives. Additionally, the evaluati ons compared spatial patterns of estimated to observed monthly average rainfall and check ed maximum planetary boundary layer (PBL) heights for reasonableness.

Qualitatively, the model fields closely matched the observed synoptic patterns, which is not unexpected given the use of nudging. The ope rational evaluation in cluded statistical comparisons of model/observed pairs (e.g., mean normalized bias, mean normalized error, index of agreement, root mean square errors, etc.) for multiple meteorological parameters. For this portion of the evaluation, five meteorological parameters were investigated: temperature, humidity, shortwave downward radiation, wind speed, and wind di rection. The three individual11 MM5 evaluations are described el sewhere (Baker, 2009a, b, c). It wa s ultimately determined that the bias and error values associated with a ll three sets of 2005 mete orological data were generally within the rang e of past meteorological modeling resu lts that have been used for air quality applications.

Table II-6 . Vertical layer structure (heights are layer top).

CAMx Layers MM5 Layers Sigma P Approximate Height (m) Approximate Pressure (mb) 0 0 1.000 0 1000 1 1 0.995 38 995 2 2 0.990 77 991

3 3 0.985 115 987 4 0.980 154 982

4 5 0.970 232 973 6 0.960 310 964

5 7 0.950 389 955 8 0.940 469 946

6 9 0.930 550 937 10 0.920 631 928 11 0.910 712 919

7 12 0.900 794 910 13 0.880 961 892 14 0.860 1,130 874

8 15 0.840 1,303 856 16 0.820 1,478 838 17 0.800 1,657 820

9 18 0.770 1,930 793 19 0.740 2,212 766

10 20 0.700 2,600 730 21 0.650 3,108 685

11 22 0.600 3,644 640 23 0.550 4,212 595

12 24 0.500 4,816 550 25 0.450 5,461 505 26 0.400 6,153 460

13 27 0.350 6,903 415 28 0.300 7,720 370 29 0.250 8,621 325 30 0.200 9,625 280

14 31 0.150 10,764 235 32 0.100 12,085 190 33 0.050 13,670 145 34 0.000 15,674 100

3. Initial and Boundary Conditions

The lateral boundary and initial species concentrations are provided by a three-dimensional global atmospheric chemistry model, the GEOS -CHEM model (standard version 7-04-11). The global GEOS-CHEM model simulates atmospheric chemical and physical processes driven by assimilated meteorological observations from the NASA’s Goddard Earth Observing System (GEOS). This model was run for 2005 with a grid resolution of 2.0 degree x 2.5 degree (latitude-longitude) and 30 verti cal layers up to 100 mb. The pred ictions were used to provide one-way dynamic boundary conditions at three-hour intervals and an initial concentration field for the 36 km CAMx simulations. The 36 km phot ochemical model simulation is used to supply initial and hourly boundary concentrations to th e 12 km domains. The future base conditions from the 36 km coarse grid modeling were used as the initial/boundary st ate for all subsequent future year 12 km finer grid modeling scenarios.12 III. Base Case Model Performance Evaluation

1. PM2.5

An operational model performance evaluation for the speciated components of PM2.5 (e.g., sulfate, nitrate, elemental carbon, organic ca rbon, etc.) was conducte d using 2005 state/local monitoring data in order to estimate the ability of the modeling system to replicate base year concentrations. The evaluation of PM2.5 componen t species includes comp arisons of predicted and observed concentrations of sulfate (SO4), nitrate (NO3), ammonium (NH4), elemental carbon (EC), and organic carbon (OC). PM2.5 ambi ent measurements for 2005 were obtained from the Chemical Speciation Network (CSN) and the Interagency Monitoring of PROtected Visual Environments (IMPROVE). The CSN sites are generally located within urban areas and the IMPROVE sites are typically in rural/re mote areas. The measurements at CSN and IMPROVE sites represent 24-hour average concentr ations. In calculating the model performance metrics, the modeled hourly specie s predictions were aggregated to the averaging times of the
measurements.

TABLE III-1. Model performance metrics for sp eciated PM2.5 averaged by quarter.

Metric Quarter Sul fate Ion Nitrate Ion Ammonium
Ion Organic Carbon Elemental Carbon Number 1 6,218 5,870 4,045 5,882 6,368 2 6,537 6,146 4,075 6,180 6,552 3 6,108 5,828 3,869 5,836 6,061 4 5,894 5,757 3,763 5,600 5,937

Mean Observed 1 2.7 2.4 1.7 1.7 0.5 (μg/m 3 ) 2 3.8 0.8 1.5 2.1 0.5
3 5.4 0.5 1.8 2.4 0.5
4 2.5 1.3 1.3 2.0 0.6

Mean Predicted 1 4.2 2.1 1.8 2.3 1.1 (μg/m 3 ) 2 4.5 0.7 1.5 1.8 0.8
3 5.4 0.3 1.5 2.4 0.8
4 3.7 1.3 1.4 2.1 1.0

Bias 1 1.6

‐ 0.2 0.4 0.7 0.6 (μg/m 3 ) 2 0.8

‐ 0.1 0.3

‐ 0.2 0.3
3 0.2

‐ 0.2 0.0 0.0 0.3
4 1.3 0.1 0.5 0.2 0.4

Error 1 1.9 1.3 0.8 1.2 0.6 (μg/m 3 ) 2 1.7 0.6 0.8 0.9 0.4
3 2.0 0.4 0.8 0.9 0.5
4 1.6 0.9 0.7 1.1 0.6

Model performance statistics were calculat ed for observed/predicted pairs of daily concentrations. The aggregated metrics and number (N) of prediction-observation pairs are shown by chemical specie and quarter in Table II I-1. The “acceptability ” of model performance was judged by comparing our 2005 performance re sults to the range of performance found in13 recent regional PM2.5 model applications for ot her, non-EPA studies. Overall, the mean bias (bias) and mean error (error) statistics shown in Ta ble III-1 are within the range or close to that found by other groups in recent applications. Th e model performance results give us confidence that our application of CAMx using this modeli ng platform provides a sc ientifically credible approach for assessing PM2.5 concentrations for the purposes of this assessment.

2. Ozone

An operational model performance evaluation fo r hourly and eight-hour daily maximum ozone was conducted in order to estimate the ability of the modeling system to replicate the base year concentrations. Ozone measurements were taken from the 2005 State/local monitoring site data in the Air Quality System (AQS) Aerometric In formation Retrieval System (AIRS). The ozone metrics covered in this evaluation include one-hour and eight-hour average daily maximum ozone bias and error. The evalua tion principally consists of st atistical assessments of model versus observed pairs that were paired in time and space on an hourly and/or daily basis, depending on the sampling frequency of each measurement site (measured data). This ozone model performance was limited to the predic tion-observation pairs where observed ozone exceeded or equaled 60 ppb. This cutoff was applie d to evaluate the model on days of elevated ozone which are more policy relevant. Aggregated performance metrics by quarter are shown in Table III-2.

TABLE III-2 . Model performance metrics for daily maximum ozone by quarter.

Quarter N Observed Predicted Bias Error
1 692 71.1 58.4

‐ 12.7 13.3 2 24,357 76.0 67.0

‐ 9.0 11.2 3 27,430 79.7 73.7

‐ 6.0 11.3 4 1,626 75.8 69.3

‐ 6.5 9.9

Quarter N Observed Predicted Bias Error
1 692 64.5 53.9

‐ 10.6 11.1 2 24,370 69.3 61.9

‐ 7.4 9.6 3 27,447 70.5 67.1

‐ 3.4 9.2 4 1,629 66.8 63.2

‐ 3.5 8.4 *metrics estimated where observed ozone > 60 ppb 8

‐ hr Daily Maximum Ozone (ppb) 1

‐ hr Daily Maximum Ozone (ppb)

3. Mercury Wet Deposition

Model estimated weekly mercury wet deposition is compared to observation data to assess model skill simulating this component of mercury de position. Mercury wet deposition measurements are weekly totals taken at sites that are part of the Merc ury Deposition Network ( http://nadp.sws.uiuc.edu/MDN/ ) which operates under the National Atmospheric Deposition Program. In addition to mercury wet deposition, the network sites also collect rainfall data which is also evaluated against estimates used by the photochemical model from prognostic meteorological model output.14 TABLE III-3. Model performance metrics for mercury wet deposition and rainfall by quarter.

Quarter N Observed Predicted Bias Error
1 866 0.15 0.03

‐ 0.12 0.12
2 943 0.27 0.04

‐ 0.23 0.23
3 907 0.31 0.05

‐ 0.25 0.26
4 808 0.13 0.02

‐ 0.10 0.11

Quarter N Observed Predicted Bias Error 1 1,162 17.32 18.15 0.83 8.55 2 1,192 20.19 24.41 4.22 14.85 3 1,213 20.50 26.34 5.84 19.58 4 1,124 19.78 17.92

‐ 1.86 9.31 Total Mercury Wet Deposition (μg/m 2 )

Rainfall (mm)15 IV. References

Baker, K., Dolwick, P., 2009a. Meteorological Modeling Performance Evaluation for the Annual 2005 Continental U.S. 36-km Domain Simulation. US Environmental Protection Agency OAQPS.

Baker, K., Dolwick, P., 2009b. Meteorological Modeling Performance Evaluation for the Annual 2005 Eastern U.S. 12-km Domain Simulation. US Environmental Protection Agency OAQPS, RTP.

Baker, K., Dolwick, P., 2009c. Meteorological Modeling Performance Evaluation for the Annual 2005 Western U.S. 12-km Domain Simulation, in: EPA, U. (Ed.). US Environmental Protection Agency OAQPS.

Baker, K., Scheff, P., 2007. Photochemical model performance for PM2.5 sulfate, nitrate, ammonium, and precursor species SO2, HNO3, and NH3 at background monitor locations in the central and eastern United States. Atmospheric Environment 41, 6185-6195.

Chang, J.S., Brost, R.A., Isaksen, I.S.A., Madronich, S ., Middleton, P., Stockwell, W.R., Walcek, C.J., 1987. A 3- DIMENSIONAL EULERIAN ACID DEPOSITION MODEL - PHYSICAL CONCEPTS AND FORMULATION. J. Geophys. Res.-Atmos. 92, 14681-14700.

ENVIRON, 2008. User's Guide Comprehensive Air Qua lity Model with Extensions. ENVIRON International Corporation, Novato.

Gery, M.W., Whitten, G.Z., Killus, J.P., Dodge, M. C., 1989. A PHOTOCHEMICAL KINETICS MECHANISM FOR URBAN AND REGIONAL SCALE COMPUTER MODELING. J. Geophys. Res.-Atmos. 94, 12925-12956.

Houyoux, M.R., Vukovich, J.M., Coats, C.J., Wheeler, N.J.M., Kasibhatla, P.S., 2000. Emission inventory development and processing for the Seasonal Model for Regional Air Quality (SMRAQ) project. Journal of Geophysical Research-Atmospheres 105, 9079-9090.

Nenes, A., Pandis, S.N., Pilinis, C., 1999. Continued de velopment and testing of a new thermodynamic aerosol module for urban and regional air quality m odels. Atmospheric Envi ronment 33, 1553-1560.

Nobel, C.E., McDonald-Buller, E.C., Kimura, Y., Allen, D.T., 2001. Accounting for spatial variation of ozone productivity in NOx emission trading. Environmental Science & Technology 35, 4397-4407.

Russell, A.G., 2008. EPA Supersites Program-related emissions-based particulate matter modeling: Initial applications and advances. J. Air Waste Manage. Assoc. 58, 289-302.

Strader, R., Lurmann, F., Pandis, S.N., 1999. Evaluation of secondary organic aerosol formation in winter. Atmospheric Environment 33, 4849-4863.

USEPA, 2007. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze, RTP.

Wagstrom, K.M., Pandis, S.N., Yarwood, G., Wilson, G.M., Morris, R.E., 2008. Development and application of a computationally efficient particulate matter apportionment algorithm in a three-dimensional chemical transport model. Atmospheric Environment 42, 5650-5659.16 APPENDIX A

Emissions Source Sectors and Revisions of the Data from Version 4 of the 2005-based modeling platform17 Platform Sector and corresponding 2005 NEI sector Description and resolution of the data input to SMOKE for the Boiler MACT, for 2005 Revisions from the 2005v5 modeling platform

IPM sector: ptipm

NEI Sector= Point 2005v2 NEI point source EGUs mapped to the Integrated Planning Model (IPM) model using the National Electric Energy Database System (NEEDS) database. For Hg: Matching was applied to the June 18, 2010 version of the inventory used for the 2005 National Air Toxics Assessment (NATA); this inventory is an update to the 2005v2 (see below row). This sector excludes sources in the industrial, commercial and institutional (ICI) boiler database developed as part of the ICI Boiler MACT rule. Added or changed ORIS Boiler IDs for some units with missing or incorrect values, and for a subset of these, recomputed annual emissions of NOX, SO2 or both using 2005 CEM data, resulting in a 1,700 tons lower NOX and 600 tons lower SO2 (net). For Hg: the v4 platform utilized the 2005v2 Hg estimate whereas the v4.1 inventory was largely based on the inventory used for the 2005 NATA.

Non-IPM sector: ptnonipm

NEI Sector=Point All 2005v2 NEI point source records not matched to the ptipm sector, annual resolution. Includes all aircraft emissions. For Hg: The June 18, 2010 version of NATA inventory was used except for ICI boilers, modifications to gold mine emissions and removal of Hg from facilities that closed prior to 2005. For ICI boilers, the Hg emissions developed for the ICI NESHAP rule was used. The NATA inventory was revised from 2005v2 by incorporating information gathered from rules such as Portland cement and hazardous waste incinerators, and comments received during state, local and tribal agency review. Revised 2005 emissions for many point sources other than EGUs to remove duplicates, improve estimates from ethanol plants, and reflect new information collected from industry through the Boiler MACT ICR. This resulted in 9000 tons less NOX; 28,000 tons less SO2 and 2000 tons less PM2.5. For Hg: the v4 platform utilized the 2005v2 Hg estimate whereas the v4.1 inventory was largely based on the inventory used for the 2005 NATA.

Average-fire sector: avefire

No NEI Sector Average-year wildfire and prescribed fire emissions derived from the 2002-based Platform avefire sector, county and annual resolution. none

Agricultural sector: ag

NEI Sector= Nonpoint NH 3 emissions from NEI nonpoint livestock and fertilizer application, county and annual resolution. none

Area fugitive dust sector: afdust

NEI Sector= Nonpoint PM 10 and PM 2.5 from fugitive dust sources from the NEI nonpoint inventory (e.g., building construction, road construction, paved roads, unpaved roads, agricultural dust), county and annual resolution. No changes to emissions but changes to temporal profiles were made for mobile-related dust categories

Remaining nonpoint sector: nonpt

NEI Sector= Primarily 2002 NEI nonpoint sources not otherwise included in other SMOKE sectors, county and annual resolution. Also includes updated Residential WoodCombustion emissions, year 2005 nonRevised Texas and Oklahoma oil and gas sector emissions. Updated the spatial surrogate for oil and gas for allocating the county-level emissions to grid cells. Removed HAP-CAP integration (affecting speciation) for pesticides,18 Nonpoint California WRAP oil and gas Phase II inventory and year 2005 Texas and Oklahoma oil and gas emissions. thereby removing benzene from the category. Removed Hg emissions from industrial, institutional and commercial boilers. Changed speciation profile for headspace vapor. Nonroad sector: nonroad

NEI Sector= Nonroad Monthly nonroad emissions from the National Mobile Inventory Model (NMIM) using NONROAD2005 version nr05c-BondBase for all states except California. Monthly emissions for California created from annual emissions submitted by the California Air Resources Board (CARB) for the 2005v2 NEI

. Changed speciation prof ile for headspace vapor (used for nonroad refueling). Added PM2.5 emissions where missing for 7 California counties.

Locomotive, and non-C3 commercial marine: alm_no_c3

NEI Sector= Nonroad Year 2002 non-rail maintenance locomotives, and category 1 and category 2 commercial marine vessel (CMV) emissions sources, county and annual resolution. Aircraft emissions are now included in the ptnonipm sector and category 3 emissions are now contained in the seca_c3 sector. None

C3 commercial marine: seca_c3

NEI Sector= Nonroad Annual point source formatted year 2005 category 3 (C3) CMV emissions, developed for the rule called “Control of Emissions from New Marine Compression-Ignition Engines at or Above 30 Liters per Cylinder”, usually described as the Area (ECA) study, originally called SO2 (“S”) ECA (see http://www.epa.gov/otaq/oceanvessels.ht

m ). Revised 2005 to reflect the final projections from 2002 developed for the category 3 commercial marine Emissions Control Area (ECA) proposal to the International Maritime Organization (EPA-420-F-10-041, August 2010). Also projected Canada as part of the ECA, using region-specific growth rates; thus the v4.1 seca_c3 inventories contain Canadian province codes. Resultant v4.1 emisisons are lower by 23,000 tons VOC, 11,000 tons of NOx and 5,000 tons SO2.

Onroad California, NMIM-based, and MOVES sources not subject to temperature adjustments: on_noadj

NEI Sector= Onroad Three, monthly, county-level components: 1) California onroad, created using annual emissions submitted by CARB for the 2005v2 NEI. 2) Onroad gasoline an d diesel vehicle emissions from MOVES2010 not subject to temperature adjustments: exhaust CO, NO X , VOC, benzene, formaldehyde, acet aldehyde, 1,3 butadiene, acrolein, naphthalene, brake and tirewear PM, and evaporative VOC, benzene, and naphthalene. 3) Onroad emissions for certain HAPs

from NMIM using MOBILE6.2, other than for California. Applies to
mercury. Updated version of MOVES (MOVES2010) was used for all criteria pollutants, for both onroad diesel and onroad gasoline vehicles (previously MOVES was not used for diesel vehicles) and key VOC HAP.

Onroad cold-start gasoline exhaust mode vehicle from MOVES subject to temperature Monthly, county-level draft MOVESbased onroad non-motorcycle gasoline emissions subject to temperature adjustments. Limited to exhaust mode only for PM species and Naphthalene. Updated version of MOVES-- MOVES2010 was used.19 adjustments: on_moves_startpm

NEI Sector= Onroad California emissions not included. This sector is limited to cold start mode emissions that contain different temperature adjustment curves from running exhaust (see on_moves_runpm sector). Onroad running gasoline exhaust mode vehicle from MOVES subject to temperature adjustments:
on_moves_runpm

NEI Sector= Onroad Monthly, county-level draft MOVESbased onroad non-motorcycle gasoline emissions subject to temperature adjustments. Limited to exhaust mode only for PM species and Naphthalene. California emissions not included. This sector is limited to running mode emissions that contain different temperature adjustment curves from cold start exhaust (see on_moves_startpm sector). Updated version of MOVES-- MOVES2010 was used.

Biogenic: biog

No NEI Sector Hour-specific, grid cell-specific emissions generated from the BEIS3.14 model -includes emissions in Canada and Mexico. None

Other point sources not from the NEI: othpt

2005 NEI Sector: N/A Point sources from Canada’s 2006 inventory and Mexico’s Phase III 1999 inventory, annual resolution. Also includes annual U.S. offshore oil 2005v2 NEI point source emissions. None

Other point sources not from the NEI, Hg only: othpt_hg

2005 NEI Sector: N/A For 2005 only, the annual year 2000 Canada speciated mercury point source emissions. None

Other nonpoint and nonroad not from the NEI: othar

No NEI Sector Annual year 2006 Canada (province resolution) and year 1999 Mexico Phase III (municipio resolution) nonpoint and nonroad mobile inventories, annual resolution. None

Other nonpoint sources not from the NEI, Hg only: othar_hg

No NEI Sector For 2005 only, the annual year 2000 Canada speciated mercury from nonpoint

sources. None

Other onroad sources not from the NEI: othon

No NEI Sector Year 2006 Canada (province resolution) and year 1999 Me xico Phase III (municipio resolution) onroad mobile inventories, annual resolution. NoneUnited StatesEnvironmental ProtectionAgencyOffice of Air Quality Planning and StandardsAir Quality Assessment DivisionResearch Triangle Park, NCPublication No. EPA-454/R-10-006December 2010