Air Quality Modeling Technical Support Document: 

Boiler Source Sector Rules 
EPA-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 27711 
1

I Introduction 

This document describes the air quality modeling 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 changes on future year: annual and 24-hour PM2.5 

concentrations, 8-hr maximum ozone, total mercury 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 physical and chemical processes involved in the 

formation, transport, and destruction of ozone, particulate matter and air toxics. In addition to 

the CAMx model, the modeling platform includes 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 inventories presented in the 

RIA. However, the air quality inventories and the final rule inventories are generally consistent, 

so the air quality modeling adequately reflects the effects of the rule. 

II. Photochemical Model Version, Inputs and Configuration 

Photochemical grid models use state of the science 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 injected 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 receptors. This type of 

emissions apportionment is useful to understand what types of sources or regions are 

contributing to pollutants estimated 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 structured 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. This 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 ozone, annual total mercury deposition levels and 

visibility impairment. Model predictions are used in a relative sense to estimate scenariospecific, future-year design values of PM2.5 and ozone. Specifically, we compare a 2016 
2

reference scenario, a scenario without the boiler sector controls, to a 2016 control scenario which 

includes the adjustments to the boiler sector. This is done by calculating the simulated air 

quality ratios between any particular future year simulation and the 2005 base. 

These predicted ratios are then applied to ambient base year design values. The design value 

projection methodology used here followed EPA guidance for such analyses (USEPA, 2007). 

Additionally, the raw model outputs are also used in a relative sense 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 model that simulates the formation and fate of 

photochemical oxidants, ozone, primary and secondary 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 that simulate the emission, production, decay, 

deposition and transport of organic and inorganic gas-phase and particle-phase pollutants in the 

atmosphere (Baker and Scheff, 2007; Nobel et al., 2001; Russell, 2008). CAMx is applied with 

ISORROPIA inorganic chemistry (Nenes 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 apportionment tools, including the original ozone 

source apportionment tool (OSAT) and the anthropogenic 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 horizontal diffusion. 

Horizontal advective fluxes for each of the regular model species that make up nitrogen oxides 

(NOX) 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 NOX and VOC limited conditions. 

Particulate matter source apportionment 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 species. Emissions of nitrogen oxides are tracked 

through all intermediate nitrogen species to particulate nitrate ion. Ammonia emissions are 

tracked to particulate ammonium ion. This modeling assessment used the PSAT approach to 

estimate source contribution to PM2.5 species and mercury and the APCA method to estimate 

source contribution to modeled ozone. 
3

B. Model domain and grid resolution 

The modeling analyses were performed for a domain 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 western U.S. The model extends vertically from the 

surface to 100 millibars (approximately 15 km) using 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 standard program changes. Table II-1 provides some 

basic geographic information regarding the photochemical model domains. 

Table II-1. Geographic elements of domains 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 millibar 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 western U.S. grid; and the blue 

inner box is the 12 km eastern 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 impacts 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 inputs to the air quality model. 

1. Emissions Input Data 

2005 baseline emissions 

The emissions data used in the base year and future reference and future emissions adjustment 

case are based on the 2005 v4.1 platform. Emissions are processed to photochemical model 

inputs with the SMOKE emissions modeling system (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 use. Unlike the 2005 v4 platform, the configuration 

for this modeling application included mercury emissions from the National Air Toxics 

Assessment Inventory and some additional boiler sector mercury emissions more consistent with 

the engineering analysis of potential control options. 

The ICR emissions are not explicitly used in the modeling assessment since the ICR database 

lacks important emissions release 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 consistent between ICR and NEI which makes removing 

boiler emissions, necessary to prevent double counting emissions in the photochemical model, 

from the NEI very problematic. 

It is important to note that the NEI is broken up into general sectors such as EGU point (PTIPM), 

non-EGU point (PTNONIPM), and stationary emissions not included in the point source 

inventories (NONPT). This non-point category is generally referred to as the “area” source 

inventory and is not a direct representation of sources classified as area sources for the purposes 

of NESHAP/MACT standards. 

2005 ICR emissions data integration with NEI 

For the non-EGU point sector (PTNONIPM), a cross-walk was used to remove boiler mercury 

emissions in the 2005 NEI at facilities with identification information that matched facilities in 

the ICR database. Boiler mercury emissions were removed from facilities in the EGU point 
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sector (PTIPM) that matched facilities in the ICR database and that had NEEDS unit design 

capacity less than 25 MW. If design capacity was not available then units with a boiler MACT 

code were removed from the inventory. All area (NONPT) mercury emissions were removed 

from the NEI inventory to prevent double counting of emissions. 

The specific procedure used to replace the Hg emissions in the NATA inventory with the boiler 

MACT ICR data is detailed below. 

NON-EGU point (PTNONIPM) sector: 

• Apply a crosswalk containing Maximum Achievable Control Technology (MACT), 

Standard Industrial Classification (SIC) and Source Classification Codes (SCC) and 

North American Industry Classsification (NAICS) codes that map to the boiler source 

category to the NATA inventory. This crosswalk contains nearly 4500 combinations of 

the above codes. 

• Remove only facilities that match facilities in the ICR database (based on the 

NEI_UNIQUE_ID field) 

• Add all Hg emissions from the ICR database that include an NEI_UNIQUE_ID (this 

procedure added all but 0.177 tons (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 boiler MACT category 

Non-point/area (NONPT) sector: 

• Remove nonpoint boiler Hg emissions to prevent double counting. 

• Boiler emissions in the nonpoint inventory utilized 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 intended to represent the emissions associated with growth and 

controls in that year projected from the 2005 simulation year. The United 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 applied to a subset of the 2005 non-EGU point and 

nonpoint to create the 2016 reference case. The 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 proposed 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 rules included in the future point source baseline 

inventory is shown in Table II-2. 

Table II-2. Control Strategies and/or Growth Assumptions 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 refinery 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 Engines1 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 Mobile Source Air Toxics (MSAT) final rule and 

the category 3 marine diesel engines Clean Air Act and International Maritime Organization 

standards which includes the establishment of emission control areas for these ships. Emission 

reductions and increases from the Renewable Fuel Standard version 2 (RFS2) are not included. 

The future case nonroad mobile emissions reductions for these years include reductions to 

locomotives, various nonroad engines including diesel 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, nationwide 
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fuel sulfur content, and evaporative emissions standards. A summary of the mobile source 

control programs included in the projected future year baseline is shown in Table II-3. 

The 2016 onroad emissions were based on MOVES2010. 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; however, 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 calculated using NMIM future-year equipment population estimates 

and control programs for 2014. Emissions for locomotives 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 Program (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 recreational 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 airports 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 the boiler sector includes growth and control for the industry 

from the 2005 sector emissions estimates. The length of time required to conduct emissions and 

photochemical modeling preclude the use of the final facility specific emissions estimates based 

on controls implemented for this rule. A 2016 “control” or emissions adjustment case was 

developed by tracking the total contribution from potentially controllable boiler sector emissions 

from the 2016 baseline inventory. This total contribution 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 shown 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 (ptipm), and point non-EGU (ptnonipm). 

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

Sector VOC NOX 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 Sector Non-EGU Point - Boiler (ptnonipm) 1 9 26 6 0 10 7 12

Boiler emissions in the non-point/area (nonpt) modeling sector are used as the basis of estimating 

air quality impacts and health benefits for the area source rule. These emissions are based on 

SCC codes shown in Table II-5. Boiler emissions 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 criteria: 1) NEI facility ID and process level fuel 

type matched to a facility in the boiler ICR database, 2) NEI source had a boiler SCC code, and 

3) unit design capacity was less than 25 MW. Major source mercury emissions were identified 

by matching facilities in the NEI/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% based on the proposed rule were tracked for source 

contribution. 
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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 Utility, Bituminous/Subbituminous Coal, Total: All Boiler Types

2101004000 Stationary Source Fuel Combustion, Electric Utility, Distillate Oil, Total: Boilers and IC Engines

2101005000 Stationary Source Fuel Combustion, Electric Utility, 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 Types
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2. Meteorological Input Data 

The gridded meteorological input data for the entire year of 2005 were derived from simulations 

of the Pennsylvania State University / National 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 projection as shown in Figure II-1. 

All three meteorological model runs 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 temperature 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 104

 for wind fields and 

temperatures and 1.0 x 105

 for moisture fields. The 12 km domain nudging weighting factors 

were 1.0 x 104 for wind fields and temperatures and 1.0 x 105 for moisture fields. 

All three sets of model runs were 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 millibar top. The MM5 and CAMx vertical structures are shown in 

Table II-6 and do not vary by horizontal grid resolution. The meteorological outputs from all 

three MM5 sets were processed to create model-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 modeling inputs. The 2005 MM5 model performance 

evaluations used an approach which included a combination of qualitative and quantitative 

analyses to assess the adequacy of the MM5 simulated fields. The qualitative aspects involved 

comparisons of the model-estimated synoptic patterns against observed patterns from historical 

weather chart archives. Additionally, the evaluations compared spatial patterns of estimated to 

observed monthly average rainfall and checked 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 operational evaluation included 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 direction. The three individual 
11

MM5 evaluations are described elsewhere (Baker, 2009a, b, c). It was ultimately determined that 

the bias and error values associated with all three sets of 2005 meteorological data were 

generally within the range of past meteorological modeling results 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 vertical layers up to 100 mb. The predictions 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 photochemical model simulation is used to supply 

initial and hourly boundary concentrations to the 12 km domains. The future base conditions 

from the 36 km coarse grid modeling were used as the initial/boundary state for all subsequent 

future year 12 km finer grid modeling scenarios. 
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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 carbon, etc.) was conducted 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 component species includes comparisons of predicted 

and observed concentrations of sulfate (SO4), nitrate (NO3), ammonium (NH4), elemental 

carbon (EC), and organic carbon (OC). PM2.5 ambient 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/remote areas. The measurements at CSN and 

IMPROVE sites represent 24-hour average concentrations. In calculating the model performance 

metrics, the modeled hourly species predictions were aggregated to the averaging times of the 

measurements. 

TABLE III-1. Model performance metrics for speciated 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/m3

) 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/m3

) 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/m3

) 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/m3

) 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 calculated 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 III-1. The “acceptability” of model performance 

was judged by comparing our 2005 performance results to the range of performance found in 
13

recent regional PM2.5 model applications for other, non-EPA studies. Overall, the mean bias 

(bias) and mean error (error) statistics shown in Table III-1 are within the range or close to that 

found by other groups in recent applications. The model performance results give us confidence 

that our application of CAMx using this modeling platform provides a scientifically credible 

approach for assessing PM2.5 concentrations for the purposes of this assessment. 

2. Ozone 

An operational model performance evaluation for 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 Information Retrieval System (AIRS). The ozone 

metrics covered in this evaluation include one-hour and eight-hour average daily maximum 

ozone bias and error. The evaluation principally consists of statistical 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 prediction-observation pairs where observed ozone 

exceeded or equaled 60 ppb. This cutoff was applied 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 deposition. Mercury wet deposition measurements 

are weekly totals taken at sites that are part of the Mercury 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/m2

)

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 Quality 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 development and testing of a new thermodynamic aerosol 

module for urban and regional air quality models. Atmospheric Environment 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 

platform 
17

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

NH3 emissions from NEI nonpoint 

livestock and fertilizer application, county 

and annual resolution. 

none 

Area fugitive dust 

sector: afdust 

NEI Sector= 

Nonpoint

PM10 and PM2.5 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 Wood 

Combustion 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 profile 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 and diesel vehicle 

emissions from MOVES2010 not 

subject to temperature adjustments: 

exhaust CO, NOX, VOC, benzene, 

formaldehyde, acetaldehyde, 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 Mexico Phase III 

(municipio resolution) onroad mobile 

inventories, annual resolution. 

None 
United States 

Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 

Publication N