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 National 

Emission Standards for Hazardous Air Pollutants for Industrial, Commercial, and Institutional 

Boilers and Process Heaters. 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 

concentrations for particulate matter less than 2.5 microns (PM2.5) c, 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 
2

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 

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 2005 base year are from the EPA’s 2005-based v4.1 modeling 

platform. This platform is based on the 2005 National Emissions Inventory (NEI), version 2. 

Emissions were processed to photochemical model inputs with the Sparse Matrix Operator 

Kernel Emissions (SMOKE) modeling system (Houyoux et al., 2000). 

This platform includes criteria pollutants and precursors: particulate matter less than 10 microns 

(PM10), PM2.5, nitrogen oxides (NOX), sulfur dioxide (SO2), carbon monoxide (CO), volatile 

organic compounds (VOC), ammonia (NH3) and hazardous air pollutants (HAP): hydrogen 

chloride, chlorine and mercury. Additionally, for some sectors, HAP emissions of benzene, 

formaldehyde, acetaldehyde and methanol are used from the inventory for chemical speciation 

VOC. For this rule, mercury emissions were added to the v4.1 platform to reflect the needs for 

the rule development and these not intended for more general use. The mercury emissions 

included in this platform are primarily from the 2005 National Air Toxics Assessment (NATA) 

inventory, which was updated from the 2005 NEI v2 in order to incorporate updated data for 

particular source categories such as cement and hazardous waste incineration, and also revised 

from comments from state and local inventory providers as a result of NATA review. 

This inventory was further modified to remove sources that were found to have shut down prior 

to 2005 and to update the gold mine emissions per information collected during the Gold Mine 

Ore and Production NESHAP. In addition, mercury emissions were revised for the boiler sector 

to allow for greater consistency with the Information Collection Request (ICR) data collected for 

this rule. In particular, we used the unit-specific ICR mercury emissions for all ICR facilities 

that could be mapped to the NATA Inventory.1

 We used the NEI to add important emissions 

release point information necessary for photochemical grid modeling such as geographic 

 

1

 The “NEI_UNIQUE_ID” field was used to map the ICR facilities to the NATA inventory. We used 7032 units’ 

Hg emissions out of 7,738 total units in the ICR. These 7032 units from the ICR sum to 4.66 tons. ICR emissions 

that were not included sum to 0.177 tons. 
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coordinates, stack coordinates, stack release height, exit temperature, exit velocity because the 

ICR lacks this information. 

The replacement of the NATA mercury with the ICR mercury is described in more detail below. 

ICR emissions were not used directly for any other pollutants used in the v4.1 platform; however 

they informed numerous corrections and updates to the inventory including the removal of 

duplicates and facilities that had shut down prior to 2005, and the inclusion of control 

information. 

 

The stationary inventory used in the v4.1 platform is separated into modeling sectors such as 

EGU point (ptipm), non-EGU point (ptnonipm), and stationary emissions not included in the 

point source inventories (nonpt). This nonpoint category is generally referred to as the “area” 

source inventory, and this category is not a direct representation of sources classified as area 

sources for the purposes of National Emissions Standards for Hazardous Air Pollutants 

(NESHAP) and Maximum Available Control Technology (MACT) standards. 

2005 ICR emissions data integration with NEI for stationary sources 

For the non-EGU point sector (ptnonipm), a cross-walk was developed and applied to remove 

boiler and process heater mercury emissions from the 2005 NATA inventory at facilities with 

identification information that matched facilities in the ICR database. This cross-walk identified 

inventory sources with boiler and process header source classification codes (SCCs) in the 

inventory that were also associated with facilities that matched the ICR database. All nonpoint 

(nonpt) boiler mercury emissions were removed from the inventory to eliminate the possibility of 

double counting of emissions with those added from the boiler ICR data. The boiler mercury 

emissions from the ICR database were then added to the inventory in place of these emissions.

In addition, boiler mercury emissions were removed from facilities in the EGU point sector 

(ptipm) that matched facilities in the ICR database and that had unit design capacity less than 25 

MW according to the 2006 version 3.02 National Electric Energy Database System (NEEDS) 

database (http://www.epa.gov/airmarkt/progsregs/epa-ipm/past-modeling.html#needs). If design 

capacity was missing and a unit had been identified in the NATA inventory as being subject to 

the Boiler MACT (based on the “MACT Code” inventory field) then its mercury emissions were 

also removed from this modeling sector inventory. 

The specific procedure used to replace the Hg emissions in the NATA inventory with the 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. 
6

• 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 in the NATA inventory 

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 the boiler sector in the modeling inventory are 4.72

 tons and all were 

included in the ptnonipm sector. 

Future Year Baseline Emissions 

The 2016 baseline emissions are intended to represent the emissions associated with growth and 

controls in 2016. The projections used for this effort are unique to this project and are not 

associated with a particular modeling platform. 

The EGU point source (ptipm) emissions estimates for the future year reference were created by 

the Integrated Planning Model (IPM) version 3.02 for criteria pollutants, hydrochloric acid, and 

mercury in 20153. For the non-EGU point (ptnonipm) and nonpoint (nonpt) sectors, both control 

and growth factors were applied to a subset of the 2005 v4.1 platform data to create the 2016 

reference case. The 2014 projection factors developed for the Transport Rule proposal (see 

http://www.epa.gov/ttn/chief/emch/index.html#transport) were further enhanced and updated for 

these 2016 baseline projections. 

The projected inventory incorporates emissions projections for the proposed Transport Rule, 

cement kiln NESHAP, RICE NESHAP, gold mine NESHAP, changes to boiler emissions based 

on the ICR database developed for this rule, and known consent decrees. A complete list of rules 

included in the future point source baseline inventory is shown in Table II-2. 

 

2

 4.66 tons from the ICR and 0.02 tons from NATA Hg inventory sources that were not covered by the ICR but were 

in the NATA Hg inventory and were added to the universe to be tagged as Boiler MACT sources. 3

 The 2015 IPM run represents the average of 2014 to 2016. 
7

Table II-2. Control strategies and/or growth assumptions included in the 2016 projection 

Projections Carried Forward from the proposed Transport Rule1,2 

Description Pollutants 

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 2016 NH3, PM 

Residential Wood Combustion Growth and Changeouts from year 2005 to 

year 2016 

All 

Gasoline Stage II growth and control from year 2005 to year 2016 VOC 

Portable Fuel Container MSAT2 inventory growth and control from year 2005 

to year 2016 

VOC 

Additional Projections Used In Boiler MACT modeling3

Emission Reductions resulting from controls put on specific boiler units (not due to MACT) 

after 2005, identified through analysis of the control data gathered from the ICR from the 

ICI Boiler NESHAP.

NOX, SO2, 

HCL

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 

New York SIP reductions VOC, NOX 

Additional plant and unit closures All 

NESHAP: Reciprocating Internal Combustion Engines4 NOX, CO, 

PM 

1. They were only changed in that the projection year was 2015 or 2016, rather than 2012 / 2014. 

2. We inadvertently did not apply closures that had been applied for the Transport Rule proposal; 

emissions from these plants sum to 3300 tons VOC, 178 tons PM2.5, 1982 tons SO2, 1639 tons NOX, 

6 tons NH3 and 379 tons CO. At the state level, the largest impact is in West Virginia (717 tons NOX, 

which is 2% of emissions in ptnonipm) and 1604 tons SO2 which is 7% of the ptnonipm sector. When 

considering emissions from other sectors, the percentages will be much smaller. All other errors are 

under 500 tons ( less than 1% of the ptnonipm sector) 

3. We inadvertently did not apply LaFarge and SaintGobain consent decrees- since one of the LaFarge 

facilities was already covered in the cement ISIS projections, the reductions missed were lower than 

estimated by the consent decree were on the order of 20,000 tons SO2, 15,000 tons NOX, 400 tons 

HCL, 200 tons PM2.5 

4. 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 

The 2016 onroad emissions reflect control program implementation through 2016 and include 

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 
8

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 baseline case nonroad mobile emissions 

reductions for these years include reductions to locomotives, various nonroad engines including 

diesel engines and various marine engine types, 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. 

Table II-3. Summary of mobile source control programs included in 2016 baseline 

National Onroad Rules: 

Tier 2 Rule (Signature date: 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) 

Category 3 marine diesel engines Clean Air Act and International Maritime 

Organization standards (April, 30, 2010) 

As with the 2005 emissions, the 2016 onroad emissions were based on MOVES 2010. Futureyear vehicle miles travelled (VMT) were projected from the 2005 NEI v2 VMT using growth 

rates from the 2009 Annual Energy Outlook (AEO) data. The same MOVES-based PM2.5

temperature adjustment factors were also applied as in 2005 for running mode emissions because 

these are not dependent on year; however, cold start emissions used 2015-specific temperature 

adjustment factors. 

2016 nonroad mobile emissions were created with NMIM using an approach that is consistent 

with that used for 2005, but emissions were calculated using NMIM 2016 equipment population 
9

estimates and control programs for 2016. 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). Emissions for Category 3 marine vessels 

(seca_c3) were computed by projecting the 2005 C3 marine using regional projection factors that 

include the impacts of the International Maritime Organization standards. 

Future Year Sector Contribution Approach

The 2016 reference scenario for the boilers affected by the rule includes 2005 emissions 

estimates with some emissions removed from the inventory because of shut downs. The length of 

time required to conduct emissions and photochemical modeling precluded the use of the final 

facility-specific emissions estimates based on controls implemented for this rule. A 2016 

“control” or emissions adjustment scenario was developed by tracking the total contribution from 

potentially controllable boiler sector emissions from the 2016 baseline inventory. This total 

contribution estimate, essentially a “zero-out”, of the sector creates a policy space where 

potential control impacts would be maximized at all locations. Since emissions reductions at 

controllable sources are not 100% (100% ~ total contribution), 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: non-point or area (nonpt), point EGU (ptipm), and point non-EGU 

(ptnonipm). These totals are the sum of emissions in the eastern and western U.S. modeling 

domains so the non-point (area) category contains some double-counting of emissions where the 

model domains overlap in the central United States (see Figure II-1). 

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

Sector VOC NOX SO2 PM25 NH3 HG0 HG2 PM25 HG

Total (TPY) Non-point - Boiler (nonpt) 14,107 394,459 1,149,402 108,386 8,489 0 0 0

Total (TPY) EGU Point - 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 Non-point - Boiler (nonpt) 0 19 82 9 5

Pct of Sector EGU Point - Boiler (ptipm) 0 0 1 0 0

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

Figure II-2 shows the locations of boilers in the NEI point source inventory used in the modeling 

analysis. Non-point boilers in the NEI do not have stack location information and are not shown. 

These boilers are spatially distributed in the modeling domain using spatial surrogates 

appropriate for this sector. 
10

Figure II-2. Locations of boilers in the NEI point inventory for the future baseline (2016). 

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. 

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
11

Table II-5. SCC codes used to identify area source sector emissions (continued) 

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

Major source mercury emissions were identified as the units added from the boiler ICR database 

plus units in the NATA Hg inventory that were not in the ICR but were considered part of the 

Boiler MACT Universe. 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. 

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 
12

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 

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. 
13

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. 
14

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 

recent regional PM2.5 model applications for other, non-EPA studies. Overall, the mean bias 
15

(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. 
16

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)
17

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. 
18

APPENDIX A 

Emissions Source Sectors in the 2005-based modeling platform, version 4.1 
19

Table 3-1. Emissions Source Sectors for Current and Future-Year Inventories, 2005-based 

Platform, Version 4.1 

Platform Sector 

and corresponding 

2005 NEI sector 

Description and resolution of the data input to SMOKE, 2005 

v4.1 platform 

IPM sector: ptipm 

NEI Sector= Point 

For all pollutants other than mercury (Hg): 2005v2 NEI point source 

EGUs mapped to the Integrated Planning Model (IPM) model using the 

National Electric Energy Database System (NEEDS) database. Revisions 

of 2005v2 NOX and SO2 emissions for some units were made due to the 

availability of additional data (ORIS codes) which allowed use of the 

2005 CEM data to replace annual emissions for these units. 

For Hg: 6/18/2010 version of the inventory used for the 2005 National 

Air Toxics Assessment (NATA) mapped to IPM using NEEDS. The 

NATA inventory is an update to the 2005v2 and was divided into IPM 

and non-IPM sectors consistent with the other pollutants. We removed 

Hg from sources that were identified in the industrial, commercial and 

institutional (ICI) database developed as part of the ICI rule because we 

included these emissions in the ptnonipm sector. 

Non-IPM sector: 

ptnonipm 

NEI Sector=Point

For all pollutants other than Hg: All 2005v2 NEI point source records not 

matched to the ptipm sector, annual resolution. Includes all aircraft 

emissions. Updates to 2005v2 were made to remove duplicates, improve 

estimates from ethanol plants, and reflect new information collected from 

industry from the Information Collection Request (ICR) for the ICI rule. 

For Hg: The 6/18/2010 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 rule were used. 

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. 

Agricultural sector: 

ag 

NEI Sector= 

Nonpoint

NH3 emissions from NEI nonpoint livestock and fertilizer application, 

county and annual resolution. 

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. 

Remaining nonpoint 

sector: nonpt 

NEI Sector= 

Nonpoint

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 non-California 

WRAP oil and gas Phase II inventory and year 2005 Texas and 

Oklahoma oil and gas emissions. Removed Hg emissions from ICI 

boilers to prevent potential double counting by inclusion of the ICI boiler 

Hg emissions from the MACT database in the Non-IPM sector. 

Nonroad sector: Monthly nonroad emissions from the National Mobile Inventory Model 
20

Platform Sector 

and corresponding 

2005 NEI sector 

Description and resolution of the data input to SMOKE, 2005 

v4.1 platform 

nonroad 

NEI Sector= 

Nonroad

(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. 

Locomotive, and nonC3 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. 

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 Emissions Control Area (ECA) study, 

(originally called SO2 [“S”] ECA -see 

http://www.epa.gov/otaq/oceanvessels.htm). Utilized final projections 

from 2002, developed for the C3 ECA proposal to the International 

Maritime Organization (EPA-420-F-10-041, August 2010). 

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, including Hg, from NMIM using 

MOBILE6.2, other than for California. 

Onroad cold-start 

gasoline exhaust 

mode vehicle from 

MOVES subject to 

temperature 

adjustments: 

on_moves_startpm 

NEI Sector= Onroad

Monthly, county-level MOVES2010 onroad gasoline vehicle emissions 

subject to temperature adjustments. Limited to exhaust mode only for 

PM species and Naphthalene. 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 MOVES2010 onroad gasoline vehicle 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). 

Biogenic: biog Hour-specific, grid cell-specific emissions generated from the BEIS3.14 
21

Platform Sector 

and corresponding 

2005 NEI sector 

Description and resolution of the data input to SMOKE, 2005 

v4.1 platform 

No NEI Sector

model -includes emissions in Canada and Mexico. 

Other point sources 

not from the NEI: 

othpt 

No NEI Sector

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. 

Other point sources 

not from the NEI, Hg 

only: othpt_hg 

No NEI Sector

Year 2000 Canada speciated mercury point source emissions; annual 

resolution. 

Other nonpoint and 

nonroad not from the 

NEI: othar 

No NEI Sector

Year 2006 Canada (province resolution) and year 1999 Mexico Phase III 

(municipio resolution) nonpoint and nonroad mobile inventories, annual 

resolution. 

Other nonpoint 

sources not from the 

NEI, Hg only: 

othar_hg 

No NEI Sector

Year 2000 Canada speciated mercury from nonpoint sources; annual 

resolution. 

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. 

 
United States 

Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 

Publication N