Bayesianspace-time downscaling fusion model 

(downscaler) -Derived Estimates of Air Quality 

for 2009 
EPA-454/R-13-003 

 November 2013 

Bayesian space-time downscaling fusion model (downscaler) -Derived 

Estimates of Air Quality for 2009 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 

 
ii

Contributors 

Ellen Baldridge (EPA/OAR) 

Halil Cakir (EPA/OAR) 

Alison Eyth (EPA/OAR) 

Dave Holland (EPA/ORD) 

David Mintz (EPA/OAR) 

Sharon Phillips (EPA/OAR) 

Adam Reff (EPA/OAR) 

Acknowledgements 

The following people served as reviewers of this document and provided valuable comments 

Jan Cortelyou (EPA/OAR) 

Dennis Doll (EPA/OAR) 

Tyler Fox (EPA/OAR) 

Neil Frank (EPA/OAR) 

James Hemby (EPA/OAR) 

Marc Houyoux (EPA/OAR), 

Dr. Bryan Hubbell (EPA/OAR) 
1

Contents

Contents .......................................................................................................................................... 1

1.0 Introduction .......................................................................................................................... 2

2.0 Air Quality Data ................................................................................................................... 5

2.1 Introduction to Air Quality Impacts in the United States ................................................. 5

2.2 Ambient Air Quality Monitoring in the United States ..................................................... 7

2.3 Air Quality Indicators Developed for the EPHT Network ............................................. 11

3.0 Emissions Data................................................................................................................... 13

3.1 Introduction to Emissions Data Development ............................................................... 13

3.2 2009 Emission Inventories and Approaches .................................................................. 13

3.3 Emissions Modeling Summary ...................................................................................... 34

4.0 CMAQ Air Quality Model Estimates ................................................................................ 55

4.1 Introduction to the CMAQ Modeling Platform .............................................................. 55

4.2 CMAQ Model Version, Inputs and Configuration ......................................................... 57

4.3 CMAQ Model Performance Evaluation ......................................................................... 62

5.0 Bayesian space-time downscaling fusion model (downscaler) -Derived Air Quality 

Estimates ....................................................................................................................................... 77

5.1 Introduction .................................................................................................................... 77

5.2 Downscaler Model ......................................................................................................... 77

5.3 Downscaler Output ......................................................................................................... 78

5.4 Overview of Downscaler Model Results for 2009 ......................................................... 79

5.5 Accuracy Assessment of Downscaler Model Results .................................................. 105

5.6 Use of EPA Downscaler Model Predictions ................................................................ 116

Appendix A - Acronyms ............................................................................................................. 117
2

1.0   Introduction

This report describes estimates of daily ozone (maximum 8-hour average) and PM2.5 (24-hour 

average) concentrations throughout the contiguous United States during the 2009 calendar 

year generated by EPA's recently developed data fusion method termed the "downscaler 

model" (DS). Air quality monitoring data from the National Air Monitoring Stations/State and 

Local Air Monitoring Stations (NAMS/SLAMS) and numerical output from the Community 

Multiscale Air Quality (CMAQ) model were both input to DS to predict concentrations at the 

2010 US census tract centroids encompassed by the CMAQ modeling domain. Information on 

EPA's air quality monitors, CMAQ model, and downscaler model is included to provide the 

background and context for understanding the data output presented in this report. These 

estimates are intended for use by statisticians and environmental scientists interested in the 

daily spatial distribution of ozone and PM2.5. 

DS essentially operates by calibrating CMAQ data to the observational data, and then uses the 

resulting relationship to predict "observed" concentrations at new spatial points in the domain. 

Although similar in principle to a linear regression, spatial modeling aspects have been 

incorporated for improving the model fit, and a Bayesian1 approaching to fitting is used to 

generate an uncertainty value associated with each concentration prediction. The uncertainties 

that DS produces are a major distinguishing feature from earlier fusion methods previously 

used by EPA such as the "Hierarchical Bayesian" (HB) model (McMillan et al, 2009). The 

term "downscaler" refers to the fact that DS takes grid-averaged data (CMAQ) for input and 

produces point-based estimates, thus "scaling down" the area of data representation. Although 

this allows air pollution concentration estimates to be made at points where no observations 

exist, caution is needed when interpreting any within-gridcell spatial gradients generated by 

DS since they may not exist in the input datasets. The theory, development, and initial 

evaluation of DS can be found in the earlier papers of Berrocal, Gelfand, and Holland (2009, 

2010, and 2011). 

The data contained in this report are an outgrowth of a collaborative research partnership 

between EPA scientists from the Office of Research and Development’s (ORD) National 

Exposure Research Laboratory (NERL) and personnel from EPA’s Office of Air and 

Radiation’s (OAR) Office of Air Quality Planning and Standards (OAQPS). NERL’s Human 

Exposure and Atmospheric Sciences Division (HEASD), Atmospheric Modeling Division 

(AMD), and Environmental Sciences Division (ESD), in conjunction with OAQPS, work 

together to provide air quality monitoring data and model estimates to the Centers for Disease 

Control and Prevention (CDC) for use in their Environmental Public Health Tracking (EPHT) 

Network. 

 

1

Bayesian statistical modeling refers to methods that are based on Bayes’ theorem, and model the world in terms

of probabilities based on previously acquired knowledge.
3

CDC’s EPHT Network supports linkage of air quality data with human health outcome data 

for use by various public health agencies throughout the U.S. The EPHT Network Program is 

a multidisciplinary collaboration that involves the ongoing collection, integration, analysis, 

interpretation, and dissemination of data from: environmental hazard monitoring activities; 

human exposure assessment information; and surveillance of noninfectious health conditions. 

As part of the National EPHT Program efforts, the CDC led the initiative to build the National 

EPHT Network (http:// www.cdc.gov/nceh/tracking/default.htm). The National EPHT 

Program, with the EPHT Network as its cornerstone, is the CDC’s response to requests calling 

for improved understanding of how the environment affects human health. The EPHT 

Network is designed to provide the means to identify, access, and organize hazard, exposure, 

and health data from a variety of sources and to examine, analyze and interpret those data 

based on their spatial and temporal characteristics. 

Since 2002, EPA has collaborated with the CDC on the development of the EPHT Network. 

On September 30, 2003, the Secretary of Health and Human Services (HHS) and the 

Administrator of EPA signed a joint Memorandum of Understanding (MOU) with the 

objective of advancing efforts to achieve mutual environmental public health goals2

. HHS, 

acting through the CDC and the Agency for Toxic Substances and Disease Registry 

(ATSDR), and EPA agreed to expand their cooperative activities in support of the CDC 

EPHT Network and EPA’s Central Data Exchange Node on the Environmental Information 

Exchange Network in the following areas: 

 Collecting, analyzing and interpreting environmental and health data from both 

agencies (HHS and EPA). 

 Collaborating on emerging information technology practices related to building, 

supporting, and operating the CDC EPHT Network and the Environmental 

Information Exchange Network. 

 Developing and validating additional environmental public health indicators. 

 Sharing reliable environmental and public health data between their respective 

networks in an efficient and effective manner. 

 Consulting and informing each other about dissemination of results obtained through 

work carried out under the MOU and the associated Interagency Agreement (IAG) 

between EPA and CDC. 

 

2

HHS and EPA agreed to extend the duration of the MOU, effective since 2002 and renewed in 2007, until June 29,

2017.  The MOU is available at www.cdc.gov/nceh/tracking/partners/epa_mou_2007.htm.
4

The best available statistical fusion model, air quality data, and CMAQ numerical model 

output were used to develop the 2009 estimates. Fusion results can vary with different inputs 

and fusion modeling approaches. As new and improved statistical models become available, 

EPA will provide updates. 

Although these data have been processed on a computer system at the Environmental Protection 

Agency, no warranty expressed or implied is made regarding the accuracy or utility of the data on 

any other system or for general or scientific purposes, nor shall the act of distribution of the data 

constitute any such warranty. It is also strongly recommended that careful attention be paid to the 

contents of the metadata file associated with these data to evaluate data set limitations, restrictions 

or intended use. The U.S. Environmental Protection Agency shall not be held liable for improper 

or incorrect use of the data described and/or contained herein. 

The four remaining sections and one appendix in the report are as follows. 

 Section 2 describes the air quality data obtained from EPA’s nationwide monitoring 

network and the importance of the monitoring data in determining health potential 

health risks. 

 Section 3 details the emissions inventory data, how it is obtained and its role as a key 

input into the CMAQ air quality computer model. 

 Section 4 describes the CMAQ computer model and its role in providing estimates of 

pollutant concentrations across the U.S. based on 12-km grid cells over the contiguous 

U.S. 

 Section 5 explains the downscaler model used to statistically combine air quality 

monitoring data and air quality estimates from the CMAQ model to provide daily air 

quality estimates for the 2010 US census tract centroid locations within the contiguous 

U.S. 

 The appendix provides a description of acronyms used in this report.
5

2.0 Air Quality Data

To compare health outcomes with air quality measures, it is important to understand the origins 

of those measures and the methods for obtaining them. This section provides a brief overview of 

the origins and process of air quality regulation in this country. It provides a detailed discussion 

of ozone (O3) and particulate matter (PM). The EPHT program has focused on these two 

pollutants, since numerous studies have found them to be most pervasive and harmful to public 

health and the environment, and there are extensive monitoring and modeling data available. 

2.1 Introduction to Air Quality Impacts in the United States

2.1.1 The Clean Air Act 

In 1970, the Clean Air Act (CAA) was signed into law. Under this law, EPA sets limits on how 

much of a pollutant can be in the air anywhere in the United States. This ensures that all 

Americans have the same basic health and environmental protections. The CAA has been 

amended several times to keep pace with new information. For more information on the CAA, 

go to http://www.epa.gov/oar/caa/. 

Under the CAA, the U.S. EPA has established standards or limits for six air pollutants, known as 

the criteria air pollutants: carbon monoxide (CO), lead (Pb), nitrogen dioxide (NO2), sulfur 

dioxide (SO2), ozone (O3), and particulate matter (PM). These standards, called the National 

Ambient Air Quality Standards (NAAQS), are designed to protect public health and the 

environment. The CAA established two types of air quality standards. Primary standards set 

limits to protect public health, including the health of “sensitive” populations such as asthmatics, 

children, and the elderly. Secondary standards set limits to protect public welfare, including 

protection against decreased visibility, damage to animals, crops, vegetation, and buildings. The 

law requires EPA to review periodically these standards. For more specific information on the 

NAAQS, go to www.epa.gov/air/criteria.html. For general information on the criteria pollutants, 

go to http://www.epa.gov/air/urbanair/6poll.html. 

When these standards are not met, the area is designated as a nonattainment area. States must 

develop state implementation plans (SIPs) that explain the regulations and controls it will use to 

clean up the nonattainment areas. States with an EPA-approved SIP can request that the area be 

designated from nonattainment to attainment by providing three consecutive years of data 

showing NAAQS compliance. The state must also provide a maintenance plan to demonstrate 

how it will continue to comply with the NAAQS and demonstrate compliance over a 10-year 

period, and what corrective actions it will take should a NAAQS violation occur after 

designation. EPA must review and approve the NAAQS compliance data and the maintenance 

plan before designating the area; thus, a person may live in an area designated as non- attainment 

even though no NAAQS violation has been observed for quite some time. For more information 

on designations, go to http://www.epa.gov/ozonedesignations/ and 

http://www.epa.gov/pmdesignations. 
6

2.1.2 Ozone 

Ozone is a colorless gas composed of three oxygen atoms. Ground level ozone is formed when 

pollutants released from cars, power plants, and other sources react in the presence of heat and 

sunlight. It is the prime ingredient of what is commonly called “smog.” When inhaled, ozone can 

cause acute respiratory problems, aggravate asthma, cause inflammation of lung tissue, and even 

temporarily decrease the lung capacity of healthy adults. Repeated exposure may permanently scar 

lung tissue. Toxicological, human exposure, and epidemiological studies were integrated by EPA 

in “Air Quality Criteria for Ozone and Related Photochemical Oxidants.” It is available at 

http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_index.html. The current (as of October 2008) 

NAAQS for ozone is a daily maximum 8-hour average of 0.075 parts per million [ppm] (for details, 

see http://www.epa.gov/ozonedesignations/). The Clean Air Act requires EPA to review the 

NAAQS at least every five years and revise them as appropriate in accordance with Section 108 

and Section 109 of the Act. 

2.1.3 Particulate Matter 

PM air pollution is a complex mixture of small and large particles of varying origin that can 

contain hundreds of different chemicals, including cancer-causing agents like polycyclic aromatic 

hydrocarbons (PAH), as well as heavy metals such as arsenic and cadmium. PM air pollution 

results from direct emissions of particles as well as particles formed through chemical 

transformations of gaseous air pollutants. The characteristics, sources, and potential health effects 

of particulate matter depend on its source, the season, and atmospheric conditions. 

As practical convention, PM is divided by sizes into classes with differing health concerns and 

potential sources32. Particles less than 10 micrometers in diameter (PM10) pose a health concern 

because they can be inhaled into and accumulate in the respiratory system. Particles less than 2.5 

micrometers in diameter (PM2.5) are referred to as “fine” particles. Because of their small size, fine 

particles can lodge deeply into the lungs. Sources of fine particles include all types of combustion 

(motor vehicles, power plants, wood burning, etc.) and some industrial processes. Particles with 

diameters between 2.5 and 10 micrometers (PM10-2.5) are referred to as “coarse” or PMc. Sources 

of PMc include crushing or grinding operations and dust from paved or unpaved roads. The 

distribution of PM10, PM2.5 and PMc varies from the Eastern U.S. to arid western areas. 

Particle pollution - especially fine particles - contains microscopic solids and liquid droplets that 

are so small that they can get deep into the lungs and cause serious health problems. Numerous 

scientific studies have linked particle pollution exposure to a variety of problems, including 

premature death in people with heart or lung disease, nonfatal heart attacks, irregular heartbeat, 

aggravated asthma, decreased lung function, and increase respiratory symptoms, such as irritation 

of airways, coughing or difficulty breathing. Additional information on the health effects of 

particle pollution and other technical documents related to PM standards are available at 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_index.html. 

 

3 The measure used to classify PM into sizes is the aerodynamic diameter. The measurement instruments used for PM 

are designed and operated to separate large particles from the smaller particles. For example, the PM2.5 instrument only 

captures and thus measures particles with an aerodynamic diameter less than 2.5 micrometers. The EPA method to 

measure PMc is designed around taking the mathematical difference between measurements for PM10 and PM2.5.
7

The current NAAQS for PM2.5 includes both a 24-hour standard to protect against short-term effects, and 

an annual standard to protect against long-term effects. The annual average PM2.5 concentration must not 

exceed 12.0 micrograms per cubic meter (ug/m3), and the 24-hr average concentration must not exceed 35 

ug/m3

. More information is available at http://www.epa.gov/air/criteria.html and 

http://www.epa.gov/oar/particlepollution/. The standards for PM2.5 values are shown in Table 2-1. 

Table 2-1. PM2.5 Standards 

Micrograms Per Cubic Meter: 

Measurement - (ug/m3) 1997 2006 2012

Annual Average 15.0 15.0 12.0 

24-Hour Average 65 35 35 

2.2 Ambient Air Quality Monitoring in the United States

2.2.1 Monitoring Networks 

The Clean Air Act requires every state to establish a network of air monitoring stations for criteria 

pollutants, following specific guidelines for their location and operation. The monitoring stations in this 

network have been called the State and Local Air Monitoring Stations (SLAMS). The SLAMS network 

consists of approximately 4,000 monitoring sites whose distribution is largely determined by the needs of 

State and local air pollution control agencies. All ambient monitoring networks selected for use in 

SLAMS are tested periodically to assess the quality of the SLAMS data being produced. Measurement 

accuracy and precision are estimated for both automated and manual methods. The individual results of 

these tests for each method or analyzer are reported to EPA. Then, EPA calculates quarterly integrated 

estimates of precision and accuracy for the SLAMS data. 

The National Air Monitoring Station network (NAMS) is about a 1,000-site subset of the SLAMS 

network, with emphasis on areas of maximum concentrations and high population density in urban and 

multi-source areas. The NAMS monitoring sites are designed to obtain more timely and detailed 

information about air quality in strategic locations and must meet more stringent monitor siting, 

equipment type, and quality assurance criteria. NAMS monitors also must submit detailed quarterly and 

annual monitoring results to EPA. 

The SLAMS and NAMS networks experienced accelerated growth throughout the 1970s. The networks 

were further expanded in 1999 following the 1997 revision of the CAA to include separate standards for 

fine particles (PM2.5) based on their link to serious health problems ranging from increased symptoms, 

hospital admissions, and emergency room visits, to premature death in people with heart or lung disease. 

While most of the monitors in these networks are located in populated areas of the country, “background” 

and rural monitors are an important part of these networks. For criteria pollutants other than ozone and 

PM2.5, the number of monitors has declined. For more information on SLAMS and NAMS, as well as 

EPA’s other air monitoring networks go to www.epa.gov/ttn/amtic. 

In 2009, approximately 43 percent of the US population was living within 10 kilometers of ozone and 

PM2.5 monitoring sites. In terms of US Census Bureau tract locations, 31,341 out of 72,283 census tract 

centroids were within 10 kilometers of ozone monitoring sites. Highly populated Eastern US and 

California coasts are well covered by both ozone and PM2.5 monitoring network (Figure 2-1).
8

Figure 2-1. Distances from US Census Tract centroids to the nearest monitoring site. 
9

In summary, state and local agencies and tribes implement a quality-assured monitoring network to 

measure air quality across the United States. EPA provides guidance to ensure a thorough understanding 

of the quality of the data produced by these networks. These monitoring data have been used to 

characterize the status of the nation's air quality and the trends across the U.S. (see 

www.epa.gov/airtrends). 

2.2.2 Air Quality System Database 

EPA’s Air Quality System (AQS) database contains ambient air pollution data collected by EPA, state, 

local, and tribal air pollution control agencies from thousands of monitoring stations. AQS also contains 

meteorological data, descriptive information about each monitoring station (including its geographic 

location and its operator), and data quality assurance and quality control information. State and local 

agencies are required to submit their air quality monitoring data into AQS within 90 days following the 

end of the quarter in which the data were collected. This ensures timely submission of these data for use 

by state, local, and tribal agencies, EPA, and the public. EPA’s Office of Air Quality Planning and 

Standards and other AQS users rely upon the data in AQS to assess air quality, assist in compliance with 

the NAAQS, evaluate SIPs, perform modeling for permit review analysis, and perform other air quality 

management functions. For more details, including how users can retrieve data, go to 

http://www.epa.gov/ttn/airs/airsaqs/index.htm. 

2.2.3 Advantages and Limitations of the Air Quality Monitoring and Reporting System 

Air quality data is required to assess public health outcomes that are affected by poor air quality. The 

challenge is to get surrogates for air quality on time and spatial scales that are useful for Environmental 

Public Health Tracking activities. 

The advantage of using ambient data from EPA monitoring networks for comparing with health outcomes 

is that these measurements of pollution concentrations are the best characterization of the concentration 

of a given pollutant at a given time and location. Furthermore, the data are supported by a comprehensive 

quality assurance program, ensuring data of known quality. One disadvantage of using the ambient data 

is that it is usually out of spatial and temporal alignment with health outcomes. This spatial and temporal 

‘misalignment’ between air quality monitoring data and health outcomes is influenced by the following 

key factors: the living and/or working locations (microenvironments) where a person spends their time 

not being co-located with an air quality monitor; time(s)/date(s) when a patient experiences a health 

outcome/symptom (e.g., asthma attack) not coinciding with time(s)/date(s) when an air quality monitor 

records ambient concentrations of a pollutant high enough to affect the symptom (e.g., asthma attack 

either during or shortly after a high PM2.5 day). To compare/correlate ambient concentrations with acute 

health effects, daily local air quality data is needed4

. Spatial gaps exist in the air quality monitoring 

network, especially in rural areas, since the air quality monitoring network is designed to focus on 

measurement of pollutant concentrations in high population density areas. Temporal limits also exist. 

Hourly ozone measurements are aggregated to daily values (the daily max 8-hour average is relevant to 

the ozone standard). Ozone is typically monitored during the ozone season (the warmer months, 

approximately April through October). However, year-long data is available in many areas and is 

extremely useful to evaluate whether ozone is a factor in health outcomes during the non-ozone seasons. 

PM2.5 is generally measured year-round. Most Federal Reference Method (FRM) PM2.5 monitors collect 

 

4

EPA uses exposure models to evaluate the health risks and environmental effects associated with exposure. These models

are limited by the availability of air quality estimates. http://www.epa.gov/ttn/fera/index.html.
10

data one day in every three days, due in part to the time and costs involved in collecting and analyzing the 

samples. However, over the past several years, continuous monitors, which can automatically collect, 

analyze, and report PM2.5 measurements on an hourly basis, have been introduced. These monitors are 

available in most of the major metropolitan areas. Some of these continuous monitors have been 

determined to be equivalent to the FRM monitors for regulatory purposes and are called FEM (Federal 

Equivalent Methods). 

2.2.4 Use of Air Quality Monitoring Data 

Air quality monitoring data has been used to provide the information for the following situations: 

(1) Assessing effectiveness of SIPs in addressing NAAQS nonattainment areas 

(2) Characterizing local, state, and national air quality status and trends 

(3) Associating health and environmental damage with air quality levels/concentrations 

For the EPHT effort, EPA is providing air quality data to support efforts associated with (2), and (3) 

above. Data supporting (3) is generated by EPA through the use of its air quality data and its downscaler 

model. 

Most studies that associate air quality with health outcomes use air monitoring as a surrogate for exposure 

to the air pollutants being investigated. Many studies have used the monitoring networks operated by 

state and federal agencies. Some studies perform special monitoring that can better represent exposure to 

the air pollutants: community monitoring, near residences, in-house or work place monitoring, and 

personal monitoring. For the EPHT program, special monitoring is generally not supported, though it 

could be used on a case-by-case basis. 

From proximity based exposure estimates to statistical interpolation, many approaches are developed for 

estimating exposures to air pollutants using ambient monitoring data (Jerrett et al., 2005). Depending 

upon the approach and the spatial and temporal distribution of ambient monitoring data, exposure 

estimates to air pollutants may vary greatly in areas further apart from monitors (Bravo et al., 2012). 

Factors like limited temporal coverage (i.e., PM2.5 monitors do not operate continuously such as recording 

every third day or ozone monitors operate only certain part of the year) and limited spatial coverage (i. e., 

most monitors are located in urban areas and rural coverage is limited) hinder the ability of most of the 

interpolation techniques that use monitoring data alone as the input. If we look at the example of 

Voronoi Neighbor Averaging (VNA) (referred as the Nearest Neighbor Averaging in most literature), 

rural estimates would be biased towards the urban estimates. To further explain this point, assume the 

scenario of two cities with monitors and no monitors in the rural areas between, which is very plausible. , 

Since exposure estimates are guaranteed to be within the range of monitors in VNA, estimates for the 

rural areas would be higher according to this scenario. 

Air quality models may overcome some of the limitations that monitoring networks possess. Models such 

as the Community Multi-Scale Air Quality (CMAQ) modeling systems can estimate concentrations in 

reasonable temporal and spatial resolutions. However these sophisticated air quality models are prune to 

systematic biases since they depend upon so many variables (i.e., metrological models and emission 

models) and complex chemical and physical process simulations. 

Combining monitoring data with air quality models (via fusion or regression) may provide the best results 
11

in terms of estimating ambient air concentrations in space and time. EPA’s eVNA5 is an example of an 

earlier approach for merging air quality monitor data with CMAQ model predictions. The downscaler 

model attempts to address some of the shortcomings in these earlier attempts to statistically combine 

monitor and model predicted data, see published paper referenced in section 1 for more information about 

the downscaler model. As discussed in the next section, there are two methods used in EPHT to provide 

estimates of ambient concentrations of air pollutants: air quality monitoring data and the downscaler 

model estimate, which is a statistical ‘combination’ of air quality monitor data and photochemical air 

quality model predictions (e.g., CMAQ). 

2.3 Air Quality Indicators Developed for the EPHT Network

Air quality indicators have been developed for use in the Environmental Public Health Tracking Network 

by CDC using the ozone and PM2.5 data from EPA. The approach used divides “indicators” into two 

categories. First, basic air quality measures were developed to compare air quality levels over space and 

time within a public health context (e.g., using the NAAQS as a benchmark). Next, indicators were 

developed that mathematically link air quality data to public health tracking data (e.g., daily PM2.5 levels 

and hospitalization data for acute myocardial infarction). Table 2-3 and Table 2-4 describe the issues 

impacting calculation of basic air quality indicators. 

Table 2-2. Public Health Surveillance Goals and Current Status 

Goal Status 

Air data sets and metadata required for air quality 

indicators are available to EPHT state Grantees. 

AQS data are available through state agencies and 

EPA’s Air Quality System (AQS). EPA and CDC 

developed an interagency agreement, where EPA 

provides air quality data along with statistically 

combined AQS and Community Multiscale Air Quality 

(CMAQ) Model data, associated metadata, and technical 

reports that are delivered to CDC. 

Estimate the linkage or association of PM2.5 and 

ozone on health to: 

Identify populations that may have higher risk of 

adverse health effects due to PM2.5 and ozone, 

Generate hypothesis for further research, and 

Provide information to support prevention and 

pollution control strategies. 

Regular discussions have been held on health-air linked 

indicators and CDC/HFI/EPA convened a workshop 

January 2008. CDC has collaborated on a health impact 

assessment (HIA) with Emory University, EPA, and 

state grantees that can be used to facilitate greater 

understanding of these linkages. 

Produce and disseminate basic indicators and other 

findings in electronic and print formats to provide 

the public, environmental health professionals, and 

policymakers, with current and easy-to-use 

information about air pollution and the impact on 

public health. 

Templates and “how to” guides for PM2.5 and ozone 

have been developed for routine indicators. Calculation 

techniques and presentations for the indicators have been 

developed. 

 

5

eVNA is described in the “Regulatory Impact Analysis for the Final Clean Air Interstate Rule”, EPA‐452/R‐05‐002, March

2005, http://www.epa.gov/cair/pdfs/finaltech08.pdf, Appendix F.
12

Table 2-3. Basic Air Quality Indicators used in EPHT, derived from the EPA data delivered to 

CDC 

Ozone (daily 8-hr period with maximum concentration—ppm—by Federal Reference Method (FRM)) 

 Number of days with maximum ozone concentration over the NAAQS (or other relevant benchmarks (by county 

and MSA) 

 Number of person-days with maximum 8-hr average ozone concentration over the NAAQS & other relevant 

benchmarks (by county and MSA)

PM2.5 (daily 24-hr integrated samples –ug/m3-by FRM) 

 Average ambient concentrations of particulate matter (< 2.5 microns in diameter) and compared to annual PM2.5 

NAAQS (by state). 

 % population exceeding annual PM2.5 NAAQS (by state). 

 % of days with PM2.5 concentration over the daily NAAQS (or other relevant benchmarks (by county and MSA) 

 Number of person-days with PM2.5 concentration over the daily NAAQS & other relevant benchmarks (by 

county and MSA)

2.3.1 Rationale for the Air Quality Indicators 

The CDC EPHT Network is initially focusing on ozone and PM2.5. These air quality indicators are based 

mainly around the NAAQS health findings and program-based measures (measurement, data and analysis 

methodologies). The indicators will allow comparisons across space and time for EPHT actions. They 

are in the context of health-based benchmarks. By bringing population into the measures, they roughly 

distinguish between potential exposures (at broad scale). 

2.3.2 Air Quality Data Sources 

The air quality data will be available in the US EPA Air Quality System (AQS) database based on the 

state/federal air program’s data collection and processing. The AQS database contains ambient air 

pollution data collected by EPA, state, local, and tribal air pollution control agencies from thousands of 

monitoring stations (SLAMS and NAMS). 

2.3.3 Use of Air Quality Indicators for Public Health Practice 

The basic indicators will be used to inform policymakers and the public regarding the degree of hazard 

within a state and across states (national). For example, the number of days per year that ozone is above 

the NAAQS can be used to communicate to sensitive populations (such as asthmatics) the number of days 

that they may be exposed to unhealthy levels of ozone. This is the same level used in the Air Quality 

Alerts that inform these sensitive populations when and how to reduce their exposure. These indicators, 

however, are not a surrogate measure of exposure and therefore will not be linked with health data.
13

3.0   Emissions Data

3.1 Introduction to Emissions Data Development

The U.S. Environmental Protection Agency (EPA) developed an air quality modeling platform based 

primarily on the 2008 National Emissions Inventory (NEI), Version 2 to process year 2009 emission data 

for this project. This section provides a summary of the emissions inventory and emissions modeling 

techniques applied to Criteria Air Pollutants (CAPs) and the following select Hazardous Air Pollutants 

(HAPs): chlorine (Cl), hydrogen chloride (HCl), benzene, acetaldehyde, formaldehyde and methanol. 

This section also describes the approach and data used to produce emissions inputs to the air quality 

model. The air quality modeling, meteorological inputs and boundary conditions are described in a 

separate section. 

The Community Multiscale Air Quality (CMAQ) model (http://www.epa.gov/AMD/CMAQ/) is used to 

model ozone (O3) and particulate matter (PM) for this project. CMAQ requires hourly and gridded 

emissions of the following inventory pollutants: carbon monoxide (CO),nitrogen oxides (NOX), volatile 

organic compounds (VOC), sulfur dioxide (SO2), ammonia (NH3), particulate matter less than or equal 

to10 microns (PM10), and individual component species for particulate matter less than or equal to 2.5 

microns (PM2.5). In addition, the CMAQ CB05 with chlorine chemistry used here allows for explicit 

treatment of the VOC HAPs benzene, acetaldehyde, formaldehyde and methanol (BAFM) and includes 

anthropogenic HAP emissions of HCl and Cl. 

The effort to create the 2009 emission inputs for this study included development of emission inventories 

for a 2009 model evaluation case, and application of emissions modeling tools to convert the inventories 

into the format and resolution needed by CMAQ. An evaluation case uses 2009-specific fire and 

continuous emission monitoring (CEM) data for electric generating units (EGUs) whereas other types of 

cases use averages for these sources. The primary emissions modeling tool used to create the CMAQ 

model-ready emissions was the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system. 

SMOKE version 3.1 was used to create emissions files for a 12-km national grid. Additional information 

about SMOKE is available from http://www.smoke-model.org. 

This chapter contains two additional sections. Section 3.2 describes the inventories input to SMOKE and 

the ancillary files used along with the emission inventories. Section 3.3 describes the emissions modeling 

performed to convert the inventories into the format and resolution needed by CMAQ. 

3.2 2009 Emission Inventories and Approaches

This section describes the emissions inventories created for input to SMOKE. The 2008 NEI, which is the 

primary basis for the input to SMOKE, includes five main categories of source sectors: a) nonpoint 

(formerly called “stationary area”) sources; b) point sources; c) nonroad mobile sources; d) onroad 

mobile sources; and e) fires. The NEI data are largely compiled from data submitted by state, local and 

tribal (S/L/T) agencies for CAPs. HAP emissions data are often augmented by EPA because they are a 

voluntary component. The 2008 NEI was compiled using the Emissions Inventory System (EIS). EIS 
14

includes hundreds of automated QA checks to help improve data quality, and also supports release point 

(stack) coordinates separately from facility coordinates. Improved EPA collaboration with S/L/T 

agencies prevented duplication between point and nonpoint source categories such as industrial boilers. 

Documentation for the 2008 NEI is available at http://www.epa.gov/ttn/chief/net/2008inventory.html# 

inventorydoc. 

2009-specific data submitted by S/L/T agencies was used for some large point sources. For EGU 

emissions, 2009 continuous emissions monitoring (CEM) data was used where it was available. For fires, 

EPA used the SMARTFIRE2 (SF2) system to develop 2009 emissions. SF2 was the first system to 

assign all fires as either prescribed burning or wildfire categories and includes improved emission factor 

estimates for prescribed burning. 2009-specific data for onroad, nonroad, and large commercial marine 

sources was also developed. Some data obtained from regional planning organizations (RPOs) was 

substituted for NEI data where the RPO data was more recently collected. California-provided mobile 

source emissions were also used. For inventories outside of the United States, including Canada, Mexico 

and offshore emissions, the latest available base year inventories were used. 

The methods used to process emissions for this project are very similar to those documented for EPA’s 

Version 5, 2007 Emissions Modeling Platform. A technical support document (TSD) for this platform is 

available at EPA’s emissions modeling clearinghouse (EMCH): 

http://www.epa.gov/ttn/chief/emch/index.html#pmnaaqs. Electronic copies of inventories similar to those 

used for this project are available in the same section of the EMCH. 

The emissions modeling process, performed using SMOKE v3.1 apportions the emissions inventories 

into the grid cells used by CMAQ and temporalizes the emissions into hourly values. In addition, the 

pollutants in the inventories (e.g., NOx and VOC) are split into the chemical species needed by CMAQ. 

For the purposes of preparing the CMAQ- ready emissions, the broader NEI emissions inventories are 

split into emissions modeling “platform” sectors; and biogenic emissions are added along with emissions 

from other sources other than the NEI, such as the Canadian, Mexican, and offshore inventories. The 

significance of an emissions sector for the emissions modeling platform is that it is run through all of the 

s programs, except the final merge, independently from the other sectors. The final merge program called 

Mrggrid combines the sector- specific gridded, speciated and temporalized emissions to create the final 

CMAQ-ready emissions inputs. 

Table 3-1 presents the sectors in the emissions modeling platform used to develop 2009 emissions for this 

project. The sector abbreviations are provided in italics; these abbreviations are used in the SMOKE 

modeling scripts and inventory file names and throughout the remainder of this section. Annual 2009 

emission summaries for the U.S. anthropogenic sectors are shown in Table 3-2 (i.e., biogenic emissions 

are excluded). Table 3-3 provides a summary of emissions for the anthropogenic sectors containing 

Canadian, Mexican and offshore sources. State total emissions for each sector are provided in Appendix 

A, a workbook entitled “Appendix_A_2009_emissions_totals_by_sector.xlsx”. 

 
15

Table 3-1. Platform Sectors Used in the Emissions Modeling Process 

2009 Platform Sector 

(Abbrev)

2009 NEI 

Sector 

Description and resolution of the data input to 

SMOKE 

IPM (ptipm) Point 

2009 NEI point source EGUs that can be mapped to 

the Integrated Planning Model (IPM) model. NEI 

values replaced with year 2009 hourly continuous 

emission monitoring (CEM) NOX and SO2 emissions.

Other pollutants are scaled from 2008 NEI using heat 

input. 

Point non-IPM (ptnonipm) Point 

A mix of 2008 NEI point source emissions with some 

2009 records where data was provided by states and 

locals and 2006 WRAP oil and gas data; these are 

emissions not matched to the ptipm sector, annual 

resolution. Includes all aircraft emissions 

Point source fire (ptfire) Fires Point source day-specific wildfires and prescribed fires 

for 2009. 

Agricultural (ag) Nonpoint 

2008 NEI nonpoint NH3 emissions from livestock and 

fertilizer application; county and annual resolution 

with some 2007 monthly resolution data provided by 

the Midwest. 

Area fugitive dust (afdust) Nonpoint 

2008 NEI nonpoint PM10 and PM2.5 from fugitive dust 

sources (e.g., building construction, road construction, 

paved roads, unpaved roads, agricultural dust), county 

and annual resolution. A land use-based transport 

fraction and 2009-based precipitation zero-out is 

applied. 

Remaining nonpoint (nonpt) Nonpoint 

Primarily 2008 NEI nonpoint for sources not included 

in other sectors mixed with 2006 WRAP oil and gas 

data, county and annual resolution. 

Nonroad (nonroad) Nonroad 

Year 2009 monthly nonroad emissions from the 

National Mobile Inventory Model (NMIM) plus 

California-provided data; county and annual 

resolution. 

C1 and C2 marine and 

locomotive (c1c2rail) Nonroad 

Year 2008 non-rail maintenance locomotives, and 

category 1 and category 2 commercial marine vessel 

(CMV) emissions sources; county and annual 

resolution; year 2009 for California. 

C3 commercial marine 

(c3marine) Nonroad 

Non-NEI, year 2009 category 3 (C3) CMV emissions 

projected from year 2002. 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- International Maritime Organization 

(ECA-IMO) study: 

http://www.epa.gov/otaq/oceanvessels.htm. (EPA420-F-10-041, August 2010). Annual resolution and 
16

treated as point sources. 

Onroad (onroad) Onroad 

Year 2009 gridded hourly emissions from onroad 

mobile gasoline and diesel vehicles from parking lots 

and moving vehicles including exhaust, evaporative, 

permeation, and brake and tire wear. Generated using 

MOVES 2010b emission factors, 2009 VMT and 

vehicle population data, and 2009 gridded met. data. In 

California, adjusted to match CA-provided emissions. 

Onroad Refueling 

(onroad_rfl) Onroad 

Year 2009 gridded hourly emissions from onroad 

mobile gasoline and diesel vehicles from parking lots 

and moving vehicles for refueling only. Generated 

using MOVES 2010b, emission actors, 2009 VMT and 

vehicle population data, and 2009 gridded met. data. 

Spatially allocated to gasoline station locations. 

Biogenic (beis) Biogenic 

Hour- and grid cell-specific emissions for 2009 

generated from the BEIS 3.14 model, including 

emissions in Canada and Mexico. 

Other point sources (othpt) N/A 

Point sources not from the NEI, including Canada's 

2006 inventory and a 2008 projection of Mexico's 

Phase III 1999 inventory; annual resolution. Also 

includes 2008 offshore oil point source emissions for 

the U.S. from the 2008 NEI. 

Other nonpoint and nonroad 

(othar) N/A 

Nonpoint and nonroad sources not from the NEI, 

including annual 2006 Canada sources at province 

resolution and a 2008 projection of annual 1999 

Mexico sources at municipio resolution. 

Other onroad sources (othon) N/A 

Onroad sources not from the NEI, including annual 

2006 Canada sources at province resolution and a 

2008 projection of 1999 Mexico sources at municipio 

resolution. 
17

Table 3-2. 2009 Continental United States Emissions by Sector (tons/yr in 48 states + D.C.) 

Sector CO NH3 NOx PM10 PM2.5 SO2 VOC 

afdust 5,823,635 816,524 

ag 3,595,429 

c1c2rail 217,984 559 1,329,661 43,528 40,733 48,487 60,809

nonpt 4,336,565 155,317 1,230,624 767,225 676,243 402,633 6,456,455

nonroad 15,053,215 1,985 1,784,297 174,562 165,768 32,169 2,249,982

onroad w/rfl. 27,221,698 127,354 6,165,415 302,003 222,002 34,973 2,736,569

ptfire 12,378,697 203,630 192,773 1,280,587 1,085,244 100,324 2,927,182

ptipm 676,123 24,015 2,046,085 298,162 205,675 5,965,968 32,955

ptnonipm 2,573,239 70,131 1,905,593 512,816 360,946 1,329,436 1,065,623

c3marine 14,757 160,083 14,515 13,326 121,120 5,725

Con.US Total 62,472,278 4,178,420 14,814,530 9,217,034 3,586,460 8,035,109 15,535,300

Table 3-3. 2009 Non-US Emissions by Sector within Modeling Domain (tons/yr for Canada, 

Mexico, Offshore) 

Country & [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] 

Sector CO NH3 NOX PM10 PM2.5 SO2 VOC 

Canada othar 3,747,303 537,912 718,757 1,421,686 393,642 97,709 1,267,472

Canada othon 4,513,915 21,810 537,704 15,004 10,634 5,430 277,874

Canada othpt 1,148,101 21,138 861,256 117,254 68,115 1,762,345 425,792

Canada Subtotal 9,409,320 580,860 2,117,717 1,553,944 472,390 1,865,484 1,971,138

Mexico othar 477,908 132,913 198,972 88,319 56,809 56,417 510,955

Mexico othon 659,536 2,971 93,839 7,935 7,348 5,738 96,218

Mexico othpt 101,309 0 344,896 122,654 90,304 740,238 78,465

Mexico Subtotal 1,238,753 135,884 637,708 218,908 154,460 802,393 685,639

Offshore othpt 82,133 0 74,277 780 769 1,021 60,756

Canada c3marine 13,394 0 160,983 13,434 12,311 99,644 5,690

Offshore c3marine 80,212 0 961,146 80,549 74,063 599,679 34,079

2009 TOTAL 10,823,812 716,744 3,951,831 1,867,615 713,994 3,368,221 2,757,303

3.2.1 Point Sources (ptipm and ptnonipm) 

Point sources are sources of emissions for which specific geographic coordinates (e.g., latitude/longitude) 

are specified, as in the case of an individual facility. A facility may have multiple emission points, which 

may be characterized as units such as boilers, reactors, spray booths, kilns, etc. A unit may have multiple 

processes (e.g., a boiler that sometimes burns residual oil and sometimes burns natural gas). The point 

sources used for this study include a limited set of emissions data for 2009 collected via the NEI process, 

with 2008 NEI data for any sources that did not report in 2009. Note that only large sources are required 
18

to report annually as opposed to triennially. This section describes NEI point sources within the 

contiguous United States. The offshore oil (othpt sector), fires (ptfire) and category 3 CMV emissions 

(c3marine sector) are point source formatted inventories discussed later in this section. Full 

documentation for the development of the 2008 NEI (EPA, 2012), is posted at: 

http://www.epa.gov/ttn/chief/net/2008inventory.html#inventorydoc. 

After removing offshore oil platforms into the othpt sector, we created two platform sectors from the 

remaining point sources for input into SMOKE: the EGU sector – also called the IPM sector (i.e., ptipm) 

and the non-EGU sector – also called the non-IPM sector (i.e., ptnonipm). This split facilitates the use of 

different SMOKE temporal processing and future-year projection techniques for each of these sectors. 

The inventory pollutants processed through SMOKE for both the ptipm and ptnonipm sectors were: CO, 

NOX, VOC, SO2, NH3, PM10, and PM2.5 and the following HAPs: HCl (pollutant code = 7647010), 

and Cl (code = 7782505). BAFM from these sectors was not utilized because VOC was speciated 

without the use (i.e., integration) of VOC HAP pollutants from the inventory (integration is discussed in 

detail in Section3.3.4). 

In the 2009 model evaluation case used in this study, for ptipm sector sources with CEM data that could 

be matched to the NEI, 2009 hourly SO2 and NOx emissions were used alongside annual emissions of all 

other pollutants. The hourly electric generating unit (EGU) emissions were obtained for SO2 and NOx

emissions and heat input from EPA’s Acid Rain Program. This data also contained heat input, which was 

used to allocate the annual emissions for other pollutants (e.g., VOC, PM2.5, HCl) to hourly values. For 

unmatched EGU units, annual emissions were temporalized to days using multi-year averages and to 

hours using state-specific averages. 

The Non-EGU Stationary Point Sources (ptnonipm) emissions were provided to SMOKE as annual 

emissions. The emissions were developed as follows: 

a. 2008 CAP and HAP data were provided by States, locals and tribes under the Consolidated 

Emissions Reporting Rule 

b. EPA corrected known issues and filled PM data gaps. 

c. EPA added HAP data from the Toxic Release Inventory (TRI) where it was not provided by 

states/locals.

d. EPA provided data for airports and rail yards. 

e. Off-shore platform data was added from Mineral Management Services (MMS). 

The changes made to the NEI point sources prior to modeling are as follows: 

 The tribal data, which do not use state/county Federal Information Processing Standards (FIPS) 

codes in the NEI, but rather use the tribal code, were assigned a state/county FIPS code of 

88XXX, where XXX is the3-digit tribal code in the NEI. This change was made because SMOKE 

requires the state/county FIPS code. 

 Stack parameters for some point sources were defaulted when modeling in SMOKE. SMOKE uses 

an ancillary file, called the PSTK file, which provides default stack parameters by SCC code to 

either gap fill stack parameters if they are missing in the NEI or to correct stack parameters if they 

are outside the ranges specified. 
19

 Replaced stack parameters with values from the 2008 NEI where 2008 values were determined to 

be more realistic. 

 Replaced facility emissions with 2008 NEI values where the 2009 NEI contained questionable 

values.

3.2.1.1 IPM Sector (ptipm) 

The ptipm sector contains emissions from EGUs in the 2009 NEI point inventory that could be matched to 

the units found in the NEEDS database, version 4.10 (http://www.epa.gov/airmarkets/progsregs/epa-ipm/

index.html). IPM provides future year emission inventories for the universe of EGUs contained in the 

NEEDS database. As described below, matching with NEEDS was done (1) to provide consistency 

between the 2009 EGU sources and future year EGU emissions for sources which are forecasted by IPM, 

and (2) to avoid double counting when projecting point source emissions. 

The 2009 NEI point source inventory contains emissions estimates for both EGU and non-EGU sources. 

When future years are modeled, IPM is used to predict the future year emissions for the EGU sources. The 

remaining non-EGU point sources are projected by applying projection and control factors to the base 

year emissions. It was therefore necessary to identify and separate into two sectors: (1) sources that are 

projected via IPM (i.e., the “ptipm” sector) and (2) sources that are not (i.e., “the “ptnonipm” sector). The 

two sectors are modeled separately in the base year as well as the future years. 

A primary reason the ptipm sources were separated from the other point sources was due to the difference 

in the temporal resolution of the data input to SMOKE. The ptipm sector uses the available hourly CEM 

data via a method first implemented in the 2002 platform and still used for the 2009 platform. Hourly 

CEM data for 2009 were obtained from the CAMD Data and Maps website3. For sources and pollutants 

with CEM data, the actual year 2009 hourly CEM data were used. The SMOKE modeling system matches 

the ORIS Facility and Boiler IDs in the NEI SMOKE-ready file to the same fields in the CEM data, 

thereby allowing the hourly SO2 and NOx CEM emissions to be read directly from the CEM data file. The 

heat input from the hourly CEM data was used to allocate the NEI annual values to hourly values for all 

other pollutants from CEM sources, because CEMs are not used to measure emissions of these 

pollutants. 

For this project, the point source inventory was reviewed to determine whether additional matches needed 

to be made. Newly identified matches for CEM and NEEDS IDs were loaded into the Emissions 

Inventory System (EIS) so they could then be written into the modeling files. Some matches were made 

outside of EIS when IDs were not mapped one to one between the systems. 

 

Emissions were scaled from 2008 levels to 2009 levels where possible based on CEM data, where 

possible. For sources not matching the CEM data (“non-CEM” sources), daily emissions were computed 

from the NEI annual emissions using a structured query language (SQL) program and state-average CEM 

data. To allocate annual emissions to each month, state-specific, three-year averages of 2008-2010 CEM 

data were created. These average annual- to-month factors were assigned to non-CEM sources by state. 

To allocate the monthly emissions to each day, the 2009 CEM data were used to compute state-specific 

month- to-day factors, which were then averaged across all units in each state. The resulting daily 

emissions were input into SMOKE. The daily-to-hourly allocation was performed in SMOKE using 

diurnal profiles. The development of these diurnal ptipm-specific profiles, considered ancillary data for 
20

SMOKE, is described in a later section. 

3.2.1.2 Non-IPM Sector (ptnonipm) 

The non-IPM (ptnonipm) sector contains all NEI point sources not included in the IPM (ptipm) sector 

except for the offshore oil and day-specific fire emissions. For the most part, the ptnonipm sector reflects 

the non-EGU component of the NEI point inventory; however, as previously discussed, it is likely that 

some small low-emitting EGUs that are not reflected in the CEMs database are present in the ptnonipm 

sector. The ptnonipm sector contains a small amount of fugitive dust PM emissions from vehicular traffic 

on paved or unpaved roads at industrial facilities or coal handling at coal mines. In previous versions of 

the platform, we would reduce these emissions prior to input to SMOKE. However, in this platform the 

reduction is not made because of a new methodology used to reduce PM dust. 

For some geographic areas, some of the sources in the ptnonipm sector belong to source categories that 

are contained in other sectors. This occurs in the inventory when states, tribes or local programs report 

certain inventory emissions as point sources because they have specific geographic coordinates for these 

sources. They may use point source SCCs (8-digit) or they may use non- point, onroad or nonroad (10-

digit) SCCs. In the 2008 NEI, examples of these types of sources include: aircraft and ground support 

emissions, livestock (i.e., cattle feedlots) in California, and rail yards. 

Some adjustments were made to the point inventory prior to its use in modeling. These include:

 Removing sources with state county codes ending in ‘777’. These are used for ‘portable’ point 

sources like asphalt plants.

 Removing sources with SCCs not typically used for modeling. 

 Adjusting latitude-longitude coordinates for sources identified to be substantially outside the 

county in which they reside.

 Removed all offshore oil records as reflected by FIPS=85000 because these sources are processed 

in the othpt sector.

 Added 2008 ethanol facilities provided by EPA’s OTAQ that were not already included in the 

2008 NEI.

 Corrected stack parameters for some units with missing or invalid parameter assignments.

 Added South Dakota emissions because they did not submit to the 2008 NEI.

 Added MeadWestVaco facility in Covington, VA because it was missing in the 2008 NEI.

 Added oil and gas emissions that were not otherwise included in the NEI from the Western 

Regional Air Partnership (WRAP) RPO created year 2006 “Phase III” oil and gas inventory 

project.

 Removed onroad refueling emissions that some states included in the point sector because these 

are modeled nationwide using MOVES2010b.

3.2.2 Nonpoint Sources (afdust, ag, nonpt) 

The nonpoint emissions sources used in this study are primarily from the 2008 NEI. Documentation for 

the 2008 NEI is available at http://www.epa.gov/ttn/chief/net/2008inventory.html#inventorydoc. Prior to 

modeling, the nonpoint portion of the 2008 NEI was divided into the following sectors for which the data 
21

3 3

3

3

is processed in consistent ways: area fugitive dust (afdust), agricultural ammonia (ag), and the other 

nonpoint sources (nonpt). This section describes stationary nonpoint sources only. Class 1 & Class 2 

(c1c2) and Class 3 (c3) commercial marine vessels and locomotives are also in the 2008 NEI nonpoint 

data category, but these sources are included in the mobile source portion of this documentation. 

Nonpoint tribal-submitted emissions were removed to prevent possible double counting with county-level 

emissions. Because the tribal nonpoint emissions are small, these omissions should not impact results at 

the 12-km scale used for modeling. This omission also eliminated the need to develop costly spatial 

surrogate data to allocate tribal data to grid cells during the SMOKE processing. Some specific types of 

nonpoint sources were not included in the modeling due to one of the following reasons: 1) the sources 

are only reported by a few states or agencies, 2) the sources are ‘atypical’ and small, and/or 3) there are 

other data available that appears to be more accurate. Additional details on nonpoint source processing 

can be found in the Version 5, 2007 Emissions Modeling Platform documentation discussed earlier. 

In the rest of this section, each of the platform sectors into which the 2008 nonpoint NEI was divided is 

described, along with any changes made to these data. 

3.2.2.1 Area Fugitive Dust Sector (afdust) 

The area-source fugitive dust (afdust) sector contains PM emission estimates for 2008 NEI nonpoint 

SCCs identified by EPA staff as fugitive dust sources. Categories included in this sector are paved roads, 

unpaved roads and airstrips, construction (residential, industrial, road and total), agriculture production 

and all of the mining 10-digit SCCs beginning with the digits “2325.” It does not include fugitive dust 

from grain elevators because these are elevated point sources. 

This sector is separated from other nonpoint sectors to allow for the application of “transport fraction,” 

and meteorology/precipitation (“MET”) reductions. These adjustments are applied via sector-specific 

scripts and make use of land use-based gridded transport fractions. The land use data used to reduce the 

NEI emissions explains the amount of emissions that are subject to transport. This methodology is 

discussed in (Pouliot, et. al., 2010), 

http://www.epa.gov/ttn/chief/conference/ei19/session9/pouliot_pres.pdf, and in Fugitive Dust Modeling 

for the 2008 Emissions Modeling Platform (Adelman, 2012). The precipitation adjustment is then 

applied to remove all emissions for days on which measureable rain occurs or there is snow on the 

ground. Both the transport fraction and MET adjustments are based on the gridded meteorological data; 

therefore, different emissions could result from different grid resolutions. Application of the transport 

fraction and MET adjustments reduces the overestimation of fugitive dust impacts in the grid modeling as 

compared to ambient samples. 

3.2.2.2 Agricultural Ammonia Sector (ag) 

The agricultural NH3 “ag” sector is comprised of livestock and agricultural fertilizer application emissions 

from the nonpoint sector of the 2008 NEI. The livestock and fertilizer emissions were extracted based on 

SCC. The “ag” sector includes all of the NH3 emissions from fertilizer contained in the NEI. However, 

the “ag” sector does not include all of the livestock ammonia emissions, as there are also some NH3

emissions from feedlot livestock in the point source inventory. To prevent double-counting, emissions 

were not included in the nonpoint ag inventory for counties in which they were in the point source 

inventory. A significant error in the 2008 NEI was corrected in the modeling platform ag sector. A 

fertilizer application source “N-P-K (multi-grade nutrient fertilizers)” (SCC=2801700010) in Luna 

county New Mexico (FIPS=35025), was 6,953 tons of NH3 in the 2008 NEI. This source was corrected 
22

by a factor of 1,000 to be 6.953 tons in the modeling platform. 

Monthly NH3 emissions provided by the Lake Michigan Air Directors Consortium were used to replace 

NEI ag sector emissions in that region due to the improved temporal resolution. 2008 NEI (annual) ag 

sector emissions were used in all other states. A new temporal allocation methodology for animal NH3 

was implemented for this modeling platform that allocates monthly emissions down to the hourly level by 

taking into account temperature and wind speed. This method is discussed in more detail in the emission 

modeling portion of this chapter. 

3.2.2.3 Other Nonpoint Sources (nonpt) 

Stationary nonpoint sources that were not subdivided into the afdust, ag or nonpt sectors were assigned to 

the “nonpt” sector. In preparing the nonpt sector, catastrophic releases were excluded since these 

emissions were dominated by tire burning, which is an episodic, location-specific emissions category. Tire 

burning accounts for significant emissions of particulate matter in some parts of the country. Because such 

sources are reported by a very small number of states, and are inventoried as county/annual totals without 

the information needed to temporally and spatially allocate the emissions to the time and location where 

the event occurred, catastrophic releases were excluded. All fire emissions, including agricultural, 

wildfire, and prescribed burning, were removed and substituted with SMARTFIRE emissions (see the 

“ptfire” sector). Locomotives and CMV mobile sources from the 2008 NEI nonpoint inventory are 

described in the mobile sources section. 

The nonpt sector includes emission estimates for Portable Fuel Containers (PFCs), also known as “gas 

cans.” The PFC inventory consists of five distinct sources of PFC emissions, further distinguished by 

residential or commercial use. The five sources are: (1) displacement of the vapor within the can; (2) 

spillage of gasoline while filling the can; (3) spillage of gasoline during transport; (4) emissions due to 

evaporation (i.e., diurnal emissions); and (5) emissions due to permeation. Note that spillage and vapor 

displacement associated with using PFCs to refuel nonroad equipment are included in the nonroad 

inventory. 

Some adjustments to the 2008 NEI nonpoint data were made using data from regional planning 

organizations (RPOs) as follows: 

 Replaced 2008 NEI oil and gas emissions (SCCs beginning with “23100”) with year 2006 Phase 

III oil and gas emissions for several basins in the WRAP RPO states. These WRAP Phase III 

emissions contain point and nonpoint formatted data are discussed in greater detail at: 

http://www.wrapair2.org/PhaseIII.aspx. These changes were made only in counties for which 

there was WRAP data. 

 Replaced 2008 NEI nonpoint agriculture burning emissions with year 2008 SMARTFIRE dayspecific county-based emissions aggregated to monthly totals. 

 Replaced open burning “land clearing” (SCC=2610000500) emissions in Florida and Georgia 

with SESARM-provided daily point data, but aggregated to county and monthly resolution. 

 Replaced open burning data (SCCs beginning with 261000x) in MARAMA states with RPOproved data. 

 Removed industrial coal combustion emissions (SCC=2102002000) in Tennessee. 

 Replaced, removed and modified much of the residential wood combustion (RWC) emissions in 
23

the MARAMA, MWRPO and SESARM states with RPO data and non-RPO corrections, 

modified the outdoor hydronic heater (OHH) emissions in all states and indoor furnaces in 

MWRPO states. 

 Removed EPA-estimated commercial cooking (SCCs 2302002100 and 2302002200) duplicate 

PM emissions in California. 

 Removed duplicate “Industrial Processes; Food and Kindred Products; Total” source 

(SCC=23020000000) in Maricopa county Arizona (FIPS=04013). 

The oil and gas changes were already discussed in the ptnonipm section. Other significant changes are 

discussed below. 

Ag burning

2008 NEI agricultural burning estimates were replaced with more specific data from the Fire 

Characteristic Classification System (FCCS) module fuel loadings map in the BlueSky Framework 

(http://blueskyframework.org/modules/fuel-loading/fccs). Year 2008-specific fire locations from 

SMARTFIRE version 1 (Sullivan, et al., 2008) were read into the FCCS module and intersected with the 

FCCS fuel-loading dataset. The module assigned an FCCS code to each fire record that reflects the 

ecosystem geography and potential natural vegetation based on remote sensing data. Prescribed or 

unclassified fires having an FCCS code equal to zero (0) were assumed to be agricultural fires. Arc GIS 

was used to categorize the fires as occurring on rangeland, cropland or other land use via USGS 2006 

National Land Cover Database (NLCD). Activity data were analyzed to restrict to cropland fires and 

assign state and crop-specific emission factors. Emissions were then appropriately weighted based on 

known statistics about each state’s crop mix. 

These SMARTFIRE-based ag burning emissions were provided in at 1km point source and day-specific 

resolution. State-county FIPS codes were assigned using GIS. The emissions were aggregated to county 

and monthly resolution and converted to SMOKE nonpoint FF10 format. This SMARTFIRE-based ag 

burning dataset includes emissions for all but these 7 of the lower 48 states: CT, DC, MA, ME, NH, RI 

and VT. These 7 states did not contain any cropland burning estimates for year 2008 based on this 

SMARTFIRE approach. 

Open burning RPO data

All 2008 NEI open burning emissions (CAPs only) were replaced in the MARAMA states with the 2007 

MARAMA open burning inventory. These MARAMA open burning emissions include estimates for 

household waste (SCC=2610030000), land clearing (2610000500) and yard waste leaf and brush 

(2610000100 and 2610000400 respectively). 

The 2008 NEI land clearing emissions in Georgia and Florida were replaced with SESARM-based year2007 data. The SESARM land clearing emissions are based on daily point emissions from the 

CONSUME v3.0 model (SESARM, 2012a). These daily point-format emissions were aggregated to 

county and monthly resolution as a separate FF10 nonpoint monthly inventory. 

TN coal combustion

Tennessee nonpoint industrial coal combustion (SCC=2102002000) emissions are significantly 

overestimated in the 2008 NEI because of incorrect reconciliation with the point source inventory. 

Nonpoint industrial coal combustion emissions were estimated by subtracting point source emissions 

rather than activity. By not accounting for controlled sources, the remaining activity for nonpoint coal 
24

combustion is significantly overestimated. EPA NEI experts determined that it would be more 

appropriate to completely remove the nonpoint component of this sector than to leave the values as they 

were. The reality for TN industrial coal combustion nonpoint sector emissions is likely much closer to 

zero than the value in the 2008 NEI because these emissions are accounted for in the point source 

inventory. 

Residential Wood Combustion

There were many modifications to the RWC emissions data. First, all RWC outdoor wood burning 

devices such as “fire pits and chimeas” (SCC=2104008700) were removed because they were only 

reported in a couple of states, RPO inventories did not include them for most states and emissions were 

generally insignificant. A market research report (Frost and Sullivan, 2010) developed in support of the 

potential RWC New Source Performance Standard (NSPS) indicated slower sales of outdoor hydronic 

heaters compared to what was assumed for growth estimates in the 2008 NEI. Therefore, outdoor 

hydronic heater appliance counts and emissions estimates (SCC=2104008610) were recomputed for all 

states, resulting in a 51% reduction to outdoor hydronic heater emissions for all states. 

In addition, all emissions in the SESARM states (i.e., AL, FL, GA, KY, MS, NC, SC, TN, VA, WV), 

including Virginia, were replaced with the SESARM year-2007 inventory (SESARM, 2012b). Urban area 

RWC were lower than the NEI estimates partially because of the assumptions about greater penetration of 

natural gas fireplaces, less access to inexpensive wood supplies and a lower proportion of housing units 

with wood burning appliances as primary heating units than rural areas. Overall, the SESARM RWC 

estimates are considerably lower than the 2008 NEI estimates for several states, particularly for 

“uncertified” and “general” wood stoves and insert categories: FL, KY, NC, TN, VA and WV. However, 

emissions in Mississippi are only slightly reduced and emissions in AL, GA and SC are very similar to 

those in the 2008NEIv2. 

The Midwest RPO (LADCO) states (i.e., IL, IL, MI, OH, WI, MN) year-2007 RWC inventory was 

similar to the 2008 NEI for most source types. However, the pellet stoves (SCC=2104008400), indoor 

furnaces (2104008510), and outdoor hydronic heater (OHH, SCC=2104008610) estimates were updated 

to reallocate the indoor furnaces and OHHs to non-MSA counties (LADCO, 2012) for several urban 

areas. Some double counting of appliances was also fixed in Wisconsin and Michigan. Overall, the 

MWRPO states totals are very similar to the 2008 NEI; however, emissions are spatially redistributed 

from urban to rural areas. Therefore, for the MWRPO states, the 2008 NEI emissions were used for all 

RWC sources except the three aforementioned SCCs that use the 2007 MWRPO data. 

Emissions from indoor wood fired furnaces (SCC=2104008510) in several MWRPO states based were 

also recomputed based on newer, improved survey data from Minnesota. The 2008 NEI for these sources 

started with an assumption of year 2002 Minnesota wood burning survey data of 38 indoor furnaces per 

100 woodstoves for Illinois, Indiana, Michigan, Ohio, and Wisconsin. More recent year 2007 MN survey 

data resulted in the much lower ratio of 7.3 indoor furnaces per 100 wood stove units. Thus, for the other 

five MWRPO states previously listed, the indoor furnace emissions are normalized by setting the indoor 

furnace count ratio to wood stoves to match the 7.6% reported value in Minnesota. The resulting 

adjustment factors reduce the indoor furnace emissions in these states by 67% (Wisconsin) to as much as 

83% in Ohio. 

The MARAMA states (i.e., CT, DE, DC, ME, MD, MA, NH, NJ, NY, PA, RI, VT) year 2007 RWC 
25

inventory was either unchanged from the 2008 NEI, or was missing for most states. The exceptions were 

New York and Pennsylvania which includes significantly revised RWC estimates compared to the 2008 

NEI. For New York, the MARAMA estimates were not split out into the refined set of 10 RWC 

appliance types/SCCs in the NEI. New York only reported “general” fireplaces (SCC=2104008100) and 

“EPA certified, non-catalytic” woodstoves (SCC=2104008320). However, similar to the SESARM and 

MWRPO improvements, the MARAMA NY RWC estimates were spatially reallocated from urban to 

more rural areas and were also lower state-wide than the NEI. For Pennsylvania, MARAMA RWC 

estimates were not much different state-wide on the aggregate, but were refined by SCC and spatially 

compared to the 2008 NEI. Therefore, the MARAMA 2007 RWC data is used for New York and 

Pennsylvania and the 2008 NEI emissions are used for all RWC sources in the rest of the MARAMA 

states. 

The uniform temporalization from month to day was modified to be day-of-year specific as discussed in 

more detail in the emissions modeling section. In short, the SMOKE program (GenTPRO) is used to 

distribute annual RWC emissions to the coldest days of the year, using maximum temperature thresholds 

by-state and/or by-county. On days where the low temperature does not drop below this threshold, RWC 

emissions are zero. Conversely, the program temporally allocates the most relative emissions to the 

coldest days. This meteorological-based temporal allocation can have a substantial impact on the amount 

of RWC emissions in an area on any given day. 

3.2.4 Day-Specific Point Source Fires (ptfire) 

Wildfire and prescribed burning emissions are contained in the ptfire sector. The ptfire sector has 

emissions provided at geographic coordinates (point locations) and has daily estimates of the emissions 

from each fires value. The ptfire sector for the 2009 Platform excludes agricultural burning and other open 

burning sources, which are included in the nonpt sector. The agricultural burning and other open burning 

sources are in the nonpt sector because these categories were not factored into the development of the 

ptfire sector. Additionally, their year-to-year impacts are not as variable as wildfires and non-agricultural 

prescribed/managed burns. 

The ptfire sector includes a satellite derived latitude/longitude of the fire’s origin and other parameters 

associated with the emissions such as acres-burned and fuel load, which allow estimation of plume rise. 

Note that agricultural burning is not included in the ptfire sector but is included in the nonpt sector. The 

point source day-specific emission estimates for 2009 fires rely on the Satellite Mapping Automated 

Reanalysis Tool for Fire Incident Reconciliation Version 2 (SMARTFIRE2) system (Raffuse, et al., 

2012). Activity data was used from the Monitoring Trends in Burn Severity (MTBS) project, Incident 

Command Summary Reports (ICS-209), and the National Oceanic and Atmospheric Administration’s 

(NOAA’s) Hazard Mapping System (HMS). 

The method involves the reconciliation of ICS-209 reports (Incident Status Summary Reports) with 

satellite-based fire detections to determine spatial and temporal information about the fires. The ICS-209 

reports for each large wildfire are created daily to enable fire incident commanders to track the status and 

resources assigned to each large fire (100 acre timber fire or 300 acre rangeland fire). The SMARTFIRE 

system of reconciliation with ICS-209 reports is described in an Air and Waste Management Association 

report (Raffuse, et al., 2007). Once the fire reconciliation process is completed, the emissions are 

calculated using the U.S. Forest Service’s CONSUMEv3.0 fuel consumption model and the FCCS fuelloading database in the BlueSky Framework (Ottmar, et. al., 2007). The detection of fires with this 
26

method is satellite-based. Additional sources of information used in the fire classification process 

included MODIS satellite and fuel moistures derived from fire weather observational data. 

The ICS-209 reports for each large wildfire are created daily to enable fire incident commanders to track 

the status and resources assigned to each large fire (100 acre timber fire or 300 acre rangeland fire). Note 

that the distinction between wildfire and prescribed burn is not as precise as with ground-based methods. 

The fire size was based on the number of satellite pixels and a nominal fire size of 100 acres/pixel was 

assumed for a significant number of fire detections when the first detections were not matched to ICS 209 

reports, so the fire size information is not as precise as ground-based methods. 

The activity data and other information were used within the BlueSky Framework to model vegetation 

distribution, fuel consumption, and emission rates, respectively. Latitude and longitude locations were 

incorporated as a post processing step. The method to classify fires as WF, WFU, RX (FCCS > 0), and 

unclassified (FCCS > 0) involves the reconciliation of ICS-209 reports (Incident Status Summary 

Reports) with satellite-based fire detections to determine spatial and temporal information about the fires. 

Because the HMS satellite product from NOAA is based on daily detections, the emission inventory 

represents a time-integrated emission estimate. For example, a large smoldering fire will show up on 

satellite for many days and would count as acres burned on a daily basis; whereas a ground-based method 

would count the area burned only once even it burns over many days. 

The SMOKE-ready “ORL” inventory files created from the raw daily fires contain both CAPs and HAPs. 

The BAFM HAP emissions from the inventory were obtained using VOC speciation profiles (i.e., a “nointegrate noHAP” use case). The BEIS3.14 model creates gridded, hourly, model-species emissions from 

vegetation and soils. It estimates CO, VOC, and NOX emissions for the U.S., Mexico, and Canada. The 

BEIS3.14 model is described further in 

http://www.cmascenter.org/conference/2008/slides/pouliot_tale_two_ cmas08.ppt. Additional references 

for this method are provided in (McKenzie, et al., 2007), (Ottmar, et al., 2003), (Ottmar, et al., 2006), and 

(Anderson et al., 2004). 

3.2.5 Biogenic Sources (beis) 

For CMAQ, biogenic emissions were computed with the BEIS3.14 model within SMOKE using 2009 

meteorological data. The BEIS3.14 model creates gridded, hourly, model-species emissions from vegetation 

and soils. It estimates CO, VOC (most notably isoprene, terpine, and sesquiterpene), and NO emissions for the 

U.S., Mexico, and Canada. The BEIS3.14 model is described further in: 

http://www.cmascenter.org/conference/2008/slides/pouliot_tale_two_cmas08.ppt. 

The inputs to BEIS include:

 Temperature data at 2 meters from the CMAQ meteorological input files,

 Land-use data from the Biogenic Emissions Landuse Database, version 3 (BELD3) that provides

data on the 230 vegetation classes at 1-km resolution over most of North America. 

3.2.6 Mobile Sources (onroad, onroad_rfl, nonroad, c1c2rail, c3marine) 

The 2009 onroad emissions are broken out into two sectors: “onroad” and “onroad_rfl”. Aircraft 
27

emissions are in the nonEGU point inventory. The locomotive and commercial marine emissions are 

divided into two sectors: “c1c2rail” and “c3marine”, and the “nonroad” sector contains the remaining 

nonroad emissions. Note that the 2008 NEI includes state-submitted emissions data for nonroad, but the 

modeling performed for this platform does not incorporate state-submitted emissions for the onroad or 

nonroad sectors, except for California. All tribal data from the mobile sectors have been dropped because 

we do not have spatial surrogate data, and the emissions are small. 

The onroad and onroad_rfl sectors are processed separately to allow for different spatial allocation to be 

applied to onroad refueling via a gas station surrogate, versus onroad vehicles that are spatially allocated 

based on roads and population. Except for California, all onroad and onroad refueling emissions are 

generated using the SMOKE-MOVES emissions modeling framework that leverages MOVES2010bgenerated outputs (http://www.epa.gov/otaq/models/moves/index.htm) and hourly meteorology. 

Emissions for onroad (including refueling), nonroad and c1c2rail sources in California were provided by 

the California Air Resources Board (CARB). 

The nonroad sector is based on NMIM except for California which uses data provided by the California 

Air Resources Board (CARB). NMIM (EPA, 2005) creates the nonroad emissions on a month-specific 

basis that accounts for temperature, fuel types, and other variables that vary by month. The 2009 NMIM 

nonroad emissions were generated using updated activity (fuels, vehicle population, etc) data, but are 

otherwise similar in methodology to those generated for the 2005 NEI. All nonroad emissions are 

compiled at the county/SCC level. Detailed inventory documentation for the 2008 NEI nonroad sectors is 

available at http://www.epa.gov/ttn/chief/net/2008inventory.html#inventorydoc. Neither NMIM nor 

MOVES generates tribal data. 

The locomotive and commercial marine vessel (CMV) emissions are divided into two nonroad sectors: 

“c1c2rail” and “c3marine”. The c1c2rail sector includes all railway and most rail yard emissions as well 

as the gasoline and diesel-fueled Class 1 and Class 2 CMV emissions. The c3marine sector emissions 

contain the larger residual fueled ocean-going vessel Class 3 CMV emissions and are treated as point 

emissions with an elevated release component; all other nonroad emissions are treated as county-specific 

low-level emissions (i.e., are in model layer 1). The 2008 NEI c3marine emissions were replaced with a 

set of approximately 4-km resolution point source format emissions. These data are used for all states, 

including California, as well as offshore and international emissions within our air quality modeling 

domain, and are modeled separately as point sources in the “c3marine” sector. 

3.2.7 Onroad non-refueling (onroad) 

For the Version 5 modeling platform, EPA estimated emissions for every county in the continental U.S. 

except for California using similar methods as for the 2008 NEI Versions 2 and 3. The modeling 

framework took into account the strong temperature sensitivity of the onroad emissions. Specifically, 

county-specific inputs and tools were used that integrated the MOVES model with the SMOKE emission 

inventory model to take advantage of the gridded hourly temperature information available from 

meteorology modeling used for air quality modeling. This integrated “SMOKE-MOVES” tool was 

developed by EPA in 2010 and is in use by states and regional planning organizations for regional air 

quality modeling. SMOKE-MOVES requires emission rate “lookup” tables generated by MOVES that 

differentiate emissions by process (running, start, vapor venting, etc.), vehicle type, road type, 

temperature, speed, hour of day, etc. 
28

To generate the MOVES emission rates that could be applied across the U.S., EPA used an automated 

process to run MOVES to produce emission factors by temperature and speed for 146 “representative 

counties,” to which every other county could be mapped as detailed below. Using the MOVES emission 

rates, SMOKE selected appropriate emissions rates for each county, hourly temperature, SCC, and speed 

bin and multiplied the emission rate by activity (i.e., VMT (vehicle miles travelled) or vehicle 

population) to produce emissions. These calculations were done for every county, grid cell, and hour in 

the continental United States. SMOKE-MOVES can be used with different versions of the MOVES 

model. For the Version 5 modeling platform, EPA used the latest publically released version: 

MOVES2010b (http://www.epa.gov/otaq/models/moves/index.htm). The MOVES default database used 

was named movesdb20120410. 

Using SMOKE-MOVES for creating emissions for modeling requires numerous steps, as described in the 

sections below: 

 Determine which counties will be used to represent other counties in the MOVES runs. 

 Determine which months will be used to represent other month’s fuel characteristics. 

 Create MOVES inputs needed only for MOVES runs. MOVES requires county-specific 

information on vehicle populations, age distributions, and inspection-maintenance programs for 

each of the representative counties. 

 Create inputs needed both by MOVES and by SMOKE, including a list of year-specific 

temperatures and activity data. 

 Run MOVES to create emission factor tables using year-specific fuel information. 

 Run SMOKE to apply the emission factors to activities to calculate emissions. 

 Aggregate the results at the county-SCC level for summaries and quality assurance. 

Some data used in the SMOKE-MOVES process is year-specific. When MOVES was run to generate the 

emission factors, gasoline and diesel properties for representing counties were based on 2009 fuel 

information (i.e., RegionalFuels_2009_20120323). The temperature and humidity inputs were also based 

on 2009 values. The VMT used by SMOKE-MOVES was generated by taking 2009 VMT by state and 

freeway/non-freeway from FHWA VM-2 tables and allocating to county and month and roadtype using 

the 2008 NEI VMT. The VMT was allocated to vehicle type using FHWA’s VM-4 table and to MOVES 

sourcetype using ratios from MOVES. Vehicle populations were then generated by applying 

VMT/vehicle default ratios from MOVES to the VMT. The same speed data used for the 2008 NEI were 

also used for this study. 

The California emissions were post-processed to incorporate both CARB supplied inventories and the 

shape of the meteorologically-based SMOKE-MOVES results by scaling the SMOKE-MOVES 

generated totals to match CARB-provided totals. Because CARB provide 2007 and 2011 emissions data, 

the data for 2009 were linearly interpolated between 2007 and 2011 levels. For more details on this 

process, see the Version 5 platform documentation. 

3.2.8 Onroad Refueling (onroad_rfl) 

Onroad refueling was modeled very similarly to the other onroad emissions. MOVES2010b was used 
29

produce emission factors (EFs) for refueling. These EFs are at the resolution of the onroad SCC and 

were run separately from the other onroad mobile sources to allow for different spatial allocation. To 

facilitate this, the EFs were separated into refueling and non-refueling tables. SMOKE-MOVES was 

then run using these EF tables as inputs and the results spatially allocated based on a gas stations spatial 

surrogate. For California, the SMOKE-MOVES generated emissions were used for onroad refueling 

without any adjustments because there were no CARB-supplied refueling emissions. 

3.2.9 Nonroad Mobile Sources — NMIM-Based (nonroad) 

The nonroad sector includes monthly exhaust, evaporative and refueling emissions from nonroad engines 

(not including commercial marine, aircraft, and locomotives) that are derived from NMIM for all states 

except California. NMIM 20090504 was run using 2009 meteorological and fuel data to create countySCC emissions by month for the 2009 nonroad mobile CAP and HAP sources. This version of NMIM ran 

the NR08a version of NONROAD. The nonroad county database was labeled 20101201_2009. The run 

incorporated Bond rule revisions to some of the base case inputs and the Bond Rule controls did not take 

effect until future years. NMIM provides nonroad emissions for VOC by three emission modes: exhaust, 

evaporative and refueling. Unlike the onroad sector, refueling emissions from nonroad sources are not 

separated into a different sector. 

EPA default inputs were replaced by state inputs where such data were provided via the 2008 NEI process. 

The 2008 NEI documentation describes this and other details of the NMIM nonroad emissions 

development. CAPs and only the necessary HAPs for the nonroad sector (i.e., BAFM, butadiene, and 

naphthalene) were included. For this study, NMIM was run separately for each county. To aid with the 

processing by SMOKE, the mode was appended to the pollutant name and the California NMIM data was 

replaced with state-supplied data. 

For California, year 2009 nonroad emissions values were interpolated between the 2007 and 2011 

emissions provided by CARB. The CARB-supplied nonroad annual inventory to monthly emissions 

values by using the aforementioned EPA NMIM monthly inventories to compute monthly ratios by 

pollutant and SCC. Some adjustments to the CARB inventory were needed to convert the provided total 

organic gas (TOG) to the VOC that was needed by SMOKE. 

3.2.10 Nonroad Mobile Sources: Commercial Marine C1, C2, and Locomotive (c1c2rail) 

The c1c2rail sector contains CAP and HAP emissions from locomotive and commercial marine sources, 

except for the category 3/residual-fuel (C3) commercial marine vessels (CMV) found in the c3marine 

sector. The “c1c2” portion of this sector name refers to the Class I/II CMV emissions, not the railway 

emissions. Railway maintenance emissions are included in the nonroad sector because these are included 

in the nonroad NMIM monthly inventories. The C3 CMV emissions are in the c3marine sector. Except 

for California, the emissions in the c1c2rail sector are year 2008 and are composed of the following 

SCCs: 2280002100 (CMV diesel, ports), 2280002200 (CMV diesel, underway), 2285002006 

(locomotives diesel line haul Class I), 2285002007 (locomotives diesel line haul Class II/III), 

2285002008 (locomotives diesel line haul passenger trains), 2285002009 (locomotives diesel line haul 

commuter lines), and 2285002010 (locomotives diesel, yard). 

The 2008 NEI Version 2 was the starting point for this sector, but several adjustments were made. First, 
30

the 2008 NEI point inventory contains rail yard emissions for several states and counties. The NEI point 

and nonpoint inventories were reviewed for counties with significant rail yard emissions in both 

inventories. It was assumed that the point inventory contained more accurate information when both 

inventories contained rail yard emissions. Therefore, nonpoint rail yards were removed from the c1c2rail 

sector for certain counties in California, Maryland, Oregon and Arizona. For more information, see the 

Version 5 2007 platform documentation. 

Analysis of the total rail emissions in the 2008 NEI showed what appeared to be missing rail line 

emissions in Texas. It was determined that line haul emissions from Texas were essentially zero in the 

2008 NEI. Therefore, all line haul emissions from the 2008 NEI were removed and information from an 

EPA default dataset of Texas line haul emissions was added. These EPA line haul emissions are 

restricted to the Class I and Class II/III operations and add approximately 52,000 tons of NOX to Texas 

that would otherwise be missing. 

For several Texas counties, the C1/C2 CMV emissions in the 2008 NEI included EPA gap filled values 

where shape IDs were not populated on submittal. The intended Texas submittal was often much smaller 

than the EPA-estimated default value for several counties. An example of this is Harris county 

(FIPS=48201) where the Texas submittal was approximately 1,200 tons of NOX for port and underway 

emissions but not all shape IDs were included. The NEI methodology used EPA emissions where Texas 

did not provide estimates and the resulting double count and overestimate of this top-down method 

resulted in over 49,000 tons of NOX in the 2008 NEI in Harris County, Texas. Therefore, the modeling 

platform used the original Texas submittal, did not append any EPA emissions, and summed up port and 

underway for the modeling files to the county level. Similar corrections to these may have been included 

in Version 3 of the 2008 NEI. Other states were impacted by a similar error in the 2008 NEI Version 2, 

but for many of these states alternative data were used as discussed below. 

For California, the California Air Resources Board (CARB) provided year 2007 and 2011 emissions for 

all mobile sources, including C1/C2 CMV and rail. These emissions are documented in a staff report 

available at: http://www.arb.ca.gov/regact/2010/offroadlsi10/offroadisor.pdf. The modeling platform uses 

2009 emissions interpolated between the 2007 and 2011 emissions. The C1/C2 CMV emissions were 

obtained from the CARB nonroad mobile dataset and include the regulations to reduce emissions from 

diesel engines on commercial harbor craft operated within California waters and 24 nautical miles of the 

California baseline. These emissions were developed using Version 1 of the CEPAM that supports 

various California off-road regulations. The locomotive emissions were obtained from the CARB trains 

dataset “ARMJ_RF#2002_ANNUAL_TRAINS.txt”. Documentation of the CARB offroad mobile 

methodology, including c1c2rail sector data, is provided here: 

http://www.arb.ca.gov/msei/categories.htm#offroad_motor_vehicles. The CARB inventory TOG 

emissions were converted to VOC by dividing the inventory TOG by the available VOC-to-TOG 

speciation factor. 

Year-2007 inventories provided by MARAMA, SESARM and the MWRPO were used for the c1c2rail 

sector emissions in their respective states. Emissions data from MARAMA rather than SESARM was 

used for Virginia because the SESARM data included some rather large emissions for Commuter Lines 

(SCC=2285002009) that were not reflected in the 2008 NEI nor the MARAMA dataset. The MWRPO 

year-2007 c1c2rail data were obtained from a subset of their version 7 emissions modeling file 

“nrinv.mwrpo_alm.baseCv7.annual.orl.txt”, where MWRPO NEI Inventory Format (NIF)-formatted data 
31

were converted to SMOKE ORL format. The MARAMA dataset was obtained from a subset of their 

version 3.3 January 27, 2012 vintage file “ARINV_2007_MAR_Jan2012.txt”. The SESARM dataset 

was obtained from a subset of the file “nrinv.alm.semap.base07.v093010.orl.txt” developed for the 

Southeastern Modeling, Analysis, and Planning (SEMAP) project. All RPO datasets were edited to 

remove non-c1c2rail sources. 

3.2.11 Nonroad mobile sources: C3 commercial marine (c3marine) 

The c3marine sector emissions data were developed based on a 4-km resolution ASCII raster format 

dataset used since the Emissions Control Area-International Marine Organization (ECA-IMO) project 

began in 2005, then known as the Sulfur Emissions Control Area (SECA). These emissions consist of 

large marine diesel engines (at or above 30 liters/cylinder) that until very recently, were allowed to meet 

relatively modest emission requirements, often burning residual fuel. The emissions in this sector are 

comprised of primarily foreign-flagged ocean-going vessels, referred to as Category 3 (C3) CMV ships. 

The c3marine inventory includes these ships in several intra-port modes (cruising, hoteling, reduced 

speed zone, maneuvering, and idling) and underway mode and includes near-port auxiliary engines. An 

overview of the C3 ECA Proposal to the International Maritime Organization (EPA-420-F-10-041, 

August 2010) project and future-year goals for reduction of NOX, SO2, and PM C3 emissions can be 

found at: http://www.epa.gov/oms/regs/nonroad/marine/ci/420r09019.pdf. The resulting ECA-IMO 

coordinated strategy, including emission standards under the Clean Air Act for new marine diesel engines 

with per-cylinder displacement at or above 30 liters, and the establishment of Emission Control Areas is 

at: http://www.epa.gov/oms/oceanvessels.htm. 

The ECA-IMO emissions data were converted to SMOKE point-source ORL input format as described in 

http://www.epa.gov/ttn/chief/conference/ei17/session6/mason.pdf, thereby allowing for the emissions to 

be allocated to modeling layers above the surface layer. As described in the paper, the ASCII raster 

dataset was converted to latitude-longitude, mapped to state/county FIPS codes that extended up to 200 

nautical miles (nm) from the coast, assigned stack parameters, and monthly ASCII raster dataset 

emissions were used to create monthly temporal profiles. Counties were assigned as extending up to 

200nm from the coast because this was the distance to the edge of the U.S. Exclusive Economic Zone 

(EEZ), a distance that defines the outer limits of ECA-IMO controls for these vessels. All non-US 

emissions (i.e., in waters considered outside of the 200nm EEZ, and hence out of the U.S. territory) are 

assigned a dummy state/county FIPS code=98001. The SMOKE-ready data were cropped from the 

original ECA-IMO data to cover only the 36-km CMAQ domain, which is the largest domain used for this 

effort, and larger than the 12km domain used in this project. 

The base year ECA inventory is 2002 and consists of these CAPs: PM10, PM2.5, CO, CO2, NH3, NOX, 

SOX (assumed to be SO2), and Hydrocarbons (assumed to be VOC). The EPA developed regional 

growth (activity-based) factors that we applied to create the 2007v5 inventory from the 2002 data. These 

growth factors are provided in Table 3-4. The East Coast and Gulf Coast regions were divided along a 

line roughly through Key Largo (longitude 80° 26’ West). 

 
32

Table 3-4. Growth factors to project the 2002 ECA inventory to 2009 

Region EEZ FIPS NOX PM10 PM2.5 VOC CO SO2

East Coast (EC) 85004 1.284 1.374 1.376 1.374 1.374 1.374

Gulf Coast (GC) 85003 1.137 1.217 1.214 1.216 1.217 1.217

North Pacific (NP) 85001 1.193 1.268 1.250 1.268 1.268 1.268

South Pacific (SP) 85002 1.334 1.429 1.427 1.417 1.415 1.434

Great Lakes (GL) n/a 1.108 1.137 1.137 1.138 1.137 1.137

Outside ECA 98001 1.252 1.338 1.338 1.338 1.338 1.338

A modification to the original ECA-IMO c3marine dataset include updating the state of Delaware county 

total emissions to reflect comments received during the Cross-State Air Pollution Rule (CSAPR) 

emissions modeling platform development: http://www.epa.gov/ttn/chief/emch/index.html#final. The 

original ECA-IMO inventory also did not delineate between ports and underway (or other C3 modes such 

as hoteling, maneuvering, reduced-speed zone, and idling) emissions; however, we used a U.S. ports 

spatial surrogate dataset to assign the ECA-IMO emissions to ports and underway SCCs - 2280003100 

and 2280003200, respectively. This has no effect on temporal allocation or speciation because all C3 

emissions, unclassified/total, port and underway, share the same temporal and speciation profiles. 

Canadian near-shore emissions were assigned to province-level FIPS codes and paired those to region 

classifications for British Columbia (North Pacific), Ontario (Great Lakes) and Nova Scotia (East Coast). 

The assignment of U.S. FIPS was also restricted to state-federal water boundaries data from the Mineral 

Management Service (MMS) that extended only (approximately) 3 to 10 miles off shore. Emissions 

outside the 3 to 10 mile MMS boundary but within the approximately 200 nm EEZ boundary in Figure 2 

8 were projected to year 2009 using the same regional adjustment factors as the U.S. emissions; however, 

the FIPS codes were assigned as “EEZ” FIPS. Note that state boundaries in the Great Lakes are an 

exception, extending through the middle of each lake such that all emissions in the Great Lakes are 

assigned to a U.S. county or Ontario. The classification of emissions to U.S. and Canadian FIPS codes is 

primarily needed only for inventory summaries and is irrelevant for air quality modeling except 

potentially for source apportionment of states contributions to transport. 

Factors were applied to compute HAP emissions (based on emissions ratios) to VOC to obtain HAP 

emissions values. Table 3-5 below shows these factors. Because HAPs were computed directly from the 

CAP inventory and the calculations are therefore consistent, the entire c3marine sector utilizes CAP-HAP 

VOC integration to use the VOC HAP species directly, rather than VOC speciation profiles. 

Table 3-5. HAP emission ratios for generation of HAP emissions from criteria emissions for C3 

commercial marine vessels 

Pollutant Apply to 

Pollutant 

Code Factor 

Acetaldehyde VOC 75070 0.0002286 

Benzene VOC 71432 9.80E-06 

Formaldehyde VOC 50000 0.0015672 
33

3.2.12 Emissions from Canada, Mexico and Offshore Drilling Platforms (othpt, othar, othon) 

The emissions from Canada, Mexico, and offshore drilling platforms are included as part of three 

emissions modeling sectors: othpt, othar, and othon. The “oth” refers to the fact that these emissions are 

usually “other” than those in the U.S. state-county geographic FIPS code, and the third and fourth 

characters provide the SMOKE source types: “pt” for point, “ar” for “area and nonroad mobile”, and 

“on” for onroad mobile. All “oth” emissions are CAP-only inventories. 

For Canada, year-2006 Canadian emissions were used but several modifications were applied to the 

inventories: 

1. Wildfires or prescribed burning were not included because Canada does not include these 

inventory data in their modeling. 

2. In-flight aircraft emissions were not included because we do not include these for the U.S. and we 

do not have a finalized approach to include in our modeling. 

3. A 75% reduction (“transport fraction”) was applied to PM for the road dust, agricultural, and 

construction emissions in the Canadian “afdust” inventory. This approach is more simplistic than 

the county-specific approach used for the U.S., but a comparable approach was not available for 

Canada. 

4. Speciated VOC emissions from the ADOM chemical mechanism were not included because we 

use speciated emissions from the CB5 chemical mechanism that Canada also provided. 

5. Residual fuel CMV (C3) SCCs (22800030X0) were removed because these emissions are 

included in the c3marine sector, which covers not only emissions close to Canada but also 

emissions far at sea. Canada was involved in the inventory development of the c3marine sector 

emissions. 

6. Wind erosion (SCC=2730100000) and cigarette smoke (SCC=2810060000) emissions were 

removed from the nonpoint (nonpt) inventory; these emissions are also absent from our U.S. 

inventory. 

7. Quebec PM2.5 emissions (2,000 tons/yr) were removed for one SCC (2305070000) for Industrial 

Processes, Mineral Processes, Gypsum, and Plaster Products due to corrupt fields after conversion 

to SMOKE input format. This error should be corrected in a future inventory. 

8. Excessively high CO emissions were removed from Babine Forest Products Ltd (British 

Columbia SMOKE plantid=’5188’) in the point inventory. 

9. The county part of the state/county FIPS code field in the SMOKE inputs were modified in the 

point inventory from “000” to “001” to enable matching to existing temporal profiles. 

For Mexico, year 2008 emissions were used that are projections of their 1999 inventory originally 

developed by Eastern Research Group Inc., (ERG, 2006) as part of a partnership between Mexico's 

Secretariat of the Environment and Natural Resources (Secretaría de Medio Ambiente y Recursos 

Naturales-SEMARNAT) and National Institute of Ecology (Instituto Nacional de Ecología-INE), the 

U.S. EPA, the Western Governors' Association (WGA), and the North American Commission for 

Environmental Cooperation (CEC). This inventory includes emissions from all states in Mexico. A 

background on the development of year-2008 Mexico emissions from the 1999 inventory is available at: 

http://www.wrapair.org/forums/ef/inventories/MNEI/index.html. 
34

The offshore emissions include point source offshore oil and gas drilling platforms. We used emissions 

from the 2008 NEI point source inventory. The offshore sources were provided by the Mineral 

Management Services (MMS). 

3.2.13 SMOKE-ready non-anthropogenic chlorine inventory 

The ocean chlorine gas emission estimates are based on the build-up of molecular chlorine (Cl2) 

concentrations in oceanic air masses (Bullock and Brehme, 2002). Data at 36 km and 12 km resolution 

were available and were not modified other than the name “CHLORINE” was changed to “CL2” because 

that is the name required by the CMAQ model. The same data was used as in the CAP and HAP 2002-

based Platform was used. See ftp://ftp.epa.gov/EmisInventory/2002v3CAPHAP/ documentation for 

additional details.

3.3   Emissions Modeling Summary

CMAQ requires emissions data to be input as hourly rates of specific gas and particle species for the 

horizontal and vertical grid cells contained within the modeled region (i.e., modeling domain). To 

provide emissions in the form and format required by the model, it is necessary to “pre-process” the 

“raw” emissions (i.e., emissions input to SMOKE) for the sectors described above. In brief, the process 

of emissions modeling transforms the emissions inventories from their original temporal resolution, 

pollutant resolution, and spatial resolution into the hourly, speciated, gridded resolution required by the 

air quality model. The pre-processing steps involving temporal allocation, spatial allocation, pollutant 

speciation, and vertical allocation of point sources are referred to as emissions modeling. 

The temporal resolution of the emissions inventories input to SMOKE for the modeling platform varies 

across sectors, and may be hourly, monthly, or annual total emissions. The spatial resolution, which also 

can be different for different sectors, may be at the level of individual point sources, county totals, 

province totals for Canada, or municipio totals for Mexico. This section provides some basic information 

about the tools and data files used for emissions modeling as part of the Version 5 platform. The 

emissions inventories were discussed in detail earlier. Therefore, we have limited the descriptions of data 

in this section to the ancillary data SMOKE uses to perform the emissions modeling steps. 

3.3.1 The SMOKE Modeling System 

For this study, emission inventories were processed into CMAQ-ready inputs using SMOKE version 3.1. 

SMOKE executables and source code are available from the Community Multiscale Analysis System 

(CMAS) Center at http://www.cmascenter.org. Additional information about SMOKE is available from 

http://www.smoke-model.org. For sectors that have plume rise, the in-line emissions capability of CMAQ 

was used, and therefore source-based emissions files were created rather than the much larger threedimensional files. For quality assurance purposes, emissions totals by specie for the entire model domain 

are output as reports that are then compared to inventory level reports generated by SMOKE to ensure 

mass is not lost or gained during this conversion process. 

3.3.2 Key Emissions Modeling Settings 

When preparing emissions for the air quality model, emissions for each sector are processed separately 
35

through SMOKE, and then the final merge program (Mrggrid) is run to combine the model-ready, sectorspecific emissions across sectors. The SMOKE settings in the run scripts and the data in the SMOKE 

ancillary files control the approaches used for the individual SMOKE programs for each sector. Table 3-

6 summarizes the major processing steps of each platform sector. The “Spatial” column shows the spatial 

approach: “point” indicates that SMOKE maps the source from a point location (i.e., latitude and 

longitude) to a grid cell; “surrogates” indicates that some or all of the sources use spatial surrogates to 

allocate county emissions to grid cells; and “area-to-point” indicates that some of the sources use the 

SMOKE area-to-point feature to grid the emissions. The “Speciation” column indicates that all sectors 

use the SMOKE speciation step, though biogenics speciation is done within BEIS3 and not as a separate 

SMOKE step. The “Inventory resolution” column shows the inventory temporal resolution from which 

SMOKE needs to calculate hourly emissions. Note that for some sectors (e.g., onroad, beis), there is no 

input inventory. Instead activity data and emission factors are used in combination with meteorological 

data to compute hourly emissions. 

Finally, the “plume rise” column indicates the sectors for which the “in-line” approach is used. These 

sectors are the only ones which will have emissions in aloft layers, based on plume rise. The term “inline” means that the plume rise calculations are done inside of the air quality model instead of being 

computed by SMOKE. The air quality model computes the plume rise using the stack data and the 

hourly air quality model inputs found in the SMOKE output files for each model-ready emissions sector. 

The height of the plume rise determines the model layer into which the emissions are placed. The 

c3marine and ptfire sectors are the only sectors with only “in-line” emissions, meaning that all of the 

emissions are placed in aloft layers and thus there are no emissions for those sectors in the twodimensional, layer-1 files created by SMOKE. In addition to the other settings, no grouping of stacks was 

performed using the PELVCONFIG file because grouping done for “in-line” processing will not give 

identical results as “offline” (i.e., processing whereby SMOKE creates 3-dimensional files). The only 

way to get the same results between in-line and offline is to choose to have no grouping. 

Table 3-6. Key emissions modeling steps by sector 

Platform sector 

Spatial Speciation 

Inventory 

resolution Plume rise 

Ptipm Point Yes daily & hourly in-line 

Ptnonipm Point Yes annual in-line 

Ptfire Point Yes Daily in-line 

Othpt Point Yes annual in-line 

c3marine Point Yes annual in-line 

Ag Surrogates Yes annual & monthly 

Afdust Surrogates Yes annual 

Beis pre-gridded landuse in BEIS computed hourly 

c1c2rail Surrogates Yes annual 

Nonpt surrogates & area-topoint Yes 

annual & monthly for 

ag burning and 

SESARM open 

b i

 

Nonroad surrogates & area-topoint Yes monthly 
36

Onroad Surrogates Yes computed hourly 

onroad_rfl Surrogates Yes computed hourly 

Othar Surrogates Yes annual 

Othon Surrogates Yes annual 

3.3.3 Spatial Configuration 

For this study, SMOKE and CMAQ were run for a 12-km modeling domain shown in Figure 3-1 

(12US1). The grid used a Lambert-Conformal projection, with Alpha = 33, Beta = 45 and Gamma = -97, 

with a center of X = -97 and Y = 40. Later sections provide details on the spatial surrogates and area-topoint data used to accomplish spatial allocation with SMOKE.

Figure 3-1. CMAQ Modeling Domain

3.3.4 Chemical Speciation Configuration 

The emissions modeling step for chemical speciation creates “model species” needed by the air quality 

model for a specific chemical mechanism. These model species are either individual chemical compounds 

or groups of species, called “model species.” The chemical mechanism used for this study is the Carbon 

Bond 05 (CB05) mechanism (Yarwood, 2005) with secondary organic aerosol (SOA) and HONO 
37

enhancements as described in http://www.cmascenter.org/help/model_docs/cmaq/4.7/

RELEASE_NOTES.txt. The mapping of inventory pollutants to model species is shown in Table 3-7. 

From the perspective of emissions preparation, the CB05 with SOA mechanism is the same as was used 

in the 2005 platform. It should be noted that the BENZENE model species is not part of CB05 in that the 

concentrations of BENZENE do not provide any feedback into the chemical reactions (i.e., it is not 

“inside” the chemical mechanism). Rather, benzene is used as a reactive tracer and as such is impacted 

by the CB05 chemistry. BENZENE, along with several reactive CB05 species (such as TOL and XYL) 

plays a role in SOA formation in CMAQ 4.7. 

Table 3-7. Model Species Produced by SMOKE for CB05 

Inventory Pollutant Model Species Model Species Description 

CO CO Carbon monoxide 

NOX NO Nitrogen oxide 

NO2 Nitrogen dioxide 

SO2 SO2 Sulfur dioxide 

SULF Sulfuric acid vapor 

NH3 NH3 Ammonia 

VOC ALD2 Acetaldehyde 

ALDX Propionaldehyde and higher aldehydes 

ETH Ethene 

ETHA Ethane 

ETOH Ethanol 

FORM Formaldehyde 

IOLE Internal olefin carbon bond (R-C=C-R) 

ISOP Isoprene 

MEOH Methanol 

OLE Terminal olefin carbon bond (R-C=C) 

PAR Paraffin carbon bond 

TOL Toluene and other monoalkyl aromatics 

XYL Xylene and other polyalkyl aromatics 

Various additional VOC 

species from the biogenics 

model which do not map to 

the above model species 

TERP Terpenes 

PM10 PMC Coarse PM > 2.5 microns and  10 microns 

PM2.5 PEC Particulate elemental carbon  2.5 microns 

PNO3 Particulate nitrate  2.5 microns 

POC Particulate organic carbon (carbon only)  2.5 

microns 

PSO4 Particulate sulfate  2.5 microns 

PMFINE Other particulate matter  2.5 microns 

The approach for speciating PM2.5 emissions supports both CMAQ 4.7.1 with five species (i.e., AE5) 

and CMAQ 5.0 that includes speciation of PM2.5 into 17 PM model species (i.e., AE6). The TOG and 
38

PM2.5 speciation factors that are the basis of the chemical speciation approach were developed from the 

SPECIATE4.3 database (http://www.epa.gov/ttn/chief/software/speciate) and is the EPA's repository of 

TOG and PM speciation profiles of air pollution sources. A few of the profiles used in the v5 platform 

will be published in later versions of the SPECIATE database. The SPECIATE database development 

and maintenance is a collaboration involving the EPA’s ORD, OTAQ, and the Office of Air Quality 

Planning and Standards (OAQPS), and Environment Canada (EPA, 2006a). The SPECIATE database 

contains speciation profiles for TOG, speciated into individual chemical compounds, VOC-to-TOG 

conversion factors associated with the TOG profiles, and speciation profiles for PM2.5. The database also 

contains the PM2.5, speciated into both individual chemical compounds (e.g., zinc, potassium, manganese, 

lead) and into the “simplified” PM2.5 components used in the air quality model. These simplified 

components for AE5 are: 

 PSO4 : primary particulate sulfate

 PNO3: primary particulate nitrate

 PEC: primary particulate elemental carbon

 POC: primary particulate organic carbon

 PMFINE: other primary particulate, less than 2.5 micrograms in diameter

NOX can be speciated into NO, NO2, and/or HONO. For the non-mobile sources, a single profile is used 

“NHONO” to split NOX into NO and NO2 with 10% NO2 and 90% NO. For the mobile sources except 

for onroad (including nonroad, c1c2rail, c3marine, othon sectors) and for specific SCCs in othar and 

ptnonipm, the profile “HONO” splits NOX into NO, NO2, and HONO with 90% NO, 9.2% NO2 and 

0.8% HONO. The onroad sector does not use the “HONO” profile to speciate NOX. Instead, 

MOVES2010b produces speciated NO, NO2, and HONO by source, including emission factors for these 

species in the emission factor tables used by SMOKE-MOVES. Within MOVES, the HONO fraction is a 

constant 0.008 of NOX. The NO fraction varies by heavy duty versus light duty, fuel type, and model 

year. The NO2 fraction = 1 – NO – HONO. For more details on the NOX fractions within MOVES, see 

http://www.epa.gov/otaq/models/moves/documents/420r12022.pdf. The SMOKE-MOVES system is 

configured to model these species directly without further speciation. 

The approach for speciating VOC emissions from non-biogenic sources has the following characteristics: 

1) for some sources, HAP emissions are used in the speciation process to allow integration of VOC and 

HAP emissions in the NEI; and, 2) for some mobile sources, “combination” profiles are specified by 

county and month and emission mode (e.g., exhaust, evaporative). SMOKE computes the resultant profile 

on-the-fly given the fraction of each specific profile specified for the particular county, month and 

emission mode. The SMOKE feature called the GSPRO_COMBO file supports this approach. 

The VOC speciation approach for the 2009 Platform includes HAP emissions from the NEI in the 

speciation process for some sectors. That is instead of speciating VOC to generate all of the species 

needed by the model, emissions of the 4 HAPs, benzene, acetaldehyde, formaldehyde and methanol 

(BAFM) from the NEI were integrated with the NEI VOC. The integration process combines the BAFM 

HAPs with the VOC in a way that does not double-count emissions and uses the BAFM directly in the 

speciation process. Generally, the HAP emissions from the NEI are believed to be more representative of 

emissions of these compounds than their generation via VOC speciation. 
39

The BAFM HAPs were chosen for this special treatment because, with the exception of BENZENE, they 

are the only explicit VOC HAPs in the base version of CMAQ 4.7 model. By “explicit VOC HAPs,” we 

mean model species that participate in the modeled chemistry using the CB05 chemical mechanism. The 

use of these HAP emission estimates along with VOC is called “HAP-CAP integration”. BENZENE was 

chosen because it was added as a model species in the base version of CMAQ 4.7, and there was a desire 

to keep its emissions consistent between multi- pollutant and base versions of CMAQ. 

For specific sources, especially within the onroad and onroad_rfl sectors, we included ethanol in our 

integration. To differentiate when a source was integrating BAFM versus EBAFM (ethanol in addition to 

BAFM), the speciation profiles which do not include ethanol are referred to as an “E-profile”, for 

example E10 headspace gasoline evaporative speciation profile 8763 where ethanol is speciated from 

VOC, versus 8763E where ethanol is obtained directly from the inventory. The specific profiles used in 

2009 are the same as used for the 2007 platform (see 2007 speciation in Table 3-6 in the 2007v5 TSD). 

The only differences between 2009 and 2007 are the GSPRO_COMBOs, which represent a different 

mixture of E0 and E10 by county between the two modeling years. 

The integration of HAP VOC with VOC is a feature available in SMOKE for all inventory formats other 

than PTDAY (the format used for the ptfire sector). SMOKE allows the user to specify the particular 

HAPs to integrate and the particular sources to integrate. The HAPs to integrate are specified in the 

INVTABLE file, and the sources to integrate are based on the NHAPEXCLUDE file (which lists the 

sources that are excluded from integration). For the “integrate” sources, SMOKE subtracts the “integrate 

” HAPs from the VOC (at the source level) to compute emissions for the new pollutant 

“NONHAPVOC.” The user provides NONHAPVOC-to-NONHAPTOG factors and NONHAPTOG 

speciation profiles. SMOKE computes NONHAPTOG and then applies the speciation profiles to allocate 

the NONHAPTOG to the other CMAQ VOC species not including the integrated HAPs. 

CAP-HAP integration was considered for all sectors and “integration criteria” were developed for some of 

those. Table 3-8 summarizes the integration approach for each platform sector. For the c1c2rail sector, the 

integration criteria were (1) that the source had to have at least one of the 4 HAPs and (2) that the sum of 

BAFM could not exceed the VOC emissions. For the nonpt sector, the following integration criteria were 

used to determine the sources to integrate:

1. Any source for which the sum of B, A, F, or M is greater than the VOC was not integrated, since 

this clearly identifies sources for which there is an inconsistency between VOC and VOC HAPs. 

2. For some source categories (those that comprised 80% of the VOC emissions), sources were 

selected for integration in the category per specific criteria. For most of these source categories, 

sources may be integrated if they had the minimum combination of B, A, F, and M. For some 

source categories, all sources were designated as “no-integrate”. 

3. For source categories that do not comprise the top 80% of VOC emissions, as long as the source 

has emissions of one of the B, F, A or M pollutants, then it can be integrated. 
40

Table 3-8. Integration status of benzene, acetaldehyde, formaldehyde and methanol (BAFM) for 

each platform sector 

Platform 

Sector 

Approach for Integrating NEI emissions of Benzene (B), Acetaldehyde (A), Formaldehyde (F) and 

Methanol (M) 

Ptipm No integration because emissions of BAFM are relatively small for this sector 

Ptnonipm No integration because emissions of BAFM are relatively small for this sector and it is not 

expected that criteria for integration would be met by a significant number of sources 

Ptfire No integration. 

Ag N/A—sector contains no VOC 

Afdust N/A—sector contains no VOC 

Biog N/A—sector contains no inventory pollutant “VOC”; but rather specific VOC species 

C1c2rail Partial integration 

C3marine Full integration 

Nonpt Partial integration 

Nonroad 

Partial integration—did not integrate California emissions, CNG or LPG sources (SCCs 

beginning with 2268 or 2267) because NMIM computed only VOC and not any HAPs for these 

SCCs. 

Onroad Full integration 

Othar No integration—not the NEI 

Othon No integration—not the NEI 

Othpt No integration—not the NEI 

The SMOKE feature to compute speciation profiles from mixtures of other profiles in user-specified 

proportions was used in this project. The combinations are specified in the GSPRO_COMBO ancillary 

file by pollutant (including pollutant mode, e.g., EXH VOC), state and county (i.e., state/county FIPS 

code) and time period (i.e., month). This feature was used for onroad and nonroad mobile and gasolinerelated related stationary sources. Since the ethanol content varies spatially (e.g., by state or sources use 

fuels with varying ethanol content, and therefore the speciation profiles require different combinations of 

gasoline and E10 profiles by county), temporally (e.g., by month) and by modeling year (i.e., future years 

have more thanol) the combo feature allows combinations to be specified at various levels for different 

years. 

The INVTABLE and NHAPEXCLUDE SMOKE input files have a critical function in the VOC 

speciation process for emissions modeling cases utilizing HAP-CAP integration, as is done for the 2009 

Platform. Two different types of INVTABLE files were developed to use with different sectors of the 

platform. For sectors in which we chose no integration across the entire sector a “no HAP use” 

INVTABLE was developed in which the “KEEP” flag is set to “N” for BAFM pollutants. Thus, any 

BAFM pollutants in the inventory input into SMOKE are dropped. This both avoids double-counting of 

these species and assumes that the VOC speciation is the best available approach for these species for the 

sectors using the approach. The second INVTABLE is used for sectors in which one or more sources are 

integrated and causes SMOKE to keep the BAFM pollutants and indicates that they are to be integrated 

with VOC (by setting the “VOC or TOG component” field to “V” for all four HAP pollutants. This 

integrate INVTABLE is further differentiated into sectors that integrate BAFM versus those that integrate 

EBAFM (e.g., the onroad and onroad_rfl sectors). 
41

Unlike other sectors, the onroad sector has pre-speciated PM. This speciated PM comes from the 

MOVES model and is processed through the SMOKE-MOVES system. Unfortunately, the 

MOVES2010b speciated PM does not map 1-to-1 to either the AE5 or AE6 species. Table 3-9 shows the 

relationship between MOVES2010b exhaust PM2.5 related species and CMAQ AE5 PM species. 

Table 3-9. MOVES exhaust PM species versus AE5 species 

MOVES2010b Pollutant Name 

Variable 

name for 

Equations 

Relation to AE5 model species 

Primary Exhaust PM2.5 – Total PM25_TOTAL 

Primary PM2.5 - Organic Carbon PM25OM Sum of POC, PNO3 and 

PMFINE 

Primary PM2.5 - Elemental 

Carbon 

PM25EC PEC 

Primary PM2.5 - Sulfate 

Particulate 

PM25SO4 PSO4 

MOVES species are related as follows: 

PM25_TOTAL = PM25EC + PM25OM + PSO4 

The five CMAQ AE5 species also sum to total PM2.5: 

PM2.5 = POC+PEC+PNO3+PSO4+PMFINE 

The basic problem is to differentiate MOVES species “PM25OM” into the component AE5 species 

(POC, PNO3 and PMFINE). The Moves2smkEF post-processor script takes the MOVES2010b species 

(EF tables) and calculates the appropriate AE5 PM2.5 species and converts them into a format that is 

appropriate for SMOKE (see http://www.smoke-model.org/version3.1/html/ch05s02s04.html for details 

on the Moves2smkEF script). 

For brake wear and tire wear PM, total PM2.5 (not speciated) comes directly from MOVES2010b. These 

PM modes are speciated by SMOKE. PMFINE from onroad exhaust is further speciated by SMOKE into 

the component AE6 species. 

Speciation profiles for use with BEIS are not included in SPECIATE. The 2009 Platform uses BEIS3.14 

and includes a species (SESQ) that was not in BEIS3.13 (the version used for the 2002 Platform). This 

species was mapped to the CMAQ species SESQT. The profile code associated with BEIS3.14 profiles for 

use with CB05 was “B10C5.”

3.3.4 Temporal Processing Configuration 

Temporal allocation or temporalization is the process of distributing aggregated emissions to a finer 

temporal resolution, such as converting annual emissions to hourly emissions. While the total emissions 

are important, the timing of the occurrence of emissions is also essential for accurately simulating ozone, 

PM, and other pollutant concentrations in the atmosphere. Typically, emissions inventories are annual or 

monthly in nature. Temporalization takes these annual emissions and distributes them to the month, the 

monthly emissions to the day, and the daily emissions to the hour. This process is typically done by 
42

applying temporal profiles—monthly, day of the week, and diurnal—to the inventories. 

The monthly, weekly, and diurnal temporal profiles and associated cross references used to create the 

hourly emissions inputs for the air quality model were similar to those used for the 2005v4.3 platform. 

Some new methodologies are introduced in this platform and updated profiles are discussed. Temporal 

factors are typically applied to the inventory by some combination of country, state, county, SCC, and 

pollutant. 

Table 3-10 summarizes the temporal aspect of the emissions processing configuration. It compares the key 

approaches used for temporal processing across the sectors. The temporal aspects of SMOKE processing 

are controlled through (a) the scripts T_TYPE (Temporal type) and M_TYPE (Mergetype) settings and (b) 

ancillary data files. In the table, “Daily temporal approach” refers to the temporal approach for getting 

daily emissions from the inventory using the Temporal program. The “Merge processing approach” refers 

to the days used to represent other days in the month for the merge step. If not “all”, then the SMOKE 

merge step runs only for representative days, which could include holidays as indicated by the right-most 

column. In addition to the resolution, temporal processing includes a ramp-up period for several days 

prior to January 1, 2009, intended to mitigate the effects of initial condition concentrations. The ramp up 

period for the national 12km grid was 10 days. For most sectors, the emissions from late December of 

2008 were used to provide emissions for the end of December, 2009. 

The Flat File 2010 format (FF10) is a new inventory format for SMOKE that provides a more 

consolidated format for monthly, daily, and hourly emissions inventories. Previously, 12 separate 

inventory files would be required to process monthly inventory data. With the FF10 format, a single 

inventory file can contain emissions for all 12 months and the annual emissions in a single record. This 

helps simplify the management of numerous inventories. Similarly, individual records contain data for 

all days in a month and all hours in a day in the daily and hourly FF10 inventories, respectively. 

SMOKE 3.1 prevents the application of temporal profiles on top of the “native” resolution of the 

inventory. For example, a monthly inventory should not have annual to month temporalization applied; 

rather, it should only have month to day and diurnal temporalization. This becomes particularly 

important when specific sectors have a mix of annual, monthly, daily, and/or hourly inventories (e.g. the 

nonpt sector). The flags that control temporalization for a mixed set of inventories are discussed in the 

SMOKE documentation.
43

Table 3-10. Temporal Settings Used for the Platform Sectors in SMOKE 

Platform sector 

Inventory 

resolution 

Monthly 

profiles 

used? 

Daily 

temporal 

approach 

1,2 

Merge 

processing 

approach 1,3 

Process 

Holidays as 

separate 

days? 

Ptipm daily & hourly All all yes 

Ptnonipm annual yes Mwdss all yes 

Ptfire Daily All all yes 

Ag annual & monthly yes all all yes 

Afdust annual yes Week all yes 

Beis hourly n/a all yes 

c3marine annual yes Aveday aveday 

c1c2rail annual yes Mwdss mwdss 

Nonpt annual & monthly yes All all yes 

Nonroad monthly Mwdss mwdss yes 

Onroad annual & monthlya all all yes 

onroad_rfl annual & monthlya All all yes 

Othar annual yes Week week 

Othon annual yes Week week 

Othpt annual yes Mwdss mwdss 1 Definitions for processing resolution:

all = hourly emissions computed for every day of the year

week = hourly emissions computed for all days in one “representative” week, representing all weeks for each month, which means

emissions have day-of-week variation, but not week-to-week variation within the month

mwdss = hourly emissions for one representative Monday, representative weekday, representative Saturday and representative Sunday for 

each month, which means emissions have variation between Mondays, other weekdays, Saturdays and Sundays within the month, but not 

week-to-week variation within the month. Also Tuesdays, Wednesdays and Thursdays are treated the same.

aveday = hourly emissions computed for one representative day of each month, which means emissions for all days of each month are the 

same.

2 Daily temporal approach refers to the temporal approach for getting daily emissions from the inventory using the Temporal program. The 

values given are the values of the L_TYPE setting.

3 Merge processing approach refers to the days used to represent other days in the month for the merge step. If not “all”, then the SMOKE 

merge step just run for representative days, which could include holidays as indicated by the rightmost column. The values given are the 

values of the M_TYPE setting.

a For onroad and onroad_rfl, the annual and monthly refers to activity data (VMT and VPOP). Emissions are computed on an hourly basis.

For the EGU emissions in the ptipm sector, hourly CEM NOX and SO2 data were used directly for 

sources that match CEMs. For other pollutants, hourly CEM heat input data were used to allocate the 

NEI annual values. For sources not matching CEM data (“non-CEM” sources), daily emissions were 

computed from the NEI annual emissions using a structured query language (SQL) program and stateaverage CEM data. To allocate annual emissions to each month, state-specific three-year averages of 

2008-2010 CEM data were created. These average annual-to-month factors were assigned to non-CEM 

sources within each state. To allocate the monthly emissions to each day, the 2009 CEM data to compute 

state-specific month-to-day factors, averaged across all units in each state. These daily emissions wee 

calculated outside of SMOKE and the resulting daily inventory is used as an input into SMOKE. 

 

The daily-to-hourly allocation was performed in SMOKE using diurnal profiles. The state-specific and 

pollutant-specific diurnal profiles for use in allocating the day-specific emissions for non-CEM sources in 
44

the ptipm sector were updated. The 2009 CEM data was used to create state-specific, day-to-hour 

factors, averaged over the whole year and all units in each state. Diurnal factors were calculated using 

CEM SO2 and NOX emissions and heat input. SO2 and NOX-specific factors were computed from the 

CEM data for these pollutants. All other pollutants used factors created from the hourly heat input data. 

The resulting profiles were assigned by state and pollutant. 

Two updated diurnal temporal profiles were incorporated into the 2009 modeling platform. For all 

agricultural burning, we used a diurnal temporal profile (McCarty et al., 2009) that puts more of the 

emissions during the actual work day and suppresses the emissions during the middle of the night was 

used. Note that all states used a uniform day of week profile for all agricultural burning emissions, 

except for the following states that for which state-specific day of week profiles were used: Arkansas, 

Kansas, Louisiana, Minnesota, Missouri, Nebraska, Oklahoma, and Texas. For residential wood 

combustion, a profile was used that placed more of the emissions in the morning and the evening when 

people are typically using these sources. This profile is based on an average of 2004 MANE-VU survey 

based temporal profiles (see http://www.marama.org/publications_folder/ResWoodCombustion/ 

Final_report.pdf). When this profile was compared to a concentration-based analysis of aethalometer 

measurements in Rochester, NY (Wang et al. 2011) for various seasons and day of the week it was found 

that the updated RWC profile generally tracked the concentration based temporal patterns. 

The temporal profile assignments for the Canadian 2006 inventory were provided by Environment 

Canada along with the inventory. They provided profile assignments that rely on the existing set of 

temporal profiles in the 2002 Platform. For point sources, they provided profile assignments by 

PLANTID. 

3.3.5 Meteorological-based Temporal Profiles 

A significant improvement over previous platforms is the introduction of meteorologically-based 

temporalization. We recognize that there are many factors that impact the timing of when emissions 

occur. The benefits of utilizing meteorology as method of temporalizing are: (1) a consistent 

meteorological dataset as is used by the AQ model (e.g. WRF) is available; (2) the meteorological model 

data is highly resolved in terms of spatial resolution; and (3) the meteorological variables vary at hourly 

resolution which can translate to hour-specific temporalization. 

The SMOKE program GenTPRO provides a method for developing meteorologically-based 

temporalization. Currently, the program can utilize three types of temporal algorithms: RWC, 

agricultural livestock ammonia, and a generic meteorology based algorithm. For the 2007 platform, we 

used the RWC and ag NH3 GenTPRO generated profiles. GenTPRO reads in gridded meteorology data 

(MCIP) and spatial surrogates and uses the specified algorithm to produce a new temporal profile that can 

be input into SMOKE. The meteorological variables and the resolution of the generated temporal profile 

(hourly, daily, etc.) depend on the algorithm and the run parameters. For more details on the 

development of these algorithms and running GenTPRO, see the GenTPRO documentation 

http://www.smoke-model.org/version3.1/GenTPRO_TechnicalSummary_Aug2012_Final.pdf and the 

SMOKE manual section http://www.smoke-model.org/version3.1/html/ch05s03s07.html. 

For the RWC algorithm, GenTPRO uses the daily minimum temperature to determine the temporal 

allocation of emissions to days. GenTPRO was run to create an annual-to-day temporal profile for the 

RWC sources within the nonpt sector. These generated profiles distribute annual RWC emissions to the 
45

coldest days of the year. On days where the minimum temperature does not drop below a user-defined 

threshold, RWC emissions are zero. Conversely, the program temporally allocates the largest percentage 

of emissions to the coldest days. Similar to other temporal allocation profiles, the total annual emissions 

do not change, just the distribution of the emissions within the year. Initially, the RWC algorithm used a 

the default temperature threshold of 50  ̊F. For most of the country, this produced a reasonable 

distribution of emissions, but for a few Southern counties all of the emissions were compressed into a few 

days creating excessively high daily emissions. GenTPRO was then modified to accept an optional input 

that defines a county/state specific alternative temperature threshold. In addition, an alternative RWC 

algorithm was created to avoid negative RWC emissions when the daily minimum temperature was 

greater than 53.3  ̊F. For the v5 platform, the alternative RWC algorithm was used for the whole country, 

with the default 50  ̊F threshold for the majority of the states, and a 60  ̊F threshold for the following 

states: Alabama, Arizona, California, Florida, Georgia, Louisiana, Mississippi, South Carolina, and 

Texas. 

For the agricultural livestock NH3 algorithm, GenTPRO algorithm is based on the Russel and Cass 

(1986) equation. This algorithm uses county-average hourly temperature and wind speed to calculate the 

temporal profile. GenTPRO was run to create month-to-hour temporal profiles for these sources. 

Because these profiles distribute to the hour based on monthly emissions, the emissions will either come 

from a monthly inventory or from an annual inventory that has been temporalized already to the month. 

For the onroad and onroad_rfl sectors, meteorology is not used in the development of the temporal 

profiles; rather, but meteorology impacts the calculation of the hourly emissions through the program 

Movesmrg. The result is that the emissions will vary at the hourly level by grid cell. More specifically, 

the on-network (RPD) and the off-network (RPV) exhaust, evaporative, and evaporative permeation 

modes use the gridded meteorology (MCIP) directly. Movesmrg determines the temperature for each 

hour and grid cell and uses it to select the appropriate EF for that SCC/pollutant/mode. For the offnetwork rate per profile (RPP) emissions, Movesmrg uses the Met4moves output for SMOKE (daily 

minimum and maximum temperatures by county) to determine the appropriate EF for that hour and 

SCC/pollutant. The result is that the emissions will vary hourly by county. The combination of these 

three processes (RPD, RPV, and RPP) is the total onroad emissions, while the combination of the two 

processes (RPD, RPV) for the refueling mode only is the total onroad_rfl emissions. Both sectors will 

show a strong meteorological influence on their temporal patterns. 

3.3.6 Vertical Allocation of Emissions 

Table 3-6 specifies the sectors for which plume rise is calculated. If there is no plume rise for a sector, the 

emissions are placed into layer 1 of the air quality model. Vertical plume rise was performed in-line 

within CMAQ for all of the SMOKE point-source sectors (i.e., ptipm, ptnonipm, ptfire, othpt, and 

c3marine). The in-line plume rise computed within CMAQ is nearly identical to the plume rise that would 

be calculated within SMOKE using the Laypoint program. See http://www.smokemodel.org/version2.7/html/ch06s07.html for full documentation of Laypoint. The selection of point 

sources for plume rise is pre-determined in SMOKE using the Elevpoint program (http://www.smokemodel.org/ version2.7/html/ch06s03.html). The calculation is done in conjunction with the CMAQ model 

time steps with interpolated meteorological data and is therefore more temporally resolved than when it is 

done in SMOKE. Also, the calculation of the location of the point source is slightly different than the one 

used in SMOKE and this can result in slightly different placement of point sources near grid cell 

boundaries. 
46

For point sources, the stack parameters are used as inputs to the Briggs algorithm, but point fires do not 

have stack parameters. However, the ptfire inventory does contain data on the acres burned (acres per day) 

and fuel consumption (tons fuel per acre) for each day. CMAQ uses these additional parameters to 

estimate the plume rise of emissions into layers above the surface model layer. Specifically, these data are 

used to calculate heat flux, which is then used to estimate plume rise. In addition to the acres burned and 

fuel consumption, heat content of the fuel is needed to compute heat flux. The heat content was assumed 

to be 8000 Btu/lb of fuel for all fires because specific data on the fuels were unavailable in the inventory. 

The plume rise algorithm applied to the fires is a modification of the Briggs algorithm with a stack height 

of zero. 

CMAQ uses the Briggs algorithm to determine the plume top and bottom, and then computes the plumes’ 

distributions into the vertical layers that the plumes intersect. The pressure difference across each layer 

divided by the pressure difference across the entire plume is used as a weighting factor to assign the 

emissions to layers. This approach gives plume fractions by layer and source. 

3.3.7 Emissions Modeling Ancillary Files 

The methods used to perform spatial allocation for the 2007 platform are summarized in this section. For 

the 2007 platform, spatial factors are typically applied by country and SCC. As described earlier, spatial 

allocation was performed for a national 12-km domain. To accomplish this, SMOKE used national 12-

km spatial surrogates and a SMOKE area-to-point data file. For the U.S., the spatial surrogates used 

2010-based data (e.g., population) wherever possible. For Mexico, the same spatial surrogates were used 

in the 2005 platform. For Canada we used a set of Canadian surrogates provided by Environment 

Canada, also unchanged from the 2005v4.3 platform. The U.S., Mexican, and Canadian 12-km 

surrogates cover the entire CONUS domain 12US1 shown in Figure 3-1. The remainder of this 

subsection provides further details on the origin of the data used for the spatial surrogates and the area-topoint data. 

The SMOKE ancillary data files, particularly the cross-reference files, provide the specific inventory 

resolution at which spatial, speciation, and temporal factors are applied. For the 2009 Platform, spatial 

factors were generally applied by country/SCC, speciation factors by pollutant/SCC or (for combination 

profiles) state/ county FIPS code and month, and temporal factors by some combination of country, state, 

county, SCC, and pollutant. 

3.3.7.1 Surrogates for U.S. Emissions 

More than sixty spatial surrogates were used to spatially allocate U.S. county-level emissions to the 

CMAQ 12-km grid cells. The Surrogate Tool was used to generate all of the surrogates. The shapefiles 

input to the Surrogate Tool are provided and documented at 

http://www.epa.gov/ttn/chief/emch/spatial/spatialsurrogate.html. The tool and updated documentation for 

it is available at http://www.ie.unc.edu/cempd/projects/mims/spatial/ and 

http://www.cmascenter.org/help/documentation.cfm?MODEL=spatial_allocator&VERSION=3.6&temp_

id=99999. The detailed steps in developing the county boundaries for the surrogates are documented at 

ftp://ftp.epa.gov/EmisInventory/ emiss_shp2006/us/metadata_for_2002_ 

county_boundary_shapefiles_rev.pdf. 

Table 3-11 lists the codes and descriptions of the surrogates. The surrogates in bold have been updated 
47

with 2010-based data, including 2010 census data at the block group level, 2010 American Community 

Survey Data for heating fuels, 2010 TIGER/Line data for railroads and roads, and 2010 National 

Transportation Atlas Data for ports and navigable waterways. For this project “Version 3” of the 2010-

baed spatial surrogates was used. Not all of the available surrogates are used to spatially allocate sources 

in the 2007 platform; that is, some surrogates shown in Table 3-11 were not assigned to any SCCs. An 

area-to-point approach overrides the use of surrogates for some airport-related sources. 

Alternative surrogates for ports (801) and shipping lanes (802) were developed from the 2008 NEI 

shapefiles: Ports_032310_wrf and ShippingLanes_111309FINAL_wrf. These surrogates were used for 

c1 and c2 commercial marine emissions instead of the standard 800 and 810 surrogates, respectively. 

For the onroad sector, the on-network (RPD) emissions were spatially allocated to roadways, which the 

off-network (RPP and RPV) emissions were allocated to parking areas. For the onroad_rfl sector, the 

emissions were spatially allocated to gas station locations. 

For the oil and gas sources in the nonpt sector, the WRAP Phase III sources have detailed basin-specific 

spatial surrogates shown in Table 3-12. The remaining oil and gas sources used the 2005-based surrogate 

“Oil & Gas Wells, IHS Energy, Inc. and USGS” (680) developed for oil and gas SCCs. The surrogates in 

Table 3-12 were applied for the counties listed in Table 3-13. 

3.3.7.3 Allocation Method for Airport-Related Sources in the U.S. 

There are numerous airport-related emission sources in the 2005 NEI, such as aircraft, airport ground 

support equipment, and jet refueling. In the 2002 platform most of these emissions were contained in 

sectors with county-level resolution — alm (aircraft), nonroad (airport ground support) and nonpt (jet 

refueling), but in the 2005 and 2008 platforms aircraft emissions are included as point sources as part of 

the ptnonipm sector. 

For the 2009 platform, the SMOKE “area-to-point” approach was used for airport ground support 

equipment (nonroad sector), and jet refueling (nonpt sector). The approach is described in detail in the 

2002 Platform documentation: http://www.epa.gov/scram001/reports/ Emissions%20TSD%20Vol1_02-

28-08.pdf. 

Nearly the same ARTOPNT file was used to implement the area-to-point approach as was done for the 

CAP and HAP-2002-based Platform. This was slightly updated from the CAP-only 2002 Platform by 

further allocating the Detroit-area airports into multiple sets of geographic coordinates to support finer 

scale modeling. The updated file was retained for the 2009 Platform. 

3.3.7.4 Surrogates for Canada and Mexico Emission Inventories 

The Mexican emissions and single surrogate (population) were the same as those used in the 2002 and 

2005 Platforms. For Canada, surrogates provided by Environment Canada with the 2006 emissions were 

used to spatially allocate the 2006 Canadian emissions for the 2005 and 2009 Platforms. 

The Canadian surrogate data described in Table 3-14 came from Environment Canada. They provided 

both the surrogates and cross references; the surrogates were outputs from the Surrogate Tool (previously 

referenced). Per Environment Canada, the surrogates are based on 2001 Canadian census data. The crossreferences that Canada originally provided were updated as follows: all assignments to surrogate ‘978’ 

(manufacturing industries) were changed to ‘906’ (manufacturing services), and all assignments to ‘985’ 
48

(construction and mining) and ‘984’ (construction industries) were changed to ‘907’ (construction 

services) because the surrogate fractions in 984, 978 and 985 did not sum to 1. Codes for surrogates other 

than population that did not begin with the digit “9” were also changed. 

Table 3-11. U.S. Surrogates Available for the 2009 Platform 

Code Surrogate Description Code Surrogate Description

N/A Area-to-point approach (see 3.3.1.2) 520 Commercial plus Industrial plus 

I tit ti l 100 Population 525

Golf Courses + Institutional +Industrial + 

Commercial

110 Housing 527 Single Family Residential

120 Urban Population 530 Residential - High Density

130 Rural Population 535Residential + Commercial + Industrial + 

Institutional

137 Housing Change 540 Retail Trade

140 Housing Change and Population 545 Personal Repair

150 Residential Heating - Natural Gas 550 Retail Trade plus Personal Repair

160 Residential Heating – Wood 555 Professional/Technical plus General 

Government

165 0.5 Residential Heating - Wood plus 0.5 Low 

Intensity Residential

560 Hospital

170 Residential Heating - Distillate Oil 565 Medical Office/Clinic

180 Residential Heating – Coal 570 Heavy and High Tech Industrial

190 Residential Heating - LP Gas 575 Light and High Tech Industrial

200 Urban Primary Road Miles 580 Food, Drug, Chemical Industrial

210 Rural Primary Road Miles 585 Metals and Minerals Industrial

220 Urban Secondary Road Miles 590 Heavy Industrial

230 Rural Secondary Road Miles 595 Light Industrial

240 Total Road Miles 596 Industrial plus Institutional plus Hospitals

250 Urban Primary plus Rural Primary 600 Gas Stations

255 0.75 Total Roadway Miles plus 0.25 Population 650 Refineries and Tank Farms

260 Total Railroad Miles 675 Refineries and Tank Farms and Gas Stations

270 Class 1 Railroad Miles 680 Oil and Gas 

280 Class 2 and 3 Railroad Miles 700 Airport Areas

300 Low Intensity Residential 710 Airport Points

310 Total Agriculture 720 Military Airports

312 Orchards/Vineyards 800 Marine Ports

320 Forest Land 801 NEI Ports

330 Strip Mines/Quarries 802 NEI Shipping Lanes 

340 Land 807 Navigable Waterway Miles

350 Water 810 Navigable Waterway Activity

400 Rural Land Area 850 Golf Courses

500 Commercial Land 860 Mines
49

505 Industrial Land 870 Wastewater Treatment Facilities

510 Commercial plus Industrial 880 Drycleaners

515 Commercial plus Institutional Land 890 Commercial Timber

Table 3-12. Spatial Surrogates for WRAP Oil and Gas Data 

Country Code Surrogate Description 

USA 699 Gas production at CBM wells 

USA 698 Well count - gas wells 

USA 697 Oil production at gas wells 

USA 696 Gas production at gas wells 

USA 695 Well count - oil wells 

USA 694 Oil production at Oil wells 

USA 693 Well count - all wells 

USA 692 Spud count 

USA 691 Well count - CBM wells 

USA 690 Oil production at all wells 

USA 689 Gas production at all wells 

Table 3-13. Counties included in the WRAP Dataset 

FIPS State County FIPS State County 

8001 Colorado Adams 30075 Montana Powder River 

8005 Colorado Arapahoe 35031 New Mexico Mc Kinley 

8007 Colorado Archuleta 35039 New Mexico Rio Arriba 

8013 Colorado Boulder 35043 New Mexico Sandoval 

8014 Colorado Broomfield 35045 New Mexico San Juan 

8029 Colorado Delta 49007 Utah Carbon 

8031 Colorado Denver 49009 Utah Daggett 

8039 Colorado Elbert 49013 Utah Duchesne 

8043 Colorado Fremont 49015 Utah Emery 

8045 Colorado Garfield 49019 Utah Grand 

8051 Colorado Gunnison 49043 Utah Summit 

8063 Colorado Kit Carson 49047 Utah Uintah 

8067 Colorado La Plata 56001 Wyoming Albany 

8069 Colorado Larimer 56005 Wyoming Campbell 

8073 Colorado Lincoln 56007 Wyoming Carbon 

8075 Colorado Logan 56009 Wyoming Converse 

8077 Colorado Mesa 56011 Wyoming Crook 

8081 Colorado Moffat 56013 Wyoming Fremont 

8087 Colorado Morgan 56019 Wyoming Johnson 

8095 Colorado Phillips 56023 Wyoming Lincoln 

8103 Colorado Rio Blanco 56025 Wyoming Natrona 
50

8107 Colorado Routt 56027 Wyoming Niobrara 

8115 Colorado Sedgwick 56033 Wyoming Sheridan 

8121 Colorado Washington 56035 Wyoming Sublette 

8123 Colorado Weld 56037 Wyoming Sweetwater 

8125 Colorado Yuma 56041 Wyoming Uinta 

30003 Montana Big Horn 56045 Wyoming Weston 

Table 3-14. Canadian Spatial Surrogates for Canadian Emissions 

Code Description Code Description 

9100 Population 9493 Warehousing and storage 

9101 Total dwelling 9494 Total Transport and warehouse 

9102 Urban dwelling 9511 Publishing and information services 

9103 Rural dwelling 9512 Motion picture and sound recording 

industries 

9104 Total Employment 9513 Broadcasting and 

telecommunications 

9106 ALL_INDUST 9514 Data processing services 

9111 Farms 9516 Total Info and culture 

9113 Forestry and logging 9521 Monetary authorities - central bank 

9114 Fishing hunting and trapping 9522 Credit intermediation activities 

9115 Agriculture and forestry activities 9523 Securities commodity contracts and 

other financial investment activities 

9116 Total Resources 9524 Insurance carriers and related 

activities 

9211 Oil and Gas Extraction 9526 Funds and other financial vehicles 

9212 Mining except oil and gas 9528 Total Banks 

9213 Mining and Oil and Gas Extract 

activities 

9531 Real estate 

9219 Mining-unspecified 9532 Rental and leasing services 

9221 Total Mining 9533 Lessors of non-financial intangible 

assets (except copyrighted works) 

9222 Utilities 9534 Total Real estate 

9231 Construction except land subdivision 

and land development 

9541 Professional scientific and technical 

services 

9232 Land subdivision and land 

development 

9551 Management of companies and 

enterprises 

9233 Total Land Development 9561 Administrative and support services 

9308 Food manufacturing 9562 Waste management and remediation 

services 

9309 Beverage and tobacco product 

manufacturing 

9611 Education Services 

9313 Textile mills 9621 Ambulatory health care services 
51

Code Description Code Description 

9314 Textile product mills 9622 Hospitals 

9315 Clothing manufacturing 9623 Nursing and residential care facilities 

9316 Leather and allied product 

manufacturing 

9624 Social assistance 

9321 Wood product manufacturing 9625 Total Service 

9322 Paper manufacturing 9711 Performing arts spectator sports and 

related industries 

9323 Printing and related support activities 9712 Heritage institutions 

9324 Petroleum and coal products 

manufacturing 

9713 Amusement gambling and recreation 

industries 

9325 Chemical manufacturing 9721 Accommodation services 

9326 Plastics and rubber products 

manufacturing 

9722 Food services and drinking places 

9327 Non-metallic mineral product 

manufacturing 

9723 Total Tourism 

9331 Primary Metal Manufacturing 9811 Repair and maintenance 

9332 Fabricated metal product 

manufacturing 

9812 Personal and laundry services 

9333 Machinery manufacturing 9813 Religious grant-making civic and 

professional and similar 

organizations 

9334 Computer and Electronic 

manufacturing 

9814 Private households 

9335 Electrical equipment appliance and 

component manufacturing 

9815 Total other services 

9336 Transportation equipment 

manufacturing 

9911 Federal government public 

administration 

9337 Furniture and related product 

manufacturing 

9912 Provincial and territorial public 

administration (9121 to 9129) 

9338 Miscellaneous manufacturing 9913 Local municipal and regional public 

administration (9131 to 9139) 

9339 Total Manufacturing 9914 Aboriginal public administration 

9411 Farm product wholesaler-distributors 9919 International and other extraterritorial public administration 

9412 Petroleum product wholesalerdistributors 9920 Total Government 

9413 Food beverage and tobacco 

wholesaler-distributors 

9921 Commercial Fuel Combustion 

9414 Personal and household goods 

wholesaler-distributors 

9922 TOTAL DISTRIBUTION AND 

RETAIL 

9415 Motor vehicle and parts wholesalerdistributors 9923 TOTAL INSTITUTIONAL AND 

GOVERNEMNT 
52

Code Description Code Description 

9416 Building material and supplies 

wholesaler-distributors 

9924 Primary Industry 

9417 Machinery equipment and supplies 

wholesaler-distributors 

9925 Manufacturing and Assembly 

9418 Miscellaneous wholesaler-distributors 9926 Distribution and Retail (no 

petroleum) 

9419 Wholesale agents and brokers 9927 Commercial Services 

9420 Total Wholesale 9928 Commercial Meat cooking 

9441 Motor vehicle and parts dealers 9929 HIGHJET 

9442 Furniture and home furnishings stores 9930 LOWMEDJET 

9443 Electronics and appliance stores 9931 OTHERJET 

9444 Building material and garden 

equipment and supplies dealers 

9932 CANRAIL 

9445 Food and beverage stores 9933 Forest fires 

9446 Health and personal care stores 9941 PAVED ROADS 

9447 Gasoline stations 9942 UNPAVED ROADS 

9448 clothing and clothing accessories 

stores 

9943 HIGHWAY 

9451 Sporting goods hobby book and music 

stores 

9944 ROAD 

9452 General Merchandise stores 9945 Commercial Marine Vessels 

9453 Miscellaneous store retailers 9946 Construction and mining 

9454 Non-store retailers 9947 Agriculture Construction and mining 

9455 Total Retail 9950 Intersection of Forest and Housing 

9481 Air transportation 9960 TOTBEEF 

9482 Rail transportation 9970 TOTPOUL 

9483 Water Transportation 9980 TOTSWIN 

9484 Truck transportation 9990 TOTFERT 

9485 Transit and ground passenger 

transportation 

9993 Trail 

9486 Pipeline transportation 9994 ALLROADS 

9487 Scenic and sightseeing transportation 9995 30UNPAVED_70trail 

9488 Support activities for transportation 9996 Urban area 

9491 Postal service 9997 CHBOISQC 

9492 Couriers and messengers 9991 Traffic 
53

REFERENCES 

Adelman, Z. 2012. Memorandum: Fugitive Dust Modeling for the 2008 Emissions Modeling Platform. UNC 

Institute for the Environment, Chapel Hill, NC. September, 28, 2012. 

Anderson, G.K.; Sandberg, D.V; Norheim, R.A., 2004. Fire Emission Production Simulator (FEPS) User's 

Guide. Available at http://www.fs.fed.us/pnw/fera/feps/FEPS_users_guide.pdf

Bullock Jr., R, and K. A. Brehme (2002) “Atmospheric mercury simulation using the CMAQ model: 

formulation description and analysis of wet deposition results.” Atmospheric Environment 36, pp 2135–

2146. 

ERG, 2006. Mexico National Emissions Inventory, 1999: Final, prepared by Eastern Research Group for 

Secratariat of the Environment and Natural Resources and the National Institute of Ecology, Mexico, 

October 11, 2006. Available at: 

http://www.epa.gov/ttn/chief/net/mexico/1999_mexico_nei_final_report.pdf

Environ Corp. 2008. Emission Profiles for EPA SPECIATE Database, Part 2: EPAct Fuels (Evaporative 

Emissions). Prepared for U. S. EPA, Office of Transportation and Air Quality, September 30, 2008. 

EPA, 2005. EPA’s National Inventory Model (NMIM), A Consolidated Emissions Modeling System for 

MOBILE6 and NONROAD, U.S. Environmental Protection Agency, Office of Transportation and Air 

Quality, Assessment and Standards Division. Ann Arbor, MI 48105, EPA420-R-05-024, December 2005. 

Available at http://www.epa.gov/otaq/models/nmim/420r05024.pdf. 

EPA 2006a. SPECIATE 4.0, Speciation Database Development Document, Final Report, U.S. Environmental 

Protection Agency, Office of Research and Development, National Risk Management Research Laboratory, 

Research Triangle Park, NC 27711, EPA600-R-06-161, February 2006. Available at 

http://www.epa.gov/ttn/chief/software/speciate/speciate4/documentation/speciatedoc_1206.pdf.

EPA, 2012a. 2008 National Emissions Inventory, version 2 Technical Support Document. Office of Air Quality 

Planning and Standards, Air Quality Assessment Division, Research Triangle Park, NC. Available at: 

http://www.epa.gov/ttn/chief/net/2008inventory.html#inventorydoc

Frost & Sullivan, 2010. “Project: Market Research and Report on North American Residential Wood Heaters, 

Fireplaces, and Hearth Heating Products Market (P.O. # PO1-IMP403-F&S). Final Report April 26, 

2010”. Prepared by Frost & Sullivan, Mountain View, CA 94041. 

Joint Fire Science Program, 2009. Consume 3.0--a software tool for computing fuel consumption. Fire Science 

Brief. 66, June 2009. Consume 3.0 is available at: 

http://www.fs.fed.us/pnw/fera/research/smoke/consume/index.shtml

Kochera, A., 1997. “Residential Use of Fireplaces,” Housing Economics, March 1997, 10-11. Also see: 

http://www.epa.gov/ttnchie1/conference/ei10/area/houck.pdf. 

LADCO, 2012. “Regional Air Quality Analyses for Ozone, PM2.5, and Regional Haze: Base C Emissions 

Inventory (September 12, 2011)”. Lake Michigan Air Directors Consortium, Rosemont, IL 60018. 

Available at: http://www.ladco.org/tech/emis/basecv8/Base_C_Emissions_Documentation_Sept_12.pdf

McCarty, J.L., Korontzi, S., Jutice, C.O., and T. Loboda. 2009. The spatial and temporal distribution of crop 

residue burning in the contiguous United States. Science of the Total Environment, 407 (21): 5701-5712. 

McKenzie, D.; Raymond, C.L.; Kellogg, L.-K.B.; Norheim, R.A; Andreu, A.G.; Bayard, A.C.; Kopper, K.E.; 

Elman. E. 2007. Mapping fuels at multiple scales: landscape application of the Fuel Characteristic 

Classification System. Canadian Journal of Forest Research. 37:2421-2437. Oak Ridge National 

Laboratory, 2009. Analysis of Fuel Ethanol Transportation Activity and Potential Distribution 

Constraints. U.S. Department of Energy, March 2009. Docket No. EPA–HQ–OAR–2010–0133. 

Ottmar, R.D.; Sandberg, D.V.; Bluhm, A. 2003. Biomass consumption and carbon pools. Poster. In: Galley, 

K.E.M., Klinger, R.C.; Sugihara, N.G. (eds.) Proceedings of Fire Ecology, Prevention, and Management. 
54

Misc. Pub. 13, Tallahassee, FL: Tall Timbers Research Station. 

Ottmar, R.D.; Prichard, S.J.; Vihnanek, R.E.; Sandberg, D.V. 2006. Modification and validation of fuel 

consumption models for shrub and forested lands in the Southwest, Pacific Northwest, Rockes, Midwest, 

Southeast, and Alaska. Final report, JFSP Project 98-1-9-06. 

Ottmar, R.D.; Sandberg, D.V.; Riccardi, C.L.; Prichard, S.J. 2007. An Overview of the Fuel Characteristic 

Classification System – Quantifying, Classifying, and Creating Fuelbeds for Resource Planning. 

Canadian Journal of Forest Research. 37(12): 2383-2393. FCCS is available at: 

http://www.fs.fed.us/pnw/fera/fccs/index.shtml

Pouliot, G., H. Simon, P. Bhave, D. Tong, D. Mobley, T. Pace, and T. Pierce . (2010) “Assessing the 

Anthropogenic Fugitive Dust Emission Inventory and Temporal Allocation Using an Updated Speciation 

of Particulate Matter.” International Emission Inventory Conference, San Antonio, TX. Available at 

http://www.epa.gov/ttn/chief/conference/ei19/session9/pouliot.pdf

Raffuse, S., N. Larkin, P. Lahm, Y. Du, 2012. Development of Version 2 of the Wildland Fire Portion of the 

[2011] National Emissions Inventory. International Emission Inventory Conference, Tampa, FL. 

Available at: http://www.epa.gov/ttn/chief/conference/ei20/session2/sraffuse.pdf 

Raffuse, S., D. Sullivan, L. Chinkin, S. Larkin, R. Solomon, A. Soja, 2007. Integration of Satellite-Detected and 

Incident Command Reported Wildfire Information into BlueSky, June 27, 2007. Available at: 

http://getbluesky.org/smartfire/docs.cfm

Russell, A.G. and G.R. Cass, 1986. Verification of a Mathematical Model for Aerosol Nitrate and Nitric Acid 

Formation and Its Use for Control Measure Evaluation, Atmospheric Environment, 20: 2011-2025. 

SESARM, 2012a. “Development of the 2007 Base Year and Typical Year Fire Emission Inventory for the 

Southeastern States”, Air Resources Managers, Inc., Fire Methodology, AMEC Environment and 

Infrastructure, Inc. AMEC Project No.: 6066090326, April, 2012 

SESARM, 2012b. “Area and Nonroad 2007 Base Year Inventories. Revised Final Report”, Contract No. S-2009-06-01, 

Prepared by Transystems Corporation, January 2012. Available at:

http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&cad=rja&ved=0CDAQFjAC&url=ftp%

3A%2F%2Fwsip-70-164-45-

196.dc.dc.cox.net%2Fpublic%2FSESARM%2FRevised%2520Final%2FSESARM%2520Base%2520Year%2520

Revised%2520Final%2520Report_Jan2012.docx&ei=xU-AUPu1F4WA0AHC5YHYCg&usg=AFQjCNFhjgx3EjhbfYmMUP4zGI_HBiqZA&sig2=hWWN0m3WYPSO28QSzn5BIA. 

Skamarock, W., J. Klemp, J. Dudhia, D. Gill, D. Barker, M. Duda, X. Huang, W. Wang, J. Powers, 2008. A 

Description of the Advanced Research WRF Version 3. NCAR Technical Note. National Center for 

Atmospheric Research, Mesoscale and Microscale Meteorology Division, Boulder, CO. June 2008. 

Available at: http://www.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf

Sullivan D.C., Raffuse S.M., Pryden D.A., Craig K.J., Reid S.B., Wheeler N.J.M., Chinkin L.R., Larkin N.K., 

Solomon R., and Strand T. (2008) Development and applications of systems for modeling emissions and 

smoke from fires: the BlueSky smoke modeling framework and SMARTFIRE: 17th International 

Emissions Inventory Conference, Portland, OR, June 2-5. Available at: 

http://www.epa.gov/ttn/chief/conferences.html

Wang, Y., P. Hopke, O. V. Rattigan, X. Xia, D. C. Chalupa, M. J. Utell. (2011) “Characterization of Residential 

Wood Combustion Particles Using the Two-Wavelength Aethalometer”, Environ. Sci. Technol., 45 (17), 

pp 7387–7393 

Yarwood, G., S. Rao, M. Yocke, and G. Whitten, 2005: Updates to the Carbon Bond Chemical Mechanism: 

CB05. Final Report to the US EPA, RT-0400675. Available at 

http://www.camx.com/publ/pdfs/CB05_Final_Report_120805.pdf.
55

4.0   CMAQ Air Quality Model Estimates

4.1 Introduction to the CMAQ Modeling Platform

The Clean Air Act (CAA) provides a mandate to assess and manage air pollution levels to protect human 

health and the environment. EPA has established National Ambient Air Quality Standards (NAAQS), 

requiring the development of effective emissions control strategies for such pollutants as ozone and 

particulate matter. Air quality models are used to develop these emission control strategies to achieve the 

objectives of the CAA.

Historically, air quality models have addressed individual pollutant issues separately. However, many of 

the same precursor chemicals are involved in both ozone and aerosol (particulate matter) chemistry; 

therefore, the chemical transformation pathways are dependent. Thus, modeled abatement strategies of 

pollutant precursors, such as volatile organic compounds (VOC) and NOx to reduce ozone levels, may 

exacerbate other air pollutants such as particulate matter. 

To meet the need to address the complex relationships between pollutants, EPA developed the Community 

Multiscale Air Quality (CMAQ) modeling system. The primary goals for CMAQ are to:

 Improve the environmental management community’s ability to evaluate the impact of air quality 

management practices for multiple pollutants at multiple scales.

 Improve the scientist’s ability to better probe, understand, and simulate chemical and physical 

interactions in the atmosphere.

The CMAQ modeling system brings together key physical and chemical functions associated with the 

dispersion and transformations of air pollution at various scales. It was designed to approach air quality as 

a whole by including state-of-the-science capabilities for modeling multiple air quality issues, including 

tropospheric ozone, fine particles, toxics, acid deposition, and visibility degradation. CMAQ relies on 

emission estimates from various sources, including the U.S. EPA Office of Air Quality Planning and 

Standards’ current emission inventories, observed emission from major utility stacks, and model estimates 

of natural emissions from biogenic and agricultural sources. CMAQ also relies on meteorological 

predictions that include assimilation of meteorological observations as constraints. Emissions and 

meteorology data are fed into CMAQ and run through various algorithms that simulate the physical and 

chemical processes in the atmosphere to provide estimated concentrations of the pollutants. Traditionally, 

the model has been used to predict air quality across a regional or national domain and then to simulate 

the effects of various changes in emission levels for policymaking purposes. For health studies, the model 

can also be used to provide supplemental information about air quality in areas where no monitors exist. 

CMAQ was also designed to have multi-scale capabilities so that separate models were not needed for 

urban and regional scale air quality modeling. The grid spatial resolutions in past annual CMAQ runs 

have been 36 km x 36 km per grid for the “parent” domain, and nested within that domain are 12 km x 12 

km grid resolution domains. The parent domain typically covered the continental United States, and the 
56

nested 12 km x 12 km domain covered the Eastern or Western United States. The CMAQ simulation 

performed for this 2009 assessment used a single domain that covers the entire continental U.S. (CONUS) 

and large portions of Canada and Mexico using 12 km by 12 km horizontal grid spacing. For urban 

applications, CMAQ has also been applied with a 4-km x 4-km grid resolution for urban core areas; 

however, the uncertainties in emissions and meteorology information can actually increase at this high of 

a resolution. Currently, 12 km x 12 km resolution is recommended for most applications as the highest 

resolution. With the temporal flexibility of the model, simulations can be performed to evaluate longer 

term (annual to multi-year) pollutant climatologies as well as short-term (weeks to months) transport from 

localized sources. By making CMAQ a modeling system that addresses multiple pollutants and different 

temporal and spatial scales, CMAQ has a “one atmosphere” perspective that combines the efforts of the 

scientific community. Improvements will be made to the CMAQ modeling system as the scientific 

community further develops the state-of-the-science. For more information on CMAQ, go to 

http://www.epa.gov/asmdnerl/CMAQ or http://www.cmascenter.org.

4.1.1 Advantages and Limitations of the CMAQ Air Quality Model 

An advantage of using the CMAQ model output for comparing with health outcomes is that it has the 

potential to provide complete spatial and temporal coverage. Additionally, meteorological predictions, 

which are also needed when comparing health outcomes, are available for every grid cell along with the 

air quality predictions.

A disadvantage of using CMAQ is that, as a deterministic model, it has none of the statistical qualities of 

interpolation techniques that fit the observed data to one degree or another. Furthermore, the emissions 

and meteorological data used in CMAQ each have large uncertainties, in particular for unusual emission 

or meteorological events. There are also uncertainties associated with the chemical transformation and 

fate process algorithms used in air quality models. Thus, emissions and meteorological data plus modeling 

uncertainties cause CMAQ to predict best on longer time scale bases (e.g., synoptic, monthly, and annual 

scales) and be most error prone at high time and space resolutions compared to direct measures.

One practical disadvantage of using CMAQ output is that the regularly spaced grid cells do not line up 

directly with counties or ZIP codes which are the geographical units over which health outcomes are 

likely to be aggregated. But it is possible to overlay grid cells with county or ZIP code boundaries and 

devise means of assigning an exposure level that nonetheless provides more complete coverage than that 

available from ambient data alone. Another practical disadvantage is that CMAQ requires significant data 

and computing resources to obtain results for daily environmental health surveillance.

This section describes the air quality modeling platform used for the 2009 CMAQ simulation. A modeling 

platform is a structured system of connected modeling-related tools and data that provide a consistent and 

transparent basis for assessing the air quality response to changes in emissions and/or meteorology. A 

platform typically consists of a specific air quality model, emissions estimates, a set of meteorological 

inputs, and estimates of “boundary conditions” representing pollutant transport from source areas outside 

the region modeled. We used the CMAQ6 model as part of the 2009 Platform to provide a national scale 

 

6 Byun, D.W., and K. L. Schere, 2006: Review of the Governing Equations, Computational Algorithms, and Other 

Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System. Applied Mechanics Reviews, 

Volume 59, Number 2 (March 2006), pp. 51-77. 
57

air quality modeling analysis. The CMAQ model simulates the multiple physical and chemical processes 

involved in the formation, transport, and destruction of ozone and fine particulate matter (PM2.5). 

This section provides a description of each of the main components of the 2009 CMAQ simulation along 

with the results of a model performance evaluation in which the 2009 model predictions are compared to 

corresponding measured concentrations. 

4.2 CMAQ Model Version, Inputs and Configuration

4.2.1 Model Version 

CMAQ is a non-proprietary computer model that simulates the formation and fate of photochemical 

oxidants, including PM2.5 and ozone, for given input sets of meteorological conditions and emissions. The 

CMAQ model version 4.7 was most recently peer-reviewed in February of 2009 for the U.S. EPA7. As 

mentioned previously, CMAQ includes numerous science modules that simulate the emission, 

production, decay, deposition and transport of organic and inorganic gas-phase and pollutants in the 

atmosphere. This analysis employed a version of CMAQ based on the latest publicly released version of 

CMAQ (i.e., version 4.7.18

) at the time of the 2009 air quality modeling. CMAQ version 4.7.1 reflects 

updates to version 4.7 to improve the underlying science which include aqueous chemistry mass 

conservation improvements and improved vertical convective mixing. The model enhancements in 

version 4.7.1 also include: 

1. Aqueous chemistry 

 Mass conservation improvements 

- Imposed one second minimum timestep for remainder of the cloud lifetime after 100 

‘iterations’ in the solver 

- Force mass balance for the last timestep in the cloud by limiting oxidized amount to mass 

available

 Implemented steady state assumption for OH 

 Only allow sulfur oxidation to control the aqueous chemistry solver timestep (previously, 

reactions of OH, GLY, MGLY, and Hg for multipollutant model also controlled the timestep) 

2. Advection

 Added additional divergence-based constraint on advection timestep 

 Vertical advection in the Yamo module is now represented with the PPM scheme to limit 

numerical diffusion 

 

7 Allen, D., Burns, D., Chock, D., Kumar, N., Lamb, B., Moran, M. (February 2009 Draft Version). Report on the Peer 

Review of the Atmospheric Modeling and Analysis Division, NERL/ORD/EPA. U.S. EPA, Research Triangle Park, NC. 

CMAQ version 4.7 was released on December, 2008. It is available from the Community Modeling and Analysis System 

(CMAS) as well as previous peer-review reports at: http://www.cmascenter.org.

8 CMAQ version 4.7.1 model code is available from the Community Modeling and Analysis System (CMAS) at: 

http://www.cmascenter.org.
58

3. Model time step determination

 Fixed a potential advection time step error 

- The sum of the advection steps for a given layer time step might not equal the output time 

step duration in some extreme cases

- Ensured that the advection steps sum up to the synchronization step

4. Horizontal diffusion 

 Fixed a potential error 

- Concentration data may not be correctly initialized if multiple sub-cycle time steps are 

required 

- Fix to initialize concentrations with values calculated in the previous sub-time step 

5. Emissions

 Bug fix in EMIS_DEFN.F to include point source layer 1 NH3 emissions 

 Bug fix to calculate soil NO “pulse” emissions in BEIS 

 Remove excessive logging of cases where ambient air temperature exceeds 315.0 Kelvin. When 

this occurs, the values are just slightly over 315

 Bug fix for parallel decomposition errors in plume rise emissions

6. Photolysis

 JPROC/phot_table and phot_sat options

- Expanded lookup tables to facilitate applications across the globe and vertical extent to 

20km

- Updated temperature adjustments for absorption cross sections and quantum yields

- Revised algorithm that processes TOMS datasets for OMI data format

 In-line option

- Asymmetry factor calculation updated using values from Mie theory integrated over log 

normal particle distribution; added special treatment for large particles in asymmetry factor 

algorithm to avoid numerical instabilities

4.2.2 Model Domain and Grid Resolution

The CMAQ modeling analyses were performed for a domain covering the continental United States, as 

shown in Figure 4-1. This single domain covers the entire continental U.S. (CONUS) and large portions 

of Canada and Mexico using 12 km by 12 km horizontal grid spacing. The model extends vertically from 

the surface to 50 millibars (approximately 19 km) using a sigma-pressure coordinate system. Air quality 

conditions at the outer boundary of the 12 km domain were taken from a global model. Table 4-1 provides 

some basic geographic information regarding the 12 km CMAQ domain.
59

Table 4-1. Geographic Information for 12 km Modeling Domain 

National 12 km CMAQ Modeling Configuration 

Map Projection Map Projection 

Grid Resolution Grid Resolution 

Coordinate 

Center 

Coordinate Center 

True Latitudes True Latitudes 

Dimensions Dimensions 

Vertical Extent Vertical Extent 

Figure 4-1. Map of the CMAQ Modeling Domain. The blue box denotes the 12 km national 

modeling domain. (Same as Figure 3-1.) 

4.2.3 Modeling Period / Ozone Episodes 

The 12 km CMAQ modeling domain was modeled for the entire year of 2009. The 2009 annual 

simulation was performed in two half-year segments (i.e., January through June, and July through 

December) for each emissions scenario. With this approach to segmenting an annual simulation we were 

able to reduce the overall throughput time for an annual simulation. The annual simulation included a 

“ramp-up” period, comprised of 10 days before the beginning of each half-year segment, to mitigate the 

effects of initial concentrations. All 365 model days were used in the annual average levels of PM2.5. For 
60

the 8-hour ozone, we used modeling results from the period between May 1 and September 30. This 153-

day period generally conforms to the ozone season across most parts of the U.S. and contains the majority 

of days that observed high ozone concentrations. 

4.2.4 Model Inputs: Emissions, Meteorology and Boundary Conditions 

2009 Emissions: The emissions inventories used in the 2009 air quality modeling are described in Section 

3, above. 

Meteorological Input Data: The gridded meteorological data for the entire year of 2009 at the 12 km 

continental United States scale domain was derived from version 3.2 of the Weather Research and 

Forecasting Model (WRF), Advanced Research WRF (ARW) core.9 Previous CMAQ annual simulations 

have typically utilized meteorology provided by the 5th Generation Mesoscale Model (MM5).10 The WRF 

Model is a next-generation mesoscale numerical weather prediction system developed for both operational 

forecasting and atmospheric research applications (http://wrf-model.org). The 2009 WRF simulation 

included the physics options of the Pleim-Xiu land surface model (LSM), Asymmetric Convective Model 

version 2 planetary boundary layer (PBL) scheme, Morrison double moment microphysics, Kain- Fritsch 

cumulus parameterization scheme and the RRTMG long-wave and shortwave radiation (LWR/SWR) 

scheme.11

The WRF meteorological outputs were processed to create model-ready inputs for CMAQ using the 

Meteorology- Chemistry Interface Processor (MCIP) package12, version 3.6, to derive the specific inputs 

to CMAQ: horizontal wind components (i.e., speed and direction), temperature, moisture, and its related 

speciated components was conducted for vertical diffusion rates, and rainfall rates for each grid cell in 

each vertical layer. The WRF simulation used the same CMAQ map projection, a Lambert Conformal 

projection centered at (-97, 40) with true latitudes at 33 and 45 degrees north. The 12 km WRF domain 

consisted of 459 by 299 grid cells. The WRF simulation utilized 34 vertical layers with a surface layer of 

approximately 38 meters. Table 4-2 shows the vertical layer structure used in WRF and the layer 

collapsing approach to generate the CMAQ meteorological inputs. CMAQ resolved the vertical 

atmosphere with 24 layers, preserving greater resolution in the PBL.

In terms of the 2009 WRF meteorological model performance evaluation, an approach which included a 

combination of qualitative and quantitative analyses was used to assess the adequacy of the WRF 

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 

 

9 Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X., Wang, W., Powers, J.G., 2008. 

A Description of the Advanced Research WRF Version 3. 

10 Grell, G. A., Dudhia, A. J., and Stauffer, D. R., 1994. A description of the Fifth-Generation PennState/NCAR Mesoscale 

Model (MM5). NCAR Technical Note NCAR/TN-398+STR. Available at http://www.mmm. ucar.edu/mm5/doc1.html. 

11 Gilliam, R.C., Pleim, J.E., 2010. Performance Assessment of New Land Surface and Planetary Boundary Layer Physics in 

the WRF-ARW. Journal of Applied Meteorology and Climatology 49, 760-774. 

12 Otte T.L., Pleim, J.E., 2010. The Meteorology-Chemistry Interface Processor (MCIP) for the CMAQ modeling system: 

updates through v3.4.1. Geoscientific Model Development 3, 243-256. 
61

spatial patterns of monthly average rainfall and monthly maximum planetary boundary layer (PBL) 

heights. The statistical portion of the evaluation examined the model bias and error for temperature, water 

vapor mixing ratio, solar radiation, and wind fields. These statistical values were calculated on a monthly 

basis. 

Table 4-2Vertical layer structure for 2009 WRF and CMAQ simulations (heights are layer top) 

Height 

(m) 

Pressure 

(mb) WRF Depth (m) CMAQ Depth (m) 

17,145 50 34 2,655 24 4,552 

14,490 95 33 1,896

12,593 140 32 1,499 23 2,749 

11,094 185 31 1,250

9,844 230 30 1,078 22 2,029 

8,766 275 29 951

7,815 320 28 853 21 1,627 

6,962 365 27 775

6,188 410 26 711 20 1,368 

5,477 455 25 657

4,820 500 24 612 19 1,185 

4,208 454 23 573

3,635 590 22 539 18 539 

3,095 635 21 509 17 509 

2,586 680 20 388 16 388 

2,198 716 19 281 15 281 

1,917 743 18 273 14 273 

1,644 770 17 178 13 178 

1,466 788 16 174 12 174 

1,292 806 15 171 11 171 

1,121 824 14 168 10 168 

952 842 13 165 9 165 

787 860 12 82 8 163 

705 869 11 81

624 878 10 80 7 160 

544 887 9 80

465 896 8 79 6 157 

386 905 7 78

307 914 6 78 5 78 

230 923 5 77 4 77 

153 932 4 38 3 76 

114 937 3 38

76 941 2 38 2 38 

38 946 1 38 1 38 
62

Initial and Boundary Conditions: The lateral boundary and initial species concentrations are provided by 

a three- dimensional global atmospheric chemistry model, the GEOS-CHEM13 model (standard version 8-

03-02 with 8-02-03 chemistry). 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 2009 with a grid resolution of 2.0 degrees x 2.5 

degrees (latitude-longitude) and 46 vertical layers up to 0.01 hPa. The predictions were processed using 

the GEOS-2-CMAQ tool and used to provide one-way dynamic boundary conditions at one-hour 

intervals.14 Ozone was evaluated from these GEOS-Chem runs by comparing to satellite vertical profiles 

and ground-based measurements and found acceptable model performance. More information is available 

about the GEOS-CHEM model and other applications using this tool at: http://wwwas.harvard.edu/chemistry/trop/geos.

4.3 CMAQ Model Performance Evaluation

An operational model performance evaluation for ozone and PM2.5 and its related speciated components 

was conducted for the 2009 simulation using state/local monitoring sites data in order to estimate the 

ability of the CMAQ modeling system to replicate the 2009 base year concentrations for the 12 km 

continental U.S. domain.

There are various statistical metrics available and used by the science community for model performance 

evaluation. For a robust evaluation, the principal evaluation statistics used to evaluate CMAQ 

performance were two bias metrics, normalized mean bias and fractional bias; and two error metrics, 

normalized mean error and fractional error. Normalized mean bias (NMB) is used as a normalization to 

facilitate a range of concentration magnitudes. This statistic averages the difference (model - observed) 

over the sum of observed values. NMB is a useful model performance indicator because it avoids 

overinflating the observed range of values, especially at low concentrations. Normalized mean bias is 

defined as: 

(NMB = 

 

 

P O

O

n

n

 



1

1

*100, where P = predicted concentrations and O = observed 

Normalized mean error (NME) is also similar to NMB, where the performance statistic is used as a 

normalization of the mean error. NME calculates the absolute value of the difference (model - observed) 

over the sum of observed values. Normalized mean error is defined as:

 

13 Yantosca, B., 2004. GEOS-CHEMv7-01-02 User’s Guide, Atmospheric Chemistry Modeling Group, Harvard University, 

Cambridge, MA, October 15, 2004. 

14 Akhtar, F., Henderson, B., Appel, W., Napelenok, S., Hutzell, B., Pye, H., Foley, K., 2012. Multiyear Boundary Conditions 

for CMAQ 5.0 from GEOS-Chem with Secondary Organic Aerosol Extensions, 11th Annual Community Modeling and 

Analysis System conference, Chapel Hill, NC, October 2012. 
63

NME = 

 

P O

O

n

n

 



1

1

*100 

Fractional bias is defined as:

FB = 

 

 

1

2

1

1

n

P O

P O

n

n



 



 

































*100

FB is a useful model performance indicator because it has the advantage of equally weighting positive and 

negative bias estimates. The single largest disadvantage in this estimate of model performance is that the 

estimated concentration (i.e., prediction, P) is found in both the numerator and denominator. 

Fractional error (FE) is similar to fractional bias except the absolute value of the difference is used so that 

the error is always positive. Fractional error is defined as: 

FE =  

1

2

1

1

n

P O

P O

n

n



 



 

































*100 

In addition to the performance statistics, regional maps which show the normalized mean bias and error 

were prepared for the ozone season, May through September, at individual monitoring sites as well as on 

an annual basis for PM2.5 and its component species. 

Evaluation for 8-hour Daily Maximum Ozone: The operational model performance evaluation for eighthour daily maximum ozone was conducted using the statistics defined above. Ozone measurements for 

2009 in the continental U.S. were included in the evaluation and were taken from the 2009 State/local 

monitoring site data in the Air Quality System (AQS) Aerometric Information Retrieval System (AIRS). 

The performance statistics were calculated using predicted and observed data that were paired in time and 

space on an 8-hour basis. Statistics were generated for the following geographic groupings in the 12-km 

continental U.S. domain15: five large subregions: Midwest, Northeast, Southeast, Central and Western 

U.S. 

The 8-hour ozone model performance bias and error statistics for each subregion and each season are 

provided in Table 4-4. Seasons were defined as: winter (December-January- February), spring (MarchApril-May), summer (June, July, August), and fall (September-October-November). Spatial plots of the 

normalized mean bias and error for individual monitors are shown in Figures 4-2 through 4-3. The 

statistics shown in these two figures were calculated over the ozone season using data pairs on days with 

observed 8-hour ozone of greater than or equal to 60 ppb. 

 

15 The subregions are defined by States where: Midwest is IL, IN, MI, OH, and WI; Northeast is CT, DE, MA, MD, ME, NH, 

NJ, NY, PA, RI, and VT; Southeast is AL, FL, GA, KY, MS, NC, SC, TN, VA, and WV; Central is AR, IA, KS, LA, MN, MO, 

NE, OK, and TX; West is AK, CA, OR, WA, AZ, NM, CO, UT, WY, SD, ND, MT, ID, and NV.
64

In general, the model performance statistics indicate that the 8-hour daily maximum ozone concentrations 

predicted by the 2009 CMAQ simulation closely reflect the corresponding 8-hour observed ozone 

concentrations in space and time in each subregion of the 12 km modeling domain. As indicated by the 

statistics in Table 4-4, bias and error for 8-hour daily maximum ozone are relatively low in each 

subregion, not only in the summer when concentrations are highest, but also during other times of the 

year. Specifically, 8-hour ozone in the summer is slightly over predicted with the greatest over prediction 

in the Southeast (NMB is 23.1 percent). Ozone performance in Spring shows better performance with 

slight over predictions in most of the subregions except in the West (slight under prediction of 0.6). In the 

winter, when concentrations are generally low, the model slightly over predicts 8-hour ozone with the 

exception of the Northeast (NMB is -11.2). In the fall, when concentrations are also relatively low, ozone 

is also slightly over predicted; with NMBs less than 24 percent in each subregion. 

Model bias at individual sites during the ozone season is similar to that seen on a subregional basis for the 

summer. The information in Figure 4-2 indicates that the bias for days with observed 8-hour daily 

maximum ozone greater than 60 ppb is within ± 20 percent at the vast majority of monitoring sites across 

the U.S. domain. The exceptions are sites in and/or near Chicago, IL, Baton Rouge, LA, Tampa and 

Orlando, FL, northern (St. Lawrence/ Franklin counties) NY, Greenville, WV, Brunswick, GA; as well as 

a few areas along the southern California coast. At these sites observed concentrations greater than 60 ppb 

are generally predicted in the range of ±20 to 40 percent. Looking at the map of bias, Figure 4-2 indicates 

that the low bias at these sites is not evident at other sites in these same areas. This suggests that the under 

prediction at these sites is likely due to very local features (e.g., meteorology and/or emissions) and not 

indicative of a systematic problem in the modeling platform. Model error, as seen from Figure 4-3, is 30 

percent or less at most of the sites across the U.S. modeling domain. Somewhat greater error is evident at 

sites in several areas most notably along portions of the Northeast Corridor and in portions of Florida, 

Louisiana, Texas, Mississippi, Alabama, South Carolina and along the California coastline. 

Table 4-3. Summary of CMAQ 2009 8-Hour Daily Maximum Ozone Model Performance Statistics 

by Subregion, by Season 

Subregion Season No. of 

Obs 

NMB 

(%) 

NME 

(%) FB (%) FE (%) 

Northeast Winter 5,472 -11.2 19.2 -11.9 21.7 

 Spring 11,995 0.8 12.0 1.7 12.7 

Summer 15,215 14.6 19.4 14.7 19.1 

 Fall 11,070 18.1 24.3 18.3 24.1 

Midwest Winter 2,708 0.5 23.9 -4.3 23.0 

Spring 11,616 2.4 13.0 3.4 13.7 

 Summer 15,914 13.2 18.3 13.4 18.1 

Fall 9,350 15.8 20.9 16.4 22.0 

 

Central States Winter 11,083 4.4 16.5 5.7 18.2 

 Spring 14,851 5.0 14.7 6.7 15.6 

Summer 16,464 21.1 26.2 21.0 25.3 

 Fall 14,495 11.0 20.2 12.8 21.5 
65

Southeast Winter 6,536 5.5 14.9 11.6 6.3 

Spring 17,194 9.8 16.4 11.1 17.0 

 Summer 19,395 23.1 26.0 22.8 25.2 

Fall 15,308 24.4 28.7 23.9 27.6 

 

West Winter 22,813 14.4 22.7 16.4 24.5 

 Spring 26,499 -0.6 12.4 -0.3 13.0 

Summer 29,460 6.8 16.5 7.4 16.6 

 Fall 26,324 8.2 17.1 9.7 17.9 

Figure 4-2. Normalized Mean Bias (%) of 8-hour daily maximum ozone greater than 60 ppb over 

the period May- September 2009 at monitoring sites in the continental U.S. modeling domain
66

Figure 4-3. Normalized Mean Error (%) of 8-hour daily maximum ozone greater than 60 ppb over 

the period May-September 2009 at monitoring sites in the continental U.S. modeling domain 

Evaluation for Annual PM2.5: The PM evaluation focuses on PM2.5 total mass and its components 

including sulfate (SO4), nitrate (NO3), total nitrate (TNO3 = NO3 + HNO3), ammonium (NH4), elemental 

carbon (EC), and organic carbon (OC). 

The PM2.5 bias and error performance statistics were calculated on an annual basis for each subregion 

(Table 4-5). PM2.5 measurements for 2009 were obtained from the following networks for model 

evaluation: Chemical Speciation Network (CSN, 24 hour average), Interagency Monitoring of PROtected 

Visual Environments (IMPROVE, 24 hour average, and Clean Air Status and Trends Network 

(CASTNet, weekly average). For PM2.5 species that are measure by more than one network, we 

calculated separate sets of statistics for each network by subregion. For brevity, Table 4-5 provides 

annual model performance statistics for PM2.5 and its component species for the five sub-regions in the 12 

km continental U.S. domain defined above (Northeast, Midwest, Southeast, Central, and West). In 

addition to the tabular summaries of bias and error statistics, annual spatial maps which show the 

normalized mean bias and error by site for each PM2.5 species are provided in Figures 4-4 through 4-17. 

As indicated by the statistics in Table 4-5, annual CMAQ PM2.5 for 2009 shows under predictions at rural 

IMPROVE monitoring sites and urban CSN monitoring sites in each subregion except in the Northeast 

and Midwest at CSN sites which shows a slight over prediction in NMB of 0 to 3 percent. Although not 

shown here, the mean observed concentrations of PM2.5 are more than twice as high at the CSN sites 

(~10μg m-3) as the IMPROVE sites (~5 μg m-3), thus illustrating the statistical differences between the 

urban CSN and rural IMPROVE networks. 

Annual average sulfate is consistently under predicted at CSN, IMPROVE, and CASTNet monitoring 

sites across the modeling domain, with NMB values ranging from -14 percent to -41 percent. Overall, 

sulfate bias performance is slightly better at rural IMPROVE sites than at urban CSN and/or suburban 
67

CASTNet sites. Sulfate performance shows moderate error, ranging from 28 to 45 percent. Figures 4-6 

and 4-7, suggest spatial patterns vary by region. The model bias for most of the Southeast, Central and 

Southwest states are within -20 to -40 percent. The model bias appears to be much less (±20 percent) 

in the Northeast, and Northwest states. A few sites in the West and in the Central U.S. have biases 

much greater than 20 percent. Model error also shows a spatial trend by region, where much of the 

Eatern states are 20 to 40 percent, the Western and Central U.S. states are 30 to 60 percent.

Annual average nitrate is over predicted at the urban and rural monitoring sites in most of the 

subregions in the12 km modeling domain (NMB in the range of 19% to 47%), except in the West 

where nitrate is under predicted (NMB in the range of -20% to -32%). The bias statistics indicate that 

the model performance for nitrate is generally best at the urban CSN monitoring sites. Model 

performance of total nitrate at sub-urban CASTNet monitoring sites shows an over prediction across 

all subregions. Model error for nitrate is somewhat greater for each subregion as compared to sulfate. 

Model bias at individual sites indicates mainly over prediction of greater than 20 percent at most 

monitoring sites in the Eastern half of the U.S. as well and in the extreme Northwest, as indicated in 

Figure 4-8. The exception to this is in the Florida and the Southwest of the modeling domain where 

there appears to be a greater number of sites with under prediction of nitrate of 20 to 80 percent. 

Model error for annual nitrate, as shown in Figure 4-9, is least at sites in portions of the Midwest and 

extending eastward to the Northeast corridor. Nitrate concentrations are typically higher in these areas 

than in other portions of the modeling domain.

Annual average ammonium model performance as indicated in Table 4-5 has a tendency for the model 

to under predict across the CSN and CASTNet sites (ranging from -1 to -25 percent). Ammonium is 

slightly over predicted in the Midwest at CASTNet sites (NMB = ~ 3 percent). There is not a large 

variation from subregion to subregion or at urban versus rural sites in the error statistics for ammonium. 

The spatial variation of ammonium across the majority of individual monitoring sites shows bias within 

±20 percent.

Annual average elemental carbon is over predicted in all subregions at urban and rural sites with the 

exception of the near negligible bias in the Central U.S. at IMPROVE sites. Similar to ammonium error 

there is not a large variation from subregion to subregion or at urban versus rural sites.

Annual average organic carbon is under predicted at both urban and rural monitoring sites in all 

subregions of the U.S. (NMB ranging from -4 to 45 percent). Similar to ammonium and elemental carbon, 

error model performance does not show a large variation from subregion to subregion or at urban versus 

rural sites (48 to 67 percent). 

Table 4-4. Summary of CMAQ 2009 Annual PM Species Model Performance Statistic 

Pollutant Monitor 

Network Subregion No. of Obs 

NMB 

(%) 

NME 

(%) FB (%) FE (%) 

PM2.5

CSN Northeast 2,754 3.0 37.7 0.4 36.6 

 Midwest 2,087 0.2 30.3 -4.6 33.2 

Southeast 2,345 -23.7 40.0 -32.8 46.1 

 Central 1,891 -14.8 41.7 -20.2 46.8 

West 2,986 -14.1 49.5 -15.2 51.0 
68

Pollutant Monitor 

Network Subregion No. of Obs 

NMB 

(%) 

NME 

(%) FB (%) FE (%) 

 

IMPROVE Northeast 2,317 -1.3 43.2 -9.0 43.3 

 Midwest 577 -8.3 33.4 -15.4 39.4 

Southeast 1,950 -25.2 44.3 -35.9 54.1 

 Central 2,500 -17.9 41.3 -24.2 49.3 

West 10,295 -27.9 56.3 -34.7 62.9 

 

Sulfate 

CSN Northeast 3,131 -20.5 34.3 -15.4 35.7 

 Midwest 2,238 -24.5 33.3 -24.7 36.4 

Southeast 2,837 -28.4 36.8 -32.7 42.9 

 Central 2,295 -30.8 42.2 -31.4 46.9 

West 3,196 -22.9 42.8 -15.6 44.7 

 

IMPROVE Northeast 2,307 -15.3 32.0 -6.3 33.7 

 Midwest 571 -25.8 35.4 -19.1 40.5 

Southeast 1,951 -27.1 37.0 -27.8 43.2 

 Central 2,446 -29.1 37.9 -24.5 41.6 

West 10,030 -14.4 44.9 -0.4 49.1 

 

CASTNet Northeast 769 -26.5 28.1 -26.7 29.9 

 Midwest 614 -30.5 31.2 -35.8 36.5 

Southeast 1,096 -35.1 36.2 -42.5 44.6 

 Central 381 -41.3 42.0 -46.4 48.3 

West 1,043 -29.8 39.2 -24.8 44.5 

 

Nitrate 

CSN Northeast 3,143 27.9 65.5 -11.7 71.1 

 Midwest 2,325 23.4 56 -8.7 67.8 

Southeast 2,851 25.2 93.3 56 106 

 Central 1,641 19.3 57.3 -24.5 80.6 

West 3,164 -32.4 61.8 -69.6 94.1 

 

IMPROVE Northeast 2,308 46.8 91.0 -13.5 91.2 

 Midwest 570 33.9 69.8 -19.7 90.7 

Southeast 1,951 24.7 108.0 -58.6 118.0 

 Central 2,445 33.9 74.1 -16.0 93.5 

West 10,016 -20.9 88.7 -76.2 122.0 

 

Total Nitrate 

(NO3 + 

HNO3) 

CASTNet Northeast 769 53.4 59.1 41.5 50.7 

 Midwest 614 33.9 43.6 33.8 40.7 

Southeast 1,096 35.6 54.3 27.0 49.9 

 Central 381 16.1 36.9 13.6 36.8 
69

Pollutant Monitor 

Network Subregion No. of Obs 

NMB 

(%) 

NME 

(%) FB (%) FE (%) 

West 1,043 4.8 40.3 15.4 43.0 

 

Ammonium 

CSN Northeast 3,131 -8.4 37.3 3.5 39.4 

 Midwest 2,238 -3.6 31.3 3.1 34.5 

Southeast 2,837 -8.1 38.9 -7.7 41.3 

 Central 2,295 -7.1 41.1 -6.5 46.0 

West 3,196 -25.9 58.1 -8.7 58.2 

 

CASTNet Northeast 769 -1.6 28.5 -1.3 30.4 

 Midwest 614 3.0 25.8 3.4 25.9 

Southeast 1,096 -15.9 30.6 -18.4 35.1 

 Central 381 -2.0 36.9 -5.8 40.4 

West 1,043 -12.8 45.3 -12.4 50.2 

 

Elemental 

Carbon 

CSN Northeast 2,978 36.1 61.5 25.1 49.2 

 Midwest 2,212 52.2 71.6 33.6 53.8 

Southeast 2,823 18.8 54.8 13.2 49.3 

 Central 2,284 48.8 79.7 33.8 60.7 

West 3,117 15.9 68.8 11.7 62.0 

 

IMPROVE Northeast 2,320 27.7 61.6 6.0 51.1 

 Midwest 577 4.1 38.9 -0.5 44.8 

Southeast 1,949 3.8 46.4 -6.5 47.4 

 Central 2,501 -0.3 40.9 -1.3 43.3 

West 10,403 15.1 77.7 3.7 60.9 

 

Organic 

Carbon 

CSN Northeast 2,900 -4.9 59.3 -6.3 60.5 

 Midwest 2,155 -14.9 53.6 -15.6 58.3 

Southeast 2,779 -39.3 55.6 -48.7 68.7 

 Central 2,239 -31.5 56.9 -35.2 66.7 

West 3,058 -21.5 57.1 -16.4 60.2 

 

IMPROVE Northeast 2,314 -3.8 63.0 -24.8 63.8 

 Midwest 574 -31.5 48.3 -41.9 64.3 

Southeast 1,951 -34.1 55.5 -61.1 74.6 

 Central 2,499 -45.2 55.9 -60.7 72.4 

West 10,238 -34.8 67.3 -37.5 72.0 

 
70

Figure 4-4. Normalized Mean Bias (%) of annual PM2.5 mass at monitoring sites in the 

continental U.S. modeling domain

Figure 4-5. Normalized Mean Error (%) of annual PM2.5 mass at monitoring sites in the 

continental U.S. modeling domain 
71

Figure 4-6. Normalized Mean Bias (%) of annual Sulfate at monitoring sites in the continental 

U.S. modeling domain 

Figure 4-7. Normalized Mean Error (%) of annual Sulfate at monitoring sites in the 

continental U.S. modeling domain 
72

Figure 4-8. Normalized Mean Bias (%) of annual Nitrate at monitoring sites in the continental U.S. 

modeling domain.

Figure 4-9. Normalized Mean Error (%) of annual Nitrate at monitoring sites in the continental 

U.S. modeling domain 
73

Figure 4-10. Normalized Mean Bias (%) of annual Total Nitrate at monitoring sites in the 

continental U.S. modeling domain

Figure 4-11. Normalized Mean Error (%) of annual Total Nitrate at monitoring sites in the 

continental U.S. modeling domain 
74

Figure 4-12. Normalized Mean Error (%) of annual Total Nitrate at monitoring sites in the continental U.S. modeling domain

Figure 4-13. Normalized Mean Error (%) of annual Ammonium at monitoring sites in the 

continental U.S. modeling domain 
75

Figure 4-14. Normalized Mean Bias (%) of annual Elemental Carbon at monitoring sites in the 

continental U.S. modeling domain

Figure 4-15. Normalized Mean Error (%) of annual Elemental Carbon at monitoring sites in the 

continental U.S. modeling domain 
76

Figure 4-16. Normalized Mean Bias (%) of annual Organic Carbon at monitoring sites in the 

continental U.S. modeling domain

Figure 4-17. Normalized Mean Error (%) of annual Organic Carbon at monitoring sites in the 

continental U.S. modeling domain
77

5.0   Bayesian space­time downscaling fusion model (downscaler) ­

Derived Air Quality Estimates

5.1   Introduction

The need for greater spatial coverage of air pollution concentration estimates has grown in recent years as 

epidemiology and exposure studies that link air pollution concentrations to health effects have become 

more robust and as regulatory needs have increased. Direct measurement of concentrations is the ideal 

way of generating such data, but prohibitive logistics and costs limit the possible spatial coverage and 

temporal resolution of such a database. Numerical methods that extend the spatial coverage of existing 

air pollution networks with a high degree of confidence are thus a topic of current investigation by 

researchers. The downscaler model (DS) is the result of the latest research efforts by EPA for performing 

such predictions. DS utilizes both monitoring and CMAQ data as inputs, and attempts to take advantage 

of the measurement data’s accuracy and CMAQ’s spatial coverage to produce new spatial predictions. 

This chapter describes methods and results of the DS application that accompany this report, which 

utilized ozone and PM2.5 data from AQS and CMAQ to produce predictions to continental U.S. 2010 

census tract centroids for the year 2009. 

5.2    Downscaler Model

DS develops a relationship between observed and modeled concentrations, and then uses that relationship 

to spatially predict what measurements would be at new locations in the spatial domain based on the 

input data. This process is separately applied for each time step (daily in this work) of data, and for each 

of the pollutants under study (ozone and PM2.5). In its most general form, the model can be expressed in 

an equation similar to that of linear regression: 

 (1 Equation (ሻݐ ,ݏሺߝ ൅ ሻݐ ,ݏሺݔ~ כ ሻݐ ,ݏଵሺߚ ൅ ሻݐ ,ݏሺ଴ߚ~ ൌ ሻݐ ,ݏሺܻ

Where: 

Y(s,t) is the observed concentration at point s and time t.

~x(s,t) is the CMAQ concentration at time t. This value is a weighted average of both the gridcell 

containing the monitor and neighboring gridcells. 

~β0(s,t) is the intercept, and is composed of both a global and a local component. 

β1(t) is the global slope; local components of the slope are contained in the ~x(s,t) term. 

ε(s,t) is the model error. 

DS has additional properties that differentiate it from linear regression: 

1) Rather than just finding a single optimal solution to Equation 1, DS uses a Bayesian approach so that 

uncertainties can be generated along with each concentration prediction. This involves drawing random 

samples of model parameters from built-in "prior" distributions and assessing their fit on the data on the 

order of thousands of times. After each iteration, properties of the prior distributions are adjusted to try 

to improve the fit of the next iteration. The resulting collection of ~β0 and β1 values at each space-time 
78

point are the "posterior" distributions, and the means and standard distributions of these are used to 

predict concentrations and associated uncertainties at new spatial points. 

2) The model is "heirarchical" in structure, meaning that the top level parameters in Equation 1 (ie 

~β0(s,t), β1(t), ~x(s,t)) are actually defined in terms of further parameters and sub-parameters in the DS 

code. For example, the overall slope and intercept is defined to be the sum of a global (one value for the 

entire spatial domain) and local (values specific to each spatial point) component. This gives more 

flexibility in fitting a model to the data to optimize the fit (i.e. minimize ε(s,t)). 

Further information about the development and inner workings of the current version of DS can be found 

in Berrocal, Gelfand and Holland (2011) and references therein. The DS outputs that accompany this 

report are described below, along with some additional analyses that include assessing the accuracy of the 

DS predictions. Results are then summarized, and caveats are provided for interpreting them in the 

context of air quality management activities. 

5.3   Downscaler Output

In this application, DS was used to predict daily concentration and associated uncertainty values at the 

2010 US census tract centroids across the continental U.S. using 2009 measurement and CMAQ data as 

inputs. For ozone, the concentration unit is the daily maximum 8-hour average in ppb and for PM2.5 the 

concentration unit is the 24-hour average in g/m3

. DS output is in the form of a comma-delimited table. 

Example output of the 2009 ozone DS run is shown in Table 5-1. Each row is specific to date and census 

tract. The columns of the output files are: 

 Date - represented by the data given in this row, in MM/DD/YYYY format. 

 Census Tract FIPS code (http://quickfacts.census.gov/qfd/meta/long_fips.htm), 

 Latitude: The y-coordinate value transformed to latitude (degrees). 

 Longitude: The x-coordinate value transformed to longitude (degrees). 

 Prediction: Daily maximum estimated 8-hour ozone concentration in ppb or 24 hour average 

PM2.5 in ug/m3

. 

 Uncertainty: The posterior standard deviation (error) of the estimated ozone or PM2.5

concentration. 
79

Table 5-1. Downscaler Model Prediction: Example Data File (Ozone) 

Date 2010 US Census 

Tract FIPS 

Code 

Latitude Longitude Daily Maximum 8-

Hour Concentration 

(ppb) 

Standard Error 

of 

Concentration 

Jan-01-2002 1001020100 32.47718 -86.49001 26.122939 7.303598 

Jan-01-2002 1001020200 32.47425 -86.47339 25.900202 7.373431 

Jan-01-2002 1001020300 32.47544 -86.4602 26.130488 7.248804 

Jan-01-2002 1001020400 32.47204 -86.4437 26.085497 7.312579 

Jan-01-2002 1001020500 32.45892 -86.42271 25.942581 7.297929 

Jan-01-2002 1001020600 32.44253 -86.47877 25.854266 7.347192 

Jan-01-2002 1001020700 32.42723 -86.44118 25.534966 7.311914 

Jan-01-2002 1001020801 32.41336 -86.5261 25.707846 7.50416 

Jan-01-2002 1001020802 32.53474 -86.51259 26.253984 7.41871 

Jan-01-2002 1001020900 32.64296 -86.52377 26.212948 7.552828 

Jan-01-2002 1001021000 32.60895 -86.75607 25.718179 7.573458 

Jan-01-2002 1001021100 32.45595 -86.73223 26.065351 7.432891 

5.4   Downscaler Model Results for the 2009 Application

Monitoring data for 2009 from the AQS database described in Chapter 2 and output from the 2009 12 km 

resolution CMAQ run described in Chapter 4 were input to DS to produce daily spatial predictions at the 

2010 continental U.S. census tract centroids. The following summary information was extracted and 

calculated for the DS ozone and PM2.5 inputs and outputs: 

 Days with the highest and greatest spatial extent of high pollution 

 Locations with the most days above the NAAQS 

 Comparison between daily AQS observations and the nearest DS census tract prediction for 

selected sites 

5.4.1 Summary of 8-hour Ozone Results 

As a summary of the overall year, Figure 5.1 shows the 4th max daily maximum 8-hour average ozone for 

AQS observations, CMAQ model predictions and downscaler model results. Based on downscaler model 

estimates for 2009, approximately 26 percent of the US Census tracts (17,280 out 66,186) have at least 

one day with an ozone value above 75 ppb. 
80

Figure 5-1. Observed, modeled and predicted annual 4th max (daily max 8-hour ozone 

concentrations) 
81

Figure 5-2 shows the location of the 4,952 census tracts that were predicted by the downscaler model to 

have annual 4th max daily max 8 hour average ozone concentrations above 75 ppb. Approximately 

24,638,377 million people (1,871,717 are 65 years in age or older) live in these 4,952 census tracts. Most 

of the high ambient ozone concentrations are in California and followed by Texas.

Figure 5-2. Census tract locations where annual 4th max daily maximum 8-hour average ozone 

concentrations estimates are above 75 ppb in 2009 

Table 5-2 ranks the days in 2009 based on the combined spatial extent and intensity of the ozone 

estimates above 75 ppb (only top 25 days are shown). There are 132 days on which at least one census 

tract was predicted to have an ozone concentration above 75 ppb. This approach ranks the days of the 

year based on two criteria: (1) spatial extent in terms of the number of census tracts where ozone 

concentrations are above 75 ppb (spatial extent criterion), and (2) average ozone concentrations at those 

locations (intensity criterion). Sunday, August 30, 2009 is the most intense day. It covers 2,696 census 

tracts with average ozone concentrations of 91 ppb. Figure 5-2 shows the location of these census tracts 

in yellow and red. August 30th is ranked second in terms of spatial extent. The combined spatial extent 

and average ozone concentration scores make this day the highest ranked ozone day in 2009. Figure 5-3 

shows that the high ozone concentrations are concentrated in a relatively small geographical area. 

However, it is spatially coincident with a highly urbanized/populated area (Los Angeles, CA) thus 

explaining the high number of census tracts. It is important to distinguish here that the spatial extent 

criterion used above is based on the number of census tracts not the geographical extent. 
82

Table 5-2. Rank order of days in 2009 based on combined spatial extent and intensity of ozone 

estimates (only top 25 days are shown) 

Day Spatial Extent in 

Terms of Census 

Tract Count

Spatial Extent 

Ranking

Average 

Ozone (ppb) 

(truncated)

Intensity 

Ranking

Overall 

Rank

Sunday, August 30, 2009 2696 2 91 1 1 

Saturday, August 29, 2009 2543 4 86 5 2 

Saturday, July 18, 2009 2177 10 89 2 3 

Sunday, September 27, 2009 2418 6 85 7 4 

Sunday, June 28, 2009 1700 15 85 9 5 

Wednesday, July 01, 2009 2622 3 83 23 6 

Wednesday, August 19, 2009 1686 16 84 12 7 

Monday, August 31, 2009 1497 20 85 10 8 

Tuesday, August 18, 2009 2078 11 84 19 9 

Thursday, September 03, 2009 1656 18 84 13 10 

Friday, August 28, 2009 1421 21 84 14 11 

Saturday, June 27, 2009 2483 5 82 31 12 

Friday, September 18, 2009 1509 19 83 22 13 

Saturday, May 30, 2009 2036 12 82 29 14 

Thursday, July 02, 2009 2380 7 82 35 15 

Sunday, July 19, 2009 1675 17 82 26 16 

Saturday, September 26, 2009 1976 13 82 32 17 

Wednesday, August 12, 2009 821 40 85 6 18 

Tuesday, August 11, 2009 1156 27 84 20 19 

Sunday, May 17, 2009 1754 14 81 37 20 

Thursday, August 20, 2009 810 42 85 11 21 

Tuesday, July 07, 2009 1075 30 83 24 22 

Monday, August 17, 2009 3024 1 80 53 23 

Saturday, August 01, 2009 744 44 84 17 24 

Saturday, July 25, 2009 714 45 84 18 25 

 

Bottom part of the Figure 5-3 shows the uncertainties (posterior standard errors) associated with the 

predictions made in August 30, 2009. Posterior standard errors are lower in the North Eastern quadrant 

of the US aided by good monitor coverage (Figure 2-1) and low observed ozone concentrations. Also 

elevations in the magnitude of errors can be seen over areas where ozone concentrations are high and the 

prediction locations are not far from the monitoring sites such as Los Angeles, CA. General trend with 

the standard errors of prediction is that the magnitude increases with high ozone concentrations and 

predictions locations further away from monitoring locations. This can be best seen in the scatter plot of 

August 30 predictions and associated posterior standard error with each prediction is color coded based 

on its distance to the nearest ozone monitor (Figure 5-4). Similar patterns observed over the predictions 

made in others days and will be discussed throughout the document. 
83

Figure 5-3. August 30, 2009, ozone concentrations for the 2010 US Census Tract locations predicted 

by downscaler model (Top) and posterior standard deviation of the predictions (Bottom) 
84

Figure 5-4. Scatter plot of predicted ozone concentrations in August 30, 2009 and associated 

posterior standard deviations. Each prediction is color coded based on its distance to the nearest 

ozone monitor. 

Monday, August 17, 2009 is the highest ozone day in terms of spatial extent covering 3,024 census tracts 

over the two largest metropolitan areas in the U.S., New York, NY and Los Angeles, CA where 

approximately 13,190,042 people live (Figure 5-5). Ozone concentrations are averaging 79.91 ppb with a 

maximum concentration of 96 ppb. New York City, NY and surrounding areas are the only East Coast 

areas predicted by the downscaler model to have concentrations above 75 ppb. Regarding to associated 

uncertainty with August 17 predictions (Figure 5-6), standard error of the predictions are elevated by high 

ozone concentrations observed over New York area. Contrary to August 30, errors in the South East are 

less than the North East of the United States on August 17. Scatter plot of the predicted ozone 

concentrations and posterior standard deviations (Figure 5-6) shows similar pattern observed in August 

30th (Figure 5-4). 
85

Figure 5-5. August 17, 2009 ozone concentrations for the 2010 US Census Tract locations predicted 

by downscaler model (Top) and standard deviations of the predictions (Bottom). 
86

Figure 5-6. Scatter plot of predicted ozone concentrations and associated standard deviations in 

August 17, 2009. Each prediction is color coded based on its distance to the nearest ozone monitor.

Shown in Figure 5-7 July 18th is another highly ranked ozone day, 2nd in intensity and 10th in spatial 

extent covering Dallas-Fort Worth, TX and Los Angeles, CA). On this day, 2177 census tracts are 

estimated to have an average ozone concentration of 89 ppb. 

During Friday, May 15, 2009 and Tuesday, March 10, 2009, only one census tract is predicted to be 

above 75 ppb, which is considered to be the lowest amongst the high ozone days. In general, August is 

the month with the highest ozone followed by July and September, respectively. In August, average 

ambient ozone concentrations are estimated to be 83 ppb on days with ozone above 75 ppb. 
87

Figure 5-7. July 18, 2009 ozone concentrations for the 2010 US Census Tract locations predicted by 

downscaler model (Top) and standard deviations of the predictions (Bottom). 
88

Figure 5-8. Scatter plot of predicted ozone concentrations and associated standard errors in July 

18, 2009. Each prediction is color coded based on its distance to the nearest ozone monitor. 

Table 5-3 ranks each census tract based on the number of days that the daily maximum 8 hour 

concentrations are above 75 ppb. Associated Figure 5-9 displays the number of ozone days above 75 ppb 

and their location. Based on the downscaler model estimates, census tracts “06071008602” and 

“06071008706” have the highest number of days (72) with ozone above 75 ppb (72 days each). For both 

census tracts the average ozone concentrations for those days is 85 ppb. On those 72 days the maximum 

ozone concentrations were 116 and 115 ppb for the two tracts, respectively. The top 18 tracts are in San 

Bernardino County in California (FIPS code “071” identified by 3rd through 5th character in Census Tract 

ID) followed by tracts that are located in Riverside County, CA (FIPS code “065”). 

 
89

Table 5-3. Census tract rankings based on ozone estimates (out of 17,280 census tracts that 

predicted to have at least one day with ozone concentration above 75 ppb, only top 30 are shown) 

Census Tract ID Number of Days 

Above 75 ppb 

Ranking based on 

Ozone Days 

Average Ozone Above 

75 ppb (truncated) 

Maximum 

Ozone 

06071008602 72 1 85 116.1 

06071008706 72 1 85 115.2 

06071007904 70 3 86 118.8 

06071008705 70 3 85 115.3 

06071008401 69 5 86 119.0 

06071008402 69 5 86 118.6 

06071008404 69 5 85 118.1 

06071008601 69 5 86 118.0 

06071008703 69 5 85 111.6 

06071008704 69 5 85 113.7 

06071008710 69 5 85 113.6 

06071007901 68 12 86 119.1 

06071007903 68 12 86 119.2 

06071008403 68 12 86 117.9 

06071008500 68 12 85 115.8 

06071008800 68 12 86 113.4 

06071008708 67 17 86 113.0 

06071011101 67 17 86 117.3 

06065043809 66 19 85 108.0 

06065043811 66 19 85 110.1 

06065044104 66 19 85 106.2 

06071007604 66 19 86 119.2 

06071008002 66 19 86 118.2 

06071008200 66 19 85 117.1 

06071008709 66 19 86 112.9 

06071011002 66 19 86 117.2 

06065043802 65 27 85 111.7 

06065043823 65 27 85 112.4 

06065044200 65 27 85 103.0 
90

Figure 5-9. Number of ozone days above 75 ppb for US census tracts predicted by DS. 

The downscaler model estimates can track the AQS observations and CMAQ predictions, and the 

downscaler model estimates can differ from either the AQS observations or the CMAQ predictions. To 

see how the daily downscaler model estimates compare to the AQS observations, we selected the 

monitors that are within 100 meters of a census tract centroid. Census tract and AQS site pairs are shown 

in Table 5.4. The associated Figure 5-10 shows the time series data for the listed sites. As shown the 

downscaler model estimates generally follow the AQS and CMAQ data. Keep in mind that the 

downscaler concentrations are point estimates trying to replicate the point measurement conditions of the 

AQS monitoring site. CMAQ concentrations represent the average conditions within 12 by 12 km grid 

cells. To further elaborate this condition, Figure 5-11 shows the relationships among the AQS ozone 

monitoring site locations, CMAQ grid cells and the US census tract centroids in the Los Angeles area. 

The CMAQ cells are on a continuous grid. EPA ozone monitor siting criteria requires States to place 

monitors in and around the urban areas with high populations. The US Census Bureau uses population 

size to define the census tract boundaries. Census tract population sizes vary between 1,200 and 8,000. 

The optimum size is 4,000 people. Therefore, in the Los Angeles area, it is not a surprise to see how 

dense the census tract locations are in the urban areas, where the monitoring sites and high population 

areas are located. Also, not surprising is how less dense the census tracts are in the rural areas where 

there are few, if any ozone monitors. 

 
91

Table 5-4. List of AQS sites that are within 100 meters of a census tract centroid 

AQS Site ID Census Tract ID County STATE

040134004 04013811200 Maricopa Arizona 

120712002 12071010801 Lee Florida 

320032002 32003004000 Clark Nevada 

420950025 42095017800 Northampton Pennsylvania 

421010004 42101019000 Philadelphia Pennsylvania 

421250200 42125754400 Washington Pennsylvania 

170310064 17031836200 Cook Illinois 
92

Figure 5-10. Daily 8-hour maximum ozone concentrations measured by AQS monitor and 

estimated by CMAQ model and Downscaler fusion for selected sites in Table 5-4 (Census centroids 

that are within 100 meters of and AQS site). 
93

Figure 5-11. Downscaler model predictions over Los Angeles, CA and surrounding areas 

5.4.2 Summary of PM2.5 Results 

As a summary of the overall year, Figure 5-12 and Figure 5.13 show the annual means and the 98th

percentile 24-hour average PM2.5 concentrations for AQS observations, CMAQ runs and downscaler 

predictions. Based on downscaler model estimates for 2009, the 98th percentile of PM2.5 values are above 

35 μg/m3

 for the 1,137 census tracts (Figure 5-14) averaging 43.8 μg/m3. 18,056 Census tracts have at 

least one day with a PM2.5 concentration above 35 μg/m3

. 7,526 census tracts have PM2.5 annual average 

concentrations above 12 μg/m3 (mean 13.2 μg/m3). 
94

Figure 5-12. Observed, modeled and predicted annual mean PM2.5 concentrations) 
95

Figure 5.13 Observed, modeled and predicted 98th percentile 24-hour average PM2.5

concentrations. 
96

Figure 5-14. Census tract locations (centroid) where the 98th percentile of 24-hour average and 

annual average PM2.5 concentrations are above 35 μg/m3

 (top) and 12 μg/m3 (bottom) respectively. 
97

Table 5-5 ranks the days in 2009 based on the combined spatial extent and intensity of the 24-Hour 

average PM2.5 concentration estimates above 35 μg/m3

 (only top 25 days are shown). There are 102 days 

on which at least one census tract was predicted to have a 24-hour average PM2.5 concentration above 35 

μg/m3. Similar to ozone in section 5.4.1, this approach ranks the days of the year based on two criteria: 

(1) spatial extent in terms of the number of census tracts where 24-hour average PM2.5 concentration 

estimates are above 35 μg/m3 (spatial extent criterion), and (2) average PM2.5 concentrations at those 

locations (intensity criterion). In overall, Thursday, January 01, 2009 is the highest ranked PM2.5 day 

based on the two criterions mentioned above (Figure 5-15). Tuesday, October 13, 2009, is the most 

intense day with average concentrations of 57 μg/m3 covering 107 census tracts (Figure 5-17). Thursday, 

January 22, 2009, is the highest PM2.5 day in terms of spatial extent covering 4,589 census tracts (Figure 

5-19). 

 

Table 5-5. Rank order of days in 2009 based on combined spatial extent and intensity of 24-Hour 

average PM2.5 concentration estimates (only top 25 days are shown) 

Day Spatial Extent in 

Terms of Census 

Tract Count

Spatial 

Extent 

Ranking

Average PM2.5

(μg/m3) 

(truncated)

Intensity 

Ranking

Overall 

Rank

Thursday, January 01, 2009 4202 2 54 6 1 

Thursday, December 10, 2009 1706 9 47 13 2 

Friday, December 11, 2009 1391 12 48 11 3 

Tuesday, January 20, 2009 836 21 55 5 4 

Wednesday, January 21, 2009 746 24 55 3 5 

Friday, January 02, 2009 3330 3 43 24 5 

Thursday, January 22, 2009 4589 1 43 26 5 

Thursday, December 03, 2009 652 29 52 7 8 

Friday, December 04, 2009 2534 5 43 31 8 

Saturday, December 05, 2009 1142 14 44 23 10 

Friday, January 16, 2009 1163 13 43 27 11 

Monday, January 19, 2009 1987 7 42 33 11 

Tuesday, January 13, 2009 725 25 46 17 13 

Wednesday, December 02, 2009 454 37 50 8 14 

Sunday, January 18, 2009 688 28 46 18 15 

Friday, December 25, 2009 1977 8 42 38 15 

Friday, December 18, 2009 2572 4 41 43 17 

Tuesday, December 29, 2009 505 35 47 14 18 

Saturday, January 17, 2009 806 22 43 29 19 

Sunday, August 30, 2009 1137 15 42 37 20 

Thursday, January 15, 2009 439 39 46 15 21 

Sunday, December 20, 2009 1410 11 41 46 22 

Saturday, January 31, 2009 452 38 44 22 23 

Tuesday, October 13, 2009 107 60 57 1 24 

Wednesday, January 14, 2009 419 41 44 21 25 

 
98

Figure 5-15. January 1, 2009 24-Hour PM2.5 concentrations for the 2010 US Census Tract locations 

predicted by downscaler model (Top) and posterior standard error of the predictions (Bottom) 
99

Figure 5-16. Scatter plot of predicted PM2.5 concentrations and associated posterior standard 

errors in January 1, 2009. Each prediction is color coded based on its distance to the nearest ozone 

monitor. 
100

Figure 5-17. October 13, 2009 24-Hour PM2.5 concentrations for the 2010 US Census Tract 

locations predicted by downscaler model (Top) and posterior standard error of the predictions 

(Bottom). Kern County, CA is highlighted with red rectangle.
101

Figure 18. Scatter plot of predicted PM2.5 concentrations and associated posterior standard errors 

in October 13, 2009. Each prediction is color coded based on its distance to the nearest ozone 

monitor. 
102

Figure 5-19. January 22, 2009 24-Hour PM2.5 concentrations for the 2010 US Census Tract 

locations predicted by downscaler model (Top) and posterior standard error of the predictions 

(Bottom). Salt lake City, UT is highlighted with red rectangle.
103

Figure 5-20. Scatter plot of predicted PM2.5 concentrations and associated posterior standard 

errors in January 22, 2009. Each prediction is color coded based on its distance to the nearest ozone 

monitor.

 
104

Table 5-6 ranks each census tract based on the number of days and average concentrations that the 24-

Hour average PM2.5 concentration estimates above 35 μg/m3

. 

Associated Figure 5-21 displays the number of PM2.5 days above 35 μg/m3 and their location. Based on 

the downscaler model estimates, census tracts “06029002814” has the highest number of days (37) with 

an average concentration of 50.8 μg/m3

. The top 30 tracts are in Kern County in California (FIPS code 

“029” identified by 3rd through 5th character in Census Tract ID). 

Table 5-63. Census tract rankings based on PM2.5 estimates (out of 18,056 census tracts that 

predicted to have at least one high concentration day of PM2.5 , only top 30 are shown) 

Census Tract ID Number of Days 

Above 35 μg/m3

Average PM2.5

(μg/m3) 

Maximum PM2.5

(μg/m3) 

Rank

06029002814 37 50.8 75.3 1 

06029002815 37 50.5 74.5 2 

06029002816 37 50.4 74.5 3 

06029002812 36 51.6 75.8 4 

06029003112 37 50.1 73.7 5 

06029002813 36 51.4 75.5 6 

06029001802 36 51.4 75.6 7 

06029001801 36 51.3 75.7 8 

06029002804 36 51.3 75.4 9 

06029001901 36 51.2 75.1 10 

06029003113 37 49.8 73.4 11 

06029002807 36 51.1 75.1 12 

06029002700 36 51.0 74.9 13 

06029002806 36 51.0 74.6 14 

06029002818 36 51.0 74.5 15 

06029003812 36 50.9 74.4 16 

06029002808 36 50.9 74.3 17 

06029002900 36 50.8 74.2 18 

06029001902 36 50.8 74.6 19 

06029003114 37 49.4 72.6 20 

06029002817 36 50.7 74.3 21 

06029003811 36 50.7 73.8 22 

06029000507 36 50.6 74.3 23 

06029002819 36 50.5 73.9 24 

06029002811 36 50.5 73.7 25 

06029000506 36 50.4 74.0 26 

06029003808 36 50.4 73.5 27 

06029001700 36 50.4 74.3 28 

06029002821 36 50.4 73.5 29 
105

Figure 5-21 Number of days 24-hour average PM2.5 concentrations are above 35 μg/m3 for the US 

census tracts predicted by DS 

5.5   Accuracy Assessment of Downscaler Model Results

This section describes the predictive performance of DS in the 2009 application. The general approach 

for this involves running DS with a subset (in this case 10%) of monitors removed from the monitoring 

data set and predicting to the spatial points of the removed monitors. This approach is sometimes called 

“cross-validation” (CV). Errors and biases can then be calculated for each prediction point by comparing 

the resulting prediction to the actual monitoring data. For this application, the default CV method in the 

DS software was followed, which involves leaving out a random sample of 10% of the monitors in each 

day of data. The sites left out on one day are not necessarily the same set of sites on another day.

Monitor- and day-specific errors and biases were then aggregated into the metrics below to provide an 

overview of the model’s accuracy. 

 

First, day-specific Root Mean Square Error (RMSEd), Mean Absolute Error (PMAEd), and biasd (or Mean 

Bias Error) were calculated to evaluate the predictive capability of the Downscaler model. Daily RMSE 

is defined as 

ܴܯܵܧௗ ൌ ቎݊ିଵ෍หܲ௝,ௗ െ ܱ௝,ௗห

ଶ

௡

௝ୀଵ ቏

ଵ

ଶൗ
106

where Pj and Oj are the DS prediction and observed concentrations respectively in location j. d is the 

specific day of the year. Even though both the RMSE and the MAE measure the average magnitude of 

the errors, it is useful to report both to diagnose the variation in the errors. Daily MAE is defined as 

ܧܣܯௗ ൌ ቎݊ିଵ෍หܲ௝,ௗ െ ܱ௝,ௗห

௡

௝ୀଵ ቏

While the MAE gives equal weight to all, the RMSE emphasizes large errors and is most useful when 

large errors are undesirable. Given the fact that the RMSE will always be larger or equal to the MAE, the 

difference between the two highlights the magnitude of the variance in the individual errors in such the 

greater the difference, the greater the variance. Daily bias is defined as 

ܾ݅ܽݏௗ ൌ ݊ିଵ෍൫ܲ௝,ௗ െ ܱ௝,ௗ൯ൌܲത

ௗ െ ܱതௗ

௡

௝ୀଵ

where ܲത

ௗ and ܱതௗ are the model-predicted and observed daily mean concentrations respectively. 

Secondly, location-specific Root Mean Square Error (RMSEi), Mean Absolute Error (MAEi), and biasi

were calculated as: 

ܴܯܵܧ ௝ൌ ൥݊ିଵ ෍หܲௗ,௝ െ ܱௗ,௝ห

ଶ

௡

ௗୀଵ

൩

ଵ

ଶൗ

where Pd and Od are the DS prediction and observed concentrations respectively in day d. j is the specific 

observation (monitor) location. 

ܧܣܯ ௝ൌ ൥݊ିଵ ෍หܲௗ,௝ െ ܱௗ,௝ห

௡

ௗୀଵ

൩

and 

ܾ݅ܽݏ ௝ൌ ݊ିଵ ෍൫ܲௗ,௝ െ ܱௗ,௝൯ൌܲത

௝ െ ܱത௝

௡

ௗୀଵ

where ܲത

௝ and ܱത௝ are the model-predicted and observed location-specific mean concentrations 

correspondingly. 

Thirdly, to further analyze how the downscaler performed over different locations, the Getis-Ord Gi* 

statistic (Getis and Ord, 1992)16 for each RMSEi and MAEi are calculated, which returned a z-score for 

each monitor location. For statistically significant positive z-scores, the larger the z-score, the more 

intense the clustering of high values indicating relatively poor model performance. For statistically 

significant negative z-scores, the smaller the z-score is, the more intense the clustering of low values 

which indicates better model performance. Getis-Ord local statistics is calculated as17: 

 

16Getis, A. and J.K. Ord. 1992. "The Analysis of Spatial Association by Use of Distance Statistics" in Geographical Analysis

24(3).

17 The ArcGIS 10.1 Resources: How Hot Spot Analysis works:

http://resources.arcgis.com/en/help/main/10.1/index.html#/How_Hot_Spot_Analysis_Getis_Ord_Gi_works/005p00000011

000000/
107

௜ܩ

௡ ௝,௜ݓ ∑ തܺ െ௝ ݔ௝,௜ݓ ∑ ൌ כ

௝ୀଵ ௡௝ୀଵ

௝,௜ݓ ∑ ݊ඨቂܵ

ଶ െ ൫∑ ݓ௜,௡ ௝

௝ୀଵ ൯

ଶ ௡

௝ୀଵ ቃ

݊െ1

where ݔ ௝is either MAEj or RMSEj for monitor j, ݓ௜, ௝is the spatial weight between monitor i and j, n is 

equal to total number of AQS monitors. ܺത and S2

 are sample mean and variance: 

ܺത ൌ ∑ ݔ௡ ௝

௝ୀଵ

݊ൗ

and 

ܵ ൌ ඨ∑ ݔ ௡ ௝ଶ

௝ୀଵ

݊ െ ሺܺതሻଶ

5.5.1 Assessment of 8-hour Ozone Run 

Daily RMSEs, MAEs and Bias values are depicted in Figure 5-22. Daily Bias values are ranging from -

1.5 to 2 ppb. Ranges for the daily RMSEd and the MAEd are 2.7 to 7.5 ppb and 2.1 to 5.4 ppb, 

respectively. On January 25th, the variance in the individual errors, the difference between the RMSE and 

the MAE were minimal. On April 5th, however, the variance was the highest. These results are 

somewhat aligned with the test results reported in the Berrocal et. al. (2009) paper which documents 

overall performance of the downscaler model. This provides some confidence that in general the 2009 

application of the downscaler model is performing reasonably well. 
108

Figure 5-22. Daily validation results 

In Figure 5-23 the location specific RMSEj, and MAEj values are presented over 1207 monitoring 

locations. Both the RMSEj and the MAEj show similar patterns over the US domain with a slightly better 

fit for the Eastern US than the Western part of the country. 

‐2

‐1

0

1

2

3

4

5

6

7

8

Daily Bias Daily RMSE Daily MAE
109

Figure 5-23. The average magnitude of the errors in 2009 predictions based on the spatio-temporal 

Downscaler model: the Mean Absolute Error (Top) and the Root mean Square Error (Bottom). 
110

The Getis-Ord Gi* statistic (pronounced G-i-star) for each RMSEj and MAEj are calculated, which 

returned a z-score for each monitor location (Figure 5-24). Clearly, the downscaler model performs 

better over the Eastern US than the Western part of the country. There was intense clustering of high 

RMSEj and MAEj values in the West (statistically significant positive z-scores) indicating relatively poor 

model performance. On the other hand over the Eastern US, there were statistically significant negative zscores indication of the clustering of low RMSEj and MAEj values which is most likely due to the higher 

density of the ozone monitoring network in the East. 

Lastly, overall DQO metrics across locations and time are calculated. For 2009, the RMSE, MAE and 

bias values are 4.7, 3.3, and 0.01 ppb, respectively. The difference between overall RMSE and MAE isn’t 

big enough to indicate the presence of very large errors however there is some variation in the magnitude 

of the errors. 

 
111

Figure 5-24. The Gi* statistic returned z-scores for each monitor locations over the United States. 

Both the RMSEj (Top) and the MAEj (Bottom) based Gi* z-scores show similar patterns with very 

slight differences. 
112

5.5.2 Assessment of PM2.5 

Daily RMSEs, MAEs and Bias values are depicted Figure 5-25. Daily Bias values are ranging from -3.8 

to 2 μg/m3

. Ranges for the daily RMSE and the MAE are 0.5 to 13.3 μg/m3 and 0.4 to 5.2 μg/m3, 

respectively. On September 27th, the variance in the individual errors, the difference between the RMSE 

and the MAE were minimal. On January 17th, the variance was the highest; it was not enough to be great 

concern however. 

Figure 5-25 Daily validation results for PM2.5

In Figure 5-26 the location specific RMSE, and MAE values are presented over 929 monitoring locations. 

Both the RMSE and the MAE show similar patterns over the US domain with a slightly better fit for the 

Eastern US than the Western part of the country. 

‐4

‐2

0

2

4

6

8

10

12

14

PM2.5 Daily Bias PM2.5 Daily RMSE PM2.5 Daily MAE
113

Figure 5-26 The average magnitude of the errors for PM2.5 in 2009 predictions based on the spatiotemporal downscaler model: the Mean Absolute Error (Top) and the Root mean Square Error 

(Bottom). 
114

Similar to ozone assessment, the Getis-Ord Gi* statistic for each RMSE and MAE are calculated for 

PM2.5, which returned a z-score for each monitor location (Figure 5-27). Statistically significant 

clustering of high values are observed in the West coast indicating relatively poor model performance. 

Significant clustering of low RMSE and MAE values are observed on the East coast indicating better 

model performance. Similar to ozone application, the downscaler application of PM2.5 performs better 

over the Eastern US than the Western part of the country. 

Lastly, overall DQO metrics across locations and time are calculated for PM2.5. For 2009, the RMSE, 

MAE and bias values are 2.8, 1.7, and 0.03 μg/m3

 respectively. The difference between overall RMSE 

and MAE isn’t big enough to indicate the presence of very large errors however there is some variation in 

the magnitude of the errors. 

 
115

Figure 5-27 The Gi* statistic returned z-scores for each PM2.5 monitoring locations over the United 

States. Both the RMSE (Top) and the MAE (Bottom) based Gi* z-scores show similar patterns with 

very slight differences. 
116

5.6   Summary and Conclusions

The results presented in this report are from an application of the DS fusion model for characterizing 

national air quality for Ozone and PM2.5. DS provided spatial predictions of daily ozone and PM2.5 at 

2010 U.S. census tract centroids by utilizing monitoring data and CMAQ output for 2009. Large-scale 

spatial and temporal patterns of concentration predictions are generally consistent with those seen in 

ambient monitoring data. Both ozone and PM2.5 were predicted with greater accuracy in the eastern 

versus the western U.S., presumably due to the greater monitoring density in the east. Another way of 

summarizing results is shown in Figure 5-28, which plots the DS predictions and ambient measurements 

paired together by the census tracts containing them. Data is plotted for all days and split out (in each 

row) by the NOAA climate regions. The outliers seen to the right of the 1:1 lines in Figure 5-28 were 

found to arise in census tracts where the AQS value was substantially higher than the surrounding CMAQ 

values. Sampling more points from the DS prediction surface and averaging across the census tracts may 

better characterize the census tract area averages, which can be explored in future analyses with the DS 

model. 

A major distinguishing feature of the DS output is the standard errors accompanying each concentration 

prediction. These standard errors give information that complements the cross-validation (CV)-

determined errors and biases. Whereas CV provides measures of accuracy, the DS-produced 

uncertainties give a measure of prediction precision. Figures 5-4, 5-6, 5-8, 5-16, 5-18, and 5-20 illustrate 

their utility: the errors demonstrate a clear increase in magnitude as distance from the nearest monitor 

increases. This numerically demonstrates the intuitively expected decrease in confidence of the 

relationship between observed and CMAQ data that DS models as monitor network density decreases, 

e.g. as in the western U.S. A total uncertainty could theoretically be constructed by combining the 

precision and bias, which could be a potentially useful tool in future network assessment and other 

sampling designing activities. 

An additional caution that warrants mentioning is related to the capability of DS to provide predictions at 

multiple spatial points within a single CMAQ gridcell. Care needs to be taken not to over-interpret any 

within-gridcell gradients that might be produced by a user. Fine-scale emission sources in CMAQ are 

diluted into the gridcell averages, but a given source within a gridcell might or might not affect every 

spatial point contained therein equally. Therefore DS-generated fine-scale gradients are not expected to 

represent actual fine-scale atmospheric concentration gradients, unless possibly multiple monitors are 

present in the gridcell. 

 
117

Figure 5-28 Downscaler predictions in each census tract versus the AQS Monitoring value in the 

same census tract. Each row pools all annual data for the specified NOAA Climate Region. 
118

Appendix A ­ Acronyms

Acronyms

ARW Advanced Research WRF core model 

BEIS Biogenic Emissions Inventory System 

BlueSky Emissions modeling framework 

CAIR Clean Air Interstate Rule 

CAMD EPA’s Clean Air Markets Division 

CAP Criteria Air Pollutant 

CAR Conditional Auto Regressive spatial covariance structure (model) 

CARB California Air Resources Board 

CEM Continuous Emissions Monitoring 

CHIEF Clearinghouse for Inventories and Emissions Factors 

CMAQ Community Multiscale Air Quality model 

CMV Commercial marine vessel 

CO Carbon monoxide 

CSN Chemical Speciation Network 

DQO Data Quality Objectives 

EGU Electric Generating Units 

Emission Inventory Listing of elements contributing to atmospheric release of pollutant 

 substances 

EPA Environmental Protection Agency 

EMFAC Emission Factor (California’s onroad mobile model) 

FAA Federal Aviation Administration 

FDDA Four Dimensional Data Assimilation 

FIPS Federal Information Processing Standards 

HAP Hazardous Air Pollutant 

HMS Hazard Mapping System 

ICS-209 Incident Status Summary form 

IPM Integrated Planning Model 

ITN Itinerant 

LSM Land Surface Model 

MOBILE OTAQ’s model for estimation of onroad mobile emissions factors 
119

MODIS Moderate Resolution Imaging Spectroradiometer 

MOVES Motor Vehicle Emission Simulator 

NEEDS National Electric Energy Database System 

NEI National Emission Inventory 

NERL National Exposure Research Laboratory 

NESHAP National Emission Standards for Hazardous Air Pollutants 

NH Ammonia 

NMIM National Mobile Inventory Model 

NONROAD OTAQ’s model for estimation of nonroad mobile emissions 

NO Nitrogen oxides 

OAQPS EPA’s Office of Air Quality Planning and Standards 

OAR EPA’s Office of Air and Radiation 

ORD EPA’s Office of Research and Development 

ORIS Office of Regulatory Information Systems (code) - is a 4 or 5 digit 

number assigned by the Department of Energy’s (DOE) Energy 

 Information Agency (EIA) to facilities that generate electricity 

ORL One Record per Line 

OTAQ EPA’s Office of Transportation and Air Quality 

PAH Polycyclic Aromatic Hydrocarbon 

PFC Portable Fuel Container 

PM2.5 Particulate matter less than or equal to 2.5 microns 

PM10 Particulate matter less than or equal to 10 microns 

PMc Particulate matter greater than 2.5 microns and less than 10 microns 

Prescribed Fire Intentionally set fire to clear vegetation 

RIA Regulatory Impact Analysis 

RPO Regional Planning Organization 

RRTM Rapid Radiative Transfer Model 

SCC Source Classification Code 

SMARTFIRE Satellite Mapping Automatic Reanalysis Tool for Fire Incident 

Reconciliation 

SMOKE Sparse Matrix Operator Kernel Emissions 

TCEQ Texas Commission on Environmental Quality 

TSD Technical support document 

VOC Volatile organic compounds 

VMT Vehicle miles traveled 

Wildfire Uncontrolled forest fire 

WRAP Western Regional Air Partnership 

WRF Weather Research and Forecasting Model