Summary Report of 

Air Quality Modeling 

Research Activities for 

2007 

EPA /600/R-09/025 

March 2009 

Summary Report of 

Air Quality Modeling 

Research Activities for 

2007

S.T. Rao, Robin Dennis, Valerie Garcia, Alice Gilliland, Rohit Mathur, 

David Mobley, Thomas Pierce, and Kenneth Schere

Atmospheric Modeling Division 

National Exposure Research Laboratory 

U.S. Environmental Protection Agency 

Research Triangle Park, NC 27711

U.S. Environmental Protection Agency 

Office of Research and Development 

Washington, DC 20460 
ii

Notice 

The research presented here was performed under the Memorandum of Understanding and 

Memorandum of Agreement between the U.S. Environmental Protection Agency (EPA) and the U.S. 

Department of Commerce’s (DOC’s) National Oceanic and Atmospheric Administration (NOAA). It has 

been subjected to EPA peer and administrative review and has been approved for publication as an EPA 

document. Mention of trade names or commercial products does not constitute endorsement or 

recommendation for use. 
iii

Abstract

Through a Memorandum of Understanding (MOU) and Memorandum of Agreement (MOA) between 

the U.S. Department of Commerce (DOC) and the U.S. Environmental Protection Agency (EPA), the 

Atmospheric Sciences Modeling Division (ASMD) of the National Oceanic and Atmospheric 

Administration’s (NOAA’s) Air Resources Laboratory (ARL) develops advanced modeling and decision 

support systems for effective forecasting and management of the Nation’s air quality. As a division within 

the EPA organizational structure, ASMD is known as the Atmospheric Modeling Division (AMD). The 

Division is responsible for providing a sound scientific and technical basis for regulatory policies to 

improve ambient air quality. The models developed by the Division are being used by EPA, NOAA, and 

the air quality community not only to understand and forecast the magnitude of the air pollution problem, 

but also to develop emission control policies and regulations. This report summarizes the research and 

operational activities of the Division for fiscal year 2007.
iv
v

Contents

Notice ............................................................................................................................................................ ii 

Abstract ........................................................................................................................................................ iii 

Figures ...........................................................................................................................................................vi 

Acknowledgements ..................................................................................................................................... vii 

Chapter 1: Introduction ...................................................................................................................................1 

Chapter 2: Providing Scientifically-Advanced Models and Tools to Support Environmental 

Policy Decisions .............................................................................................................................................3 

Introduction .............................................................................................................................................3 

Research Description ...............................................................................................................................3 

Accomplishments ....................................................................................................................................4 

Next Steps ...............................................................................................................................................6 

Impacts and Transition of Research to Applications................................................................................6 

Chapter 3: Evaluating the Impact of Regulatory Policies on Air Quality and Ecosystems ............................8 

Introduction .............................................................................................................................................8 

Research Description ...............................................................................................................................8 

Accomplishments ....................................................................................................................................9 

Next Steps ...............................................................................................................................................9 

Impacts and Transition of Research to Applications................................................................................9 

Chapter 4: Linking Sources to Human Exposure .........................................................................................11 

Introduction ...........................................................................................................................................11 

Research Description .............................................................................................................................12 

Accomplishments ..................................................................................................................................12 

Next Steps .............................................................................................................................................13 

Impacts and Transition of Research to Applications .............................................................................15 

Chapter 5: Linking Sources to Ecosystem Exposure.....................................................................................16 

Introduction ...........................................................................................................................................16 

Research Description .............................................................................................................................16 

Accomplishments ..................................................................................................................................17 

Next Steps .............................................................................................................................................18 

Impacts and Transition of Research to Applications .............................................................................18 

Chapter 6: Providing Air Quality Forecast Guidance for Health Advisories ................................................20 

Introduction ...........................................................................................................................................20 

Research Description .............................................................................................................................20 

Accomplishments ..................................................................................................................................20 

Next Steps .............................................................................................................................................21 

Impacts and Transition of Research to Applications..............................................................................21 

Chapter 7: Understanding the Relationships between Climate Change and Air Quality ..............................23 

Introduction ...........................................................................................................................................23 

Research Description .............................................................................................................................23 

Accomplishments ..................................................................................................................................23 

Next Steps .............................................................................................................................................25 

Impacts and Transition of Research to Applications .............................................................................25 

Appendix A: Division Staff Roster ..............................................................................................................26 

Appendix B: Division and Branch Descriptions............................................................................................27 

Appendix C: Awards and Recognition..........................................................................................................29 

Appendix D: Publications .............................................................................................................................30 

Appendix E: Abbreviations ..........................................................................................................................35 
vi

Figures

Number Page

1-1 The Division’s role in the Source-Exposure-Dose-Effects Continuum ..................................................2 

1-2 Strategy to meet user needs.....................................................................................................................2 

2-1 Schematic of CMAQ modeling system, including meteorology, emissions, and air quality models .....7 

3-1 Assessing the impact of regulations on ecosystems and human health endpoints showing 

the indicators (boxes) and process linkages (arrows) associated with the NOx Budget Trading 

Program.................................................................................................................................................10 

4-1 Multiple scales in air quality modeling.................................................................................................15 

5-1 WDT screen capture showing the CMAQ 2002 annual total nitrogen deposition (kg-N/ha) 

for the 36-km grid resolution with the overlay of 8-digit Hydrologic Unit Code (HUC) delineations 

for the Cape Fear Basin and Albemarle-Pamlico Sound system...........................................................19 

5-2 WDT screen capture showing the average 2002 annual total nitrogen deposition (kg-N/ha) 

to each watershed segment in the Cape Fear Basin and Albemarle-Pamlico Sound system.................19 

6-1 Forecast surface-level 8-hour maximum O3 concentrations on August 15, 2007. Color-coded 

diamonds indicate corresponding observed levels................................................................................22 

7-1 Average summer (June-August, or JJA) difference between future – current regional climate scenarios 

for temperature, isoprene emissions, and solar radiation reaching the surface. ....................................24 

7-2 Increase (future-current) in O3 concentrations under future climate conditions when comparing the 

95th % of the O3 distribution (i.e., high O3 episodes). The summer (JJA) and fall (September and 

October) months are compared.............................................................................................................24 

7-3 Average summer increase (future-current) in O3 when methane concentrations increase from 1.8 ppm 

to 2.4 ppm. ............................................................................................................................................25 
vii

Acknowledgments

The authors acknowledge the support of Patricia McGhee of the Division for technical editing and 

manuscript preparation.
1

Chapter 1 

Introduction

The National Oceanic and Atmospheric Administration 

(NOAA) Atmospheric Sciences Modeling Division (ASMD) 

works within the frameworks of the Memorandum of 

Understanding and Memorandum of Agreement between the 

U.S. Department of Commerce (DOC) and the U.S. Environmental Protection Agency (EPA). These agreements are 

implemented through long-term Interagency Agreements 

DW13938483 and DW13948634 between EPA and NOAA. 

The Division is organized into four research branches: 

• Atmospheric Model Development Branch 

• Model Evaluation and Applications Branch 

• Air-Surface Processes Modeling Branch 

• Applied Modeling Branch 

The first three branches above constitute the Atmospheric 

Modeling Division (AMD) of the National Exposure Research 

Laboratory (NERL) of the Office of Research and Development (ORD) within EPA’s organizational structure. The 

fourth branch listed is part of the Air Quality Assessment 

Division of the Office of Air Quality Planning and Standards 

(OAQPS) within EPA’s organizational structure. Throughout 

this report, these NOAA-EPA branches will be collectively 

referred to as “the Division.” The appendices to this report 

contain a list of Division employees (Appendix A), descriptions of the Division and its branches (Appendix B), a list of 

awards earned by Division personnel (Appendix C), and a list 

of Division publications (Appendix D). 

The Division’s role within the source-to-outcome continuum 

is to conduct research that improves the Agency’s understanding of the linkages from source to exposure (see Figure 

1-11

). Through its research branches, the Division provides 

atmospheric sciences expertise, air quality forecasting support, 

and technical guidance on the meteorological and air quality 

modeling aspects of air quality management to various EPA 

offices (including OAQPS Regional Offices), other federal 

agencies, and state and local pollution control agencies. 

 

1

Adapted from “A Conceptual Framework for U.S. EPA’s National Exposure 

Research Laboratory,” November 2007 Draft by EPA/NERL. 

The Division provides this technical support and expertise 

using an interdisciplinary approach that emphasizes integration and partnership with EPA and public and private research 

communities. Specific research and development activities are 

conducted in-house and externally via contracts and 

cooperative agreements. 

The Division has completed a major strategic planning process 

begun in 2002. We identified six outcome-oriented Theme 

Areas:

• Providing scientifically advanced models and tools to 

support environmental policy decisions 

• Evaluating the impact of regulatory policies on air 

quality and ecosystems 

• Linking sources to human exposure 

• Linking sources to ecosystem exposure 

• Providing air quality forecast guidance for health 

advisories 

• Understanding the relationships between climate 

change and air quality. 

Research tasks were developed within each Theme Area, by 

considering these questions: 

• Over the next two to three years, who are the major 

clients and what are their needs? 

• What research investments are needed to further the 

science ways that helps the client(s)? How will we 

lead or influence the science in this area? 

• What personnel expertise, resources, and partners are 

needed to do this work? 

• Does the proposed work fall within the current scope 

and plans of existing projects, or would personnel 

resources need to be shifted from other projects to 

make this happen? 

The result is a research strategy for meeting user needs that is 

built around the six major Theme Areas and supported by the 

four branches of the Division, as depicted in Figure 1-2. The 

Division’s Applied Modeling Branch also supports the three 

research- and development-focused branches by facilitating 

the transition of atmospheric modeling systems and other 

research tools to regulatory applications. 
2

This report summarizes the research and operational activities 

of the Division for fiscal year 2007. It includes descriptions of 

research and operational efforts in air pollution meteorology, 

in meteorology and air quality model development, and in 

model evaluation and applications. The rest of this report 

(Chapters 2 through 7) is organized according to the six major 

program themes listed above, also shown in Figure 1-2. 

Figure 1-1. The Division’s role in the source-exposure-dose-effects continuum. 

Figure 1-2. The Division’s strategy to meet user needs.

Ambient 

Concentrations 

Source / Origin 

Fate and 

Transport 

Exposure 

Dose 

Early Signs of 

Effects 

Adverse 

Outcome 

Source-to-Outcome Continuum 

Division’s 

Role 
3

Chapter 2 

Providing Scientifically Advanced Models and Tools to Support 

Environmental Policy Decisions 

Introduction 

The Clean Air Act (CAA) requires that EPA set National 

Ambient Air Quality Standards (NAAQS) for air pollutants 

considered harmful to public health and the environment. 

Thresholds for six criteria pollutants have been established: 

carbon monoxide (CO), lead (Pb), nitrogen oxides (NOx), fine 

particulate matter (PM2.5) and coarse particulate matter 

(PM10), tropospheric ozone (O3), and sulfur oxides (SOx). EPA 

reviews each NAAQS every five years, and proposes changes 

if the most current science on health and ecological effects 

suggests changing the standards. For example, in 2006 EPA 

revised the standards for daily average PM2.5 from 65 to 35 

μg/m3

, and eliminated the annual average standard for PM10, 

leaving only the daily standard of 150 μg/m3. 

When a geographic area exceeds the NAAQS for a criteria 

pollutant, EPA may designate that area as being in 

“nonattainment.” In response, the state containing that area 

must develop a State Implementation Plan (SIP) that explains 

how the state will achieve compliance with the NAAQS. The 

principal tools that EPA and the states use to demonstrate this 

compliance are air quality simulation models. Each SIP must 

include a modeling demonstration illustrating how the state 

intends to mitigate emissions (usually through additional 

emission controls) to achieve compliance with the standard. 

In addition to the NAAQS for the criteria pollutants, EPA and 

the states also study mitigation strategies for other types of 

pollutants, such as hazardous air pollutants (HAPS, or air 

toxics) and global pollutants (mercury, for example, falls into 

both those categories). While there is a range of air quality 

policy-related issues that are tracked separately for individual 

pollutants, the pollutants’ chemistry and the sources involved 

in producing harmful air quality conditions are interrelated. 

Therefore, a multipollutant model is needed that can simulate 

the atmospheric processes and emission source inputs that 

contribute to all of these chemical species and conditions. The 

Division develops, evaluates, applies, and refines such models. 

These models represent, in as much detail as possible, the 

various dynamical, physical, and chemical processes 

regulating the atmospheric transport and fate of pollutants. 

The principal modeling platform, the Community Multiscale 

Air Quality (CMAQ) modeling system, includes components 

for meteorology, emissions, air quality, and analysis with 

visualization (see Figure 2-1). 

Research Description 

Within this Theme Area, the principal elements of the 

modeling program are Model Development and Model 

Evaluation. These elements are interrelated and form an 

iterative process: model evaluation provides information for 

improving the models; models are then improved through 

research and development; the improved models are reevaluated; and (assuming successful re-evaluation) the 

improved models are then available for regulatory application. 

Through the Model Development program element, the 

Division develops and improves the CMAQ air quality model 

for a variety of spatial scales (urban through continental) and 

temporal scales (days to years) and for a variety of pollutants 

(O3, PM, mercury and other air toxics, visibility, acid deposition). The multipollutant model approach permits the testing 

of emission control strategy impacts on the target pollutant, as 

well as collateral impacts on other pollutants. 

Focus areas of model development include the following: 

• Turbulence and diffusion within the planetary 

boundary layer (PBL) in the meteorological and air 

quality models 

• Data assimilation 

• Consistent linkage of the meteorology model with the 

air quality and emissions models 

• Source emissions modeling, including biogenic, 

wildfire, dust, ammonia, and other anthropogenic 

emissions 

• Gas- and aqueous-phase chemistry 

• Aerosol chemistry, physics, and thermodynamics 

• Sub-grid parameterization and modeling techniques 

• Numerical advection and other solution techniques 

• Code parallelization and efficiency 

Integrating meteorology and chemistry modeling is a new 

program priority designed to provide feedback from air quality 

parameters (e.g., aerosols) that affect meteorological 
4

parameters (e.g., radiation). Developmental areas are guided 

by the model evaluation results and by model sensitivity and 

uncertainty tests. New CMAQ model versions are released for 

public access roughly every one to two years. Workgroups 

have been formed to focus on these research topics: 

• Atmospheric chemistry and aerosols 

• Two-way interactive meteorology–chemistry-transport modeling 

• Weather Research and Forecasting (WRF) Model 

• Air toxics modeling 

Through the Model Evaluation program element, the 

Division evaluates the models to characterize the accuracy of 

model predictions and to identify improvements needed in 

model processes or model inputs. This requires comparisons 

against observational data. We compare different CMAQ simulations (e.g., different model versions, different chemical 

mechanisms, different vertical layer structuring) to identify the 

impact of model changes or options on model performance. 

Uncertainties in meteorological predictions and emission 

estimates are considered to help identify where improvements 

are needed. Regulatory applications of CMAQ are evaluated 

by comparing model-predicted changes in ozone and aerosols 

to changes in emission precursors. We conduct model evaluation through workgroups dealing with these issues: 

• Operational evaluations supporting the CMAQ model 

releases 

• Model diagnostics (chemistry, meteorology) 

• Model dynamics (i.e., tracking simulated and 

observed changes in air quality over time) 

• Probabilistic evaluation (exploring limits to the 

deterministic use of model predictions) 

• Spatial and temporal analyses of modeled and 

observed air pollutants 

Through these efforts, the Division facilitates the transition of 

research to the regulatory community. 

Accomplishments 

In the area of model development, a multipollutant version of 

the CMAQ modeling system was developed to predict ozone, 

PM, and mercury and 38 other HAPs in a single model 

configuration. We created this model version in response to 

increasing interest in modeling multiple pollutants, including 

criteria and hazardous air pollutants, within a single modeling 

framework for air quality management. The new model will 

support regional and urban studies that assess the potential cobenefits and effectiveness of various emission control programs, such as the Clean Air Interstate Rule (CAIR)∗

, Clean 

Air Mercury Rule (CAMR)∗

, Clean Air Visibility Rule 

(CAVR), and various onroad and nonroad mobile source rules. 

It will also support future assessment studies based on 

 

∗ Note that CAIR and CAMR are currently in litigation and that the research 

programs may be adjusted by the resolution of the legal issues. 

integrated national emission inventories containing both HAPs 

and criteria pollutants. The multipollutant model was developed by modifying and merging algorithms for gas-phase 

chemistry, aerosols, clouds, and emissions used in the mercury 

and HAPs versions of CMAQ. The Carbon Bond 05 (CB05) 

chemical mechanism was combined with the chemical 

reactions for chlorine, mercury, and HAPs, and implemented 

into the CMAQ modeling system. A normalization process 

was performed to test the model and to ensure that the 

multipollutant model is consistent with the original versions. 

Results suggest that consistency is achieved by including the 

emissions and chemistry of molecular chlorine (Cl2) and 

hydrochloric acid (HCl) in each model version. The multipollutant model will be included in the 2008 release of 

CMAQ. 

During 2007, in collaboration with a variety of private and 

governmental research organizations, the Division completed 

the analysis of results from the North American Mercury 

Model Intercomparison Study (NAMMIS). The NAMMIS 

employed global-scale modeling of atmospheric mercury to 

define initial and boundary conditions for three regional-scale 

mercury models that were the primary subjects of the study: 

the CMAQ model, developed and applied by the Division; the 

Regional Modeling System for Aerosols and Deposition 

(REMSAD), developed and applied by ICF International; and 

the Trace Element Analysis Model (TEAM), developed by 

Atmospheric and Environmental Research, Inc. The CMAQ, 

REMSAD, and TEAM simulations of the air concentration 

and wet and dry deposition of various mercury species during 

the 2001 test period were compared on time scales from 

weekly to annual. The simulations of wet deposition of 

mercury from CMAQ, REMSAD, and TEAM were also compared against observations from the Mercury Deposition 

Network on time scales from weekly to annual. Considerable 

model-to-model differences were found for air concentration, 

dry deposition, and wet deposition. Statistical agreement 

between simulated annual wet deposition and the corresponding observation was found to be largely scaled to the 

statistical accuracy of the precipitation data input to all three 

models; these data were derived from prior meteorological 

modeling. On shorter time scales, the statistical agreement for 

mercury wet deposition was weaker than for the input 

precipitation data, indicating that the physicochemical 

processes controlling the wet deposition of mercury may still 

not be accurately treated in any of the models tested. At the 

end of 2007, results from the NAMMIS were being described 

in two manuscripts intended for publication in peer-reviewed 

scientific journals. 

We also worked to improve the representation of reactive 

nitrogen chemistry in CMAQ. Similar to other air quality 

models, the CMAQ model currently accounts for only the 

homogeneous chemical reactions of nitrous acid (HONO). 

Studies have indicated that air quality models that take into 

account only the homogeneous reactions are not adequate to 

explain the observed ambient HONO. Recent evidence 
5

suggests that direct emissions and a heterogeneous reaction 

involving nitrogen dioxide and water vapor may play an 

important role in HONO chemistry. To improve the model 

performance for HONO, these additional sources have been 

included in CMAQ. The inclusion of these sources does 

indeed improve the model performance for HONO. 

During 2007, several advances were made in the simulation of 

aerosol chemistry and physics. We focused on heterogeneous 

nitrogen chemistry, coarse-particle chemistry, trace-elemental 

composition of particles, and aerosol thermodynamics. The 

heterogeneous reaction probability of N2O5 on wetted particle 

surfaces (γN2O5) is an influential parameter affecting 

wintertime predictions of fine-particulate nitrate (NO3

-

). 

Division scientists discovered a typographical error in the 

published parameterization of γN2O5 that had been translated 

into the CMAQ v4.6 code. Correcting that error led to a 

degradation in the model predictions of wintertime NO3

-

. 

Therefore, a detailed a study of the underlying laboratory data 

was conducted and a new γN2O5 parameterization was developed. This parameterization is the first to include the effects of 

temperature, humidity, particle composition, and phase state 

on γN2O5. When incorporated into the next version of CMAQ, 

the new γN2O5 parameterization is expected to mitigate current 

overpredictions of wintertime NO3

-

 under conditions prevalent 

in the midwestern U.S. In a separate effort during 2007, we 

made considerable progress in simulating the dynamic 

interaction between gaseous species (e.g., nitric acid) and 

coarse particles. These thermodynamically driven interactions 

are currently neglected in the CMAQ model, resulting in a 

gross underprediction of particulate nitrate and an overestimation of particulate chloride in coastal urban areas. The 

interactions have been successfully simulated in a stand-alone 

box model of the CMAQ aerosol module and will be 

incorporated into the full modeling system next year. Progress 

was also made in modeling the source origin of various trace 

elements in fine particulate matter. This development will 

introduce a number of new ways to evaluate model performance for primary PM in urban areas. Finally, we resumed 

efforts to improve the numerical stability of the gas/particle 

thermodynamic calculations in CMAQ. This work is being 

conducted in collaboration with researchers at the Georgia 

Institute of Technology. 

Efforts were also devoted toward transitioning to the Weather 

Research and Forecasting (WRF) model as the meteorological 

driver for CMAQ. The Pleim-Xiu land-surface model (PX 

LSM), the Asymmetric Convective Model version 2 (ACM2) 

boundary layer model and surface layer scheme, historically 

used as physics options in the Fifth-Generation Mesoscale 

Model (MM5), have been added to the WRF model. We 

provided the codes for these models to the National Center for 

Atmospheric Research (NCAR) for inclusion in the next 

release of WRF (version 3.0), due to be released in the spring 

of 2008. Evaluation of WRF simulations using these new 

physics components and comparisons to other LSM and PBL 

options in the WRF system have shown generally comparable 

or better results for temperature, humidity, and winds. This 

work has also led to some other significant improvements, 

including a new indirect nudging scheme for soil temperature, 

improved treatment of seasonal changes in vegetation, and 

improved parameterizations for soil, vegetation, and snow heat 

capacity. To facilitate the linkage between the WRF and 

CMAQ modeling systems, version 3.3 of the MeteorologyChemistry Interface Processor (MCIPV3.3) was prepared and 

delivered to the Community Modeling and Analysis System 

(CMAS) Center for release to the CMAQ user community; 

major changes included updates for WRF fields, improvements to dry deposition, removal of outdated science options, 

and addition of metadata to MCIP output files. The Division 

also completed a systematic investigation of the impacts of 

data assimilation in meteorological models on air quality 

predictions from CMAQ. Analyzing MM5 and CMAQ 

simulations confirmed that the use of nudging throughout the 

simulation period leads to improved prediction of ozone. 

MM5 simulations maintain nearly constant statistical 

performance on average when nudging is used throughout the 

simulation period; however, CMAQ predictions of ozone tend 

to degrade as the run time in MM5 increases. A two-part paper 

summarizing the findings from this investigation was accepted 

for publication in the Journal of Applied Meteorology and 

Climatology. Additional investigation into this phenomenon 

will continue in 2008 using WRF rather than MM5. 

Finally, model development efforts were also devoted toward 

developing and testing an on-line integrated meteorology–

atmospheric-chemistry modeling system. Integrating meteorology and chemistry modeling is a new program priority 

designed to provide feedback from air quality parameters (e.g., 

aerosols) that affect meteorological parameters (e.g., radiation). A coupled WRF-CMAQ system capable of simultaneous integration of meteorology and chemistry with two-way 

data exchange has been developed and tested. Development 

efforts on the feedback effects of aerosols on solar radiation 

are progressing. 

In the area of model evaluation, the Division continued to 

probe the performance of the CMAQ system using operational, diagnostic, dynamic, and probabilistic evaluation techniques. A number of publications examined performance 

under various synoptic regimes, with alternative chemical 

mechanisms, and with varying degrees of vertical resolution. 

Using operational and diagnostic evaluation techniques, an 

examination of CMAQ’s PM2.5 predictions showed that the 

PM “other” component was a major contributor to CMAQ’s 

overprediction of total PM2.5 in the fall and winter. Work is 

continuing to reduce the uncertainty of PM “other,” by 

looking at possible biases in emissions. Another thorny issue 

that was tackled during 2007 was how to improve the 

comparison between observed and predicted PM2.5 species to 

account for artifacts in the observations that are not accounted 

for in the CMAQ predictions. These components include 
6

nitrate volatilization and retained water mass, which can 

significantly impact the measured PM2.5 mass. The efforts to 

account for these artifacts will ultimately provide more 

accurate comparisons between observed and predicted PM2.5

mass. 

We made significant progress with probabilistic evaluation 

techniques, with an initial focus on CMAQ ozone predictions. 

Because all models are a simplification of the phenomena they 

aim to represent, it is often more useful to estimate a model 

result as a probabilistic range rather than as a single “best” 

result. A key challenge is that ensemble approaches require a 

large number of expensive simulations of independent 

modeling systems. We implemented a computationally efficient method to generate ensembles with hundreds of 

members based on several structural configurations of a single 

air quality modeling system and using the Decoupled Direct 

Method (DDM) to directly calculate how ozone concentrations 

change as a result of changes in input parameters. The 

modeled probabilistic range was compared to observations and 

was shown to perform better than more ad hoc estimates of the 

uncertainty in ozone predictions. Because this technique can 

generate large ensembles efficiently, it is well suited for 

diagnosing structural errors in the air quality modeling system. 

Exploration into new statistical methods for evaluating 

comparisons of monitoring data with model predictions also 

took place. Advanced statistical methods can aid the evaluator 

by making the best use of the limited monitoring data 

available, accounting for the differences between point-based 

measurements (monitors) and grid cell averages (model 

output), and assessing the model output for grid cells in which 

no monitors are located. While a variety of approaches could 

reasonably be utilized, the focus has been on methods that 

allow one to better understand and utilize the spatial 

correlation of pollutant fields, such as kriging-based methods. 

One example is Hierarchical Bayesian Modeling which is used 

to investigate the relationship between ammonium wet 

deposition and precipitation, and kriging with adjustments for 

anisotropy, used to better understand ozone and PM2.5

concentrations in the northeastern U.S. In addition, we have 

recently assessed the impact on model evaluation of 

incommensurability—that is, the mismatch between pointbased measurements and areal averages (model output). Ideas 

for improving regional air quality model evaluation techniques 

were explored at an American Meteorological Society (AMS)- 

and Division-sponsored workshop during the summer of 2007. 

Lastly, the Atmospheric Model Evaluation Tool (AMET) was 

made publically available. AMET is a combination of opensource software that includes a relational database to store 

paired observed-predicted values and a statistical program to 

create various plots and calculate statistics. AMET is a valuable tool that can aid in the evaluation of both meteorological 

and air quality simulations. Because AMET utilizes a 

relational database, the user can query data in the database 

based on any number of criteria, making it ideal for 

identifying any specific problems that may exist in the model 

predictions. Work to improve AMET and extend its capabilities will continue in the future. 

Next Steps 

Over the next several years, science and technology 

advancements planned for the CMAQ modeling system 

include enhanced emissions modeling and additional model 

system evaluation. Some of the planned milestones under this 

Theme Area are the following: 

FY-2008 

• Release and evaluate new version of CMAQ 

modeling system that will include improved simulations of aerosol processes, especially secondary 

organic aerosol (SOA) production; 

• Develop prototype of two-way integrated 

meteorology-chemistry simulation model based on 

WRF and CMAQ models; 

FY-2009 

• Add fugitive windblown dust emissions module to 

CMAQ modeling system; 

• Investigate the impacts of aerosol feedbacks on radiation on simulated meteorology and air quality using 

the integrated WRF-CMAQ modeling system; 

FY-2010 

• Refine the capability in CMAQ to accurately model 

the size, composition, and morphology of ultrafine 

particles; 

• Develop improvements in representation of physical, 

chemical, and dynamical processes to accurately 

represent air quality at fine scales down to 1 km and 

finer resolutions. 

Impacts and Transition of Research to 

Applications 

The Division releases versions of the CMAQ model and 

associated programs to the public through the ORD-supported 

CMAS Center; the Center also provides user support and 

training. The community air quality modeling concept, 

especially the CMAQ model, has seen growing acceptance 

since the model was first released in 1998. An annual CMAQ 

model users conference now attracts over 200 people each 

year from North and South America, Europe, and Asia. 

EPA/OAQPS and the states use CMAQ for assessments 

conducted during national air quality rulemaking and in their 

SIPs, respectively. OAQPS has used the model to assess the 

potential effectiveness of the CAIR and the CAMR. The 

states, through their Regional Planning Organizations (RPOs), 

are using CMAQ for visibility assessments in support of the 

Regional Haze Rule (RHR) and for upcoming SIP assessments 

for O3 and PM2.5. The CMAQ model is also being used in 

Canada, the U.K., Spain, Eastern European countries, China, 
7

Korea, and many other nations in programs to improve 

regional air quality management. NOAA’s National Weather 

Service (NWS), in a collaborative project with EPA, is using 

CMAQ to make publicly available short-term (next-day) 

forecasts of ozone air quality across the eastern United States 

(see Chapter 6). 

The end result of all of these efforts will be the ability to better 

inform (1) the public on current air quality conditions (from 

forecasting applications), to help them make decisions on 

health-related exposures to air pollution, and (2) policy makers 

(from air quality model assessments) to guide them in making 

the best long-term emission control decisions to reduce air 

pollution. 

The part of the Division organizationally associated with 

OAQPS oversees and facilitates the process of transitioning 

the tools we develop and evaluate to regulatory applications, 

thus providing the foundation for scientifically sound 

regulatory decisions. 

Figure 2-1. Schematic of CMAQ modeling system, including meteorology, emissions, and air quality models. 

SMOKE 

Anthropogenic and Biogenic Emissions Processing

Meteorological Model 

(WRF or MM5)

CMAQ AQ Model 

Chemistry-Transport Computations

Met-Chem Interface Processor 

(MCIP) 

Met. Data Processing

Weather Data

EPA Emissions Inventory

Hourly 3-D Gridded Chemical Concentrations

CMAQ Modeling System
8

Chapter 3 

Evaluating the Impact of Regulatory Policies on Air Quality and Ecosystems

Introduction 

 

As discussed in the introduction to Chapter 2, air quality 

management in the United States is implemented for criteria 

pollutants through NAAQS. States with nonattainment areas 

(areas that do not meet the NAAQS for one or more 

pollutants) must submit SIPs that demonstrate how the state 

will reduce emissions to achieve attainment. Most criteria 

pollutants are transported across state boundaries, which 

complicates the nonattainment issue. Recent rulemakings have 

recognized that this transport must be considered, requiring 

that a regional perspective be used when developing strategies 

for air pollution nonattainment. 

In 1998, EPA finalized a rule known as the NOx Budget 

Trading Program (NBP), requiring 22 states and the District of 

Columbia to submit SIPs that address the regional transport of 

ground-level ozone. The actions directed by these plans 

include reducing emissions of nitrogen oxides (which are a 

precursor to ozone formation), thereby decreasing the 

formation and transport of ozone across state boundaries. 

The Clean Air Rules of 2004 are a suite of actions designed to 

improve air quality. Three of the rules specifically address the 

transport of pollution across state borders. The CAIR∗ will 

permanently cap emissions of sulfur dioxide (SO2) and NOx

from utilities in the eastern United States. When fully 

implemented in 2015, CAIR will reduce SO2 emissions in 

these states by over 70% and NOx emissions by over 60% 

from 2003 levels. CAMR∗

 will build on CAIR to reduce 

mercury emissions from coal-fired power plants. The NonRoad Diesel Rule will reduce emissions from future non-road 

diesel engines by changing the way diesel engines function (to 

reduce emissions) and the way diesel fuel is refined (to 

remove sulfur). 

 

∗ Note that CAIR and CAMR are currently in litigation and that the research 

programs may be adjusted by the resolution of the legal issues. 

Deposition of atmospheric nitrogen, sulfur, and mercury to 

land and water surfaces contributes significant loadings to 

receiving water bodies, affecting the health of ecosystems. For 

example, atmospheric deposition of nitrogen accounts for 

about 30% of the nitrogen coming into the Chesapeake Bay. 

CAA regulations, including the NBP, CAIR, and CAMR, are 

expected to reduce the atmospheric deposition of these 

pollutants. 

Research Description 

Given the significant costs associated with these rules and 

control measures, it is important to demonstrate their 

effectiveness. The Division has demonstrated reductions in 

observed and modeled ozone concentrations resulting from 

actions of the NBP. Research will continue to develop ways to 

systematically track and periodically assess our progress in 

attaining national, state, and local air quality goals—

particularly those related to criteria pollutants regulated under 

the NAAQS and the Clean Air Rules. 

Research under this Theme Area falls into two categories: 

• Evaluating changes in ambient air pollutant concentrations and atmospheric deposition due to the 

implementation of emission reductions 

• Investigating relationships among emissions, ambient 

pollutant concentrations, human exposure, and 

human health endpoints 

The major research questions addressed by this research 

include the following: 

• Did our control strategies result in the anticipated 

emission reductions? 

• Did our models accurately predict the changes in 

pollutant concentrations and atmospheric deposition 

due to the control strategies? 

• What are the human and ecosystem health consequences of these reductions? 

The CMAQ modeling system is used to characterize air 

quality before and after the implementation of a target 

regulation and to evaluate correlations between changes in 

emissions and changes in pollutant concentrations or 
9

atmospheric deposition. Various scenarios are modeled to 

estimate the anthropogenic contribution to total ambient 

concentrations and the impact of not implementing the 

regulation. Methods have been developed to differentiate 

changes attributable to emission reductions from those that 

result from other factors, such as weather and annual and 

seasonal variations in emissions. Trajectory models, such as 

NOAA’s Hybrid Single Particle Lagrangian Integrated 

Trajectory (HYSPLIT) model, are used to investigate the 

transport of primary and secondary pollutants from their 

sources to downwind regions. 

Research is initially focusing on regulations affecting NOx and 

SO2, for which emissions monitoring data are available (e.g., 

NBP and CAIR regulations). Later research will investigate 

using other sources of information, such as remote sensing, to 

evaluate regulations that impact pollutants such as PM and 

mercury, for which emissions data are sparse or uncertain. 

Specifically, we are developing indicators to assess changes in 

emissions and air quality associated with regulatory actions, 

and modeling approaches to characterize the processes that 

impact the relationships between these indicators (process 

linkages). Figure 3-1 indentifies the full suite of indicators and 

process linkages associated with the evaluation of the NBP 

rules. Previous efforts performed under this Theme Area 

developed the indicators characterizing changes in emissions 

and ambient NOx and ozone concentration levels. Models and 

data analyses were used to relate the changes in emissions to 

the changes in ambient NOx (emitted precursor pollutant) and 

ozone (secondary pollutant) concentrations by directly relating 

the fate and transport of these pollutants to levels downwind 

of their sources. 

Accomplishments 

The results of research under this Theme Area have indicated 

that when major point sources of NOx were reduced by the 

NBP, this decreased ozone concentration levels by 5-8 parts 

per billion (ppb) at downwind locations. In 2007, evaluations 

of the chemical and physical processes further indicated that, 

while a dramatic reduction in maximum ozone chemical 

production rates occurred downwind of major point sources 

affected by the NBP, net ozone production efficiency actually 

increased due to the greater decrease in reactive nitrogen 

product species (NOz). This and other results indicate that the 

chemical regime has shifted toward more NOx-limited 

conditions in the plume-impacted areas downwind of the 

sources, meaning that relatively small increases in NOx

emissions (e.g., from the transport corridors in the eastern 

U.S.) can result in a relatively large increase in ozone, due to 

changes in production rate efficiencies. Overall, our research 

has shown that emission control programs implemented under 

the NBP have been effective in meeting the objective of 

reducing interstate ozone transport, and have helped improve 

ozone air quality in source areas of the eastern United States. 

These results contributed to the annual assessment of the NBP 

in Report EPA-430-R-07-009, NOx Budget Trading Program 

2006: Program Compliance and Environmental Results.

 

Next Steps 

Research over the next five years will 

(1) continue the assessment of the NBP by applying 

ambient concentration and exposure indicators to a 

health and risk assessment in the greater New York 

State area; and 

(2) assess the impact of the phased implementation of 

CAIR through the application and further development of indicators and process linkage methodologies 

developed for assessing the NBP. 

 

The process of developing indicators and process linkages for 

assessing the NBP will not only establish an approach for 

assessing CAIR, but will also establish a baseline description 

of the state of the environment before the implementation of 

CAIR. Major deliverables anticipated from this research 

include the following: 

FY-2008 

• Develop methods to quantify the impact of the NBP 

on ambient ozone concentrations and atmospheric 

transport of pollutants, including assessing the 

impacts of not implementing the regulation, and to 

quantify the anthropogenic contribution; 

FY-2009 

• Develop methods to quantify the probability of ozone 

exposure above exceedance levels to populations 

before and after the NBP was implemented; 

• Conduct a prototype risk assessment to examine the 

health impact of simulated emission scenarios; 

FY-2010 

• Develop approaches for characterizing the magnitude 

of changes in hospital admissions in New York State 

resulting from the NBP; 

FY-2012 

• Apply prototype ambient concentration tracking 

method to evaluate impact of CAIR on ambient 

concentrations and deposition rates; 

• Apply prototype deposition approach to evaluate 

impact of CAIR on ecological exposure endpoints in 

major water bodies. 

Impacts and Transition of Research to 

Applications 

Quantifying the improvement in air quality and human and 

ecological health brought about by costly regulations is critical 

in evaluating whether these actions are making the difference 

originally anticipated. Research under this Theme Area 

evaluates the effectiveness of specific regulatory actions. 

Methods developed for these evaluations will also provide a 
10

framework for assessing future regulatory actions. These 

methods will include 

• Data combination techniques 

• Model evaluations for different regulatory and 

emission scenarios 

• Approaches for tracking trends embedded within 

spatial and temporal signals and confounded by 

factors such as meteorology 

• Evaluation of the impacts of regulatory actions on 

human and ecological exposure and health 

This effort transitions research results to applications by 

demonstrating the use of the CMAQ and HYSPLIT models 

and statistical techniques to evaluate the impact of regulations 

implemented to improve air quality. 

Figure 3-1. Assessing the impact of regulations on ecosystems and human health endpoints showing 

the indicators (boxes) and process linkages (arrows) associated with the NOx Budget Trading Program. 
11

Chapter 4 

Linking Sources to Human Exposure

Introduction 

The goal of this research theme is to reduce uncertainties in 

quantifying the link between sources of atmospheric pollution 

and human exposure. The CAA requires EPA to assess which 

HAPs pose the greatest risk to humans in the United States, 

and to develop strategies for controlling harmful concentrations of these compounds. These assessments typically 

involve the application of different models, depending on 

program objectives—global, regional, urban, or local scale 

(Figure 4-1). Performing these assessments often requires a 

linkage between ambient air quality and human exposure 

models. The Division conducts research to build this linkage 

by combining the features of grid-based, regional-scale 

chemistry-transport models and urban-scale dispersion 

models. This research facilitates the use of air quality model 

concentrations in human exposure modeling and health risk 

assessments, which historically have been limited by their 

need to rely upon monitored concentrations at a central site. 

For exposure assessments, air quality modeling should include 

local-scale features, long-range transport, photochemistry, and 

deposition to provide the best estimates of air concentrations. 

Generally speaking, the two major types of air quality models 

are source-based Gaussian dispersion models and grid-based 

chemistry-transport models. Chemistry-transport models, such 

as CMAQ, can provide estimates of photochemically formed 

pollutants typically at a 36- to 4-km grid scale, but not locallevel details. CMAQ provides volume-average concentration 

values for each grid cell in the modeling domain for given 

conditions. Emissions are assumed to be instantaneously wellmixed within the grid cell in which they are emitted. While 

grid-based models are preferred for simulation of chemically 

reactive airborne pollutants, dispersion models (such as the 

AMS/EPA Regulatory Model Improvement Committee 

[AERMIC] Model [AERMOD] have been developed to 

simulate the near-field fate of airborne pollutants that are 

relatively chemically inert. 

For multipollutant assessments, a suite of toxic compounds 

needs to be included in the CMAQ modeling system, and 

model results should be evaluated with ambient observational 

data. This research need is closely linked to other research 

themes within the Division that involve the development and 

evaluation of the modeling system, improvements in chemical 

and physical characterization of air toxics, and the measurement of ambient air toxics concentrations. 

Because exposure assessments are primarily for urban areas, 

air quality simulation models should accurately depict the 

physical-chemical processes that occur in these areas. 

Concentration fields derived from models run at grid resolutions on the order of 4 km or larger (such as CMAQ) do not 

account for the variability of high emission gradients typical in 

urban areas. Several approaches are available that may yield a 

better characterization of urban “hot spots,” including bruteforce simulations with finer-scale grid models, hybrid 

modeling that combines chemistry-transport models with 

dispersion models, and sub-grid variability distribution 

estimates of concentrations. Meteorological models such as 

the MM5 and WRF modeling systems now include the 

capability to assimilate advanced urban canopy descriptions, 

including building, vegetation, and street canyon characteristics. Databases of high-resolution urban morphological 

features are needed to support these advanced models for 

future urban evaluation and application. 

A growing number of health studies have identified adverse 

effects—including respiratory disease, cancer, and death—for 

populations exposed to air pollution near major roadways, thus 

raising concerns about building schools near roadways and the 

general health of people living near roads. Performing nearroadway risk assessments requires characterizing atmospheric 

processes in complex urban settings, especially near major 

roadways. Near-road air pollution has been selected as a 

central theme in EPA/ORD’s multiyear clean air research 

plan, because it is a problem that is of pressing importance (as 

identified by EPA’s stakeholders), and it requires an 

integrated, multidisciplinary field and laboratory scientific 

approach. 

Research Description 

The Division’s work in this Theme Area is broken into the 

following two research tasks: 

• Multiscale modeling of toxic air pollutants 

• Near-roadway modeling 
12

Within the first task, multiscale modeling of air toxics 

involves 

(1) including chemistry and physics for additional toxic 

air pollutants in CMAQ; 

(2) applying CMAQ with toxics for problems of interest 

to Program and Regional Offices, and the transfer of 

information to these stakeholders; 

(3) reformatting the results from air quality model 

simulations for use in the Stochastic Human 

Exposure and Dose Simulation (SHEDS) model; and 

(4) developing methods and tools that can be used to 

predict air pollutant concentrations at urban (or 

neighborhood) scales, and using these tools to assess 

the magnitude and variability of concentrations to 

which urban populations are exposed.

To incorporate the salient features of both grid-based and 

plume-dispersion approaches, we have been testing a hybrid 

approach that combines results from CMAQ with the 

AERMOD model. The CMAQ grid model provides the 

regional background concentrations and urban-scale photochemistry, and the AERMOD local plume dispersion model 

provides the air concentrations that are due to local emission 

sources. The results of both model simulations are combined 

to provide ambient air concentrations for use in exposure 

models. The advantage of this modeling approach is that 

researchers can incorporate the spatial and temporal variation 

of air pollution within a study area without having to rely on 

dense ambient monitoring networks. This hybrid approach is 

currently being explored in several studies, including an air 

quality and exposure study in Detroit, MI, and an 

accountability study in New Haven, CT. 

As a complement to hybrid modeling, we are exploring other 

methods to obtain model concentration fields at spatial scales 

needed for improved exposure and risk assessments. This 

entails running CMAQ with higher-resolution grid meshes 

(smaller grid cells) than is the normal practice. We are also 

investigating the use of urbanized versions of the MM5 and 

WRF models to drive the CMAQ model at 1-km grid 

resolutions. In addition, partnerships with external collaborators are being leveraged to study ways to parameterize 

concentration distribution statistics to augment CMAQ outputs 

at 12- or 4-km grid resolutions, based on outputs of fine-scale 

grid models and/or use of hybrid modeling approaches. 

To support improved urban-scale meteorological modeling, 

the Division is leading the creation of the National Urban 

Database and Access Portal Tool (NUDAPT). As part of this 

effort, we are conducting collaborative studies with NCAR on 

the urbanized version of the WRF model. 

Regarding near-roadway modeling, the second task within this 

Theme Area: Before 2007, the Division was engaged in a 

number of loosely coordinated research projects involving the 

near-road environment, including research to support 

homeland security efforts. In 2007, EPA/ORD initiated a 

cross-laboratory coordinated near-road research program. The 

Division is meeting the physical and numerical dispersion 

modeling needs of this program, by assisting in the design and 

analysis of field experiments, by conducting laboratory 

dispersion studies, and by developing improved numerical 

algorithms for modeling near-road dispersion of emissions 

from major roadways. Our focus is to examine the significance of near-road emissions from varied roadway conditions 

on human exposure and related health risks, and to develop 

tools for addressing this issue. 

Accomplishments 

The CMAQ modeling system has been modified to include 

HAPs, and results from the revised model have been coupled 

with the near-field dispersion model AERMOD to account for 

urban-scale gradients of air toxics. In addition, outputs from 

this coupled system have been successfully linked to the 

SHEDS model and the Hazardous Air Pollutant Exposure 

Model (HAPEM). This research has been performed in 

collaboration with scientists from EPA/ORD/NERL’s Human 

Exposure and Atmospheric Sciences Division (HEASD) and 

EPA/OAQPS. 

During the past two years, we have developed the hybrid 

approach to estimate concentrations for multiple pollutants 

that reflects both local features (hot spots) and regional 

transport. The local impacts from mobile sources and significant stationary sources are estimated using AERMOD, and 

the combined concentrations are used for subsequent human 

exposure analysis. During 2007, we demonstrated an 

application of this linkage for New Haven, CT. This project is 

a collaborative effort with state and local agencies, including 

government, academia, and the New Haven community, to 

apply and evaluate air quality and human exposure models 

that can be used with health data. The project goal is to assess 

the feasibility of using this information to conduct an air 

accountability study (i.e., to trace the impact of air quality 

changes through to human health impacts). 

Efforts have continued on methods to derive sub-grid 

variability (SGV) distributions from a combination of ~1-kmgrid-resolution CMAQ model simulations and hybrid model 

results. With this approach, each 4- or 12-km CMAQ grid cell 

is assigned SGV characteristics, such as the type of distribution or the range of concentrations corresponding to userprescribed percentile values. Initial efforts have focused on 

SGV distribution functions derived from the Wilmington, DE, 

and Houston, TX, modeling results. The use of Weibull 

distributions seems promising. The SGV approach may prove 

useful for applications that can incorporate estimates of SGV 

on an a priori basis, such as with population exposure studies. 

For urban-scale meteorological modeling, a prototype version 

of NUDAPT was completed for the Houston area. NUDAPT’s 

portal features allow users to adapt processed fields of urban 
13

canopy parameters and other gridded fields for use with 

different grid resolutions and map projections. NUDAPT also 

includes sets of advanced urban canopy parameter 

implementations for the MM5 and WRF modeling systems. A 

workshop of the federal, state, academic, and private 

collaborators was conducted in Boulder, CO, during spring 

2007 as a means to perform the initial implementation of 

NUDAPT. Sensitivity studies using urbanized MM5 and WRF 

to drive CMAQ for urban applications were begun. For 

Houston, sensitivity runs of MM5 at 1-km resolution are being 

performed using both the standard MM5 model and an 

urbanized MM5 based on an urban canopy approach. 

Similarly, collaborations are underway with NCAR regarding 

their urbanized WRF model. Preliminary CMAQ-Toxics 

(CMAQ-TX) simulations have been made at 1-km grid 

resolution for the Houston and Wilmington domains; the 

Delaware modeling is being performed in collaboration with 

the State of Delaware. A preliminary survey of the distribution 

functions has been conducted from the Wilmington and 

Houston CMAQ results. A special session at the 2007 annual 

CMAS conference showcased the NUDAPT efforts, including 

a demonstration of the NUDAPT initial prototype. 

During 2007, the Near-Roadway and School Infiltration 

Research Initiative project continued. Fourteen roadway 

configurations were identified, and a physical model was 

created for each configuration for performing modeling in the 

Division’s Meteorological Wind Tunnel. The configurations 

included a flat roadway with no surrounding obstacles (base 

case), noise barriers of varied heights and distances from 

roadway, two different porous barriers intended to simulate 

rows of vegetation, and depressed and elevated roadways. The 

experiments generated three-dimensional data sets comprising 

winds, turbulence, and tracer-gas concentrations. Preliminary 

results show that the solid noise barriers have a substantial 

effect on downwind concentrations. When winds blow across 

the roadway, the barriers increase vertical turbulence. This 

causes the plume from the roadway to mix more vigorously in 

the vertical direction, which results in decreased ground-level

concentrations immediately downwind of the road. For a 

single upwind barrier, the downwind concentration (near the 

edge of the roadway) decreases by a factor of four compared 

to the base case. By adding a second solid barrier on the 

downwind edge of the roadway, the downwind concentration 

decreases by a factor of six compared to the base case. With 

an upwind “vegetation” barrier of 58% porosity, only minor 

differences are seen in the downwind concentrations compared 

to the base case. Although the simulated upwind vegetation 

causes a modest increase in the vertical extent of the plume, 

downwind concentrations decrease less than 7% over the base 

case. A vegetation barrier with less porosity (23%, representing more dense vegetation) shows a decrease in near-road 

downwind concentrations of about a factor of two. Denser or 

taller vegetation would be expected to produce greater 

differences in the concentration field. Finally, depressed 

roadways are found to affect the downwind concentration 

fields in a way similar to the case with noise barriers on both 

sides of the road. 

A number of journal articles are being prepared using the 

results from the wind tunnel, and the data obtained are being 

used to verify numerical algorithms and to improve the linesource algorithm used in near-roadway dispersion models. 

Several members of the Division participated in the Raleigh 

2006 Pilot Field Study, which was an EPA/ORD multilaboratory collaborative effort involving EPA/NERL and 

EPA’s National Health and Environmental Effects Research 

Laboratory (NHEERL) and National Risk Management 

Research Laboratory (NRMRL). The Raleigh field study, 

conducted in the summer of 2006, was designed to provide 

data to characterize the influence of traffic-generated 

emissions in the near-road environment, especially to help 

assess their impact on air quality and particle toxicity near a 

heavily traveled highway. The study included several realtime and time-integrated sampling devices that measured air 

quality concentrations at multiple distances and heights from 

the road. Pollutants analyzed included EPA-regulated gases, 

particulate matter (coarse, fine, and ultrafine), and air toxics. 

Pollutant measurements were synchronized with real-time 

traffic and meteorological monitoring devices to provide 

continuous and integrated assessments of the variation of nearroad pollutant concentrations and particle toxicity with 

changing traffic and environmental conditions, as well as 

distance from the road. This research task helped provide the 

analysis used to demonstrate the temporal and spatial impact 

of traffic emissions on near-road air quality. 

Using funding from the EPA/ORD near-roadway research 

initiative, a support contract provided a comprehensive review 

of available/operational air quality and emission models. A 

final draft report was completed, and work is underway to 

transform this report into a review article, possibly for the 

Journal of the Air Waste and Management Association. This 

review will provide the air quality modeling community with a 

convenient summary of the current state-of-science and will 

serve as a guide for the research needed to develop improved 

near-roadway air quality and emission models. 

Next Steps 

During the next few years, the Division is expected to increase 

emphasis in the areas of near-roadway modeling and linkage 

of air quality models with human exposure models to assess 

human health. A major effort during 2008 will be to create and 

implement a version of the SAPRC07-TX chemical 

mechanism within the CMAQ system. This mechanism will 

go directly to a “multipollutant” form. We also plan to add 

cloud chemistry for chromium compounds and address adding 

arsenic compounds into the CMAQ-TX model. If time 

permits, we will start studying the inclusion of polycyclic 

aromatic hydrocarbons (PAHs) into CMAQ-TX. We hope to 

simulate the seven to sixteen PAHs that are suspected of 
14

causing damage to human health, based on laboratory studies. 

Besides requiring revision of model algorithms, PAHs present 

an additional difficulty because parts of the emissions inventory for HAPs lump several PAHs into one emissions rate. 

Also during 2008, simulations with CMAQ-TX for Baltimore 

will be performed, as well as simulations to examine the effect 

of alternative mobile-source fuel composition on 

concentrations of toxic aldehydes. In 2009, we will analyze 

output from the Baltimore simulations and provide the 

ambient concentration predictions for input to Environmental 

Benefits Mapping and Analysis Program (BenMAP) benefits 

model analyses being performed by EPA/OAQPS. 

To support urban modeling, a “fine-scale” Division workgroup has been established to develop a more detailed research 

plan for investigating the efficacy of adapting WRF/CMAQ to 

a grid mesh of less than 4 km for various test beds. This plan 

will be used to guide and perhaps redirect research within the 

Division during 2008 through 2011. 

Via an existing collaboration with the State of Delaware, we 

will continue examining and refining the characteristics of the 

SGV distributions using both fine-scale and hybrid modeling 

approaches. Methods to utilize these distributions will be 

investigated for developing parameterization of SGV variations for coarse (4- and 12-km) grid resolutions. We may also 

investigate parameterizations of SGV distributions with either 

off-the-shelf or alternative software specifically developed for 

deriving parametric forms of the distribution function. 

Further, we plan to explore developing an easy-to-use method 

to create modeling input files for on-road mobile sources at a 

link-based level, and to assess the impact of more spatially 

resolved emissions on modeled ambient air pollutant concentrations. We will continue conducting uncertainty analyses 

to evaluate model results and explore an “ensemble-based” 

approach for generating probabilistic concentration fields. 

Follow-on collaboration with NCAR will investigate use of 

the urbanized version of WRF for driving CMAQ for urban 

applications. We will also perform sensitivity studies using 

advanced urbanized versions of MM5 and WRF with CMAQ 

to examine the impact of using the NUDAPT data in urban 

areas. 

Follow-on wind tunnel dispersion studies will be conducted 

over several years to expand the near-roadway scenarios to 

include the influences of wind direction variations, nearby 

buildings, and upwind and downwind vegetation. The 

Division and NOAA’s Air Resources Laboratory (ARL) Field 

Research Division (FRD) are planning to perform a tracer-gas 

dispersion experiment to enhance a field study that will be 

conducted in Las Vegas during 2008-2009. The overall 

purpose of the EPA field study is to characterize the spatial 

gradient of pollution within ~200 m of a major highway. The 

research plan envisions the release of SF6 from a 100-m 

perforated pipe under various flow regimes. Plans have been 

proposed to deploy sonic anemometers near I-15 in Las Vegas 

to characterize the decay of vehicular-induced turbulence as a 

function of distance from the roadway. Further discussions 

between the Division and FRD will occur during 2008. Design 

of the field study will be supported by a wind tunnel study of 

the flow and dispersion in a 1:200 scale model of the selected 

Las Vegas field site, which will be performed during summer 

2008. Data collected in the field and the laboratory, as well as 

detailed numerical modeling studies from models such as the 

Quick Urban and Industrial Complex (QUIC) model, will be 

analyzed and a refined line-source algorithm will be proposed 

for inclusion in the AERMOD model. 

In future years (2009-2011), the Division plans to return to 

wind tunnel studies of urban street canyon flows within a scale 

model of a large urban center (Midtown Manhattan). We will 

examine the general structure of complex urban boundary 

layers. The Midtown Manhattan model provides a “target of 

opportunity” because a physical model already exists from an 

earlier homeland security project. These data, in combination 

with those from similar wind tunnel studies of urban centers 

(Lower Manhattan, Oklahoma City, etc.) and from computational fluid dynamics modeling (existing, from studies 

performed outside of the Division) of the same Midtown area, 

can be used to characterize the influence of the urban 

landscape on grid-average concentrations within regionalscale modeling analyses, and could provide the basis for 

improved urban dispersion algorithms within near-field and 

hybrid modeling approaches. 

Research under this Theme Area is expected to contribute to 

the following research milestones: 

FY-2008 

• Linkage of CMAQ-TX output with BenMAP for the 

Baltimore area; 

• Enhanced air quality and exposure modeling tools to 

address finer-scale air toxics concentrations and 

exposures (EPA Annual Performance Measure #397); 

• Report on the NUDAPT Prototype; 

• Characterization of ambient air quality near roadways 

with data collected at the Division’s Meteorological 

Wind Tunnel and development of recommendations 

for improved line-source algorithms for urban-scale 

air quality modeling; 

• CMAQ model system release and evaluation, 

including concurrent multipollutant modeling 

capability (O3, PM, air toxics, mercury); 

FY-2009 

• Report on model results for Baltimore and on effects 

of control strategies intended to reduce harmful 

concentrations of multiple pollutants ;

• Hybrid modeling results for exposure assessment 

studies of interest to health scientists/epidemiologists; 
15

• Report on the sensitivity of alternative fine-scale 

meteorological/air quality modeling approaches in 

urban areas; 

FY-2010 

• Development and evaluation of an improved linesource algorithm for characterizing near-roadway 

impacts in air quality models; 

FY-2011 

• Improved CMAQ modeling system for use in urbanscale residual nonattainment areas (joint effort with 

the model development and evaluation research 

theme discussed in Chapter 2); 

FY-2012 

• Identification and evaluation of assessment tools to 

aid urban planners in considering near-road health 

effects. 

Impacts and Transition of Research to 

Applications 

Development and application of linked models of ambient air 

quality and human exposure will help epidemiologists reduce 

uncertainty as they assess the risk of air pollutants to human 

health, and will help policy-makers reduce uncertainty as they 

develop control strategies that target air pollutants identified 

as posing the greatest risk to humans. These uncertainty 

reductions should result in more accurate risk assessment 

results and in policies that are more likely to protect human 

health. 

Figure 4-1. Multiple scales in air quality modeling.
16

Chapter 5 

Linking Sources to Ecosystem Exposure 

Introduction 

Ecosystems provide us with many life-sustaining benefits—

resources and services that contribute to our physical, social, 

and economic welfare; examples of ecosystem services 

include clean air and water, fertile soil for crop production, 

pollination, and flood control. A long-term goal of environmental management is to achieve sustainable ecological 

resources through a comprehensive assessment of current and 

projected ecosystem health and services. Such an assessment 

must include identification of the major threats (the specific 

stressors) to ecosystem health, the sources of those stressors, 

and how they move through the environment. This is 

fundamentally a problem of multimedia pollution. 

The overall objective of this Theme Area is to develop the 

atmospheric components of multimedia modeling and 

assessment tools to allow better management and protection of 

ecosystems and their associated resources and services. The 

Division is developing a suite of linked models, tools, and 

technology to provide long-range ecological forecasts and a 

scientific basis for decision-making to protect terrestrial and 

aquatic ecosystems. This research supports EPA’s expanded 

definition of air quality management that includes ecosystem 

protection in assessments of air pollution regulations, i.e., in 

the setting of secondary NAAQS. It also supports the new 

emphasis of EPA’s Ecosystem Research Program (ERP) on 

linking sources to exposure in a multipollutant context and 

developing capabilities for ecosystem services assessments. 

The interaction between the atmosphere and the underlying 

surface is increasingly being recognized as a significant factor 

in multimedia issues. Atmospheric deposition is an important 

source of ecosystem stressors, in particular for acidification, 

eutrophication of coastal estuaries due to excess nitrogen, and 

bioaccumulation of mercury. Managing the nitrogen cycle is a 

central issue of the ESRP. Critical-load is the amount of 

deposition above which natural resources can be negatively 

affected and is intended as a protective threshold. The National Academy of Sciences (NAS) has recommended that EPA 

consider a deposition-based approach such as critical loads to 

ecosystem management.2

 In support of the ESRP thrust and 

 

2

Committee on Air Quality Management in the United States, National 

Research Council. 2004. Air Quality Management in the United States. 

Washington, DC: National Academy of Sciences. 

the NAS recommendation, the Division conducts research to 

provide the most accurate atmospheric deposition estimates 

possible. 

The Clean Water Act administered by the EPA requires states 

to develop Total Maximum Daily Load limits (TMDLs), the 

maximum amount of a pollutant that a body of water can 

receive while still meeting water quality standards. The 

atmosphere is an important contributor to stressors such as 

excess nutrients, but atmospheric deposition is seldom 

considered in the development of TMDLs. The Division is 

conducting research to improve the understanding of the 

atmospheric contribution of stressors to TMDLs. 

Research Description 

For this research theme, the Division has identified research 

areas that have the most potential to reduce critical uncertainties in atmospheric deposition, to assess program accountability, and to link atmospheric deposition to ecosystem 

resources and services. 

Specific research tasks are grouped under the following 

research program elements: 

• Air-Surface Research and Development 

• Multimedia Applications 

• Multimedia Tool Development 

Through the Air-Surface Research and Development

program element, the Division develops and enhances airsurface exchange modules for CMAQ, and advances the linkage between CMAQ and the underlying land-use categories to 

facilitate improved interactions with ecosystem models. We 

also develop and enhance air-surface exchange modules for 

monitoring network operations using an inferential method for 

dry deposition, focusing primarily on sulfur, nitrogen, and 

mercury species. The bidirectional air-surface exchange process is a new feature of this program element. 

Focus areas of the Air-Surface Research and Development 

program element include the following: 

• Unidirectional deposition of gases and particles 

• Bidirectional flux (air-surface exchange) of ammonia 

• Bidirectional flux of mercury 
17

• Land-surface interface within the CMAQ system to 

support bidirectional fluxes 

• Land-use-specific flux determination by CMAQ for 

linkage with ecosystem models 

• Dry deposition and bidirectional flux module adaptations for network operations 

Through the second program element, Multimedia Applications, the Division develops and improves linkages between 

air and water models, and develops and maintains connections 

to ecosystem resources and services through participation with 

partners in multimedia assessments. Simulation of deposition 

estimates at a National scale is an important output from these 

efforts. 

Focus areas of Multimedia Applications include the following: 

• Chesapeake Bay 2007/2008 re-evaluation and 2010 

TMDL assessment 

• Tampa Bay nitrogen deposition assessment (TMDL) 

• Coastal air-water model linkage development to 

address water quality issues 

• Studies to address management of the nitrogen cycle 

Through the Multimedia Tool Development program 

element, the Division develops tools for specialized 

multimedia analyses and applications involving atmospheric 

models. The need for specialized tools is especially pertinent 

to bringing atmospheric components together with watershed 

components for multimedia management analyses. Most offthe-shelf tools do not address the specialized needs encountered in analyzing data from a multimedia perspective. 

Significant effort is often required to analyze observations and 

model results and provide them in forms that are required for 

supporting management decisions. 

Focus areas of Multimedia Tool Development include the 

following: 

• Conversion of land-use information to National Land 

Cover Data (NLCD) and allocation of spatial data to 

a CMAQ-usable gridded form 

• Development of watershed deposition tool to overlay 

gridded CMAQ output onto selected watershed 

segment polygons 

• Updating CMAQ visualization tools to be based on 

the Java programming language 

Accomplishments 

An evaluation of the Multi-Layer Model (MLM) used in 

EPA’s Clean Air Status and Trends Network (CASTNET) for 

estimating dry deposition pointed to areas for model improvement. In response, we developed the Multilayer Biochemical 

Model (MLBC) as a replacement for the MLM. New versions 

of MLM and MLBC were developed for network operations: 

MLMNet and MLBCNet. A new interface capable of running 

both models was also developed. We delivered first versions 

of the two new model versions and the interface to the 

EPA/Office of Air and Radiation (OAR) Clean Air Markets 

Division (CAMD) in 2007. 

The Division also partnered with EPA’s Chesapeake Bay 

Program Office (CBPO) to define a series of CMAQ estimates 

of future atmospheric nitrogen deposition out to 2030, 

simulating growth and implementation of new air quality 

regulations. The new regulations include CAIR∗

, CAMR∗, and 

CAVR. A significant decrease in nitrogen deposition from 

NOx emission reductions is expected, but the growth in 

ammonia emissions erodes these benefits. We also partnered 

with the CBPO to define a series of desired CMAQ enhancements for air-water model studies. 

We used a brute-force sensitivity approach with CMAQ to 

estimate the relative contribution of NOx emissions from each 

of the six Bay states (Delaware, Maryland, New York, 

Pennsylvania, Virginia, and West Virginia, plus the District of 

Columbia) to oxidized nitrogen deposition in the Chesapeake 

Bay watershed. The brute-force sensitivity approach produced 

an unacceptable degree of nonlinearity in the combined estimate of the relative contribution from the Bay states. We then 

modified the Decoupled Direct Method in Three Dimensions 

(DDM3-D) sensitivity approach in CMAQ, which mathematically accounts for the nonlinearities, to include the tracking of nitrogen deposition from NOx emissions. Contributions 

from mobile sources, power plants, and industry in the Bay 

states to nitrogen deposition to the Bay watershed will be resimulated with the new DDM3-D implementation in CMAQ 

in 2008. 

To support development of the nitrogen TMDL for the Tampa 

Bay watershed, the Division partnered with the Tampa Bay 

Estuary Program (TBEP) to define annual nitrogen deposition 

simulations for Tampa Bay watershed segments in order to 

improve the understanding of nitrogen deposition to the 

watershed. In addition to providing deposition estimates for 

current (2002) emission conditions with CMAQ-UCD (a 

version of CMAQ that uses a sectional approach to represent 

the aerosol size distribution), we performed sensitivity simulations to answer questions about (1) the relative contribution of 

within-watershed NOx emissions to watershed nitrogen 

deposition, (2) the deposition reduction benefit expected to 

result from court-ordered NOx reductions in emissions from 

two power plants located alongside Tampa Bay, and (3) the 

nitrogen deposition reduction benefit expected to result from 

the 23-state CAIR air emission reductions in 2010. 

The Watershed Deposition Tool (WDT) is designed to allow 

users to read seasonally or annually averaged CMAQ files in 

native format and calculate a weighted-average deposition or 

change in deposition for selected watershed hydrologic units. 

 

∗ Note that CAIR and CAMR are currently in litigation and that the research 

programs may be adjusted by the resolution of the legal issues. 
18

In 2007 we implemented a second set of improvements to the 

WDT and released it to the public via the ASMD web site. 

Examples of outputs from the WDT are shown in Figures 5-1 

and 5-2. Also in 2007, an initial version of the Visualization 

Environment for Rich Data Interpretation (VERDI) was 

delivered to the Division. VERDI is an open-source Java tool 

for visualizing CMAQ and other environmental data. The 

tool’s open-source and Java-based aspects will allow many 

users to contribute to its development and enhancement. 

VERDI is designed to replace the Package for Analysis and 

Visualization of Environmental data (PAVE), which is the 

current package used for visualizing CMAQ data. 

Next Steps 

Over the next several years, advancements are planned for the 

multimedia Theme Area to investigate more sophisticated 

futures scenarios for air-water linkages, and to adapt CMAQ 

to calculate bidirectional exchange of ammonia and mercury 

and to more closely couple to ecosystems models. Some of the 

planned milestones are as follows: 

FY-2008 

• Complete key Chesapeake Bay CMAQ modeling 

scenarios with 12-km grid cell size and sea salt 

influence for the Chesapeake Bay Program (CBP) 

TMDL analysis and ESRP baseline;

• Incorporate bidirectional NH3 and mercury flux 

algorithms into research version of CMAQ;

• Incorporate new mosaic land-use interface in CMAQ 

for better communication with ecosystem models; 

• Complete preliminary air-water model linkage for 

Cape Fear River basin in North Carolina;

FY-2009 

• Convert mosaic land-use interface to NLCD for 

consistency with ecosystem models, and test CMAQ 

for land-use-change scenario analysis; 

• Simulate Chesapeake Bay futures scenarios with 

CMAQ at 12-km grid cell size, and incorporate NH3

bidirectional exchange influence for Chesapeake 

sensitivity; 

• Complete preliminary air-water model linkage for 

North Carolina Albemarle-Pamlico estuarine system; 

FY-2010 

• For ESRP place-based scenario analyses (Carolinas, 

Midwest, Tampa), simulate nitrogen, sulfur, and 

ozone deposition futures incorporating land-use 

changes ; 

• Incorporate into a science version of CMAQ a 

generalized land-surface layer to support multipollutant bidirectional flux calculations. 

Impacts and Transition of Research to 

Applications 

CASTNET monitors concentrations and dry depositions at 

sites across the country to assess long-term trends in air 

quality, dry deposition, and environmental protection resulting 

from regulatory policies and emission reductions required 

under the CAA. CASTNET is considered the primary source 

for estimates of dry acidic deposition and is vital to EPA’s 

efforts to protect terrestrial and aquatic ecosystems. The 

Division’s development of MLBC, an improved model for dry 

deposition estimates, is key to CASTNET’s success. 

The major connection between the atmosphere and ecosystems 

is through air-surface exchange, which includes deposition, 

and for some pollutants also includes a bidirectional flux. 

Significant ecosystem stressors that result from air-surface 

exchange include acidifying deposition of nitrogen and sulfur, 

neutralizing deposition of base cations, and eutrophying 

deposition of reduced and oxidized nitrogen. EPA program 

offices, such as the Office of Water and OAR, and the states 

use this information to support their policy decisions affecting 

TMDLs, atmospheric emissions, and coastal management. 

Estimates of the expected changes in atmospheric deposition 

to the Chesapeake Bay watershed contribute significant information on nitrogen loading that is used by the CBP to manage 

the Bay. This supports the CBP’s commitment to reducing 

nitrogen loads in the Bay by 2010 with the help of reductions 

in atmospheric deposition. In addition, this work provides an 

important test bed for linking atmospheric models with 

watershed models. and is a flagship of multimedia planning 

and benefits assessment for a coastal estuary. 

Air deposition reductions are a key element of the Tampa Bay 

TMDL implementation strategy required by the Clean Water 

Act. This work will significantly reduce the uncertainty in the 

estimates of nitrogen loading due to atmospheric deposition to 

Tampa Bay watershed basins and bay segments used in 

establishing the Tampa Bay TMDL. The model-estimated 

effect of court-ordered NOx emissions reductions from two 

power plants adjacent to the Bay will provide Tampa the best 

estimate of nitrogen deposition reductions across the Bay and 

the watershed attributable to known NOx emission reductions 

expected to occur by 2010. The model-estimated effects of 

deposition reductions due to the recent Clean Air Rules will 

allow assessment of whether these rules are keeping up with, 

or being outpaced by, the effects of growth. 

Addressing multimedia issues often requires working with 

multiple types of models and data sets. Proper software tools 

allow environmental scientists and managers to perform their 

work more efficiently and enable them to develop insights that 

they might otherwise miss. The software tools developed by 

the Division are for community use, and will also allow EPA 

and the states to conduct their work more effectively and provide for a more complete multimedia approach. These tools 
19

will allow new users to take advantage of the results of the 

more advanced air quality models for multimedia applications. 

The tools will also allow ecosystem and watershed managers 

to take atmospheric deposition into account in their planning. 

Figure 5-1. WDT screen capture showing the CMAQ 2002 annual total nitrogen deposition (kg-N/ha) 

for the 36-km grid resolution with the overlay of 8-digit Hydrologic Unit Code (HUC) delineations 

for the Cape Fear Basin and Albemarle-Pamlico Sound system. 

Figure 5-2. WDT screen capture showing the average 2002 annual total nitrogen deposition (kg-N/ha) 

to each watershed segment in the Cape Fear Basin and Albemarle-Pamlico Sound system.
20

Chapter 6 

Providing Air Quality Forecast Guidance for Health Advisories

Introduction 

An increasing number of clinical and epidemiological studies 

have associated adverse health effects in humans with exposure to ambient O3 and PM2.5. Local air quality agencies 

therefore need accurate forecasts of atmospheric pollutant 

concentrations so they can alert sensitive populations about 

the onset, severity, and duration of unhealthy air, and to 

encourage the public and industry to reduce emissionsproducing activities. Forecasting local and regional air pollution events is challenging because the processes governing the 

production and accumulation of ozone and fine PM are 

complex and nonlinear. Comprehensive atmospheric models 

provide a scientifically sound tool for preparing air quality 

forecast guidance. The Division uses modeling to forecast the 

day-to-day variability in air pollutant concentrations. The 

principal modeling platform for this purpose is the CMAQ 

modeling system linked with the North American Mesoscale 

(NAM) model, a NOAA/NWS operational weather prediction 

model. 

Research Description 

In 2003, EPA and NOAA signed a Memorandum of Agreement to collaborate on the design and implementation of a 

system to produce daily air quality modeling forecast information. The Division has linked together NOAA’s operational 

NAM and EPA’s CMAQ model to form the core of this forecast system. The preliminary system provided ground-level 

ozone predictions over the northeastern United States. 

Through an ongoing collaborative program of phased 

development and testing with the NWS, we are expanding the 

system’s capabilities. In August 2005, the operational domain 

was extended over the entire eastern United States. In 2007, 

based on extensive testing of model upgrades, the operational 

domain of the NAM-CMAQ modeling system was expanded 

to cover the entire continental United States (Figure 6-1), and 

the Division continued developmental testing for PM2.5

forecasts over the continental United States. Over the next few 

years, our research will investigate extending the forecast 

guidance period, expanding coverage to include Alaska and 

Hawaii, refining the spatial resolution of forecast guidance, 

improving the representation of the physical and chemical 

processes dictating moderate-to-high ambient O3 levels, and 

adding PM2.5 to the model forecast capability. 

NOAA is supporting the basic infrastructure for air quality 

forecasting, with NOAA-EPA/AMD personnel providing 

much of that support. The Division 

• Contributes to the CMAQ model improvements 

through comprehensive diagnostic analyses of model 

forecasts 

• Builds an air quality forecast database at EPA 

consisting of the daily meteorological, emissions, and 

air quality outputs from the NAM-CMAQ forecast 

system 

• Improves the accuracy of predicted pollutant 

distributions through development and application of 

bias-adjustment methods to correct model errors in 

forecasts, either in real-time or in post-process 

• Makes these data available to the air quality 

management community and the general public 

• Provides value-added analyses of the data contained 

in this long-term database (e.g., re-analysis or data 

fusion with observations to create long-term archive 

of ambient air quality and deposition surfaces for 

linkage with exposure studies; analysis of long-term 

spatial and temporal trends in ambient air quality and 

deposition; exploring relationships between ambient 

concentrations and meteorological conditions) 

Accomplishments 

During 2007, several major changes were implemented in the 

air quality forecast modeling system: 

• Based on extensive testing of model upgrades 

addressing the representation of PBL processes, dry 

deposition, cloud mixing, and emission source 

specification, the operational domain of the NAMCMAQ modeling system was expanded to cover the 

entire continental United States. Hour-by-hour ozone 

forecasts, through midnight of the following day, are 

available online, providing information about the 

onset, severity, and duration of poor air quality from 

coast to coast. 
21

• To reduce errors associated with interpolation of 

meteorological data from the WRF-Nonhydrostatic 

Mesoscale Model (NMM) coordinate and grid structure to that of CMAQ, we improved the coupling and 

consistent use of data between the two models. The 

first step in this effort—improving the coupling in the 

vertical direction such that the CMAQ calculations 

are performed with the same hybrid sigma-P vertical 

coordinate system that is utilized in the WRFNMM—was successfully implemented in 2006, and 

further tested in 2007. The updated system provides a 

more accurate representation of the three-dimensional 

meteorological fields. Efforts are also underway to 

include consistent coupling between the horizontal 

coordinate and grid system of the two models. 

• The emission inventories used by the air quality 

modeling forecast system were updated to represent 

the 2007 conditions. We used Continuous Emission 

Monitoring (CEM) data from 2005 to generate a base 

year of emission estimates for NOx and SO2 from 

electricity generating units (EGUs). For other 

pollutants and non-EGUs, base year 2001 emissions 

were utilized. Annual Energy Outlook data from the 

Department of Energy were used to project energyrelated point-source emissions from the base year to 

2007. Vehicle miles traveled projected out to 2007, 

along with updated 2007 fleet information, were used 

to estimate mobile-source emissions. The emissions 

inventory was also augmented with updated emission 

information from some states. 

• We added diagnostic tracers to CMAQ to track and 

quantify the influence of lateral boundary conditions 

specified for O3. Analysis of simulated tracer distributions indicated that the simulated surface-level 

background O3 is highly dependent on lateral 

boundary conditions specified in the free troposphere. 

We investigated the use of potential vorticity estimates (based on NAM meteorological predictions) as 

a surrogate for O3 associated with stratospheric 

intrusion events. The impacts were evaluated against 

extensive ozonesonde measurements from the 2006 

Intercontinental Chemical Transport Experiment 

(INTEX) Ozonesonde Network Study. 

Extensive evaluation of archived forecast results for a variety 

of trace species was also conducted through comparisons with 

measurements from surface sites as well as aircraft deployed 

during the 2004 International Consortium for Atmospheric 

Research on Transport and Transformation field study and the 

2006 Texas Air Quality Study. 

Through detailed comparisons with measurements from a 

variety of surface networks, we performed continuous evaluation of PM forecast results from the developmental 

simulations. Performance characteristics for PM2.5 forecasts 

over an entire year were investigated with an emphasis on 

understanding seasonal biases. To characterize model performance during the wintertime, we completed a detailed 

comparison of PM2.5 and constituent concentration forecasts 

with measurements from various surface networks. 

The Division developed and tested methods to characterize 

real-time emissions from wildfires using satellite information 

from the Hazard Mapping System to detect the locations of 

fires. We also developed a method to estimate the emissions of 

gaseous and particulate matter constituents from these fires for 

input to CMAQ. Initial testing indicates that the new wildfire 

estimates improved model forecast performance for both O3

and PM2.5 in regions impacted by pollution plumes from the 

fires. 

An extensive investigation of postprocessing bias-adjustment 

techniques that incorporate recent model forecasts with 

observations to adjust real-time O3 forecasts was conducted. 

We found the methods to be effective in reducing systematic 

errors in model O3 forecasts. Extensions to the Kalman filter 

bias-adjustment method were investigated to reduce the 

unsystematic (random) component of the model forecast error. 

Next Steps 

FY-2008 

• Incorporate the updated CB05 chemical mechanism 

and updated emission estimates into the NAMCMAQ air quality forecasting system; 

• Continue populating the air quality data archive at 

EPA with WRF-NMM-CMAQ daily air quality 

forecasts and meteorological data for 2008; 

• Conduct initial testing of WRF-NMM-CMAQ linkage on the native WRF model E-Grid structure; 

• Develop and evaluate postprocessing bias-adjustment 

techniques to achieve improved model forecasts; 

FY-2009 

• Develop improvements in representation of PM 

processes in air quality forecast models; 

• Analyze and evaluate developmental PM forecast 

simulations over the continental United States; 

FY-2010 

• Conduct experimental testing of daily PM forecast 

simulations (with NOAA/NWS); 

• Create improved methods to specify lateral chemical 

boundary conditions for forecast applications through 

linkage with global models. 

Impacts and Transition of Research to 

Applications 

Since early 2003, the Division has worked with NOAA/NWS 

to develop and deploy a model-based national air quality 

forecast guidance system, which currently operates at the 

NWS. Hourly ozone forecasts through midnight of the 
22

following day are available online, providing information on 

the onset, severity, and duration of poor air quality to more 

than 290 million people across the country. Local and state air 

quality forecasters use this tool to create daily air quality 

outlooks and issue air quality alerts, based on EPA’s healthbased Air Quality Index. 

Analysis of model forecasts of air quality allow EPA and 

NOAA researchers to continuously assess and improve model 

performance. Forecast guidance products have also been used 

for in-field guidance for flight planning during specialized 

field campaigns, such as the 2004 International Consortium 

for Atmospheric Transport and Transformation and the 2006 

Texas Air Quality Study. Detailed post-mission analyses and 

comparisons of model forecast results with extensive measurements from these field campaigns have also provided 

diagnostic information on model performance, helping 

improve the science in CMAQ. 

EPA’s archive of the forecast products provides a rich 

repository of daily air quality information that can potentially 

be used to understand long-term trends in air quality, the 

effectiveness of emission control programs in reducing 

population exposure, and relationships between air pollution 

and human health. 

Figure 6-1. Forecast surface-level 8-hour-maximum O3 mixing ratios on August 15, 2007. 

Color-coded diamonds indicate corresponding observed levels. 
23

Chapter 7 

Understanding the Relationships between Climate Change and Air Quality 

Introduction 

It is well known that meteorology strongly influences ozone 

and aerosol variability, both spatially and temporally. 

Meteorology over many decades includes variations on synoptic, seasonal, and interannual time scales. In addition to the 

interannual variability, research suggests the presence of an 

increasing trend in temperature over the past century, and this 

trend is projected to continue into the future. It is important to 

understand the potential impacts of climate change on air 

quality and how these impacts may influence projected 

improvements in air quality from regulatory control programs. 

Conversely, we must also understand the influences of air 

quality on climate. For example, sulfate aerosols can have a 

cooling effect on the atmosphere through radiation scattering; 

thus, emission controls resulting in substantial decreases in 

sulfate concentrations are likely to affect climate. Using 

modeling tools that can simulate these interactions between 

climate and air quality, Division personnel work toward this 

Theme Area’s key goals of improving our understanding of 

the impacts of future climate change on air quality, and 

identifying potential influences on climate from major changes 

in aerosol loadings. 

Research Description 

The focus of the ongoing Climate Impacts on Regional Air 

Quality (CIRAQ) project is characterizing potential effects of 

climate change on regional air quality between now and 2050. 

The results from this project have been generated using a 

coupled global-to-regional downscaled modeling approach. 

Modeling results suggest that a midrange climate scenario 50 

years into the future could introduce a moderate increase in 

ozone and a decrease in aerosols in the eastern United States; 

however, future emission scenarios would introduce a much 

larger difference that has uncertainties in both magnitude and 

direction. In the current phase, the CIRAQ project is investigating future emission scenarios developed in collaboration 

with EPA/NRMRL and modeling the resulting impacts on air 

quality. The results from the first series of simulations contributed to the 2007 U.S. EPA national air quality assessment 

report; the emission scenario tests will contribute to the 2012 

EPA national air quality assessment report. CIRAQ results 

will support two of the synthesis and assessment reports 

planned for the Climate Change Science Program (CCSP), a 

multi-agency program aimed at improving our understanding 

of the science of climate change and its potential impacts. 

In addition to the series of simulations and analyses developed 

under the current CIRAQ project, future research plans 

include additional downscaled regional climate simulations 

using advanced global-scale models that were applied for the 

recent Intergovernmental Panel on Climate Change (IPCC) 

Fourth Assessment Report, such as the NOAA Geophysical 

Fluid Dynamics Laboratory (GFDL) global-scale model. 

GFDL’s global models include scientific updates for climate 

and chemistry, and together with the Division’s regional-scale 

models would provide an advanced global- to regional-scale 

modeling tool for this research. Preliminary linkages and tests 

are underway, and current planning under the NOAA air 

quality and climate programs may provide additional support 

for this effort. 

The WRF mesoscale weather model will be used to produce 

meteorological data fields for CMAQ air quality simulations. 

The integrated WRF-CMAQ model will provide direct 

feedbacks from aerosols in CMAQ to radiation predictions in 

WRF. The Division will use this modeling tool to conduct 

sensitivity simulations to evaluate the potential impact of 

future air quality programs on regional climate. For example, 

we will investigate whether large-scale reductions in sulfate 

concentrations might contribute to warming in the United 

States. 

Accomplishments 

During the past several years, the CIRAQ project members 

have collaborated with Pacific Northwest National Laboratory 

(PNNL) and Harvard University to develop and evaluate a 

series of 10 years of current and 10 years of future (2050) 

downscaled regional climate simulations. Dr. Ruby Lueng 

(PNNL) led the effort to generate the downscaled climate 

scenarios. The Division has archived and evaluated these 

regional climate model outputs and used these regional 

climate scenarios to develop a model sensitivity study of the 

impact of future climate on air quality. We have prepared a 

series of scientific papers that discuss evaluation of these 

simulations for current time periods and characterization of 

the differences between the current-year and future-year 

predictions. 
24

With 5 years of current and 5 years of 2050 air quality simulations completed using these downscaled regional climate 

simulations, a series of model sensitivity tests and analyses 

were conducted in 2007 to assess climate-related air quality 

sensitivities. Results from this work have contributed in 2007 

to several products, including the EPA National Center for 

Environmental Assessment (NCEA) interim report on climate 

and air quality, CCSP’s Synthesis and Assessment Products 

(SAPs) 3.2 and 4.6, and a series of journal articles. The 

following results from Nolte et al. (2008) (Figure 7-1) suggest 

that increasing temperatures, isoprene emissions, and surface 

radiation would increase high O3 events, which are characterized here as the O3 levels in the 95th percentile of the 

distribution of O3 mixing ratios (Figure 7-2). 

Figure 7-1. Average summer (June-August, or JJA) difference 

between future – current regional climate scenarios for 

temperature, isoprene emissions, and solar radiation reaching 

the surface. 

Figure 7-2. Increase (future – current) in O3 concentrations 

under future climate conditions when comparing the 95th% of 

the O3 distribution (i.e., high O3 episodes). The summer (JJA) 

and fall (September and October) months are compared.

Unique new findings from this study included the suggestion 

of an extension of the O3 season into the fall months (Figure 

7-2, lower plot). Another interesting study outcome came from 

the sensitivity tests altering the methane concentrations 

assumed in CMAQ from current levels to the levels projected 

under the IPCC A1B storyline. Results suggest an increase in 

O3 of approximately 0.5-1 ppb across the U.S., which suggests 

a broad increase in the “background” O3 levels (Figure 7-3). 

While global chemistry models had suggested similar impacts 

of methane on background O3, this was the first regional-scale 

modeling study to demonstrate that. 
25

Figure 7-3. Average summer increase (future – current) in O3

when methane concentrations increase from 1.8 ppm to 2.4 

ppm. 

Next Steps 

FY-2008 

• Develop air quality emission scenarios for the 2050 

time period (in collaboration with EPA/NRMRL); 

FY-2009 

• Prepare a report on the impact of climate change on 

U.S. PM concentrations. Conduct model sensitivity 

tests and report on the PM concentration changes in 

the U.S. under a future climate scenario with and 

without future emission scenarios (in collaboration 

with EPA/NCEA and EPA/NRMRL); 

FY-2010 

• Test model linkage approaches for downscaling 

global to regional climate in order to contribute to the 

EPA 2012 national air quality assessment report and 

to address future modeling tool needs in air quality 

management. 

Impacts and Transition of Research to 

Applications 

Air quality planning procedures rely on present meteorological 

conditions when developing future emission control strategies. 

The research conducted under this Theme Area will help identify the uncertainty introduced when future climate influences 

are not included in analyses for future years. Modeling tools, 

including WRF-CMAQ and global model linkages developed 

in this research, will be made available for use in addressing 

air quality management issues that must consider climate 

variability and trends. Sensitivity studies will provide an 

additional assessment of the role of short-lived pollutants on 

the radiative budget. 
26

Appendix A 

Atmospheric Modeling Division Staff Roster 

As of 12/31/2007 

Office of the Director 

S. T. Rao, Director 

Patricia McGhee, Assistant to the Director 

Veronica Freeman-Green, Secretary 

Sherry Brown, Support Specialist 

Val Garcia, Deputy Director 

Linda Green, Budget Analyst 

John Irwin (contractor) 

David Mobley (EPA), Associate Director 

Ken Schere, Science Advisor 

Gary Walter, Information Technology Manager 

Jeff West, Quality Assurance Manager 

Air-Surface Processes Modeling Branch 

Tom Pierce, Chief 

Jane Coleman (SEEP1

), Secretary 

Jesse Bash 

Bill Benjey 

Jason Ching 

Ellen Cooter 

Robin Dennis 

Vlad Isakov 

Jehn-Yih Junag (ORISE2

) 

George Pouliot 

Donna Schwede 

George Bowker, Fluid Modeling Facility 

David Heist, Fluid Modeling Facility 

Steve Perry, Fluid Modeling Facility 

Bill Peterson (contractor), Fluid Modeling Facility 

Ashok Patel (SEEP), Fluid Modeling Facility 

John Rose (SEEP), Fluid Modeling Facility 

Applied Modeling Branch

Mark Evangelista, Chief 

Dennis Atkinson 

Desmond Bailey 

Pat Dolwick 

Rich Mason 

Brian Orndorff 

Joe Touma 

Atmospheric Model Development Branch 

Rohit Mathur, Chief 

Shirley Long (SEEP), Secretary 

Prakash Bhave 

Russ Bullock 

Ann Marie Carlton 

Tianfeng Chai (contractor) 

Brian Eder 

Rob Gilliam 

Jerry Herwehe 

Bill Hutzell (EPA) 

Jim Kelly (EPA), Postdoc 

Daiwen Kang (contractor) 

Hsin-mu Lin (contractor) 

Deborah Luecken (EPA) 

Tanya Otte 

Jon Pleim 

Adam Reff (EPA), Postdoc 

Shawn Roselle 

Golam Sarwar (EPA) 

John Streicher 

Daniel Tong (contractor) 

David Wong 

Jeff Young 

Shaocai Yu (contractor) 

Yang Zhang (ORISE) 

Model Evaluation and Applications Branch 

Alice Gilliland, Chief 

Melanie Ratteray (SEEP), Secretary 

Wyat Appel 

Kristen Foley 

Jim Godowitch 

Steve Howard 

Sergey Napelenok 

Chris Nolte 

Rob Pinder 

Jenise Swall 

Alfreida Torian 

1

SEEP: Senior Environmental Employee Program 2

ORISE: Oak Ridge Institute for Science and Education 
27

Appendix B 

Division and Branch Descriptions 

Atmospheric Modeling Division 

The Division implements the Memorandum of Understanding and Memorandum of Agreement between the U.S. 

Department of Commerce and the U.S. Environmental 

Protection Agency. In this capacity the Division develops 

and evaluates predictive atmospheric models on all spatial 

and temporal scales for forecasting the Nation’s air quality, 

and for assessing changes in air quality and air pollutant 

exposures, as affected by changes in ecosystem management and regulatory decisions. The Division is responsible 

for providing a sound scientific and technical basis for 

regulatory policies to improve ambient air quality. The 

models developed by the Division are being used by EPA, 

NOAA, and the air quality community to understand and 

forecast the magnitude of the air pollution problem, as well 

as to develop emission control policies and regulations. 

Established in 1955, the Division serves as the vehicle for 

implementing the agreements between NOAA and EPA, 

which funds the research efforts. 

The Division conducts atmospheric research in-house and 

through contracts and cooperative agreements with other 

agencies, academia, and the private sector. With a staff of 

NOAA and EPA scientists, the Division provides technical 

information, observational and forecasting support, and consulting on all meteorological and modeling aspects of the air 

pollution control program. In addition to facilitating 

research in the fields of air pollution meteorology and 

atmospheric modeling, the Division interacts extensively 

with academic and other scientific institutions in the U.S. 

and abroad to help support NOAA’s and EPA’s missionoriented efforts as well as to ensure that the environmental 

community has the benefit of the highest-quality peerreviewed science in dealing with air pollution problems. 

Atmospheric Model Development Branch 

The Atmospheric Model Development Branch (AMDB) 

develops, tests, and refines analytical, statistical, and 

numerical models used to describe and assess relationships 

between air pollutant source emissions and resultant air 

quality, deposition, and pollutant exposures to humans and 

ecosystems. The models are applicable to spatial scales 

ranging from local/urban and mesoscale through regional, 

including linkage with global models. AMDB is a key 

advocate in the meteorological modeling community for air 

quality applications. The Branch adapts and extends 

meteorological models to couple effectively with chemistrytransport models to create comprehensive air quality modeling systems, including the capability for two-way communication and feedback between the models. AMDB conducts 

studies to describe the atmospheric processes affecting the 

transport, diffusion, transformation, and removal of pollutants in and from the atmosphere using both theoretical 

approaches and analyses of monitoring and field study data. 

AMDB converts these and other study results into models 

for simulating the relevant physical and chemical processes 

and for characterizing pollutant transport and fate in the 

atmosphere. The Branch conducts model exercises to assess 

the sensitivity and uncertainty associated with model input 

databases and applications results. AMDB’s modeling 

research is designed to produce tools to serve the nation’s 

need for science-based air quality decision-support systems. 

The Air Quality Forecasting program fosters collaborations 

between NOAA and EPA in developing, applying, and 

evaluating comprehensive models for operational use for 

providing short-term air quality forecast guidance. 

Model Evaluation and Applications Branch 

The Model Evaluation and Applications Branch (MEAB) 

develops and applies advanced methods for evaluating the 

performance of models in reproducing the observed air 

quality. MEAB provides routine and high-performance 

computing support needed by the Division in the development, evaluation, and application of environmental models. 

The Branch applies the Division’s models to important 

environmental problems, providing scientific guidance on 

their use in air quality decision making. MEAB fosters the 

application of new computational techniques and tools to 

environmental simulation modeling and contributes to the 

interagency Information Technology Research and Development program. 
28

Air-Surface Processes Modeling Branch 

The Air-Surface Processes Modeling Branch (APMB) performs process-based modeling research for the Division’s 

atmospheric pollutant models, with a focus on three research 

themes: (1) emissions modeling, (2) deposition onto sensitive ecosystems, and (3) linkage of air quality with human 

exposure. APMB’s emissions modeling effort (with a 

special emphasis on natural sources such as windblown 

fugitive dust, wildfires, and biogenic emissions) helps 

ensure that meteorologically influenced emissions are 

properly considered in air quality models. APMB’s deposition research uses state-of-the-art trace-gas flux measurements to develop tools for assessing nutrient loadings and 

ecosystem vulnerability. APMB’s urban-scale modeling 

program (which includes collection and integration of 

experimental data from its Fluid Modeling Facility) is 

focused on building “hot-spot” air toxic analysis algorithms 

and linkages to human exposure models. 

Applied Modeling Branch

The Applied Modeling Branch (AMB) evaluates, modifies, 

and improves atmospheric modeling systems and simulation 

techniques to ensure appropriateness and consistency with 

established scientific principles. The Branch evaluates the 

effect of meteorological conditions on air quality and on the 

environmental decisions that are based upon air quality 

assessments and simulations. 
29

Appendix C 

Awards and Recognition for 2007 

NOAA Administrator’s Award

• Robin Dennis, Val Garcia, Alice Gilliland, Rohit 

Mathur, Tom Pierce, S.T. Rao, and Ken Schere – 

Leadership in the Development of “One 

Atmosphere” Air Quality Model 

EPA Bronze Medals 

• Prakash Bhave, David Mobley, Adam Reff, and 

Golam Sarwar – SPECIATE Version 4 Release 

NATO International Technical Meeting 

• Sergey Napelenok – Best Presentation by Young 

Professional 

 

EPA Special Act/Time Off Awards 

• George Bowker – QUIC Applications for Near 

Roadway 

• Bill Hutzell – Evaluation of Air Toxic Modeling 

with CMAQ 

• Deborah Luecken – VOC Reactivity Support to 

OAQPS Regulatory Program 

• David Mobley – Administrative Support to AMD 

• Adam Reff – Advancing PM2.5 Source 

Appointment Science 

• Golam Sarwar – Atmospheric Chemistry 

Enhancements to CMAQ 

NERL Special Achievement Awards 

• Russ Bullock, Bill Hutzell, Deborah Luecken, 

Shawn Roselle, and Golam Sarwar – Goal 1: 

Mission Support: Multipollutant Development 

Team 

• David Mobley – Goal 4 Science Integration – 

Inter-divisional-laboratory research: SPECIATE 

project coordination with OAQPS, NRMRL, 

OTAQ, and NERL 

• Bill Benjey, Ellen Cooter, Rob Gilliam, Alice 

Gilliland, and Chris Nolte – Goal 5: Identifying 

and Addressing Future Issues: CIRAQ Team 

NOAA CIYA/Special Act/Time-Off Awards 

• Ann Marie Carlton – SOA Module 

• Ellen Cooter – Leadership of the eco-system 

program and APMB 

• Robin Dennis – Multimedia Modeling 

• Kristen Foley – Advancing Model Evaluation 

Methods 

• Rob Gilliam, Tanya Otte, and Jon Pleim – 

Transitioning from MM5 to WRF 

• Jim Godowitch – Support to NOx SIP Call 

Evaluation 

• David Heist – Fluid Modeling Facility Support to 

Near Roadway 

• Steve Howard – AMI Air Accountability Project 

Support 

• Vlad Isakov – Near Roadway and School 

Infiltration Initiative 

• Rob Pinder – Evaluating NOx Emission Budgets 

with CMAQ 

• Shawn Roselle – Architect of CMAQ 

Multipollutant Model 

• Joe Touma – Support to the Mobile Source Air 

Toxics Rule 

• Jeff Young – Optimizing CMAQ 

• Val Garcia, Alice Gilliland, Rohit Mathur, Tom 

Pierce, S.T. Rao, and Ken Schere – Scientific 

Leadership

Recognition 

• Acting Director of the Ecosystems Research 

Division in Athens, GA – Tom Pierce 

• Acting Branch Chief of APMB – Ellen Cooter 

• New Zealand Embassy Science Fellow – David 

Mobley
30

Appendix D 

Publications for 2007 

(Division authors are in bold) 

Journal Articles 

Appel, K.W., A.B. Gilliland, G. Sarwar, and R.C. 

Gilliam. Evaluation of the Community Multi-scale Air 

quality (CMAQ) model version 4.5: Sensitivities impacting 

model performance; Part1 ozone. Atmospheric Environment, 

41(40): 9603-9615 (2007). 

Badendreier, J., L.S. Matott, J. Hameedi, R. Dennis, C. 

Knightes, R. Mathur, Y. Mohamoud, J.M. Johnson, G. 

Laniak, N. Gaber, P. Pascual, and R. Araujo. Managing 

multimedia pollution for a multimedia world. EM: Air and 

Waste Management Association Magazine for 

Environmental Managers, 6-11(2007). 

Bhave, P.V., G.A. Pouliot, and M. Zheng. Diagnostic 

model evaluation for carbonaceous PM2.5 using organic 

markers measured in the southeastern U.S. Environmental 

Science & Technology 41: 1577-1583 (2007). 

Bowker, G.E., R. Baldauf, V. Isakov, A. Khlystov, and W. 

Petersen. The effects of roadside structures on the transport 

and dispersion of ultrafine particles from highways.

Atmospheric Environment, 41: 8128-8139 (2007). 

Bowker, G.E. and H.C. Crenshaw. Electrostatic forces in 

wind-pollination: Part 1, Measurement of the electrostatic 

charge on pollen. Atmospheric Environment, 41: 1587-1595 

(2007). 

Bowker, G.E. and H.C. Crenshaw. Electrostatic forces in 

wind-pollination: Part 2, simulations of pollen capture.

Atmospheric Environment, 41: 1596-1603 (2007). 

Bowker, G.E., D. Gillette, G. Bergametti, B. Maticorena, 

and D. Heist. Sand flux simulations at a small scale over a 

heterogeneour mesquite area of the northern Chihuahuan 

desert. Journal of Applied Meteorology and Climatology 

(special Issue), 46(9): 1410-1422 (2007). 

Carlton, A.G., B.J. Turpin, K.E. Eltieri, S. Seitzinger, A. 

Reff, H-J. Lim, and B. Ervens. Atmospheric oxalic acid and 

SOA production from glyoxal: Results of aqueous photo 

oxidation experiments. Atmospheric Environment, 41(35): 

7588-7602 (2007). 

Chow, J.C., J.G. Watson, H.J. Feldman, J.E. Nolen, B. 

Wallerstein, G.M. Hidy, P.J. Lioy, D. Mobley, K. Baugues, 

and J. Bachmann. Will the circle be unbroken: A history of 

the U. S. National Ambient Air Quality Standards. Journal 

of the Air and Waste Management Association, 57: 1151-

1163(2007). 

Cook, R., M. Strum, J. Touma, T. Plama, J. Thurman, D. 

Ensley, and R. Smith Inhalation exposure and risk from 

mobile source air toxics in future years. Journal of Exposure 

Analysis & Environmental Epidemiology, 17: 95-105 

(2007). 

Cook, R., J.S. Touma, A. Fernandez, D. Brzezinski, C. 

Bailey, C. Scarbro, J. Thurman, M. Strum, D. Ensley, and 

R. Baldauf. Impact of underestimating the effects of cold 

temperature on motor vehicle start emissions of air toxics in 

the United States. Journal of the Air and Waste 

Management Association, 57: 1469-1479(2007). 

Cooter, E.J., J. Swall, and R. Gilliam Comparison of 700-

hPa NCEP-R1 and AMIP-R2 wind patterns over the 

continental United States using cluster analysis. Journal of 

Applied Meteorology and Climatology, 46(11): 1744-

1758(2007). 

Dennis, R., R. Haeuber, T. Blett, J. Cosby, C. Driscoll, J. 

Sickles, and J. M. Johnston. Sulfur and nitrogen deposition 

on ecosystems in the United States. EM: Air and Waste 

Management Association Magazine for Environmental 

Managers, 12-17(2007). 

Gego, E., P.S. Porter, A. Gilliland, and S.T. Rao. 

Observation-based assessment of the impact of nitrogen 

oxides emissions reductions on ozone air quality over the 

eastern United States. Journal of Applied Meteorology and 

Climatology (special issue), 46(7): 994-1008 (2007). 
31

Hogrefe, C.. W. Hao, K. Civerolo, J.-Y. Ku, G. Sistla, R. S. 

Gaza. L. Sedefian, K. Schere, A. Gilliland, and R. 

Mathur. Daily Simulation of ozone and fine particulates 

over New York State: Findings and challenges. Journal of 

Applied Meteorology and Climatology (special issue), 

46(7): 961-979 (2007). 

Hudman, R.C., D.J. Jacob, S. Turquety, E.M. 

Leibensperger, L.T. Murray, S. WU, A.B. Gilliland, M. 

Avery, T.H.Bertram, W. Brune, R.C. Cohen, J.E. Dibb, 

F.M. Flocke, A. Fred, J. Holloway, J.A. Neuman, R. 

Orville, A. Perring, X. Ren, G.W. Sachse, H.B. Singh, A. 

Swanson, and P.J. Wooldridge. Surface and lightning 

sources of nitrogen oxides over the United States: 

magnitudes, chemical evolution, and outflow. Journal of 

Geophysical Research, 112(D12S05):1-14 (2007). 

Irwin, J.S., W.B. Petersen, and S. Howard. Probabilistic 

characterization of atmospheric transport and diffusion. 

Journal of Applied Meteorology and Climatology (special 

issue), 46(7): 980-993 (2007). 

Isakov, V., J. Irwin, and J. Ching. Using CMAQ for 

exposure modeling and characterizing the sub-gird 

variability for exposure estimates. Journal of Applied 

Meteorology and Climatology (special Issue, 46(9): 1354-

1371 (2007). 

Isakov, V., J.S. Touma, A. Khlystov. A method of 

assessing air toxics concentrations in urban areas using 

mobile platform measurements. Journal of the Air & Waste 

Management Association 57: 1286-1295 (2007). 

Isakov V., A. Venkatram, J. Touma, D. Koracin, and T. 

Otte. Evaluating the use of outputs from comprehensive 

meteorological models in air quality modeling applications. 

Atmospheric Environment, 41(8):1689-1705 (2007). 

Kang, D., R. Mathur, K. Schere, S. Yu, and B. Eder. New 

categorical metrics for air quality model evaluation. Journal 

of Applied Meteorology and Climatology: Special Issue 

NOAA/EPA Golden Jubilee, 46(4): 549-555 (2007). 

Koracin, D., A. Panorska, V. Isakov, J.S. Touma, and J. 

Swall. A statistical approach for estimating uncertainty in 

dispersion modeling: An example of application in 

southwestern USA. Atmospheric Environment, 41(3): 617-

628 (2007). 

Liao, K.-J., E. Tagaris, K. Manomaiphiboon, S.L. 

Napelenok, J-H. Woo, S. He, P. Amar, and A.G. Russell. 

Sensitivities of ozone and fine particulate matter formation 

to emissions under the impact of potential future climate 

change. Environmental Science and Technology, 41(24): 

8355-8361(2007). 

Lin, Che-Jen, P. Pongprueksa, O.R. Bullock, Jr., S.E. 

Lindberg, S.O. Phkonen, C. Jang, T. Braverman, and T.C. 

Ho. Scientific uncertainties in atmospheric mercury models 

II: Sensitivity analysis in the CONUS domain. Atmospheric 

Environment, 41(31): 6544-6560 (2007). 

Lindberg, S.,O.R. Bullock, Jr, R. Edinghaus, D. Engstrom, 

X. Feng, W. Fitzgerald, N. Pirrone, E. Prestbo, and C. 

Seigneur. A synthesis of progress and uncertainties in 

attributing the sources of mercury in deposition. Ambio, A 

Journal of the Human Environment, 36 (1):19-33 (2007). 

Mathur, R., W. Frick, G.G. Lear, and R. Dennis. 

Ecological forecasting: Microbial contamination and 

atmospheric loading of nutrients to land and water. EM: Air 

and Waste Management Association Magazine for 

Environmental Managers, 36-40(2007). 

McKeen, S., S.H. Chung, J. Wilczak, G. Grell, I. Djalalova, 

S. Peckham, W. Gong, V. Bouchet, R. Moffet, G.R. 

Carmichael, R. Mathur, and S. YU. Evaluation of several 

PM2.5 forecast models using data collected during the 

ICARTT/NEAQS 2004 field study. Journal of Geophysical 

Research, 112: 1-20 (2007). 

Pennell, W., R. Scheffe, J. Brook, K. Demerjian, G. Hidy, J. 

Vickery, and J. West. Implementing accountability within a 

multi-pollutant air quality management framework. EM: Air 

and Waste Management Association Magazine for 

Environmental Managers 21-24 (2007). 

Pleim, J.E. A combined local and non-local closure model 

for the atmospheric boundary layer. Part 1: Model 

description and testing. Journal of Applied Meteorology and 

Climatology (special issue). 46(9): 1383-1395 (2007). 

Pleim, J.E.A combined local and non-local closure model 

for the atmospheric boundary layer. Part 2: Application and 

evaluation in a mesoscale meteorology model. Journal of 

Applied Meteorology and Climatology (special issue).

46(9): 1396-1409 (2007). 

Pour-Biazar, A., R.T. McNider, S.J. Roselle, R. Suggs, G. 

Jedlovec, S. Haines, S. Kim, D.W. Byun, J.C. Lin, and T.C. 

Ho. Assimilation of GOES satellite data in CMAQ: 

Correcting photolysis rates based on observed clouds.

Journal of Geophysical Research, 112:(D10302): 1-17 

(2007). 

Rao, S.T. Linking Air, Land, and Water Pollution for 

Effective Environmental Management. EM: Air and Waste 

Management Association Magazine for Environmental 

Managers, 5(2007). 

Reff, A., B.J. Turpin, J.H. Offenberg, C.P. Weisel, J. Zang, 

M. Morandi, T. Stock, S. Colome, and A. Winer. A 
32

functional Group Characterization of organic PM2.5 

exposure: Results from the RIOPA study. Atmospheric 

Environment, 41(22): 4585-4598 (2007). 

Reff, A.H., S.I. Eberly, and P. Bhave. Receptor modeling 

of ambient particulate matter data using positive matrix 

factorization review of existing methods. Journal of the Air 

& Waste Management Association, 57 (2):146-154, (2007). 

Roy, B., G. Pouliot, A. Gilliland, T. Pierce, S. Howard, P. 

Bhave, and W. Benjey. Refining fire emissions for air 

quality modeling with remotely sensed fire counts: A 

wildfire case study. Atmospheric Environment, 41: 655-665 

(2007). 

Roy, B., R. Mathur, A. Gilliland, and S. Howard. A

comparison of CMAQ-based aerosol properties with 

IMPROVE, MODIS and AERONET data. Journal of 

Geophysical Research, 112:(D14): 1-17 (2007). 

Ryaboskapko, A., O.R. Bullock, J. Christensen, M. Cohen, 

A. Dastoor, I. Iiyin, G. Petersen, D. Syrakov, R.S. Artz, D. 

Davignon, R.R. Draxler, and J. Munthe. Intercomparison 

Study of Atmospheric Mercury Models: 1. Comparison of 

models with short-term measurements. Science of the Total 

Environment, 376: 228-240 (2007). 

Ryaboskapko, A., O.R. Bullock, J. Christensen, M. Cohen, 

A. Dastoor, I. Iiyin, G. Petersen, D. Syrakov, R.S. Artz, D. 

Davignon, R.R. Draxler, and J. Munthe. Intercomparison 

study of atmospheric mercury models: 2. Modeling results 

vs. long-term observations and comparison of country 

atmospheric balances. Science of the Total Environment. 

377: 319-333 (2007). 

Sarwar, G and P.V. Bhave. Modeling the effect of chlorine 

emissions on ozone levels over the eastern United States.

Journal of Applied Meteorology and Climatology (special 

issue), 46(7): 1009-10190(2007). 

Singh, R.B., A.H. Huber, and J.N. Braddock. Sensitivity 

analysis and evaluation of MicroFac PM: A microscale 

motor vehicle emission factor model for PM emissions. 

Journal of the Air & Waste Management Association, 57(4): 

420-433 (2007). 

Smolarkiewicz, P., R. Sharman, J. Weil, S.G. Perry, D. 

Heist, and G. Bowker. Building resolving large- eddy 

simulations and comparison with wind tunnel experiments. 

Journal of Computational Physics. 227(1): 633-653 (2007). 

Stein, Airel F., V. Isakov, J. Godowitch, and R.R. Draxler. 

A hybrid modeling approach to resolve pollutant 

concentrations in an urban area. Atmospheric Environment, 

41(40): 9410-9426 (2007). 

Tong, D., R. Mathur, K. Schere, D. Kang, and S. Yu. The 

use of air quality forecasts to assess impacts of air pollution 

on crops: Methodology and case study. Atmospheric 

Environment, 41(38): 8772-8784(2007). 

Touma, J.S., V. Isakov, A. Cimorelli, B. Anderson, and R. 

Brode. Using Prognostic Model Generated Meteorological 

Output in the AERMOD dispersion model: An illustrative 

application in Philadelphia, PA. Journal of Air & Waste 

Management Association, 57(5):586-595, (2007). 

Venkatram, A., V. Isakov, E. Thoma, and R. Baldauf. 

Analysis of air quality data near roadways using a 

dispersion model. Atmospheric Environment, 41: 9481-9497 

(2007). 

Yu, S., R. Mathur, K.L. Schere, D. Kang, J.A. Pleim, and 

T.L. Otte. A Detailed Evaluation of the ETA-CMAQ 

Forecast Model Performance for O3, Its Related Precursors, 

and Meteorological Parameters during The 2004 ICARTT 

Study. Journal of Geophysical Research-Atmospheres,

112(D12S14):1-24, (2007). 

Yu, S., P.V. Bhave, R.L. Dennis, and R. Mathur. Seasonal 

and regional variations of primary and secondary organic 

aerosols over the continental United States: Semi-empirical 

estimates and model evaluation. Environmental Science & 

Technology, 41(13): 46904697 (2007). 

Zheng J., J.L. Swall, W.M. Cox, and J.M. Davis. Interannual variation in meteorologically adjusted ozone levels 

in the eastern United States: A comparison of two 

approaches. Atmospheric Environment, 41(4):705-716 

(2007). 

Published Reports 

Rao, S.T., R. Dennis, V. Garcia, A. Gilliland, R. Mathur, 

D. Mobley, T. Pierce, and K. Schere. Summary Report of 

Air Quality Modeling Research Activities for 2006. U.S. 

Environmental Protection Agency, EPA/600/R-07/103, 

(2007). 

 

Conference Papers and Proceedings 

Rao, S.T., A.B. Gilliland, K. Foley, and C. Hogrefe. 

Evaluating and using Air Quality Models. International 

Conference on Urban Air Quality, Limassol, Cyprus, March 

29, 2007. 

Sarwar, G., S. Roselle, R. Mathur, W. Appel, and R. 

Philbrick. A comparison of Community Multi scale Air 

Quality (CMAQ) Modeling System predictions with 

observations from the Northeast oxident and particle study. 

100th Air and Waste Management Association Annual 
33

Conference and Exhibition, Pittsburgh, Pennsylvania, June 

26-29, 2007 

Heist, D.K., S.G. Perry, L. Brixey, and G.E. Bowker. 

Wind tunnel simulations of pollution from roadways. 

International Workshop on Physical Modeling of Flow 

dispersion Penomena (PHYSMOD2007). University of 

Orleans, France, August 29-31, 2007. 

Pullen, Julie, J. Ching, W. Sailor, W. Thompson, B. 

Bornstein, and D. Koracin. Summary and Highlights of the 

AMS 7th Conference on Coastal, Atmospheric and, Oceanic 

Prediction and Process and 7th Symposium on the Urban 

Environment. AMS 7th Conference on Coastal, Atmospheric 

and, Oceanic Prediction and Process and 7th Symposium 

on the Urban Environment. San Diego, CA, September 9-

13, 2007. 

Ching, J. National urban database and access portal tools 

(NUDAPT): a project overview. AMS 7th Symposium on the 

Urban Environment. San Diego, CA, September 10-13, 

2007. 

Huber, A. Evaluation study of building-resolved urban 

dispersion models. AMS 7th Annual Symposium on the 

Urban Environment. San Diego, CA, September 13, 2007. 

Huber, A. Preliminary results of CFD simulation for the 

scenario of a recent field study in an urbanized domain. 

AMS 7th Symposium on the Urban Environment. San Diego, 

CA, September 13, 2007. 

Bullock, O.R., Jr. The effect of lateral boundary values on 

atmospheric mercury simulations with the CMAQ model. 

29th NATO/ SPS International Technical Meeting on Air 

Pollution Modeling and its Application, Aveiro, Portugal, 

September 24-28, 2007. 

Davidson, P, K. Schere, R. Draxler, S. Kondragunta, R.A. 

Wayland, J. F. Meagher, and R. Mathur. Toward a US 

national air quality forecast capability: Current and planned 

capabilities. 29th NATO/ SPS International Technical 

Meeting on Air Pollution Modeling and its Application, 

Aveiro, Portugal, September 24-28, 2007. 

Gilliam, R., J. Pleim, and A, Xiu. Implementation of the 

pleim-xiu land surface model and asymmetric convective 

model in the WRF model. 29th NATO/ SPS International 

Technical Meeting on Air Pollution Modeling and its 

Application, Aveiro, Portugal, September 24-28, 2007. 

Gilliland, A.B., J.M. Godowitch, C. Hogrefe, and S.T. 

Rao. Evaluating regional-scale air quality models. 29th 

NATO/ SPS International Technical Meeting on Air 

Pollution Modeling and its Application, Aveiro, Portugal, 

September 24-28, 2007. 

Hogrefe, C., J. Y. Ku, G. Sistla, A. Gilliland, J.S. Irwin, 

P.S. Porter, E. Gego, P. Kasibhatla, and S.T. Rao. Has the 

performance of regional-scale photochemical modeling 

systems changed over the past decade? 29th NATO/ SPS 

International Technical Meeting on Air Pollution Modeling 

and its Application, Aveiro, Portugal, September 24-28, 

2007. 

Isakov, V., H. Ozkaynak. A modeling methodology to 

support evaluation of public health impacts of air pollution 

programs. 29th NATO/ SPS International Technical Meeting 

on Air Pollution Modeling and its Application, Aveiro, 

Portugal, September 24-28, 2007. 

Luecken, D. Evaluating the effects of emission reductions 

on multiple pollutants simultaneously. 29th NATO/ SPS 

International Technical Meeting on Air Pollution Modeling 

and its Application, Aveiro, Portugal, September 24-28, 

2007. 

Mathur, R., S. Roselle, and G. Pouliot. Diagnostic analysis 

of the three-dimensional sulfur distributions over the 

Eastern United States using the CMAQ model and 

measurements from the 2004 ICARTT Field experiment. 

29th NATO/ SPS International Technical Meeting on Air 

Pollution Modeling and its Application, Aveiro, Portugal, 

September 24-28, 2007. 

Mobley, D., L. Beck, G. Sarwar, A. Reff, and M. 

Houyoux. SPECIATE - EPA's Database of Speciated 

Emission Profiles. 29th NATO/ SPS International Technical 

Meeting on Air Pollution Modeling and its Application, 

Aveiro, Portugal, September 24-28, 2007. 

Napelenok, S.L., R.W. Pinder, A.B. Gilliland, and R.V. 

Martin. Developing a method for resolving NOx emission 

inventory biases using discrete Kalman filter inversion, 

direct sensitivities, and satellite-based NO2 columns. 29th 

NATO/ SPS International Technical Meeting on Air 

Pollution Modeling and its Application, Aveiro, Portugal, 

September 24-28, 2007. 

Nolte, C., A.B. Gilliland, and C. Hogrefe. Linking global to 

regional models to simulate U.S. air quality in the year 

2050. 29th NATO/ SPS International Technical Meeting on 

Air Pollution Modeling and its Application, Aveiro, 

Portugal, September 24-28, 2007. 

Pleim, J., J. Young, D. Wong, R. Gilliam, W. Hutzell, T. 

Otte, and J. Walker. Bi-directional surface chemical fluxes 

for 2-way coupled meteorology and air quality modeling. 

29th NATO/ SPS International Technical Meeting on Air 
34

Pollution Modeling and its Application, Aveiro, Portugal, 

September 24-28, 2007. 

Roy, B.A., G.A. Poulit, D. Mobley, T. G. Pace, A. J. Soja, 

J. J. Szykman, and J. Al-Saadi. Development of an 

inventory of fire emissions using satellite data. 29th NATO/ 

SPS International Technical Meeting on Air Pollution 

Modeling and its Application, Aveiro, Portugal, September 

24-28, 2007. 

Sarwar, G., R. Dennis, and B. Vogel. The effect of 

heterogeneous reactions of model performance for nitrous 

acid. 29th NATO/ SPS International Technical Meeting on 

Air Pollution Modeling and its Application, Aveiro, 

Portugal, September 24-28, 2007. 

Davis, J.M., P. Bhave, and K. Foley. Parameterization of 

N2O5 Reaction probabilities for inclusion in CMAQ. 6th

Annual CMAS Conference Preprints, Chapel Hill, NC, 

October 1-3, 2007. 

Roselle, S., D.J. Luecken, W.T. Hutzell, O.R. Bullock, G. 

Sawar, and K. Schere. Development of a Multi pollutant 

version of the Community Multi scale Air Quality (CMAQ) 

modeling system. 6th Annual CMAS Conference Preprints, 

Chapel Hill, NC, October 1-3, 2007. 

Schwede, D., N. Collier, A. Dolph, M.A. Widing, and T. 

Howe. A New Tool for analyzing CMAQ modeling results: 

Visualization Environment for rich data 

interpretation(VERDI). 6th Annual CMAS Conference 

Preprints, Chapel Hill, NC, October 1-3, 2007 
35

Appendix E 

Abbreviations 

ACM.....................Asymmetric Convective Model 

AERMIC ..............AMS/EPA Regulatory Model 

Improvement Committee 

AERMOD ............AMS/EPA Regulatory Model 

AMB.....................Applied Modeling Branch 

AMD ....................Atmospheric Modeling Division 

AMDB..................Atmospheric Model Development 

Branch 

AMET ..................Atmospheric Model Evaluation Tool 

AMS .....................American Meteorological Society 

APMB ..................Air-Surface Processes Modeling Branch 

ARL......................Air Resources Laboratory 

ASMD ..................Atmospheric Sciences and Modeling 

Division 

BenMAP...............Benefits Mapping and Analysis Program 

CAA .....................Clean Air Act 

CAIR ....................Clean Air Interstate Rule 

CAMD..................Clean Air Markets Division 

CAMR..................Clean Air Mercury Rule 

CASTNET............EPA's Clean Air Status and Trends 

Network 

CAVR...................Clean Air Visibility Rule 

CB05 ....................Carbon Bond 05 

CBP ......................Chesapeake Bay Program 

CBPO ...................Chesapeake Bay Program Office 

CCSP....................Climate Change Science Program 

CEM .....................Continuous Emission Monitoring 

CIRAQ .................Climate Impacts on Regional Air Quality 

CIYA....................Cash In Your Account 

CMAQ..................Community Multiscale Air Quality 

Model 

CMAQ-TX ...........Community Multiscale Air Quality 

Model-Texas 

CMAQ-UCD ........University of California Davis aerosol 

module coupled to the Community 

Multiscale Air Quality model 

CMAS ..................Community Modeling and Analysis 

System 

DDM ....................Decoupled Direct Method 

DDM3-D ..............Decoupled Direct Method-3d 

DOC .....................Department of Commerce 

EGU .....................Electric Generating Units 

EPA ..................... Environmental Protection Agency 

ESRP ................... Ecological Services Research Program 

FRD ..................... NOAA’s Field Research Division 

FY........................ Fiscal Year 

GFDL................... Geophysical Fluid Dynamics Laboratory 

GHG .................... greenhouse gas 

HAPEM ............... Hazardous Air Pollutant Exposure Model 

HAPS................... Hazardous Air Pollutants 

HUC..................... Hydrological Cataloging units 

HYSPLIT............. Hybrid Single Particle Lagrangian 

Integrated Trajectory 

INTEX ................. Intercontinental Chemical Transport 

Experiment 

IPCC .................... International Panel on Climate Change 

MCIP ................... Meteorology-Chemistry Interface 

Processor 

MEAB ................. Model Evaluation and Applications 

Branch 

MLBC.................. Multilayer biochemical model 

MLM ................... Multi-Llyer Model 

MM5.................... Fifth Generation of the Penn 

State/UCAR Mesoscale Model 

MOA.................... Memorandum of Agreement 

MOU.................... Memorandum of Understanding 

NAAQS ............... National Ambient Air Quality Standard 

NAM.................... North American Mesoscale 

NAMMIS............. North American Mercury Model 

Intercomparison Study 

NAS..................... National Academy of Sciences 

NBP ..................... NOx Budget Trading Program 

NCAR.................. National Center for Atmospheric 

Research 

NCEA .................. National Centers for Environmental 

Prediction 

NERL................... National Exposure Research Laboratory 

NHEERL ............. National Health and Environmental 

Effects Research Laboratory 

NLCD .................. National Land Cover Data 

NMM................... Nonhydrostatic Mesoscale Model 

NOAA ................. National Oceanic and Atmospheric 

Administration 
36

NRMRL................National Risk Management Research 

Laboratory 

NUDAPT..............National Urban Database and Access 

Portal Tool 

NWS.....................National Weather Service 

OAQPS.................Office of Air Quality Planning and 

Standards 

OAR .....................Office of Oceanic and Atmospheric 

Research 

ORD .....................Office of Research and Development 

PAH......................Polycyclic Aromatic Hydrocarbons 

PAVE ...................Package for Analysis and Visualization 

of Environmental data 

PBL ......................planetary boundary layer 

PM........................particulate matter 

PMML..................Predictive Model Markup Language 

ppb........................parts per billion 

ppm.......................parts per million 

PX LSM ...............Pleim-Xiu Land Surface Model 

QUIC....................Quick Urban Industrial Complex 

REMSAD .............Regional Modeling System for Aerosols 

and Deposition 

RHR..................... Regional Haze Rule 

RPO ..................... Regional Planning Organization 

SAP...................... Statewide Air Pollution Research Center 

SAPRC07-TX...... Statewide Air Pollution Research Center 

– 2007 toxics version of the chemical 

mechanism - Texas 

SGV..................... sub-grid variability 

SHEDS ................ Stochastic Human Exposure and Dose 

Simulation 

SIP ....................... State Implementation Plans 

SMOKE ............... Sparse Matrix Operator Kernel 

Emissions 

SOA..................... secondary organic aerosol 

TBEP ................... Tampa Bay Estuary Program 

TEAM.................. Trace Element Analysis Model 

TMDL.................. Total Maximum Daily Load 

VERDI................. Visualization Environment for Rich Data 

Interpretation 

WDT.................... Watershed Deposition Tool 

WRF .................... Weather Research and Forecasting 
Office of Research and Development 

(8101R) 

Washington, DC 20460 

Official Business 

Penalty for Private Use 

$300 

EPA 600/R-09/025 

March 2009 

www.epa.gov

Recycled/Recyclable

Printed with vegetable-based ink on paper that 

contains a minimum of 50% post-consumer 

fiber and is processed chlorine free.