1 

Report 

on the 

Peer Review 

of the 

Atmospheric Modeling and Analysis Division 

National Exposure Research Laboratory 

Office of Research and Development 

U.S. Environmental Protection Agency 

Research Triangle Park, North Carolina 

February, 2009 

Peer Review Panel: 

David Allen, Chair 

Douglas Burns 

David Chock 

Naresh Kumar 

Brian Lamb 

Michael Moran 
2 

Table of Contents 

Table of Contents..........................................................................................................................................2

Executive Summary......................................................................................................................................4

Introduction and Charge to the Panel........................................................................................................4

Accomplishments and Strengths of the Division......................................................................................5

Research Quality.......................................................................................................................................6

Responsiveness to Agency Needs.............................................................................................................7

Impact .......................................................................................................................................................8

Leadership in the Scientific Community ..................................................................................................8

Introduction and Charge to the Panel............................................................................................................9

Theme 1. Model Development and Diagnostic Testing.............................................................................12

1A. Introduction......................................................................................................................................12

1B. Research ...........................................................................................................................................12

Accomplishments and Strengths.........................................................................................................12

Limitations ..........................................................................................................................................15

Future Directions ................................................................................................................................15

Suggestions for Improvement .............................................................................................................16

1C. Advice and Assistance .....................................................................................................................17

1D. Leadership........................................................................................................................................18

1E. Summary and Recommendations .....................................................................................................19

Theme 2. Model Evaluation: Establishing Model’s Credibility ................................................................23

2A. Introduction......................................................................................................................................23

2B. Research ...........................................................................................................................................23

Accomplishments and Strengths.........................................................................................................23

Limitations ..........................................................................................................................................25

Future Directions ................................................................................................................................25

Suggestions for Improvement .............................................................................................................26

2C. Advice/Assistance ............................................................................................................................27

2D. Leadership........................................................................................................................................27

2E. Summary and Recommendations .....................................................................................................28

Theme 3. Linking Air Quality and Human Health ....................................................................................29

3A. Introduction......................................................................................................................................29

3B. Research ...........................................................................................................................................29

Accomplishments and Strengths.........................................................................................................29

Limitations ..........................................................................................................................................30

Future Directions ................................................................................................................................31

Suggestions for Improvement .............................................................................................................31

3C. Advice/Assistance ............................................................................................................................32

3D. Leadership........................................................................................................................................32

3E. Summary and Recommendations .....................................................................................................33

Theme 4. Climate Change and Air Quality Interactions............................................................................34

4A. Introduction......................................................................................................................................34

4B. Research ...........................................................................................................................................34

Accomplishments and Strengths.........................................................................................................34

Limitations ..........................................................................................................................................35

Future Directions ................................................................................................................................35

4C. Advice/Assistance ............................................................................................................................36
3 

4D. Leadership........................................................................................................................................37

4E. Summary and Recommendations .....................................................................................................38

Theme 5. Linking Air Quality and Ecosystem Health...............................................................................40

5A. Introduction......................................................................................................................................40

5B. Research ...........................................................................................................................................40

Accomplishments and Strengths.........................................................................................................40

Limitations ..........................................................................................................................................41

Suggestions for Improvement .............................................................................................................41

5C. Advice/Assistance ............................................................................................................................42

5D. Leadership........................................................................................................................................42

5E. Summary and Recommendations .....................................................................................................43

Acronyms....................................................................................................................................................45
4 

Executive Summary 

Introduction and Charge to the Panel 

The National Exposure Research Laboratory (NERL) conducted a peer review of its 

Atmospheric Modeling and Analysis Division (AMAD), with the goals of assessing the quality 

of the science being performed within the Division and the responsiveness of the Division to the 

needs of the Environmental Protection Agency (referred to in this report as EPA, or the Agency). 

EPA’s Atmospheric Modeling and Analysis Division is one of the largest air-quality model 

development and evaluation groups in the world. A primary focus of AMAD has been the 

development, evaluation and application of the CMAQ (Community Multi-scale Air Quality) 

modeling system, which consists of the CMAQ chemical transport model (CCTM), and its 

meteorological driver models and pre- and post- processing systems. The CMAQ modeling 

system provides a quantitative link between emissions of pollutants to the atmosphere and 

human and ecosystem exposures. This modeling tool supports a wide variety of Agency 

activities and decision-making processes, and has a wide and deep user base throughout the 

world. 

A review panel, consisting of individuals with air quality modeling research and project 

management expertise, met with Division staff and management at Research Triangle Park, 

North Carolina from January 27 through January 29, 2009. Members of the panel were: 

Panel Chair 

Dr. David Allen, Gertz Regents Professor, University of Texas at Austin 

Panel Members 

Dr. Douglas Burns, Research Scientist, U.S. Geological Survey 

Dr. David Chock, Senior Scientist, Ford Research Laboratory 

Dr. Naresh Kumar, Senior Program Manager, Electric Power Research Institute 

Dr. Brian Lamb, Professor, Washington State University 

Dr. Michael Moran, Research Scientist, Environment Canada 

The panel was charged with addressing four primary questions: 

1. What is the quality of the scientific research being performed by the Division? (Quality) 

2. Is the Division responsive to Agency needs and problems? (Responsiveness) 

3. Is the Division’s work having impact on Agency decisions? (Impact) 

4. Are Division staff leaders in their scientific communities? (Leadership) 

Since the Division consists of four existing branches and a fifth, emerging, research area, the 

charge questions were addressed separately for each of five topical areas. Detailed findings and 

recommendations for the areas of (1) Model Development, (2) Model Evaluation, (3) Linking 

Air Quality and Ecosystem Health, (4) Climate Change and Air Quality Interactions, and (5) 

Linking Air Quality and Human Health are provided in the main body of the report. This 
5 

summary presents a synthesis of those findings and recommendations for the Division as a 

whole. 

Accomplishments and Strengths of the Division 

Before addressing specific charge questions, it is useful to briefly summarize some of the overall 

accomplishments of the Division. More detailed summaries of the accomplishments associated 

with specific areas are described in the body of the report. Among the most significant 

accomplishments of the Division are the following: 

• The Division has established the CMAQ models as a leading state-of-the-science regional 

air quality modeling system, with a large user community. 

• The Division has delivered timely and relevant modeling products to EPA and its broader 

user community. 

• The Division has, on its own initiative, developed an independent and on-going external 

review mechanism for the CMAQ modeling system, and has developed a rigorous 

internal model evaluation program. 

• The Division has developed a balanced portfolio of core research and applied activities in 

support of EPA needs. 

• The Division has established continuing and substantive interactions with the Office of 

Air Quality Planning and Standards (OAQPS) and other Agency clients and partners. 

• The Division has established an effective outreach mechanism to serve the broader user 

community. 

• The Division has aggressively addressed the issue of staff renewal. 

The Division had, at the time of the review, 47 current scientific and technical staff. This 

represents a very significant research commitment to AQ model development, evaluation, and 

application. A significant expenditure of resources is, however, no guarantee of research 

productivity, quality, or relevance. The Division’s overall success can be attributed in no small 

part to the talent and experience of its staff, to its forward-looking, thoughtful, and informed 

management, and to good teamwork and a positive culture. Some specific strengths of the 

Division include the following: 

• A large fraction of the AMAD staff, 42 of 50, hold post-graduate degrees (28 Ph.D., 14 

M.Sc.); there is a good mix of complementary disciplines for a numerical modeling program, 

including several statisticians. 

• An organizational arrangement by both research theme and functional team that provides the 

required mix of skill for different projects and tasks. 

• Very close cooperation and coordination between model development and model evaluation 

that facilitates the identification and prioritization of additional model development tasks. 

• Successful ongoing renewal of the Division’s staff through its use of ORD’s postdoctoral 

fellowship program and the hiring of a cadre of very able young scientists to fill gaps left by 

staff turnover (five retirements and four transfers to other divisions or organizations in the 

past five years). 
6 

• Maintenance of close working relationships with key clients, such as regular monthly 

meetings between AMAD and OAQPS managers and weekly or biweekly meetings between 

AMAD staff and OAQPS staff working on two key CMAQ inputs, emissions and 

meteorological modeling. 

• Significant attention given to collaborations to improve CMAQ in order to leverage Division 

resources; collaborators have included other Agency divisions, other government agencies, 

and university faculty. 

• Strategic thinking about future directions, especially in regard to neighborhood-scale 

modeling to be coupled with human-exposure models, and hemispheric- or global-scale 

modeling to understand the impact of climate change on air quality. 

Research Quality 

For each of five theme areas, the panel identified strengths and weaknesses of the research 

programs and made recommendations. These are summarized in the main body of the report. 

Summarized here is a synthesis of those findings for the Division. 

The panel identified multiple strengths of the research program in regional air quality modeling. 

The division has developed uniformly strong research teams, with a good balance of skills and 

good interaction across branches. Extensive interactions across branches of the Division have led 

to creative development, evaluation and application of modeling tools. The Division has 

documented its work in the peer-reviewed scientific literature and these publications are cited at 

rates above the norm for both the Office of Research and Development (ORD) and the general 

scientific community. 

These long-standing research strengths in regional air quality modeling and analysis are now 

being complemented by a number of new initiatives at global and neighborhood scales. 

Maintaining the Division’s record of excellence as it enters these new areas will pose a number 

of challenges. Among the most significant of these challenges will be adding the necessary 

expertise and resources to the Division. Adding expertise and resources may be done in a variety 

of ways. For example, the Division may form new partnerships (e.g., with NASA on global 

change), or it might add new staff with targeted expertise, or it might provide significant 

professional development opportunities for existing staff. All of these alternatives are likely to 

require significant resources, yet there is a continuing demand for the Division’s current 

activities and it would not be prudent to draw down resources dedicated to existing areas. Unless 

resources are expanded or some activities are dropped, the Division is unlikely to be able to 

continue to meet its core regional modeling mission and simultaneously address the new 

initiatives. 

Recommendation: Preserve the Division’s existing resources for regional air quality 

modeling while responding to new Agency initiatives. This may necessitate difficult choices 

regarding which new initiatives the Division is able to pursue. 
7 

Responsiveness to Agency Needs 

The Division has selected model development activities that address critical and evolving science 

questions that underlie many of the policy questions facing the Agency and many of the 

scientific initiatives of ORD. While the activities of the Division are highly relevant to Agency 

needs, current staffing and resources prevent the Division from addressing all of the topics of 

interest to the EPA and the broader CMAQ user community. If the Division keeps all CMAQ 

model development within the Division, it will face difficult choices about which modeling 

needs merit the use of its limited resources. However, if the Division can mobilize the broad and 

capable CMAQ user base to promote additional model development, evaluation and application, 

it may significantly expand the capabilities of CMAQ. Mobilizing the user community in model 

development, evaluation and application has the added benefits of more deeply engaging the user 

community and investing them in the continued success of the models. The panel made a series 

of recommendations that, if implemented, could facilitate greater user contributions to the 

evolution of CMAQ. The panel noted, however, that user engagement in model evolution must 

be done carefully, with appropriate quality assurance mechanisms built into the process. 

Recommendation: Develop a public strategic plan, with a 3-5 year time horizon, for the 

continued development, evaluation and support of the CMAQ model system. As the 

Division’s own internal plans for model development and evaluation are more widely known, the 

broader community can identify complementary initiatives. 

Recommendation: Define, as transparently as possible, pathways for users to contribute to 

the CMAQ modeling system. Presently, the manner in which the Division identifies scientific 

results that drive model development initiatives is not widely understood. It is also not clear to 

the broader user community what types of quality assurance and model evaluation criteria need 

to be met for user-developed tools to be incorporated into the modeling system. Making these 

processes transparent will promote user engagement. 

Recommendation: Make available, as possible, inputs and results for selected CMAQ 

simulations, which can serve as model application and test platforms for the user 

community. The Division has invested significant effort in building a variety of long time series 

simulations (e.g., seasonal, annual, and decadal modeling runs) that have proven very valuable in 

model performance evaluations within the Division. Making the inputs and results for these 

simulations broadly available, with appropriate documentation, will facilitate wider model 

evaluation and analysis activities by the user community. It will also make the model 

evaluations more straightforward to interpret, since simulations would be based on a consistent 

set of inputs. These types of “test problems” are common in other modeling communities. 

Recommendation: Seek focused, expert input on Division methodologies, tools and models 

at critical steps in their development. The Division has a strong track record of soliciting 

frequent external peer reviews of the CMAQ modeling system. These reviews have tended to be 

comprehensive examinations of the modeling system. These should continue, but the Division 

should add focused teams of external experts that can examine more narrowly defined emerging 

model system developments (e.g., WRF-CMAQ, inverse emission modeling, or the Carbon 

apportionment tool). These focused teams should provide input at project initiation, pre-release 

and other key model development stages. 
8 

Impact 

The CMAQ modeling system has been used in a large number of Agency decisions and in air 

quality decisions made by other branches of the federal government, by states, by local 

governments, other interest groups and other countries. A notable example, and one that 

suggests a more general recommendation, is the development of a national air quality forecasting 

system. The Division was instrumental in developing an air quality forecasting system, and as a 

successful on-going application, its operation has been transferred to another agency. Yet this 

forecasting capability provided significant model evaluation opportunities (as it was being 

developed) that should not be lost. In addition, as the CMAQ model continues to evolve, model 

improvements generated by the Division should be incorporated into the operational forecast 

model.

Recommendation: Continue a formal working relationship with air quality forecasting 

operations. If this specific relationship is successful, formal methods for integrating operational 

applications with Division research may become a more general practice.

Leadership in the Scientific Community 

The Division has played an important role in developing a regional air quality modeling 

community directed toward meeting ambient air quality standards, and the Division has 

established high scientific standards for model development, evaluation and application. The 

panel’s recommendations have addressed how the Division might further strengthen its 

leadership position in the regional air quality community; however, the Division’s community 

will broaden as CMAQ is used to address air quality issues at smaller scales and at larger scales. 

The Division’s role in leading this expanded community has not yet been articulated and needs 

to be defined as direction from the Agency emerges. 

In conclusion, the panel reiterates its findings that the Division has established the CMAQ 

models as a leading state-of-the-science regional air quality modeling system, with a large user 

community; that the Division has delivered timely and relevant modeling products to EPA and 

its broader user community; and that the Division has developed a balanced portfolio of core 

research and applied activities in support of EPA needs. The panel also notes that the process for 

conducting the peer review was well managed and effective, however, in future peer reviews, 

separate panel meetings with junior staff, in addition to poster sessions and meetings with senior 

management, would be useful. 
9 

Introduction and Charge to the Panel 

The National Exposure Research Laboratory (NERL) conducted a peer review of Atmospheric 

Modeling and Analysis Division (AMAD), with the goals of assessing the quality of the science 

being performed within the Division and the responsiveness of the Division to the needs of the 

Environmental Protection Agency (EPA, or the Agency). 

A review panel, consisting of individuals with air quality modeling research and project 

management expertise, met with Division staff and management at Research Triangle Park, 

North Carolina from January 27 through January 29, 2009. Members of the panel were: 

Panel Chair 

Dr. David Allen, Gertz Regents Professor, University of Texas at Austin 

Panel Members 

Dr. Douglas Burns, Research Scientist, U.S. Geological Survey 

Dr. David Chock, Senior Scientist, Ford Research Laboratory 

Dr. Naresh Kumar, Senior Program Manager, Electric Power Research Institute 

Dr. Brian Lamb, Professor, Washington State University 

Dr. Michael Moran, Research Scientist, Environment Canada 

The panel was charged with addressing four primary questions: 

1. What is the quality of the scientific research being performed by the Division? (Quality) 

2. Is the Division responsive to Agency needs and problems? (Responsiveness) 

3. Is the Division’s work having impact on Agency decisions? (Impact) 

4. Are Division staff leaders in their scientific communities? (Leadership) 

The panel’s main findings and recommendations, as described in the Executive Summary, are 

organized using these four primary questions as a framework. During the review, the Division’s 

work was presented in five major topical areas: (1) Air Quality Model Development, (2) Air 

Quality Model Evaluation, (3) Linking Air Quality and Human Health, (4) Climate Change and 

Air Quality Interactions, and (5) Linking Air Quality and Ecosystem Health. For each of these 

topical areas, the panel was directed, in its charge statement, to organize its findings and 

recommendations into a framework consisting of three sections: 

1. Research 

2. Advice and assistance 

3. Leadership 

NERL requested that both the quality of the Division’s work and Division responsiveness to 

agency needs be addressed in each section. Examples of issues to be addressed in each of these 

sections, as listed in the charge statement, are given below: 
10 

Research 

Quality Responsiveness 

Division’s approach to a given environmental 

problem 

Examples: 

• Has the Division identified major uncertainties and 

appropriate research priorities? 

• Are approaches scientifically sound? 

• In what ways has the Division advanced scientific

understanding of the problem? (Has it had an impact?) 

• Are future directions sound? 

Division’s resources (assume fixed numbers) 

Examples: 

• Are Division’s resources effectively and strategically 

allocated across problems (appropriate depth and 

breadth)? 

• Is the skill mix optimized for the scientific direction 

taken? 

• Does workforce maintain cutting-edge knowledge and 

skills? 

Mechanisms and extent to which findings/products 

are disseminated to scientific audience in a timely

fashion

Division’s responsiveness to Agency needs 

Examples: 

• Is research driven by Agency priorities? 

• Does the research address the critical issues within 

EPA’s mission? 

• Is the Agency using the Division’s data/products?

• Does the Agency adopt approaches or methods 

developed by the Division? 

• Does the Division provide information necessary for 

EPA users to meet statutory requirements or other policy 

needs? 

• What problems has the Division solved for the 

Agency? 

Balance between core and problem-driven 

research 

Mechanisms and extent to which findings/products 

are disseminated to Agency in timely fashion

Advice and Assistance 

Quality Responsiveness 

Extent to which Division’s assistance is 

sought by, or provided to, the scientific 

community 

Examples: 

• Do scientists serve on national/international 

workgroups, symposia, professional societies, 

publication boards? 

• Are they members of research review 

boards (e.g., study sections) for other organizations? 

• Do they provide scientific or technical 

guidance to local, state, tribal and international 

governments?

Extent to which Division’s advice/ technical 

support is sought by the Agency (Program 

Offices, Regional Offices) 

Examples: 

• Does Division staff participate on major within-Agency 

workgroups? 

• Do Division scientists assist the Agency in 

developing testing guide-lines, interpreting 

research advances, reviewing Program 

Office or Regional documents?

Leadership 

Quality Responsiveness 

Division’s leadership role in the scientific 

community (influence on agendas, 

decisions, priorities of other 

researchers/organizations) 

Examples: 

• Do scientists lead collaborative research efforts at the 

national/international level? 

• Do they serve on advisory boards of other major 

agencies/organizations? 

• Are they invited to chair major committees? 

• Do they organize major conferences, symposia? 

• Do they receive awards/honors for scientific 

contributions?

Division’s leadership role in the Agency 

(influence on research planning efforts, 

decisions, and priorities of the Agency) 

Examples: 

• Does Division staff lead research planning 

and coordination efforts across Divisions, 

Agency Labs and Offices? 

• Do scientists represent ORD/Agency on 

workshops or workgroups addressing major risk 

assessment or environmental issues? 

• Do they receive major Agency awards/honors? 
11 

The following sections of this report describe the panel’s assessment of AMAD Research, 

Advice/Assistance and Leadership in the 5 theme areas of (1) Air Quality Model Development, 

(2) Air Quality Model Evaluation, (3) Linking Air Quality and Human Health, (4) Climate 

Change and Air Quality Interactions, and (5) Linking Air Quality and Ecosystem Health. 
12 

Theme 1. Model Development and Diagnostic Testing 

1A. Introduction 

EPA’s AMAD division may be the largest air-quality-model development, evaluation, and 

application group in the world. An important thrust of this NERL division is model development 

and diagnostic testing. For the past 15 years or more, the primary focus of AMAD’s 

development efforts has been the CMAQ (Community Multiscale Air Quality) modeling system, 

which now consists of the CMAQ chemical transport model (CCTM), its meteorological driver 

models (MM5, WRF), emissions processing and emissions modeling systems (SMOKE, BEIS), 

and other supplemental software and data sets. 

The CMAQ numerical modeling system provides a quantitative link between emissions of target 

pollutants or their precursors to the atmosphere and the resulting ambient air concentrations and 

dry and wet removal of these pollutants. CMAQ can thus be used to (a) evaluate and assess 

proposed AQ control measures and management programs, (b) make short-range (i.e., several 

days) AQ forecasts (c) assess AQ responses to long-term changes (i.e. climate change), 

(d) provide atmospheric inputs to other models (e.g., water-chemistry and ecosystem models), 

(e) help design measurement networks, and (f) test scientific hypotheses. 

1B. Research 

AMAD research contributions to AQ model development and testing have been wide-ranging, 

scientifically sound, and of high quality, and they have contributed significantly both to the AQ 

modeling community in general and to Agency programs in particular. The development of an 

operational AQ forecasting system in response to a direct request from Congress is one 

particularly noteworthy example. 

Accomplishments and Strengths 

In the past five years, the Division has had many significant accomplishments in model 

development and diagnostic testing, including improvements to the CMAQ CCTM, emissions 

models, and meteorological models: 

• In collaboration with NOAA, development and transition to operation in only four years of a 

U.S. operational national AQ forecast system for ozone (Eta-CMAQ, then WRF-NMMCMAQ). 

• Release of six public versions of CMAQ (v4.2.2 through v4.7) to the external user 

community in the 2003-2008 period (see www.cmaq-model.org). 

• Development of a multi-pollutant version of CMAQ (v4.7) that can model criteria pollutants 

(O3, PM2.5, PM10) and toxic pollutants (Hg plus 40 other species) simultaneously. 
13 

• Design and development of AMET and WDT, two post-processing tools to assist client and 

user communities in making better use of CMAQ results. 

• Establishment of a number of targeted collaborations with various other EPA Divisions, 

other U.S. federal departments and agencies, universities, international organizations, and 

consulting companies to improve CMAQ. These collaborations have allowed the Division to 

access complementary external expertise and external facilities. Some examples include the 

development of the CMAQ sea-salt emissions module, the AMET and WDT post-processing 

tools, and the CB05 gas-phase chemistry mechanism, the extension of ISORROPIA to base 

cations, the use of meteorological satellite data to adjust photolysis rates, and various 

computational efficiency improvements, including parallelization of the CCTM code. 

• Initiation of three external peer reviews of the CMAQ modeling system (in 2003, 2005, and 

2006) through the CMAS Center to provide critiques and advice on the CMAQ development 

program, including future directions and priorities (see www.cmaq-model.org). 

• Implementation of numerous improvements to the CMAQ modeling system during the 2003-

2008 period. Table 1 lists notable new features and upgrades. They include improvements 

to most aspects of the modeling system, including the specification of input emissions, the 

meteorological driver, the chemical transport model, and post-processing capabilities. A 

number of these improvements were made possible by recent advances in understanding of 

important atmospheric chemical and physical processes. 
14 

Table 1. Significant developments to the CMAQ modeling system implemented by AMAD 

during the 2003-2008 time period. 

1 Improved computational efficiency, especially in the aerosol module; 

2 Development of a “side-by-side” two-way-coupled meteorological and chemical transport 

modeling system (WRF-CMAQ) that can be applied to both AQ-climate studies and to finer 

spatial scale modeling; 

3 Development of a sectional version of CMAQ; 

4 Significant upgrades to parameterization of secondary organic aerosol (SOA) formation 

(including contributions of isoprene, sesquiterpenes, benzene, glyoxal, and methylglyoxal 

precursors and polymerization, acid catalysis, and nox-dependent processes); also required a 

new release of BEIS (v3.14) to include sesquiterpene emissions; 

5 Roll-out of a new CB05 gas-phase chemistry mechanism, including Cl2 chemistry, and work 

to implement the SAPRC07 mechanism, an update of SAPRC99; 

6 Implementation of a chemically active PM coarse mode for semi-volatile inorganic ions and 

elimination of some numerical instabilities in ISORROPIA; 

7 Development of in-line photolysis rate module with aerosol feedback; 

8 Improved treatment of N2O5 hydrolysis; 

9 Upgrades to HONO emissions and chemistry; 

10 Modifications to representation of cloud mixing in the convective cloud module; 

11 Closer and more consistent connections in treatment of meteorological surface fluxes and 

dry deposition; 

12 Development of treatment of bi-directional surface fluxes of NH3 and Hg; 

13 Development of a sea-salt emission flux parameterization that considers both open-ocean 

and surf-zone emissions (relevant for the National Estuary Program); 

14 Development of the Atmospheric Model Evaluation Toolkit (AMET), a meteorological and 

AQ model evaluation package (released in 2008), and the Watershed Deposition Tool 

(WDT), a GIS tool to map gridded CMAQ output fields to irregularly-shaped watersheds; 

15 Implementation of an “instrumented” version of CMAQ for process analysis, sensitivity 

analysis, and source attribution, including sulfur and carbon tracking tools and DDM-3D 

version; 

16 Modifications to the WRF meteorological model to make it a more suitable and compatible 

“driver” for the CMAQ CCTM, including incorporation of the Pleim-Xiu land-surface 

model and “nudging” FDDA; 

17 Development of an improved PBL parameterization (ACM2) that has been implemented in 

both a meteorological model and an AQ model (CMAQ);

18 Development of PBL data assimilation techniques; 

19 Development of methodology to build national, model-ready emissions inventory of dayspecific daily wildfire and prescribed-burn emissions based on both satellite-remote sensing 

and surface reports (U.S. wildfire inventories now available for 2002 to 2006 period). 
15 

• Associated with all of these model developments and improvements have been ongoing 

evaluation studies and diagnostic testing using data from major field programs. Field studies 

can provide (for short periods) detailed sets of measurements of a broad set of chemical 

species with high time resolution and sometimes high spatial resolution and above-surface 

measurements. AMAD made use of short-term data sets from the following programs and 

field studies during the 2003-2008 period to advance CMAQ development: 

o 2004-08 NOAA-EPA AQ forecast system evaluation for a wide range of seasonal 

and synoptic conditions; 

o analysis of carbonaceous aerosol vs. SEARCH network daily measurements using 

new process analysis tools in CMAQ; 

o 2001 Northeast Oxidant and Particle Study (NEOPS) field campaign; 

o 2002 Northeastern AQ Study (NEAQS) field program; 

o 2002 Bay Regional Air Chemistry Experiment (BRACE) field study in Tampa Bay, 

Florida and interactions of gas-phase species and coarse PM; 

o 2004 ICARTT northeast U.S. field program and vertical profiles of CMAQ; 

o 2006 TexAQS field campaign. 

Limitations 

• The Division is in danger of trying to cover too much ground with too few resources, 

especially in relation to future model development plans to extend the scale of CMAQ to the 

neighborhood scale and the hemispheric scale. Note that this same concern was raised in 

2003 by the 1st CMAQ external peer review panel that was organized by CMAS. 

Future Directions 

Plans for future directions presented to the review panel by AMAD included the following: 

• improved treatment of chemical lateral boundary conditions; 

• increase of maximum CMAQ domain size from continental to hemispheric scale; 

• further development of modeling system to handle finer grid resolutions; 

• improvements to chemical interactions between gas-aerosol-aqueous phases; 

• improvements to the chemical mechanisms; 

• improvements to model performance during cold seasons; 

• improvements for optical and radiative properties, especially for AQ-climate applications; 

• continued effort to characterize unspeciated PM2.5; 

• implementation of wind-blown dust emissions module and treatment of chemical interactions 

between crustal base cations and gases; 

• development of a parameterization for lightning NOx emissions, a source of nitrogen that is 

currently missing from the model; 

• transition from BEIS to the MEGAN biogenics emissions module (benefits should include 

better treatment of vegetation phenology, more up-to-date emission factors, applicability to 

other continents); 

• investigation of use of non-traditional upper air data in meteorological data assimilation; 
16 

• use of National Land Cover Dataset (NLCD) in calculation of land-surface fluxes and dry 

deposition; 

• improvements to treatment of stable boundary layers, including nighttime and winter 

conditions; and 

• continued development of coupled WRF-CMAQ system for both AQ-climate and fine-scale 

modeling (key to two FY11 Annual Performance Measures). 

All of the above future directions should contribute to improved simulation for one or more of 

the primary CMAQ applications, but some of these model improvements will be harder to 

achieve than others due to limitations in data availability or current scientific understanding. For 

example, being able to model wind-blown dust emissions will improve short-term PM and 

visibility simulations and also long-term acid deposition estimates. However, modeling windblown dust requires information about some soil properties that are not typically available, 

information about seasonal variations in vegetation cover, and accurate predictions of soil 

moisture. Stable boundary layers are a common occurrence but the parameterization of stable 

boundary layers in AQ models has not advanced significantly in the last three or four decades. 

Improvements here would be very beneficial but are likely to be hard-won. 

Suggestions for Improvement 

There are a few other emerging issues that were not raised by AMAD during the on-site review. 

The Division might also consider addressing the following issues, with the understanding that the 

future directions discussed above may take precedence depending upon Agency needs: 

• PM number density and the ultrafine mass fraction. These aspects of PM have been raised 

by some in the health community as being more closely linked to health impacts, including 

by studies of near-roadway PM exposure. Modeling these aspects of PM, however, will 

place new demands on the development of emissions inventories and on the numerical 

treatment of the PM size distribution in order to conserve both number and mass 

simultaneously; 

• Hemispheric AQ modeling. Moving to this spatial scale requires improvements to both 

model inputs and the model itself, including the specification of hemispheric emissions (i.e., 

Asian and European anthropogenic emissions, biogenic emissions), hemispheric 

meteorological modeling and data assimilation from a wide range of data sources, including 

satellites, aircraft, buoys, and ships, the treatment of background chemistry and emissions 

(e.g., DMS, CH4), and the treatment of upper boundary conditions and stratospheretroposphere exchange; 

• Possibility that semi-volatile VOC emissions from vehicular and other sources are not 

accounted for in current emission inventories and AQ models. Work by the group at 

Carnegie-Mellon, for example, suggests that these emissions may be a missing organic PM 

source; 

• Data fusion techniques. There have been some efforts to combine AQ model predictions and 

AQ and other measurements (e.g., precipitation, cloud cover) by various approaches, 

including kriging, model output statistics, objective analysis, and model-space and 

observation-space methods in data assimilation, but further improvements and applications of 

such techniques to CMAQ are likely possible. 
17 

• Development of a 2-way nested domain capability or the use of a non-uniform grid system 

that would provide higher resolution in specific areas of interest within larger domain 

simulations. Such a capability is fairly common in meteorological modeling and would be 

useful for modeling situations where chemical feedbacks from a high-resolution domain 

would have significant impacts on the coarser scale as well. One example might be AQ 

modeling for mega-cities, where a realistic treatment of small-scale chemistry and dispersion 

in the urban core and urban plume could conceivably change the predicted regional-scale 

impacts. 

• A more in-depth study of horizontal physical diffusivity and its parameterization for different 

grid scales is in order. Presently, numerical diffusion is often lumped together with physical 

diffusion, and the true impact of this lumping is often unknown. What is known though is 

that the errors would tend to be large near the peak concentration regions and regions with 

large concentration gradients. 

1C. Advice and Assistance 

As demonstrated by the following examples, AMAD’s model development research has had a 

significant impact in supporting EPA’s policy, planning, and priorities. 

• Application of CMAQ by the Agency (OAQPS) to provide technical foundations and benefit 

assessments for a number of Agency regulations, including: 

o Clean Air Interstate Rule (CAIR) 

o Clean Air Mercury Rule (CAMR) 

o Revised O3 NAAQS final rule 

o Locomotive and Commercial Marine final rulemaking 

o NOx/SOx secondary NAAQS review (underway) 

o Renewable Fuels Standards Act-2 rulemaking (upcoming) 

• Contributions to Agency science assessments (e.g., 5-year NAAQS reviews, incorporation of 

CIRAQ results in ORD interim national AQ assessment) 

• Application of CMAQ to State Implementation Plans (SIPs) by U.S. regional planning 

organizations (RPOs), individual states, and consultants 

• Establishment of the Community Modeling and Analysis System (CMAS) Center at the 

University of North Carolina, which provides support to the large external CMAQ 

community of users (~1000), has organized a series of seven regional AQ modeling 

conferences focused on CMAQ, and offers courses on using CMAQ and its emissions, 

meteorological, AQ, and post-processing components; the external community in turn has 

authored more than 25 CMAQ publications (e.g., see weblink 

http://www.cmascenter.org/resources/bibliography.cfm?temp_id=99999#CMAQ_DEV) 

• Improvements to the comprehensiveness of U.S. national emission inventories through the 

development of methodologies to estimate emissions from non-traditional sources (e.g., 

biogenics, wildfires, ocean wave-breaking) 
18 

Work on model improvements has been prioritized based on relevance to Agency issues (i.e., 

application needs) and expected impact. 

• Significant internal recognition of AMAD’S contributions to EPA itself appears strong, as 

suggested by significant number of intra-agency awards 

• Dec. 2008 memo from AQAD director (Richard Wayland) is very supportive overall of 

AMAD contributions to Agency programs (see Section 14 of Peer Review Support 

Document) 

• Large number of research collaborations with groups inside Agency (Table 11.4 of Peer 

Review Support Document) 

• Participation in and contributions to ORD and NERL planning activities 

• AMAD has also been responsive to recommendations from past external reviews. For 

example, it was noted in both the 2nd and 3rd CMAQ External Peer Review reports (see 

weblink http://www.cmaq-model.org/) that AMAD had already made significant responses to 

a number of the recommendations contained in the 1st and 2nd CMAQ External Peer Review 

reports, respectively. 

The division has provided additional advice and assistance both to the Agency and to outside 

organizations and groups, including the following examples: 

• The Division has worked with the National Park Service to assess air concentration and 

deposition at Shenandoah National Park; 

• The Division has provided assistance to the Chesapeake Bay Program since 1992 and more 

recently to the Tampa Bay Estuary Program; 

• The Division has worked with the New York Department of Environmental Conservation to 

establish a state-level AQ forecasting program; 

1D. Leadership 

AMAD has been actively engaged with the rest of the Agency and with the external science 

community during the 2003-2008 period, providing leadership within these communities in a 

number of ways. 

• development of a state-of-the-science regional AQ modeling system 

• transfer of EPA-developed meteorological parameterizations to NCAR’s Weather Research 

Forecast (WRF) meteorological model 

• choice of research problems as an example to the larger scientific community 

• large number of research collaborations with groups inside and outside the Agency (cf. Table 

11.4 of the Peer Review Support Document) 

• strong publishing record: 50 AMAD staff (including some now retired or transferred) 

contributed to 161 journal articles published during 2003-2008 period, including 104 firstauthored publications (i.e., > 25 articles per year) 
19 

• Division staff hold 5 adjunct professorships, sit on 7 journal editorial boards, and serve as 

officers in 3 professional societies and as committee members in 4 professional societies 

• Division staff have mentored ~8 post-doctoral fellows and students 

• creation of CMAS to provide support to the external community of CMAQ users 

• organization of conferences, workshops, and special issues (e.g., 2005 NOAA-EPA Golden 

Anniversary modeling conference, 2006 special issue of Atmospheric Environment on model 

evaluation, 2007 AMS-EPA regional AQ model evaluation workshop, 2009 AQMEII model 

evaluation workshop) 

1E. Summary and Recommendations 

Findings: 

• The Division has developed CMAQ, a leading state-of-the-science regional air quality 

modeling system, and has continued to innovate in improving the model science. 

• The Division has generally been responsive to the needs of CMAQ users in developing 

and providing updated versions of the model. This includes a concerted effort to improve 

the model’s computational efficiency as well as its science. 

Recommendations: 

• Develop a public strategic plan, with a 3-5 year time horizon, for the continued 

development, evaluation and support of the CMAQ modeling system. It is clear that the 

Division has identified a set of future directions and formulated a development plan for 

CMAQ, but these are not widely known outside of the Division. As the Division’s own 

internal plans for model development and evaluation become more widely known, the 

broader CMAQ community can identify complementary initiatives and avoid duplicative 

research. 

• Define, as transparently as possible, pathways for users to contribute to the CMAQ 

modeling system. Presently, the manner in which the Division identifies scientific results 

that drive model development initiatives is not widely understood. It is also not clear to 

the broader user community what types of quality assurance and model evaluation criteria 

need to be met for user-developed tools to be incorporated into the modeling system. 

Making these processes transparent will promote user engagement and may in turn 

benefit the Division. 

We note that a related concern was raised in the 2nd CMAQ External Peer Review report 

(p. 12), where the suggestion was made by that review panel that extramural 

improvements to CMAQ such as CMAQ-MADRID should be brought into the core 

model version so that the code changes would not need to be “migrated” into each new 

CMAQ release. AMAD responded that this suggestion could be very time-consuming, 

although it was noted that CMAS had set up and maintains a multi-version repository of 

research versions of CMAQ. This response was re-iterated by AMAD during the 3rd
20 

CMAQ External Peer Review (see p. 8), but it was also noted by AMAD that some 

modules developed outside of the EPA have been incorporated into the core model (e.g., 

ISORROPIA, UCD sectional PM version, DDM-3D). It was suggested that the existence 

of an active collaboration between AMAD and the external developers made this transfer 

more likely to happen. 

In view of AMAD’s efforts to encourage the development of an external CMAQ 

community, it would be worthwhile for AMAD to communicate its position on this 

subject to the community and to describe the minimum requirements that should be met. 

• Establish a process to communicate and consult with the external user community before 

features are removed from CMAQ and provide a rationale for such removals when a new 

model version is released. Many new features and improvements have typically been 

introduced in each CMAQ release, but before version 4.7 was released in Sept. 2008, 

very few existing features had been removed (the RADM2 gas-phase chemistry 

mechanism was dropped in version 4.5). When CMAQ version 4.7 was released, 

however, five CMAQ options were dropped: plume-in-grid (PinG); CB4 gas-phase 

chemistry mechanism; AERO3 module and AE3 mechanism; RADM cloud module; and 

the stand-alone mercury version of CMAQ. The loss of support for selected model 

features means a loss of backward compatibility. It would be helpful to the larger CMAQ 

user community if advance warning of such decisions were given and an opportunity to 

comment was made available. It would also be informative as part of the release of a 

new model version to provide reasons for the removal of any features. 

For this panel, the loss of support for the CMAQ PinG module in version 4.7 was the 

most surprising. AMAD’s response to the 2nd CMAQ External Peer Review report (p. 4) 

stated that new research and development on the PinG module was to be phased out after 

2005 and a reason was given, but the PinG module was then upgraded in CMAQ version 

4.6 before being phased out in version 4.7. Others in the CMAQ user community might 

have been similarly surprised. 

• Seek focused, expert input on Division methodologies, tools and models at critical steps 

in their development. The Division has a strong track record of soliciting frequent 

external peer reviews of the CMAQ modeling system. These reviews have tended to be 

comprehensive examinations of the modeling system. These should continue, but the 

Division should add focused teams that can examine more narrowly defined emerging 

model system developments (e.g., coupled models and WRF-CMAQ, hemispheric 

CMAQ, inverse emission modeling, or the Carbon apportionment tool). These focused 

teams should provide input at project initiation, pre-release, and other key model 

development stages. 

Finding: 

• In collaboration with NOAA, AMAD was responsible for the development and transition 

to operation in a short time (four years) of the first U.S. operational national AQ forecast 

system for ozone. In 2003 the EPA and NOAA signed a Memorandum of Agreement to 

collaborate on the design, development, evaluation, and implementation of an operational 
21 

daily AQ forecast system. The resulting operational air-quality forecast model (AQFM) 

was based in large part on the CMAQ CCTM and other key components of the CMAQ 

modeling system. The Division has suggested (see Peer Review Support Document, p. 4-

11) that this collaboration was concluded successfully at the end of FY08 with “the 

transfer of AMAD’s models and modeling guidance to NOAA’s operational arm” at the 

same time that the NOAA staff seconded to the EPA were formally transferred to 

EPA/NERL/AMAD. However, two of the original design goals for the AQFM have not 

yet been met: (1) that the AQFM forecast domain should include all 50 U.S. states, 

including Alaska and Hawaii; and (2) that the AQFM forecasts include both ozone and 

PM2.5 forecasts. At the present time the AQFM forecast domain only includes the 

contiguous 48 states and PM2.5 forecasts are not yet operational. 

Recommendation: 

• AMAD should continue a formal working relationship with NOAA on operational AQ 

forecasting. Maintaining such a relationship would benefit both parties. NOAA would 

continue to have a formal mechanism to access AMAD expertise during the transition to 

a larger forecast domain that supports AQ forecasts for Alaska and Hawaii and to 

operational PM2.5 forecasting. This agreement would also institutionalize NOAA’s 

access to the EPA’s latest anthropogenic emission inventories, to AMAD’s models and 

methodologies to estimate natural emissions, and to the near-real-time provision of 

continent-wide O3 and PM2.5 measurements via the EPA’s AIRNow meta-network. 

AMAD in turn would continue to learn from having CMAQ applied 24/7 over a 

continental-scale domain using a different meteorological driver (WRF-NMM) with 

advanced meteorological data assimilation techniques to forecast both gas and aerosol 

species. Such an agreement would also provide EPA with a formal procedure to provide 

NOAA with newer versions of CMAQ, thus supporting an ongoing transition of research 

advances to the operational arena and avoiding a divergence of AQ forecasts being made 

by the EPA for policy purposes with those being made by NOAA for short-term 

guidance. If this specific relationship is successful, formal methods for integrating 

operational applications with Division research may become a more general practice.

Finding: 

• AMAD’s model development research has had a significant impact in supporting EPA’s 

policy, planning, and priorities. The CMAQ modeling system has been used to provide 

analysis and benefits assessment underlying such Agency regulations as the Clean Air 

Interstate Rule, the Clean Air Mercury Rule, the revised O3 NAAQS Final Rule, and the 

Locomotive and Commercial Marine Final Rule. It has also been used by the states in 

preparing State Implementation Plans for submission to the Agency. 

Recommendation: 

• When required, the Division’s core activities should be expanded in order to maintain any 

Division capabilities that are not currently required to support Agency policy activities 

but which have been required in the past and are likely to be needed again in the near 

future. A significant investment is usually required to build the scientific expertise and 

modeling tools needed to bring leading-edge science to support policy development. In a 

research environment where science continues to evolve at a rapid pace, both expertise 
22 

and models must also be updated on an ongoing basis to be of most use. For example, 

CMAQ mercury modeling has contributed to Agency policy in the recent past and will 

almost certainly be needed in the future. Efforts should be made by the Division to 

maintain this capability. 
23 

Theme 2. Model Evaluation: Establishing Model’s Credibility 

2A. Introduction 

A Long-Term Goal of the Office of Research and Development's Clean Air Research Multiyear 

Plan is to reduce uncertainty in standard setting and air quality management decisions. Model 

evaluation is a key component of the AMAD activities that allow the ORD to move toward this 

goal. Confidence in the model predictions can only be increased if the model describes the 

chemical and meteorological processes reliably, has reliable emissions, boundary and initial 

conditions as input, and has an accurate computational methodology. An evaluation of all these 

components and the model as a whole also requires reliable observational data for comparison 

purposes. However, because of the very wide range of the temporal and spatial scales of the 

processes involved, and the fact that most of the processes can only be studied through 

observation rather than experimentation, model evaluation becomes a very challenging task. 

 

Model evaluation helps identify and potentially improve weaknesses in the model or its 

components, and can also help improve the design of data collection and measurement processes. 

The AMAD has a rather elaborate and comprehensive framework to evaluate CMAQ. The 

evaluation framework consists of four different components: operational, diagnostic, dynamic, 

and probabilistic. Operational evaluation compares model-predicted and routinely measured 

concentrations of the end-point pollutants of interest. Diagnostic evaluation attempts to identify 

areas where improvement is needed, and is tied directly to model development activities of 

AMAD. Dynamic evaluation is concerned with how model predictions respond to changes in 

meteorology and/or emissions. Probabilistic evaluation attempts to incorporate model and input 

uncertainties in the characterization of model predictions and to capture the stochastic nature of 

the atmospheric processes through the judicious usage of deterministic-model runs. 

 

2B. Research 

Accomplishments and Strengths 

• An outstanding strength of the AMAD in research is the team work and spirit of the staff. 

In particular, members of the Model Development and Diagnostic Testing Team and the 

Model Evaluation Team have collaborated closely to help facilitate model improvement. 

• The AMAD places eight full-time equivalents of scientists on model evaluation. This 

indicates the high priority AMAD places on this activity. The AMAD’s strong support and 

the positive attitudes of the scientists help assure the high quality of the research. 

• To facilitate and standardize the model evaluation effort, the AMAD has developed the 

Atmospheric Model Evaluation Toolkit (AMET), which is a package of data analysis and 

graphical tools for use by both the model developers and users. This tool has been 

enthusiastically received by practitioners and is a great asset for model improvement. 

• As noted in the review of Theme 1, to evaluate the direct relevance and usefulness of 

CMAQ for 24-hour real-time forecasting operations, the AMAD formed a partnership with 
24 

NOAA to develop the National Air Quality Forecast System (NAQFS). This partnership 

has the advantage of both supporting the air quality forecasting operation and helping 

identify shortcomings in the model. 

• In a remarkably short time frame, the AMAD updated the meteorological model in NAQFS 

(WRF-NMM). The subsequent spatial and temporal evaluations of the NAQFS show that 

the WRF-NMM-CMAQ model generally performs well but with some regional systematic 

errors and biases. Overall, the WRF-NMM-CMAQ has great potential to improve both the 

science and applicability of the model. 

• The AMAD has explored and developed post-processing bias-adjustment techniques. One 

of these, based on the Kalman Filter, can significantly reduce model bias and should help 

improve air quality forecasting even though it is not designed to identify model shortfalls. 

• The Division has statisticians as members of the staff. This is a remarkable asset to the 

Division. By using block kriging, they help illustrate the role of spatial correlation structure 

in understanding the incommensurability issue between grid-averaged concentrations and 

point measurements. The methodology implicitly highlights the need to adjust model grid 

size to reduce incommensurability. Application of this methodology can be very helpful in 

human exposure studies for pollutants of different spatial and temporal scales. 

• The AMAD researchers use instrumented models for diagnostic evaluations to investigate 

how the model responds to perturbations in various model parameters. Three such models 

are considered: the Direct Decoupled Method (CMAQ-DDM-3D), carbon apportionment 

(CMAQ-CA), and sulfur tracking (CMAQ-ST). The CMAQ-DDM-3D is used to estimate 

the impact of precursor-emission changes on ozone, quantify uncertainties in model 

outputs when model structural and/or parametric uncertainties are explicitly considered, 

and estimate emissions via inverse modeling. CMAQ-CA is designed to track the size, 

composition and sources of primary carbonaceous aerosol. This activity has led to 

improvement in the National Emissions Inventory. CMAQ-ST tracks emitted sulfur 

through its different chemical and physical pathways. The usefulness of these 

instrumented models has been well demonstrated. If the emissions and emission changes 

are reliable, these tracking tools can also be an excellent test for model performance. 

• Emissions are among the largest sources of uncertainty in air quality modeling. The 

AMAD has used inverse modeling to reconstruct emissions for seasonal agricultural 

ammonia emissions and urban NOx emissions. The resulting ammonia seasonal emission 

estimates have been incorporated directly into the SMOKE emission system. In the NOx 

study, satellite NO2 information was used to help reveal a NOx bias in the free troposphere. 

Whether the bias is a chemistry issue, a transport issue, or both needs to be investigated. 

• In dynamic model evaluation, AMAD has performed a ground-breaking study to compare 

the model-predicted changes and observed changes in ozone concentrations during the 

summer months in the Eastern US as a result of NOx emission reductions dictated by the 

NOx SIP call. The AMAD had an elaborate modeling design with different combinations 

of emission changes, meteorological changes, and changes in chemical mechanism. The 

years selected for the study were 2002 (pre-NOx-SIP call), and 2004 and 2005 (post-NOxSIP call). This project allowed the EPA to conclude that there was a general 

underprediction of ozone reductions compared to observations, and that CB05 with 

updated emission inventories appears to perform better in predicting ozone response to 

NOx reduction. There was also a significant underprediction of ozone and NOx at 

altitudes above 2000 m. The AMAD plans to conduct diagnostic analyses of modeled and 
25 

observed vertical concentration and wind profiles to assess transport and vertical mixing 

processes. Dynamic model evaluation is a critical area of activity for the evaluation of the 

applicability of air-quality models for policy-related simulations and AMAD has done an 

outstanding job in undertaking this activity. 

• The AMAD has also undertaken probabilistic model evaluation. Bayesian model 

averaging has been used to construct a probability distribution function of ozone 

concentration at a specific location and time. The distribution function is in turn an 

observation-calibrated weighted average of model-prediction distributions generated by 

allowing for structural and parametric uncertainties in the model. This approach would be 

very useful if the weights, which are the posterior models' probabilities for inclusion in the 

averaging process, are relatively stable under a relatively wide range of environmental 

conditions. Probabilistic model evaluation is a new activity at AMAD and in the airquality modeling community in general. It allows the use of results from deterministic 

models like CMAQ in a stochastic context. And it also conveys the degree of confidence 

in the range of model-predicted concentrations in describing actual observations. This 

valuable activity would not have been effectively undertaken in a timely manner had the 

Division not brought well-trained statisticians on board. 

 

Limitations 

• In inverse modeling, use of only one set of the DDM sensitivity coefficients can impose 

substantial restrictions on the outcome of the emission estimates. For example, if the 

sensitivity is with respect to an emission-change factor applied to the emission profile, then 

the basic temporal and spatial variations of the emission profile has been implicitly 

adopted, which may or may not match reality. 

• The AMAD model evaluation effort has largely been confined to CMAQ, although the 

mercury version of CMAQ was included in two model inter-comparisons. Accordingly, 

opportunities for new insights gained by evaluating non-CMAQ models are not available. 

• The AMAD is expanding its neighborhood-scale modeling capability and concurrently 

plans to greatly enhance its modeling capability at the global and/or hemispheric scale, and 

global-to-regional downscaling. All these efforts are likely to pose a severe challenge to 

resource allocation, and perhaps constrain some of the model evaluation efforts. 

 

Future Directions 

• Further interaction between AMAD and the NAQFS group would be a mutually beneficial 

collaboration. Such a collaboration would allow for the potential incorporation of the postprocessing, bias-adjustment technique based on the Kalman Filter or the Bayesian model 

averaging method to provide confidence-level estimates of the air quality forecast. This is a 

highly valuable approach for health protection. 

• The AMAD intends to continue to update and expand the suite of instrumented models. In 

particular, the AMAD is exploring developing in-house capabilities in adjoint modeling. 

This is a worthwhile approach to understand how a perturbation or sensitivity coefficient of 

a given species concentration can be traced back to its origin at an earlier time according to 

a given model like CMAQ. Within the limit of its applicability, the approach can help 
26 

identify potential areas for improvement in CMAQ when predicted changes in emissions 

can be compared with the actual changes. 

• Use of highly-resolved NO2 satellite data coupled with improved model chemistry, once 

available, is strongly encouraged for inverse modeling of NOx emissions. There may be a 

grid scale problem for inverse modeling of carbon monoxide, especially in the regions of 

interest, in the microenvironments near the emission sources. For elemental carbon, such a 

study may be useful if the concern is the regional transport of the pollutant. In this 

connection, a hemispheric or global model may be more suitable because elemental carbon, 

and many other pollutants and pollutant precursors can be transported long range. 

• Applying dynamic model evaluation to the most current CMAQ version is recommended 

especially when there are sufficient and significant changes from the version used in the 

earlier evaluation. To evaluate the ability of MOBILE6 (and possibly link-based mobile 

emissions) to actually capture the NOx emission changes may necessitate a smaller-scale 

model, with a 2x2 km2

 or at most 4x4 km2 grids. The 12x12 km2 grid may be less useful 

because the NOx emissions are rather granular spatially in an urban environment, and 

beyond an urban area where the larger grid may be useful, interferences from other 

emission sources, chemical and physical processes may blur the signals of the mobilesource NOx emission changes. 

• The AMAD intends to better characterize the model uncertainty by improving the 

uncertainty estimates of sector emissions. It also intends to incorporate the NO2 data in 

addition to the ozone data to help weight ensemble members in Bayesian model averaging. 

These are useful efforts to improve the robustness of the weights. Applying the 

methodology to PM2.5 is perhaps worthwhile, but characterizing model uncertainties is 

more challenging because of the added complexity and uncertainty of aerosol chemistry. 

Applying the methodology to regional downscaling is much less useful presently because 

of the huge uncertainty associated with regional climate predictions from existing general 

circulation models, and there are no bona fide observations for comparison purposes. 

Suggestions for Improvement 

• It is recommended that AMAD take advantage of information and data gained from the 

NAQFS to develop an in-depth and systematic model evaluation of episodic air quality 

events for different parts of the country. This could be conducted in close collaboration 

with the NCEP NAQFS group. 

• Air quality models contain structural, parametric (including emissions) and numerical 

uncertainties. In inverse modeling, it would be helpful to ascertain how some of the nonemission uncertainties in the model are translated into uncertainties in the emission 

estimates. This is especially important because the emission profile leading to good 

agreement between observation and model prediction is simply an outcome of a closedloop iterative effort driven by the least-squares assumption in inverse modeling, not an 

indication that the derived emissions are more reliable. 

• To minimize profile presumptions on the emissions, it would be useful in the inverse 

modeling approach to look into the use of Kalman filter with an explicit transfer matrix 

linking emissions to concentrations (instead of a Jacobian based on the sensitivity 

coefficients) for time-dependent inversions to construct some relatively granular 

temporally and spatially dependent emission profiles. 
27 

• The validity of the sensitivity coefficients beyond say, a 10-20% emission perturbation can 

become an issue for pollutants undergoing nonlinear processes. So if the converged 

emissions are significantly different from those of the initial emissions assumed by inverse 

modeling, what is the potential implication? This issue needs to be investigated. The 

AMAD has been careful in the choice of species or species groups for study in this regard. 

• In assessing the transport and vertical mixing processes in the model study of the NOx-SIP 

Call, it is also necessary to assess the reliability of the horizontal transport and mixing 

processes in the model. The notorious numerical problems associated with advection 

should not be ignored. 

• The model-predicted ozone changes due to emission changes like the NOx-SIP Call is 

most certainly grid-size dependent. An investigation of this issue would be helpful but 

challenging. This issue is also tied to the issue of subgrid parameterization of chemistry, 

which can be quite intractable. Simply reducing the grid size and re-running the model 

does not fully address the problem. 

• It would be worthwhile to compare the performance of CMAQ and non-CMAQ models 

like CAMx. Considerable insight would be gained in such a comparison, leading to 

subsequent model improvement. Also, it would be worthwhile to look into the 

incorporation of non-CMAQ models in Bayesian model averaging. 

2C. Advice/Assistance 

• Through CMAS, the AMAD has played a major role in advising and assisting the user 

community, including members of federal, state, tribal and regional regulatory agencies, 

and international governmental agencies. 

• The implementation of AMET is another clear indication of the responsiveness of AMAD 

to the needs of the user community. 

• Members of the AMAD have worked closely with members of the Air Quality Assessment 

Division (AQAD), keeping the latter informed on the science and process issues as the new 

version of CMAQ (v4.7) was being developed and evaluated. The same is true in the 

development of the coupled WRF-CMAQ system. 

• Members of the AMAD have also served on EPA review panels on grants proposals. 

2D. Leadership 

• Because the AMAD has a very high concentration of expertise over a wide spectrum of air 

quality issues, its leadership role in the scientific research communities is unchallenged. 

The long record of awards/honors received by members of the AMAD bears this out. 

• Many members of the AMAD are invited speakers and/or chairs or organizers of many 

national and international conferences, workshops, and symposia. Many also serve on 

professional societies and publication review boards, and some are on the faculties of 

universities. 
28 

2E. Summary and Recommendations 

Findings: 

• Overall, the AMAD has done an outstanding job in model evaluation. The research quality 

is high, and the effort is responsive to the needs of the Agency. 

• The Division has worked closely with AQAD to inform, deliver and help implement as 

necessary new versions of CMAQ for applications. 

• The Division has taken the role of assisting the user community. Many of the members are 

also playing a leadership role in air quality modeling research. 

• There is tremendous value to be gained if the heretofore fruitful NAQFS partnership 

between AMAD and NOAA could be re-affirmed and institutionalized. 

Recommendations: 

• As the Division takes on new areas of activities in climate-change impact on air quality, 

prioritization of projects becomes necessary. Prioritization ought to be based on the direct 

practical relevance of the project and its ability to improve understanding and/or to truly 

reduce uncertainty. 

• The AMAD is encouraged to work with non-CMAQ model developers to gain from each 

others' insights and modeling experience. 

• In considering air quality modeling of the neighborhood scale and hemispheric scale, it 

would be useful if AMAD could lay out potential revisions and new emphasis in the 

approaches for model evaluation. For example, in the case of hemispheric modeling and 

multi-year simulation, a pertinent issue would be how to handle emissions that contribute 

to long-range transport and formation of target pollutants like ozone. In the neighborhood 

scale, one issue would be the extent of the spatial and temporal granularity of the pollutants 

of interest compared to the resolution of the model. 

• Diagnostic analyses of modeled and observed vertical concentrations and wind profiles to 

assess transport and vertical mixing processes are not only necessary, but long overdue. 

The role of physical transport has generally been taken for granted and assumed to be taken 

care of by mass adjustment and artificial diffusion in the numerical methods. This is 

unfortunate. A careful evaluation of the dispersion characteristics, both vertical and 

horizontal, within and above the planetary boundary layer is strongly encouraged. Past and 

planned (if any) field studies with tracer releases should be useful for this purpose. 

 
29 

Theme 3. Linking Air Quality and Human Health

3A. Introduction

The goal of this research theme is to reduce uncertainties in linking air pollution sources and 

health exposure. The AMAD attempts to achieve this by combining grid-based, regional-scale 

models with urban-scale, dispersion models to provide spatially and temporally resolved air 

pollution concentrations. The ultimate goal is to use these high resolution air quality model 

predictions in exposure modeling as compared to using measured pollution concentrations, 

because measurements are lacking in spatial coverage in most cases. The three main projects 

under this research area are: 

• Developing a hybrid modeling approach by combining results from a regional scale 

model (CMAQ) and a dispersion model (AERMOD). 

• Improving the near-road component of AERMOD to better assess the spatial variability 

of air quality near roadways. 

• Evaluating the effectiveness of regulatory actions by investigating the relationships 

among emissions, ambient pollutant concentrations, human exposures and health 

outcomes. 

3B. Research 

Accomplishments and Strengths 

• AMAD has coupled the CMAQ model with the AERMOD model, thereby developing an 

approach that can be used to simulate local impacts from pollution sources. This 

approach provides spatially resolved ambient concentration estimates at the census block 

group level; this information can be input to human exposure models. This approach 

could serve as a good tool to assess the relative changes in local air quality from different 

pollution control measures.

• The Division is one of the few groups conducting research on improving the 

understanding of the impacts of traffic pollution on local air quality and exposures. Some 

of the laboratory and field measurements, including use of a mobile sampling platform 

for pollutant and air toxic measurements, conducted by the Division should help in 

developing better algorithms for and evaluating parameterizations of near-roadway 

dispersion models.

• The Division explored the use of the linked hybrid air quality and exposure modeling 

results as part of a feasibility study to assess public health impacts of emission reduction 

activities in New Haven, CT.

• EPA’s meteorological wind tunnel is an important tool for examining near-road 

dispersion, and the Division has used that resource to examine flow and dispersion 

around roadways with different configurations (e.g., barriers, road cuts).
30 

• Based on previous wind tunnel studies, the Division has developed an algorithm for 

inclusion into AERMOD for simulating the influence of depressed roadways.

• The Division has conducted significant research to assess various statistical methods to 

combine observed and modeled pollutant concentrations to provide improved air quality 

surfaces for use in exposure modeling and health studies. Currently, 5 summers of 

combined daily ozone surfaces are being used in exposure modeling, and 9 years of 

combined ozone surfaces are being used in an epidemiology study.

Limitations 

• The AMAD has correctly identified the problem of few ambient observations to provide 

metrics of exposure in epidemiological studies. These sparse measurements fail to 

capture the large spatial variability in air pollutant concentrations found near roadways or 

major industrial sources. AMAD has developed an approach to use data obtained from air 

quality models coupled with high resolution data from local scale dispersion models and 

their research direction is focused towards improving this modeling approach. The one 

major limitation of this approach is that although the coupled model results are highly 

resolved, they cannot be assumed to be better than a single point measurement until the 

performance of the fine spatial scale model is assessed. Improving the algorithms in 

dispersion models (such as better simulation of near-roadway conditions) is worthwhile; 

however a more real problem that needs to be addressed is the lack of available fine-scale 

meteorological data when running these dispersion models and fine scale concentration 

measurements to assess the performance of the models. AMAD’s future research aims to 

better evaluate the dispersion models by using data from special studies, but unless these 

models are evaluated when they are applied in new settings, their applicability will be 

questionable. 

• It seems AMAD is also planning to use the hybrid approach to provide air quality data for 

use in epidemiological studies to determine relationships between air quality and health 

effects. There is a critical need to establish the adequacy of using model predicted air 

quality concentrations in epidemiological studies before using this technique for that 

purpose. Although it may fall outside the scope of AMAD, there should also be a focus 

on developing interpolation techniques to better estimate ambient air quality populationweighted exposure metrics when relatively higher numbers of measurements are 

available in an urban area. It would be instructive to compare the results of 

epidemiological studies obtained using interpolation of observed air quality data versus 

using the air quality data obtained from air quality models. 

• It is not clear why AMAD has decided to use a regulatory model like AERMOD for 

linking with CMAQ to develop the hybrid modeling approach. There are other dispersion 

models in the public domain (such as SCICHEM) that could be good candidates for 

coupling with CMAQ. In fact, SCICHEM has already been interfaced with CMAQ by 

other researchers for simulating the short-term chemistry of power plant plumes. 

• AMAD is also examining the effectiveness of regulatory actions by investigating the 

relationship among emissions reductions, air quality changes, and exposure. A more 

comprehensive approach to evaluate the effectiveness of regulatory actions would be to 

determine if the observed health effects have decreased over time and if those decreases 
31 

in adverse effects can be attributed to a reduction in exposure to pollutants controlled via 

various regulatory actions. AMAD may need to work with other divisions to fully 

evaluate these relationships.

Future Directions 

• AMAD is planning to continue to improve the near-road component of AERMOD as part 

of the hybrid modeling approach. Improving the capabilities to simulate near-road 

concentrations is a worthwhile exercise, as health studies point to a relationship between 

health effects and pollution from traffic sources. A major part of this future work should 

focus on evaluating any new parameterizations that are developed for near-road treatment 

in AERMOD. Moreover, a guidance document should be developed that clearly 

delineates the conditions under which the hybrid approaches can be used with a high 

degree of confidence.

• AMAD is also planning to conduct further wind tunnel studies to examine additional 

roadway configurations and different atmospheric stability conditions. These studies 

should provide data that can be used to improve not only AERMOD, but other dispersion 

models as well.

• AMAD intends to conduct computational fluid dynamics modeling of near-road 

dispersion. This is an exciting area of research but it is also resource intensive, especially 

when a three-dimensional system with potentially complex geometry is used. Many twodimensional systems have been developed in the past, but their applicability is rather 

limited

• AMAD is also planning to collaborate with other EPA laboratories and the FHWA on 

conducting field campaigns in different cities (Las Vegas, Detroit, and Raleigh) to better 

understand the relationship between traffic emissions and roadway-related air pollution 

concentration gradients. This is another worthwhile activity that should help advance the 

basic science in understanding the impact of traffic-related emissions on ambient 

concentrations at local scales.

• A future direction in this research theme is to investigate the ability to embed hybrid finescale modeling within the CMAQ grid structure. This is a very good idea provided that 

there is also a fine-scale emission inventory upwind of the area of interest to enhance the 

granularity of the model predictions. Nested models have been used by others and it 

would be a good idea to take advantage of insights and experience that are already 

available to speed up the effort.

• AMAD is planning to develop an approach for assessing the effectiveness of future 

regulations to include metrics (predictions of changes associated with promulgation of a 

rule) and indicators (actual levels of the same or closely related parameters observed 

during implementation) established a priori. This is certainly a worthwhile exercise and 

it is hoped that the Division will draw upon the latest NARSTO assessment on multipollutants and accountability.

Suggestions for Improvement 

• There is a critical need to develop better techniques to provide high resolution data for 

exposure models and for epidemiological studies. AMAD is to be commended in its 
32 

efforts to improve dispersion models by providing high resolution data at local scales. It 

is recommended that AMAD convene a workshop with experts from the health and 

exposure sciences and the air quality community to critically examine the approach 

selected by the Division and to consider alternative methodologies. 

• Whatever approach is deemed appropriate, it is recommended that AMAD develop a 

guidance document on the use of these new approaches to link air quality and exposure 

models with an emphasis on the limitations of these approaches. 

• AMAD should explore the use of other dispersion models or at a minimum describe the 

rationale for using AERMOD as the model of choice. Familiarity with the model should 

not be a sufficient criterion.

• To explore the interface of air quality models and exposure estimates, the Division could 

conduct a study where different metrics of exposure (derived from nearest measurements, 

interpolated concentration surfaces, and model simulations) are used to examine how 

epidemiological results may differ depending on the source of exposure data.

3C. Advice/Assistance

• The Division’s research under this theme directly addresses the agency’s mission of 

protecting public health by examining the link between air quality and health. As 

mentioned earlier, the Division may need to expand its research scope to fully resolve the 

accountability issue. 

• AMAD’s research in improving the hybrid approach is leading to improvements in 

models like AERMOD that are used by other groups at EPA and beyond. 

• The coupled urban/regional modeling system can be a good tool to assess the impact of 

various control measures by projecting future air quality and exposures at urban and local 

scales. 

• Division scientists are active in the national and international scientific community and 

openly share their expertise and scientific products with other scientists and other 

government agencies. They serve on various national and international workgroups, 

professional societies, and editorial boards of scientific publications. 

• Division scientists actively participate in various collaborations with other governmental 

agencies (state, local, tribal, and international) and various cross-disciplinary workgroups 

within the agency. 

3D. Leadership

• Division scientists are actively seeking leadership positions in various capacities at the 

national and international level. Senior staff of the division lead collaborative research 

efforts at different levels. Most of the scientists are active in scientific 

conferences/workshops in various capacities (conference chairs; session chairs; invited 

speakers).

• Many of the division scientists have received awards/honors for their scientific 

contributions.
33 

• Division scientists play many leadership roles in leading research planning and 

coordination efforts across other divisions and labs within the agency. They participate as 

agency representatives in addressing major environmental issues at national and 

international levels. 

• Division staff have received major agency awards for their contributions. 

3E. Summary and Recommendations 

Findings: 

• Overall, AMAD has identified a key gap in exposure modeling, i.e., the lack of high 

resolution spatial and temporal ambient data to use in exposure models and 

epidemiological studies. The approach selected by AMAD to address this issue relies 

heavily on model simulated data, however it remains to be seen how robust this approach 

may be in real applications. 

• AMAD is advancing the science to better characterize near-road variability in pollutant 

concentrations. The basic research results obtained through field and laboratory 

measurements could be used to advance the algorithms in various dispersion models. 

• AMAD’s goal to address the issue of accountability is commendable, but it is very 

challenging to determine if a change in health effects is a result of changes in air quality 

or some other factors. A simple use of exposure models to determine the change in 

exposures may not be sufficient to answer the accountability question. 

Recommendations: 

• AMAD should explore convening a workshop with experts from health and exposure 

sciences and air quality community to critically examine the hybrid approach selected by 

the Division and to consider alternative methodologies. Different approaches may be 

appropriate for different needs.

• Whatever approach is deemed appropriate, it is recommended that AMAD develop a 

guidance document on the use of these new approaches to link air quality and exposure 

models with emphasis on limitations of these approaches. 
34 

Theme 4. Climate Change and Air Quality Interactions 

4A. Introduction 

Beginning in 2002, EPA initiated a significant effort to assess the impact of global change on 

future air quality. A number of extramural projects were funded via the EPA STAR grant 

program, while AMAD undertook a parallel effort within the agency. Results from a number of 

these studies, including the AMAD work, were integrated in an interim assessment report in 

2007. Also in 2007, the focus on linkages between air quality and climate underwent a 

significant change when the Supreme Court ruled that EPA consider CO2 as an air pollutant 

under the Clean Air Act. Subsequent events provided an injection of funds into AMAD, and the 

AMAD research direction has been modified to include investigation of feedbacks between air 

pollutants and regional climate. In this review, elements from the initial assessment and initial 

work addressing greenhouse gas (GHG) emissions and feedback on regional climate are 

addressed. 

4B. Research 

To approach an assessment of the impact of global change on air quality, the agency held a 

workshop on the topic and used this as a starting point for the extramural grant program and for 

AMAD work. The general approach was to focus on comparisons between current conditions 

and projected conditions in the year 2050 and to apply a variety of modeling methods to conduct 

the assessment. Many of the individual groups employed output from global circulation and 

chemistry models to set meteorological and chemical boundary conditions for continental and 

regional scale simulations for current and future conditions. In most cases, the downscaling 

involved using MM5 to simulate current and future weather conditions, and a separate chemical 

transport model (such as CMAQ) to simulate current and future air quality conditions. Within 

AMAD, this approach was employed where the GISS II’ global model output was used with 

MM5 and global chemical conditions were obtained from GEOS-Chem to provide BC for 

CMAQ simulations. Both were global runs for the IPCC A1B scenario. 

In the second area of work, AMAD has initiated the linkage of WRF-CMAQ as a coupled, online 

system. This step was taken primarily to provide a tool to address questions related to feedback 

of aerosol pollutant levels on regional climate. 

Accomplishments and Strengths 

• The pilot project on climate downscaling has been completed. AMAD has focused on 

evaluation of the current regional climate and chemical transport simulations and 

comparing these with air quality under future weather conditions while keeping US 

emissions constant in all cases. This approach provides information about the effects of 

climate change upon air quality and avoids the uncertainties in the projected land use 

change and global or US pollutant emission changes. Thus, the strength of this approach 
35 

is that it can provide relatively unambiguous information about climate change effects on 

air quality. 

• A new WRF-CMAQ prototype has been developed and evaluated. Given the popularity 

of WRF-CHEM, the development of WRF-CMAQ is a welcome addition to the field and 

provides a second avenue for investigation of climate/aerosol feedbacks. There is a large 

CMAQ user community that has invested considerable resources in learning to use and 

helping to improve CMAQ. There is an extensive infrastructure associated with CMAQ 

in terms of emissions processing which is largely missing for WRF-CHEM. In addition, 

CMAQ has capabilities that are not readily available in WRF-CHEM in terms of 

chemical mechanism options, diagnostic modeling codes (process analysis, etc), and 

other features. Thus, it is understandable and desirable that WRF-CMAQ as a coupled 

system be developed and made available to the community. 

• The climate change/air quality assessment team appears to have a good mix of expertise. 

The level of effort for the climate assessment with AMAD has been at a relatively low 

level, but this is magnified since it is part of the larger EPA extramural climate 

assessment program. 

• The division has established good collaborations with global modeling groups in order to 

access the appropriate global modeling expertise and results. This will be a key 

advantage in terms of future work. 

Limitations 

• The AMAD group has served as one of the ensemble of EPA climate/air quality 

assessment groups, but AMAD is in a position to provide more leadership to promote 

greater collaboration and sharing of datasets, tools, and results among the extramural 

research groups. 

• The development of WRF-CMAQ has not involved the community to any large extent, 

and the panel believes there would be benefits from greater community participation in 

this effort. 

• So far, attention has been paid to the impact of temperature increase. The AMAD may 

also want to look into the impact of drought on the biosphere and its subsequent 

emissions and air quality. An increase in drought intensity and/or occurrence can also 

increase the incidents of forest fires and shorten the lifespan of trees. The resulting 

impacts on air quality are issues worthy of further investigation. 

• If changes in future anthropogenic emissions are to be included in the climate-change 

study, it will be necessary to also consider the emission impact of changes in land 

use/land cover due to potential changes in agricultural practice and likely expansion of 

arable and marginal lands for renewable fuel production, and substantial shifts in 

emissions and emission sources due to the likely increased use of other clean fuels like 

low-carbon electricity, nuclear energy, and other factors. 

Future Directions 

• The group is well poised to continue to expand the work on downscaling of global 

change and investigation of the effects of global change on air quality. However, it will 

be important to strategically choose future simulations to create a small ensemble that 
36 

covers the range of uncertainties in future projections. An important aspect will be the 

need to review the directions being pursued within the NCER grants program to fill gaps 

and to optimize the mix of modeling approaches. In this regard, the AMAD group could 

play a key coordination role among the NCER research projects. 

• The investigation of alternative downscaling techniques coupled with the use of different 

advanced general circulation models, and completion of a series of downscaled climate 

simulations will provide a foundation for a wide range of global change impact 

investigations that go far beyond air quality. The AMAD group is in a position to partner 

with other EPA divisions and with outside groups to provide these simulations and 

expertise for applications related to water quality, human and ecosystem health, and other 

climate issues. 

• Investigation of greenhouse-gas mitigation strategies is another arena of climate change 

where the AMAD group can play a large role. For example, what is the impact of 

biofuels on air quality and climate change or what is the impact of an all electric mobile 

fleet upon air quality and climate change? Also, as the role of methane is better 

recognized in transcontinental ozone formation, its emissions, both natural and 

anthropogenic, are in need of an in-depth study. These issues could provide a separate, 

but related area of investigation, which will require strategic decisions regarding 

resources. 

• It would be very worthwhile to conduct parallel WRF-CMAQ and WRF-CHEM 

simulations for a range of conditions to help evaluate both systems and to provide both 

communities with some degree of confidence in these coupled systems. In this regard, 

special attention is needed to evaluate the coupled system to determine whether the 

coupling correctly treats aerosol feedbacks to the meteorology. Evaluation methods that 

go beyond the normal paired meteorological and air pollutant concentrations will be 

required, and these evaluations may require special field programs and datasets. 

4C. Advice/Assistance

The pilot project, establishment of partnerships with the global modeling community, and 

development of the WRF-CMAQ prototype are each very positive steps taken by AMAD in 

response to the agency need to address the interactions of climate change and air quality. 

• The pilot project, together with the NCER grant results, can be formulated in a way to 

provide guidelines to regions and states about how to account for climate change effects 

for incorporation into control strategy planning. The interim assessment document is the 

first step in this process. An important aspect of this approach is the requirement that the 

regional downscaling of climate conditions be fully evaluated and essentially 

demonstrated as correct. AMAD found that for the global model employed, the regional 

climate simulations were biased to warmer temperatures and lower precipitation 

compared to current conditions. Further work using other global models and the WRF 

met model is needed to correct this deficiency. 

• The AMAD results fit well within the overall climate and air quality research program 

and AMAD has helped advance our understanding of the impact of climate change on 

future air quality. Within the context of the larger collection of EPA funded projects, 

some groups followed a similar path of constant emissions, while others addressed the 
37 

effects of land use and emission changes. The net result is that information is now 

available that provides insight into the relative importance of climate change versus land 

use and emission changes. In addition, information is available that helps document the 

uncertainties associated with using different models, different model configurations, and 

different IPCC global scenarios. 

• The AMAD research specifically targeted the climate effects portion of the assessment 

and results from this will be valuable for establishing guidelines and tools for regional 

and state planning with regard to climate effects. 

• The AMAD provided data used in the current interim EPA assessment document; this is 

the primary product provided to states and regions from the AMAD climate assessment 

work. 

• Development of WRF-CMAQ is a very positive contribution both in terms of linking the 

models in a way that addresses time step and mass consistency issues and in terms of 

providing a tool to investigate aerosol /climate interactions at the regional scale 

• It does not appear that any more specific guidance or products have been developed for 

regional or state use. At the same time, it seems clear that agencies and states are 

beginning to ask how climate effects should be addressed. However, the timeline 

associated with these requests is usually aimed at 2020 or 2030 as opposed to 2050. A 

key question is whether the AMAD and the larger agency effort can be used to provide 

guidance for these shorter timelines. In addition, the regions and states are interested in 

results that address climate effects as well as changing atmospheric composition 

(background levels) over these same timelines. In this regard, how is AMAD connected 

to the EPA long range transport program? Collaboration with this group would be quite 

useful for addressing boundary conditions questions. Further work will be required to 

address these needs. 

4D. Leadership 

• Within the climate change/air quality effort, AMAD scientists have made presentations at 

a number of national and international meetings, results have been published in the peer 

reviewed literature, and the scientists have been active as journal reviewers. 

• AMAD scientists have played a significant role in all of the workshops and meetings 

associated with the agency climate change/air quality program. In addition, most of the 

staff are involved in a wide range of other agency activities and appear to participate in a 

number of different workgroups. 

• As indicated above, the AMAD scientists have been involved in the agency climate 

change/air quality program which is the only national program addressing this topic. 

Staff serve on the CMAS external advisory board and have received agency awards for 

their work. 

• Beyond these contributions, it is difficult to judge whether there is much involvement in 

other outside activities such as membership on societal committees. There is no 

evidence, positive or negative, for their role in providing guidance to state or local 

agencies or tribes. 
38 

4E. Summary and Recommendations 

Findings: 

• Overall, the AMAD climate change/air quality research program fits well within the 

larger EPA program and AMAD scientists are making valuable contributions to our 

understanding of the effects of global change on air quality. These contributions are in 

line with the level of resources previously available and similar in scope to those from the 

extramural climate assessment grants. 

• The research directly addresses Agency priorities and has the potential to provide 

valuable guidance to states and regions for air quality planning needs. 

Recommendations: 

• Guidance to the states is not yet available and feedback should be solicited, in 

collaboration with OAQPS, from the regions and states as to the type of information 

needed. There is currently a void in terms of how states and regions account for climate 

and global changes effects in air quality planning projects. 

• Attention should be given in further downscaling simulations for documenting the range 

of results for potential land use and emission changes. 

Finding: 

• The development of WRF-CMAQ as a coupled system is a welcome addition for the 

CMAQ modeling community 

Recommendations: 

• Further work is warranted to complete the initial development of WRF-CMAQ and make 

it available to the community. 

• Evaluation of the coupled system will require new thinking about how to evaluate the 

coupled aspect of the model and to demonstrate its utility. This should include some 

careful comparisons with WRF-CHEM simulations for a range of conditions. 

Finding: 

• The division has a good publication rate and in the area of global change, papers are 

beginning to appear in the literature. 

Recommendation: 

• Dissemination of these research results should continue to emphasize peer reviewed 

journals. Global change work would be very appropriate for high impact journals, 

including the Journal of Geophysical Research, Environmental Science and Technology, 

and Atmospheric Chemistry and Physics, and this is encouraged. 

 

Finding: 

• The AMAD scientists working in this area are all involved in a number of coordinated 

research projects outside of this effort and seem to contribute to agency workgroups 

across a range of topics. The scientists are active in terms of peer reviewed publications 
39 

and presentations at a variety of technical meetings. There is less evidence for their role 

at the national or international level in terms of non-Agency organizations and programs. 

 

Recommendation: 

• As the Division extends its work to continental and hemispheric scales, new expertise 

should be added to the Division. Adding expertise and resources may be done in a 

variety of ways. For example, the Division may form new partnerships (e.g., with NASA 

on global change), or it might add new staff with targeted expertise, or it might provide 

significant professional development opportunities for existing staff. 
40 

Theme 5. Linking Air Quality and Ecosystem Health 

5A. Introduction

The research in this theme focuses on ecosystem exposure through wet and dry atmospheric 

deposition inputs as predicted by CMAQ and its meteorological driver model. The principal 

focus over the past several years has been on nitrogen and mercury. The work on nitrogen 

deposition has had a principal emphasis on deposition to estuarine ecosystems---first the 

Chesapeake Bay, and later Tampa Bay. The principal application for mercury deposition was in 

support of the Clean Air Mercury Rule. The inclusion of sea salt effects on dry deposition of 

nitrate, completed as part of the BRACE Tampa Bay study, was a key model development. 

Another model development that will greatly assist ecosystem effects work is the inclusion of bidirectional exchange of mercury and ammonia in the latest version of CMAQ. New directions 

for ecosystem work include greater emphasis on the issue of ecosystem acidification and 

development of the ability to better link CMAQ to watershed and ecosystem models through 

development of the Watershed Deposition Tool (WDT). This tool should encourage greater 

collaboration between AMAD scientists, CMAQ, and applied ecosystem modeling work such as 

critical loads studies. 

5B. Research 

Accomplishments and Strengths 

• No current measurement networks account for all of the atmospheric deposition of 

nitrogen, which makes the CMAQ model an important ecosystem tool because it 

accounts for many unmeasured species. 

• Assistance provided to the Chesapeake Bay and Tampa Bay Programs in terms of 

providing those groups with modeled nitrogen deposition estimates is to be commended 

and represents a significant accomplishment. AMAD scientists have a long and 

successful history of working with these programs, especially the Chesapeake Program. 

AMAD accomplishments and contributions were noted by two support letters from 

leaders of each of these estuary programs. 

• The development of the Watershed Deposition Tool represents an important step in 

reaching out to watershed modelers in support of critical loads, TMDL, and acidification 

studies. The tool is an attempt to overcome the issue of applying gridded CMAQ output 

to the irregular grids used by various watershed and ecosystem models. It will be 

interesting to see how widely this tool is used in the future, and whether it helps to form a 

bridge between AMAD and terrestrial and aquatic scientists. The WDT is in an advanced 

beta test at the moment, and the program can be obtained by contacting a member of the 

AMAD staff. Follow through to make this an open source tool available through the 

AMAD web site or through CMAS is planned and is encouraged by the peer panel. 

• The continued development of the CMAQ mercury model is noted. In addition to the 

bidirectional exchange of mercury, some updated chemical reactions have been added to 
41 

the model. The work on model inter-comparisons at the North American and global 

scales is important given our rapidly evolving knowledge of controls on the mercury 

cycle. 

• Involvement by AMAD personnel in the Ad-Hoc Critical Loads Subcommittee (CLAD) 

of the National Atmospheric Deposition Program is commendable and provides a good 

link to investigators doing critical loads studies. CLAD provides a good forum for 

AMAD to feature CMAQ capability to support critical loads studies as well as an 

opportunity to identify potential collaborators and new users of CMAQ. 

Limitations 

• Some limitations in applying CMAQ to critical loads studies were noted, including the 

current inability to simulate base cation deposition in CMAQ. This limitation was 

discussed with AMAD staff at the review, and limitations with natural dust emissions in 

the current emissions inventory were noted. Plans call for updates that may allow base 

cation deposition to be modeled in the future. There are also some limitations in applying 

CMAQ at many of the long-term watershed study sites such as those in the EPA 

LTM/TIME program where critical loads estimates are needed. Many of these sites have 

watersheds that drain only a few square kilometers, whereas CMAQ national domain 

simulations use either a 36 km or 12 km grid-cell size. This limitation was discussed with 

AMAD staff at the review, and an ability to simulate at the 4 km grid scale was noted as 

well as early attempts to utilize orographic precipitation databases such as PRISM. An 

ability to simulate orographic precipitation enhancement at as fine a spatial scale as 

possible, would vastly improve the ability to apply CMAQ to critical loads studies at 

many long-term sites where data are available. 

• The panel noted the curious lack of any modeling efforts related to ozone effects on 

terrestrial ecosystems. This seems to be a result of a decision by the EPA Ecological 

Research Program to focus on aquatic rather than terrestrial resources as stated on page 3-

17 of the Peer Review Support Document. This criticism applies equally to the 

acidification issue as well because recovery of aquatic biota, while strictly an aquatic 

problem, depends on recovery within terrestrial ecosystem compartments such as the soil. 

• The inability of the CMAQ model to account for the deposition of organic nitrogen is 

recognized, and was discussed with pertinent staff at the review. Current scientific 

knowledge does not seem to allow accurate modeling of organic nitrogen deposition, but 

this area should be monitored for possible inclusion in future versions of CMAQ. 

Suggestions for Improvement 

• Incorporate base cation deposition into CMAQ once emissions inventory updates and 

modeling of wind-blown dust allow reliable estimates. 

• Continue improving ability to link CMAQ with watershed studies, TMDL studies, and 

critical loads studies by further reducing spatial scale for modeling and further exploring 

ability to simulate enhancement of wet deposition through orographic effects. 

• Include consideration of ozone deposition results in tandem with other deposition studies. 

• Continue adding new or updated reactions of mercury chemistry, including the halogen 

reactions, as our knowledge in that area is improving. 
42 

5C. Advice/Assistance 

• This group has done an admirable job advising EPA on likely outcomes of CAIR and 

CAMR. 

• AMAD has been successful in helping to stimulate the development of a nascent effort 

within the National Atmospheric Deposition Program to measure ammonia using passive 

sample collection techniques. This information will not only help improve ammonia 

deposition modeling within CMAQ, but will help to close the gap in terms of unmeasured 

nitrogen deposition species. 

• One can argue that the Agency failed to adequately foresee that the Acid Rain Program 

within the Clean Air Act Amendments of 1990 would not stimulate widespread 

ecosystem recovery in many impacted areas. We are not certain of the role AMAD may 

have played in this process, but it is clear that ecosystem compartments such as soils were 

not adequately considered (e.g., calcium depletion), and the time lags involved in 

migration of biota to extirpated locations may not have been fully considered within 

EPA. This possible oversight appears to now be the topic of some concern as evidenced 

by what appears to be a commitment to use CMAQ to address critical ecosystem loads, 

and to make the model easily available to others with similar interests. 

• AMAD modelers appear to have used the model to describe to the Agency the unintended 

consequences of clean air legislation such as the effects of reducing sulfur dioxide 

emissions on increasing ammonium deposition based on the representation of 

atmospheric chemistry in the model. 

5D. Leadership 

• The staff generally publishes at a good rate in the peer-reviewed literature, is actively 

involved in national and international collaborative research efforts, and is active in 

working committees within professional societies and with other groups. Overall, the 

staff appears to be leaders among their scientific peers and within EPA. 

• Several of the AMAD staff who work on ecosystems effects studies have received 

various awards from EPA such as the Bronze Medal. The awarding of these medals 

points to the leadership role of these modelers within the Agency. 

• Participation in international and continental mercury model comparisons efforts is 

noteworthy, and will help in further refining the CMAQ mercury model. 

• Involvement and activities within the Chesapeake and Tampa Bay Programs has 

established AMAD’s leadership role in use of CMAQ to model the role of atmospheric N 

deposition in estuarine nitrogen budgets. It appears that the model is being used by 

estuarine programs in Connecticut and other locations. 
43 

5E. Summary and Recommendations 

Findings: 

• Although the ecosystem work represents a small part of the overall effort within AMAD, 

this work is important in developing links to the ecosystem/watershed research 

community, supporting EPA policy efforts such as CAIR, and further expanding the 

CMAQ user community. 

• Development of the Watershed Deposition Tool represents a positive step by providing 

an opportunity for ecosystem and watershed modelers to more easily use CMAQ output 

in their models. 

• Future climate change work within EPA may present opportunities for the ecosystem 

effects modeling group within AMAD. 

• The CMAQ mercury deposition model successfully informed EPA about the expected 

effects of CAMR on mercury deposition. 

• Mercury modeling with CMAQ in support of CAMR highlights the likely limited benefit 

and limited geographic area where improvements in mercury bioaccumulation would be 

expected. These results highlight the global nature of the mercury problem. 

Recommendations: 

• AMAD should continue to support ecosystem effects work utilizing CMAQ. 

• Continued development of the Watershed Deposition Tool is encouraged. Future 

developments should consider a process to further reduce the grid scale size of output so 

that the model can more easily be applied to small watersheds, and incorporation of 

orographic precipitation effects which greatly influence deposition in complex terrain. 

Continued development of the Tool should further enlarge the CMAQ modeling 

community and highlight the usefulness of CMAQ as an ecosystem effects/watershed 

modeling tool. 

• Depending on the direction of future climate change research within EPA, opportunities 

may arise to link AMAD work with ecosystem change studies. This type of work would 

provide another avenue (in addition to the climate change – air pollutant effects work) 

through which CMAQ could prove to be a useful scientific tool. This direction is 

encouraged if available future funds allow. 

• Continued development of the CMAQ mercury model is encouraged. Since mercury 

science is at a fairly early stage of development, new reactions and rates are likely to 

appear often, and this new knowledge and information should be incorporated into the 

mercury model on a regular basis. The recent rejection of CAMR by the court further 

highlights the need to support the CMAQ mercury model by continued updating in 

anticipation of likely future mercury policy explorations within EPA. 

• An increasing body of scientific information suggests strong links between mercury and 

sulfur deposition in predicting levels of methyl mercury in fish. Future explorations of 

the mutual roles of mercury and sulfur deposition in mercury bioaccumulation within 

ecosystems is encouraged. Development of combined sulfur and mercury deposition 

modeling scenarios using CMAQ would allow researchers a means to test hypotheses 

about how strongly sulfur deposition affects mercury bioaccumulation. 
44 

• Continued mercury model intercomparisons with CMAQ and other leading models are 

encouraged with an aim of further refining the temporal and spatial scales from emissions 

to deposition. Such comparisons will help to better bound the geographic range of 

sources of mercury deposition to ecosystems within the US. 
45 

Acronyms 

ACM2 Asymmetric Convective Model version 2 

AMS American Meteorological Society 

AMAD Atmospheric Modeling and Analysis Division 

AMET Atmospheric Model Evaluation Tool 

AQ Air Quality 

AQAD Air Quality Assessment Division 

AQFM Air Quality Forecast Model 

AQME Air Quality Model Evaluation 

BEIS Biogenic Emissions Inventory System 

BENMAP Environmental Benefits Mapping and Analysis Program 

BRACE Bay Regional Air Chemistry Experiment 

CAIR Clean Air Interstate Rule 

CAMR Clean Air Mercury Rule 

CB Carbon Bond (chemical mechanism) 

CCTM CMAQ Chemical Transport Model 

CIRAQ Climate Impact on Regional Air Quality

CLAD Critical Loads Ad-Hoc Subcommittee of the National Atmospheric Deposition 

Program 

CMAQ Community Multi-scale Air Quality (model) 

CMAQ-CA CMAQ-Carbon Apportionment 

CMAQ-ST CMAQ-Sulfur Tracking 

CMAS Community Modeling and Analysis System

DDM Decoupled Direct Method 

DMS Dimethyl sulfide 

EPA Environmental Protection Agency 

FDDA Four dimensional data assimilation 

FHWA Federal Highway Administration 

GEOS Goddard Earth Observing System 

GHG Greenhouse Gas 

GISS Goddard Institute for Space Studies

ICARTT International Consortium for Atmospheric Research on Transport and 

Transformation

IPCC Intergovernmental Panel on Climate Change 

ISORROPIA not an acronym – meaning is “equilibrium” in Greek 

MEGAN Model of Emissions of Gases and Aerosols from Nature

MM5 Mesoscale Meteorological Model (version 5) 

NAAQS National Ambient Air Quality Standard 

NAQFS Nationwide Air Quality Forecast System

NASA National Aeronautics and Space Administration

NCLD National Land Cover Dataset 

NEAQS Northeast Air Quality Study 

NEOPS Northeast Oxidant and Particle Study 

NERL National Exposure Research Laboratory 
46 

NOAA National Oceanic and Atmospheric Administration 

NMM NCEP Meteorological Model 

OAQPS Office of Air Quality Planning and Standards 

ORD Office of Research and Development 

PinG Plume in Grid 

PM Particulate Matter 

PRISM Parameter-elevation Regressions on Independent Slopes Model 

PBL Planetary Boundary Layer 

RADM Regional Acid Deposition Model 

RPO Regional Planning Organization 

SAPRC State-wide Air Pollution Research Center (chemical mechanism) 

SEARCH Southeastern Aerosol Research and Characterization Study 

SMOKE Sparse Matrix Operational Kernel Emissions 

SOA Secondary Organic Aerosol 

TexAQS Texas Air Quality Study 

TMDL Total Maximum Daily Load 

VOC Volatile Organic Compounds 

WDT Watershed Deposition Tool 

WRF Weather Research and Forecasting 

WRF-CHEM Weather Research and Forecast model coupled with Chemistry 

WRF-CMAQ Weather Research and Forecast model with coupled online CMAQ