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NOAA Technical Memorandum OAR ARL-245 

FISCAL YEAR 2001SUMMARYREPORT OF THE NOAAATMOSPHERIC SCIENCES 

MODELING DIVISION TO THE U.S.ENVIRONMENTALPROTECTION AGENCY 

Evelyn M. Poole-Kober 

Herbert J. Viebrock 

(Editors) 

Atmospheric Sciences Modeling Division 

Research Triangle Park, North Carolina 

Air Resources Laboratory 

Silver Spring, Maryland 

June 2002 

UNITED STATES NATIONAL OCEANIC AND Oceanic and Atmospheric DEPARTMENT OF COMMERCE ATMOSPHERIC ADMINISTRATION Research Laboratories 

Donald L.Evans VADMConradC. Lautenbacher,Jr. David L. Evans 

Secretary Under Secretary for Oceans Director 

and Atmosphere/Administrator 
NOTICE 

Mention of a commercial company or product does not constitute an endorsement by NOAA. 

Use for publicity or advertising purposes of information from this publication concerning 

proprietary products or the tests of such products is not authorized. 

For sale by the National Technical Information Service, 5285 Port Royal Road, Springfield, VA 22161. 

ii 
PREFACE


This document summarizes the Fiscal Year 2001 research and operational activities of the 

Atmospheric Sciences Modeling Division (ASMD), Air Resources Laboratory, working under 

Interagency Agreements EPA DW13938483 and DW13948634 between the U.S. Environmental 

Protection Agency (EPA) and the National Oceanic and Atmospheric Administration (NOAA). 

The summary includes descriptions of research and operational efforts in air pollution 

meteorology, atmospheric modeling, air pollution control activities, and abatement and 

compliance programs. 

Established in 1955, the Division serves as the vehicle for implementing the agreements 

with EPA, which funds the research efforts in air pollution meteorology and atmospheric 

modeling. ASMD conducts research activities in-house and through contract and cooperative 

agreements for the EPA’s National Exposure Research Laboratory and other EPA groups. With 

a staff consisting of NOAA and EPA, ASMD also provides technical information, observational 

and forecasting support, and consulting on all meteorological aspects of the air pollution control 

program to many EPA offices, including the Office of Air Quality Planning and Standards. The 

primary groups within ASMD are the Atmospheric Model Development Branch, Modeling 

Systems Analysis Branch, Applied Modeling Research Branch, and Air Policy Support Branch. 

The staff is listed in Appendix G. Acronyms, publications, and other professional activities are 

listed in the remaining appendices. 

Any inquiry on the research or support activities outlined in this report should be sent to 

the Director, Atmospheric Sciences Modeling Division (MD-E-243-02), U.S. Environmental 

Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711. 

iii 
iv

CONTENTS


Page 

PREFACE .................................................................. iii


FIGURES .................................................................. viii


ABSTRACT .................................................................. 1 


1. INTRODUCTION ........................................................... 1 


2. PROGRAM REVIEW ....................................................... 2 


2.1 Office of the Director ................................................. 2 


2.1.1 NATO Committee on the Challenges of Modern Society .............. 2 


2.1.2 United States/Russia Joint Environmental Committee ................ 2 


2.1.3 Interdepartmental Meteorological Committee ....................... 3 


2.1.4 Board on Atmospheric Sciences and Climate ....................... 4 


2.1.5 Standing Air Simulation Work Group ............................. 4 


2.1.6 European Monitoring and Evaluation Program ...................... 4 


2.1.7 	Chesapeake Bay Program Air Subcommittee and Chesapeake Bay Program


Modeling Subcommittee ...................................... 5 


2.1.8 Regional Acid Deposition Model Application Studies ................ 5 


2.1.9 Nitrogen Deposition to Coastal Estuaries .......................... 6 


2.1.10 	Community Multiscale Air Quality Model Evaluation Studies ......... 6 


2.1.11 	Ozone Forming Potential of Organic Emissions .................... 7 


2.1.12 	United States-Germany Environmental Agreement .................. 8 


2.1.13 	Atmospheric Sciences Modeling Division Library .................. 8 


2.1.13.1 	International Association of Aquatic and Marine Science 


 Librarians and Information Centers ..................... 9 


2.1.13.2 Southeast Affiliate of IAMSLIC Librarians ................ 9 


2.1.13.3 Atmospheric Science Librarians International .............. 9 


2.1.13.4 	Atmospheric Sciences Modeling Division Library Web 


Page ............................................. 10


2.2 Atmospheric Model Development Branch ................................ 10


2.2.1 Community Multiscale Air Quality Modeling System ............... 10


2.2.1.1 	Introduction ......................................... 10


2.2.1.2 	Research and Development Scope of the Community Multiscale 


 Air Quality Modeling System ......................... 11


2.2.1.3 	Photolysis Rates ..................................... 16


2.2.1.4 	Cloud Dynamics and Aqueous-Phase Chemistry Module .... 16


v 
2.2.1.5 	Sub-grid Scale Plume-in-Grid Modeling in the CMAQ 


Modeling System ..................................... 16


2.2.1.6 	Air Quality Modeling of Particulate Matter and Air Toxics at 


Neighborhood Scales ............................... 17


2.2.1.7 	Aggregation Research for Models-3/CMAQ ............... 18


2.2.1.8 	Collaborative Model Evaluation Studies for Particulate 


Matter ........................................... 19


2.2.1.9 	A Preliminary Evaluation of Models-3/CMAQ for Particulate 


Matter ........................................... 22


2.2.1.10 	Ozone Initial and Boundary Concentrations for 


 Models-3/CMAQ From Ozone Climatology ............. 23


2.2.2 Aerosol Research and Modeling ................................ 24


2.2.3 Atmospheric Toxics Pollutant Research .......................... 25


2.2.4 Meteorological Modeling Studies ............................... 27


2.2.4.1 	Meteorology Modeling for Models-3/CMAQ Applications .... 27


2.2.4.2 	Advanced Land-Surface and Planetary Boundary Layer 


Modeling in MM5. ................................. 28


2.2.4.3 Urban Canopy Parameterization in MM5 .................. 28


2.2.4.4 	Linking Meteorology and Chemistry Models ............... 29


2.2.5 Dry Deposition Studies ....................................... 30


2.2.5.1 	Dry Deposition Research .............................. 30


2.2.5.2 	Dry Deposition Modeling .............................. 32


2.2.6 Technical Support ........................................... 32


2.2.6.1 	North American Research Strategy for Tropospheric Ozone ... 32


2.2.6.2 	Western Regional Air Partnership Air Quality Technical 


Forums .......................................... 33


2.2.6.3 	Climatological and Regional Analyses of CASTNet Data 


from 1989-1999 ................................... 33


2.2.6.4 	Evaluation of the Use of NEXRAD Stage IV Data in the 


Multimedia Modeling of Pollutant Transport ............. 34


2.3 Modeling Systems Analysis Branch ..................................... 34


2.3.1 Technology Support for the Models-3/CMAQ Modeling System ....... 35


2.3.1.1 	Models-3 Version 4 ................................... 35


2.3.1.2 	Models-3/CMAQ Stand-Alone Version ................... 35


2.3.1.3 	Models-3/CMAQ Reverse Gridding Utility ................ 36


2.3.1.4 	Models-3/CMAQ Support and Training ................... 36


2.3.2 Support for Multimedia Modeling ............................... 37


2.3.2.1 	Development of the Multimedia Integrated Modeling System .. 37


2.3.2.2 	Rain Gauge Network .................................. 38


2.3.3 Research on Emissions Modeling ............................... 38


2.3.3.1 	Anthropogenic Emissions .............................. 38


2.3.3.2 	Biogenic Emissions ................................... 39


2.3.3.3 	Inverse Modeling of Ammonia Emissions ................. 40


vi 
2.4 Applied Modeling Research Branch ..................................... 41


2.4.1 Compartmental Multimedia Modeling ........................... 41


2.4.2 Application of the Concept of Saltation-Driven Resuspension of PM10 .. 42


2.4.3 	Comparison of Wind-Tunnel Measurements of the Flow and Turbulence


Fields in Arrays of Two- and Three-Dimensional Buildings .......... 44


2.4.4 Atmospheric Correction / Image Processing ....................... 49


2.4.5 Local Scale Modeling of Human Exposure Microenvironments ........ 49


2.5 Air Policy Support Branch ............................................ 52


2.5.1 Modeling Studies ............................................ 52


2.5.1.1 	CMAQ Proof of Concept: Continental United States 


Application ....................................... 52


2.5.1.2 	The National-Scale Air Toxics Assessment ................ 55


2.5.1.3 	Statistical Evaluation of Model Performance ............... 56


2.5.1.4 	The Krakow Urban Air Pollution Project .................. 57


2.5.1.5 Estimating Background Concentration for Diesel PM ........ 59


2.5.1.6 	A Simplified Approach for Estimating Secondary Production 


 of Hazardous Air Pollutants Using the OZIPR Model ...... 61


2.5.1.7 	Long Range Transport Screening Calculations Using 


CALPUFF ........................................ 63


2.5.1.8 	Uncertainty Characterization in Air Quality Modeling ........ 63


2.5.1.9 Trajectory Analyses for PM2.5 ........................... 64


2.5.1.10 Enhancements to AERMET ........................... 64


2.5.2 Modeling Guidance .......................................... 65


2.5.2.1 Support Center for Regulatory Air Models ................. 65


2.5.2.2 Models-3 Help Desk .................................. 65


2.5.2.3 	Ad Hoc Meteorological Modeling Workgroup .............. 66


3. REFERENCES ............................................................ 67


vii 
APPENDIX A: ACRONYMS, ABBREVIATIONS, AND DEFINITIONS ................ 76


APPENDIX F: HIGH SCHOOL, UNDERGRADUATE, AND GRADUATE STUDENTS, 


APPENDIX G: ATMOSPHERIC SCIENCES MODELING DIVISION STAFF 


APPENDIX B: PUBLICATIONS ................................................ 80


APPENDIX C: PRESENTATIONS .............................................. 87


APPENDIX D: WORKSHOPS AND MEETINGS .................................. 95


APPENDIX E: VISITING SCIENTISTS ......................................... 103


AND POSTDOCTORAL RESEARCHERS ................................. 106


AND AWARDS ....................................................... 108


viii 
Page 

FIGURES 

1.	 Illustrates maximum 1-hr average ozone concentration (ppmV) in each grid cell between 

7:00 AM and midnight EDT .............................................. 12


2.	 Illustrates 24-hr averages of sulfate concentrations (micrograms/m3) ............... 13


3.	 Illustrates 24-hr averages of PM2.5 .......................................... 14


4.	 Illustrates noontime EDT visibility (deciview, note insert) in each grid cell ......... 15


5.	 The three compartment FCCM phase I pilot model ............................ 42


6.	 An example of a dust storm during May 2–3, 2001 ............................. 46


7.	 Building arrays in the wind tunnel: a) two-dimensional buildings, flow left to right; b)


three-dimensional buildings, looking into the flow ............................. 47


8.	 Mean velocity vectors and turbulent kinetic energy (m2s

-2) in the centerplane of the wind


tunnel for the area surrounding the first three rows of buildings ................... 48


9.	 Water vapor highlighted scene, with river, lakes, and clouds in black .............. 49


10.	 Example effect of urban buildings on wind profiles. ............................ 51


11.	 Example vertical slice of full 3-D solution for wind ............................ 51


12.	 Example vertical slice of full 3-D solution for concentration ..................... 52


13.	 Comparison of winter average (December - February) Interagency Monitoring of


PROtected Visual Environments (IMPROVE) site concentrations and model


concentrations of sulfate ion, nitrate ion, and organic carbons by region for the 1996


national CMAQ application. .............................................. 53


14.	 Comparison of annual average IMPROVE site concentrations and model concentrations of


PM2.5 (paired in space and time) for the 1996 national CMAQ application.. ......... 54


15.	 Comparison of observed and estimated A) annual average and B) monthly average SO2


concentration values. Linear regression results (with zero intercept) and site locations are


annotated within the figures ............................................... 58


ix 
16. Relative emissions (percent) for January and June 1998 from the three source 

inventories ............................................................ 59


17.	 The percentage contribution to the total SO2 concentration estimated for Site 5 from each


of the three emission inventories. Black line is simulated total SO2. ............... 59


18.	 Schematic map of census tract centroids and rings of radius 50 km, 300 km. ........ 61


x 
FISCAL YEAR 2001 SUMMARY REPORT OF THE NOAA


ATMOSPHERIC SCIENCES MODELING DIVISION TO THE


 U.S. ENVIRONMENTAL PROTECTION AGENCY


ABSTRACT. During Fiscal Year 2001, the Atmospheric Sciences Modeling 

Division provided meteorological and air quality modeling assistance to the U.S. 

Environmental Protection Agency. This ranged from research studies and model 

applications to the provision of advice and guidance. Research efforts 

emphasized the development and evaluation of air quality simulation models. 

Among the research studies and results were the release of the FY-2001 version of 

the Community Multiscale Air Quality (CMAQ) modeling system, the integration 

of the Sparse Matrix Operator Kernel Emissions model into CMAQ, the inclusion 

of the aerosol algorithm into the Plume-in-Grid (PinG) model, the preliminary 

evaluation of the CMAQ particulate matter module, the continued evaluation and 

modification of CMAQ-Hg, development of the Multi-Layer Bio-Chemical dry 

deposition model, continuation of compartmental multimedia modeling, and 

development of a method for estimating the vertical flux of resuspended particles. 

1. INTRODUCTION 

In Fiscal Year 2001, the Atmospheric Sciences Modeling Division (ASMD) continued its 

commitment for providing goal-oriented, high-quality research and development, and operational 

support to the U.S. Environmental Protection Agency (EPA). Using an interdisciplinary 

approach emphasizing integration and close cooperation with EPA and public and private 

research communities, the Division's primary efforts focused on studying processes affecting 

dispersion of atmospheric pollutants, modeling pollutant dispersion on all temporal and spatial 

scales, and developing multi-media modeling frameworks in a high performance computing and 

communications environment. The research products developed by the Division are transferred 

to the public and private national and international user communities. Section 2.1 discusses 

Division participation in international activities, while Sections 2.2 through 2.4 outline the 

Division research activities in support of the short- and long-term needs of the EPA and 

environmental community. Section 2.5 discusses Division support to the operational programs 

and general air quality model user community. 
2. PROGRAM REVIEW 

2.1 Office of the Director 

The Office of the Director provides direction, supervision, program management, and 

administrative support in performing the Division's mission and in achieving its goals of 

advancing the state of the atmospheric sciences and enhancing the protection of the environment. 

The Director's Office also engages in several domestic and international research exchange 

activities. 

2.1.1 NATO Committee on the Challenges of Modern Society 

The North Atlantic Treaty Organization (NATO) Committee on the Challenges of 

Modern Society (CCMS) was established in 1969 with the mandate to examine how to improve, 

in every practical way, the exchange of views and experience among the Allied countries in the 

task of creating a better environment for their societies. The Committee considers specific 

problems of the human environment with the deliberate objective of stimulating corrective action 

by member governments. The Committee's work is carried out on a decentralized basis through 

pilot studies, discussions on environmental issues, and fellowships. 

The Division Director serves as the U.S. representative on the Scientific Committee for 

International Technical Meetings (ITMs) on Air Pollution Modeling and Its Application, 

sponsored by NATO/CCMS. A primary activity within the NATO/CCMS Pilot Study on Air 

Pollution Control Strategies and Impact Modeling is organizing a symposium every eighteen 

months that deals with various aspects of air pollution modeling. The meetings are rotated 

among different NATO and Eastern Bloc countries, with every third ITM held in North America 

and the two intervening ITMs held in European countries. 

The former Division Director served as the Conference Chairman of the Millennium 

(24th) NATO/CCMS International Technical Meeting held in Boulder, Colorado, during May 

15–19, 2000. The proceedings were published by Kluwer Academic/Plenum Publishers (Air 

Pollution Modeling and Its Application XIV, 2001). 

2.1.2 United States/Russia Joint Environmental Committee 

The former Division Director served as the United States Co-Chairman of the United 

States/ Russia Working Group 02.01-10 on Air Pollution Modeling, Instrumentation, and 

Measurement Methodology, and as Co-Leader of the United States/Russia Project 02.01-11 on 

Air Pollution Modeling and Standard Setting. The purpose of the 1972 Nixon-Podgorny 

Agreement forming the US/USSR Joint Committee on Cooperation in the Field of 

Environmental Protection was to promote, through mutual visits and reciprocal assignments of 

personnel, the sharing of scientific and regulatory research results related to the control of air 

2

pollution. Activities under this agreement have been extended to also comply with the 1993 

Gore-Chernomyrdin Agreement forming the United States/Russia Commission on Economic and 

Technological Cooperation. There are four Projects under Working Group 02.01-10: 

Project 02.01-11: Air Pollution Modeling and Standard Setting 

Project 02.01-12: Instrumentation and Measurement Methodology 

Project 02.01-13: Remote Sensing of Atmospheric Parameters 

Project 02.01-14: Statistical Analysis Methodology and Air Quality

 Trend Assessment 

Progress under this Working Group continued during FY-2001. An annual Working 

Group meeting was held during November 2000 at the Main Geophysical Observatory in St. 

Petersburg, Russia. The Russian scientists presented information regarding the development of a 

new version of the Russian regulatory dispersion model, which will be introduced next year as a 

national guideline. They informed the group about current work aimed at developing and 

generalizing a methodology for the computational and hybrid monitoring jointly using the results 

of measurements and computations. The Russian work in forecasting urban air pollution was 

outlined. An English version of the official report, Air Quality in Major Russian Cities for Ten 

Years, was presented. 

The U.S. Co-Chairman informed his Russian counterparts about the latest developments 

in the implementation of the Models-3/Community Multiscale Air Quality (CMAQ) model. A 

computer demonstration was presented, which visualized the results of several application runs 

with Models-3/CMAQ. These runs illustrated the relative efficacies of different emission control 

strategies in improving ozone, visibility, acid deposition, and PM2.5 in the eastern United States. 

In recognition of the impending retirement of the U.S. Working Group Co-Chairman, a 

letter from the Head of the Russian Federal Service for Hydro-meteorology and Environmental 

Monitoring was presented by the Director of the Voeikov Main Geophysical Observatory. The 

letter acknowledged the organization and promotion of cooperation between Russian and 

American scientists working in the area of air pollution modeling and monitoring during the last 

fifteen years. 

2.1.3 Interdepartmental Meteorological Committee 

The Division Director serves as the U.S. Environmental Protection Agency representative 

on the Interdepartmental Committee for Meteorological Services and Supporting Research 

(ICMSSR). The Committee, composed of representatives from 14 Federal government agencies, 

was formed in 1964 under Public Law 87-843 and OMB Circular A-62 to provide the Executive 

Branch and the Congress with a coordinated, multi-agency plan for government meteorological 

services and for those research and development programs that directly support and improve 

these services. The Committee prepared the annual Federal Plan for Meteorological Services 

and Supporting Research (U.S. Department of Commerce, 2001). 

3

The Division Director also serves on the ICMSSR Committee for Cooperative Research. 

Other Division members serve on the ICMSSR Working Group for Atmospheric Transport and 

Diffusion and on the ICMSSR Working Group for Climate Services. 

2.1.4 Board on Atmospheric Sciences and Climate 

The Division Director serves as the Agency liaison to the Board on Atmospheric Sciences 

and Climate (BASC) of the National Research Council, National Academy of Sciences. The 

BASC seeks to advance our understanding of the atmosphere and climate, and to improve our 

ability to apply this knowledge for our benefit. The Board (1) reviews in broad perspectives both 

basic and applied research dealing with the atmosphere and with the geophysical systems 

influencing weather and climate; (2) provides advice and guidance to appropriate government 

agencies on problems and programs within the Board’s interest and expertise; and (3) counsels 

the United States participation in international research and application programs relating to the 

atmosphere and climate such as the World Climate Program and its research activities. 

2.1.5 Standing Air Simulation Work Group 

The Division Director serves as the EPA Office of Research and Development 

representative to the Standing Air Simulation Work Group (SASWG), which serves as a forum 

for issues relating to air quality simulation modeling of criteria and other air pollutants from 

point, area, and mobile sources. Its scope encompasses policies, procedures, programs, model 

development, and model application. The work group fosters a consensus between the Agency 

and the State and local air pollution control programs through semi-annual meetings of members 

representing all levels of enforcement. 

2.1.6 European Monitoring and Evaluation Program 

A Division scientist serves as the United States representative to the European 

Monitoring and Evaluation Program (EMEP) that oversees the cooperative program for 

monitoring and evaluation of the long-range transmission of air pollutants in Europe. The 

primary goal of EMEP is to use regional air quality models to produce assessments evaluating 

the influence of one country's emissions on another country's air concentrations or deposition. 

The emphasis has shifted from acidic deposition to ozone and there is now interest in fine 

particulates and toxic chemicals. The United States and Canadian representatives report on 

North American activities related to long-range transport. The Division scientist also evaluates 

European studies of special relevance to the program, providing technical critiques of the EMEP 

work during formal and informal interactions, and develops and coordinates such programs with 

EMEP as the modeling studies of the Modeling Synthesizing Center-West at the Norwegian 

Meteorological Institute in Oslo, Norway. 

4

2.1.7	 Chesapeake Bay Program Air Subcommittee and Chesapeake Bay Program 

Modeling Subcommittee 

A Division scientist is a member of the Air Subcommittee, a working subcommittee of 

the Chesapeake Bay Program. The subcommittee has responsibility for advice and leadership on 

issues of atmospheric deposition to the watershed and the Bay, on overseeing application of the 

Extended Regional Acid Deposition Model (Extended RADM) to link atmospheric deposition 

with Bay watershed models, and in dealing with the potential role of atmospheric deposition 

reductions anticipated by the 1990 Clean Air Act Amendments (CAAA) on Bay restoration 

efforts. The Air Subcommittee also works with other Chesapeake Bay committees to define the 

top priority air quality scenarios to be simulated by the Extended RADM. The Division scientist 

is also an ex officio member of the Modeling Subcommittee of the Implementation Committee. 

This Subcommittee has responsibility for overseeing the application of water quality models and 

coordinating their linkage with the Extended RADM and interpretation of the findings. 

Work in FY-2001 with the Extended RADM focused on a re-evaluation of the potential 

magnitude of reductions in oxidized nitrogen deposition stemming from the CAAA and possible 

additional reductions from point sources beyond requirements of the CAAA. The CAAA 

reductions were associated with the new heavy duty diesel rule being promulgated by EPA for 

2020 and the additional point source reductions were those being examined by the EPA Office of 

Air Programs, Clean Air Markets Division. The Extended RADM was also used to estimate the 

relative contribution each of the six Bay states’ (Maryland, Pennsylvania, Virginia, New York, 

Delaware, and West Virginia) nitrogen oxide emissions make to the oxidized nitrogen deposition 

to the Chesapeake Bay major tributaries. 

2.1.8 	Regional Acid Deposition Model Application Studies 

During FY-2001, an operational version of the Extended RADM, which operates on the 

Cray® T3DTM1

 massively parallel computer, was finalized for use with adjusted ammonia 

emissions, using a primitive model inversion. The Extended RADM incorporates the full 

dynamics of secondary inorganic fine particle formation and is, therefore, able to simulate 

ammonia (reduced nitrogen) deposition in addition to oxidized nitrogen deposition, simulate the 

partitioning of total ammonia into gaseous ammonia and particulate ammonium, and the 

partitioning of total nitrate into gaseous nitric acid and particulate nitrate. The Extended RADM 

represents a step in the transition to Models-3/CMAQ for application simulations. 

A Division scientist is participating in an integrated assessment of Shenandoah National 

Park that is being coordinated by the U.S. National Park Service. The first purpose of the 

assessment is to estimate how air pollution stressors will be reduced due to ozone and acid rain 

controls on air emissions that stem from CAAA and how ecosystem damage from ozone and acid 

1

Cray is a registered trademark and Cray T3D is a trademark of Cray Research. 

5 
rain will be changed or mitigated and second, and how air quality related values connected to 

visibility degradation will be affected. The second purpose of the assessment is to provide an 

approximate sense of how much farther air emissions would need to be reduced to provide full 

protection of the ecosystems from ozone and acid rain damage. During FY-2001, the Extended 

RADM was run in aggregation mode for a set of four future emission scenarios to define the 

changes in seasonal ozone, annual acidic deposition, and annual inorganic fine particulates 

relative to an updated 1990 base case consistent with the future scenarios. Extensive use of the 

EPA National Environmental Supercomputing Center (NESC) Cray® T3DTM computer resource 

was made. Two of the scenarios represented estimates of the effects of, first, acid rain controls 

and, second, acid rain mobile source Tier II standards, and new heavy duty diesel vehicle 

regulations. Two of the scenarios represented potential cases with extreme emissions reductions 

beyond those called for in the 1990 Amendments to the Clean Air Act (CAAA). The changes in 

air pollution stressor metrics over Shenandoah were then passed to the ecosystem modelers. In 

addition, the range of influence mappings from the RADM and Extended RADM were used to 

define airsheds for the Shenandoah National Park associated with sulfur deposition, oxidizednitrogen deposition and sulfate air concentrations. The airsheds were further subdivided to 

characterize from where and how far away the emissions emanate that have the greatest 

importance to stressing the ecosystems of the Shenandoah National Park. Work in FY-2002 will 

focus on writing the Shenandoah assessment. 

2.1.9 Nitrogen Deposition to Coastal Estuaries 

Using the procedure developed for the Chesapeake Bay and outlined in Dennis (1997), 

airsheds for 20 coastal watersheds along the East and Gulf Coasts were developed. These 

oxidized nitrogen airsheds are expected to be available on the Division’s multi-media web site in 

FY-2002. This work is presented in the NOAA assessment of atmospheric deposition to coastal 

estuaries (Paerl et al., 2001). 

Ammonia deposition is a major new focus of assessment for deposition to the 

Chesapeake Bay watershed and Bay surface waters, and to the Neuse River Estuary and Pamlico 

Sound of North Carolina. In FY-2001, reduced nitrogen range of influence mapping with the 

Extended RADM was completed for 12 emission source subregions, bringing the total to 56 

subregions, to support the development of airshed estimates for ammonia. Reduced nitrogen 

airsheds for Chesapeake Bay, Neuse River Estuary, and Pamlico Sound, Apalachee Bay, and 

Long Island Sound watersheds were developed. These reduced nitrogen airsheds will be 

available on the Division’s multi-media web site in FY-2002. 

2.1.10 Community Multiscale Air Quality Model Evaluation Studies 

Ozone continues to be a pollutant of concern because national standards are still being 

violated. CMAQ represents a new, multi-pollutant tool to support development of ozone-related 

controls on emissions. Division scientists conducted an initial, operational evaluation of CMAQ 

6

for ozone using databases from two field studies. The scientists evaluated model performance in 

terms of bias and gross error for averaging periods associated with the form of the ozone standard 

and of interest to regulators, particularly the ability to reproduce the 1-hour peak and 8-hour 

average daily maximum ozone levels. To better challenge CMAQ, a two-week period from July 

5–18, 1995, that included some of the highest and lowest daily maxima observed in that year at 

approximately 560 ozone monitoring sites in the eastern United States was used. Initial 

evaluation of the model indicates that CMAQ is functioning satisfactorily for ozone. CMAQ 

performed quite well for high-ozone days, but less satisfactorily for low-ozone days and for days 

with larger mean-to-maximum ranges. However, CMAQ is operating in a range consistent with 

other large ozone models in the United States, while predicting a full suite of multiple pollutants. 

The model evaluation results support CMAQ’s wide-spread use (Arnold and Dennis, 2001). 

Fine particulate matter was found to have a significant impact on human health. Sulfate 

is a dominant constituent of fine particulate mass across the eastern United States and Canada. In 

1995, sulfur dioxide (SO2) emissions were reduced in accordance with the CAAA. Trend 

analysis of the Clean Air Status and Trends Network (CASTNet) ambient data showed that 

reduction of sulfate air concentrations was smaller than reduction of SO2 concentrations by a 

significant amount. Division scientists evaluated the ability of CMAQ to replicate the nonlinear 

response of sulfate. A sulfate tracking version of CMAQ was specially constructed for the 

analysis and was able to successfully replicate the main features of the sulfate changes due to 

reductions in SO2 emissions. Preliminary results of the study were presented at the NARSTO 

International Symposium on Particulate Matter in Querétaro, Mexico, in October 2000. 

Sensitivity analyses with the CMAQ sulfate tracking model show that both meteorology and 

chemical nonlinearity contribute to the less-than-proportional response of sulfate and that, for the 

June 1990 to 1995 period studied, meteorology has by far the larger influence on sulfate nonproportionality. However, the role of oxidant limitation is seen to be quite important to the 

production of sulfate fine particulate matter, particularly when meteorology is held constant in 

the model comparisons, and is much larger than its role in acidic deposition. 

2.1.11 Ozone Forming Potential of Organic Emissions 

Volatile organic emissions (VOC) have different reactivities or potentials for producing 

ozone in the atmosphere. They are classified into negligibly reactive (exempt from ozone 

regulations), reactive, and highly reactive, the latter two classes being subject to inventory and 

control regulations. Evidencing and documenting negligible reactivity is a highly sensitive and 

often controversial subject as it determines the outcome of petitions submitted to EPA by 

industry for exempting new VOC emission products or processes from ozone regulations. This, 

and also the large uncertainties of the reactivity-based VOC control strategies currently in place 

and the substantial costs of such strategies, led the U.S. Congress to earmark monies for and 

direct EPA to undertake a substantial research/development effort to advance the reactivity 

science and generate improved reactivity data. Through its National Exposure Research 

Laboratory (NERL), EPA responded by enhancing its in-house reactivity program, by founding a 

national Reactivity Research Work Group (RRWG) composed of Government, industry, and 

7

university research institutions, and by awarding several cooperative agreements and contracts 

for reactivity research to universities. Work in FY-2001 focused on in-house reactivity modeling 

studies and reviewing of industry petitions, on building a new generation smog chamber facility 

for improved reactivity testing of VOCs at the University of California, Riverside, on completing 

through RRWG a substantial effort to identify scientific and policy issues and develop responsive 

research plans in the reactivity area, on initiating cooperative and contract research programs, and 

on drafting an Ozone Criteria Document Section on VOC Reactivity. In the reactivity modeling 

area, a fixed parameter version of the SAPRC99 chemical mechanism and the SMOKE©2 

emissions processor were incorporated into the Models3/CMAQ modeling system. Such a 

system will be used eventually for substantially improved model-computation of reactivity. 

Overall, the FY-2001 activities are of benefit in that they sensitize the scientific and regulatory 

communities to uncertainties and gaps in reactivity science and will lead ultimately to the 

development of scientific basis for reactivity-based VOC control policies. 

2.1.12 United States-Germany Environmental Agreement 

The United States-Germany Environmental Agreement for information exchange was 

initiated in 1988. Information exchange is effected mainly through participation in biennial 

Workshops organized jointly by the two countries. To date, there have been six Workshops. 

Workshop scope was initially limited to the photochemical ozone pollution problem, but was 

recently expanded to include also photochemical aerosols. Participation also was expanded 

recently to include countries from Eastern Europe and non-European countries. The Workshop 

agenda is designed so as to include presentations on subjects of mutual interest. In past years, 

United States interest was mainly in European atmospheric chemistry studies, particularly those 

at the newly built, large, ultra-modern outdoor smog chamber facility in Valencia, Spain. 

European interest was mostly in modeling studies and regulatory policies and strategies in the 

United States. Efforts in FY-2001 included completing the Proceedings from the 6th Workshop, 

and planning of the 7th Workshop scheduled for the second week of October 2002 in Germany. 

Anticipated benefit from 7th Workshop is new information leading to updating of the chemical 

mechanisms for ozone and fine particulate matter species and models used in the United States. 

2.1.13 Atmospheric Sciences Modeling Division Library 

The Division supports a research library that originated in 1958 at the Robert S. Taft 

Sanitary Engineering Center in Cincinnati, Ohio. In 1971, the Library became part of the EPA at 

Research Triangle Park, North Carolina, and NOAA at Silver Spring, Maryland, when the EPA 

and NOAA were established in the Executive Branch by Reorganization Plans Nos. 3 and 4 of 

1970. The Library is unique in that its collection is included in both the EPA and NOAA library 

networks. 

2

Copyright 2000 MCNC–Environmental Modeling Center 

8 
The Library is part of the ASMD Office of Director. Its mission is to serve the NOAA 

Division assigned to support EPA’s National Exposure Research Laboratory, Office of Air 

Quality Planning and Standards, and Environmental Research Center staff located in Research 

Triangle Park, North Carolina, and EPA and NOAA nationwide. Because the Library is a 

participating member of OCLC (Online Computer Library Center), it provides services to all 

members of that organization. The major field of interest is the meteorological aspects of air 

pollution, including numerical and physical model development and application. The Library is 

a member of American Meteorological Society. 

2.1.13.1 	International Association of Aquatic and Marine Science Librarians and 

Information Centers 

The Division Librarian participated in the 26th International Association of Aquatic and 

Marine Science Libraries and Information Centers (IAMSLIC) Annual Conference, September 

28–October 6, 2000, in Victoria, British Columbia, Canada. At the conference, the Librarian 

presented an informational poster on the membership representation of the Atmospheric Science 

Librarians International (ASLI) organization. The Librarian introduced the Director of the Law 

Library and Professor of Law at the University of North Carolina at Chapel Hill, who led a twohour copyright workshop. The Librarian served on the officers nomination committee. 

2.1.13.2 	Southeast Affiliate of IAMSLIC Librarians 

The Division Librarian attended the SAIL 2001 Conference, May 2–4, 2001, in Sarasota, 

Florida, at the Mote Marine Laboratory. The Mote scientists, library professors, and librarians 

presented relevant topics for the attendees. SAIL members made other presentations about 

activities in their areas. The 35 attendees included NOAA Librarians, and librarians and 

scientists from the academic and municipal community, and a non-profit laboratory. The ASMD 

Librarian was elected SAIL archivist. 

2.1.13.3 	Atmospheric Science Librarians International 

The Division Librarian attended the Fourth Annual Conference of the Atmospheric 

Science Librarians International (ASLI), which was held in conjunction with the 81st Annual 

Meeting of the American Meteorological Society, January 9–14, 2001, in Albuquerque, New 

Mexico. At the third annual conference in Long Beach, California, the Librarian was elected 

Chair of ASLI for 2000; therefore, as Chair, the Librarian planned the 2001 program and acted as 

moderator of the meeting (http://www.lib.noaa.gov/asli/asli2001ag.htm). At the conference, the 

Librarian also served at the ASLI exhibit booth and at the Air Resources Laboratory (ARL) 

booth in the exhibit hall. During 2001, the Librarian served as a member of the ASLI Executive 

Board and Membership Chair. 

9

2.1.13.4 Atmospheric Sciences Modeling Division Library Web Page 

The ASMD Library maintains a world-wide web (WWW) page 

(http://www.epa.gov/asmdnerl/library/library.htm), which provides information about the 

Library's history, location, and services to the Division staff and other users in Research Triangle 

Park, North Carolina, and other locations. The home page provides WWW interface connections 

to the EPA and NOAA on-line catalogs in which the Library's book and journal collections are 

cataloged. In addition, the page provides links to other information resources through the 

agencies' home pages and to other WWW resources that reflect the Library's collection and staff 

needs. The Librarian provides PDF documents of publication citations for inclusion on the 

Division’s home page (http://www.epa.gov/asmdnerl/) and publication citations for the NOAA 

Air Resources Laboratory home page (http://www.arl.noaa.gov/ ). During the year, the process 

of updating the web site to be in compliance with Section 508 of the Rehabilitation Act of 1973 

was completed. 

2.2 Atmospheric Model Development Branch 

The Atmospheric Model Development Branch develops and evaluates analytical and 

numerical models that describe the transport, dispersion, transformation, and 

removal/resuspension of atmospheric pollutants on local, urban, and regional scales. These are 

comprehensive air quality modeling systems that incorporate state-of-science formulations 

describing physical and chemical processes. 

2.2.1 Community Multiscale Air Quality Modeling System 

2.2.1.1 Introduction 

EPA released the Community Multiscale Air Quality (CMAQ) modeling system, initially 

in June 1998, and several subsequent revisions in FY-1999, FY-2000, and FY-2001. Models3/CMAQ is a numerical modeling system that can simultaneously simulate the transport, 

physical transformation, and chemical reactions of multiple pollutants across large geographic 

regions. The system is useful to states and other government agencies for making regulatory 

decisions on air quality management, as well as to research scientists for performing atmospheric 

research. Models-3, a flexible software framework, and the CMAQ modeling system, support air 

quality applications ranging from regulatory issues to scientific research on atmospheric 

processes. A modular science design of CMAQ allows the user to build different chemistrytransport models for various air quality problems. The CMAQ models can be operated 

independently of the Models-3 system framework, providing more flexibility for advanced 

research and applications. The CMAQ models were tested for several air quality studies, 

including photochemical ozone and particular matter episodes in 1995 in the northeastern United 

States (NARSTO-NorthEast field study) and southeastern United States (Nashville, Tennessee, 

Southern Oxidants Study (SOS)) for the period July 2–18, 1995. The test results are very 

10

promising when compared with observed surface ozone concentrations and aircraft 

measurements. Preliminary model assessments for particulate matter indicate that the CMAQ 

model is performing quite well for modeling particulate sulfate, but is overpredicting nitrate, and 

underpredicting organic aerosols. Work continued through FY-2001 in diagnosing the reasons 

for these biases in the model. 

2.2.1.2 	Research and Development Scope of the Community Multiscale Air Quality 

Modeling System 

After the initial release of the Models-3/CMAQ modeling system, development continued 

to improve its science content and expand the operational platforms. The Models-3/CMAQ 

science paradigm embodies the one-atmosphere concept for air quality modeling. To simulate 

weather and air quality phenomena realistically, adaptation of a one-atmosphere perspective 

based mainly on first principle science descriptions of the atmospheric system is necessary. This 

perspective emphasizes the interactions among multiple air pollutants at different dynamic 

scales. For example, processes critical to producing oxidants, acid and nutrient depositions, and 

fine particles are too closely related to be treated separately. 

Figures 1, 2, 3, and 4 demonstrate the one-atmosphere modeling concept and illustrate 

Models-3/CMAQ simulation results for ozone and fine particulate matter (PM2.5), sulfates, and 

visibility for July 6, 1999, for the contiguous United States at 32-km horizontal grid dimension, a 

period of widespread ambient pollution in the nation. Figure 1 illustrates maximum 1-hr average 

ozone concentration (ppmV) in each grid cell between 7:00 AM and midnight EDT. Figure 2 

illustrates 24-hr averages of sulfate concentrations (micrograms/m3). Figure 3 illustrates 24-hr 

averages of PM2.5 concentrations (micrograms/m3

) in each 32-km grid cell. Figure 4 illustrates 

noontime EDT visibility (deciview, note insert) in each grid cell. 

Science modules in the CMAQ system are the Fifth Generation Pennsylvania State 

University/National Center for Atmospheric Research (NCAR) Mesoscale Meteorological Model 

(MM5), the Sparse Matrix Operator Kernel Emissions (SMOKE)©3 system, and the CMAQ 

Chemical Transport Model (CCTM). There are several interface processors that link other model 

input data to CCTM. The Meteorology-Chemistry Interface Processor (MCIP) processes MM5 

output to provide a complete set of meteorological data for CCTM. Initial and boundary 

conditions are processed with the processors, ICON and BCON, respectively. A photolytic rate 

constant processor, which is based on RADM’s JPROC, computes species-specific photolysis 

rates for a set of predefined zenith angles and altitudes. In addition, a Plume Dynamics Model 

(PDM) is used to provide major elevated point-source plume dispersion characteristics for 

driving the plume-in-grid processing within CMAQ. The continued improvement of many 

elements of the CMAQ system is described below. 

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Copyright 2000 MCNC–Environmental Modeling Center 

11 
Figure 1. Models-3/CMAQ simulation results for July 6, 1999, for the contiguous United States at 32-km horizontal 

grid spacing showing maximum 1-hr average ozone concentration (ppmV) in each grid cell between 7:00 AM and 

midnight EDT. 
Figure 2. Models-3/CMAQ simulation results for July 6, 1999, for the contiguous United States at 32-km horizontal 

grid spacing showing 24-hr averages of sulfate concentrations (micrograms/m3

). 
Figure 3. Models-3/CMAQ simulation results for July 6, 1999, for the contiguous United States at 32-km horizontal 

grid spacing showing 24-hr averages of PM2.5 concentrations (micrograms/m3

) in each 32-km grid cell. 
Figure 4. Models-3/CMAQ simulation results for July 6, 1999, for the contiguous United States at 32-km horizontal 

grid spacing showing noontime EDT visibility (deciview, note insert) in each grid cell. 
The latest public release of the CMAQ modeling system occurred in February 2001. In 

this version, the computationally-efficient SMOKE© emissions model replaced the older 

Models-3 Emissions Processing and Projection System. A new efficient numerical solver for the 

gas-phase chemistry, a modified Euler backward iterative technique, was implemented. Model 

testing and evaluation will continue in FY-2002, and will include examining the impacts of using 

new land-surface, deposition, and turbulence schemes in the modeling system, as well as using a 

new chemical kinetic mechanism to treat gas-phase reactions. 

2.2.1.3 Photolysis Rates 

The photolysis rate model in CMAQ computes rates for various altitudes, latitudes, and 

hours from local noon using a radiative transfer model, typical atmospheric conditions, detailed 

satellite ozone data (if available), modeled cloud fields, a standard aerosol profile, and surface 

albedo. In FY-2001, development continued in coupling aerosols and gas-phase chemistry to 

include the effects of using dynamically-calculated aerosol profiles as an adjustment to 

photolysis rate estimates (Park, 2001). Alternative methods for treating cloud cover in the 

radiative transfer calculations are being developed and tested, including the use of GOES satellite 

data as an alternative source for cloud fields. Testing and evaluation will continue in FY-2002. 

2.2.1.4 Cloud Dynamics and Aqueous-Phase Chemistry Module 

The cloud module in CMAQ consists of a sub-grid cloud model and a grid-resolved cloud 

model. The sub-grid cloud model, which is based on the RADM cloud model (Walcek and 

Taylor, 1986; Chang et al., 1990; Dennis et al., 1993), simulates convective precipitating and 

non-precipitating clouds. The grid-resolved cloud model simulates clouds that occupy the entire 

grid cell and were resolved by the meteorological model. Development and evaluation continued 

on the CMAQ cloud module, referred to as the Sulfate-Tracking Model. The cloud module 

tracks the sources of sulfate (gas-phase production, aqueous-phase production, initial and 

boundary conditions, and emissions) at any location and at any time. Alternative models for deep 

convective, shallow convective, and grid-resolved clouds will be examined and considered for 

incorporation into CMAQ. Research will be initiated to re-examine and update the model for 

aqueous chemistry in CMAQ. 

2.2.1.5 Sub-grid Scale Plume-in-Grid Modeling in the CMAQ Modeling System 

The plume-in-grid (PinG) approach provides a realistic scientific treatment of the 

physical and chemical processes affecting pollutant concentrations in elevated, major-point 

source plumes in the CCTM. The PinG algorithm simulates the gradual growth of sub-grid scale 

plumes due to dispersion processes that are important factors impacting the temporal evolution of 

photochemistry in plume cells during the sub-grid scale phase. The description of the 

capabilities of the CMAQ/PinG model treatment and its technical formulation are described in 

16

Gillani and Godowitch (1999). The key modeling components include a plume dynamics model 

(PDM) processor and a Lagrangian reactive plume model (PinG module), which are designed to 

simulate the relevant plume processes at the proper spatial and temporal scales. The PDM 

processor determines the position and physical dimensions of individual plume sections by 

simulating plume rise, vertical and horizontal plume growth, and plume transport. The PinG 

model simulates the relevant plume processes with a moving array of attached cells representing 

a plume horizontal cross-section. The PinG treatment is capable of simulating a single plume or 

multiple point-source plumes with hourly emission releases. The PinG module is fully integrated 

into the CCTM grid model. PinG is executed concurrently during a CCTM simulation and 

employs appropriate grid concentrations as boundary conditions for various plume sections. An 

important feedback occurs when a plume section reaches the grid cell size as the sub-grid plume 

treatment ceases and plume concentrations are incorporated into the Eulerian grid system. 

A notable, new extension for the PinG module is the inclusion of an aerosol/particulate 

modeling algorithm to treat particulate matter and aerosol species in the sub-grid plumes. The 

Binkowski (1999) aerosol treatment, which was the aerosol modeling component of the CCTM 

grid model, was adapted and integrated into the PinG module so that gas-phase chemistry and 

aerosols can be simulated in the same manner as in the grid model. Test simulations were 

underway with a group of major point sources exhibiting a wide range of NOx and SOx emission 

rates within a regional modeling domain encompassing the greater Nashville, Tennessee, area. 

Preliminary model test results reveal interesting differences in the formation of sulfate aerosol 

concentrations in plumes from the various point sources. Simulations are being conducted with 

the RADM2 and Carbon Bond-IV (CB-IV) gas-phase photochemical schemes to assess 

differences due to the choice of chemical mechanisms. For the gas-phase species, previous PinG 

results at various downwind distances were encouraging with the modeled plume NOx and ozone 

concentrations exhibiting the same evolutionary pattern found in real-world plume measurements 

(Godowitch and Young, 2000). An initial, limited comparison of the PinG modeled plume 

concentrations against ozone, selected nitrogen species, and SO2 data obtained from aircraft 

traverses across plumes was reported by Godowitch (2001). Further evaluation of the PinG 

module is underway with modeled gas and aerosol species comparisons with the observed plume 

data collected on various airborne platforms during the1995 and 1999 SOS field experiments. 

Additional simulations using CCTM/PinG and CCTM/NoPinG model versions are being 

performed over a two-week period to examine gridded concentration fields with PinG and 

without the PinG approach for the largest point-source emitters. 

2.2.1.6 	Air Quality Modeling of Particulate Matter and Air Toxics at 


Neighborhood Scales


Risks to human health from exposure to ozone, fine particulate matter (PM2.5) and air 

toxics are undergoing rigorous scientific examination. Risk assessments from epidemiological 

and source-to-dose studies depend on exposure assessments, including exposure modeling, which 

are driven by data from central site monitors. Some types and classes of pollutants are 

characterized by high temporal and/or spatial variability; thus, for implementation of the National 

17

Ambient Air Quality Standards of the CAAA, data and models used to drive human exposure-topollutants assessments will be required at commensurate scales. Further, the source contribution 

to exposure from biogenic and different anthropogenic sources (e.g., transportation, 

industrial/commercial, residential activities) needs to be delineated in the assessments. 

To support source-to-dose assessments as part of human population exposure modeling 

for EPA’s risk assessment/risk management paradigm and also for assessment and management 

of risk associated with PM2.5 and air toxics, an emissions-based modeling capability is being 

developed to provide air quality concentration fields and sub-grid variability characterization 

from regional to neighborhood scales. These modeled concentrations may augment and/or 

supplant observations from monitors that, by themselves, provide limited or negligible 

characterization of the requisite temporal variability and spatial features of the pollutant field. 

Additionally or as an alternative, model predictions may serve as a surrogate for pollutant 

compounds that are not measured. 

With its multiscale modeling capability the CMAQ modeling system provides a powerful 

modeling platform for this effort. The interest is to extend the utility of CMAQ to grid sizes that 

exhibit gradients at fine scale. Modifications to MM5 were made to accommodate urban 

building morphological and detailed land-use data at fine scale resolution (Otte and Lacser, 

2001). Additional modeling techniques are needed to describe the sub-grid scale variability, 

including those that are derived from turbulence-induced concentration fluctuations using largeeddy simulation (LES) techniques (Herwehe, 2000) and dispersion of point- and area-sources 

from street canyon flows, using a combination of computational fluid dynamics (CFD) and wind 

tunnel modeling techniques. This variability information, in the form of probability density 

functions (PDF), provides statistical information (e.g., peak-to-mean ratios) and spatial structure 

functions of the variability as a function of the building morphology. The first application from 

this system is to provide ambient concentration fields to various micro-environments in the 

population-based Stochastic Human Exposure and Dose Simulation (SHEDS) model (Burke et 

al., 2001). 

Preliminary results from a proof-of-concept case study for the Philadelphia area (Ching et 

al., 2001) show an example where the fine mode component of the PM2.5 size distribution 

exhibited greater spatial variability than the PM2.5 by mass. In addition, the extent of the resolved 

scale spatial variability is seen to vary with each pollutant and also, the grid-resolved variability 

does not necessarily increase monotonically with increased grid resolution. This means that the 

grid resolution selected for use in exposure modeling may need to be ascertained by numerical 

experiments. Results to date for the sub-grid modeling are from the use of a coupled LES with 

chemistry model (LESchem) in which the degree of the variability is governed by the chemical 

reactivity of the species, the atmospheric mixture, and the turbulent structure of the mixed layer. 

18

2.2.1.7 Aggregation Research for Models-3/CMAQ 

In support of studies mandated by the CAAA, the Models-3/CMAQ model is used by the 

EPA Program Offices to estimate deposition and air concentrations associated with specified 

levels of emissions. Assessment studies require CMAQ-based distributional estimates of ozone, 

acidic deposition, PM2.5, as well as visibility, on seasonal- and annual-time frames. In practice, 

CMAQ may be executed for a finite number of episodes or events, which are selected to 

represent a variety of meteorological classes. A statistical procedure called aggregation is then 

applied to the outputs from CMAQ to derive the required seasonal and annual estimates (Cohn et 

al., 2001). 

The aggregation approach utilized cluster analysis of the 700 h Pa u and v wind field 

components over the time period 1984–1992 to define homogeneous meteorological clusters. 

Alternative schemes were compared using relative efficiencies and meteorological 

considerations. An optimal scheme was defined to include 20 clusters (five per season), and a 

stratified sample of 40 events was selected from the 20 clusters using a systematic sampling 

technique. The light-extinction coefficient, which provides a measure of visibility, was selected 

as the primary evaluative parameter for two reasons. First, this parameter can serve as a 

surrogate for PM2.5, for which little observational data exist. Second, of the air quality 

parameters simulated by CMAQ, this visibility parameter has one of the most spatially and 

temporally comprehensive observational data sets. Results suggest that the approach reasonably 

characterizes synoptic-scale flow patterns and leads to strata that explain the variation in 

extinction coefficient and other parameters (temperature and relative humidity) used in this 

analysis, and, therefore, can be used to achieve improved estimates of these parameters relative 

to estimates obtained using other methods (Eder et al., 2000a). Moreover, defining seasonally 

based clusters further improves the ability of the clusters to explain the variation in these 

parameters, and, therefore, leads to more precise estimates. 

2.2.1.8 Collaborative Model Evaluation Studies for Particulate Matter 

Three collaborative model evaluation projects are underway based upon utilization of the 

Models-3/CMAQ system: 

Cooperative Agreement with University of Alabama-Huntsville. 

A modeling center was started at the University of Alabama-Huntsville to develop a 

Models-3/CMAQ capability to serve present and future needs of the Southern Oxidant Study 

(SOS) community. As part of the functions of this Center for Models-3/CMAQ, some 

performance evaluation studies were conducted for specific episode simulations. The modeldata intercomparisons were made for selected case studies using targeted data from aircraft, 

meteorological observations, and surface chemistry sites from the 1995 and 1999 Nashville SOS 

field experiments. The first set of studies conducted by the modeling team focused on the July 

1995 SOS experimental period. Several modeling runs were made to study and evaluate the 

19

sensitivity of the use of the PinG approach in CMAQ, and to examine alternative methods for 

introducing initial and boundary conditions (IC/BCs). Experiments are being conducted to arrive 

at the most satisfying IC. Alternative runs with and without PinG are under study. Special 

attention is being given to the evaluation of the meteorology fields from MM5 as contrasted to 

the Regional Atmospheric Modeling System (RAMS), using as a basis wind profiler data for 

winds and satellite data for cloud predictions. One emphasis will be on the predictability of the 

nocturnal jets and their subsequent effects on plume dispersion. Statistical evaluations will 

provide the basis for an assessment and improvements to nudging coefficients used in MM5. 

These methods and evaluation studies are being conducted with different horizontal grid 

dimensions (e.g., 36, 12 and 4 km). 

Work is underway on a test case that will cover the period of June 22–August 17, 1999, 

and the simulation will use MM5. This period spans the major SOS field activities in Nashville 

(June 22–July 22) and Atlanta (August 1–August 31). The models will be run in two modes: a 

control run without satellite assimilation and a run with satellite assimilation. The satellite 

assimilation run will include insolation and soil moisture retrievals. Also, GOES visible 

broadband transmittance, surface albedo, and IR (infrared) cloud tops will be used in the CMAQ 

photolysis calculations. The MM5 control run was completed for the June 22–July 31 period. 

The satellite assimilation was carried out for June 22–29. Meteorological evaluation has started 

using the same techniques incorporated in the 1995 cases and include comparison against special 

observations such as profilers and standard National Weather Service observations. 

Cooperative Agreement with the Washington University Center for Air Pollution 

Impacts and Trend Analysis (CAPITA) in St. Louis, Missouri. 

A collaborative study between ASMD and the Center for Air Pollution Impacts and 

Trends Analysis (CAPITA), undertaken to evaluate the performance of Models-3/CMAQ, was 

completed. As part of this effort, CAPITA assessed the suitability of using visibility as a 

surrogate for PM2.5 concentrations in the Models-3/CMAQ aggregation technique for producing 

annual- and long-term averages. Both efforts utilized CAPITA's consolidated database of PM 

data sets. CAPITA performed its evaluation of the CMAQ model using the IMPROVE 

(Interagency Monitoring of PROtected Visual Environments) mass and chemically speciated 

concentration data. The comparison period, July 4–18, 1995, was during a major ozone and PM 

episode covering much of the eastern United States. The daily average measured species 

concentrations were compared to the corresponding averages from the 36-km model grid. The 

model performance was evaluated based on the correlation of the model-data pairs. The model 

results were also examined on contour maps for each aerosol component to evaluate the spatial 

biases compared to the IMPROVE and AIRS (Aerometric Information Retrieval System) PM10 

data. The measured and modeled PM2.5 mass concentrations at the 18 eastern United States 

IMPROVE sites were found to be highly correlate (R2= 0.84). There was an offset in the 

correlation, which implies an unaccounted background mass of about 4 mg/m3

. The model 

values were somewhat lower than the measured values (slope 0.84). There was no evidence of 

systematic spatial bias in the model-data PM2.5 mass comparison. The modeled and measured 

fine particle sulfate concentrations over the eastern United States showed an excellent correlation 

20

(R2

=0.86) and zero offset. The model sulfate was somewhat higher, but there was no evidence of 

systematic spatial bias in the model-data sulfate comparison. 

The measured total organics in the IMPROVE data showed poor correlation with the 

model values (R2= 0.03). In fact, the model organics were virtually constant at about 1.8 mg/m3, 

while the corresponding measured values at the IMPROVE sites ranged from 2. 5 to 7 mg/m3

. 

The poor model-data comparison for organics is attributed to sources not considered in the 

model, such as biomass smoke. Qualitative biomass tracers in the IMPROVE data, (non-soil 

potassium), indicate that during the July 1995 summer episode, biomass smoke was a significant 

contributor to organic PM over the eastern United States. Unfortunately, the main quantitative 

features of biomass smoke emissions and ambient smoke composition are poorly understood. 

The IMPROVE data indicate that in July 1995, fine particle soil components contributed 2-10 

mg/m3

 to the PM2.5 over the eastern United States. The chemical fingerprints and transport also 

indicate that long-range transport of Sahara dust was a major fraction of fine dust over the eastern 

United States in July. Fine soil sources and intercontinental-scale transport are not included in 

the current model. The measured PM10 data from the extensive AIRS (600 sites) and IMPROVE 

networks are generally higher then the model values. The spatial pattern of the data-model 

difference shows large excess measured PM10 (up to ~30 ug/m3

) over the more arid western part 

of the domain, where windblown dust is significant. However, near urban areas the model 

exceeded the measured values, indicating that urban primary sources in the model were 

overemphasized. 

Interagency Agreement with the U.S. National Park Service. 

This collaboration involves the development, implementation, and utilization of 

Models-3/CMAQ to assess and develop strategic and tactical strategies to deal with existing and 

emerging pollution issues pertinent to the Class I natural areas in the West. This collaboration 

with the U.S. National Park Service includes principals from the Cooperative Institute for 

Research in the Atmosphere (CIRA) at the Colorado State University, Fort Collins, Colorado. 

After implementing Models-3/CMAQ at CIRA, the effort is developing and incorporating 

algorithms for advanced smoke emission processing from fires (prescribed, agricultural, and 

natural) into Models-3/CMAQ. This project will serve to facilitate the use of Models-3/CMAQ 

in the West to develop science-based strategic plans for dealing with smoke emission 

management issues and interstate transport affecting regional haze, PM2.5, PM10, and ozone. By 

design, the effort to develop the smoke emission processor will involve introducing several major 

components including: 

•	 System to identify fire boundaries determined from the Geographic Information System 

(GIS) coverage generated for fires from various data sources. The basic output from this 

step is spatially identified fire boundaries as a function of time (daily or hourly). The 

National Fire Occurrence database, a spatial database with 1-km resolution that includes 

most fires for the period of record, 1986–1996, is being utilized for the first generation 

product. 

. 

21

•	 Fuel models that introduce vegetation coverage and fuel loading data associated with the 

fires using the EPA vegetation mapping coverage and the U.S. Forest Service risk 

analyses system. The emission production models require data about the amount and 

characteristics of fuel loading present on a piece of land. Generally, these are tied to the 

fuel models of the National Fire Danger Rating System, which includes handling of dead 

fuels moisture as well as live fuel moisture or greenness maps derived weekly from 

Normalized Difference Vegetation Index data observed by satellites. 

•	 Fuel moisture model that compares the current fuel moisture content to the fuel moisture 

content threshold for flammability. Thus, the moisture content of each of the different 

fuel types is a critical determinant of flammability. The finer fuel moisture content will 

be calculated from the time weather conditions including temperature, relative humidity, 

and cloudiness data from the MM5 system. 

•	 A fire generation processor based on spatial coverage of historical wildfires from satellite 

observations or individual fire records of a stochastic fire generator based on 

precipitation, humidity, drought determined from a drought index, strong convection as 

an index of lightning, indication of lightning or human ignition probability (might be 

based on population density). 

•	 Processor for determining fire behavior or biomass consumption, and 

•	 Processor for providing emissions profiles for speciated wildfire emission pollutants to be 

determined using current or recent research studies. 

2.2.1.9 	A Preliminary Evaluation of Models-3/CMAQ for Particulate Matter 

Ambient air concentrations of particulate matter continue to be a major concern. High 

concentrations of fine particles were linked to detrimental health effects (including an increase in 

mortality) and visibility degradation. Accordingly, the 1990 Clean Air Act Amendments called 

for an assessment of current and future regulations designed to protect human health and welfare. 

The most reliable tool for carrying out such assessments are air quality models like the 

Models3/CMAQ, which simulate air concentrations and deposition of particulate matter as well 

as several measures of visibility associated with specified levels of emissions. These simulations 

can be used to support both regulatory assessment and scientific studies on a myriad of spatial 

and temporal scales. To determine the accuracy of the simulations involving visibility and 

particulate matter, a preliminary evaluation of the aerosol component of CMAQ was performed 

(Mebust et al., accepted for publication). 

The visibility portion of the evaluation compared visibility observations in the eastern 

United States, for July 11–15, 1995, against the CMAQ simulations, using both the Mie 

efficiency theory approximation and the IMPROVE mass reconstruction technique (Eder et al., 

2000b). Comparison of model results with observed spatial and temporal patterns of visibility 

22

degradation during the specified time period reveals reasonable agreement, as CMAQ captured 

the main visibility gradients, maxima and minima. The Mie approximation and mass 

reconstruction methods for calculating deciviews generally underpredict visibility degradation by 

~7 and ~10 dv, respectively. The correlation coefficient between the two methods, r2

 = 0.97, 

shows excellent agreement for the five-day simulation. However, only marginal agreement 

exists between each method and the observations; r

2

 = 0.32 for the Mie theory approximation and 

r

2

 = 0.30 for the mass reconstruction method. 

The particulate matter evaluation, using observations of sulfate, nitrate, PM2.5, PM10, and 

organic carbon from 18 stations of the IMPROVE network for eight days in June 1995, reveals 

that, with the exception of sulfate, the model generally underpredicts aerosol concentrations 

(Eder et al., 2001a). These underpredictions are consistent across the model domain throughout 

the simulation period. More specifically, good agreement was found between simulated and 

observed sulfate concentrations (r

2 = 0.63; median bias = 0.01), with both the simulated mean 

(4.98:g/m3), and coefficient of variation (79.16%) closely matching those observed (4.83:g/m3, 

71.63%). Conversely, very poor agreement was found between simulated and observed nitrate 

concentrations. Although the simulated mean (0.21 :g/m3

) was relatively close to the observed 

mean (0.31 :g/m3

), the overall r

2

 (0.005) and median bias (-0.88) were very low. Fair agreement 

occurred between simulated and observed PM2.5 concentrations (r

2

 = 0.55; median bias = -0.34). 

The simulated mean (9.06 :g/m3) and coefficient of variation (62.06%) reasonably match those 

observed (12.96 :g/m3

, 55.39%). The level of agreement between simulated and observed PM10 

concentrations was not as good as for PM2.5. Although the simulated mean (13.74 :g/m3) and 

coefficient of variation (73.74%) are reasonably close to those observed (19.40 :g/m3

, 50.26%), 

the overall r

2

 (0.13) and median bias (-0.38) were low. The simulated mean (1.53 :g/m3) and 

coefficient of variation (44.23%) reasonably match those observed (2.32 :g/m3, 47.34%). 

Potential sources of error in these model simulations, for both visibility degradation and 

speciated aerosol concentrations, include uncertain emission inventories, erroneous input 

meteorological data, and an incomplete understanding of aerosol dynamics in the CMAQ aerosol 

component. Inadequacies in evaluation data sets were identified. EPA recently implemented the 

National PM2.5 Monitoring Network, consisting of mass monitoring (1100 sites), routine 

chemical speciation (300 sites), and supersite characterization. These network measurements 

will eventually produce a valuable database, allowing a more thorough evaluation of CMAQ. 

2.2.1.10 	Ozone Initial and Boundary Concentrations for Models-3/CMAQ From 

Ozone Climatology 

The setting of initial and boundary concentration (IC/BC) of air species for the CMAQ 

system represents clean ambient conditions in the eastern-half of the United States. The first 

examination is on the sensitivity of the IC/BC for ozone. The study noted large differences in 

observed vertical and horizontal distributions of ozone compared to those distributions used as 

IC/BC in CMAQ. The magnitude of the difference is large at upper tropospheric levels. Because 

of stratosphere-troposphere exchange, the upper troposphere is characterized with high 

23

concentrations of ozone (hundreds of ppbv). However, the current IC/BC artificially sets the 

ozone level as 70 ppbv in the upper troposphere throughout the model domain. The large 

difference of the standard ozone IC/BC specification from a more realistic situation might limit 

the capability of the CMAQ system, and cause uncertainty in CMAQ’s performance. 

The purpose of this research was to improve the IC/BC setting for the Models-3/CMAQ 

modeling system, and to assess the influence of introducing stratospheric ozone into the 

troposphere on regional and urban air quality and on the tropospheric ozone budget. The 

simulation covered the entire United States with 108-km grid cell size from July 2–12, 1988. 

The domain was divided into 34 layers vertically up to 40 millibars. In addition to a base case 

with standard IC/BC, ozone initial and boundary concentrations were specified based on ozone 

climatology (Logan, 1999), which was derived from 15 years of surface, satellite, and 

ozonesonde data across the globe. The new IC/BC specification enabled the CMAQ model to 

study ozone cross-tropopause flux transporting to the lower troposphere, and to analyze the 

impact of intercontinental ozone transport. Potential vorticity, which is usually used as a 

dynamic tracer of ozone flux, was calculated in the model and its trend related to ozone variation. 

The tropospheric ozone residual (TOR) data derived from satellite observational results were 

used for comparison with the modeled tropospheric ozone budget. 

Since ozone climatology was based on observations, the derived ozone IC/BCs were in 

better agreement with the real atmosphere than the standard IC/BC. While this project is still 

ongoing, CMAQ simulation with the new IC/BC demonstrated transport of ozone vertically 

through the 34 layer domain from the stratosphere and upper troposphere down to the surface. It 

was responsible for more than 15 ppbv ozone increase within the boundary layer after the first 

four days of simulation. The change of IC or BC alone could lead to significant ozone variation. 

Sensitivity studies showed that after four days of simulation, the initial concentrations could 

cause over 10 ppbv ozone difference at the ground, and high concentrations of ozone at the 

boundaries could cause strong horizontal advection (up to ~40 ppbv/hr increase) in the upper 

troposphere. The comparison with TOR data (Fishman and Balok, 1999) shows promising 

consistency. Studies to relate the relationship of ozone variation, potential vorticity, and TOR 

are underway. The influence of IC/BC on ozone and its precursors will be studied further. A 

recommendation was made to introduce improved IC/BC into CMAQ, with an initial 

methodology using ozone climatology from a global scale observations database. 

2.2.2 Aerosol Research and Modeling 

The next release of CMAQ in FY-2002 will incorporate a new mechanism for treating 

secondary organic aerosol (SOA). The new method is based upon Schell et al. (2001) and 

partitions the species that form SOA between the vapor and particle phases. The same precursors 

as in the earlier versions of CMAQ are considered. These are toluene, xylene, cresol, long chain 

alkanes, internal alkanes, and monoterpenes. The surrogate for internal alkanes was changed to 

cyclohexene. The monoterpenes are now lumped according to the contribution to the national 

biogenic emissions inventory. The solution for the system is an algebraic system of nonlinear 

24

quadratic equations solved by a Newton-Raphson technique. Schell et al. (2001) showed that 

this approach is efficient even in three-dimensional simulations. The advantage of this approach 

over the previous method of using fractional aerosol yields is the incorporation of recent 

laboratory results showing the dynamic nature of the partitioning mechanism. 

2.2.3 Atmospheric Toxics Pollutant Research 

Development of new atmospheric simulation modeling capabilities for toxic pollutants 

during FY-2001 focused on mercury and two semi-volatile substances, atrazine and dioxin. The 

latter focus is on researching modeling capacities for additional slow reacting and toxic 

pollutants. These modeling efforts involve the use of CMAQ as the basis for air toxics pollutant 

modeling, but the relevant scientific issues and modeling approaches for mercury differ 

somewhat from those for atrazine and dioxin. Each effort is discussed separately below. 

Modeling of Mercury. 

During FY-2001, ASMD personnel continued participation in the first phase of an 

intercomparison study of numerical models for long-range atmospheric transport of mercury 

sponsored by the European Monitoring and Evaluation Programme (EMEP) and organized by 

EMEP’s Meteorological Synthesizing Center - East in Moscow, Russia. This first phase of the 

intercomparison involves the simulation of mercury chemistry in a closed cloud volume given a 

variety of initial conditions. Results obtained from the CMAQ mercury (CMAQ-Hg) model and 

the other participating models from Russia, Germany, Sweden, and the United States were 

compared to identify key scientific and modeling uncertainties, and a report was issued to the 

EMEP governing body (Ryaboshapko et al., 2001). The second phase of intercomparison 

involves full-scale model simulations of the emission, transport, transformation, and deposition 

of mercury over Europe and comparison of the modeling results to field measurements of 

elemental mercury gas, reactive gaseous mercury, and particulate mercury in air. 

During FY-2001, a number of modification were made to the CMAQ-Hg model. Based 

on information derived from the model intercomparison study described above, the mechanism 

for sorption of Hg2+ compounds to elemental carbon aerosol in aqueous suspension was modified 

to use sorption equilibrium constants based on Seigneur et al. (1998). Also, new rate constants 

for the gas-phase oxidation of Hg0

 by molecular chlorine and hydroxyl radical were incorporated 

into the CMAQ-Hg model (as determined by Calhoun and Prestbo, personal communication, 

2001) and Sommar et al. (2001), respectively. Finally, the Fortran subroutine for the CMAQ 

aqueous chemistry mechanism was optimized to more efficiently calculate the mercury chemistry 

in concert with the standard CMAQ mechanism. Further modification of the CMAQ-Hg 

chemical mechanism for mercury is expected in FY-2002 as additional chemical reactions are 

identified and as new rate constants are determined. 

During FY-2001, an evaluation of the CMAQ-Hg model against observations of wet 

deposition of mercury was conducted. Measurements of the wet deposition of mercury obtained 

25

from the Mercury Deposition Network (MDN) (Vermette et al., 1995) at 11 locations in the 

central and eastern parts of the United States were compared to CMAQ-Hg model simulations 

during two four-week periods in 1995. The results showed relatively good agreement between 

modeled and observed wet deposition of mercury for the period from April 4 to May 2 for which 

a Pearson correlation coefficient of 0.657 was computed. However, for the period from June 20 

to July 18, the Pearson correlation coefficient was only 0.329. The inferior performance during 

the summer period was found to be largely due to the inability of the meteorological driver, 

MM5, to accurately define small-scale convective precipitation elements at the 36-km grid cell 

size employed for the CMAQ-Hg evaluation. The results of the CMAQ-Hg evaluation were 

described in a journal article (Bullock and Brehme, accepted for publication). Further 

CMAQ-Hg model evaluation will be performed during FY-2002 against MDN data collected in 

2000 as the necessary MM5-derived meteorological input data become available. 

Modeling of Semi-Volatile Compounds. 

During FY-2001, the research effort involving atrazine was largely completed. It adapted 

CMAQ to simulate atrazine concentrations in air and precipitation from April to July of 1995 

over the eastern two-thirds of the United States. To accomplish the simulation, chemistry and 

deposition algorithms were modified, while a new algorithm for gas-to-particle partitioning was 

added to account for the semi-volatile nature of atrazine. An initial examinaton showed that 

predictions were roughly consistent to ranges of air and deposition observations. A more 

complete evaluation used studies along the Mississippi River (Foreman et al., 2000; Majewski et 

al., 2000) and Lake Michigan (Miller et al., 2000). Regarding observations along the Mississippi 

River, the model underpredicted precipitation concentrations and overpredicted air 

concentrations summed over gas and particulate forms within a mean factor of two. In particular, 

the model severely overpredicted lower limits in gas-phase concentrations. These errors appear 

to stem from the precipitation and atrazine emissions data that supported the simulations. 

Further work regarding atrazine may be conducted to support the papers that presented this 

research (Cooter and Hutzell, accepted for publication; Cooter et al., accepted for publication). 

In addition, research was initiated to develop versions of CMAQ that simulate the fate of 

dioxins and such simple aromatic compounds as benzene. The dioxins version will treat 17 

Polychlorinated Dibenzofuran and Dibenzodioxin congeners because of their significant toxicity. 

The model expands upon the atrazine research because such similar processes control atrazine 

and dioxins as gas to particle exchange, low reactivity, and insignificant cloud chemistry 

(Lohmann and Jones, 1998). The expansion refines the new algorithm for gas to particle 

exchange based on effects from aqueous and organic aerosol components. Emphasis was placed 

on improving dry deposition and simplifying chemical mechanisms. In CMAQ, algorithms for 

dry deposition do not include organic factors that contribute to the deposition of dioxins and 

other persistent organic pollutants (Lorber and Pinsky, 2000). Simplifying the chemistry 

considers the low reactivity of dioxins and reduces computational requirements without 

compromising accuracy. The second model adapts chemical mechanisms within CCTM that 

represent aromatics as aggregated species. 

26

2.2.4 Meteorological Modeling Studies 

The Fifth-Generation Pennsylvania State University/NCAR Mesoscale Model (MM5) is 

the primary tool for providing meteorological input for Models-3/CMAQ. MM5 is widely used 

to generate meteorological characterizations throughout the air quality modeling community. For 

Models-3/CMAQ, MM5 is applied to several case studies at a variety of spatial scales using a 

series of one-way nested domains. MM5 is run retrospectively using four-dimensional data 

assimilation (FDDA) for a dynamic analysis of the simulation period. The output represents a 

dynamically consistent multiscale meteorology simulation for various horizontal grid spacings 

and from continental to urban areal coverage. The MM5 output is ultimately used in the CMAQ 

emissions and chemistry modules to describe the planetary boundary layer characteristics and the 

atmospheric state variables. 

2.2.4.1 Meteorology Modeling for Models-3/CMAQ Applications 

Several projects were underway during FY-2001 using MM5 to support 

Models-3/CMAQ applications. MM5 Version 3 Release 4 (MM5v3.4) was released by the 

National Center for Atmospheric Research (NCAR), Boulder, Colorado, in November 2000. 

MM5v3.4 included the first general release of the ASMD-developed Pleim-Xiu Land-Surface 

Model (Pleim and Xiu, 1995; Xiu and Pleim, 2001). During FY-2001, MM5v3.4 was tailored 

for air quality applications with some minor modifications, and it was used for internal projects. 

During FY-2001, a research effort was initiated to use MM5 to drive CMAQ for episodic 

evaluation based on the photochemical field studies from the SOS in Nashville during the 

summer of 1999. Several aspects of the 1999 case studies differ from the 1995 case studies that 

dominated previous research. The first change was to use NOAA-generated archived Eta model 

analyses as first-guess fields for MM5. The archived Eta fields have considerably better 

horizontal (40 km, degraded from 32 km) and vertical (25 levels) resolutions than the global 

fields (2.5-degree latitude/longitude with 10 levels). As a result, the need for a large-scale (e.g., 

108 km) intermediate domain was eliminated. In addition, the nesting ratio was changed to 4:1 

for one-way-nested simulations in an effort to better simulate the finest-scale runs. Based on 

experience with the 1995 NARSTO-NE case studies and published research, the new 

configurations were set up to better simulate resolved convection and neighborhood scale 

processes. The new grid configurations and input data resolutions require a new FDDA strategy, 

and research in this area is on-going. Addition research is planned for 1999 photochemical field 

studies in Atlanta and Philadelphia and the 2000 Texas Air Quality Study (TexAQS 2000) based 

on the same continental 32-km domain, but with finer-scale nests focused over each area. 

A preliminary demonstration of anisotropic weighting functions in FDDA was reported 

by Otte et al. (2001). The research used an observing-system simulation experiment to illustrate 

that anisotropic weighting functions have subtle but important impacts in FDDA. An extension 

of this work to air quality modeling is planned for FY-2002. 

27

2.2.4.2 Advanced Land-Surface and Planetary Boundary Layer Modeling in MM5 

MM5 was coupled to an advanced land-surface and planetary boundary layer (PBL) 

model to improve simulation of surface fluxes and PBL characterization. Such surface and PBL 

quantities as surface air temperature and PBL height are critical to realistic air quality modeling. 

The new land-surface model, known as the PX-LSM (Pleim and Xiu, 1995; Xiu and Pleim, 

2001), includes explicit soil moisture and vegetative evapotranspiration along with the ACM 

PBL scheme (Pleim and Chang, 1992). A key feature of the model is an indirect data 

assimilation scheme where soil moisture is nudged according to biases in surface air temperature 

and humidity as compared to gridded surface analyses. The data assimilation scheme helps 

overcome the difficulties in soil moisture initialization and model errors. 

The PX LSM was first released in the NCAR-supported MM5v3.4 in the fall of 2000. An 

updated version was recently released in MM5v3.5. Instructions for use of the PX LSM can be 

found in the MM5 tutorial that is on the NCAR MM5 website at 

http://www.mmm.ucar.edu/mm5/mm5-home.html. 

Evaluation and further improvement of the PX- LSM through comparison to field 

experiment data are continuing. In addition to previous studies comparing model runs to surface 

fluxes over corn, grass, and soybeans, case studies were made for a mixed deciduous-coniferous 

forest field study in the Adirondack area of New York in July 1998, and two sites, grass and 

soybeans, near Nashville, Tennessee, that were part of the SOS 1999 field study (Pleim et al., 

2001). These comparison studies included evaluation of such meteorological parameters as 

surface level temperature and humidity, and surface fluxes of heat, moisture, and net radiation as 

well as ozone dry deposition velocity from a dry deposition model (see Section 2.2.5.2) that 

couples to the PX- LSM. Such comparisons to field surface flux and meteorology measurements 

are extremely valuable for improvement of the model's ability to simulate a variety of vegetation 

types. 

2.2.4.3 Urban Canopy Parameterization in MM5 

During FY-2001, an urban canopy parameterization (UCP) was developed and 

implemented in MM5 for fine-scale (~1-km horizontal grid spacing) simulations (Otte and 

Lacser, 2001). The urban canopy is defined as the volume from the surface to the tops of the 

buildings within grid points that have an urban land-use classification. The UCP accounts for the 

drag exerted by urban structures, the increase in turbulent kinetic energy particularly near the tops 

of buildings, and the changes to the energy budget due to anthropogenic heating and absorption 

and emission of radiation within the urban canopy. 

The UCP was implemented into MM5v3.4 via modifications to the Gayno-Seaman 

planetary boundary layer parameterization and the Rapid-Radiative Transfer Model radiation 

scheme. The UCP was initially evaluated in a 1.3-km domain using a case study from the 

NARSTO-NE photochemical field study in 1995 with a focus over Philadelphia. Since the UCP 

28

is applied to the urban canopy, the vertical resolution of the MM5 UCP simulations was 

increased from 30 to 40 levels with 10 layers within the lowest 100 meters and the lowest level at 

2 meters AGL so that several levels are within the urban canopy. The UCP was implemented in 

stages to evaluate its behavior in MM5. Incremental improvements included the impact on the 

momentum and turbulent kinetic energy (TKE) equations, various changes to the energy budget 

at the surface and within the urban canopy, and a sub-categorization of the urban areas into 

various zones (e.g., residential, industrial, high-rise) to allow for more appropriate application of 

the UCP within urban areas. Sensitivity tests involved the specification of the canopy area 

density function and settings of urban characteristics within the urban sub-categories. 

Initial evaluation of the UCP in MM5 is promising, and evaluations will continue through 

FY-2002. Ultimately, the MM5 fields generated with the UCP will be used in CMAQ for 

neighborhood scale studies. 

2.2.4.4 Linking Meteorology and Chemistry Models 

The Meteorology-Chemistry Interface Processor (MCIP) is the key processor allowing the 

consistent off-line linkage between meteorological models and CMAQ. It is essential that MCIP 

be compatible with upgrades to MM5 to preserve numerical and physical consistency between 

the meteorology and chemistry models. During FY-2001, the MCIP software was completely 

revised and upgraded with additional scientific capability. The revision enabled dynamic 

allocation of computational grid space and improved the program for both developers and users. 

The scientific upgrades included pass-through capabilities for MM5-output planetary boundary 

layer and radiation fields that were formerly re-calculated in MCIP; this enabled closer coupling 

of MM5 with the CCTM. MCIP was configured to support both MM5v2- and MM5v3­

formatted output fields. Special treatment of new fields generated by the Pleim-Xiu land-surface 

model in MM5 was also added to MCIP. In addition, the Models-3 dry deposition scheme 

(M3Dry) was implemented, and two new toxic dry deposition species were added. Several new 

output fields were created in MCIP, as well, to support more sophisticated cloud microphysics, 

the Pleim-Xiu land-surface model, modeling of air toxics, and biogenic emissions processing. 

The upgraded software program (MCIP Version 2) was released in late FY-2001 to 25 beta 

testers in the CMAQ community, and early feedback was very positive. MCIP Version 2 will be 

officially released to the CMAQ community in early FY-2002. 

As other meteorological models are used within the CMAQ community, MCIP will also 

be modified to process data from those sources, notably from the next-generation meteorology 

model, the Weather Research and Forecast (WRF) model. MCIP development will continue 

through FY-2002 and beyond. 

29

For on-line linkage of the meteorology and chemistry models, work is underway to 

implement the MCNC4

 MCPL©5 (model couple) module in the CMAQ system. This will enable 

the meteorology model and CCTM to be run simultaneously (on-line) with data transfer at 

granularities as fine as either model’s integration time step. Using MCPL in an on-line mode 

also facilitates feedback from CCTM back to the meteorology model during integration. MCPL 

also offers an off-line capability to mimic the functionality of the MCIP program. 

2.2.5 Dry Deposition Studies 

2.2.5.1 Dry Deposition Research 

Models. 

The initial development of the next generation deposition velocity model, which is called 

the Multi-Layer Bio-Chemical dry deposition model (MLBC) was completed, and a journal 

article describing the model was prepared. The model uses an improved Gaussian quadrature 

integration scheme, which reduces the number of layers needed in the integration and is more 

accurate; a significantly revised aerodynamic resistance model based on similarity theory; a 

simplified boundary layer resistance model; and a revised and enhanced short- and long-wave 

canopy radiation model. Rather than based on the typical Jarvis scheme, the stomatal resistance 

is based on a model of Farquhar et al. (1980), which calculates stomatal conductance by 

considering photosynthesis and respiration processes. This method provides more insights into 

the biochemical mechanisms governing photosynthesis and respiration, and how these are tied to 

stomatal conductance considering the direct and indirect effects of environmental factors. 

MLBC also has a new cuticular resistance model. Plant cuticles are a lipophilic polymer 

membrane that consists of an insoluble bipolymer cutin and waxlike lipids. Diffusion across this 

layer can be either directly from the air to the layer, or from the air to a thin water film that 

usually exists on outdoor surfaces, and from the water layer to the cuticle. Diffusion equations 

for each of these pathways are developed that depend on the chemistry of the cuticle, water, and 

each pollutant. Tests of this model against data collected in the field studies show significant 

improvement over the present generation of models. After its completion, MLBC will be 

modified for use in Models-3/CMAQ, and combined with other improvements in deposition 

velocity modeling discussed below. 

Model Evaluation. 

A journal article has been prepared describing the results of the evaluation and sensitivity 

tests of the MLBC. Results show that the model accurately predicts fluxes of CO2 and the 

4

MCNC, Research Triangle Park, North Carolina 

5

Copyright 1997-2001 MCNC, Inc. 

30 
deposition velocity of O3 and SO2. Tests show improvements over previously used deposition 

velocity models. Differences of the model outputs between different sites with different plants 

are not large. Model sensitivity to the changes of input variables was also conducted. The model 

produces very reasonable response to the environmental conditions (air temperature, leaf 

temperature, pressure, humidity, wind speed, solar radiation, vertical temperature gradient, and 

CO2 concentration etc.), indicating that the model has the application potential to a wide range of 

climate conditions, and can be used in the Clean Air Status And Trends Network (CASTNet). 

The model outputs depend on the accuracy and precision of input variables; therefore, a good 

quality assurance measurement program is required to get good performance of the model. 

Statistical analyses suggest that the photosynthesis model works better for C3 plants than 

for C4 plants. The scheme for C4 plant photosynthesis proposed by von Caemmerer and Furbank 

(1999) considers more variables, and may be worth testing in future studies. The model 

overestimated the amplitudes of both seasonal and diurnal cycles for SO2 deposition velocity. 

The model also shows high sensitivity to changes in humidity. This could be due to the weight 

relative humidity was given in the function computing the water thickness on a leaf surface. 

Therefore, further testing should be performed on this empirical function, or it could be replaced 

with the method described by Xiao et al. (2000). Evaporation from wet leaves occurs in real 

situations, but is not considered in the model, resulting in underestimation in both the weekly 

daytime averages and hourly averages. This further suggests that the method developed by Xiao 

et al. (2000), or a similar method be used in the model. Analysis shows that stomata are the 

dominant control factor for CO2, H2O, and O3 fluxes, while cuticle and aerodynamic resistance 

play important roles for SO2 flux. 

Field Measurements for Model Applications. 

The atmospheric resistance term in the model, Ra, depends on onsite measurements of 

turbulence. The previous version of the model, the MLM (Multi-Layer inferential dry deposition 

Model), was designed to use a simple and robust measurement of turbulence, the standard 

deviation of the wind direction, F2. The MLBC was designed to use either similarity theory, with 

the requirement to measure the difference of temperature with height, )T, or the simpler F2

method. Tests over both a soybean field and a forest show that the differences between the two 

methods are small, although the similarity theory method is slightly better. 

Similarity Theory Evaluation. 

Similarity theory is used extensively in both the dry deposition model and other parts of 

atmospheric dispersion models. The theory was evaluated extensively over ideal conditions, but 

very infrequently over the rough real world sites where it is usually used. The extensive set of 

meteorological measurements were recorded as part of the dry deposition field studies and used 

to evaluate similarity theory over agricultural fields and forests. More particularly, research 

focused on looking at the integral forms of the stability correction factors for temperature and 

momentum, Qm and QT. Preliminary results show that similarity theory seems to do a good job, 

even over the forests. Existing models of Qm and QT from Paulson (1970) and Byun (1990) do 

31

quite well in moderate stability, but less well in stronger stability. In some cases, the models do 

well for unstable conditions, but in others they overestimate the correction needed to the neutral 

case. The models seem to do their worst over corn fields, where roughness elements are less 

dense than the leaf canopy of a forest or soybean plants. 

2.2.5.2 Dry Deposition Modeling 

As part of the CMAQ development, a new method for modeling dry deposition of 

gaseous chemical species was developed to take advantage of the more sophisticated surface 

model, PX LSM, implemented in MM5. Since the PX-LSM has an explicit parameterization for 

evapotranspiration, the same stomatal and canopy conductances can be used to compute dry 

deposition velocities of gaseous species. This technique has the advantage of using more 

realistic conductance estimates resulting from the integrated surface energy calculation where the 

soil moisture is continually adjusted to minimize model errors of temperature and humidity. The 

new dry deposition model, M3Dry, was incorporated into the new version of the (MCIPv2). 

The combined PX-LSM and M3Dry models were previously evaluated for ozone 

deposition by comparing model results with field measurements at Bondville, Illinois, and 

Keysburg, Kentucky (Pleim et al., 1996; Pleim et al., 1997). Further evaluation studies were 

performed for a 1998 mixed forest study in New York (Pleim et al., 2001), and two sites, grass 

and soybeans, near Nashville, Tennessee, that were part of the SOS 1999 field study. Sensitivity 

of CMAQ to the new dry deposition scheme was studied for the NARSTO 1995 modeling study 

(Pleim and Byun, 2001). Further sensitivity experiments will be performed for the 1999 

SOS/Nashville study. 

2.2.6 Technical Support 

2.2.6.1 North American Research Strategy for Tropospheric Ozone 

NARSTO (formerly known as the North American Research Strategy for Tropospheric 

Ozone) is a coordinated 10-year research strategy to pursue the science-based issues that will lead 

to better management of the North American tropospheric ozone, particulate matter, and other air 

quality problems. It includes a management function for performing this coordination across the 

public and private sector organizations sponsoring air quality research, as well as those groups 

performing the research, including the university community. Canada and Mexico are also 

participating in the continental NARSTO program. During FY-2001, the ongoing NARSTO 

science assessment of tropospheric ozone was completed, with the publication of a special 

NARSTO issue of Atmospheric Environment (2000). This Special Issue contains a set of critical 

review papers, commissioned specially for the ozone assessment that covers relevant areas 

including ambient measurements and networks, field studies, source emissions, atmospheric 

chemistry, and meteorological and air quality models. The second part of the ozone assessment, 

32

an Assessment Report (NARSTO Synthesis Team, 2000) relating the state-of-science to 

outstanding air quality management issues, was published in October 2001. 

2.2.6.2 Western Regional Air Partnership Air Quality Technical Forums 

The Western Regional Air Partnership (WRAP) is a broad-based regional air quality 

coordinating organization composed of States and Tribes in the western United States, U.S. 

Departments of Agriculture and Interior, EPA, and other affected stakeholders representing 

industry, environmental groups, and other interested parties. A Division scientist participated in 

the Air Quality Modeling Forum (AQMF) and Research and Development Forum (R&DF), 

which are two of several committees of WRAP formed to provide technical guidance. WRAP is 

a follow-on organization to the Grand Canyon Visibility Transport Commission, whose objective 

was to provide technical and policy input needed to regulate regional haze in the western United 

States. AQMF provides WRAP with technical analyses needed to meet practical, real-world 

objectives, especially as they relate to meeting the regulatory requirements of the EPA regional 

haze rule (RHR) published July 1, 1999 (Regional Haze Regulations, 1999). Specific AQMF 

modeling assessments on regional visibility include (1) relative incremental contribution of a 

given source or source control on visibility at one or more Class I areas; (2) cumulative impact of 

regional source growth or control on Class I areas throughout the region; (3) impact of regional 

sources during periods of high and low visibility conditions; and (4) evaluation of cost-effective 

alternatives for improving regional haze. Time frames required by RHR are (1) near-term (SO2 

regional emission trading program plan due October 1, 2000); (2) intermediate to long-term 

(additional requirements for regional visibility modeling due December 31, 2003); and (3) longterm (modeling to support State Implementation Plans due no later than December 31, 2008). 

WRAP AQMF is using Models-3/CMAQ for performing the intermediate- and long-term 

modeling for RHR. 

The AQMF strategy is to facilitate the conducting of both the short term (RHR 109) and 

full implementation of the RHR Section 108 requirements. This approach involves setting up 

two teams, one to perform modeling startup of the CMAQ and other modeling systems for the 

short term tasks, and one to establish the WRAP regional modeling center run by the University 

of California at Riverside, to implement the various modeling requirements for the RHR Section 

108. Efforts are underway to address the need to develop and implement an improved modeling 

methodology and parameterizations for fugitive dust emissions and for the modeling of smoke 

emissions from both wildland and prescribed-burn fires into the CMAQ modeling system. 

2.2.6.3 Climatological and Regional Analyses of CASTNet Data from 1989-1999 

- The spatial and temporal variability of ambient air concentrations of SO2, SO42-, NO3 , 

HNO3, NH4

+

 and O3 obtained from the CASTNet was originally examined in 1998 using an 

objective, statistically based technique called rotated principal component analysis (Eder and 

Sickles, 1998). This initial analysis, which covered the period October 1989 through August 

33

1995, allowed for the identification and subsequent characterization of homogeneous influence 

regimes associated with each of the six species. Depending on the species, either two (NO3

-

), 

three (SO2, SO4

2-, NH4+

, O3), or four (HNO3) influence regimes were identified by the analysis. 

Examination of the temporal variability of these homogeneous influence regimes through time 

series and spectral analysis revealed various seasonal and annual cycles of differing strengths and 

timing. 

This analysis provided evidence of, and considerable insight into the regional-scale 

behavior of these species’ air concentrations, which suggested that exclusively local strategies to 

reduce their concentrations through reductions in emissions may be wholly inadequate without 

parallel management of regional emissions. Research using data from the period August 1995 

through December 1999 will help characterize the impact (on various spatial and temporal 

scales) of the Clean Air Act Title IV’s Phase I emission reductions that began in 1995 for SOx 

and 1996 for NOx. 

2.2.6.4 	Evaluation of the Use of NEXRAD Stage IV Data in the Multimedia 

Modeling of Pollutant Transport 

The Multimedia Integrated Modeling System (MIMS) is being designed to model the 

cycling of pollutants and nutrients between the atmosphere and the earth’s surface, including 

water bodies and groundwater. The ability to accurately model atmospheric, hydrological, and 

surface processes that transport chemicals is highly dependent on precipitation types, rates, and 

totals. Historically, the only data available for model input was from the National Weather 

Service rain gauge networks. More recently, however, a data set called NEXRAD Stage IV has 

become available that assimilates both rain gauge data and WSR-88D (Weather Surveillance 

Radar 1988 Doppler Version) into a comprehensive hourly, national data set with a 4-km2 

resolution. 

Since these data are available on a much finer scale than that of the rain gauge network, it 

was assumed that they would be superior for input into atmospheric and hydrological models. 

The purpose of this research is to investigate this supposition, while identifying possible 

limitations of the NEXRAD Stage IV data through a comparison with ground truth data obtained 

from a small but very dense rain gauge network near Lizzie, North Carolina (Eder et al., 2001b). 

This research, which has entered into its second year of data collection will use a variety of 

visualization and spatial statistical techniques. Accuracy, bias, and the confidence with which 

areal estimates on various scales can be made. 

2.3 Modeling Systems Analysis Branch 

The Modeling Systems Analysis Branch provides Information Technology (IT) and 

facilities infrastructure support to the Division, heads development of the Multimedia Integrated 

Modeling System (MIMS) framework, provides leadership to EPA’s IT Research and 

34

Development (formerly HPCC) program, and performs research on biogenic and anthropogenic 

emissions modeling. 

2.3.1 Technology Support for the Models-3/CMAQ Modeling System 

2.3.1.1 Models-3 Version 4 

A new release of Models-3 (Version 4.1) was prepared and distributed during FY-2001. 

This release included the SMOKE© emissions model, an accompanying SMOKE© Tool, and a 

revised and improved File Converter tool. The File Converter is capable of converting files of 

known content between ASCII, SAS®6, and the network Common Data Format (CDF) 

Input/Output Application Interface (I/O API) formats. The science algorithms, supporting 

processors, and tutorial input files were made available by anonymous ftp 

(ftp://ftp.epa.gov/amd). 

The Models-3 framework Version 4.1 for an UNIX®7 operating system, was developed on 

a SunTM8 workstation with a Solaris 2.7 operating systemTM9, and released in June 2001. Models-3 

documentation was made available via the Web at http://www.epa.gov/asmdnerl/models3. The 

updated Models-3 framework was released for SunTM workstations and Microsoft® WindowsTM 

NT®10 operating system. The framework has been encumbered with performance problems and 

licensing expenses of OrbixTM11, a commercial software component of the framework supporting 

distributed applications using object-oriented client-server technology. Further development of 

Models-3 framework is now directed away from the use of products that require licensing fees to 

the runtime user and version compatibility between the products. Specifically, during FY-2001, 

substantial progress was made on a Java-based modeling framework with a new operating 

paradigm supported under the MIMS development program, which is expected to replace the 

current framework during FY-2002. 

2.3.1.2 Models-3/CMAQ Stand-Alone Version 

6

SAS is a registered trademark of SAS Institute, Inc. 

7

UNIX is a trademark of AT and T. 

8

Sun is a trademark of Silicon Graphics, Inc. 

9

Solaris 2.7 operating system is a trademark of Sun Microsystems. 

10Microsoft and Windows NT are registered trademarks of Microsoft Corporation; and 

NT is a registered trademark of Northern Telcom Limited. 

11Orbix is a registered trademark of IONA Technologies Ltd. 

35 
During the Fall 2000, a stand-alone version of the Models-3/CMAQ modeling system 

was released with numerous enhancements, including: 

•	 Code needed to run the CMAQ initial conditions preprocessor (ICON), CMAQ 

emissions-chemistry interface preprocessor (ECIP), and CMAQ Chemistry-Transport 

(CTM) model on a parallel multi-processor platform like the CRAY® T3ETM12 . 

•	 CMAQ codes became Fortran 90 compliant, so that applications could be run on a 

parallel platform. 

•	 Single-source codes that can be compiled on any platform, eliminating the need to 

maintain separate codes for different platforms. 

•	 New aerosol module, which incorporated variable standard deviations into the modal 

approach using a new aerosol surface area species. 

2.3.1.3 	Models-3/CMAQ Reverse Gridding Utility 

The spatial allocation of emission-related data to grid cells is computationally one of the 

most time-consuming aspects of processing emission data for air quality modeling. In the 

Models-3/CMAQ modeling framework, spatial allocation is accomplished with a Geographic 

Information System (GIS). Typically, when data are provided by such political units as state or 

county, the state and county identifier codes are removed as part of the gridding process. 

Normally, this is not a problem, because gridded emission and modeled air-quality concentration 

data are evaluated on the basis of the grid cells. However, until recently, it has been impossible 

to reverse allocate the data to geographic units without the use of GIS. To address this problem, 

a generic reverse-gridding utility was created for the Models-3 system. Although the utility was 

written in Fortran, it can be used either independently from or within the Models-3 system. The 

reverse gridding utility accepts gridded-emission data in the format used by the Models-3 system, 

and produces either ASCII or network CDF I/O API formatted files of emission data by state or 

county. This is accomplished by using the ASCII spatial surrogate files prepared as part of the 

emission data gridding. The reverse gridding utility may be extended for use with such other 

geographic areas as hydrological units (drainage basins), which could aid multi-media modeling 

applications of nutrient deposition. After additional testing, the reverse gridding utility was 

released via the Models-3 web page during the Fall 2001 as a patch to the Models-3 Version 4.1 

release. 

2.3.1.4 	Models-3/CMAQ Support and Training 

12Cray is a registered trademark and Cray T3E is a trademark of Cray Research. 

36 
Community Modeling and Analysis System. 

ASMD entered into a cooperative agreement for a four-year research grant to initiate the 

Community Modeling and Analysis System (CMAS) center for user support, maintenance, and 

improvements of Models-3. CMAS is intended to encourage membership and participation from 

the state and local governments, industry and academia, as well as federal agencies. The initial 

work of CMAS will be to provide direct user support and training to users of Models-3/CMAQ. 

Models-3 Help Desk. 

The EPA Help Desk continued support for Models-3/CMAQ. The Help Desk has a 

support structure consisting of a collection of individual scientists to answer user questions in 

specific areas. The telephone number for the Help Desk is 919-541-0157. The Models-3/CMAQ 

web site (http://www.epa.gov/asmdnerl/models3/) also provided user support for the stand-alone 

version, i.e., without the Models-3 framework. These codes may be downloaded and adapted to 

execute on any computing platform. The downloaded files contain code programmed to ingest 

the data sets for a tutorial using the CB-IV chemical mechanism. The web site also contains 

Model Change Bulletins where known problems with the system are listed along with the 

instructions to solve the problem. Models-3 Public Forum area continued on the SCRAM 

(Support Center for Regulatory Air Models) web site (http://www.epa.gov/scram001). This area 

provides a central location for the discussion of issues related to the operation and use of 

Models-3. A mailing list was used to communicate with Models-3 users and others by sending 

messages to m3list@tempest.rtpnc.epa.gov. Registration of users on the mailing list is managed 

by the Models-3 Help Desk. 

2.3.2 Support for Multimedia Modeling 

2.3.2.1 Development of the Multimedia Integrated Modeling System 

Successful modeling cross-media ecosystems entails solving such scientific and 

computational challenges as ensuring that consistent assumptions are used at the boundary of the 

media and managing the large number of models and data sets that are typically required. The 

Multimedia Integrated Modeling System (MIMS) project is conducting research and developing 

solutions for some of those challenges. The MIMS framework is addressing the computational 

and data management issues. The framework is a software infrastructure or environment that 

will support constructing, composing, executing, and evaluating complex modeling studies. 

The MIMS development team started development of the MIMS framework during 

FY-2001. The focus of the effort was on developing software to support configuration and 

management of complex model executions. The team adopted Argonne National Laboratory’s 

Dynamic Information Architecture System (DIAS) (Christiansen, 2000) as the central approach 

for specifying model interactions and coordinating execution. DIAS organizes concepts to be 

modeled as domain objects such as the atmosphere, a group of people, and a chemical. Each 

37

domain object has a well defined set of parameters that describes its state and processes that 

changes its state. Models implement domain objects’ processes and use the domain objects’ 

parameters as inputs and outputs. Writing or wrapping models to work with domain objects 

eliminates direct dependencies between models. To support this paradigm, Argonne has 

developed a software library that provides discrete event-based coordination of models. 

The MIMS framework layers on top of the DIAS library a number of capabilities to assist 

modelers when they configure and execute their models. The framework’s graphical user 

interface provides a visual representation of the models’ configurations, interconnections, and 

execution statuses. The configuration is represented by an extensible set of parameters that 

correspond to model inputs and outputs. Types of parameters range from integers and strings to 

such complex concepts as a regular grid on the earth’s surface or a set of chemical reactions. 

New types of parameters and models can easily be supported, which allows basic graphical user 

interfaces for models to be developed more quickly in MIMS than would be possible with a 

custom user interface. 

The framework also includes support for the repeated execution of models. This is 

commonly used in evaluations of alternative management approaches, sensitivity and uncertainty 

studies, and optimization and calibration runs. The framework includes a foundation to support 

these various types of repeated execution and a full implementation for stepping forward through 

time. Several prototype versions of the framework have been provided to interested parties 

within EPA to prompt feedback on how to better meet their needs. The first public release of the 

MIMS framework is planned for Spring 2002 to support Models-3/CMAQ. 

2.3.2.2 Rain Gauge Network 

A network of 10 rain gauges was established within a small watershed near the Neuse 

River in eastern North Carolina. This region is being scrutinized by environmental scientists 

because of the region’s explosive growth of animal husbandry and concerns about estuary 

eutrophication and nitrogen deposition. This rural area consists of a mixture of farmlands, swine 

facilities, and densely-wooded snake-invested bottomlands. Precipitation data from this network 

will complement other surface hydrology, water quality, and ground water measurements that are 

being collected by scientists from U.S. Geological Survey (USGS) and the NERL/Athens 

Division to support modeling studies of the watershed. Precipitation data from the gauges will 

also be useful for evaluating daily NEXRAD data. 

2.3.3 Research on Emissions Modeling 

2.3.3.1 Anthropogenic Emissions 

During FY-2001, the Sparse Matrix Operator Kernel Emissions (SMOKE)© system was 

introduced as an integral part of the Models-3 air quality modeling system, replacing the 

38

Models-3 Emission Processing and Projection System (MEPPS). SMOKE© was originally 

developed as a prototype for the Models-3 system by the MCNC-North Carolina Supercomputing 

Center in cooperation with the Division (Houyoux and Vukovich, 1999). The sparse matrix 

approach to repetitive computations with large emission databases decreases processing time by 

at least an order of magnitude (hours to minutes). In addition to being modified to run with the 

current Models-3 framework, SMOKE© was enhanced to allow user-defined specification of 

such major elevated point-source emissions (MEPSE) as large electric utility stacks. The user 

may define MEPSEs by the mass of daily emissions for different pollutants and/or by physical 

stack parameters (height, diameter, temperature, flow). Enhancement to allow use of hourly 

continuing emission monitoring data from electric utilities was initiated in FY-2001. ASMD 

provided source category-specific emission-species data that allow SMOKE© to produce 

particulate matter species from reported PM2.5 emissions for use in CMAQ. 

During FY-2001, the SMOKE© Tool was developed and released as part of the Models-3 

system as a utility to provide input files and quality control functions for SMOKE©. The 

SMOKE© Tool is derived from MEPPS, and consequently remains based on the SAS® software 

system. The SMOKE© Tool includes the ability to import and quality assure emission-related 

data, tools to analyze and visualize the emission data using the ARC/InfoTM13 geographic 

information system (also required to use SMOKE© Tool), and the ability to output a user defined 

modeling grid and gridded spatial surrogate data files required by SMOKE©. Reducing licensing 

costs and eliminating dependencies on third-party software are key goals of future Models-3 

development. SMOKE© Tool can be used either within the current Models-3 framework or 

independently without the framework. A separate Spatial Tool to define grids and grid spatial 

surrogate data (coded in C) is under development. SMOKE© will be used in the new Java-based 

Models-3 framework based on the MIMS framework planned for release in FY-2002. SMOKE© 

Tool will not be in the new framework. However, its functions will gradually be replaced by new 

tools not dependent on ARC/Info® or SAS®, which may be used independently or in the new 

Models-3 framework. 

2.3.3.2 Biogenic Emissions 

ASMD continued to develop and test algorithms for simulating airborne emissions from 

natural and biogenic sources. These sources include hydrocarbons from vegetation, nitric oxide 

and ammonia from soils, nitric oxide from lightning, and ammonia from livestock operations. 

The algorithms were integrated into the Biogenic Emissions Inventory System (BEIS), the third 

generation of which was incorporated into the FY-2001 release of SMOKE©. A Division web 

site was created to provide additional information on the Division’s biogenic emissions research, 

slides of presentations, and access to data and computer algorithms. The web address is 

http://www.epa.gov/asmdnerl/biogen.html. 

13ARC/Info is a trademark of ESRI. 

39 
Regional air quality models need accurate characterization of vegetation cover to estimate 

biogenic emissions and dry deposition. However, most satellite-derived data sets, while 

providing good spatial resolution, do not resolve vegetation species and crop types. Isoprene 

emissions vary among tree species, with extremely high emissions from oaks, but negligible 

emissions from maples. Division scientists have constructed a 1-km vegetation database for 

North America, called the Biogenic Emissions Land use Database (BELD3). The USGS1-km 

land-use/land-cover (LULC) data set derived from the NOAA Advanced Very High Resolution 

Radiometer (AVHRR) satellite imagery was coupled with forest inventory data from the U.S. 

Forest Service and the 1992 Agricultural Census. The 1990 Census was used to denote 

urbanized regions. Each 1-km pixel includes percent forest cover, percent crop cover, Federal 

Information Processing Standard code, and the USGS LULC class. In the United States, each 

pixel is further divided into tree species and crop types. This data set provides much greater 

spatial resolution than earlier county-based land-use data sets developed for biogenic emission 

calculations. It should provide a more accurate basis for vegetation-sensitive calculations for 

such regional air quality models as CMAQ. The data set can be accessed at 

http://www.epa.gov/asmdnerl/biogen.html. 

2.3.3.3 Inverse Modeling of Ammonia Emissions 

Approximately 85% of NH3 emissions are estimated to come from agricultural non-point 

sources, with a suspected strong seasonal pattern in these NH3 emissions. However, current NH3 

emission inventories’ lack intra-annual variability. The Branch is applying the adaptive-iterative 

Discrete Kalman Filter inverse modeling technique (Gilliland and Abbitt, 2001; Haas-Laursen et 

al., 1996) to estimate seasonally varying NH3 emission for the eastern United States. The Branch 

used the CMAQ modeling and ammonium (NH4

+

) wet concentration data from the National 

Atmospheric Deposition Program network for this application. The inverse modeling technique 

estimates the emission adjustments that provide optimal modeled results with respect to wet 

[NH4

+

], observational data error, and emission uncertainty. Final products of this research will 

include monthly emission estimates from each season of 1990. Results for January, April, and 

June 1990 are currently available. The results confirm that annual average NH3 emission 

estimates can introduce substantial errors into air quality modeling results. Based on the inverse 

modeling method, the annual emission values should be decreased by 64% for January 1990, 

26% for April 1990, and increased by 25% for June 1990. More details from these results can be 

found in Gilliland et al. (in press). 

Simulations are currently underway to develop emission adjustments for October 1990, 

which along with results from January, April, and June will provide a first estimate of the 

seasonal variation in NH3 emissions. The next step in this research will be to expand to more 

recent years to provide information about the interannual variability, as well as the seasonality, in 

NH3 emissions. Additionally, the inverse modeling method will be modified and tested to 

address spatial variability in the emissions by considering individual source types and their 

spatial distribution. 

40

2.4 Applied Modeling Research Branch 

The Applied Modeling Research Branch investigates and develops applied numerical 

simulation models of sources, transport, fate, and mitigation of air toxic pollutants in the near 

field and conducts research on exchange of air pollutants with other media. Branch scientists 

also conduct research to develop and improve human exposure predictive models, focusing 

principally on urban environments where exposures are high. Databases are assembled and used 

to model development and research on flow characterization, dispersion modeling, and human 

exposure. Using the Fluid Modeling Facility (FMF), the Branch conducts simulations of 

atmospheric flow and pollutant dispersion in complex terrain, in and around such obstacles as 

buildings, in convective boundary layers and dense gas plumes, and in other situations not easily 

handled by mathematical models. 

2.4.1 Compartmental Multimedia Modeling 

The transport and environmental fate of toxic pollutants is an inherently multimedia chain 

of events. One key area of uncertainty in current spatial, multimedia models is the lack of proper 

characterization of intermedia fate and transport elements. This is a principle strength of fully 

coupled hybrid compartmental (FCCM) models. FCCM models include both well-mixed or 

uniform as well as non-uniform media descriptions that are tightly integrated through well-posed 

intermedia physical boundary conditions. Simultaneous solution of the media expressions 

imposes a chemical mass balance across the entire multimedia system. Recent implementations 

of this modeling approach include the mechanistic descriptions of intermedia transport 

parameters, the accounting for time-dependent parameter variability, and the inclusion of 

vegetation and aquatic biota compartments. These models are most appropriate for the study of 

widely distributed (i.e., non-point source), relatively non-reactive organic chemicals at scales 

ranging from a small watershed up to 103 km2. An internal EPA Grant was awarded in 1999 to 

support research regarding the development of a “next generation” of these models, using the 

FCCM model MEND-TOX, as a point of departure. 

Several case studies using MEND-TOX were completed that explore various aspects of 

FCCM model behavior. During FY-2001, a manuscript was accepted for publication that 

describes the linkage of MEND-TOX and ongoing Division research regarding the development 

of enhanced algorithms describing gas-phase dry deposition to vegetated surfaces (Cooter and 

Cohen, accepted for publication). Subsequently, this work was combined with previous MENDTOX applications to produce two manuscripts— a description of the FCCM approach, and a 

summary of case study results— for publication (Cohen and Cooter, accepted for publication; 

Cohen and Cooter, accepted for publication). Internal reports summarizing the potential 

implementation and advancement of FCCM concepts under Multimedia Integrated Modeling 

System (MIMS) framework were assembled during FY-2001. Critical design and 

implementation issues were highlighted and will be explored during FY-2002 through a limited 

series of evolving pilot models. The first such pilot, in which all media are assumed to be 

homogeneous, is illustrated in Figure 5. 

41

Air

(gas only)

ad'O,"'iOn/VOIa'ili""t Iwet depo,ition 

Water Biota I 

uPtake/~eas~ excr tion - - - - - - - - - - J 

feedin:< A. 

Figure 5. The three compartment FCCM phase I 

pilot model. 

Specific MIMS framework issues exclusive to the Phase I compartmental model study 

include using the framework to build and define a set of model equations from individual objects 

or components, use of interchangeable model components for re-specification of the model 

equations, input from several information sources spanning a wide range of time scales, 

implementation of a numerical solver suitable for systems of stiff ordinary differential equations, 

and the iterative solution of the equation system that defines our multimedia environment. 

During FY-2002, the results ofthe Phase I pilot will be evaluated. Based on the results of the 

evaluation, the pilot could be expanded to address a variety of such other design issues as the 

inclusion of non-homogeneous media (described via partial differential equations), additional 

mechanistic transfer processes, additional media, and linkages with other Division model 

implementations under the MIMS framework. 

42
2.4.2 Application of the Concept of Saltation-Driven Resuspension of PM10 

A method for estimating the vertical flux of resuspension particles smaller than 10 

micrometers (PM10) emitted during wind erosion was developed for Owens Lake, a large dustsource area in California. Owing to the size of the potentially dust-emitting dry lake bed (about 

130 km2

 ) and the large effort and expense to rigorously measure vertical mass fluxes using 

micometeorological methods, an alternate method using cheaper and more easily accomplished 

measurements was developed. The method assumed that the emission mechanism for vertical 

flux of PM10 is dominated by sandblasting by hopping (saltating) sand particles. Sand grains 

absorb momentum from the wind and deliver it to the surface. When the kinetic energy of the 

impaction of sand grain overcomes the binding energy holding particles d<10:m in place, 

resuspension occurs. 

Using the assumption that sandblasting dominates PM10 emissions, the model of PM10 

dust emissions used measurements of sand mass flux on the surface of the lake bed, a model of 

dust transport, and high quality measurements of PM10 at several locations near the shoreline of 

Owens Lake. The method allowed the estimation of mass fluxes for the most active areas of dust 

emissions at Owens Lake as follows: 

! Sand fluxes are presently being measured in a 1 km by 1 km grid of 130 sand flux 

samplers. 

! The concentration of PM10 (particle mass for particle size smaller than 10 :m) is 

measured at several locations along the shoreline of Owens Lake using continuous 

measuring TEOM (Tapered-Element Oscillating Microbalance) instruments. 

! The concentrations are modeled using CALPUFF. Initially, a first guess value for the 

ratio of vertical mass flux of PM10 to horizontal sand mass flux (Fa/q) is used with the 130 

q values to give the Fa (vertical fluxes of PM10) for each 1 km2 area of Owens Lake. 

These Fa values are used in the model and the concentration field is calculated. 

! The ratios of the calculated concentrations at the locations of the TEOM instruments to 

the actual concentrations are found. Using the mean of these ratios the first guess Fa/q 

value is adjusted so that the predicted and measured concentrations agree. 

! One Fa/q value is found for each hour of a dust storm. 

! An example of a dust storm that took place on May 2–3, 2001, is shown in Figure 6. 

Owens Lake is shown having a grid work with a sand motion detector at the center of 

each grid square. The number appearing in each square is the total mass flux of sand for 

the entire storm. Superimposed are boundary observations of the dust plumes taken for 

each hour of the storm from three mountain locations surrounding the lake. TEOM PM10 

instruments are shown on the map at Olancha, Dirty Socks, Shell Cut, Flat Rock, Keeler, 

and Lone Pine. 

43

! All dust storms are analyzed. Owens Lake is divided into three areas having distinct 

surface characteristics. Because these areas are the dominant source areas for given 

TEOM receptors, during defined wind direction ranges, the times are restricted to those 

hours when winds were within the wind direction range. One-hour Fa /q values that apply 

to the three distinct source areas of the lake were calculated. 

! Micrometeorologically and wind-tunnel determined Fa/q values for small areas of the 

lake were found and were compared to the large-scale Fa/q values. Agreement was 

satisfactory. 

! Using this method, areas of the strongest emissions of Owens Lake were identified and 

estimates of the total production of PM10 dust were calculated. 

2.4.3 	Comparison of Wind-Tunnel Measurements of the Flow and Turbulence Fields in 

Arrays of Two- and Three-Dimensional Buildings 

A laboratory study of the complex flow patterns within arrays of two-dimensional and 

three-dimensional buildings to simulate the types of flow fields that might be found in urban 

street canyons was conducted in the meteorological wind tunnel at the Fluid Modeling Facility 

(FMF). This study is an integral component of the Division’s modeling of particulates and air 

toxics at neighborhood scales (Poole-Kober and Viebrock, 2000), a project to develop linkages 

between Eulerian grid-based air quality models and human exposure at finer scales. 

Additionally, the FMF staff have been collaborating with Los Alamos National Laboratory and 

Lawrence Livermore National Laboratory to utilize the study data for improving computational 

fluid dynamics models of flow and dispersion in urban and industrial areas. Previous annual 

report entries (Poole-Kober and Viebrock, 1999; 2000) describing this long-term project have 

discussed a series of flow visualizations and surface pressure measurements on the buildings and 

the mean flow and turbulence in the array of two-dimensional buildings. Here, the focus is on a 

comparison of the flow and turbulence fields for arrays of two- and three-dimensional buildings. 

44

Figure 6. An example of a dust storm during May 2–3, 2001. 

45

For the 2D array in the wind tunnel, seven rectangular buildings, each the width of the 

tunnel and height and downwind dimension equal to 15 cm, were placed with an alongwind 

separation of 15 cm (Figure 7a). The 3D array consisted of seven rows of eleven cubical 

buildings where each cube had a height of 15 cm (Figure 7b). High resolution measurements of 

the three components of mean and turbulent velocities were obtained around and above the 

buildings. The approach flow to the building array was a simulated neutral atmospheric 

boundary layer with a depth of approximately 12 building height. There was sufficient upwind 

fetch for the boundary layer to grow to equilibrium before reaching the upwind edge of the 

buildings. The approach flow at z = building height (H) was 3m/s, ensuring that Reynolds 

number independence was satisfied. Measurements of the three components of velocity and the 

turbulence intensities were obtained with pulsed-wire anemometry (Bradbury and Castro, 1971). 

Measurements (on the longitudinal centerline of the building array) were collected from 3.5 H 

upstream to 7.5 H downstream of the building arrays and up to 3 H in the vertical. Figure 8 

shows a cross section of the mean wind and the turbulent kinetic energy (TKE) fields around and 

upstream of the first three buildings in the 2D array and the first three rows of cubical buildings 

in the 3D array. The cross sections are along the centerline of the tunnel, which in the 3D array 

is centered on buildings (not in a street canyon). 

There are several noticeable differences in the flow fields of the 2D and 3D cases. At the 

leading edge and immediately above the rooftop of the first building row, the TKE is much larger 

in the 2D case. The presence of the wide buildings requires the air to move more forcibly over 

the obstruction resulting in stronger jetting action and associated shear induced turbulence 

beyond the leading top edge. This region of high turbulent energy is advected downwind over 

several more buildings (rows). The strong recirculation just upwind of the first 2D building, 

again as the result of the blockage by the wide buildings, appears both in the flow vectors and in 

high turbulence levels. Also, in the flow for the 2D buildings there is significant vertical motion 

for up to several building heights above the first row whereas the flow becomes nearly horizontal 

just a short distance above the buildings in the 3D case. The primary reason for these various 

differences is that the recirculation, jetting and vertical motions are much less pronounced 

because the air stream can flow between as well as over the buildings in the 3D arrangement. 

Finally, distinct differences appear in the regions of circulation within the street canyons. 

Between the 2D buildings the vortex is symmetric and is centered in the canyon. With the 3D 

building array, the vortex is clearly asymmetric with the center shifted toward the upwind 

building face leaving a broad area of downward motion. This asymmetry is likely due to the 

lateral (cross-stream) canyon motions that result from the edge effects as the flow moves around 

the sides of the cubical buildings. 

46

Figure 7. Building arrays in the wind tunnel: a) two-dimensional buildings, flow left to right; b) threedimensional buildings, looking into the flow. 
Figure 8. Mean velocity vectors and turbulent kinetic energy (m2

s

-2) in the centerplane of the 

wind tunnel for the area surrounding the first three rows of buildings. 
2.4.4 Atmospheric Correction / Image Processing 

One of the physical state variables characterizing a landscape is its spectral albedo, or 

reflectance, function. During June 1999, spectral upwelling radiance was measured for a 10 km 

x 60 km swath of the lower Neuse River Basin by NASA/Dryden field operations. The 

detector— Airborne Visible and Infrared Imaging Spectrometer (AVIRIS)—is a nadir-looking 

spectral radiometer that collects radiance data [W/m2/sr/nm] in 224 bands (10 nm bandwidth) 

from 380 nm to 2510 nm, with a spacial resolution of 20 m. Processing of AVIRIS data to derive 

surface reflectance requires compensation for atmospheric effects, including backscatter and 

absorption. The MODTRAN radiative transfer model was used to create data for a look-up table 

interpolation approach (inverting radiance to 

determine reflectance has no closed form 

since reflectance-to-radiance is strongly nonlinear). Using MODTRAN, upwelling 

radiance was calculated for atmospheric 

conditions and geodesic configuration 

prevailing at the time of overflight. Pixelwise correction for water vapor is necessary 

due to its great spacial variability. Scene-wise 

correction was adequate for other gasses and 

aerosols having negligible spacial variability 

within the scene. One unexpected discovery 

of this work is the difference in detectable 

precipitable column water vapor over 

agricultural fields vs. forest canopies (Figure 

9). This could lead to an automated 

classification scheme. Automated image 

classification will be validated by field 

reference data. 

Figure 9. Water vapor highlighted scene, with 

river, lakes, and clouds in black. 

2.4.5 Local Scale Modeling of Human Exposure Microenvironments 

A project to specifically improve the methodology for real-time site specific modeling of 

human exposures to motor vehicle emissions is ongoing. This project is being pursued in 

collaboration with other projects. The goal is to develop improved methods for modeling air 

pollution from the source through the air pathway to human exposure in significant 

microenvironments. Local-scale modeling refers to spatial scales from the size of an individual 

vehicle to the order of 1 km. A complete modeling framework from source-to-exposure, together 

with some measurements, is principally being set up in the Research Triangle Park area of North 

Carolina and in New York City, which can be transferred to other locations. Human exposure 

models use simplified assumptions based on a few fixed air monitoring stations or modeled 

49

concentrations from regional-scale motor vehicle emission/transport models resulting in great 

uncertainty in their estimations. The refined modeling will be used to provide improved linkage 

between source emissions and human exposures to provide refined exposure factors for 

significant microenvironments. 

Real-time site-specific motor vehicle emission models capable of capturing real-world 

emissions are needed to support human exposure modeling. Development of a real-time 

Microscale automobile emission Factor model for Particulate Matter (MicroFacPM) was 

completed and supporting papers should be published during FY-2002. Now MicroFacPM along 

with the Microscale automobile emission Factor model for Carbon Monoxide (MicroFacCO) 

will be applied to roadway dispersion models to evaluate there performance. These models can 

be used to demonstrate the sensitivity of emission estimates to real-time input parameters for 

vehicle fleet composition, vehicle speed, and meteorological conditions. 

Refined modeling using computational fluid dynamics (CFD) simulations and 

measurements are being applied to develop refined air dispersion models for linkage to human 

exposure microenvironmental models. This modeling framework will help to establish the 

direct relationships between source-to-exposure concentrations specific to the particular exposure 

microenvironment. Output from this deterministic modeling of microenvironmental 

concentrations and measured microenvironmental concentrations for a range of scenarios will be 

used to develop distributions of potential exposure that is probabilistically-based to support 

population-based human exposure modeling. Figures 10, 11, and 12 provide examples of CFD 

simulation models for a multi-block area in New York City that were set up for evaluation. CFD 

simulations provide opportunities for expanding and improving capabilities for modeling 

exposures to environmental pollutants. A cooperative research project with Fluent, Inc., is 

examining and evaluating the application of CFD models for simulating air pollution along the 

pathway from source to human exposures. While the goal ultimately is to model a large urban 

neighborhood, there is first a need to examine the simulation of an atmospheric boundary layer 

and pollutant dispersion in absence of urban obstacles. The detailed spatial resolution of 

environmental pollution concentrations that is possible from CFD simulations can provide 

important information that is not available from a single-point measurement. Output of CFD 

simulations can be used to develop better simplified modeling methods in the same way as field 

and wind tunnel study measurements are used. Through further research, validation and testing, 

CFD modeling has the potential to become a reliable tool for estimating pollutant concentrations 

for situations that today have no reliable modeling method. 

50

Figure 10. Example effect of urban buildings on wind 

profiles. 

Figure 11. Example vertical slice of full 3-D solution for wind. 

51

Figure 12. Example vertical slice of full 3-D solution for concentration. 

2.5 Air Policy Support Branch 

The Air Policy Support Branch supports the activities of the EPA Office of Air Quality 

Planning and Standards (OAQPS). The Branch responsibilities include evaluating, modifying, 

and improving atmospheric dispersion and related models to ensure adequacy, appropriateness, 

and consistency with established scientific principles and Agency policy; preparing guidance on 

evaluating models and simulations techniques that are used to assess, develop, or revise national, 

state, and local pollution control strategies for attainment and maintenance of National Ambient 

Air Quality Standards; and providing meteorological assistance and consultation to support 

OAQPS in developing and enforcing Federal regulations and standards and assisting EPA 

Regional Offices. 

2.5.1 Modeling Studies 

2.5.1.1 CMAQ Proof of Concept: Continental United States Application 

As part of a proof-of-concept effort designed to more fully understand the details of the 

Models-3/CMAQ modeling system and lay the groundwork for using CMAQ in regulatory 

support modeling exercises, Models-3/CMAQ was configured and successfully applied for the 

entire year of 1996 over the continental United States. The simulations were completed for a 36­

km grid with eight vertical layers. The meteorological input fields were developed using MM5 

and the emissions were based on a version of the National Emissions Trend inventory. 

52

A base case simulation using the May 2000 release of CMAQ was completed and 

evaluated against a limited ambient database. Generally, comparisons between model 

predictions and ambient data for particulate matter (PM) smaller than 2.5 microns 

(PM2.5)—paired in space and time—showed good agreement as shown in Figure 13. 

Figure 13. Comparison of annual average IMPROVE site concentrations 

and model concentrations of PM2.5 (paired in space and time) for the 

1996 national CMAQ application. Dashed lines indicate 25% error. 

However, based on this particular CMAQ application, there appear to be some fine 

particulate performance issues that still need to be resolved. This may not necessarily be 

indicative of a problem with the air quality model itself, as some combination of meteorological 

and emission inputs could also be responsible. It should also be noted that most other existing 

PM models are subject to the same PM2.5 performance questions. The considerable 

overestimation (factor of 5) of particulate nitrates in the model, especially in the winter (Figure 

14), is of particular concern. Several diagnostic tests are planned to better determine the causes 

of these over predictions. 

53

Figure 14. Comparison of winter average (December-February) Interagency 

Monitoring of PROtected Visual Environments (IMPROVE) site 

concentrations and model concentrations of sulfate ion, nitrate ion, and 

organic carbons by region for the 1996 national CMAQ application. 

Several diagnostic simulations were completed investigating the effects of certain model 

inputs and physiochemical options. The first set of tests quantified the impacts of employing the 

RADM2 chemical mechanism as opposed to the CB-IV mechanism, which was used in the base 

case. For the most part, the simulated particulate concentrations were not greatly affected. The 

second set of tests looked at the impacts of reducing the highly uncertain ammonia emissions 

inventory by 50% for January 1996. While this improved the model nitrate predictions 

considerably, additional emissions development research is required before such a change can be 

justified. The third set of tests looked at the impacts of varying boundary conditions on CMAQ 

model predictions. A qualitative assessment determined that model performance for ozone could 

be improved by using climatological average inputs as opposed to the default concentrations that 

are built into CMAQ. These runs also highlighted the potential impacts of intercontinental 

transport on air quality in the western United States. 

54

2.5.1.2 	The National-Scale Air Toxics Assessment 

Under the CAAA of 1990, EPA is required to regulate emissions of 188 listed air toxic 

pollutants. The EPA National-Scale Air Toxics Assessment includes 33 air toxics that present 

the greatest threat to public health in the largest number of urban areas. These 33 air toxics 

include 32 hazardous air pollutants (HAPs) and diesel particulate matter. The goal of the 

national-scale assessment is to characterize risks and health effects of these air toxics on a broad 

scale to identify pollutants of greatest concern to the greatest number of people. The results will 

be used to identify pollutants and areas of the country requiring additional investigation. 

The Assessment System for Population Exposure Nationwide (ASPEN) model was used 

to estimate the 1996 ambient concentrations based on the1996 emissions (U.S. Environmental 

Protection Agency, 2000). This model is based on the Industrial Source Complex Long-Term 

model (ISCLT), which simulates the behavior of the pollutants after they are emitted into the 

atmosphere. ASPEN uses estimates of toxic air pollutant emissions and meteorological data 

from more than 350 National Weather Service Stations to estimate air toxics concentrations 

nationwide. The ASPEN model takes into account such important determinants of pollutant 

concentrations as rate of release, location of release, the height of release, wind speed and 

direction from the meteorological stations nearest to the release, breakdown of the pollutants in 

the atmosphere after being released (i.e., reactive decay), settling of pollutants out of the 

atmosphere (i.e., deposition), and transformation of one pollutant into another (i.e., secondary 

formation). The model estimates toxic air pollutant concentrations for every census tract in the 

continental United States, Puerto Rico, and the Virgin Islands. Census tracts are land areas 

defined by the U.S. Bureau of the Census and typically contain about 4,000 residents each. 

Census tracts are usually smaller than 2 square miles in size in cities, but much larger in rural 

areas. 

Modeled ambient concentration maps allow states to view 1996 ambient concentration 

estimates (in micrograms per cubic meter) based on the median concentration in each county. 

The maps are color-coded (by percentile breakdown relative to the rest of the country) to show 

how each county's median concentration compares to the rest of the United States. The median 

concentration is the value for which 50% of the census tracts in the county have ambient 

concentrations less than the median, and 50% of the census tracts in the county have ambient 

concentrations greater than the median. Lists of the modeled ambient concentrations for benzene 

for each state is available at http://www.epa.gov/ttn/atw/nata/. Several conclusions can be 

reached from the results of this study: 

•	 Concentration estimates are a complex function of a number of factors, including 

emissions density (number of sources in a particular area), meteorology, and source 

characteristics, rather than just related to total emissions. Both emissions and estimated 

concentrations of the 32 HAPs available to date are generally higher in urban than in rural 

areas. 

55

•	 Some pollutants are more evenly distributed around the country (e.g., benzene, which is 

present in gasoline), while others are linked to areas of industrial activity (e.g.,vinyl 

chloride). 

•	 There is considerable variability between the national, state, and the county level in terms 

of contributions by source type. 

•	 The highest ambient average concentration of the individual pollutants occurs in different 

States (i.e., no one State has the highest concentrations of all the pollutants), because 

different types of sources are contributing to emissions in different areas of the country. 

•	 Of the four main source types (area and other, major, on-road, non road), no one type is a 

main contributor to the estimated concentrations of the 32 HAPs available to date. The 

results show that, on a national level, about half of the pollutants have area and other 

sources as the dominant contributing source type. Source type contributions for benzene 

concentration in each state is available at http://www.epa.gov/ttn/atw/nata/. 

2.5.1.3 	Statistical Evaluation of Model Performance 

Within the American Society for Testing and Materials (ASTM) work continues on the 

development of an ASTM Standard Guide that would provide guidance on the construction of 

objective statistical procedures for comparing air quality simulation modeling results with tracer 

field data. Thus far, those most involved in the development of this ASTM Guide have been 

scientists within the European community, where there is still strong interest in short-range 

plume and puff dispersion models. In December 2000, the guide was finalized and published by 

ASTM with the designation number of D-6589-00, and entitled, Standard Guide for Statistical 

Evaluation of Atmospheric Dispersion Model Performance (available from the ASTM web site, 

http://www.astm.org/). The publication of this guide is an important step towards developing 

statistical evaluation procedures for use in selecting and recommending preferred air quality 

simulation models for use in routine regulatory assessments. 

Work will now be directed toward establishing test methods for use in evaluating plume 

dispersion models. In finalizing the guide, a series of investigations were completed to refine 

and test a draft evaluation procedure that is described within D-6589 (Irwin, 2001). During the 

Fall 2001, work was completed on revising Fortran software and databases that implement a draft 

evaluation procedure described in the annex of D-6589, for public use and evaluation. This 

procedure measures how well short-range dispersion models characterize the variation of the 

centerline maximum concentration at the surface as a function of transport distance and stability. 

To demonstrate this procedure, work is underway to evaluate several modern plume dispersion 

models using tracer field data from three extensive field experiments. If these results prove 

successful, work will be initiated to convert the D-6589 annex into a formal ASTM Standard 

Procedure, which can then be formally recommended for use. 

56

2.5.1.4 The Krakow Urban Air Pollution Project 

The former Polish capitol, Krakow, is located in the Vistula River Valley and frequently 

experiences extended periods of stagnation, especially in wintertime. With emissions from such 

old technologies as factories, steelworks, and small family manufacturing sites, Krakow 

frequently experiences air pollution problems. In recent years, Krakow has seen a rapid increase 

in the numbers of cars and associated traffic congestion resulting from a lack of bypasses 

surrounding the city, which is anticipated to add to existing air pollution impacts. Starting in 

1991 with support from the EPA Office of International Activities, a pilot program for Poland 

was initiated to develop a formal air pollution abatement program. A seven-station automated 

air-monitoring network was installed in Krakow and training was initiated in the collection and 

analysis of air monitoring data. In December 1998, the pilot program was extended to include 

training in the application of air dispersion modeling, for the ultimate purpose of investigating 

the benefits of alternative control abatement strategies. The CALMET/CALPUFF (Scire et al., 

2000a; 2000b) modeling system was selected to provide a flexible system that would prove 

useful throughout Poland. In the Spring 2001, the comparisons of simulation results for 

sulfur-oxide (SO2)with monitoring data for seven sites in Krakow for 1998 were completed 

(Irwin et al., 2001a). 

The comparison of annual and monthly concentration values is shown in Figure 15. The 

larger variance in the estimates versus observed, may signal a need for local emission inventory 

adjustments. For instance, the bias to overestimate concentration at Site 3, Figure 15(A), is seen 

in Figure 15(B) to relate to overestimation of the winter month concentration values (solid red 

circles). The bias to underestimate at Site 4, Figure 15(A), is seen in Figure 15(B) to relate to 

underestimation of the winter month concentration values (solid blue circles). 

57

Figure 15. Comparison of observed and estimated A) annual average and B) monthly average 

SO2 concentration values. Linear regression results (with zero intercept) and site locations are 

annotated within the figures. 

Figure 16 illustrates the estimated relative emissions for January and June 1998. Figure 

15 illustrates the contribution to the total SO2 concentration estimated for Site 5 from each of the 

three emission inventories. Only results for Site 5 are shown in Figure 17 since the contribution 

from the three emission inventories (point, heat, mobile) to the total SO2 concentration estimated 

at each site is fairly consistent across all sites. Figure 17 shows the definite seasonal variation in 

the relative impact from the three source types, and the relative importance during the heating 

season of the heat production emissions. These results shown in Figures 16 and 17 illustrate that 

although the emissions from tall stacks can be considerably larger than from low level releases, 

the local impact from these emissions is limited to periods of the day and seasons of the year 

when these emissions reach the surface. 

58

Figure 16. Relative emissions (percent) for Figure 17. The percentage contribution to 

January and June 1998 from the three source the total SO2 concentration estimated for Site 

inventories 5 from each of the three emission inventories. 

Black line is simulated total SO2. 

The basis for sponsorship by the U.S. Agency for International Development for the 

Krakow Urban Air Pollution Project was removed with the acceptance of Poland into the North 

Atlantic Treaty Organization Alliance. The project successfully trained the staff in the operation 

of the CALPUFF modeling system, and provided a basis for investigating the benefits of 

alternative control abatement strategies in future years. 

2.5.1.5 Estimating Background Concentration for Diesel PM 

Background concentrations are an essential part of the total air quality concentration to be 

considered in determining source impacts. Background air quality includes pollutant 

concentrations due to natural sources, nearby sources that are unidentified in the inventory, and 

long-range transport into the modeling domain. Typically, monitored air quality data should be 

used to establish background concentrations. 

The ASPEN model is based on ISCLT Version 2.0 Gaussian plume model. The ASPEN 

model calculates concentrations at receptors in a concentric grid of 12 rings and 16 radial 

distances with a maximum distance of 50 km. However, sources at distances more than 50 km 

from the receptor contribute to the total concentration at the receptor location. 

59

For diesel PM, a modeling based approach was developed to provide a rough 

approximation of concentrations due to transport from sources located between 50 km and 300 

km from the receptor. A schematic plot showing the relationship between the census tract 

centroids at a distance 50 km – 300 km and modeling receptors allocated at census tract centroids 

is shown in Figure 18. In this figure, the receptor is shown as a blue star and a contribution from 

emission sources within a ring of 50 km – 300 km (shown in red) is considered. The background 

concentration at each receptor is the sum of concentrations resulting from all sources within the 

50 km – 300 km radius. 

This approximation was based on results from existing CALPUFF simulations from an 

elevated source (35 m) and a surface release (2 m) for three geographical areas: Boise, Idaho, 

Medford, Oregon, and Pittsburgh, Pennsylvania. These simulations were made as part of a series 

of simulations to compare ISCLT results with CALPUFF results (U.S. Environmental Protection 

Agency, 1993). CALPUFF is a Lagrangian puff model, which was originally designed for 

mesoscale applications, and it can operate in a range of 0 – 300 km from the source (Scire et al., 

2000b). For these CALPUFF simulations, CALPUFF was run using ISC meteorology. 

Therefore, these CALPUFF results are not the result of a full-scale refined analysis, in which the 

meteorological conditions are allowed to vary in space and time. 

The approach has several limitations. The estimates assume a complete and accurate 

inventory. Use of the ISCLT meteorology in CALPUFF does not account for wind flow in rivers 

and valleys as in mountainous terrain. The local wind flow patterns could cause concentrations 

to be significantly different at specific locations. Using three specific locations to obtain a 

national average parameterization is simplistic. Finally, using CALPUFF with site specific 

information on emission release height, stack parameters, wet and dry deposition, meteorological 

wind field, etc., would give different estimates. Thus, these estimates of the impact of emissions 

located greater than 50 km but less than 300 km are considered as an approximation of 

background concentration until more reliable estimates can be obtained from monitoring data or 

when improved modeling techniques are developed. 

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Figure 18. Schematic map of census tract centroids and rings of radius 50 km, 300, km. 

2.5.1.6 	A Simplified Approach for Estimating Secondary Production of Hazardous 

Air Pollutants Using the OZIPR Model 

Title III of the CAAA regulates chemicals classified as hazardous air pollutants (HAPs). 

These are substances that are either known to cause, or are suspected of causing, a threat of 

adverse human health effects. These compounds are found in the atmosphere as a result of 

primary emissions or from the transformation of organic compounds emitted by stationary or area 

sources. Carbonyl compounds represent an important class of organic compounds found on the 

list of HAPs. This study examines three of these compounds, formaldehyde, acetaldehyde, and 

acrolein, to determine the relative importance of their formation through primary and secondary 

processes. 

61

Several complex models exist that include both dispersion and atmospheric chemistry to 

yield HAPs concentration estimates. However, these models are very expensive to execute, often 

requiring the use of supercomputers. The goal of this study was to explore whether a simplified 

approach could provide useful estimates of total HAPs concentrations. The approach taken was 

to estimate secondary HAPs production with a stand-alone model run in a personal computing 

environment that incorporated only nondispersive processes such as photochemistry. Results 

from this model would then be coupled to those from a relatively simple dispersion model to 

estimate total ground-level HAPs concentrations. The study results indicate that such a process 

is possible and yields reasonable estimates. 

The photochemical modeling used the OZIPR (OZone Isopleth Plotting program, 

Research version one) model, a one-dimensional box model with a time-varying box height. 

Emissions were added to the box by time of day; such factors as temperature, relative humidity, 

atmospheric pressure, solar radiation, and deposition were used to determine chemical reaction 

rates. The reaction mechanism used in the study is based on the widely used SAPRC97 

mechanism. The model produces chemical concentration estimates as a function of time. These 

estimates can then be used in conjunction with output from other models that account for 

dispersion, but not for chemical transformations. The output data from the OZIPR model are 

presented in several ways (e.g., annual and seasonal averages, time series profiles) to facilitate 

their use with dispersion models. 

Ten study areas were selected for this project: Atlanta, Boston, Chicago, Denver, 

Houston, Los Angeles, Phoenix, Pittsburgh, Seattle, and Washington DC. In each study area, 

urban and rural counties were chosen. The urban counties are centered on the cities in question; 

the rural counties are near enough to the urban areas to have similar meteorological patterns, but 

different emissions based on their lower population and distinct land use patterns. As many as 

48 model runs were needed to adequately characterize each city. 

The results show that secondary formation generally accounted for approximately 90% of 

the ambient formaldehyde and acetaldehyde, and approximately 85% of acrolein; these 

percentages varied only slightly between cities (U.S. Environmental Protection Agency, 1999a). 

Annual averages for the urban secondary formation of formaldehyde ranged from 3.0 :g m-3 for 

Seattle to 13.4 :g m-3 for Los Angeles. For acetaldehyde, the corresponding numbers are 5.0 :g 

m-3 for Phoenix to 18.0 :g m-3 for Los Angeles; for acrolein, the values are 0.2 :g m-3 (five cities) 

to 0.7 :g m-3 for Los Angeles. Generally, the rural values for each of the three HAPs ranged 

between 30% and 50% less than the urban values. The ambient formaldehyde concentrations 

were usually greater for southern cities. 

The user can use results of this study in a number of ways that are fully described in the 

report (U.S. Environmental Protection Agency, 1999b). The most elaborate information can be 

obtained from running OZIPR using city-specific parameters. If that is not feasible, tables in the 

report can be used in conjunction with a dispersion model to provide seasonal and hourly 

adjustments to account for secondary production of HAPs. Finally, when the location of interest 

is dissimilar from those studied here and the OZIPR model cannot be run, the report provides 

62

guidance on adjusting estimates of aldehyde and the other pollutants to account for secondary 

HAPs production. 

2.5.1.7 Long Range Transport Screening Calculations Using CALPUFF 

During the New Source Review (NSR) process, there are occasions when it becomes 

necessary to assess impacts to Class I areas at distances beyond 50 kilometers (km) from the 

proposed source. Because EPA determined that steady-state plume dispersion models are not 

appropriate beyond 50 km, the agency proposed in 1998 to adopt the CALPUFF/CALMET 

modeling system for distances beyond 50 km. EPA and the Federal Land Managers (FLMs) 

developed a CALPUFF screening method to expedite the NSR process. The screening method 

provides permit applicants, permitting authorities, and the FLMs with a quick and easy method to 

estimate concentration values and impacts to such Air Quality Related Values (AQRVs) as 

visibility, and deposition of sulfur and nitrogen to soils and vegetation. To illustrate the 

capabilities of the CALPUFF screening technique, a series of investigations were summarized 

and presented at a conference (Irwin and Notar, 2001). The investigation focused on the extent 

of the receptor ring needed for the screening analysis in the context of simulating the impact of 

primary emissions of SO2. Results for the two investigations revealed that year-to-year variations 

occur in the screening results on the order of 25-30%, which confirmed previous conclusions 

(U.S. Environmental Protection Agency, 1998). Results for Mammoth Cave National Park 

confirmed previous conclusions (U.S. Environmental Protection Agency, 1998) that the 

screening procedure may not always provide results higher than would be generated by a refined 

analysis. The screening results for the 3-hr averaging time were of the right order of magnitude, 

but were over 20% lower than that determined by the refined analysis. Using receptor arcs that 

extend 90 degrees to either side of the extent of the Class I area(s) of concern seems to be 

sufficient. This was a tentative conclusion, which needs to be confirmed in more situations. 

 Herb, this paragraph was different from the one I used. 

2.5.1.8 Uncertainty Characterization in Air Quality Modeling 

The characterization of uncertainty in the air quality modeling results is often called upon 

in formal risk assessments. Air quality modeling involves a series of linked models, from the 

characterization of the emissions, to the characterization of the meteorological conditions, to the 

transport and fate of the emissions, each of which has its own uncertainties. To stimulate 

discussion, a review of concerns was prepared and presented at a conference (Irwin et al., 2001b) 

and a workshop. This discussion reviews some of the issues involved in summarizing not only 

the combined uncertainty of risk estimates, but also methods for apportioning the uncertainty to 

the various models (emissions, transport, transformation, removal, exposure, and risk). The goal 

of the discussion was to make the environmental modeling community aware that there is interest 

in developing a consensus on technical procedures and methods for use in characterizing 

uncertainty in modeling. If there is sufficient interest and in the spirit of the National Transfer 

and Advancement Act of 1995 (Section 12(d) of Public Law 104-113), it was proposed that a 

63

community of participants be formed, where ideas can be openly debated, collaboration 

stimulated, and consensus promoted for general usage. Two alternatives were considered: form 

an ad hoc workgroup, or join an existing group whose purpose is to stimulate collaboration and 

development of consensus guidance. In this regard, the American Society for Testing and 

Materials provides an open, consensus development process. Committee D-22 on Sampling and 

Analysis of Atmospheres has experts whose interest are ambient air, workplace atmosphere, 

source emissions, indoor air, acidic deposition, meteorological conditions, sampling strategies, 

calibration procedures, quality assurance practices, and development of international standards in 

these fields. 

2.5.1.9 Trajectory Analyses for PM2.5 

The Hybrid Single-Particle Lagrangian Trajectory (HYSPLIT) model is being run for a 

selected number of sites for specific time periods in 2000. It was concluded that a back trajectory 

database would be helpful to determine potential source origins of elevated ambient air 

concentrations of PM2.5. The trajectory database can be used as input to residence time analyses, 

and can be used in combination with wind data to help locate the probable sources for the high 

PM2.5 values that were measured during the year 2000. The procedure for executing the 

HYSPLIT model consisted of running it for five levels, four times per 24-hour period and for 

five days in length backwards in time. The Eta Data Assimilation System (EDAS), which covers 

the United States, was the input meteorological data set chosen for the trajectories. The archived 

data set has a horizontal grid size of 80 km. 

2.5.1.10 Enhancements to AERMET 

AERMET, the meteorological preprocessor for EPA’s new state-of-the-science regulatory 

air quality dispersion model, was upgraded and released for beta testing. AERMET processing 

is solidly based on the planetary boundary layer (PBL) theory; important divisions within the 

PBL (e.g., the surface layer and the mixed layer) are modeled separately. Similarity theory is 

used to model the surface layer. AERMET algorithms provide hourly estimates of the following 

surface and mixed layer parameters: Monin-Obukhov length, friction velocity, surface heat flux, 

and convective scaling velocity. Hourly estimates of convective and mechanical mixing heights 

are also provided. 

To make these estimates, AERMET uses a variety of data sources including National 

Weather Service surface and upper-air data, and where available, data from an on-site 

meteorological monitoring program (e.g., data from a meteorological tower). These data are 

processed in three stages. In the first stage, data are extracted from one or more archives and 

subjected to various quality assessment checks. In the second stage, data from the surface, 

upper-air, and on-site archives are merged and written to a merge file in 24-hour blocks. In the 

third stage, data are processed to provide hourly PBL estimates. 

64

Enhancements in this upgrade to AERMET include the addition of code for extracting 

and processing data from National Climatic Data Center (NCDC) archives for surface- and 

upper-air data. This will facilitate the use of meteorological data as it becomes available from 

NCDC. The upgrade also includes the addition of a bulk Richardson algorithm for processing 

the stable boundary layer, improvements to make it user friendly, improvements in error 

reporting, and numerous debugging. 

2.5.2 Modeling Guidance 

2.5.2.1 Support Center for Regulatory Air Models 

During FY-2001, a restructured SCRAM (Support Center for Regulatory Air Models) 

website was released to the general public. The new website re-packages the models with 

descriptions and their component files: model executable, source code, users guide, test cases, 

and other relevant files. The restructuring process was modeled after the Code of Federal 

Regulations (CFR) area that provides classification of models and recommendations for specific 

model use (40CFR Part 51, Appendix W). The SCRAM reorganization was accomplished at the 

request of the modeling community to provide a concise organization of modeling information. 

A SCRAM presentation was made to the Atmospheric Science Librarians International 

conference at the 81st American Meteorological Society Annual Meeting in Albuquerque, New 

Mexico, held on January 14–18, 2001. The presentation, Air Dispersion Models at the U.S. EPA, 

provided an overview of the different classifications of models and their uses. In addition, an online demonstration of SCRAM, its models, and other utilities was provided. 

2.5.2.2 Models-3 Help Desk 

Models-3/CMAQ is a multiscale air quality model, which provides modeling in a one 

atmosphere environment using a graphical user interface-based framework, accounting for such 

processes as chemistry and aerosol interactions, and providing graphical and tabular output. The 

Models-3 Help Desk is an OAQPS initiative to provide full-time assistance to Models-3/CMAQ 

users, during both the installation and model application. A formal support network comprises 

the Help Desk with capable and accessible technical experts who are knowledgeable in the 

different modules and scientific processes that are performed within the model. During 

FY-2001, the Models-3 Help Desk responded to various users including Regional and State EPA 

offices, and international users in Canada and Great Britain. The new release of Models-3 

(version 4.1) was distributed by the Help Desk via digital linear tapes (DLT). Nineteen users 

received DLT tapes. In addition, a Models-3 version compatible with the Windows NT® 

operating system was prepared on CD-ROM and sent to the National Technical Information 

Service for distribution. Proprietary software needed to run Models-3 is also provided with the 

CDs. 

65

2.5.2.3 Ad Hoc Meteorological Modeling Workgroup 

The Branch hosted a gathering of over 30 members of the meteorological modeling 

community in August 2001 as part of the 2nd annual meeting of the Ad Hoc Meteorological 

Modeling Workgroup. The purpose of the meeting was to foster a community exchange of 

information related to numerical meteorological modeling for eventual air quality purposes. 

There were 16 individual presentations summarizing current MM5 and Regional Atmospheric 

Modeling System (RAMS) meteorological modeling activities and several roundtable discussions 

of issues related to best practices in using simulated meteorological data for air quality modeling. 

Among the issues discussed were model performance evaluations, computing platforms, analysis 

software, MM5 physics options, and the status of the RAMS model. Participants included staff 

from State governmental air management agencies, modelers from regional planning 

organizations, meteorological modeling consultants, industry representatives, university 

researchers, as well as staff from other Federal government agencies. 

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3. REFERENCES


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Proceedings of the Millennium (24th) NATO-CCMS International Technical Meeting on 

Air Pollution Modeling and Its Application, May 15–19, 2000, Boulder, Colorado, 

Kluwer Academic/Plenum Publishers, New York, NY, 782 pp. (2001). 

Arnold, J.R., and R.L. Dennis. First results from operational testing of the U.S. EPA 

Models3/Community Multiscale Model for Air Quality (CMAQ). In Air Pollution 

Modeling and Its Application XIV. Gryning, S.-E., and F.A. Schiermeier (Eds.). Kluwer 

Academic/Plenum Publishers, New York, NY, 651–658 (2001). 

Atmospheric Environment. Special Issue: The NARSTO ozone assessment — Critical Reviews. 

K.L. Schere, G.M. Hidy, and H.B. Singh (Guest Eds.). Atmospheric Environment 

34(12-14):1853–2332 (2000). 

Binkowski, F.S. Aerosols in Models-3 CMAQ. In Science algorithms of the EPA Models-3 

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75

APPENDIX A: ACRONYMS, ABBREVIATIONS, AND DEFINITIONS 

ACM Asymmetric Convective Model 

AERMET Meteorological preprocessor 

AIRS Aerometric Information Retrieval System 

AQMF Air Quality Modeling Forum 

AQRV Air Quality Related Values 

ARL Air Resources Laboratory, NOAA 

ASLI Atmospheric Science Librarians International 

ASMD Atmospheric Sciences Modeling Division 

ASPEN Assessment System for Population Exposure Nationwide 

ASTM American Society for Testing and Materials 

AVHRR Advanced Very High Resolution Radiometer 

AVIRIS Airborne Visible and Infrared Imaging Spectrometer 

BASC Board on Atmospheric Sciences and Climate (NRC/NAS) 

BCON Boundary CONditions processor 

BEIS Biogenic Emissions Inventory System 

BELD3 Biogenic Emissions Land use Database 

CAAA Clean Air Act Amendments of 1990 

CALMET A diagnostic meteorological model 

CALPUFF CALifornia PUFF (transport and dispersion) model 

CAPITA Center for Air Pollution Impacts and Trends Analysis 

CASTNet Clean Air Status and Trends Network 

CB-IV Carbon Bond-IV 

CCMS Committee on the Challenges of Modern Society 

CCTM CMAQ Chemistry-Transport Model 

CDF Common Data Format 

CD-ROM Compact Disk - Read Only Memory 

CFD Computational Fluid Dynamics 

CFR Code of Federal Rgulations 

CIRA Cooperative Institute for Research in the Atmosphere 

CMAQ Community Multiscale Air Quality modeling system 

CMAQ-Hg CMAQ mercury model 

CMAS Community Modeling and Analysis System 

CTM Chemistry-Transport Model 

DIAS Dynamic Information Architecture System 

DLT Digital Linear Tape 

ECIP Emissions-Chemistry Interface Processor 

EDAS Eta Data Assimilation System 

EMEP European Monitoring and Evaluation Programme 

EPA Environmental Protection Agency 

Extended RADM Regional Acid Deposition Model with full dynamics of secondary 

inorganic fine particle formation taken from the RPM 

FCCM Fully Coupled hybrid Compartmental Model 

76

FCMSSR Federal Committee for Meteorological Services and Supporting 

Research 

FDDA Four-Dimensional Data Assimilation 

FLMs Federal Land Managers 

FMF Fluid Modeling Facility (EPA) 

FTP File Transfer Protocol 

FY Fiscal Year 

GIS Geographic Information System 

HAP Hazardous Air Pollutant 

HPCC High Performance Computing and Communications 

HTML HyperText Markup Language 

HYSPLIT Hybrid Single-Particle Lagrangian Trajectory model 

IAMSLIC International Association of Aquatic and Marine Science Libraries and 

Information Centers 

ICMSSR Interdepartmental Committee for Meteorological Services and 

Supporting Research 

IC/BC Initial and boundary conditions 

ICON Initial CONditions processor 

IMPROVE Interagency Monitoring of PROtected Visual Environments 

I/O Input/Output 

ISCLT Industrial Source Complex model - Long Term 

ITMs International Technical Meetings 

JPROC Photolysis rate processor 

LULC Land Use/Land Cover 

LES Large-eddy simulation 

M3Dry Models-3 dry deposition scheme 

MCIP Meteorology-Chemistry Interface Processor 

MCPL Model couple module in the CMAQ system 

MDN Mercury Deposition Network 

MEND-TOX Multimedia ENvironmental Distribution of TOXics 

MEPPS Models-3 Emission Processing and Projection System 

MEPSE Major Elevated Point-Source Emissions 

MicroFacCO Microscale automobile emission Factor model for Carbon Monoxide 

MicroFacPM Microscale automobile emission Factor model for Particulate Matter 

MIMS Multimedia Integrated Modeling System 

MLBC Multi-Layer Bio-Chemical dry deposition model 

MLM Multi-Layer inferential dry deposition Model 

MM5 Mesoscale Model - Version 5 

Models-3 Third generation air quality modeling system 

MODTRAN MODerate resolution TRANSmittance 

NARSTO Formerly North American Research Strategy for Tropospheric Ozone 

NARSTO-NE NARSTO-NorthEast 

NASA National Aeronautics and Space Administration 

NATO North Atlantic Treaty Organization 

77

NATO/CCMS 

NCAR 

NCDC 

NERL 

NESC 

NEXRAD 

NOAA 

NSR 

NWS 

OAQPS 

OCLC 

OMB 

OZIPR 

PBL 

PDF 

PDM 

PinG 

PM 

PM2.5 

PM10 

PX LSM 

QA/QC 

RADM 

RAMS 

R&DF 

RHR 

RPM 

RPO 

RRWG 

SAIL 

SAPRC99 

SASWG 

SBL 

SCRAM 

SHEDS 

SIP 

SMOKE 

SO2 

SOA 

SOS 

TEOM 

TexAQS 2000 

TKE 

North Atlantic Treaty Organization Committee on Challenges of Model 

Society 

National Center for Atmospheric Research 

National Climatic Data Center 

National Exposure Research Laboratory 

National Environmental Supercomputing Center (EPA) 

NEXt generation RADar 

National Oceanic and Atmospheric Administration 

New Source Review 

National Weather Service 

Office of Air Quality Planning and Standards (EPA) 

Online Computer Library Center 

Office of Management and Budget 

OZone Isopleth Plotting program, Research version 

Planetary Boundary Layer 

Probability Density Function 

Plume Dynamics Model 

Plume-in-Grid algorithm 

Particulate Matter 

Particulate matter smaller than 2.5 microns 

Particulate matter up to 10 micrometers in size 

Pliem-Xiu Land-Surface Model 

Quality Assurance/Quality Control 

Regional Acid Deposition Model 

Regional Atmospheric Modeling System 

Research and Development Forum 

Regional Haze Rule 

Regional Particulate Model 

Regional Planning Organization 

Reactivity Research Work Group 

Southeast Affiliate of IAMSLIC Librarians 

Chemical mechanism 

Standing Air Simulation Work Group 

Stable Boundary Layer 

Support Center for Regulatory Air Models 

Stochastic Human Exposure and Dose Simulation 

State Implementation Plan 

Sparse Matrix Operator Kernel Emissions 

Sulfur Dioxide 

Secondary organic aerosol 

Southern Oxidants Study 

Tapered-Element Oscillating Microbalance instruments 

2000 Texas Air Quality Study 

Turbulent Kinetic Energy 

78

TOR Tropospheric ozone residual 

UCP Urban canopy parameterization 

USGS U.S. Geological Survey 

US/USSR United States/Union of Soviet Socialist Republics 

UV Ultraviolet 

VOC Volatile organic compounds 

WRF Weather Research Forecasting 

WRAP Western Regional Air Partnership 

WSR-88D Weather Surveillance Radar 1988 Doppler Version 

WWW World-Wide Web 

79

APPENDIX B: PUBLICATIONS


Air Pollution Modeling and Its Application XIV. Gryning, S.-E., and F.A. Schiermeier (Eds.). 

Proceedings of the Twenty-Fourth NATO-CCMS International Technical Meeting on Air 

Pollution Modeling and Its Application, May 15–19, 2000, Boulder, Colorado. Kluwer 

Academic/Plenum Publishers, New York, NY, 834 pp. (2000). 

Arnold, J.R., and R.L. Dennis. First results from operational testing of the U.S. EPA 

Models3/Community Multiscale Model for Air Quality (CMAQ). In Air Pollution 

Modeling and Its Application XIV. Gryning, S.-E., and F.A. Schiermeier (Eds.). Kluwer 

Academic/Plenum Publishers, New York, NY, 651–658 (2001). 

Atmospheric Modeling Division, EPA Systems Development Center, and Science Applications 

International Corporation. Project Officer William Benjey. Models-3 installation 

procedures for a Sun Workstation with a Unix-based operating system (Models-3 Version 

4.1). EPA-600/R-01/037, National Exposure Research Laboratory, U.S. Environmental 

Protection Agency, Research Triangle Park, NC, 200 pp. (2001). 

Atmospheric Modeling Division, EPA Systems Development Center, and Science Applications 

International Corporation. Project Officer William Benjey. Models-3 installation 

procedures for a personal computer with a NT operation system (Models-3 Version 4.1). 

EPA-600/R-01/046, National Exposure Research Laboratory, U.S. Environmental 

Protection Agency, Research Triangle Park, NC, 132 pp. (2001). 

Atmospheric Modeling Division, EPA Systems Development Center, and Science Applications 

International Corporation. Project Officer William Benjey. SMOKE tool for Models-3 

Version 4.1. Structure and operation documentation. EPA 600/R-01/048, National 

Exposure Research Laboratory, U.S. Environmental Protection Agency, Research 

Triangle Park, NC, 66 pp. (2001). 

Atmospheric Modeling Division, EPA Systems Development Center, and Science Applications 

International Corporation, Project Officer William Benjey. Release notes for Models-3 

version 4.1 patch: SMOKE tool and file converter. EPA/600/R-01/076, National 

Exposure Research Laboratory, U.S. Environmental Protection Agency, Research 

Triangle Park, NC, 74 pp. (2001). 

Barnard, W., C. Claiborn, D. Gillette, D. Latimer, T. Pace, and J. Watson. Methodology for 

estimating fugitive windblown and mechanically resuspended road dust emissions 

applicable for regional scale air quality modeling. Prepared for Western Governors’ 

Association, April 2001. Available at www.wrapair.org (2001). 

80

Benjey, W.G.; M.R. Houyoux, and J.W. Susick. Implementation of the SMOKE emission data 

processor and SMOKE tool input data processor in Models-3. The Emission Inventory 

Conference: One Atmosphere, One Inventory, Many Challenges; Denver, Colorado. U.S. 

Environmental Protection Agency, Office of Air Quality Planning & Standards, 

Research Triangle Park, NC (2001). Available at 

http://www.epa.gov/ttn/chief/conferences.html (accessed July 2002). 

Bullock, O.R., Jr. Modeling assessment of transport and deposition patterns of anthropogenic 

mercury air emissions in the United States and Canada. The Science of the Total 

Environment 259:145–157 (2000). 

Byun, D.W. Description of atmospheric transport processes in Eulerian air quality models. In 

Mesoscale Atmospheric Dispersion. Z. Boybeyi (Ed.). WIT Press, Boston, 289–341 

(2001). 

Byun, D.W., and J.E. Pleim. Sensitivity of ozone and aerosol predictions to the transport 

algorithms in the Models-3 Community Multiscale Air Quality (CMAQ) modeling 

system. In Air Pollution Modeling and Its Application XIV. Gryning, S.-E., and F.A. 

Schiermeier (Eds.). Kluwer Academic/Plenum Publishers, New York, NY, 203–212 

(2001). 

Ching, J. K. S., A. Lacser, D. Byun, and W. Benjey. Air quality modeling at neighborhood scales 

to improve human exposure assessment. Proceedings of the Third International 

Conference on Urban Air Quality, Loutraki, Greece, March 19–23, 2001. CD-ROM. 

Sponsored by AWMA, IUAPPA and EURASAP, .Paper No. 3.1 (2001). 

Ching, J., A. Lacser, T. Otte, D. Byun, and J. Herwehe. Modeling air toxics and PM2.5 

concentration fields as a means for facilitating human exposure assessments. 

Proceedings of the 10th International Symposium Transport and Air Pollution, September 

17–19, 2001, Boulder, Colorado. National Center for Atmospheric Research, Boulder, 

17–24 (2001). 

Cohn, R.D., B.K. Eder, S.K. LeDuc, and R.L. Dennis. Development of an aggregation and 

episode selection scheme to support the Models-3 Community Multiscale air quality 

model. Journal of Applied Meteorology 40:210–228 (2001). 

Cooter, E.J., and Y. Cohen. Model evaluation of dry deposition to vegetation for volatile and 

semi-volatile organic compounds in a multimedia environment. In Air-Surface Exchange 

of Gases and Particles (2000). Proceedings of the 6th International Conference on AirSurface Exchange of Gases and Particles, Edinburgh, July 3–7, 2000. D. Fowler, C. 

Pitcairn and J-W Erisman ( Eds.). Kluwer Academic Publishers: Dordrecht, 285–294 

(2001). 

81

R.L. Dennis, and R. Mathur. 	Airshed domains for modeling atmospheric deposition of oxidized 

and reduced nitrogen to the Neuse/Pamlico System of North Carolina. Hydrological 

Science and Technology 17(1-4):107–117 (2001). 

Dennis, R.L, G.S. Tonnesen, and R. Mathur. Nonlinearities in the sulfate secondary fine 

particulate response to NOx emissions reductions as modeled by the Regional Acid 

Deposition Model. In Air Pollution Modeling and Its Application XIV. Gryning, S.-E., 

and F.A. Schiermeier (Eds.). Kluwer Academic/Plenum Publishers, New York, NY, 

193–202 (2001). 

Dolwick, P., C. Jang, N. Possiel, B. Timin, G.L. Gipson, and J. Godowitch. Summary of results 

from a series of Models-3/CMAQ simulations in ozone in the western United States. 

Proceedings of the 94th Annual Meeting of the Air & Waste Management Association, 

Orlando, Florida, June 24–28, 2001. (CD-ROM). Session No. AB-2c. Advances in 

Photochemical Air Quality Modeling. Air & Waste Management Association, Pittsburgh, 

Paper No. 957 (2001). 

Draxler, R.R., D.A. Gillette, J.S. Kirkpatrick, and J. Heller. Estimating PM10 air concentrations 

from dust storms in Iraq, Kuwait and Saudi Arabia. Atmospheric Environment 

35:4315–4330 (2001). 

Eder, B.K., S.K. LeDuc, A.B. Gilliland, and P.L. Finkelstein. On the use of NEXRAD Stage IV 

data in the multimedia modeling of pollutant transport. Preprints, Symposium 

Precipitation Extremes: Prediction, Impacts, and Responses, January 15–18, 2001, 

Albuquerque, New Mexico. American Meteorological Society, Boston, 284–285. (2001). 

Finkelstein, P.L., and P.F. Sims. Sampling error in eddy correlation flux measurements. Journal 

of Geophysical Research 106(D4):3503–3509 (2001). 

Gillette, D.A. Particle production and aeolian transport from a “supply-limited” source area in 

the Chihuahuan desert. Journal of Geophysical Research 106:5267–5278 (2001). 

Gillette, D.A., and W. Chen. Particle production and aeolian transport from a “supply-limited” 

source area in the Chihuahuan desert, New Mexico, United States. Journal of 

Geophysical Research 106(D6):52–5278 (2001). 

Gillette, D.A., T.C. Niemeyer, and P.J. Helm. Supply-limited horizontal sand drift at an 

ephemerally crusted, unvegetated saline playa. Journal of Geophysical Research 

106(D16):18,085–18,098 (2001). 

82

Gillette, D.A. Regional scale vertical dust flux is a small fraction of the local field-scale 

horizontal fugitive dust flux. Western Governors Association, Western Regional Air 

Partners, Expert Panel on Fugitive Dust. Available at www.wrapair.org under reports ­

Final Report on Fugitive Dust available under R&D Forum Committee, April 2001 

(2001). 

Gilliland, A.B., and P.J. Abbitt. A sensitivity study of the discrete Kalman filter (DKF) to initial 

condition discrepancies. Journal of Geophysical Research 106(D16):17,939–17,952 

(2001). 

Godowitch, J.M. Results of photochemical simulations of subgrid scale point source emissions 

with the Models-3 CMAQ modeling system. Preprints, Millennium Symposium on 

Atmospheric Chemistry: Past, Present and Future of Atmospheric Chemistry, January 

15–18, 2001, Albuquerque, New Mexico. American Meteorological Society, Boston, 

43–49 (2001). 

Huber, A.H., M. Freeman, S. Rida, K. Kuehlert, and E. Bish. Development and applications of 

CFD in support of air quality studies of roadway and building microenvironments. 

Proceedings of the 94th Annual Meeting of the Air & Waste Management Association, 

Orlando, Florida, June 24–28, 2001. (CD-ROM). Session No. AB-3e. Applications of 

Computational Fluid Dynamics to Air Pollution Modeling. Air & Waste Management 

Association, Pittsburgh, Paper No. 1035 (2001). 

Irwin, J.S. Statistical evaluation of centreline concentration estimates by atmospheric dispersion 

models. International Journal Environment and Pollution 14(1-6):28–38 (2000). 

Irwin, J.S. Comparison of two sampling procedures for the statistical evaluation of the 

performance of atmospheric dispersion models in estimating centerline concentration 

values. In Air Pollution Modeling and Its Application XIV. Gryning, S.-E., and F.A. 

Schiermeier (Eds.). Kluwer Academic/Plenum Publishers, New York, NY, 665–673 

(2001). 

Irwin, J.S., J. Niedzialek, and J. Burzynski. A comparison of CALPUFF air quality simulation 

results with monitoring data for Krakow, Poland. Proceedings of the 7th International 

Conference on Harmonisation Within Atmospheric Dispersion Modelling For Regulatory 

Purposes, Belgirate, Italy, May 28–31, 2001. C. Cuvelier (Ed.). Environment Institute, 

Joint Research Centre, European Commission, I-21020, Ispra, Italy, 217–221 (2001). 

Jang, C., P. Dolwick, N. Possiel, B. Timin, and J. Tikvart. Annual application of U.S. EPA’s 

third-generation air quality modeling system over the continental United States. 

Proceedings of the 94th Annual Meeting of the Air & Waste Management Association, 

Orlando, Florida, June 24–28, 2001. (CD-ROM). Session No. AB-2c. Advances in 

Photochemical Air Quality Modeling. Air & Waste Management Association, Pittsburgh, 

Paper No. 379 (2001). 

83

LeDuc, S.K. Supporting the data needs of the coastal community II: Hurrican Floyd post-event. 

Preprints, 17th Conference on Interactive Information and Processing Systems for 

Meteorology, Oceanography and Hydrology; January 14–19, 2001, Albuquerque, New 

Mexico. American Meteorological Society, Boston, 437–438 (2001). 

Leduc, S.K., K.L. Schere, J.M. Godowitch, and G.L. Gipson. Models-3/CMAQ applications 

which illustrate capability and functionality. (Poster). In Air Pollution Modeling and Its 

Application XIV. S.-E. Gryning, and F.A. Schiermeier (Eds.). Kluwer Academic/Plenum 

Publishers, New York, NY, 737–738 (2001). 

Meyers, T., J. Sickles, R. Dennis, K. Russell, J. Galloway, and T. Church. Atmospheric nitrogen 

deposition to coastal estuaries and their watersheds. In Nitrogen Loading in Coastal 

Water Bodies. An Atmospheric Perspective. Richard A. Valigura et al., (Eds.). Coastal 

and Estuarine Studies, 57. American Geophysical Union, Washington, DC, 53–76 

(2001). 

NARSTO Ozone Synthesis Team. Assessing policy-relevant science for managing ozone air 

quality. EM. 6:11–15 (2001). 

Okin, G.S., and D.A. Gillette. Distribution of vegetation in wind-dominated landscapes: 

Implications for wind erosion modeling and landscape processes. Journal of Geophysical 

Research 106(D9):9673–9683 (2001). 

Otte, T.L., and A. Lacser. Implementation of an urban canopy parameterization in MM5 for 

meso-gamma-scale air quality modeling applications. Preprints, Ninth Conference on 

Mesoscale Processes, July 30–August 2, 2001, Ft. Lauderdale, Florida. American 

Meteorological Society, Boston, 78–81 (2001). 

Otte, T.L., N.L. Seaman, and D.R. Stauffer. A heuristic study on the importance of anisotropic 

error distributions in data assimilation. Monthly Weather Review 129:766–783 (2001). 

Paerl, H., W. Boynton, R. Dennis, C. Driscoll, H. Greening, J. Kremer, N. Rabalais, and S. 

Seitzinger. Atmospheric deposition and nitrogen in coastal waters: Biogeochemical and 

ecological implications. In Nitrogen Loading in Coastal Water Bodies. An Atmospheric 

Perspective. Richard A. Valigura et al., (Eds.). Coastal and Estuarine Studies, 57. 

American Geophysical Union, Washington, DC, 11–52 (2001). 

Pleim, J.E., and D.W. Byun. Application of a new land-surface, dry deposition, and PBL model 

in the Models-3 Community Multiscale Air Quality (CMAQ) modeling system. In Air 

Pollution Modeling and Its Application XIV. Gryning, S.-E., and F.A. Schiermeier 

(Eds.). Kluwer Academic/Plenum Publishers, New York, NY, 297–305 (2001). 

84

Pleim, J.E., and A. Xiu. Updates and evaluation of the PX LSM in MM5. Preprints, Eleventh 

PSU/NCAR Mesoscale Modeling System Users’ Workshop, June 25–27, 2001, Boulder, 

Colorado. National Center for Atmospheric Research, Boulder, 98–101 (2001). 

Pleim, J.E., A. Xiu, P.L. Finkelstein, and T.L. Otte. A coupled land-surface and dry deposition 

model and comparison to field measurements of surface heat, moisture, and ozone fluxes. 

Water, Air, and Soil Pollution: Focus 1:243–252 (2001). 

Poole-Kober, E.M., and H.J. Viebrock (Eds.). Fiscal year 2000 summary report of the NOAA 

Atmospheric Sciences Modeling Division to the U.S. Environmental Protection Agency. 

NOAA Technical Memorandum ERL ARL-239, Atmospheric Sciences Modeling 

Division, Research Triangle Park, NC, 97 pp. (2000). 

Possiel, N., G. Stella, R. Ryan, T. Pace, W. Benjey, A. Beidler, E. Kinnee, M.. Houyoux, and Z. 

Adelman. Development of an anthropogenic emission inventory for annual nationwide 

Models-3/CMAQ simulations of ozone and aerosols. The Emission Inventory 

Conference: One Atmosphere, One Inventory, Many Challenges; Denver, Colorado. U.S. 

Environmental Protection Agency, Office of Air Quality Planning & Standards, 

Research Triangle Park, NC (2001). Available at 

http://www.epa.gov/ttn/chief/conferences.html (accessed July 2002). 

Roelle, P.A., V.P. Aneja, B. Gay, C. Geron, and T. Pierce. Biogenic nitric oxide emissions from 

cropland soils. Atmospheric Environment 35(1):115–124 (2001). 

Schell, B., I.J. Ackermann, H. Hass, F.S. Binkowski, and A. Abel. Modeling the formation of 

secondary organic aerosol within a comprehensive air quality model system. Journal of 

Geophysical Research 106(D22):28,275–28,293 (2001). 

Schere, K.L. Foreword NARSTO critical reviews. Atmospheric Environment 34:1853–1860 

(2000). 

Schwede, D.B., W.B. Petersen, and S.K. LeDuc. Simulating atmospheric exposure using an 

innovative meteorological sampling scheme. In Air Pollution Modeling and Its 

Application XIV. Gryning, S.-E., and F.A. Schiermeier (Eds.). Kluwer 

Academic/Plenum Publishers, New York, NY, 735–736 (2001). 

Singh, R.B., and A.H. Huber. Sensitivity analysis and evaluation of MicroFacCO: A microscale 

motor vehicle emission factor model for CO Emissions. Journal of the Air & Waste 

Management Association 51:1087–1099 (2001). 

85

Singh, R.B., A.H. Huber, and J.N. Braddock. Development of a Microscale emission Factor 

model for Particulate Matter (MicroFacPM) for predicting real-time motor vehicle 

emissions. Proceedings of the 94th Annual Meeting of the Air & Waste Management 

Association, Orlando, Florida, June 24–28, 2001. (CD-ROM). Session No. AS-1d. 

Mobile Source Particulate Emissions. Air & Waste Management Association, Pittsburgh, 

Paper No. 262 (2001). 

Singh. R.B., and A.H. Huber. Development of a microscale emission factor model for CO for 

predicting real-time motor vehicle emissions. Journal of the Air & Waste Management 

Association 50:1980–1991 (2000). 

Sokolik, I.N., D.M. Winker, G. Bergametti, D.A. Gillette, G. Charmichal, Y.J. Kaufman, L. 

Gomes, L. Schuetz, and J.E. Penner. Introduction to special section: Outstanding 

problems in quantifying the radiative impact of mineral dust. Journal of Geophysical 

Research 106(D16):18,015–18,027 (2001). 

Strum, M.L., N. Frank, J.S. Touma, and R. Mason. Emissions processing and air quality 

modeling initial National Scale Air Toxics Assessment. Proceedings of the 94th Annual 

Meeting of the Air & Waste Management Association, Orlando, Florida, June 24–28, 

2001. Air & Waste Management Association, Pittsburgh, Paper No. 432 (2001) 

Sugata, S., D.W. Byun, and I. Uno. Simulation of sulfate aerosol in east Asia using Models3/CMAQ with RAMS meteorological data. In Air Pollution Modeling and Its 

Application XIV. Gryning, S.-E., and F.A. Schiermeier (Eds.). Kluwer 

Academic/Plenum Publishers, New York, NY, 267–275 (2001). 

Venkatram, A., R. Brode, A. Cimorelli, R. Lee, R. Paine, S. Perry W. Peters, J. Weil, and R. 

Wilson. A complex terrain dispersion model for regulatory applications. Atmospheric 

Environment 35:4211–4222 (2001). 

Vukovich, F., A. Gilliland, A. Venkatram, and J. Sherwell. On performing long-term predictions 

of ozone using the SOMS model. Atmospheric Environment 35:569–578, 2001. 

Walsh, K., and A. Gilliland. Regional variations in sulfate and nitrate on annual, seasonal, and 

synoptic time scales. Journal of Air & Waste Management Assocociation 51:174–185 

(2001). 

Weil, J.C., W.H. Snyder, R.E. Lawson, Jr., and M.S. Shipman. New developments in dispersion 

experiments and models for the convective boundary layer. In Air Pollution Modeling 

and Its Applications XIV. Volume 24. Gryning, S-E., and F.A. Schiermeier (Eds.). 

Kluwer Academic/Plenum Publishers, New York, 445–454 (2001). 

Xiu, A., and J.E. Pleim. Development of a land surface model. Part I: Application in a 

Mesoscale Meteorological Model. Journal of Applied Meteorology 40:192–209 (2001). 

86

APPENDIX C: PRESENTATIONS


Benjey, W.G., and S.K. LeDuc. Status and implications of Models-3. Presentation at the Fall 

2000 Meeting of the Standing Air Emissions Working Group, Washington, DC, October 

20, 2000. 

Benjey, W.G. Emission modeling procedures. Presentation to the Neighborhood Modeling 

Team of the Atmospheric Sciences Modeling Division, Research Triangle Park, NC, 

March 29, 2001. 

Benjey, W.G. The status of smoke and reactivity capabilities. Presentation to the Joint Industry 

Committee on Atmospheric Reactivity, Research Triangle Park, NC, April 16, 2001. 

Benjey, W.G. Anthropogenic emissions and SMOKE in MIMS. Presentation at the Multimedia 

Integrated Modeling Systems Peer Review, Research Triangle Park, NC, April 4, 2001. 

Benjey, W.G. Emission Modeling: Status and Future . Presentation to the International 

Transport of Air Pollution Committee, Washington, D.C., August 28, 2001. 

Benjey, W.G. Models-3/CMAQ and forestry SMOKE emissions. Presentation to the U.S. Forest 

Service Regional Modeling Center managers, Athens, GA, August 23, 2001. 

Bullock, O.R., Jr. The importance of emission speciation to the atmospheric transport and 

deposition of mercury. Presentation at the Conference on Assessing and Managing 

Mercury from Historic and Current Mining Activities, San Francisco, CA, November 29, 

2000. 

Bullock, O.R., Jr. Modeling and monitoring of atmospheric mercury. Presentation at the U.S. 

EPA Region 5 Office, Chicago, IL, January 18, 2001. 

Ching, J.K.S. Air quality modeling of PM and air toxics at neighborhood scales to improve 

human exposure assessments. Presentation at the International Society of Exposure 

Analysis 10th Annual Conference, Monterey Peninsula, CA, October 25, 2000. 

Ching, J.K.S. Modeling PM and air toxics at neighborhood scales and their linkages to human 

population and personal exposure models. Presentation at the 7th International 

Conference on Atmospheric Sciences and Applications to Air Quality Modeling 

Challenges, Taipei, Taiwan, November 2, 2000. 

Ching, J.K.S. Neighborhood scale modeling to support exposure modeling. Presentation at the 

Human Health, Remote Sensing and Urbanization Meeting, Atlanta, GA, April 10, 2001. 

Ching, J.K.S. Air quality modeling—Models-3/CMAQ. Presentation at the SIAM Conference, 

University of Houston, TX, April 27, 2001. 

87

Ching, J.K.S. Status of neighborhood scale modeling project. Briefing to the Director of the 

National Exposure Research Laboratory and staff, Research Triangle Park, NC, July 3, 

2001. 

Ching, J.K.S. Neighborhood scale modeling. Presentation at the NERL/NHEERL Exposure to 

Dose Modeling Workshop, Research Triangle Park, NC, July11, 2001. 

Ching, J.K.S. Modeling gridded PM and air toxics concentrations and their sub-grid 

concentration distributions for use in exposure modeling. Presentation at the Third 

Annual Meeting of the NERL/ORD University Partnership Agreements for Conducting 

Research on Source-to-Dose Human Exposure Modeling, Research Triangle Park, NC, 

August 28, 2001. 

Cooter, E.J. Regional modeling of the atmospheric transport and deposition of atrazine. 

Presentation at the Society of Environmental Toxicology and Chemistry 21st Annual 

Meeting, Nashville, TN, November 14, 2000. 

Cooter, E.J. Next generation compartmental modeling. Presentation at the Multimedia 

Integrated Modeling Systems Peer Review, Research Triangle Park, NC, April 3, 2001. 

Dennis, R.L. Atmospheric deposition and airsheds. Seminar at the U.S. Environmental 

Protection Agency, Region 2 Office, New York City, NY, October 5, 2000. 

Dennis, R.L. Operational evaluation of CMAQ for ozone. Presentation at the SASWG Meeting 

(by phone), Washington, DC, October 20, 2000. 

Dennis, R.L. A modeling study of the effect of acid rain controls on fine particle concentrations 

of sulfate in eastern North America. Presentation at NARSTO Symposium — 

Tropospheric Aerosols: Science and Decisions in an International Community, Queretaro, 

Mexico, October 25, 2000. 

Dennis, R.L. Determination of the airshed domains for modeling atmospheric deposition of 

oxidized and reduced nitrogen to the Neuse River Basin. Presentation at the American 

Institute of Hydrology on Atmospheric, Surface, and Subsurface Hydrology and 

Interactions, Research Triangle Park, NC, November 7, 2000. 

Dennis, R.L. Atmospheric transport and deposition. Presentation at the Chesapeake Bay Shared 

Resources Workshop, Dewey Beach, DE, November 15, 2000. 

Dennis, R.L. Atmospheric transport and deposition of nitrogen to coastal estuaries. Seminar at 

the Duke Nicholas School of the Environment, Durham, NC, November 27, 2000. 

88

Dennis, R.L. Issues related to nitrogen meteorology and atmospheric chemistry. Presentation at 

the North Carolina State Workshop on Future Directions in Air Quality Research: 

Ecological, Atmospheric, Regulatory/ Policy/ Economic and Educational Issues, Research 

Triangle Park, NC, February 14, 2001. 

Dennis, R.L. Atmospheric exchange and deposition. Presentation at the Multimedia Integrated 

Modeling Systems Peer Review, Research Triangle Park, NC, April 4, 2001. 

Dennis, R.L. Coastal studies — Nitrate and ammonia. Presentation to the Air Resources 

Laboratory program review, Research Triangle Park, NC, May 9, 2001. 

Dennis, R.L. Sulfate nonlinear response to SO2 emissions reduction. Presentation to the OAQPS 

Modelers Meeting, Research Triangle Park, NC, May 11, 2001. 

Dennis, R.L. Science questions. Presentation at the EMEP/EPA Workshop on Photo-oxidants, 

Fine Particles, and Haze Across the Arctic and North Atlantic: Transport Observations 

and Models, Columbia University Lamont-Doherty Earth Observatory, Palisades, NY, 

June 12, 2001. 

Dolwick, P.D. CMAQ proof of concept: Western United States ozone modeling. Presentation at 

the LADCO Models-3/CMAQ training session, Research Triangle Park, NC, January 31, 

2001. 

Dolwick, P.D. The Models-3/Community Multiscale Air Quality modeling system: Introduction 

and preliminary results. Presentation to the EPA Deputy Assistant Administrator, 

Research Triangle Park, NC, April 17, 2001. 

Dolwick, P.D. CMAQ proof of concept modeling. Presentation at the April 2001 SASWG 

Meeting (by phone), Chicago, IL, April 20, 2001. 

Dolwick, P.D. OAQPS proof of concept CMAQ modeling. Presentation at the Ad Hoc Air 

Quality Group, Research Triangle Park, NC, May 10, 2001. 

Dolwick, P.D. Numerical air quality forecasting: Description of planned projects and 

preliminary recommendations for U.S. EPA involvement. Presentation to the Air Quality 

Index—Long Range Planning Team, Durham, NC, May 16, 2001. 

Dolwick, P.D. Plans for BEIS-3 sensitivity modeling within U.S. EPA/OAQPS, Des Plaines, IL, 

May 22, 2001. 

Dolwick, P.D. Background and goals of the Ad Hoc Meteorological Modeling Workgroup, 

Research Triangle Park, NC, August 23, 2001. 

89

Eder, B.K. Preliminary evaluation of CMAQ using visibility parameters. Briefing for the 

OAQPS, Emissions, Monitoring and Analysis Division Director, Research Triangle Park, 

NC, October 17, 2000. 

Eder, B.K. Preliminary evaluation of CMAQ using visibility parameters. Presentation at 

SASWG Meeting (by phone) in Washington, DC, October 20, 2000. 

Eder, B.K. On the use of NEXRAD Stage IV data in the multimedia modeling of pollutant 

transport. Presentation at the Multimedia Integrated Modeling Systems Peer Review, 

Research Triangle Park, NC, April 3, 2001. 

Eder, B.K. A preliminary evaluation of Models-3/CMAQ using visibility and aerosol data. 

Presentation at the Ad-Hoc Air Quality Group, Research Triangle Park, NC, May 11, 

2001. 

Eder, B.K. A preliminary evaluation of Models-3/CMAQ using visibility and aerosol data. 

Invited speaker, North Carolina State University, Department of Marine Environmental 

Atmospheric Science Seminar Series, Raleigh, NC, September 10, 2001. 

Eder, B.K. A preliminary evaluation of Models-3/CMAQ using aerosol data. Presentation at 

the BOSC Meeting, Research Triangle Park, NC, September 19, 2001. 

Eder, B.K. Presentation on the dangers of lightning, Kingswood Elementary, Raleigh, NC, 

September 28, 2001. 

Gillette, D.A. The concept of vegetation "streets" in dust emission modeling. Seminar presented 

at the University of Paris 12 (Creteil cedex), France, October 13, 2000. 

Gilliland, A.B. Seasonal estimates of ammonia using inverse modeling techniques. Poster 

presentation at the Shared Resources Workshop, Dewey Beach, DE, November 16,2000. 

Gilliland, A.B. Multimedia Integrated Modeling System (MIMS) development and challenges 

for multimedia modeling. Presentation at the Multimedia Integrated Modeling Systems 

Peer Review, Research Triangle Park, NC, April 3, 2001. 

Gilliland, A.B. Seasonal estimates of ammonia using inverse modeling techniques. Poster 

presentation at the Multimedia Integrated Modeling Systems Peer Review, Research 

Triangle Park, NC, April 3, 2001. 

Gilliland, A.B. Overview of multimedia integrated modeling activities. Presentation at the 

NOAA ARL peer review, Research Triangle Park, NC, May 9, 2001. 

Gilliland, A.B. Seasonal estimates of ammonia using inverse modeling techniques. Poster 

presentation at the ARL peer review, Research Triangle Park, NC, May 9, 2001. 

90

Huber, A.H. Advances in human exposure modeling. Presentation at the EMPACT National 

Meeting (by phone), Philadelphia, PA, January 25, 2001. 

Irwin, J.S., K. Steinberg, C. Hakkarinen, and H. Feldman. Uncertainty in air quality modeling 

for risk calculations. Fifth Annual George Mason University Transport and Dispersion 

Modeling Workshop, Fairfax, VA. July 18, 2001. 

Lacser, A., and T.L. Otte. Implementation of an urban canopy parameterization in a mesoscale 

model. Fifth Annual George Mason University Transport and Dispersion Modeling 

Workshop, Fairfax, VA. July 18, 2001. 

Leduc, S.K. Floods. Presentation to three fifth-grade classes at Lincoln Trail Elementary 

School, Mahomet, IL, November 8, 2000. 

Leduc, S.K. Floods. Presentation to three first-grade classes at Sangamon Elementary School, 

Mahomet, IL, November 8, 2000. 

Leduc, S.K. Floods. Presentation to three first-grade classes at Memorial Elementary School, 

Paris, IL, November 9, 2000. 

Leduc, S.K. Modeling air quality. Presentation at an honors colloquium at Meredith College, 

Raleigh, NC, November 29, 2000. 

Leduc, S.K. Supporting the data needs of the coastal community II: Hurricane Floyd post-event. 

Oral and Poster presentation at the AMS 17th International Conference on Interactive 

Information Processing Systems for Meteorology, Oceanography, and Hydrology, 

Albuquerque, NM, January 14, 2001. 

Otte, T.L. Weather basics: Evaporation and condensation. Presentation to 2nd grade (two 

sessions), Fuquay-Varina Elementary School, Fuquay-Varina, NC, December 12, 2000. 

Otte, T.L. Weather basics: Evaporation and condensation. Presentation to 2nd grade (five 

sessions), Dillard Drive Elementary School, Raleigh, NC, December 15, 2000. 

Otte, T.L. Weather maps and forecasting. Presentation to 5th grade (four sessions), Swift Creek 

Elementary School, Raleigh, NC, January 9, 2001. 

Otte, T.L. MM5 overview. Presentation at the Neighborhood Scale Modeling Group, Research 

Triangle Park, NC, January 23, 2001. 

Otte, T.L. Weather basics: Evaporation and condensation. Presentation to 2nd grade (2 sessions), 

Olive Chapel Elementary School, Apex, NC, March 9, 2001. 

91

Otte, T.L., and S.K. LeDuc. Weather Jeopardy. (2 sessions). Presentation at 9th Expanding Your 

Horizons Workshop, North Carolina State University, Raleigh, NC, March 13, 2001. 

Otte, T.L. Some modeling activities at ASMD. Presentation at NARSTO-NE-OPS Workshop, 

University of Maryland, College Park, MD, March 23, 2001. 

Otte, T.L. Weather basics: Evaporation and condensation. Presentation to 2nd grade (four 

sessions), Swift Creek Elementary School, Raleigh, NC, March 30, 2001. 

Otte, T.L. Weather Jeopardy. Presentation at the Science Day (four sessions), St. Mary 

Magdalene School, Apex, NC, June 6, 2001. 

Otte, T.L. EPA/ORD meteorological modeling activities. Presentation at the Ad Hoc 

Meteorological Modeling Workgroup, Research Triangle Park, NC, August 23, 2001. 

Otte, T.L. MCIP Version 2. Atmospheric Model Development Branch Seminar, Research 

Triangle Park, NC, August 27, 2001. 

Pierce, T.E. Guidance on biogenic emission inventory preparation. Presentation at the 

Emissions Inventory Improvement Plan Workshop, Raleigh, NC, October 10, 2000. 

Pierce, T.E. Development of biogenic emission inventories and improved vegetation databases. 

Presentation at the Multimedia Integrated Modeling Systems Peer Review, Research 

Triangle Park, NC, April 3, 2001. 

Pierce, T.E. Development of the third generation of the Biogenic Emissions Inventory System 

(BEIS). Presentation at the NOAA ARL peer review, Research Triangle Park, NC, May 

9, 2001. 

Pierce, T.E. Recommendations for biogenic emissions modeling to support regional air quality 

models. Presentation at a Biogenic Emissions Workshop sponsored by the Lake 

Michigan Air Directors for the Regional Planning Offices, Chicago, IL, May 22, 2001. 

Pierce, T.E. Meteorology can be exciting. Presentation to two first-grade classes, Morrisville 

Elementary School, Morrisville, NC, August 31, 2001. 

Pleim, J.E. Evaluation of a coupled land-surface and dry deposition model. Invited presentation 

at the American Geophysical Union Fall 2000 Meeting, San Francisco, CA, December 

17, 2000. 

Pleim, J.E. A new land surface model in MM5. Invited presentation at the WRF Land-Surface 

Modeling Workshop, NCEP/EMC, Camp Springs, MD, October 23, 2000. 

92

Pleim, J.E. U.S. EPA’s Models-3/Community Multiscale Air Quality Model (CMAQ). Invited 

presentation at the DOE Atmospheric Programs Annual Meeting, Raleigh, NC, February 

22, 2001. 

Poole-Kober, E.M. Atmospheric Science Librarians International. Poster presentation at the 26th 

IAMSLIC Annual Conference, Victoria, British Columbia, Canada, October 3, 2000. 

Roselle, S.J. U.S. EPA's multi-pollutant Models-3/Community Multiscale Air Quality model. 

Presentation at NARSTO Symposium — Tropospheric Aerosols: Science and Decisions 

in an International Community, Queretaro, Mexico, October 25, 2000. 

Roselle, S.J. CMAQ Sulfate Tracking Model. Presentation at Ad Hoc Air Quality Group, 

Research Triangle Park, NC, May 11, 2001. 

Schiermeier, F.A. Demonstration of the Supercomputing Center and Scientific Visualization 

Laboratory. Presentation for the U.S. Department of Defense Officials, Research 

Triangle Park, NC, October 16, 2000. 

Schiermeier, F.A. Atmospheric Sciences Modeling Division regional deposition modeling 

applications. Presentation at the Standing Air Simulation Work Group Meeting, 

Washington, DC, October 21, 2000. 

Schiermeier, F.A. Computer demonstration of Models-3/CMAQ control scenario applications. 

Presentation at the Twenty-Second United States/Russia Working Group Meeting on Air 

Pollution Modeling, Instrumentation, and Measurement Methodology, Main Geophysical 

Observatory, St. Petersburg, Russia, November 8, 2000. 

Schiermeier, F.A. Interim summary of EPA involvement in the Committee for Agency 

Cooperative Research. Presentation for the ICMSSR Committee for Agency Cooperative 

Research, Washington, DC, December 14, 2000. 

Schere, K.L. Uncertainty in applying and evaluating air quality models. Presentation at the U.S. 

EPA Models Evaluation and Peer Review Workshop, Alexandria, VA, November 29, 

2000. 

Schere, K.L. Status of U.S. EPA’s Models-3/CMAQ. Presentation at the PM Supersites 

Workshop, U.S. Environmental Protection Agency, Research Triangle Park, NC, March 

21, 2001. 

Schere, K.L. Status of Models-3 and CMAQ. Presentation at the EPA/OAQPS — Briefing for 

the Director, Office of Air Quality Planning and Standards, and staff, Research Triangle 

Park, NC, April 5, 2001. 

93

Schere, K.L. Models-3/CMAQ: Community Multiscale Air Quality modeling. Presentation at 

the NOAA ARL Peer Review, Research Triangle Park, NC, May 9, 2001. 

Schere, K.L. The NARSTO ozone assessment and long-range transport. Presentation at the 

MIT Symposium on Exporting and Importing Air Pollution, Regional and Global 

Transport, Dedham, MA, July 11, 2001. 

Schere, K.L. WRF model development and status. Presentation at the Ad-Hoc Meteorological 

Modeling Workgroup, Research Triangle Park, NC, August 24, 2001. 

Streicher, J.J. Computer simulation graphics as an exposure analysis tool: Applications in solar 

UV exposure. Presentation at the RACHET seminar, Research Triangle Park, NC, 

December 7, 2000. 

Streicher, J.J. Use of surface reflectance for ecological monitoring and change detection in the 

Lower Neuse River Basin, NC, at the Tenth NASA/JPL Airborne Earth Sciences 

Workshop, Pasadena, CA, February 28, 2001. 

Streicher, J.J. Modeling continuous exposure to solar radiation. Presentation at the 29th meeting 

of the American Society for Photobiology, Chicago, IL, July 10, 2001. 

Streicher, J.J. Advances in photobiology using computer graphical analysis. Presentation at the 

EMPACT National Meeting, Philadelphia, PA, August 7, 2001. 

West, J.L. Air quality research: Future directions. Poster presentation the NARSTO Meeting, 

Research Triangle Park, NC, February 12, 2001. 

West, J.L. Quality systems science center activities and future directions. Presentation at the 

NARSTO Annual Executive Assembly Meeting, March 6, 2001. 

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APPENDIX D: WORKSHOPS AND MEETINGS 

NARSTO-NE Ozone and Particle Study Workshop, Albany, NY, October 2–4, 2000. 

K.L. Schere 

EPA Region 2 Office Meeting on Air and Water, New York City, NY, October 5, 2000. 

R.L. Dennis 

Workshop on the Role of Mineral Aerosols in Quaternary Climate Cycles: Models and Data, 

Max Planck Institute, Jena, Germany, October 8–10, 2000. 

D.A. Gillette 

SASWG Meeting, Washington, DC, October 20, 2000. 

R.L. Dennis 

Fall 2000 Standing Emissions Air Working Group, Washington, DC, October 20–21, 2000. 

W.G. Benjey 

WRF Land-Surface Modeling Workshop, National Center for Environmental 

Prediction/Environmental Modeling Center, Camp Springs, MD, October 23–24, 2000. 

J.E. Pleim 

NARSTO—Tropospheric Aerosols: Science and Decisions in an International Community. A 

Technical Symposium on Aerosol Science, Querétaro, Mexico, October 23–26, 2000. 

R.L. Dennis 

S.J. Roselle 

K.L. Schere 

Committee on Climate Services, Washington, DC, October 24, 2000. 

S.K. Leduc 

Workshop on Air Quality Modeling Challenges, Taipei, Taiwan, November 3, 2000. 

J.K.S. Ching 

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2000 Annual Meeting of the American Institute of Hydrology on Atmospheric, Surface, and 

Subsurface Hydrology and Interactions, Research Triangle Park, NC, November 5–8, 2000. 

A.B. Gilliland (Session chair: Integrated Multimedia Modeling) 

R.L. Dennis 

Chesapeake Bay Shared Resources Conference on Ammonia, Dewey Beach, DE, November 

14–16, 2000. 

R.L. Dennis 

A. B. Gilliland 

T. E. Pierce 

U.S. EPA Models Evaluation and Peer Review Workshop, Alexandria, VA, November 28–30, 

2000. 

E.J. Cooter 

K.L. Schere 

Western Governors Association, Western Regional Air Partnership Research and Development 

Forum, Windblown and Mechanically Entrained Road Dust Workshop, Las Vegas, NV, 

December 14, 2000. 

D.A. Gillette 

Workshop on Biogenic Emissions Research, California Air Resources Board, Sacramento, CA, 

December 12–14, 2000. 

T.E. Pierce 

Electric Utilities Environmental Conference, Tucson, AZ, January 8–9, 2001. 

J.L. West 

AMS 17th International Conference on Interactive Information Processing Systems for 

Meteorology, Oceanography, and Hydrology, Albuquerque, NM, January 14–19, 2001. 

S. K. Leduc 

Integrated Mercury Monitoring Network Workshop, Chicago, IL, January 18, 2001. 

O.R. Bullock, Jr. 

96

The 84th Annual Meeting of the American Meteorological Society, January 13–17, 2001. 

E.M. Poole-Kober 

The Fifth Annual Meeting of Atmospheric Science Librarians International, Orlando, FL, January 

16–18, 2001. 

E.M. Poole-Kober 

NARSTO Reactivity Research Work Group, Research Triangle Park, NC, January 17–18, 2001. 

J.L. West 

Southern Oxidant Study Review Meeting, Research Triangle Park, NC, January 29–31, 2001. 

R.L. Dennis 

Southern Oxidant Study Workshop, Research Triangle Park, NC, January 30, 2001. 

B.K. Eder 

T.L. Otte 

Fifth Annual NERL Planning Meeting, Las Vegas, NV, January 30–February 1, 2001. 

K.L. Schere 

MIMS Neuse River Meeting, Research Triangle Park, NC, January 31, 2001. 

R.L. Dennis 

Conference on Future Directions in Air Quality Research, Research Triangle Park, NC, February 

12–15, 2001. 

S.K. Leduc (Conference organizer; Session chair: Carbon Dioxide) 

R.L. Dennis 

J.L West 

International Atmospheric Mercury Model Intercomparison, Moscow, Russia, February 14–15, 

2001. 

O.R. Bullock, Jr. 

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Meeting of the Office of the Federal Coordinator for Meteorology Committee on Climate and 

Monitoring, Washington, DC, February 26, 2001. 

S.K. Leduc 

Implementation of Canada-Wide Standards Conference, Toronto, Canada, March 7–8, 2001. 

J.L. West 

Ninth Expanding Your Horizons Workshop, North Carolina State University, Raleigh, NC, 

March 13, 2001. 

S.K. LeDuc 

T.L. Otte 

PM Supersites Workshop, U.S. EPA, Research Triangle Park, NC, March 21–21, 2001. 

R.L. Dennis 

B.K. Eder 

K.L. Schere 

Workshop on the Revised Protocol for Identification of Dust Producing Areas on Owens Lake, 

at Great Basin Unified Air Pollution Control District, Bishop, CA, March 21–23, 2001. 

D.A. Gillette 

NARSTO-NE-OPS Workshop, University of Maryland, College Park, MD, March 22–23, 2001. 

T.L. Otte 

Federal Information Technology Research and Development Meeting, Washington, DC, March 

26, 2001. 

S.S. Fine 

Annual ASTM D22 Committee and Subcommittee Meetings, Phoenix, AZ, April 2–5, 2001. 

J.S. Irwin 

MIMS Program Review, Research Triangle Park, NC, April 3–5, 2001. 

R.L. Dennis 

98

NARSTO Reactivity Research Work Group, Research Triangle Park, NC, April 11–12, 2001. 

J.L. West 

NARSTO Executive Assembly Meeting, Toronto, Canada, April 17–18, 2001. 

R.L. Dennis 

J.L. West 

Workshop: New Visions on Software Design, Washington, DC, April 17–19, 2001. 

S.S. Fine 

Human Exposure Program Review, U.S. EPA, Research Triangle Park, NC, April 18, 2001. 

J.K.S. Ching 

NOAA 2001 Constituent Workshop, Washington, DC, April 18, 2001. 

S.K. Leduc 

Spring 2001 Standing Emissions Air Working Group, Chicago, IL, April 20–21, 2001. 

W.G. Benjey 

EPA Emissions Inventory Conference, Denver, CO, April 30–May 3, 2001. 

T.E. Pierce (Session chair: Ammonia Emissions) 

High Performing Organization Workshop, Chapel Hill, NC, May 1–3, 2001. 

W.G. Benjey 

Air Resources Laboratory Program Review, Research Triangle Park, NC, May 9–10, 2001. 

R.L. Dennis 

99

Ad Hoc Air Quality Group, Research Triangle Park, NC, May 10–11, 2001. 

R.L. Dennis 

P.D. Dolwick 

B.K. Eder 

M.L. Evangelista 

B.L. Orndorff 

S.J. Roselle 

Weather Research and Forecasting Model-Chemistry Working Group, NCAR, Boulder, CO, May 

22–23, 2001. 

K.L. Schere 

LADCO Biogenics Modeling Meeting, Chicago, IL, May 22–23, 2001. 

P. D. Dolwick 

Persistant Bioaccumulative Toxin (PBT) Monitoring Strategy Development, Silver Spring, MD, 

May 22–23, 2001. 

O.R. Bullock, Jr. 

Institutional Ecological Economics Modeling Meeting, Solomons, MD, May 28–29, 2001. 

S.S. Fine 

American Geophysical Union Spring 2001 Conference, Boston, MA, May 29-31, 2001. 

A.B. Gilliland (Session chair: Watershed-Ecosystem Coupling) 

SEDRIS Technology Conference 2001, Lake Tahoe, NV, June 5–8, 2001. 

S.K. Leduc 

EMEP/EPA Workshop on Photo-Oxidants, Fine Particles, and Haze Across the Arctic and North 

Atlantic: Transport Observations and Models, Palisades, NY, June 12–15, 2001. 

R.L. Dennis 

P. D. Dolwick 

Committee on Climate Services, Washington, DC, June 15, 2001. 

S.K. Leduc 

100

Federal Interagency Ecosystem Modeling Meeting, Rockville, MD, June 17–19, 2001. 

S.S. Fine 

Shenandoah Assessment Workshop, Shenandoah National Park, VA, June 19–21, 2001. 

R.L. Dennis 

Eleventh PSU/NCAR Mesoscale Model Users' Workshop, Boulder, CO, June 25–27, 2001. 

T.L. Otte 

J.E. Pleim 

MIT Symposium on Exporting and Importing Air Pollution, Regional and Global Transport, 

Dedham, MA, July 10–12, 2001. 

K.L. Schere 

NARSTO NE-OPS Meeting, Philadelphia, PA, July 26–27, 2001. 

J.L. West 

AMS 9th Conference on Mesoscale Processes, 18th Conference on Weather Analysis and 

Forecasting, and 14th Conference on Numerical Weather Prediction, Ft. Lauderdale, FL, July 

30–August 3, 2001. 

T.L. Otte 

Air Quality Index Meetings with the Puget Sound Clean Air Agency, Seattle, WA, August 6–8, 

2001. 

P. D. Dolwick 

Texas Air Quality Study (TexAQS2000) Workshop, Austin, Texas, August 7–10, 2001. 

R.L. Dennis 

B.K. Eder 

T.L. Otte 

K.L. Schere 

J.L. West 

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August 2001 Technical Meeting of the Regional Planning Organizations, St. Louis, MO, August 

14–16, 2001. 

P. D. Dolwick 

M. Evangelista 

CASES-99 Workshop II, Research Triangle Park, NC, August 20–22, 2001. 

J.K.S. Ching 

Ad Hoc Meteorological Modeling Workgroup, Research Triangle Park, NC, August 23–24, 

2001. 

P. D. Dolwick 

M.L. Evangelista 

B.L. Orndorff 

T.L. Otte 

EMEP Steering Body Meeting, Geneva, Switzerland, September 3–5, 2001. 

R.L. Dennis 

High-Performing Organization Training, Research Triangle Park, NC, September 4–6, 2001. 

W.G. Benjey A.G. Gilliland S.J. Roselle 

F.S. Binkowski J.M. Godowitch J.H. Rudisill, III 

S.A. Brown S.C. Howard K.L. Schere 

O.R. Bullock, Jr. T.L. McDuffie D.B. Schwede 

J.K.S. Ching T.L. Otte J.J. Streicher 

E.J. Cooter S.G. Perry R.S. Thompson 

R.L. Dennis T.E. Pierce A.R. Torian 

B.K. Eder J.E. Pleim H.J. Viebrock 

S.S. Fine E.M. Poole-Kober J.O. Young 

P.L. Finkelstein S.T. Rao 

National Institute of Standards Workshop, Gaithersburg, MD, September 6–7, 2001. 

J.L. West 

State and Regional Representatives Emissions Modeling Meeting, Chicago, IL, September 

20–21, 2001. 

W.G. Benjey 

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APPENDIX E: VISITING SCIENTISTS 

1.	 Dr. Kiran Alapaty 

MCNC 

Research Triangle Park, NC 

Dr. Alapaty visited the Division on January 9, 2001, to present a seminar on Techniques to 

Improve Boundary Layer Simulations for Air Quality Applications. 

2.	 Yoram Cohen, Professor 

UCLA Department of Chemical Engineering 

University of California 

Los Angeles, CA 

Professor Cohen visited the Division on February 27, 2001, to present a seminar on Multimedia 

Modeling. 

3.	 Dr. Douglas Fox Mr. John Vimont Mr. Al Riebau 

CIRA National Park Service US Forest Service 

Fort Collins, CO Oak Ridge, TN Washington, DC 

Dr. Fox, and Messr. Vimont and Riebau visited the Division during May 8–9, 2001, to discuss 

the IAG Project Review on fire emissions modeling for the CMAQ model. 

4.	 Drs. Noor Gillani, Richard McNider, and Arastoo Biazar 

University of Alabama-Huntsville 

Huntsville, AL 

Drs. Gillani, McNider, and Biazar visited the Division during April 4–5, 2001, to discuss the 

Cooperative Agreement Project Review on the CMAQ modeling center at the University of 

Alabama-Huntsville, Huntsville, AL. 

5.	 Emily L. Harris, M.P.H. 

Senior Epidemiologist 

Arkansas Department of Environmental Quality 

Little Rock, AR 

Ms. Harris visited the Division on April 29, 2001, to discuss advanced procedures on performing 

biogenic emission calculations for the State of Arkansas as part of a State Implementation Plan. 

103

6.	 Dr. Avraham Lacser 

Israel Institute for Biological Research 

Ness Ziona, Israel 

Dr. Lacser, on a two-year sabbatical from his home institute, is working with Division scientists 

on neighborhood-scale modeling issues with the MM5 and CMAQ models. 

7.	 Dr. Alberto Martilli 

Laboratory of Air and Soil Pollution 

Swiss Federal Institute of Technology 

Lausanne, Switzerland 

Dr. Martilli visited the Division during September 26–29, 2001, to present a seminar on the 

development and evaluation of an urban surface exchange parameterization for mesoscale 

models, and to discuss the topic with Division staff. 

8.	 Drs. S.T. Rao, Christian Hogrefe, and Michael Ku 

New York State Department of Environmental Conservation 

Albany, NY 

Drs. Rao, Hogrefe, and Ku visited the Division on June 20, 2001, to discuss CMAQ modeling. 

9.	 Drs. A. Russell, and T. Odman Messrs. T. Tomiyama, and A. Hayashi 

Georgia Institute of Technology Drs. S. Kobayashi, H. Kunimi, and S. Yamazaki 

Atlanta, GA Petroleum Energy Center 

Tokyo, Japan 

Drs. Russell, Odman, Kobayashi, Kunimi, and Yamazaki, and Messr. Tomiyama, and Hayashi 

visited the Division during July 16–17, 2001, to discuss collaboration on street-corner scale 

modeling with the U.S. Environmental Protection Agency, Office of Research and Development, 

Japan Clean Air Program, and Georgia Institute of Technology. 

10.	 Dr. Fred Vukovich 

Science Applications International Corporation 

Raleigh, NC 

Dr. Vukovich visited the Division on October 18, 2000, to present a seminar on remote sensing 

databases for providing IC/BC inputs to CMAQ. 

104

11.	 Drs. Vikram Vyas, Qing Sun, Sheng-Wei Wang, and Arunundram Chandrasekar, 

Environmental Occupational Science Health Institute (EOSHI) 

Rutgers University 

Rutgers, NJ 

Drs. Vyas, Sun, Wang, and Chandrasekar visited the Division during November 14–15, 2000, to 

discuss collaboration on air quality modeling at neighborhood scales. 

12. Dr. Satoshi Yamazaki 

Toyota Research 

Tokyo, Japan 

Dr. Akira Hayashi 

Petroleum Energy Center 

Tokyo, Japan 

Dr. Kazuhiko Suzuki 

Idemitsu Kosan Company 

Tokyo, Japan 

Dr. Shiunji Kobayashi 

NIES and Petroleum Energy Center 

Tokyo, Japan 

Drs. Yamazaki, Hayashi, Suzuki, and Kobayashi, a group representing the Japan Clean Air 

Program, visited the Division on February 15, 2001, to discuss collaborating on CMAQ model 

development studies, especially on fine particulate matter and microscale modeling. 

105

APPENDIX F: HIGH SCHOOL, UNDERGRADUATE, AND GRADUATE


STUDENTS, AND POSTDOCTORAL RESEARCHERS 

1.	 Dr. Jeffrey R. Arnold 

University Corporation for Atmospheric Research 

Boulder, Colorado 

Dr. Arnold, a postdoctoral researcher, is in his third year with the Division. Dr. Arnold is 

developing more advanced methods to extend the state of the art of diagnostic model evaluation 

applicable to complex, nonlinear photochemical models, to codify the new evaluation techniques, 

and to make weight-of-evidence approaches objective. 

2.	 Ms. Megin Chapman 

606 Oak Avenue 

Hamlet, NC 

Ms. Chapman, a student in the School of Information and Library Science, North Carolina 

Central University (NCCU), Durham, North Carolina, worked in the library from May through 

July 2001. Ms. Chapman did this through NCCU as a practicum and earned three credit hours. 

She participated in handling library services during that period to gain experience in interlibrary 

loans, reference, and cataloging. 

3.	 Dr. Shan He 

University Corporation for Atmospheric Research 

Boulder, CO 

Dr. He, a post-doctoral researcher, is working with the Division on air quality model evaluation 

for particulate matter. He began a two-year visit with the Division on August 21, 2000. 

4.	 Ms. Cassandra L. Hunsucker 

1713 Carolina Street 

High Point, NC 

Ms. Hunsucker, a student in the School of Information and Library Science, University of North 

Carolina at Greensboro, spent April 14, 2001, in the library observing the activities and services 

provided and interviewing the Librarian as a class assignment. Ms. Hunsucker also visited with 

some of the research scientists, asking them questions about the benefits of having the library 

within the Division. 

106

5.	 Rokjin Park 

Department of Meteorology 

University of Maryland 

College Park, MD 

Mr. Park, a graduate student, is working with the Division to develop a method for adjusting 

photolysis rates for the presence of aerosols. 

6.	 Mr. Jason Smith 

University of North Carolina at Chapel Hill 

Chapel Hill, NC 

Mr. Smith, a research assistant, assisted in the software framework design of the Multimedia 

Intergrated Modeling System. 

7.	 Dr. Gail S. Tonnesen 

University Corporation for Atmospheric Research 

Boulder, Colorado 

Dr. Tonnesen, a postdoctoral researcher, completed her third year with the Division. Dr. 

Tonnesen investigated the identification of indicator ratios of ambient concentrations of 

photochemically active trace gases that might distinguish the sensitivity of the local production 

of ozone to NOX and VOC emissions in the ambient atmosphere for the testing of air quality 

models. The tests were developed from theoretical considerations of atmospheric 

photochemistry. 

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APPENDIX G: ATMOSPHERIC SCIENCES MODELING DIVISION


STAFF AND AWARDS


All personnel are assigned to the U.S. Environmental Protection Agency from the National 

Oceanic and Atmospheric Administration, except for those designated EPA, who are employees 

of the EPA, or SEEP, who are part of the EPA Senior Environmental Employment Program. 

Office of the Director 

Francis A. Schiermeier, Supervisory Meteorologist, Director (Until February 2001) 

William B. Petersen, Supervisory Physical Scientists, Acting Director (Since February 

2001) 

Herbert J. Viebrock, Meteorologist, Assistant to the Director 

Dr. Robin L. Dennis, Physical Scientist 

Dr. Peter L. Finkelstein, Physical Scientist 

Bruce W. Gay, Jr. (EPA), Program Manager 

Evelyn M. Poole-Kober, Librarian 

Jeffrey L. West, Physical Science Administrator 

Dr. Basil Dimitriades (SEEP), Physical Scientist 

Barbara R. Hinton (EPA), Secretary 

Atmospheric Model Development Branch 

Kenneth L. Schere, Supervisory Meteorologist, Chief 

Dr. Francis S. Binkowski, Meteorologist 

O. Russell Bullock, Jr., Meteorologist


Dr. Daewon W. Byun, Physical Scientist (Until May 2001)


Dr. Jason K.S. Ching, Meteorologist


Dr. Brian K. Eder, Meteorologist


Gerald L. Gipson (EPA), Physical Scientist


James M. Godowitch, Meteorologist


Dr. William T. Hutzell (EPA), Physical Scientist 


Dr. Michelle R. Mebust (EPA), Physical Scientist


Tanya L. Otte, Meteorologist 


Dr. Jonathan E. Pleim, Physical Scientist


Shawn J. Roselle, Meteorologist


Tanya L. McDuffie, Secretary


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Modeling Systems Analysis Branch 

Thomas E. Pierce, Supervisory Physical Scientist, Chief (Since August 2001) 

Dr. William G. Benjey, Physical Scientist 

Dr. Steven S. Fine, Computer Specialist 

Dr. Alice B. Gilliland, Physical Science Administrator 

Steven C. Howard, Computer Specialist 

Dr. Sharon K. LeDuc, Physical Scientist (Until October 2001) 

John H. Rudisill, III, Equipment Specialist 

Alfreida R. Torian, Computer Specialist 

Gary L. Walter, Computer Scientist 

Dr. Jeffrey O. Young, Mathematician 

Ruby S. Borden (SEEP), Secretary 

Applied Modeling Research Branch 

William B. Petersen, Supervisory Physical Scientist, Chief (Until February 2001) 

Dr. Ellen J. Cooter, Supervisory Meteorologist, Acting Chief (Since February 2001) 

Dr. Dale A. Gillette, Physical Scientist 

Dr. Alan H. Huber, Physical Scientist 

Dr. Steven G. Perry, Meteorologist 

Donna B. Schwede, Physical Scientist 

John J. Streicher, Physical Scientist 

Roger S. Thompson, Physical Scientist 

Lawrence E. Truppi, Meteorologist 

Ashok Patel (SEEP), Engineer 

John Rose (SEEP), Machinist/Model Maker 

Bruce Pagnani (SEEP), Computer Programmer 

Sherry A. Brown, Secretary 

Air Policy Support Branch 

Mark L. Evangelista, Supervisory Meteorologist, Chief 

Dennis A. Atkinson, Meteorologist 

Dr. Desmond T. Bailey, Meteorologist 

Patrick D. Dolwick, Physical Scientist 

John S. Irwin, Meteorologist 

Brian L. Orndorff, Meteorologist 

Jawad S. Touma, Meteorologist 

Thomas E. Pierce, Supervisory Physical Scientist, Chief, MSAB, received the EPA Bronze 

Medal —“For outstanding efforts in coordinating with State and local agency staff and industries 

to develop consensus approaches for generating emission inventory guidance under the Emission 

Inventory Improvement Program” 

109