NOAA Technical Memorandum OAR ARL-249 

FISCAL YEAR 2002 SUMMARY REPORT OF THE NOAA ATMOSPHERIC SCIENCES 

MODELING DIVISION TO THE U.S. ENVIRONMENTAL PROTECTION AGENCY 

Evelyn M. Poole-Kober 

Herbert 1. Viebrock 

(Editors) 

Atmospheric Sciences Modeling Division 

Research Triangle Park, North Carolina 

Air Resources Laboratory 

Silver Spring, Maryland 

June 2003 

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

Donald L. Evans VADM Conrad C. Lautenbacher, Jr. Daniel L. Albritton 

Secretary Under Secretary for Oceans Acting Assistant Administrator 

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 report summarizes the Fiscal year 2002 research and operational activities of the 

Atmospheric Sciences Modeling Division (ASMD), Air Resources Laboratory, National Oceanic 

and Atmospheric Administration (NOAA), working under Interagency Agreements EPA 

DW13938483 and DW13948634 between the U.S. Environmental Protection Agency (EPA) and 

U.S. Department of Commerce. The summary includes descriptions of research and operational 

efforts in air pollution meteorology, meteorology and air quality model development and 

evaluation, and air pollution abatement and compliance programs. 

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

with EPA, which funds the research efforts. ASMD conducts research activities in-house and 

through contract and cooperative agreements for the EPA National Exposure Research 

Laboratory and other EPA groups. With a staff consisting of NOAA and EPA employees, 

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. ASMD is organized into 

three research Branches - Atmospheric Model Development Branch, Modeling Evaluation and 

Applications Research Branch, and Air-Surface Processes Modeling Branch, and an operational 

Branch - Air Policy Support Branch. The report is organized by major program themes reflecting 

the ASMD strategic plan. 

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

the Director, Atmospheric Sciences Modeling Division, MD-E243-02, U.S. Environmental 

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

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CONTENTS
 

Page 

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

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

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

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

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

2.1 Atmospheric Model Development ....................................... 2
 

2.1.1 Meteorological Modeling for CMAQ Applications ................... 3
 

2.1.2 Linking Meteorology and Chemistry Models ....................... 4
 

2.1.3 Land Surface and Planetary Boundary Layer Modeling ............... 5
 

2.1.4 Anthropogenic Emissions ...................................... 5
 

2.1.5 Biogenic Emissions ........................................... 6
 

2.1.6 Modeling Smoke Emissions From Fires ........................... 7
 

2.1.7 Fugitive Dust Modeling ........................................ 8
 

2.1.8 Effects of Desert Vegetation Type on Fugitive Dust Emissions ......... 9
 

2.1.9 	Implementation and Testing of New and Refined Chemical Mechanisms
 

and Chemical Solvers in CMAQ .............................. 11
 

2.1.10 	Aerosol Mechanism Improvements in CMAQ .................... 12
 

2.1.11 	Plume-in-Grid Modeling ..................................... 13
 

2.1.12 	CMAQ Code Integration and 2002 Release ....................... 14
 

2.1.13 	Development and Testing of an Air Quality Forecast Model ......... 15
 

2.1.14 	AERMET ................................................. 16
 

2.1.15 	Recommendations for Observations-Based Methods ............... 16
 

2.2 Atmospheric Model Evaluation and Application Activities ................... 17
 

2.2.1 Improve O3 Vegetation-Atmosphere Exchange Models .............. 17
 

2.2.2 Modeling Studies in Coastal Environments ........................ 19
 

2.2.3 Chesapeake Bay 2005 Re-Evaluation ............................ 20
 

2.2.4 	Bay Regional Atmospheric Chemistry Experiment: Evaluation and
 

Assessment ................................................ 21
 

2.2.5 Ammonia Budgets for Coastal Systems ........................... 21
 

2.2.6 Inverse Modeling for Ammonia ................................. 22
 

2.2.7 Diagnostic Metrics for Ozone and Particulate Matter ................ 23
 

2.2.8 	CMAQ Model Evaluation to Assess Readiness for State Implementation
 

Plans ..................................................... 24
 

v 
2.2.9 	Sensitivity of CMAQ Control Strategy Predictions to Model Input
 

Uncertainties for CMAQ and MM5 Configurations ................ 24
 

2.3 Air Toxics Modeling ................................................. 25
 

2.3.1 CMAQ Extensions to New Air Toxics Gaseous Species ............. 25
 

2.3.2 CMAQ Extensions to Semi-Volatile and Involatile Compounds ....... 26
 

2.3.3 CMAQ Mercury Model Refinements and Evaluation ................ 27
 

2.3.4 Estimating Background Concentrations ........................... 28
 

2.3.5 Urban-Scale Analyses ........................................ 28
 

2.4 Fine-Scale Modeling ................................................. 30
 

2.4.1 Urban Canopy Parameterization in MM5 ......................... 30
 

2.4.2 	Air Quality Modeling of Particulate Matter and Air Toxics 	 
 

at Neighborhood Scales ...................................... 32
 

2.4.3 Modeling the World Trade Center Disaster Site .................... 34
 

2.4.4 Meteorological Measurements Near the World Trade Center .......... 37
 

2.4.5 Estimating the Plume from the World Trade Center Recovery Site ..... 38
 

2.5 Multimedia Modeling ................................................ 41
 

2.5.1 Develop Tools to Support Sensitivity and Uncertainty Studies ......... 41
 

2.5.2 Development of Compartmental Modeling Tools for Toxics .......... 43
 

2.5.3 Urban Drainage Decision Support System Prototype ................ 45
 

2.6 Global Climate Change Impacts on Air Quality ............................ 45
 

2.7 Specialized Client Support ............................................ 47
 

2.7.1 Community Modeling and Analysis System Center ................. 47
 

2.7.2 	American Meteorological Society/Environmental Protection Agency
 

Regulatory Model Screening Tool .............................. 48
 

2.7.3 	Joint Action Group for Selection and Evaluation of Atmospheric 
 

Transport and Diffusion Models ............................... 49
 

2.7.4 AERMOD Screening Model Comparison ......................... 50
 

2.7.5 Modeling Significant Increases in Concentrations of PM2.5 ........... 50
 

2.7.6 Developing Statistical Forecasting Methods for PM2.5 ............... 51
 

2.7.7 NARSTO Program Support .................................... 51
 

2.7.8 European Monitoring and Evaluation Program ..................... 52
 

2.8 Regulatory Support .................................................. 52
 

2.8.1 Support Center for Regulatory Air Models ........................ 52
 

2.8.2 Trajectory Analyses .......................................... 52
 

2.8.3 Effects of Additional VOC/NOx Reductions in 2010 ................ 53
 

2.8.4 	Operational Evaluation of Proposed Clear Skies Initiative Ozone 	 
 

Modeling ................................................. 54
 

vi 
REFERENCES .............................................................. 55
 

APPENDIX A: ACRONYMS, ABBREVIATIONS, AND DEFINITIONS ................ 61
 

APPENDIX B: PUBLICATIONS ................................................ 64
 

APPENDIX C: PRESENTATIONS .............................................. 71
 

APPENDIX D: WORKSHOPS AND MEETINGS .................................. 80
 

APPENDIX E: VISITING SCIENTISTS .......................................... 89
 

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

AND POSTDOCTORAL RESEARCHERS .................................. 91
 

APPENDIX G: ATMOSPHERIC SCIENCES MODELING DIVISION STAFF AND 
 

AWARDS ............................................................ 92
 

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Page 

FIGURES 

Figure 1.-- Mean daily sand mass horizontal flux versus date of collection for the means of five
 

ecosystem types and the Scrape Site. The averaging period was approximately three months. 
 

Each mean is for values measured at three Net Primary Productivity sites having the same
 

ecosystem type. The Scrape Site mean is for three measurements taken within an area usually
 

dominated by mesquite bushes but having been scraped free of any vegetation. All sampled sites
 

are located within a circle having a radius of 10 km centered at the headquarters of the U.S.
 

Department of Agriculture, Jornada Experimental Range (JER). The JER is located between Las
 

Cruces and Alamagordo, New Mexico ............................................ 10
 

Figure 2.-- Installation of the meteorological tripod and sonic anemometer at the site inside the
 

forest canopy ................................................................ 18
 

Figure 3.-- Ozone values measured at four heights outside the forest ..................... 19
 

Figure 4.-- Annual average concentrations (ug/m3) of benzene modeled by allocating onroad
 

emissions to road segments and nonroad emissions to grid cells ........................ 31
 

Figure 5. -- A 1:600 scale model of Lower Manhattan as it appears on the turntable within the
 

Meteorological Wind Tunnel .................................................... 35
 

Figure 6. -- Neutrally-buoyant smoke released from nine positions within the simulated World
 

Trade Center rubble pile ....................................................... 36
 

Figure 7. -- Smoke visualization for neutrally buoyant release from the simulated World Trade
 

Center site (release area just left of center in this photo); flow is from left to right .......... 37
 

Figure 8. -- View of the Meteorological Instrumentation Cluster along the Hudson River looking
 

Southeast ................................................................... 39
 

Figure 9. -- View of the Meteorological Instrumentation Cluster from above Pier 25 ........ 39
 

Figure 10. -- Map (50-km by 50-km centered on ground zero) of example 1-hour averaged plume
 

dilution of a volume source from ground zero. Red numbers are PM2.5 hourly measurements .. 40
 

Figure 11. -- Map of September 11, 2001, through December 8, 2001, averaged plume dilution of
 

a volume source from ground zero. The wind rose is presented for this same period ........ 40
 

Figure 12. -- An example display of meteorological and particulate matter concentrations with
 

the 3-dimensional model of buildings for the Lower Manhattan area ..................... 41
 

viii 
Figure 13. -- The pilot Three Compartment Model (3cm) as implemented under the MIMS
 

framework .................................................................. 44
 

ix
 
FISCAL YEAR 2002 SUMMARY REPORT OF THE NOAA
 

ATMOSPHERIC SCIENCES MODELING DIVISION TO THE


 U.S. ENVIRONMENTAL PROTECTION AGENCY
 

ABSTRACT. During Fiscal Year 2002, 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 application of meteorological and air quality 

simulation models. Among the research studies and results were the release of the 

latest version of the Community Multiscale Air Quality (CMAQ) modeling 

system in June 2002, continued development and improvement of CMAQ and its 

modules, wind tunnel modeling of the World Trade Center disaster site, 

estimation and characterization of the dispersion of particulate matter from the 

World Trade Center recovery site after September 11, 2001, study of the 

requirements for air quality modeling at fine or neighborhood scales, and initial 

work on an air quality forecasting system using the Eta model. 

1. INTRODUCTION 

In Fiscal Year (FY) 2002, the Atmospheric Sciences Modeling Division (ASMD) 

continued its commitment to 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 the dispersion of atmospheric pollutants through numerical modeling as well as 

physical modeling; and developing and evaluating meteorological and air quality models on all 

temporal and spatial scales. The research products developed by the Division are transferred to 

the public and private national and international user communities. 

Division research is focused on five areas: new developments in air quality modeling; 

global climate change and its impact on regional air quality; multimedia modeling; data 

management and analysis; and air quality forecasting. The Division was reorganized to prepare it 

to respond effectively to these new research directions as more fully described in the following 
sections. A new version of the Community Multiscale Air Quality (CMAQ) modeling system, 

incorporating the latest advances in state-of-science in modeling ozone, fine particles, visibility, 

and other pollutants was released in June 2002. Research was initiated to develop and apply 

statistical techniques for evaluating air quality model performance in reproducing the spatial and 

temporal features embedded in the observational data. In collaboration with the NOAA National 

Weather Service and Office of Oceanic and Atmospheric Research, work was begun to link 

CMAQ with the NOAA operational meteorological model, Eta. To improve the simulation of 

the transport and fate of airborne agents in the near-field, a scale model of Lower Manhattan was 

designed for use in the wind tunnel to study the impact of pollutants released from ground zero. 

These studies will help improve the predictions using computational fluid dynamics models and 

mesoscale models to quantify the adverse impacts from the collapse of the World Trade Center. 

2. PROGRAM REVIEW 

2.1 Atmospheric Model Development 

This research is aimed at providing state-of-science air quality models and guidance for 

their use in the implementation of National Ambient Air Quality Standards (NAAQS) for ozone 

and fine particulate matter (PM2.5). The principal effort is to develop and improve the 

Models-3/Community Multiscale Air Quality (CMAQ) modeling system, a multiscale and 

multi-pollutant chemistry-transport model (CTM). Specific research components include: 

meteorological modeling, land-surface and planetary boundary layer (PBL) modeling, emissions 

modeling, gas-phase chemical mechanisms and solvers, aerosol representations in grid-based air 

quality models, plume-in-grid treatment for large elevated sources of pollution, CMAQ code 

integration and efficiencies, and air quality forecasting. 

The objectives of this research program are to continuously develop and improve the 

mesoscale (regional through urban scale) air quality simulation models, including CMAQ, as air 

quality management and NAAQS implementation tools. The CMAQ CTM includes the 

necessary critical science process modules for handling atmospheric transport, deposition, cloud 

mixing, emissions, gas- and aqueous-phase chemical transformation processes, and aerosol 

dynamics and atmospheric chemistry. Research is conducted to develop and test appropriate 

chemical and physical mechanisms, improve the accuracy of emissions and dry deposition 

algorithms, and to develop and advance state-of-science meteorology models and contributing 

process parameterizations. 

By design, CMAQ is expected to be used by both scientists and policy makers for 

assessment activities, research module developments, and detailed model evaluation studies. 

Scientists can thus incorporate additional air quality science process modules into the system. A 

generalized coordinate approach used in CMAQ allows the CMAQ CTM to be configured 

dynamically consistent with the driver meteorology model. Tested model configurations can be 

established for use by the policy community to develop and analyze implementation strategies for 

2
 
air quality management. CMAQ supports the vision of “one atmosphere” approach to air quality 

modeling. It is capable of concurrently simulating gridded fields of oxidants, fine particles,

 visibility degradation, and acidic and nutrient deposition and loadings to ecosystems at urban 

and regional scales. As our understanding of atmospheric processes, input data, and model 

formulations and parameterizations improve, it will be essential to continue to upgrade or 

provide science options for future releases of CMAQ. Therefore, activities that facilitate the 

maintenance and science process evolution within CMAQ will be required. The work described 

below includes additional model development and testing leading to the June 2002 release of the 

CMAQ modeling system. 

2.1.1 Meteorological Modeling for CMAQ Applications 

The Fifth-Generation Pennsylvania State University (PSU)/National Center for 

Atmospheric Research (NCAR) Mesoscale Model (MM5) is the primary tool for providing 

meteorological input for Models-3/CMAQ. MM5 is widely used to generate meteorological 

characterizations of the atmosphere throughout the air-quality modeling community. For 

Models-3/CMAQ, MM5 is applied to several case studies (both episodic and seasonal) 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 ranging from continental to urban scales. The MM5 output is 

ultimately used in the SMOKE (emissions) and CMAQ (chemistry) modules to describe the 

atmospheric state variables and the planetary boundary layer characteristics. 

Several projects were underway during FY-2002 using MM5 to support 

Models-3/CMAQ applications. MM5 Version 3 Release 5 (MM5v3.5) was made available to the 

modeling community by NCAR in December 2001. MM5v3.5 featured scientific revisions to the 

Pleim-Xiu land-surface model (Xiu and Pleim, 2001), as well as updated land-use and soil 

databases, all of which are of particular interest for air-quality modeling. During FY-2002, 

MM5v3.5 was tailored for air quality applications with minor modifications to the science 

algorithms and parameters, and it was used in various research projects. 

During FY-2002, MM5 was used to drive CMAQ for a 10-week summer modeling period 

based on the photochemical field studies from the Southern Oxidants Study (SOS) in Nashville 

and Atlanta during the summer of 1999. Meteorological and chemical observations were made 

in Nashville during June and July 1999, and chemical observations were made in Atlanta during 

August 1999. The modeling of SOS 1999 consisted of a common domain with 32-km horizontal 

grid spacing and separate 8-km and 2-km domains over each of the focal cities. Preliminary 

results from the Nashville study have been summarized by Pleim et al. (2002). 

In addition, modeling domains were established for simulating the periods observed 

during the 2000 Texas Air Quality Study of Houston (TexAQS 2000) and the 2002 Bay Regional 

Atmospheric Chemistry Experiment (BRACE) of Tampa , Florida. Tentatively, these modeling 

3
 
projects will use the same 32-km grid spacing that was used for the SOS 1999 studies and 8-km 

and 2-km domains over the focal cities. Some of the plans for the TexAQS 2000 application 

have been discussed by Ching et al. (2002). In-house modeling for TexAQS 2000 and BRACE 

is expected to commence in FY-2003. 

Also in FY-2002, development continued on the implementation of alternate land-use 

databases in MM5v3. The alternate land-use databases theoretically will add value to the 

meteorology simulations through use of higher-resolution base data (on the order of meters rather 

than kilometers) and through more detailed categorization. Specifically, the Biogenic Emissions 

Land cover Database version 3 (BELD-3) has been selected for implementation in MM5 to 

facilitate linkage between the meteorology and emissions components of CMAQ. It is 

anticipated that these new databases will also be of particular benefit for the neighborhood-scale 

modeling activities. 

Finally, preliminary plans for a transition from MM5 to the Weather Research and 

Forecast Model (WRF) were discussed. WRF is expected to be the next-generation meteorology 

model that will include many of the features currently in MM5. WRF is a developmental 

community model, and it is available to the modeling community for testing. WRF is attractive 

for air-quality modeling applications because it contains mass-conserving equations, whereas 

MM5 does not. The primary drawback of WRF is that there are no plans to develop MM5’s 

FDDA system for WRF, and the FDDA would be a requirement for in-house WRF applications. 

WRF’s developers have recognized the need for FDDA as an unfunded requirement. As a result, 

the ASMD transition to WRF has been tentatively postponed until late FY-2003, at the earliest. 

2.1.2 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 revisions enabled dynamical 

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 was also added to MCIP. In addition, the Models-3 dry deposition scheme (M3Dry) was 

implemented, and three new dry deposition species were added. Several new output fields were 

created in MCIP, as well, to support more sophisticated cloud micro physics, the Pleim-Xiu landsurface model, modeling of air toxics, and biogenic emissions processing. 

In FY-2002, the upgraded software program, MCIP Version 2 (MCIP2), was released to 

30 beta testers in the CMAQ community representing federal and state regulatory agencies, 

4
 
industry, and academia. The beta-testing exercise enabled the creation of more test cases to build 

a more robust MCIP2, allowed users to become familiar with the new code structure, provided a 

vehicle for user feedback, and infused the new science into the user base. Three beta releases of 

MCIP2 were made in early FY-2002. The MCIP2 code was released to the community in March 

2002, three months ahead of the remainder of the CMAQ system. 

2.1.3 Land Surface and Planetary Boundary Layer Modeling 

Realistic simulation of land surface and planetary boundary layer (PBL) processes are 

critically important for both meteorology and air quality modeling. Interactions between surface 

characterization, surface fluxes, and PBL processes are very tightly coupled. In addition, surface 

fluxes and PBL mixing of chemical constituents closely follow the meteorological processes. 

Hence, work involves both the meteorology and chemical transport models to develop realistic 

and consistent modeling of surface and PBL processes. 

The bulk of this effort years has focused on the development, testing, and implementation 

of the Pleim Xiu land surface model (PX LSM) (Xiu and Pleim, 2001) in the MM5 and the 

M3dry dry deposition model in CMAQ. The M3dry scheme is linked to the PX LSM by use of 

the canopy (bulk stomatal) resistance and aerodynamic resistance directly from the PX LSM. 

These modules are now in public releases of both models (MM5 and CMAQ), providing the 

capability of using the same PBL scheme for both meteorological and chemical species. The 

Asymmetric Convective model (ACM) is part of the PX LSM implemented in MM5 and has also 

been added to CMAQ in the 2002 release. 

2.1.4 Anthropogenic Emissions 

During FY-2002, the Sparse Matrix Operator Kernel Emission (SMOKE)©1 modeling 

system (www.emc.mcnc.org/products/smoke) was enhanced to allow user definition and 

grouping of major elevated point sources by stack parameters, emissions, emission rank, plant 

identification number, source identification number, calculated plume rise, or any combination of 

the preceding. A variety of software bugs were fixed. In addition, the Biogenic Emission 

Inventory System Version 3 (BEIS-3) and 1-km spatial resolution Biogenic Emission Land use 

Data Base (BELD) were installed in SMOKE©. SMOKE© development began under ASMD 

sponsorship, however, it has become a true community model. Development of SMOKE© is 

continuing with the contribution of several entities for modeling of wild fire emissions (ASMD 

and U.S. Forest Service), toxic emissions (EPA Office of Air Quality Planning and Standards 

(OAQPS)), and alternative land cover and wildfire emissions (Western Regional Air 

Partnership). ASMD is collaborating with OAQPS in defining the methodology by which 

SMOKE© will model toxic emission data for input to CMAQ. The initial estimation of toxic 

1

Copyright 1999 MCNC—North Carolina Supercomputing Center 

5 
emissions by SMOKE© will be limited to mobile sources, although application of the complete 

National Toxic Emission Inventory is planned in the near future. 

The SMOKE© Tool was phased out during FY-2002 along with the old Models-3 

modeling framework and graphical interface. The old framework was dependent on obsolete 

commercial software and had become problematic. The core Models-3 modeling components 

(CMAQ, MCIP, SMOKE) are often run independently and are not affected by the phase-out of 

the old framework and interface. The Tool was more tightly bound with the old framework. The 

SMOKE© Tool was used to define major elevated point sources and to grid emission data and 

related spatial surrogates for input to CMAQ. Point source definition is now done within 

SMOKE© and gridding may be accomplished using the new Spatial Allocator tool of the 

Multimedia Integrated Modeling System (MIMS). Unlike the SMOKE© Tool, the Spatial 

Allocator does not require the use of expensive SAS®2 or Arc/Info®3 software licenses. Finally, 

SMOKE© can be run either independently using scripts or with MIMS as a graphical interface. 

MIMS has been tested as a new, optional, robust interface for operation of SMOKE and CMAQ. 

ASMD continued collaboration with the EPA National Risk Management Research 

Laboratory (NRMRL) on the development of a Modal Mobile Model for improved estimation of 

mobile source emissions. At the end of FY-2002, a detailed plan for completion of the coding 

and testing of the model was finished. The model will combine emission factors produced by the 

EPA Mobile6 mobile source emission model and spatial road network information following the 

approach of the spatially detailed Modal Model to produce spatially distributed mobile emission 

model results. The results will be more spatially refined than Mobile 6 outputs, but less inputdate intensive than the Modal Model. The concepts testing with the Modal Mobile Model may 

be used in the new Multiscale Motor Vehicle and Equipment Emission System (MOVES), under 

development by the EPA Office of Transportation and Air Quality (OTAG), will eventually 

replace the regulatory mobile source emission model Mobile6. MOVES is designed as a 

modular model, thereby allowing components to be run individually or in combination. MOVES 

will accommodate modeling of mobile source emissions at various spatial scales, ranging from 

individual vehicles (micro-scale) to producing regionally-distributed mobile emission data 

suitable for use in such regional air quality transport models as CMAQ. MOVES will be a 

Geographic Information System (GIS)-based multiscale mobile emission model. MOVES 

version for greenhouse gases is planned for release in FY-2006. 

2.1.5 Biogenic Emissions 

Version 3.10 of the Biogenic Emissions Inventory System (BEIS) was introduced as part 

of an upgrade to CMAQ (Pierce et al., 2002a). BEIS3.10 features a 1-km vegetation database for 

2

SAS is a registered trademark of SAS Institute, Inc. 

3

ARC/Info is a registered trademark of ESRI 

6 
the contiguous United States, which resolves forest canopy coverage by tree species; normalized 

emission factors for 34 chemicals, including 14 monoterpenes and methanol; a soil nitric oxide 

emissions algorithm that accounts for soil moisture, crop canopy coverage, and fertilizer 

application; and, speciation factors for the CB-IV, RADM2 (Regional Acid Deposition Model, 

version 2), and SAPRAC99 chemical mechanisms. During 2002, BEIS3.09 was formally 

imbedded as part of the SMOKE modeling system (Vukovich and Pierce, 2002). 

In an effort to improve the characterization of vegetation cover for biogenic emissions 

and other air quality modeling processes, vegetation cover and isoprene emission estimates were 

compared with three contemporary databases (Pierce et al., 2002b). These databases included (1) 

the North American Land Cover Characteristics (NALCC) version 2 database, (2) the Biogenic 

Emissions Land cover Database (BELD3), and (3) the National Land Cover Database (NLCD). 

The NALCC database, which is released by the U.S. Geological Survey (USGS) and supported 

by the National Center for Atmospheric Research (NCAR) for use with MM5, consists of 1-km 

resolved land-cover classes derived from Advanced Very-High Resolution Radiometer 

(AVHRR) satellite data (U. S. Geological Survey, 2001). BELD3 provides vegetation data to the 

Biogenic Emissions Inventory System (U. S. Environmental Protection Agency, 2001). It 

combines the NALCC data with U.S. Forest Service (USFS) and U.S. Department of Agriculture 

databases so that tree and crop cover (by species) are resolved to 1-km. The NLCD was released 

by the Multi-Resolution Land Characteristics Consortium (U.S. Geological Survey, 2002). It is 

based on Landsat-TM data and available at ~30-m resolution. The relative distribution of forest 

and agriculture cover contained in the popular NALCC database was found to differ considerably 

from the two other contemporary databases across the mixed agricultural/forested region of the 

Tennessee Valley. Isoprene emission estimates varied by a factor of two depending on the source 

of vegetation data. Therefore, caution is urged in using such broadly-defined vegetation classes 

as those found in the NALCC data to derive biogenic emissions. Finally, it was recommended 

that future work consider the use of other databases, such as the NLCD, coupled with tree species 

distribution information to simulate other meteorologically-related processes that depend on the 

characterization of vegetation data. 

2.1.6 Modeling Smoke Emissions From Fires 

A prototype, stand-alone emissions processor is being developed that will introduce 

smoke from fires (prescribed and wildfires) into the Models-3/CMAQ modeling system. The 

goal of this project is to build a tool to generate emissions from forest burning for use in regional 

air quality modeling with the following characteristics: 

•	 horizontal scale from regional to national with grid spacings ranging from 

1 km to 36 km; 

•	 temporal resolution ranging from hourly to multi-year; 

•	 chemical species, including all NAAQS and visibility components and 

their precursors; and 

•	 accuracy equivalent to other emissions estimates. 

7
 
This prototype system, Community Smoke Emission Model, consists of a set of 

processors based on state-of science algorithms developed primarily by the USFS. This 

development is a cooperative effort with the U.S. National Park Service (USNPS) and includes 

principals at the Cooperative Institute for Research in the Atmosphere, Colorado State 

University, Fort Collins, Colorado. This project will serve to facilitate the use of Models3/CMAQ to develop science-based strategic plans for dealing with smoke emission management 

issues and interstate transport affecting regional haze, PM2.5, PM10, and ozone. The effort to 

develop the smoke emission processor will involve introducing several major components, 

including: 

•	 a system to identify fire boundaries determined from Geographic
 

Information Systems (GIS) coverage;
 

•	 fuel models to introduce vegetation coverage and fuel loading data
 

associated with the fires;
 

•	 a fuel moisture model; 

•	 a fire generation processor based on spatial coverage of historical wildfire; 

•	 a processor based on the USFS Consume4

 model for determining fire 

behavior or biomass consumption; and 

•	 a processor for computing plume rise and providing emissions profiles for 

speciated wildfire emission pollutants. 

The outputs and variables from these various modules are to be shared through linkages to the 

other CMAQ processors that are being set up for testing. An EPA/USNPS Interagency 

Agreement is in place to continue the collaborations and coordination for implementing the 

modeling system into the Models-3/CMAQ SMOKE emissions processor. 

2.1.7 Fugitive Dust Modeling 

Windblown and fugitive dust from on- and off-road activities, industrial and construction 

activities, and agricultural tillage practices are sources of PM10 in the atmosphere. These 

contributions are not incorporated in CMAQ because of a lack of an acceptable emission 

processing system to models these fluxes. Clearly, models for windblown and fugitive dust must 

involve complex atmospheric processes and linkages with spatially and temporally variable land 

surfaces, soil types, and soil conditions. Initial development of a prototype windblown dust 

model to be used in CMAQ at 36-km resolution was begun. The basis for the wind blowndust 

formulation is derived from use of threshold friction velocity parameterizations, and 

incorporation of gridded databases prepared with information on soil types, surface soil moisture 

content, weather, and vegetation type and coverages. Due to the variability of vegetation 

coverages, and the non-homogeneous distribution of wind-erodible land-use types, and the 

4

A fuel consumption model, which predicts total smoldering fuel consumption during 

wildfires. 

8 
interception of uplifted dust particles by tree and vegetation canopies, a series of modeling 

sensitivity studies was performed. The purpose of these studies was to understand the model 

sensitivities to such input information such as percentage distribution of wind-erodible land-use 

types and vegetative coverage at fine grid resolutions in modifying the estimated dust flux at 

36-km grid resolution. The development of an algorithm for determining the flux of dust from 

on/off road, construction, and agriculture tillage will result from the studies on windblown dust. 

2.1.8 Effects of Desert Vegetation Type on Fugitive Dust Emissions 

The objective of the investigation was to determine the effects of desert land vegetation 

type on the emissions of fugitive dust. The study area was the Jornada del Muerto located in the 

northern part of the Chihuahuan desert. A previous study showed that sandy soils have the 

lowest threshold friction velocities of any non-disturbed soil type, and consequently, have the 

greatest potential for sand movement. In the sandy soils, mesquite (Prosopis glandulosa) is the 

dominant plant, creosote (Larrea tridentata) is widespread, and black grama grass (Bouteloua 

eriopoda), dominant about 50 years ago, is still found. Other locally dominating vegetative types 

are tar bush, and mixed grass species found in topographic lows (playas). Both tar bush and 

playa vegetation are found in heavier soils, silty for tar bush and clay for playas, that are 

associated with larger threshold velocities for wind erosion. 

Sand fluxes were measured at three different sites for five dominant plant types: 

mesquite, tar bush, creosote bush, black gramma grass, and mixed grasses on playas. For 

comparison, measurements were also made at a vegetation-free, flat site having sandy soil similar 

to that found at the mesquite sites. 

The daily mean, Q, is a measure of the accumulated mass of sand movement. It is 

accumulated flux of sand transported through a surface having unit width, perpendicular to the 

ground and to the wind direction and extending to the height of the highest transported sand 

grain. Integrated, 3-month horizontal sand flux measurements (Q) were made for almost 3 years 

at 15 Net Primary Productivity sites having vegetation typical of the northern Chihuahuan Desert. 

A comparison site with no vegetation (Scrape Site) shows the effect of long-term removal of all 

vegetation on sand movement. Mean sand emission values for mesquite-dominated sites are 

higher than the mean emission rates for other kinds of vegetation (Figure 1). The other four 

means corresponding to different vegetation types are grouped by dominant plant type: creosote, 

tarbush, grama grass, and playa vegetation. Each of these means is composed of data from three 

individual sites. 

Daily mean Q data for the vegetation types, tar bush, creosote, playa and grama, appear to 

be varying almost randomly with time, giving rough means of about 0.1 g/cm per day for the 

3-month collections. The lack of correlation of Q for the non-mesquite vegetation types with the 

Q from the Scrape Site shows that non-mesquite site emissions have a weak relationship with the 

Scrape Site emissions. The mesquite Q means, however, seem to correlate quite well with the 

Scrape Site Q means (Figure 1). This was interpreted as evidence that response to wind at the 

9
 
Figure 1. Mean daily sand mass horizontal flux versus date of collection for the means of five 

ecosystem types and the Scrape Site. The averaging period was approximately three months. 

Each mean is for values measured at three Net Primary Productivity sites having the same 

ecosystem type. The Scrape Site mean is for three measurements taken within an area usually 

dominated by mesquite bushes but having been scraped free of any vegetation. All sampled 

sites are located within a circle having a radius of 10 km centered at the headquarters of the U.S. 

Department of Agriculture, Jornada Experimental Range (JER), which is located between Las 

Cruces and Alamagordo, New Mexico. 

mesquite site is roughly proportional to that at the Scrape Site. For the five lowest values of the 

Scrape Site Q, four of the mesquite Q means are almost the same as the means for the other 

vegetative sites. This may be showing that the threshold velocities for the mesquite sites are 

higher than those for the Scrape Site and that those threshold velocities were not exceeded or 

barely exceeded for those time periods, as was the case at the other vegetative sites. During these 

times, however, threshold velocity was exceeded at the Scrape Site. Lastly, except for the four of 

the time periods mentioned above, the mesquite Q are always higher than for any other 

10
 
vegetative type. The study may be summarized by the conclusion that land dominated by 

mesquite (Prosopis glandulosa) is the most important area for active sand movement at the 

Jornada Experimental Range. 

2.1.9 	Implementation and Testing of New and Refined Chemical Mechanisms and 

Chemical Solvers in CMAQ 

The treatment of atmospheric, gas-phase chemistry is a critical component of the CMAQ 

modeling system. The ability of CMAQ to accurately predict ambient concentrations of trace 

gases in the atmosphere is fundamentally dependent upon the validity of the gas-phase chemical 

interactions and transformations contained in the chemical mechanism that is used in CMAQ. 

The accurate representation of gas-phase chemistry is also vital for the simulation of such other 

important atmospheric processes as the formation of aerosols, the chemical transformations 

taking place in the liquid phase, and the deposition of air contaminants to land and water 

surfaces. Commensurate with the need for an accurate chemistry representation is the need for 

gas-phase chemistry solution techniques that are both highly accurate and computationallyefficient. Since numerical solution techniques that have been used historically consume about 50 

to 75 percent of the computer time required for model simulations, any substantial computational 

efficiencies that can be gained will significantly lower the computational requirements of the 

model. The underlying objectives of this research effort are twofold: (1) to improve and enhance 

the representation of atmospheric gas-phase chemistry in CMAQ by refining existing chemical 

mechanisms, by adding new chemical mechanisms, and by investigating new approaches for 

increasing chemical information in the model, and (2) to reduce computer time required to 

simulate gas-phase chemistry by enhancing the computational efficiency of existing solvers, by 

investigating new approaches that can be used in conjunction with existing solvers to lower 

computational requirements without sacrificing the numerical accuracy, and by testing and 

evaluating new chemistry solver algorithms. The results of this work will help improve the 

scientific integrity of CMAQ by incorporating new scientific knowledge in the area of 

atmospheric chemistry, and will increase the practicality of using CMAQ as a modeling tool in 

regulatory/operational modeling applications by lowering the computational burden. 

During FY-2002, several enhancements have been made to the treatment of gas-phase 

chemistry in CMAQ. First, a new gas-phase chemical mechanism, SAPRC99, was added to the 

existing array of chemical mechanisms in CMAQ. SAPRC99, developed by researchers at the 

University of California Riverside, is a detailed mechanism of the reactions of volatile organic 

compounds (VOC) and nitrogen oxides (NOx) that can be used for both urban and rural areas. It 

includes recent kinetic and mechanistic atmospheric chemistry data, and represents the state-of 

the-science in atmospheric chemistry mechanisms as of mid-1999. It has been fully implemented 

in CMAQ, including linkages to aerosol and aqueous chemistry to enable a “one-atmosphere” 

modeling approach. Second, a new form of gas-phase chemistry solver— the Modified Euler 

Backward Iterative (MEBI) method—was included. This solver is about two to three times faster 

than the solvers included with the original CMAQ modeling system. Because MEBI is not a 

generalized chemistry solver, a unique version must be formulated for each chemical mechanism 

11
 
with which it is to be used. Thus far, CMAQ MEBI solvers have been developed and released 

for the following chemical mechanisms: Carbon Bond-IV (CB-IV), RADM2 with 4-product 

isoprene chemistry, and SAPRC99. Since the MEBI solvers are more computationally-efficient, 

but still provide a relatively high level of accuracy, this class of solver is usually preferred for 

most CMAQ modeling applications. Their availability often enables the conduct of CMAQ 

model applications that are more detailed and extensive than those that could be performed using 

one of the slower solvers included in previous CMAQ releases. 

2.1.10 Aerosol Mechanism Improvements in CMAQ 

Ongoing development of the CMAQ aerosol module includes the investigation of sea salt 

dynamics for aerosols in a marine environment. Several sea spray generation functions (Andreas, 

1998; Monahan et al., 1986; Smith and Harrison, 1998; Smith et al., 1993) were examined to 

determine the relative merits and drawbacks of each. The lognormal form of the Smith and 

Harrison (1998) function appears best-suited to CMAQ’s modal approach to aerosol modeling 

and the necessary equations to calculate sea salt emissions according to Smith and Harrison 

(1998) have been coded into a stand-alone box model for testing. These equations include 

number, volume, and mass emissions; vertical wind profiles; and roughness length. To date, box 

model testing has included the investigation of wind profile functions for determining sea salt 

emissions; performance of a sensitivity analysis on selected input terms; examination of the 

dependence of sea spray generation on various values of friction velocity u*; and whether or not 

equilibrium between the gas and aerosol phases may be assumed for marine environments (Allen 

et al., 1989; Hildemann et al., 1984; Nenes et al., 1998; Quinn et al., 1992). Upon sufficient 

testing of the box model, sea salt dynamics will then be added to the CMAQ model. 

The most recent version of the aerosol component, AE3, has a new approach to 

calculating the secondary organic aerosol (SOA) mass produced by biogenic and anthropogenic 

precursors. The method used to calculate SOA is based upon work by Schell et al. (2001), 

modified to be consistent with the CMAQ modeling paradigm. The precursors of SOA 

considered in AE3 are aromatics, monoterpenes, internal alkenes, and high molecular weight 

alkanes. Schell et al., (2001) considered a lumped class of precursor aromatics, and alpha pinene 

and limonene as separate precursors. AE3 considers toluene, xylene, and cresol as separate 

aromatic precursors, and a lumped class of monoterpenes, as well as long chain alkanes and 

internal alkenes. The partition coefficients for the biogenics were taken from Griffin et al. 

(1999); those for the aromatics, toluene and xylene, are from the high and low yield curves, 

respectively, in Odum et al. (1997). The partition coefficients for the alkanes and cresol are from 

Strader et al. (1999); those for the internal alkenes are from Kalberer et al., (2000). Temperature 

corrections for the partition coefficients were made using the methodology of Sheehan and 

Bowman (2001), and the biogenics were lumped using the methodology of Bian and Bowman 

(2002). In lumping the biogenics, the fractional contribution of the biogenic species was taken as 

0.4 alpha pinene, 0.25 beta pinene, 0.15 delta 3-carene, and 0.1 each for sabinene and limonene. 

These fractions reflect a national rather than a regional distribution of the species. The lumping 

12
 
scheme was provided for use in CMAQ by Professor Bowman (personal communication) prior to 

publication. 

2.1.11 Plume-in-Grid Modeling 

The plume-in-grid (PinG) approach contained in the CMAQ provides a realistic scientific 

treatment of the physical and chemical processes impacting pollutant concentrations in isolated, 

major point-source plumes. The PinG treatment simulates gradual plume growth due to 

dispersion processes, in contrast to instant mixing into the entire volume of a large Eulerian 

model grid cell in which the elevated point source is located, which can have a strong impact on 

the temporal evolution of photochemistry in the near-field. The description of the capabilities of 

the CMAQ/PinG modeling treatment and its technical formulation have been described in Gillani 

and Godowitch (1999). The key modeling algorithms are a plume dynamics model (PDM) and a 

Lagrangian reactive plume model (PinG module), which are designed to simulate the relevant 

plume processes at the proper spatial and temporal scales for CMAQ regional modeling domains 

with typical grid sizes of about 10-40 km. The PinG treatment is capable of simulating multiple 

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

CMAQ, and it is exercised concurrently during a simulation to use grid cell concentrations as 

boundary conditions for each plume section. An important feedback occurs when a plume 

section attains the model grid cell size, as the subgrid plume treatment ceases and plume 

concentrations are incorporated into the Eulerian grid system. 

A notable FY-2002 update for the PinG module has been the inclusion of an 

aerosol/particulate modeling algorithm to simulate PM and aerosol species along with gas-phase 

pollutant species in the subgrid plumes. The Binkowski (1999) aerosol algorithm, which has 

been employed as the aerosol modeling component in the CTM grid model, was adapted and 

integrated into the PinG module. A series of model simulations were conducted with the 

RADM2 and CB-IV photochemical mechanisms and with the three available chemical solver 

methods: QSSA (quasi-steady state approximation), MEBI (modified Euler backward iterative), 

and Gear approaches to assess differences in aerosol species formation due to the choice of 

chemical mechanism and numerical solver approach. The Gear solver is the preferred method 

when applying either chemical mechanism with PinG. Model simulations were also performed 

for a set of major point-sources exhibiting a wide range of NOx and SOx emission rates within a 

regional grid model domain encompassing the greater Nashville, Tennessee, area. Preliminary 

model results presented in Godowitch (2002) revealed interesting differences in sulfate aerosol 

(SO4) concentrations and PinG results appeared to agree with emerging observed plume aerosol 

data. For point sources with comparable SO2 emissions, greater sulfate formation occurred in 

those plumes with lower NOx emission rates. 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 PinG modeled plume 

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

traverses across plumes was reported by Godowitch (2001). In addition, analyses of CTM results 

13
 
from 12-km and 36-km domains for a July 1995 period were performed from separate model 

simulations, which either included the PinG treatment or omitted it. Comparisons of modeled 

and observed hourly ozone concentrations indicated that the CTM/PinG results displayed better 

agreement and less bias than the CTM/NoPinG results, particularly in areas where numerous 

large point sources exist. Further modeling results are anticipated with modeled photochemical 

pollutant and aerosol species to be compared to available observed plume data collected by 

various airborne platforms during the SOS 1999 field experiments. 

2.1.12 	CMAQ Code Integration and 2002 Release 

ASMD redesigned, and tested the CMAQ system code to optimize performance and assist 

with public releases of the CMAQ modeling system code. The main focus was on modifying 

CMAQ to maintain the high quality science processing, while restructuring code and data 

structures to speed up the computational time. 

The latest version of CMAQ was released for public use in June 2002. The modeling 

system is available for downloading at ftp://ftp.epa.gov/amd/stand_alone_models3/cmaq/. The 

scripts for this release have been tested on LinuxTM5

 (specifically, Redhat®6

 Linux 2.1 with the 

Portland Group F90 compiler, pgf90 version 3.2). This version of CMAQ features several major 

changes: 

1.	 Addition of a new aerosols module, AE3, which includes improved 

treatment for secondary organic aerosol formation by including semivolatile compounds that partition between gas and aerosol phases, updated 

process for sulfate nucleation, heterogeneous conversion of N2O5 to nitric 

acid, and the ISORROPIA model for aerosol thermodynamics; 

2.	 Incorporation of aerosol emissions in the vertical diffusion process; 

3.	 Change in the order of the time splitting science processes, from chemistry 

-> clouds -> aerosols, to clouds -> chemistry -> aerosols, to provide a 

better linkage between gas-phase chemistry and aerosols; 

4.	 Addition of the SAPRC-99 gas-phase mechanism; 

5.	 Addition of the MEBI solver for all variants of CB-IV, SAPRC-99, and 

the RADM2-Carter-4-Product-Isoprene mechanisms; 

5

Linux is a trademark of Linus Torvaldo 

6

Red Hat is a registered trademark of Red Hat, Inc. 

14 
6.	 Addition of a new optional vertical diffusion module, ACM; 

7.	 Change to dynamically allocate the horizontal grid for the chemistry model 

and the initial and boundary concentrations pre-processors (ICON and 

BCON), thus allowing one executable to run any horizontal domain that 

CMAQ supports; 

8.	 Ability for CMAQ to window from meteorology and emission data sets, so 

that runs on many subdomains can be executed using one data set that 

encompasses all those subdomains; and 

9.	 Addition of an hour-average concentration output option for any chemical 

species. 

2.1.13 	Development and Testing of an Air Quality Forecast Model 

As part of a joint NOAA-EPA project on air quality forecasting, an optimized version of 

the CMAQ model is being linked with the NOAA/National Weather Service (NWS) mesoscale 

Eta meteorological model. The project started toward the end of FY-2002, and will continue into 

FY-2003. Appropriate linkage software to drive CMAQ with the Eta model output will be 

developed and tested. Other objectives of this work will be to make significant improvements in 

the computational performance of the air quality model, which uses the CMAQ science. The 

current CMAQ was designed to be a highly flexible community modeling system, capable of 

running on various computer architectures including parallel processors. The main focus was not 

speed of execution, but generality and ease of facilitating model science versions for the general 

modeling community. The air quality forecast version of CMAQ, however, will aim towards a 

platform-specific air quality forecasting model that will trade off much of generality for 

computational speed. 

The development of an air quality forecasting model will require significant performance 

improvements over the current version of CMAQ. To add to the challenge, some processing 

presently done in the meteorology-chemistry interface processor (e.g., deposition velocity 

calculations) and some emissions processing that depends on dynamic meteorology (e.g., 

biogenic emissions) will have to be included with the set of operator-splitting, science processes 

already done in CTM. 

To achieve the necessary speedup structurally, it is envisioned that CMAQ will have to be 

changed in two major areas. First, a new model architecture is being developed that will be 

tuned for a specific parallel platform. Second, code modifications are being made to optimize 

use of such specific hardware features as memory access (cache) and hardware floating point 

units. In addition to structural modifications, ASMD is also looking at algorithms and data 

organization changes to achieve performance gains. The initial version of the forecast model 

15
 
(FY-2003) will include ozone and photooxidants, but will not include prediction of PM, whose 

capability will be added to a subsequent version of the air quality forecast model. 

2.1.14 AERMET 

Work continued on necessary improvements and upgrades to the meteorological 

preprocessors, MPRM (Meteorological Preprocessor for Regulatory Modeling) and AERMET 

(American Meteorological Society and Environmental Protection Agency Regulatory Model for 

Meteorology). These programs process meteorological data for use with the EPA regulatory 

dispersion models. Upgrades to these programs are necessary from time-to-time to keep pace 

with changes to the meteorological data archives maintained by the NOAA National Climatic 

Data Center, and to support ongoing improvements in regulatory dispersion models. MPRM, the 

older of the two preprocessors, supports dispersion models that rely on Pasquill-Gifford 

dispersion coefficients (U.S. Environmental Protection Agency, 1996). EPA continues to 

support these older models; however, the intent is to eventually phase out such support. 

AERMET, which supports the newest generation of dispersion models, is based on 

planetary boundary layer (PBL) theory. Important processes within the PBL (e.g., the surface 

layer and the mixed layer) are modeled separately (U.S. Environmental Protection Agency, 

1998). 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. Upgrades and improvements to AERMET 

continued throughout FY-2002 in response to user comments on the beta versions of the software 

released in September 2001 and again in March 2002. In addition, support was also provided to 

the EPA OAQPS staff involved in evaluating and testing AERMOD. These evaluations, which 

are required for the promulagation of AERMOD, are being conducted using multiple test data 

sets and meteorological databases. The necessary preprocessing of these data provides an 

excellent opportunity for exercising AERMET. 

2.1.15 Recommendations for Observations-Based Methods 

The NARSTO Ozone Assessment of 2000 suggested the use of observations-based 

methods (OBMs) as diagnostic tools (1) to diagnose ozone production and production efficiency 

in both the real atmosphere and in the model-simulated atmosphere, and (2) to assess and help 

explain the effectiveness of emission controls over time. Diagnostic analyses and OBMs require 

a special set of non-routine measurements, because they typically involve ratios of special 

species. Earlier work (Tonnesen and Dennis, 2000a; 2000b) had identified a set of OBMs, which 

were refined and described subsequently in an EPA report (Dennis et al., 2001). During 

FY-2002, the main elements of the recommended set of OBMs, based on the measurement of O3, 

NO+true NO2 = NOX, NOY, and NOY-NOX = NOZ, were tested for their ability to effectively and 

diagnostically compare CMAQ’s results and measured ozone production. The test data sets 

16
 
came from SOS data taken in Nashville, Tennessee, in the summers of 1995 and 1999 and in 

Atlanta during August 1999. For Nashville, the OBM set worked well. However, for Atlanta, 

interpretation of the OBM measures turns out to be more complicated due to differences in air 

mass histories, and further interpretive work is planned. As data permit, other recommended 

measures are now being examined, to assess the overall robustness of the OBM measures. 

2.2 Atmospheric Model Evaluation and Application Activities 

2.2.1 Improve O3 Vegetation-Atmosphere Exchange Models 

Biologists and plant physiologists interested in studying the effects of pollutants on 

vegetation have learned that it is not sufficient to relate plant symptoms to local pollutant 

concentration. The air concentration above or near the plant canopy is not the same as the 

amount of pollutant that actually gets into the plant, or its exposure, which for the same 

concentration can vary depending on species, weather, time of day, season of the year, and health 

of the plant. While they do a good job of predicting exposure of the total canopy, normal 

deposition models are not designed to operate within the canopy at the leaf level where pollutant 

damage occurs. Yet understanding deposition at the leaf level is necessary if a complete 

understanding of the pollutant, plant-damage relationship is to be understood for the 

development of defensible secondary ambient air quality standards as required by the Clean Air 

Act (CAA). To estimate dosage and exposure, a model designed to estimate fluxes and plant 

exposure within a plant canopy is needed. However, a sub-canopy model is not simple. Because 

wind and pollutant profiles within a canopy are not log-linear as they are in the normal 

atmospheric boundary layer, simple gradient-transfer models are inappropriate, and it is 

necessary to use the more complex second-order closure models. These models have been 

developed for use in tree canopies from theoretical considerations, but have received little 

evaluation. For use in herbaceous perennials, which are frequently studied because of their 

sensitivity to pollutants, these models have never been evaluated. ASMD has started a model 

evaluation and improvement program for this reason, adapting a second order closure model 

developed by NOAA’s Atmospheric Turbulence and Diffusion Division at Oak Ridge, 

Tennessee, and collaborating in the evaluation and improvement program. This study is part of a 

larger study of ozone damage to sensitive plants, with additional collaborators at Auburn 

University, Auburn, Alabama, University of Newcastle, United Kingdom, and Appalachian State 

University, Boone, North Carolina. The funding comes from the USNPS, the National 

Geographic Society, and other sources. 

A first field study was completed in the summer of 2002 at Purchase Knob in the Great 

Smoky Mountains National Park, a high elevation location set aside by the USNPS for 

educational and scientific activities. Two study sites were selected, one on the outside edge of a 

forest, and one inside the forest where the plants grew in dappled shade. The species under study 

was Rudbeckia laciniata (cutleaf coneflower), which has been shown to be very sensitive to 

ozone. Populations of this plant are not uncommon in the park at higher elevations. 

17
 
To evaluate the sub-canopy deposition model, measurements of the important inputs and 

outputs were made. Along with the necessary routine meteorological variables, measurements of 

radiation and atmospheric turbulence directly above the coneflower canopy had to be made. 

Turbulence measurements were made with a fast-response sonic anemometer that measures the 

wind speed by recording the doppler shift in a sound wave traveling between two sensors located 

a few centimeters apart. Figure 2 displays the installation of the meteorological tripod and sonic 

anemometer at the site inside the forest canopy. The sonic anemometer is at the top of the tripod, 

the other sensors on the crossarms, and the dataloger is in the white box. Ozone was measured at 

four levels: one above, and three within the canopy. Along with observations of the 

meteorological variables, measurements of photosynthetic rate, water use, anthocyanin 

concentration, leaf area index, and stomatal conductance were made. 

The field study was completed in July 2002. Data have been processed, screened, and put 

into a database for analysis. Much of the preliminary analysis and model evaluation has also 

been completed. Ozone values were measured at four heights at the site outside the forest 

(Figure 3). At the first height within the canopy (1.0 m) the vegetation is still very sparse, and 

the ozone hardly diminished at all from the levels measured above the canopy at 2.35 m. At the 

next level down (0.57 m) in the densest section of leaves, the ozone levels are much lower. And 

at the lowest levels, just 0.2 m above the ground, the ozone levels are lower yet. 

Figure 2. Installation of the meteorological tripod and sonic anemometer at 

the site inside the forest canopy. 

18
 
Figure 3. Ozone values measured at four heights outside the 

forest. 

The task now is to run the model of subcanopy deposition to see if it can reproduce these 

measurements, determine its strengths and weaknesses, diagnose reasons for the weaknesses, and 

find improvements. In addition to ozone concentration, profiles of leaf conductance, 

photosynthethically active radiation, and higher moments of turbulence measured with the sonic 

anemometer will be compared with the model to test model performance. The database of 

observations from the field program will be put into a form accessible to the public, and located 

on the ASMD website (http://www.epa.gov/asmdnerl/). 

2.2.2 Modeling Studies in Coastal Environments 

Simulations with the Extended RADM were used in a joint effort with the USNPS to 

develop an environmental assessment for the Shenandoah National Park. Such an assessment is 

fairly unique in Park history. 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 the 

1990 Clean Air Act Amendments (CAAA), estimate how ecosystem damage from ozone and 

acid rain will be changed or mitigated, and how air quality related values connected to visibility 

19
 
degradation will be affected. The second purpose 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. The Extended RADM was used to estimate the degree to 

which surrounding states and regions are responsible for multi-pollutant exposures of ecosystems 

of the Shenandoah National Park to acidic deposition, inorganic fine particle concentrations, and 

seasonal ozone. Airsheds for Shenandoah were found to be quite large, covering roughly 13 

states. The Extended RADM was also used to explore the degree to which CAA regulations 

using two scenarios, and subsequent, significant emission reductions beyond the CAA using two 

additional scenarios, could reduce acidic deposition and seasonal ozone exposures to achieve 

ecosystem-protective levels of exposure. The Shenandoah assessment has undergone internal 

Park Service and external science review. Completion of the Shenandoah assessment report is 

expected during FY-2003. Besides the analyses for the assessment, a concise description of the 

Extended RADM as a multi-pollutant model was also written for an appendix, in response to 

reviewer requests, which serves as an aid to scientific documentation. 

2.2.3 Chesapeake Bay 2005 Re-Evaluation 

ASMD has established a long-term relationship with the EPA and NOAA Chesapeake 

Bay Programs to address multi-media environmental problems where the atmosphere is an 

important pathway. Atmospheric deposition of reduced and oxidized nitrogen is an important 

source of new nitrogen to coastal estuaries, contributing to eutrophication problems that have 

been growing, and Chesapeake Bay is a leader in using multi-media modeling. Two major 

Chesapeake Bay re-evaluations or assessments of required nitrogen load reductions to the Bay 

have already occurred. The next re-evaluation is slated for 2005 or shortly thereafter. 

Chesapeake Bay has been placed on EPA’s list of impaired waters. A 1999 court 

agreement set 2011 as the deadline for writing an enforceable cleanup plan, a TMDL (Total 

Maximum Daily Load) plan. The Chesapeake 2000 agreement calls for preempting the need for 

a TMDL plan by cleaning up the Bay by 2010. The Bay 2005 re-evaluation is a critical step in 

this process towards the 2010 cleanup and delisting, and ASMD is participating in the reevaluation process. The best science is desired for the re-evaluations, and during the period 

between major re-evaluations, ASMD is changing its multi-media modeling of nitrogen from the 

Extended RADM to its new model, CMAQ. CMAQ has been sufficiently evaluated for 

deposition to show that it is an improvement over the Extended RADM. A newly designed 

aggregation data set with 40 cases has been developed for CMAQ that can directly address 

seasonal deposition, an improvement over the older aggregation set. The outer, continental grid 

resolution is 36-km, a significant reduction over the 80-km resolution used with the Extended 

RADM. For Chesapeake Bay multi-media simulations, a 12-km nest over the Northeast within 

the 36-km grid covering most of the Chesapeake Bay airshed has been developed. This inner 

nest will better resolved the Bay surface compared to the 20-km nest used with the Extended 

RADM. The MM5 meteorological runs for the 36-km aggregation set and the 12-km nest were 

completed during FY-2002. Prior to establishing new base cases with CMAQ, testing is now 

underway to determine the best method for parameterizing the mixing height, because the 

20
 
CMAQ MCIP has new capabilities available. In FY-2002, post-analyses were completed to help 

tributaries and Bay states set the stage for the re-evaluation, based on current model runs using 

the Extended RADM. The relative contribution from each of the six main Bay states to nitrogen 

deposition to the watershed tributaries was calculated, using runs that coordinated with the 

Shenandoah assessment. 

2.2.4 Bay Regional Atmospheric Chemistry Experiment: Evaluation and Assessment 

The Tampa Bay Estuary Program and the Florida Department of Environment (FDEP) 

have asked EPA/National Exposure Research Laboratory (NERL) and NOAA/Air Resources 

Laboratory (ARL) to enter into a partnership to lead the application of CMAQ to help them 

understand the sources of nitrogen deposition affecting Tampa Bay. The majority (60 percent) of 

the nitrogen deposition to the estuary and watershed is estimated to come from sources local to 

Tampa Bay, which is unusually high, due to Tampa’s isolation from other large source regions. 

ASMD was asked to work with the Tampa Bay National Estuary Program to assess the 

atmospheric contribution of nitrogen to Tampa Bay. Tampa Bay provides an important 

atmospheric multi-media problem significantly involving coarse particles and sea salt, and 

creates an excellent research opportunity to test advances in the science in CMAQ to deal with 

coastal nitrogen deposition. Two of the largest power plants in the nation, in terms of NOX 

emissions, are on the shores of the Bay and there are serious questions as to how much of the 

atmospheric deposition is due to the power plants versus mobile sources in the area surrounding 

the Bay. CMAQ was selected as the model for the Tampa Bay Assessment, in part because 

CMAQ will incorporate sea salt in its fine particle module in FY-2003. Prior to any Tampa Bay 

assessment, it was agreed that CMAQ needs to be evaluated against high-quality local data and 

that the nitrogen budget around Tampa Bay needs to be more carefully characterized. 

The Bay Regional Atmospheric Chemistry Experiment (BRACE) was designed for these 

two purposes. BRACE was conduced in May 2002. Division scientists, along with ARL 

colleagues, were significantly involved in the planning of BRACE. Division scientists played a 

major role in establishing the location of the BRACE supersite and in establishing the suite of 

measurements for the supersite and several surrounding measurement sites that would to the best 

degree feasible delineate the full nitrogen budget and photochemical processing behind the 

budget. ASMD scientists worked on deployment of true-NO2 monitors and with Hillsborough 

and Pinellas Counties’ air quality professional on deployment of NOY instruments. ASMD and 

NOAA ARL, Silver Spring, Maryland, scientists took the lead on siting three wind profilers 

around the Bay, and helped define the complete chemistry package of instruments for the NOAA 

Twin Otter aircraft. 

2.2.5 Ammonia Budgets for Coastal Systems 

An important fraction of atmospheric nitrogen deposition is reduced nitrogen 

(ammonia/ammonium). In the future, with successful implementation of the EPA regulations on 

21
 
NOx emissions for control of ozone and increases in animal operations in the eastern seaboard 

states, reduced nitrogen is expected to become a majority of the nitrogen deposited from the 

atmosphere. However, ammonia is not receiving the attention it deserves, in part, because many 

ecologists dealing with marine estuaries and watersheds believe ammonia deposits instantly so 

that none leaves the immediate area. Long-range transport of ammonia is ignored. ASMD has 

an opportunity to correct this misinterpretation of data through modeling and model-data 

interpretation studies using the regional models. Model atmospheric budget analyses were 

performed in FY-2002 for North Carolina ammonia emissions associated with the large increase 

in the hog population. The analysis, covering a short summer period and reported at the 

International N2001 Conference, show that only 5 to10 percent of the NHx budget dry-deposits 

locally while most of the ammonia emissions are involved in long-range transport. This is 

contrary to conventional wisdom. The model results are consistent with spatial and temporal 

trends in the ammonia wet deposition data. A regional exploration of the ammonia budget was 

performed for the eastern United States using the Extended RADM. The conclusion thus far is 

that the conventional wisdom that assumes there is no long-range transport of ammonia is 

incorrect. Nonetheless, the conventional wisdom persists and distorts studies of nitrogen-cycling 

in coastal estuaries, introducing significant errors in them. Because it is important to correctly 

assess the ammonia/ammonium transport and deposition to coastal estuaries and coastal oceans, 

further analyses will be undertaken with new CMAQ simulations to build a case against the 

conventional wisdom. 

2.2.6 Inverse Modeling for Ammonia 

One of the key uncertainties affecting emissions-based air quality model predictions is the 

uncertainty in the emissions input. Ammonia emissions have one of the highest degrees of 

uncertainty as compared to other emissions, and they have a significant impact on fine particle 

predictions. Variations in ammonia emissions will cause large variations in predicted nitrate PM 

concentrations. The EPA National Emission Inventory (NEI) for ammonia emissions is based on 

a 1994 report where European emission factors were extrapolated for the United States. These 

emission estimates suggest that the largest source of ammonia emissions is animal husbandry, 

which comprises about 80 percent of the total. Fertilizer is also a substantial source. Both of 

these sources are expected to have seasonal variations because of the temperature dependence of 

ammonia volatilization from animal waste and because of the seasonal patterns in fertilizer 

application. However, the emission factors used in the NEI do not have any dependence on 

temperature, and great uncertainty exists in the factors themselves. For these reasons, the 

uncertainty in ammonia emissions is both with respect to their seasonality (they are expected to 

vary significantly between winter and summer) and in their overall annual magnitude. 

Ammonia emission studies traditionally have been based on flux observations at 

individual sites, which are dependent on such local factors as the meteorological conditions, 

housing conditions, and feed for the animals studied. To evaluate the current ammonia emissions 

inventory on a regional scale, inverse modeling is used to estimate monthly ammonia emission 

fields for the eastern United States. Using the Kalman Filter technique with CMAQ, inverse 

22
 
modeling is used to estimate monthly ammonia emissions that produce optimized predictions of 

wet concentrations of ammonium for 1990. Further, uncertainties in the modeled precipitation 

have been accounted for by introducing the standard error of the precipitation estimate into the 

Kalman filter. As anticipated, results suggest that the emissions should be highest during 

summer conditions and lower during fall and winter. Findings from this study will be presented 

in a journal article. 

As a next phase of this study, the inverse modeling of ammonia will be extended to the 

United States continental domain for a more current time period. Then the work will be 

extended to examine different sources of emissions that have different spatial patterns, such as 

cattle versus hogs. The objective is to assess whether a particular source has a greater degree of 

uncertainty than other sources and to consider spatial uncertainty in the emissions based on 

source-specific information. The ammonia emission estimates developed from this study support 

the wok of OAQPS, which is currently evaluating the NEI ammonia inventory and considering 

improvements. This work also supports the air quality modeling community whose modeling 

results can be detrimentally affected by large uncertainties in ammonia emission inputs to the 

model. 

2.2.7 Diagnostic Metrics for Ozone and Particulate Matter 

Diagnostic metrics enable us to examine the process side of the model to better examine 

the degree of reliability of control strategy predictions. They require a special set of non-routine 

measurements, because they typically involve ratios of species involved in photochemical 

production. Earlier work (Tonnesen and Dennis, 2000a; 2000b) had identified a set of metrics 

based on measurement of O3, NO+true NO2 = NOX, NOY, and NOY-NOX = NOZ. These 

diagnostic tests were applied to CMAQ for Nashville, Tennessee, for the 1995 SOS field 

measurements. The model simulations examined for Nashville were at 12-km and 4-km 

resolutions. The modeling results from 36-km grid spacing were not examined because they 

were considered to be too coarse for ozone predictions for a moderate-sized urban area. The 

diagnostic metrics suggest that the 4-km grid cell size produces slightly better predictions relative 

to ozone process fidelity. The metrics also highlighted sites that were being influenced by local 

emissions sources that were missing in the model, pointing out sites where model prediction of 

ozone changes might be biased because of missing emissions (or extra emissions) in the model 

inputs. New statistical procedures for the assessment of ozone production efficiency were 

developed as part of this work because the metric used to define ozone production efficiency, the 

relationship between hourly O3 and aged nitrogen oxides (NOZ) (as typically illuminated in 

scatterplots) is curvilinear and involves effectively two independent variables, rather than 

dependent and independent variables. Thus, standard procedures are not appropriate. Also, 

approaches for partitioning the data into age and ozone productivity classes were developed for a 

cleaner comparison and interpretation of observataions and model predictions. The metrics are 

now being applied to other time periods (SOS Nashville 1999 data) and other cities (SOS Atlanta 

1999) to test their robustness. 

23
 
2.2.8 CMAQ Model Evaluation to Assess Readiness for State Implementation Plans 

Model evaluation is an essential component of such science-based air quality models as 

CMAQ. Operational or performance evaluations examine the pollutant end-points that are of 

direct interest to client users of CMAQ and mostly relate to ozone, fine particles, and acidic and 

nitrogen deposition. Where possible, these performance evaluations are supported by diagnostic 

evaluations. ASMD has participated in the NARSTO model intercomparison for ozone. For this 

study, the comparison period of July 5–18, 1995, was resimulated with the new SMOKE 

emissions processor, using the 2001 release version of CMAQ to correct errors that had been 

uncovered. To make the comparisons more relevant, control strategy runs were also included in 

this round. To examine CMAQ’s performance for deposition, wet deposition simulations for 

January and June 1990 were compared to the National Atmospheric Deposition Program 

measurements and reported at the Spring 2002 American Geophysical Union Conference in 

Washington, DC. The updates to the cloud module in CMAQ to better account for ice and snow 

improved the wintertime predictions over those using the older cloud formulation of RADM. 

The comparisons were quite reasonable although errors in predicted precipitation created 

significant scatter. In a sensitivity simulation to examine the importance of possible errors in the 

dry deposition rate for SO2 it was found that wet deposition changed by about 10 to15 percent for 

a factor of 3 change in the SO2 dry deposition rate. 

A major evaluation task was the testing of CMAQ for the 2002 release. The evaluation 

was continental in scope for the 2-week period from June 30 to July 14, 1999. The focus was on 

ozone (against data from the AIRS sites) and fine particulate matter (against data from 

IMPROVE and CASTNet). The simulations were generated with a 32-km continental resolution 

and an 8-km grid over the Southeast. During the checkout, several errors were uncovered and 

corrected in the emissions processing by SMOKE. A bench run with the older 2001 science 

modules selected (RADM2 vs. SAPRC99; AE2 vs. AE3; BEIS2 vs. BEIS3) was included. In 

addition, several versions of CMAQ with incremental science changes were run on both domains 

to test the effect of the new science. Sulfate continued to be predicted well with the newer 

science, while the nitrate over-prediction bias increase and organic carbon predictions changed 

from an under-prediction to an over-prediction. Ozone is biased higher with the new chemical 

mechanism. A full 1999 evaluation from June 15 to August 31, 1999, was begun for full ozone 

evaluations against the Nashville 1999 data set (June 15–July 15, 1999) and full fine particle 

evaluations against the Atlanta 1999 supersite data set (August 3–August 31, 1999). Three grid 

structures will be available: 32-km (continental), 12-km (southeast), and 2-km (Nashville and 

Atlanta). 

2.2.9 	Sensitivity of CMAQ Control Strategy Predictions to Model Input Uncertainties for 

CMAQ and MM5 Configurations 

Sensitivity analyses are important adjuncts to model-data comparisons. A key use of the 

air quality models is prediction of the effects of emission controls on air concentrations. These 

predictions can be affected by model input uncertainties, model parameter uncertainties, and 

24
 
structure of the model itself. The focus of the sensitivity analyses was principally on the 

chemical mechanism and vertical mixing algorithms, because a major new chemical mechanism, 

SAPRC99, was newly available for CMAQ. Grid size effects were also examined because finer 

grids have a large computational burden, limiting the number of analyses that can be performed. 

The results of this sensitivity work were reported at the U.S.-German Workshop in Bad Breisig, 

Germany, in October 2002. The key findings were: 

•	 Differences in the type of vertical mixing (meteorology) create the largest 

differences in predicted daily peak 8-hour ozone, but differences in 

chemical mechanisms cause the largest difference in control strategy 

response, for the same grid resolution. 

•	 The reduction in ozone from NOX emission reductions is smaller with the 

older mechanisms, like RADM2, compared to the newer SAPRC99 

mechanism, and may include ozone increases in certain locations. There is 

no difference in the VOC control response between mechanisms. 

Additional sensitivities are underway to further explore the reason for the chemical mechanism 

differences and to provide user guidance regarding the sensitivity results. Further exploration of 

the influence of vertical mixing on control strategy predictions will be undertaken. 

2.3 Air Toxics Modeling 

2.3.1 CMAQ Extensions to New Air Toxics Gaseous Species 

The 1990 CAAA identified nearly 200 individual compounds or mixtures of compounds 

as hazardous air pollutants that have the potential for causing adverse health effects. Air quality 

models for predicting ambient concentrations of these toxic compounds are needed to provide 

human exposure estimates for both risk assessment and risk management. To obtain accurate 

estimates of the ambient concentrations of these compounds, the important processes that control 

their fate must be properly simulated. For many compounds, this necessitates inclusion of the 

chemical reactions that lead to the formation and transformation of those compounds in the 

atmosphere. The objective of this work is to add to CMAQ the capability to model specific 

gaseous toxic compounds. 

Two approaches are being evaluated for modifying CMAQ to include gaseous toxic 

compounds. The first approach involves treating each toxic compound explicitly by including its 

atmospheric chemical reactions directly in the CMAQ chemical mechanism. This approach 

provides the most accurate representation of the atmospheric chemistry, but has the disadvantage 

of requiring chemical mechanism and solver modifications for each toxic compound that is 

added. A second approach involves approximating the atmospheric chemistry of the toxic 

compounds outside the chemical mechanism. This approach is preferable because the need for 

modifications to the core chemistry mechanism and solver are lessened and because the 

25
 
computational expense is lowered by not including the compound directly in the chemistry 

computations. This approach is also more amenable to generalization, which would facilitate 

adding new gaseous toxic compounds in the future. The major disadvantage is that it may not be 

feasible for those toxic compounds that have many chemical production and loss pathways, or for 

those compounds that react very fast. 

During FY-2002, initial work was completed on incorporating 18 toxic compounds 

explicitly in CMAQ . Model tests were conducted both with toxics included explicitly in the 

mechanism and with the reactions of several toxics approximated outside the chemical 

mechanism. The results of these tests have shown that, of the 18 toxics considered, 14 

compounds can be modeled outside the chemical mechanism with little loss in accuracy. The 

exceptions identified thus far are formaldehyde and acetaldehyde which have numerous loss and 

production pathways, and hence, must always be included in the mechanism explicitly. Two 

others, acrolein and 1,3-butadiene, are also included explicitly in the chemical mechanism 

because of their interactions (the atmospheric reaction of 1,3-butadiene leads to the formation of 

acrolein). To conduct these tests, both the SAPRC99 and CB-IV chemical mechanisms and 

associated MEBI chemistry solvers in CMAQ were modified to accommodate the inclusion of 

the toxic compounds. For most compounds, virtually identical results were obtained with either 

mechanism. This effort has laid the foundation for conducting a comprehensive modeling 

assessment of these compounds in FY-2003. 

2.3.2 CMAQ Extensions to Semi-Volatile and Involatile Compounds 

During FY-2002, a version of CMAQ was developed that simulates air concentrations 

and deposition for toxic congeners of Poly-Chlorinated Dibenzo-Furans and Dibenzo-p-Dioxins 

(PCDF’s and PCDD’s). The effort expanded upon previous research on atrazine (Cooter and 

Hutzell 2002; Cooter et al., 2002) with appropriate modifications for dry deposition and 

atmospheric chemistry. The model represents each congener’s mass in gaseous and aerosol 

forms that exchange mass based on theoretical coefficients for gas to particle partitioning. 

Gaseous forms undergo chemical transformations based on congener-dependent rate constants 

and concentrations of hydroxyl radicals determined by a chemical mechanism within CMAQ. 

Dry deposition removes all forms; however, wet deposition removes only particulate forms 

because gaseous forms have extremely low solubility in water. Dry deposition velocities for 

gaseous forms do not include effects from organic factors because their contribution is still 

uncertain. 

This version of CMAQ was evaluated through simulations that covered July 1999. One 

part of the evaluation compared model predictions against observed air concentrations from the 

National Dioxins Air Monitoring Network (Cleverly et al., 2000). Model predictions had good 

correlations with observations for lower chlorinated congeners of PCDF’s, but had poor 

correlations with most PCDD congeners. Emission inventories could be the cause of these 

problems because PCDF and PCDD emissions have been historically difficult to identify and 

quantify (Cohen et al., 2002). Model results were also evaluated regarding observed partitioning 

26
 
between gaseous to particle forms. Predictions reproduced observed values within observational 

and modeling uncertainties. A project by the National Center for Environmental Assessment 

may support further evaluations of the developed model. The Center will inter-compare 

predictions between several models that use an improved emissions inventory (National Center 

for Environmental Assessment, 2001). 

2.3.3 CMAQ Mercury Model Refinements and Evaluation 

During FY-2002, the first phase of an intercomparison study of numerical models for 

long-range atmospheric transport of mercury was completed and a second phase was begun. This 

study is being sponsored by the European Monitoring and Evaluation Programme (EMEP) and is 

organized by EMEP’s Meteorological Synthesizing Center - East in Moscow, Russia. The first 

phase of the intercomparison involved 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, Canada, 

and the United States were compared to identify key scientific and modeling uncertainties. A 

project report previously submitted to the EMEP governing body was published (Ryaboshapko et 

al., 2002). 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. Comparison of the various model results against observations, 

and between the models, will continue into FY-2003. 

During FY-2002, a number of minor modifications were made to CMAQ Hg to more 

efficiently calculate the gas-phase and aqueous-phase mercury chemistry in concert with the 

chemistry mechanisms in the latest published versions of the standard CMAQ code. These 

modifications were made in anticipation of a planned public release of CMAQ Hg scheduled 

toward the end of FY-2003. 

During FY-2002, the results from a comparison of CMAQ Hg simulations against 

observations of wet deposition of mercury were published (Bullock and Brehme, 2002). 

Measurements of the wet deposition of mercury obtained from the Mercury Deposition Network 

(MDN) (Lindberg and Vermette, 1995; Vermette et al., 1995) at 11 locations in the central and 

eastern parts of the United States were compared to the CMAQ-Hg simulations during each week 

of two 4-week periods in 1995. The results showed relatively good agreement between modeled 

and observed weekly wet deposition of mercury during the period from April 4 to May 2, 1995. 

However, during the period from June 20 to July 18, 1995, the agreement was found to be much 

weaker 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. Further CMAQ-Hg 

evaluation will be performed against MDN data collected in 2001 as the necessary MM5-derived 

meteorological input data become available. 

27
 
2.3.4 Estimating Background Concentrations 

In the 1990 CAAA, Congress established a list of 188 toxic chemicals designated as 

hazardous air pollutants (HAPs), also known as air toxics. These pollutants are associated with a 

wide variety of adverse health and environmental effects. The sources of the air toxics include 

major point sources, area sources, mobile sources, and transport from other areas. As part of the 

implementation of the CAAA, EPA manages the National-Scale Air Toxics Assessment 

(NATA), a nationwide study of the potential inhalation exposures and health risks associated 

with 32 HAPs and diesel PM. To estimate total ambient concentrations, however, a value for 

background must be estimated and added to modeled concentrations. As defined, background 

accounts for natural sources, nearby sources (farther than 50 km), and unidentified sources. 

In the recently completed NATA, corresponding to calendar year 1996 emissions and 

meteorological data, estimates for background were determined from open literature. There are 

two limitations to this approach. First, estimates based on literature or data correspond to points 

in time that were far removed from the 1996 temporal scope of the recent NATA study. Second, 

the literature was used to identify a constant, nationwide background estimate for each HAP 

under study. It is believed that such an approach may not be realistic for those HAPs that are 

expected to exhibit spatially heterogeneous backgrounds across the United States. New and 

improved values for background need to be determined. 

A study (Bortnick et al., 2002) uses statistical algorithms to estimate background levels at 

different localities. Estimates were obtained for such localities with available data as California, 

Texas, and parts of the Northeast and East Coast. The challenges ahead include extrapolating 

these finding to the rest of the United States where there are few or no available monitoring data. 

2.3.5 Urban-Scale Analyses 

The 1990 CAAA Section 112(k) requires EPA to reduce hazardous air pollutant (HAP) 

risks in urban areas. The EPA's strategy for reducing these risks is discussed in the Integrated 

Urban Air Toxics Strategy (U.S. Environmental Protection Agency, 2002). To help understand 

the air toxics problem in an urban area, it is necessary to know the concentrations of air toxics to 

which people are exposed. Unfortunately, air monitoring data are scarce and limited. Another 

means for estimating HAP concentrations is through the use of air quality models. Urban areas 

can vary greatly in terms of the types of emission sources and the legal enforcement options 

provided by state and local programs to control air toxics emissions. Air quality models allow 

state and local agencies to test the effectiveness of alternative control measures in reducing 

ambient levels of toxic contaminants. 

The intent of urban-wide air toxics modeling applications is to provide data inputs for use 

in exposure and risk calculations and prioritization, obtain a higher degree of geographic 

resolution than those obtained from national-scale studies, identify data gaps and help allocate 

resources toward particular issues of interest or concern, and support the planning and 

28
 
implementation of ambient air monitoring programs. Regarding the higher geographic resolution 

of assessment results, an important benefit of refinement by an urban-scale application is 

illustrated by considering the methods by which emissions data are applied to the models. To 

achieve the objective of a national-scale assessment within a feasible scope of time and 

resources, assumptions about emissions allocation are typically made to simplify the modeling. 

An example would be the allocation of unknown emission source locations to the centroid of the 

census tract. While this approach allows the national-scale assessment to broadly identify 

pollutants of potential concern across large geographical areas, the assigning of source locations 

or other sensitive parameters limits resolution. The urban-scale modeling effort is more localized 

and can compliment the national-scale assessment by increase in specificity. However, the 

number of assumptions made in urban-scale modeling precludes the use in specifying the 

individual sources that contribute to the total concentrations or the impact of specific sources in 

specific areas such as a neighborhood. For determining impacts of specific sources, more 

detailed analysis than is warranted in an urban-scale application is needed. 

Urban areas contain major sources, numerous smaller area sources, and mobile source. 

As a result, modeling analyses for large numbers of air toxics sources possess special challenges. 

Although most HAPs are emitted directly, some are produced and destroyed through reactions in 

the atmosphere. These issues, as well as receptor selection, meteorological data processing, and 

background concentration selection pose significant technical challenges to the air quality 

modeler. Although many air quality models can be used for estimating urban wide ambient 

concentrations, this document deals with the applications of the Industrial Source Complex 

(ISCST3) model, a model that can estimate close distance impacts from industrial facilities. This 

model has been extensively used in analyzing impacts from a single or a few facilities and this 

report should help provide transition to the more complex issues associated with urban-wide 

applications. Several conclusions can be drawn from the results of this study: 

•	 Increasing the receptor density near high emission sources changed the location of 

maximum concentrations between ISCST3 base and ISCST3 fine grid. The ISCST3 fine 

grid results also illustrated the concentration gradients that can occur near high emission 

sources. These findings illustrate the importance of the receptor placement and receptor 

density to model performance. 

•	 Allocating the onroad mobile emissions to road segments can improve the model 

predicted concentrations when compared to monitor observations. The benzene ISCST3 

base underpredicted the average concentrations at the seven monitors. Road segment 

allocation (ISCST3 Roads) resulted in better model-monitor agreement and also changed 

the location of maximum concentrations when compared to ISCST3 base. Allocating 

onroad mobile emissions to road segments also increased the maximum total 

concentration for formaldehyde but as seen in Figure 4, the nonroad mobile 

concentrations still dominated the maximum total concentration. 

29
 
•	 Higher concentrations are located near the higher emission regions for the five HAPs 

presented. A majority of the high emissions were located in eastern and northern Harris 

County, as were the higher concentrations. Among the five HAPs, the trend is that the 

HAPs with higher emissions also have higher maximum concentrations in the near-field. 

•	 It is important that emission inventory development continue to be refined in order to 

define emission parameters, sources, emission amounts, and locations for input into 

dispersion models. This will aid in improving model predictions and their use in 

assessing exposure to toxic pollutants and designing emission control strategies for toxic 

air contaminants. 

2.4 Fine-Scale Modeling 

2.4.1 Urban Canopy Parameterization in MM5 

Urban canopy parameterizations (UCPs) are being developed and implemented in MM5 

to improve fine-scale (~1-km grid spacing) simulations. This effort is important to ultimately 

improve the pollutant transport for neighborhood-scale CMAQ simulations that will be linked to 

modeling human exposure to airborne toxic pollutants. The UCPs involve changes to the 

momentum and energy budgets in MM5, as well as acquisition and implementation of various 

databases on the urban morphology of the modeled cities. The UCPs in MM5 leverage the 

published work of international scientists. 

In FY-2002, research continued on a UCP that was initially developed and implemented 

in MM5 in the previous fiscal year. This UCP is targeted for applications of MM5 that are on the 

order of 1-km horizontal grid spacing. This UCP accounts for the drag exerted by urban 

structures, the increase of turbulent kinetic energy particularly near the tops of buildings, and the 

changes to the energy budget due to anthropogenic heating and the absorption and emission of 

radiation within the urban canopy (i.e., from the surface to the tops of buildings). In FY-2002, 

the energy treatment was extended, and a preliminary evaluation of the UCP on Philadelphia was 

performed. Details of the UCP and preliminary results from the application on Philadelphia are 

described in Lacser and Otte (2002) and Otte and Lacser (2002). In addition, development began 

of an advanced UCP that includes coupling with an urban soil model, SM2-U, to add a more 

sophisticated and specific treatment of the energy balance in the urban areas as well as a 

treatment of vegetation and rural zones within the modeling domain. Initial tests of the advanced 

UCP in MM5 used the same Philadelphia study as the UCP described above. A more rigorous 

evaluation of the advanced UCP is planned for FY-2003 using Houston. 

In preparation for the Texas 2000 modeling study, a detailed database of the urban 

morphology for Houston and the surrounding areas was purchased. Several key parameters were 

identified (e.g., building density and height, vegetation density and height, plan area density, and 

many more). The processing of the morphology database is expected to be completed in early 

FY-2003. 

30
 
Figure 4. Annual average concentrations using ISC Roads for Houston, Texas, 1996, 

benzene, all sources (onroad segments and nonroad gridded) with no background. 

31 
2.4.2 Air Quality Modeling of Particulate Matter and Air Toxics at Neighborhood Scales 

Air quality simulation models provide a basis for developing and implementing the 

NAAQS and are tools for performing risk-based assessments for air toxics and developing and 

testing environmental management strategies. Both PM2.5 and airborne air toxic pollutants have 

characteristically different degrees of spatial and temporal variability, especially in urban areas 

and different geographical-climatic regimes. The performance of human exposure assessments 

requires the capability for air quality model simulations at neighborhood scales for situations 

where pollutants can exhibit higher spatial and temporal variability than currently modeled. 

Exposure models will need concentration fields at neighborhood scale resolution to address such 

issues as environmental justice and community-based risk assessments, and conduct hot spot 

analyses. The Models-3/CMAQ system has been used in multiscale simulations to span a range 

of grid resolutions from 36 (or 32) km to 4 km to provide concentration distributions at regional 

to urban scales respectively. Also, exposure models are currently using either limited amounts of 

monitoring data and or crude dispersion modeling tools. Thus, there is a need to improve the 

capability of the modeling tools to provide accurate simulations at neighborhood scale 

resolutions. 

In this study, the requirements are explored for modeling air quality at neighborhood 

scales. This effort investigates the science and operational requirements for running CMAQ at 

about 1-km grid resolution. Another requirement is the development of stochastic methods to 

handle additional concentration variability that arise from within grid sources and atmospheric 

processes. Recognizing that much of the attention for exposure assessments will be focused in 

urban areas, it is important to ensure that the flow fields in urban areas are accurately modeled, 

including the effects of heterogeneous building features in urban areas. A prototype UCP at 

neighborhood scales (~1 km horizontal grid spacing) was introduced into the PSU/NCAR 

Mesoscale Model Version 5, (MM5), which provides the meteorological fields for the 

chemistry-transport model in CMAQ. UCP is based on the drag approach, and is applied to grid 

cells in MM5 that have a non-zero fraction of urban land use. For this investigation, a model 

domain and study area were centered on the Philadelphia Metropolitan Area and MM5 and 

CMAQ were run for simulations at horizontal grid dimensions of 36 km, 12 km, 4 km, and 1.33 

km. The 1.33-km simulation represents the neighborhood scale. The introduction of the UCP 

parameterization produced significant differences in the predicted mean and turbulent fields 

within the urban canopy, especially in areas characterized by high density of tall buildings. The 

MM5, with UCP, simulations of vertical profiles of wind speed and turbulent kinetic energy were 

highly consistent with results from wind tunnel studies. Results illustrate the importance of 

accounting for the urban morphological structures in modeling the flow in urban areas. It 

provides a basis for more accurately resolving the magnitude and spatial details of the modeled 

air quality fields, especially for pollutant species that may have fine spatial gradients that are 

important for human exposure assessment. 

During the course of the study, it was found that the introduction of a new mixing length 

parameterization applied to the basic urban canopy parameterizations provided a means to 

eliminate spurious wave-like features in the meteorological fields and subsequent pollutant fields 

32
 
at 1.33-km grid resolution. Thus, with UCP in MM5, a limited set of pollutant concentration 

fields using CMAQ were examined, including NOx, ozone, several PM species (including 

particle numbers for fine and accumulation modes), formaldehyde, and acetaldehyde. It was 

found that the spatial representation of the concentration fields to enhanced grid resolution was 

different for different pollutant species. For most pollutant species, the increased grid resolution 

from 4-km to 1.33-km resulted in enhancements to coarse resolution features and the emergence 

of additional spatial features, accompanied by an increase in the magnitude of the concentration 

distribution pattern. For example, in the simulation for NOx, the concentration features for the 

areas of dense mobile sources and point sources were considerably sharpened both in terms of 

horizontal gradients and magnitude at 1.33-km resolution. Consequently, the simulations 

showed a corresponding decrease in ozone due to the effect of titration from the high NO 

features. While the simulations for PM mass were relatively insensitive to increased grid 

resolution, the number of aitken mode particles as predicted by the model exhibited spatial 

enhancements. The simulation of formaldehyde and acetaldehyde demonstrated that modeling 

these (and other toxic pollutants) at fine resolution can lead to identification and details of toxic 

pollutant hot spots. This project is a multi-year study. Several ongoing efforts underway are: 

• refinements to modeling UCP at neighborhood scales, 

• development of subgrid parameterizations, and 

• model evaluation studies. 

A new set of UCPs will be introduced into an advanced urban-soil canopy-air model, 

SM2-U (Dupont, 2001), to be subsequently implemented in MM5/CMAQ. These refinements 

will be applied to the Houston, Texas, area and tests performed against the Texas 2000 air quality 

study database. An important component of this study is the production of a new set of gridded, 

high resolution urban canopy parameters needed by the SM2-U model, and is to be based on 

building and tree canopies data for Harris County and the Houston Ship Channel area (derived 

from airborne lidar measurements). This effort is underway under a contract with the University 

of Arkansas, Fayetteville, Arkansas. The effort to formulate methodologies to produce the subgrid concentration distributions is directed towards development of probability density functions 

(PDFs) for the CMAQ grids. Studies continue on deriving the PDFs for the sub-grid scale 

variability from turbulence-induced concentration fluctuations using large-eddy 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. Ultimately, using these modeled PDFs, various statistical parameters (e.g., peak-tomean ratios) and spatial structure functions of the variability as a function of the building 

morphology will compliment the CMAQ grid predictions. Initial results to date have focused on 

the results of using the coupled LES with the chemistry model (LESchem). They provide 

indications that this form of variability is governed by the chemical reactivity of the species, the 

atmospheric pollutant mixture, and the turbulent intensity and structure of the mixed layer. In 

addition to the Texas 2000 study, a collaboration was begun with the Ecole Centrale Nationale of 

the University of Nantes, France, for studies relevant to the neighborhood scale modeling project. 

This effort involves evaluation of neighborhood scale modeling techniques using a special 

database called ESCOMPTE, a major European sponsored intensive field study program. The 

33
 
ESCOMPTE database (Mesteyer, 2002) contains specialized urban-canopy scale boundary layer 

and chemical measurements made in Marseilles, France, in 2001. 

2.4.3 Modeling the World Trade Center Disaster Site 

The September 11, 2001, destruction of the World Trade Center (WTC) in New York 

City resulted in the release of a large volume of airborne pollutants in the form of fugitive PM 

and various combustion products. These emissions persisted at varying degrees for weeks and 

months after the initial catastrophic event. This event has elevated the need for reliable models 

to predict concentrations of such contaminants within complex urban areas. Pollutant monitoring 

alone cannot provide the temporal and spatial details generally needed for exposure assessments 

and associated risks due to unplanned releases in urban regions. Models are critical for providing 

timely and reliable estimates of human exposures. The successful development and evaluation of 

such models demands relevant laboratory and/or field measurements of flow and dispersion in a 

variety of urban settings. Toward this database development goal, a laboratory study has been 

initiated in the Fluid Modeling Facility’s meteorological wind tunnel to characterize the near and 

mid-range distribution of concentrations of pollutants emitted from the WTC site. The study will 

provide both flow and concentration fields for comparison against those estimated by CFD 

models being developed and tested both within the Division and by collaborators in other 

national laboratories. This site-specific and detailed study of the WTC surroundings, in 

conjunction with previous (and more generic) building-array, wind-tunnel studies, is integral in 

the attempts to understand the variability of concentrations in complex urban areas as part of the 

neighborhood scales program (Ching et al., 2002). The laboratory study consists of eight main 

components: 

1.	 study design; 

2.	 preparation of the wind tunnel instrumentation and development of an 

appropriate approach flow boundary layer; 

3.	 construction of the scale model of Lower Manhattan and the WTC rubblepile source area, and installation on the turntable within the meteorological 

wind tunnel; 

4.	 establishment of Reynolds number independence within the narrow street 

canyons of Manhattan; 

5.	 extensive visible and laser-enhanced smoke visualizations of releases from 

the WTC site; 

6.	 vertical profiles of flow (velocities, turbulence, Reynold stresses) at 

approximately 30 to 40 selected locations within the city; 

7.	 tracer releases and associated concentration measurements throughout and 

downwind of Lower Manhattan; and 

8.	 analysis and documentation. 

The current study design includes data collection for three wind directions relevant to the 

climatology of the Lower Manhattan area during the months following September 11, 2001. 

34
 
During mid to late FY-2002, the first four study components were completed and the 

smoke visualizations were initiated. A 1:600 scale model of Lower Manhattan has been 

constructed and placed into the Meteorological Wind Tunnel (Figure 5). This model represents 

the full width (Hudson to East Rivers) of the southern most 2 kilometers of Manhattan Island. In 

addition to the many buildings, structures, and general roughness features of the city, the model 

includes an emulation of the WTC rubble pile with smoke emissions that were common for 

weeks after the collapse (Figure 6). The approach flow boundary layer has a depth of 

approximately 1.8 m with a 0.16 power law wind profile. The wind profile and approach flow 

roughness elements relate to a full scale roughness length of approximately 0.1 m. For the wind 

directions of this study, the actual approach to Manhattan is first over the relatively flat areas of 

suburban New Jersey followed by 1 to 3 kilometers of water. 

Figure 5. A 1:600 scale model of Lower Manhattan as it appears on the turntable within the 

Meteorological Wind Tunnel. 

35
 
Figure 6. Neutrally-buoyant smoke released from nine positions within the simulated World 

Trade Center rubble pile. 

Prior to including the scale model in the wind tunnel, a series of experiments were 

performed (Heist, 2002) to examine the flow and turbulence characteristics within some generic 

urban-like landscapes. This preliminary study was focused on an investigation of the Reynolds 

number independence within street canyons similar to the most extreme ones found in Manhattan 

(very narrow and deep). For the street canyon widths as little as 5 to 6 cm in this urban model, 

the study suggests that a freestream velocity of 4.0 ms-1 is needed to maintain sufficiently high 

Reynolds numbers (approximately 10,000) for flow independence. Smoke visualization for the 

first wind direction was initiated in late FY-2002. These qualitative studies will help in 

identifying the important flow features and in the final design of the flow and dispersion 

sampling arrays. Figures 7 depicts a neutrally-buoyant smoke release from the WTC site with 

flow from left to right and with visible light illuminating the smoke. This example, for a wind 

direction of 270 degrees, shows plume material almost immediately vented upward by the flow 

36
 
features associated with the tallest buildings in the vicinity of the site. Because of this, much of 

the resultant plume is lofted well above the buildings by the time the material passes beyond the 

downwind urban boundary. The laboratory study is expected to be concluded in mid FY-2003 

with data made available for model comparisons by late FY-2003. 

Figure 7. Smoke visualization for neutrally buoyant release from the simulated World Trade 

Center site (release area just left of center in this photo); flow is from left to right. 

2.4.4 Meteorological Measurements Near the World Trade Center 

The Meteorological Instrumentation Cluster of 3 (MIC3) portable trailers (10-m Tower 

instrumented for winds and temperature at 3 levels; AV Model 4000 miniSODAR; and AV 

Model 2000 SODAR) operated on Pier 25 near ground zero in Lower Manhattan from November 

2001 through April 2002 (Figures 8 and 9). Observations from this equipment are being used to 

supplement routine meteorological observations and model applications for the surrounding 

New York City area to support assessments of the environmental impact of emissions from 

ground zero. These measurements provide the only data above the surface for assessing 

37
 
meteorological models and for providing boundary conditions for fine scale numerical 

simulations necessary to support human exposure assessment studies. 

2.4.5 Estimating the Plume from the World Trade Center Recovery Site 

A general characterization of the dispersion of the PM from the WTC recovery site in the 

ambient air during the three months following the September 11, 2001, events was completed. 

Hourly estimates of plume transport and unit source dilution were made using a blended 

observational and dispersion modeling approach. A derivative of the CALMET-CALPUFF 

dispersion model was employed to simulate the plume dilution for a 50-km x 50-km square area 

surrounding Lower Manhattan. This modeling was completed in collaboration with the North 

Carolina State Climate Office at North Carolina State University, Raleigh, North Carolina. An 

example hour-average plume and the plume averaged over the full study period are presented in 

Figures 10 and 11. The results of this 3-month simulation were developed to support the 2002 

assessment of the environmental impact of the events following September 11, 2001. The plume 

characterization study has been very valuable in supporting the human health assessment studies. 

A digital model replication of all buildings south of Canal Street in lower Manhattan was 

developed using licensed digital information from Vexcel Corporation, Boulder, Colorado. 

Meteorological information from both the EPA (two SODAR’s and a 10-m tower) and U.S. 

Bureau of Land Management sites (six 2-m stations) in Lower Manhattan are being displayed 

with monitored PM concentrations to examine the influences of meteorology on air pollution 

concentration levels. Figure 12 displays a 1-hour example of these data within the building 

model. The lower left hand corner of the picture includes the winds reported at the five National 

Weather Service sites in the metropolitan area. A digital model of each building has been 

developed to support construction for wind tunnel model studies and the computation fluid 

dynamics numerical simulations studies of the fine-scale pollution dispersion near ground zero, 

which will be completed during FY-2003. 

38
 
Figure 8. View of the Meteorological Instrumentation Cluster along the 

Hudson River looking Southeast. 

Figure 9. View of the Meteorological 

Instrumentation Cluster from above Pier 25. 
Figure 10. Map (50-km by 50-km centered on ground zero) of Figure 11. Map of September 11, 2001, through December 8, 

example 1-hour averaged plume dilution of a volume source 2001, averaged plume dilution of a volume source from 

from ground zero. Red numbers are PM2.5 hourly measurements. ground zero. The wind rose is presented for this same period. 
Figure 12. An example display of meteorological and particulate matter concentrations with 

the 3-dimensional model of buildings for the Lower Manhattan area. 

2.5 Multimedia Modeling 

2.5.1 Develop Tools to Support Sensitivity and Uncertainty Studies 

Estimating the sensitivity of models to their inputs and the effect of input uncertainties on 

model results provides valuable information for model developers and decision makers. 

Unfortunately, estimating such values can be difficult, typically requiring the execution of 

numerous simulations and the interpretation of their results. An even more common challenge is 

to execute simulations for many different situations, such as time periods, management 

strategies, or locations. In this case, the analysis of the results might not be as complex as it is 

for sensitivity and uncertainty analysis, but the management of simulations can still be a 

significant problem. 

41
 
To support modelers who perform modeling studies that require repetitive computations, 

ASMD developed new capabilities for the Multimedia Integrated Modeling System (MIMS). 

MIMS supports the composition, configuration, application, and evaluation of complex systems 

of models. MIMS addresses some of the data management, software engineering, and 

mechanical bottlenecks that limit people’s ability to perform large modeling studies, allowing 

them to focus more attention on essential policy, scientific, and modeling issues. The primary 

new capabilities that support repetitive simulations are composite models, general approaches for 

iteration, and the ability to execute models on remote computers. 

Often a set of models and/or associated processors versus a single model are required to 

address an issue. This set of models forms a conceptual unit that is used in larger studies. For 

instance, a set of models that predicts the response of an estuary to watershed management 

practices might then be used as part of a sensitivity study to identify management actions that 

have a significant effect on the estuary’s health. To simplify the process of composing models, 

the MIMS team developed capabilities that allow modelers to identify a set of models and 

programs that should be treated as one model. This allows modelers to compose complex 

simulations from hierarchical units, similar to how programmers organize functionality into 

subroutines. 

Sensitivity analysis, uncertainty analysis, calibration, optimization, and application of a 

model to a series of data sets all have the common aspect of running a model repeatedly for 

different input data. To support all of these activities, the MIMS team designed and 

implemented generic support for iteration in the form of an abstract class that provides the 

common behaviors required for iteration. These common behaviors include providing user 

interfaces to connect inputs and outputs and managing the execution of simulations. Specific 

types of iteration are implemented by extending the abstract class and specifying three primary 

behaviors: 1) initialization of the iteration, 2) processing the result of one simulation, and 3) 

finishing the iteration. The first two behaviors can specify inputs for simulations that should be 

performed. When there are no more simulations to be performed, the third behavior is invoked. 

This design supports all types of iteration and allows developers to easily add new types of 

iteration to MIMS. To evaluate this design, the team implemented iterators for Monteo Carlo 

studies and for list of values. The team also worked with collaborators who are developing an 

urban drainage decision support system prototype in MIMS, to help them implement advanced 

uncertainty and nonlinear optimization iterators in MIMS. Iterators can apply to any model, 

which can include composite models consisting of a number of programs. 

For some types of studies, thousands of model executions might be required. When inputs 

for multiple simulations can be specified simultaneously, it is possible to expedite such studies 

by utilizing multiple computers. Monte Carlo studies, sensitivity studies, and simulations of 

different, independent locations are examples of problems where task parallel processing can be 

beneficial. To support task parallelism on multiple computers in MIMS, the team created a 

general design for executing programs that allows easy implementation of new approaches for 

working with remote computers. This design allows users to easily specify that their simulations 

should be executed on multiple computers. The team implemented support for MIMS 

42
 
controlling computations on remote computers via secure shell. Additional methods for 

distributing computations, such as utilizing Condor®7

(http://www.cs.wisc.edu/condor/),will be 

supported in the future. 

The MIMS team has also developed a variety of capabilities that were required to support 

applications in MIMS. These capabilities included generating model control text files by 

combining a user-supplied template and model input parameters, reading and writing columnar 

files containing time-series data, and basic plotting. 

2.5.2 Development of Compartmental Modeling Tools for Toxics 

The release, transformation, transport, and environmental fate of toxic pollutants are an 

inherently multimedia chain of events. FY-2002 multimedia research, regarding fully coupled, 

multimedia, dynamic, hybrid compartmental models, focused on the development and 

implementation of a pilot compartmental modeling tool under the MIMS framework. 

Programming support was provided by the Argonne National Laboratory and consultative 

assistance was provided by Multimedia Envirosoft Corporation, Los Angeles, California. The 

Three Compartment Model (3cm) application builds, solves, and displays the results of a system 

of ordinary differential equations (ODEs), describing the dynamic movement of chemical mass 

through multiple media (Figure 13)—a significant functional extension of the previous MIMS 

framework. 

The 3cm multimedia system includes uniform air, water, and biota (fish) compartments. 

The initial test case follows the movement of chlorobenzene, a volatile organic chemical used as 

a solvent and in the production of other chemicals, through air and water media to Alewife (prey) 

and Salmon (predator) endpoints. The initial simulation period spans 3-summer months (~2160 

hours) for a water body with geographic characteristics similar to Lake Michigan and 

climatological conditions similar to Detroit, Michigan. The 3cm is not intended to accurately 

reproduce observed chemical concentrations, but 1) to test the ability to implement the modeling 

approach under the MIMS framework, 2) begin modification of an existing commercial model 

configuration, MEND-TOX (Cohen and Cooter, 2002a; 2002b) to facilitate integration with air 

quality model development research, and 3) facilitate comparison of pilot results to a previous 

modeling study. The 3cm makes extensive use of the general approach described in Cohen and 

Cooter (2002a), but was developed entirely in-house, i.e., no propriatary code. Modifications to 

Cohen and Cooter (2002a) include the addition of a linear food-chain model, use of alternative 

algorithms to increase compatibility with other ASMD modeling activities, and the introduction 

of dynamic time varying media volumes and meteorology. The biota model includes fish 

growth, uptake of chemical in the water via the gills, mechanical uptake via feeding, egestion and 

degradation. The published MEND-TOX model employs air/water mass transfer algorithms 

valid only for a neutral atmosphere. This was replaced with a more flexible stability-dependant 

7

Condor is a trademark of Condor Team, University of Wisconsin-Madison 

43 
estimate similar to that used by CMAQ. Effects of water surface roughness (characterized by 

Reynolds number) on mass transfer and temperature dependent chemical diffusivity, neither of 

which are prominent in CMAQ (U.S. Environmental Protection Agency, 1999) but present in 

MEND-TOX, were included in the 3cm. The use of dynamic (hourly) boundary layer height and 

meteorological conditions is a marked departure from the traditional application of the 

compartmental modeling approach, but compliments other ASMD research. 

Figure 13. The pilot Three Compartment Model (3cm) as implemented under the 

MIMS framework. 

Model building activities during FY-2002 included code checking and quality assurance, 

installation and testing of the public domain ODE solver LSODE (Livermore Solver for Ordinary 

Differential Equations) under the MIMS framework, and several multimedia system performance 

checks to establish that at chemical steady state agreement exists between numerical and 

analytical solutions. This later test revealed additional coding errors that were corrected, and 

established appropriate error tolerance limits for the ODE solver. 

There are still several outstanding implementation issues. For instance, initial interest in 

this modeling approach stemmed from its combination of significant mechanistic detail with 

simulation speed. The 3cm application simulation is unacceptably slow. The original 3cm pilot 

was overly input/output bound and coding errors resulted in LSODE convergence errors. Once 

corrected, some speed improvement has been achieved, but the system is still much slower than 

44
 
the more complete Fortan 90 MEND-TOX implementation. This shortcoming will need to be 

resolved to achieve the system performance required for the long-time period, large-domain 

applications required by ASMD clients. A second unanswered question is the impact of dynamic 

media volumes and meteorology on the assumption of local equilibrium. Similar questions have 

arisen with regards to the semi-volatile partitioning model developed for the CMAQ toxics. 

Their solution will be studied for possible adaptation in the 3cm. A final question concerns 

variable process time-steps across media. This issue has been raised, but has not been adequately 

answered by the 3cm model. It gains importance as the FY-2003 task begins of adding multiphase, non-uniform media to the pilot system, e.g., sediment, soils. Coupling of uniform and 

non-uniform media will require definition of a new system of partial differential equations 

(PDEs) whose solution will require implementation of a PDE solver in MIMS, the definition of 

appropriate boundary condition expressions and development of an acceptable uniform/nonuniform media coupling paradigm. 

2.5.3 Urban Drainage Decision Support System Prototype 

A 2-year cooperative agreement was awarded in FY-2002 to develop and test the 

integration of the Storm Water Management Model (SWMM) with decision support tools to 

identify cost-effective and reliable watershed management strategies. A team of scientists from 

MCNC Environmental Programs, North Carolina State University, and University of North 

Carolina at Chapel Hill are the recipients of the cooperative agreement. The team is using the 

MIMS framework as an integration platform for developing the prototype urban watershed 

decision support tools. The prototype is being designed with local and city planners who manage 

urban point and non-point sources as potential users. 

During the first year of the cooperative agreement, work was underway to design and 

implement the decision support and analysis tools. Included are such uncertainty analysis tools 

as a Monte Carlo and Latin Hypercube approach for uncertainty propagation, importance 

sampling, and uncertainty importance estimation. Several optimization approaches based on 

genetic algorithms are being tested as well. These uncertainty and optimization tools will be 

integrated into the MIMS framework during FY-2003 for use with SWMM and other models 

within MIMS. Also during FY-2002, code development, testing, and management approaches 

were established for quality assurance and control of the software development. Finally, the 

integration of SWMM with the uncertainty and optimization tools is planned for FY-2003 along 

with the testing of the coupled system in a case study of Rouge River near Detroit, Michigan. 

This case study will address many of the use-case scenarios for which the system is designed. 

2.6 Global Climate Change Impacts on Air Quality 

This project was initiated in FY-2002 and will directly contribute to the EPA Global 

Change Research Program’s (EPA GCRP) assessment of global climate change impacts on air 

quality. The Division’s role in the assessment is to simulate regional emissions and air quality, 

45
 
specifically ozone and PM, using CMAQ. The planned products for this effort are designed to 

provide results and analysis in a timely manner for the EPA GCRP 2007 air quality assessment 

report. FY-2002 activities included: 

EPA GCRP Workshop on Global Climate Change Impacts on Air Quality. During 

December 5–2001, an EPA GCRP workshop was held that included scientists from universities, 

national laboratories, and other research organizations. Approaches for studying the influence of 

global climate change on air quality were discussed. It was decided that an incremental analysis 

would be necessary. In the first phase, the impact of climate change would be assessed without 

the added influence of future scenarios of emissions and land use. In the second phase, future 

emission scenarios would be included in the CMAQ simulations to consider the additive effects. 

It was also determined that multi-year simulations would be necessary during current and future 

periods to capture the climate change signals. Based on these points, a detailed methodology was 

developed during FY-2002 for the first incremental analysis of the air quality assessment, where 

all other factors except climate change remain unaffected. 

STAR Request for Assistance. ASMD worked closely with the EPA GCRP in the 

development of a Request for Assistance (RFA) funding solicitation on global climate change 

and air quality. Cooperative agreements will be awarded in FY-2003 to groups doing research 

with global climate models and chemical transport models. Collaboration with recipients of this 

RFA will be necessary for linkages with CMAQ and the air quality assessment. 

Regional Climate Downscaling Effort. To support this project and ultimately the air 

quality assessment, EPA GCRP is funding the Department of Energy’s (DOE) Pacific Northwest 

National Laboratory (PNNL) to develop current and future regional climate simulations. These 

simulations will rely on MM5 with initial and boundary conditions from global climate 

simulations under current and future Intergovernmental Panel on Climate Change (IPCC) 

greenhouse gas scenarios. 

Based on these activities, the primary outcome for FY-2002 was to finalize the approach 

for the assessment of climate change on air quality. The project approach will first focus on 

developing linkages of the meteorology with Global Climate Models (GCMs). DOE's PNNL 

will be performing regional climate modeling simulations using MM5 with information from 

several GCMs, including the NASA (National Aeronautics and Space Administration) GISS 

(Goddard Institute for Space Studies) models as boundary conditions and large-scale input. 

Simulations will be performed for a reference period (e.g., 1995 ± 5 years) and a future period 

under climate change conditions (e.g., 2050 ± 5 years). As regional climate modeling 

simulations become available, ASMD will archive the data and develop the model-ready 

meteorology fields needed for CMAQ and the emissions processor SMOKE (Sparse Matrix 

Operator Kernel Emission)8

. 

8

Copyright 2000 MCNC - Environmental Modeling Center 

46 
During FY-2003, test simulations will be performed with the NASA GISS GCM under a 

future IPCC greenhouse gas emission scenario. By analyzing GCM output from a current year 

through 2050, it will be determined how many years of CMAQ simulation are needed to separate 

out the influence of climate change trends from interannual variability. The GISS simulations 

will include passive tracers to follow stagnation events in addition to a standard analysis of 

temperature and mixing height changes. Results from this analysis will be used to finalize the 

decision regarding the number of years of CMAQ simulation necessary for the analysis of 

climate change impacts on air quality. 

Once the EPA STAR cooperative agreements are awarded in FY-2003, ASMD will 

identify collaborators for linking global chemical transport model (GCTM) results with CMAQ. 

It is anticipated that a GCTM will be used to provide initial and boundary conditions for ozone, 

PM, and related precursors. These activities will be ongoing during FY-2003–04, followed by 

the development of model-ready emissions for CMAQ based on base emission factors for current 

conditions and the regional climate simulations for current and future periods. CMAQ 

simulations for the first incremental analysis of global climate change effects on air quality 

without future air quality emission scenarios will then be performed and analyzed during 

FY-2005–07 for the 2007 EPA GCRP assessment report. 

2.7 Specialized Client Support 

2.7.1 Community Modeling and Analysis System Center 

In FY-2002, efforts continued under the cooperative agreement between EPA and the 

MCNC North Carolina Supercomputing Center to establish and operate the Community 

Modeling and Analysis System (CMAS) in support of the Models-3/CMAQ modeling system. 

CMAS is crucial to the growth and sustenance of the CMAQ user community for collaboration in 

model improvements, training, and support. During FY-2002, CMAS staff established the basis 

for operation, including an 18 member External Advisory Committee (EAC) representing the 

user community, an Internet support site featuring model documentation, e-mail based user 

support, and online links to relevant modeling news (www.cmascenter.org). EAC consists of 

members representing state regulatory agencies, academia, industry, air quality consultants, 

foreign users, and ex officio EPA members. The web site provides a community-based Models3/CMAQ Help Desk. Supported Models-3/CMAQ features include the CMAQ CTM, SMOKE, 

the Input Output/Applications Programming Interface convention that allows Models-3 system 

components to pass information, and MIMS interface for optional use with CMAQ and SMOKE. 

To guide the efforts, protocols were prepared and reviewed by EAC for intellectual property, 

testing, and acceptance of new Models-3/CMAQ components, membership, and user support. 

Recognition of CMAS by the Models-3/CMAQ user community has grown substantially. There 

are now users in at least 25 states and 6 foreign countries. In addition, a complementary Models3/CMAQ applications support center is being established at the University of Waterloo in 

Waterloo, Ontario, Canada. CMAS planned a CMAQ User’s Workshop and training courses for 

CMAQ, SMOKE, and MIMS, for October 21–23, 2002. 

47
 
2.7.2 	American Meteorological Society/Environmental Protection Agency Regulatory 

Model Screening Tool 

During FY-2001 and FY-2002, an effort was underway to develop a procedure based on 

AERMOD that would be easy to implement and would provide worst-case surface concentration 

estimates of inert species released from a point source, where relatively simple transport and 

diffusion plume models are appropriate. The procedure would provide concentration values that 

would in general be greater than AERMOD would predict, if the application of AERMOD was 

tailored to the particular location and terrain (i.e., run in a refined mode). Such screening tools 

have traditionally been used to provide a fast estimate of worst-case concentration values from 

which one can assess whether a more refined modeling assessment is needed. 

Given the treatment of the interplay between the seasonal variation of local land-use and 

its effect on the profiles of wind and temperature within the AERMOD modeling system, it was 

decided to replace the meteorological processing requirements with the screening tool for 

AERMOD. A two-step plan was devised. In step one, a team of Federal and State air quality 

modelers proficient in the refined application of AERMOD applied the AERMOD modeling 

system to over 20 locations in the United States and its territories. From these analyses, a 

database of worst-case meteorology was complied for the sources simulated, which ranged from 

area and volume sources to point sources with various stack heights and buoyancy release 

conditions. In the second step, a Fortran-based program called MakeMet was developed that 

would generate worst-case meteorology, which were to be close to that determined through the 

actual application of AERMOD. 

In conducting the analyses, it became apparent that novice users of AERMOD would 

have difficulty determining the characteristics of the land use and the variation of these land use 

characteristics over a 1-year period. To resolve this, another team was formed to develop a 

Fortran program called AirSurface that would compute the land-use characteristics for 

inhomogeneous (e.g., is a mixtures of forested areas, water bodies, urbanize areas), and provide 

effective values of the land-use characteristics averaged over the domain of application of the 

AERMOD modeling system. Following the investigations of Batchvarova and Gryning (2001) 

and Batchvarova et al. (2001), methods were derived for computing these effective values. To 

complete this analysis, an uncertainty assessment was conducted to determine the number of 

significant figures needing to be reported so as to provide a realistic expectation of when 

differences are significant. It was determined that one significant figure is likely sufficient, 

especially since it is difficult to specify these inputs to less than 30 percent uncertainty even with 

on-site instrumentation and from land-use maps. Work is ongoing to develop and test the 

programs MakeMet and AirSurface. It is anticipated that versions of these programs will be 

released for public testing and evaluation during the Summer of 2003. 

48
 
2.7.3 	Joint Action Group for Selection and Evaluation of Atmospheric Transport and 

Diffusion Models 

In December 2001, the Office of the Federal Coordinator for Meteorology (OFCM) 

hosted the Workshop on Effective Emergency Response-Selecting a Suitable Dispersion Model 

for a Given Application. The participants considered what kinds of models are used in specific 

situations and how atmospheric transport and diffusion (ATD) modeling systems are evaluated. 

The goal of the workshop was to define a framework for supporting the objective determination 

of the most appropriate dispersion modeling system to be used in a given situation. This 

workshop served as the impetus for the OFCM to lead the Federal ATD modeling community in 

a concerted effort to evaluate the ATD models available to address typical threats. 

In January 2002, OFCM formed the Joint Action Group for Selection and Evaluation of 

Atmospheric Transport and Diffusion Models (JAG/SEATD) and charged the participants with 

the following tasks: review the ATD modeling systems in use by the Federal agencies at 

operational modeling centers; conduct a preliminary analysis of gaps in understanding of the 

processes on which the modeling systems are constructed; recommend areas for research and 

development to improve the Nation's capabilities to use ATD modeling systems in response to 

the terrorist threat; review the ATD model evaluation processes used by the agencies; and review 

plans to conduct field experiments. JAG was constituted from representatives of all Federal 

agencies actively employing atmospheric dispersion models for emergency response applications. 

The agencies included several arms of the Departments of Defense and Energy, the 

Environmental Protection Agency, the Federal Emergency Management Administration, Nuclear 

Regulatory Commission, and NOAA. The JAG/SEATD met monthly through July 2002, and 

summarized its findings in a report (Hicks et al., 2002). 

The JAG/SEATD members coordinated with modelers, emergency managers, and 

first-responders to understand the needs of decision makers and the end-users. The emergency 

managers and first-responders require the best available information regarding the spatial extent 

and timing of hazardous areas. Key decisions are usually made within the first hour of an 

incident. Therefore, because timeliness of dispersion forecasts is critical, the dispersion services 

and products that are needed must be available in time to be used in the decision-making process. 

The modeler requires the best available information regarding the characterization of the source 

and the physical environment. 

While there are over 140 documented ATD models that are used for regulatory purposes, 

research and development, and emergency operations, the JAG/SEATD narrowed the list to only 

29 non-proprietary modeling systems that are used operationally either by first-responders and/or 

at the Federal operational modeling centers. These 29 modeling systems were the focus of the 

JAG/SEATD's evaluation. The JAG/SEATD noted that there are differences among the 

operational modeling systems and that none encompassed the entire breadth of needs. 

Moreover, the JAG/SEATD noted important differences between the basic requirements 

confronting military and civilian developers of the dispersion forecasting methodologies. It was 

49
 
further noted that the two major civilian capabilities operate from opposite shores, with the DOE 

capability operating from California and the NOAA system from the Washington, DC, area. 

After careful examination of the end-user and decision-maker's needs, eight scenarios 

were developed for review. The scenarios covered a wide range of potential threats and 

exercised a variety of model capabilities. For each scenario, individual modeling system 

applicability was evaluated over two spatial scales (local and mesoscale), two modeling 

environments (urban and rural), and three temporal scales (immediate response, intermediate 

response and short-term planning, and recovery and long-term planning). The evaluation 

included an assessment of the availability of data for model evaluation.

 The analysis of the gaps in the understanding and capabilities of the ATD modeling 

systems yielded factors that require further research and development. The factors range from 

source characterization to the study of the effects of complex terrain, coastal influences, and 

urban areas. Four barriers to progress were identified: the not invented here barrier, the 

military-civilian barrier, the education barrier, and the emergency response/public 

communications barrier. Efforts to bridge those barriers are addressed in the JAG/SEATD 

report. Modeling system evaluation procedures were reviewed, and general guidelines of what 

needs to be considered in the evaluation of ATD models were developed. The JAG/SEATD 

concluded that the quality assurance planning, documentation, and scrutiny should be consistent 

with the intended use. 

2.7.4 AERMOD Screening Model Comparison 

A series of SCREEN3 model runs were performed using the cavity option to compare 

results from the SCREEN3 cavity concentration with AERMOD cavity concentrations. The 

purpose of this effort was to assess the cavity results from both models, since the AERMOD 

screening model, AERSCREEN, may not be ready when AERMOD is released. About 38 

SCREEN3 model runs were made with different building sizes, urban/rural options, and other 

specifications. Preliminary comparisons indicate SCREEN3 cavity results are both higher and 

lower than the AERMOD cavity results. On-going study is required to detect patterns or trends 

in the various building configurations and their results. 

2.7.5 Modeling Significant Increases in Concentrations of PM2.5 

Analyses were performed of modeling the concentration of PM2.5 to determine the 

increase in emissions needed to affect a significant increase in concentrations for a range of 

point, area, and volume sources. Estimates were provided for both primary particulate emissions 

and precursor emissions (SO2 for ammonium sulfate and NOX for ammonium nitrate). The latter 

is a unique and, perhaps, a first time application of the precursor option in CALPUFF. These 

analyses were completed in support of policy making for the New Source Review. Similar 

analyses are anticipated in support of the EPA’s Regional Haze Regulations. 

50
 
2.7.6 Developing Statistical Forecasting Methods for PM2.5 

Public saturation of real-time ozone mapping and forecasting programs have fueled the 

demand for increased coverage of air quality and health information. Providing advanced 

warning of unhealthy air quality to the public is becoming increasingly important for EPA, as 

well as for state and local air quality agencies. Ozone forecasts now appear regularly in USA 

TODAY, or on The Weather Channel, and in other local media for many parts of the United 

States during the ozone season. 

Division staff are managing a project designed to extend the forecasting efforts to fine 

PM by developing city-specific air quality forecasting techniques for 21 major metropolitan areas 

in the United States. The goal is to develop statistical models relating the increasingly available, 

continuous, real-time PM2.5 ambient data to various meteorological scenarios, with an eye toward 

generation of next-day public health advisories using the EPA's Air Quality Index. In addition to 

the development of the regression-based forecasting tools, the project is in the process of 

transferring the relevant software and knowledge bases to the state and local air quality agencies 

that will be ultimately responsible for issuing air quality alerts. 

2.7.7 NARSTO Program Support 

The NARSTO program is a multinational, public/private partnership of over 70 

organizations sponsoring and participating in ozone and particulate air quality research in North 

America. The coordination of communication and planning activities for air quality research, 

science plans, and state-of-science assessments with the NARSTO membership are important 

tasks performed by the NARSTO managers. ASMD provides a full-time associate management 

coordinator for the NARSTO program. Part of the management function includes project officer 

duties on an interagency agreement with the DOE’s Oak Ridge National Laboratory to provide 

infrastructure support to the NARSTO program, assistance agreements related to the SOS 

program, contracts for technical support, and grants to the National Research Council. The 

associate management coordinator also provides technical assistance and leadership to the 

NARSTO Quality System Science Center, which is charged with developing and maintaining the 

NARSTO permanent data archive as well as a comprehensive quality management system. 

An ASMD scientist serves as a co-chair of the NARSTO Modeling and Chemistry Team. 

As part of those duties, several NARSTO document reviews were conducted, including the 

internal review of the NARSTO PM Assessment and a review of a major report done under a 

Coordinating Research Council contract for NARSTO in which several ozone models were 

compared. The scientist was invited to write a descriptive analysis (conceptual model) for 

particulate concentrations in the northeastern United States for the NARSTO PM Assessment 

Report. 

The principal authors of the NARSTO state-of-science suspended PM assessment met at 

the University of Minnesota in Minneapolis to discuss the comments from the National Research 

51
 
Council's (NRC) review of the draft NARSTO report. NARSTO received the NRC review report 

in September and with all the comments received, the Assessment team began the process of 

producing a final report. All comments were discussed at the intensive meeting and each author 

was given the task of responding to the comments as determined by the team. The 500 page 

report, to be available on CD, will be formally released at the American Association for Aerosol 

Research meeting on March 30, 2003, in Pittsburgh, Pennsylvania. 

2.7.8 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. 

2.8 Regulatory Support 

2.8.1 Support Center for Regulatory Air Models 

During FY-2002, the SCRAM (Support Center for Regulatory Air Models) website was 

modified to comply with the official EPA web page template, as directed by the Office of 

Environmental Information. Although the web site files and technical materials within SCRAM 

remained nearly the same, infrastructure modifications affected all web pages. Some additional 

links were created to provide easier maneuvering to specific models and related files. Thirteen 

SCRAM products were added and/or modified during FY-2002. Most of these changes were 

beta test files related to the work resulting from the 7th Modeling Conference for Air Quality 

Modeling (http://www.epa.gov/scram001/tt26.htm). SCRAM serves as the sole Internet source 

for the EPA’s air quality dispersion models, guidance, and related programs and information. 

2.8.2 Trajectory Analyses 

A plan was developed to use the HYSPLIT4 (Hybrid Single-Particle Lagrangian 

Integrated Trajectory) model to help determine possible source locations for high PM fine values 

for some of the EPA trends network. Initially, as part of the plan, the HYSPLIT4 model was run 

52
 
over several sites and a number of time periods using EDAS (Eta Data Assimilation System) and 

FNL (Final) meteorological data. These data were plotted simultaneously and analyzed to 

determine the approximate differences in running the model utilizing different meteorological 

fields. 

The execution of the plan was completed for three sites. Trajectories were run for an 

approximate 2-year time period over the years 2001 and 2002 using the EDAS data set as the 

primary data and FNL as a backup. This involved running the trajectory model numerous times 

at different levels and different starting times within a 24-hour period. Analyses were then 

performed on the trajectories to help determine source locations of high PM fine values. 

2.8.3 Effects of Additional VOC/NOx Reductions in 2010 

A series of modeling analyses were completed to examine the impact of further precursor 

reductions on future levels of 8-hour ozone over the eastern United States. The air quality model 

used to conduct the analyses was the Comprehensive Air Quality Model with Extensions version 

3.10 (CAMx). Three sets of additional precursor reductions were layered atop the proposed 2010 

Clear Skies Initiative control scenario. The modeling analyses were intended to answer three 

main questions: 

1. How effective will additional low-level anthropogenic NOx, anthropogenic VOC, or 

combined emissions reductions be in reducing 8-hour ozone concentrations from the levels to be 

achieved through existing and planned regional and national controls? 

Based on the modeling analyses, it was determined that additional low-level 

anthropogenic NOx, anthropogenic VOC, and/or combined emission reductions will generally be 

effective in reducing 8-hour ozone concentrations above and beyond the levels to be achieved 

through existing and planned controls. For instance, the number of projected nonattainment 

areas is decreased from 53 in the 2010 Clear Skies control case, to 40 areas after 35 percent 

low-level anthropogenic VOC control, to 27 areas after 35 percent low-level anthropogenic NOx 

control, and to 19 areas after a 35 percent combined strategy. 

2. Will reducing anthropogenic VOC help to reduce ozone in all projected 8-hour ozone 

areas? 

Major metropolitan areas in the northern United States are expected to be responsive to 

additional VOC control. For example, the extent of the projected nonattainment area is reduced 

by 22 percent in Boston and 18 percent in Chicago after the imposition of a 35 percent low-level 

anthropogenic VOC reduction. Conversely, urban areas in the southern United States, which 

generally contain large quantities of biogenic VOC, are less responsive to VOC controls. For 

instance, modeled ozone levels in Atlanta are only reduced by 1 to 3 percent when low-level 

anthropogenic VOC emissions are cut by 35 percent. Other southeastern metropolitan areas 

essentially show no change when VOC is reduced. 

53
 
3. In 2010, are there areas where further NOx reductions beyond existing and planned 

controls might result in increased ozone? 

There are some areas where further NOx reductions beyond existing and planned controls 

might result in increased ozone. In particular, the urban centers of Chicago, Detroit, and New 

York City showed increases in projected future ozone peaks when NOx alone was reduced by 35 

percent. That said, regional NOx control is generally beneficial. For example, the extent of the 

projected nonattainment area (grid cells >= 85 ppb) is reduced by 65 percent in Boston and 19 

percent in Chicago after the imposition of a 35 percent low-level NOx. 

2.8.4 Operational Evaluation of Proposed Clear Skies Initiative Ozone Modeling 

EPA is applying CAMx to estimate the degree of future year ozone nonattainment of the 

NAAQS with and without the emissions reductions associated with the proposed Clear Skies 

Initiative. As part of this analysis, three 1995 base case episodes were simulated at resolutions of 

36 km and 12 km. An operational evaluation was completed consisting entirely of comparisons 

against surface ozone data for the entire domain, as well as several dozen subregions. 

At the synoptic scale, the model was found to exhibit little overall bias (-2.1 percent) or 

error (21.1 percent). However, for some of the individual subregions, particularly those that 

included coastal areas, model biases and errors approached +/- 40 percent. Unlike similar 

regional modeling applications, the model did not appear to exhibit any trend in bias over the 

course of the episodes. Ultimately, the model was deemed acceptable for use in a relative sense, 

i.e., for projecting changes in future year air quality. 

54
 
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60
 
APPENDIX A: ACRONYMS, ABBREVIATIONS, AND DEFINITIONS 


ACM Asymmetric Convective Model 

AE3 Aerosols component version 3 

AERMIC American Meteorological Society/Environmental Protection 

Agency Regulatory Model Improvement Committee 

AERMET AERMOD Meteorological Preprocessor 

AERMOD AERMIC Model 

AERSCREEN AERMOD screening model 

ARL Air Resources Laboratory 

ASMD Atmospheric Sciences Modeling Division 

ASPEN Assessment System for Population Exposure Nationwide 

ATDD Atmospheric Turbulence and Diffusion Division, NOAA 

AVHRR Advanced Very High Resolution Radiometer 

BCON Boundary Concentrations 

BELD Biogenic Emissions Land cover Database 

BELD-3 Biogenic Emissions Land cover Database version 3 

BEIS-3 Biogenic Emissions Inventory System version 3 

BRACE Bay Regional Atmospheric Chemistry Experiment 

CAA Clean Air Act 

CAAA Clean Air Act Amendments 

CALMET-CALPUFF A diagnostic meteorological model/puff dispersion model 

CAMx Comprehensive Air Quality Model with extensions 

CB-IV Carbon Bond version 4 

CCM Community Climate Model 

CIRA Cooperative Institute for Research in the Atmosphere 

CMAQ Community Multiscale Air Quality modeling system 

CMAQ-Hg Community Multiscale Air Quality modeling system mercury 

model 

CMAS Community Modeling and Analysis System 

CONSUME A fuel consumption model, which predicts total smoldering fuel 

consumption during wild fires. 

CSEM Community Smoke Emission Model 

CTM Chemistry-Transport Model 

DOE Department of Energy 

EDAS Eta Data Assimilation System 

EMEP European Monitoring and Evaluation Program 

EPA Environmental Protection Agency 

ESCOMPTE A European sponsored intensive field study program 

Eta National Center for Environmental Prediction Mesoscale Model 

Extended RADM Regional Acid Deposition Model with full dynamics of secondary 

inorganic fine particle formation taken from the RPM 

FDDA Four-Dimensional Data Assimilation 

61
 
FY Fiscal Year 

GCM Global Climate Models 

GCRP Global Change Research Program 

GCTM Global Chemical Transport Model 

GIS Geographic Information System 

GISS Goddard Institute for Space Studies 

HAP Hazardous Air Pollutant 

HYSPLIT4 Hybrid Single-Particle Lagrangian Integrated Trajectory model 

ICON Initial Concentrations 

IPCC Intergovernmental Panel on Climate Change 

ISORROPIA A computationally efficient thermodynamic model 

LADCO Lake Michigan Air Directors Consortium 

LES Large-eddy simulation 

LESchem LES with the chemistry model 

LSODE Livermore Solver for Ordinary Differential Equation 

M3DRY Models-3 Dry Deposition Scheme 

MARAMA Mid-Atlantic Regional Air Management Association 

MCIP Meteorology-Chemistry Interface Processor 

MCIP2 Meteorology-Chemistry Interface Processor version 2 

MDN Mercury Deposition Network 

MEBI Modified Euler Backward Iterative 

MEND-TOX Multimedia Environmental Distribution of TOXics 

MIC3 Meteorological Instrumentation Cluster of 3 

MIMS Multimedia Integrated Modeling System 

MM5 Mesoscale Model - version 5 

Mobile6 Mobile Source Emission Model 

MOVES Multiscale mOtor Vehicle and equipment Emission System 

MPRM Meteorological Preprocessor for Regulatory Modeling 

NAAQS National Ambient Air Quality Standards 

NALCC North American Land Cover Characteristics 

NASA National Aeronautics and Space Administration 

NATA National Air Toxics Assessment 

NCAR National Center for Atmospheric Research 

NEI National Emission Inventory 

NERL National Exposure Research Laboratory 

NHEERL National Health and Environmental Effects Research Laboratory 

NLCD National Land Cover Database 

NOAA National Oceanic and Atmospheric Administration 

NOx Nitrogen oxides 

NPS National Park Service 

NRMRL National Risk Management Research Laboratory 

NSR New Source Review 

NWS National Weather Service 

OBMs Observations-Based Methods 

62
 
ODE	 Ordinary Differential Equation 

OFCM	 Office of the Federal Coordinator for Meteorology 

OTAG	 Office of Transportation and Air Quality 

PBL	 Planetary Boundary Layer 

PCDF’s	 Poly-Chlorinated Dibenzo-Furans 

PCDD’s	 Poly-Chlorinated Dibenzo-p-Dioxins 

PCM	 Parallel Climate Model 

PDE	 Partial Differential Equation 

PDFs	 Probability Density Functions 

PDM	 Plume Dynamics Model 

PinG	 Plume-in-Grid 

PinG Module	 Plume-in-Grid Model 

PM	 Particulate Matter 

PNNL	 Pacific Northwest National Laboratory 

PX LSM	 Pleim Xiu Land-Surface Model 

QSSA	 Quasi-Steady State Approximation 

RADM2	 Regional Acid Deposition Model version 2 

RFA	 Request for Assistance 

RPM	 Regional Particulate Model 

SAPRC99	 A gas-phase chemical mechanism 

SCRAM	 Support Center for Regulatory Air Models 

SM2-U	 An urban soil model 

SMOKE	 Sparse Matrix Operator Kernel Emission model 

SOA	 Secondary Organic Aerosol 

SOS	 Southern Oxidants Study 

SWMM	 Storm Water Management Model 

TexAQS	 Texas Air Quality Study of Houston 

TKE	 Turbulent Kinetic Energy 

TMDL	 Total Maximum Daily Loan 

TTCP-CBD	 The Technical Cooperation Program (TTCP) Chemical, Biological 

and Radiological Defense (CBD) 

UCPs	 Urban Canopy Parameterizations 

USFS	 U.S. Forest Service 

USGS	 U.S. Geological Survey 

VOC	 Volatile Organic Compounds 

WRF	 Weather Research and Forecast 

WTC	 World Trade Center 

63
 
APPENDIX B: PUBLICATIONS
 

Athanassiadis, G., S.T. Rao, J.Y. Ku, and R. Clark. Boundary layer evolution and its effects on 

ground-level ozone concentrations. Preprints, 4th Conference on Atmospheric Chemistry: 

Urban, Regional and Global-Scale Impacts of Air Pollutants, January 13–17, 2002, 

Orlando, Florida. American Meteorological Society, Boston, 97–102 (2002). 

Benjey, W.G., and R.E. Imhoff. Establishment of a community modeling and analysis support 

mechanism. In The Eleventh International Emission Inventory Conference: Emission 

Inventories - Partnering for the Future, Atlanta, Georgia, April 15–18, 2002. Office of 

Air Quality Planning & Standards, U.S. Environmental Protection Agency, Research 

Triangle Park, NC. Available online from 

http://www.epa.gov/ttn/chief/conference/ei11/index.html#ses-8 (2002). 

Bird, S.L., S.G. Perry, S.L. Ray, and M.E. Teske. Evaluation of the AgDISP aerial spray 

algorithms in the AgDRIFT model. Environmental Toxicology and Chemistry 

21:672–681 (2002). 

Biswas, J., C. Hogrefe, and S.T. Rao. Temporal and spatial variability of ozone production 

efficiencies over the eastern United States. Proceedings of the 95nd Annual Meeting of 

the Air & Waste Management Association, Baltimore, Maryland, June 23–27, 2002. 

(CD-ROM). Session AB-2c. Advances in Photochemical Air Quality Modeling, Air & 

Waste Management Association, Pittsburgh, Paper # 43110 (2002). 

Bullock, O.R., Jr., and K.A. Brehme. Atmospheric mercury simulation using the CMAQ model: 

formulation description and analysis of wet deposition results. Atmospheric Environment 

36:2135–2146 (2002). 

Cai, C., C. Hogrefe, J. Biswas, S.T. Rao, N. Seaman, A. Gibbs, G. Kallos, P. Katsafados, C. 

Walcek, J. Chang, and K.L. Demerjian. An experimental air quality forecast modeling 

system (AQFMS) for the northeast United States: A demonstration study. Preprints, 4th 

Conference on Atmospheric Chemistry: Urban, Regional and Global-Scale Impacts of 

Air Pollutants, January 13–17, 2002, Orlando, Florida. American Meteorological 

Society, Boston, 47–52 (2002). 

Ching, J.K.S. Modeled PM and air toxics concentration fields to drive human exposure models. 

Preprints, 11th Annual Meeting of the International Society of Exposure Analysis, 

Charleston South Carolina, November 4–8, 2001. International Society of Exposure 

Analysis, Boston, p. 208 (2001). 

64
 
Ching, J., A. Lacser, T.L. Otte, J. Herwehe, and D. Byun. Neighborhood scale modeling of PM2.5 

and air toxics concentration distributions to drive human exposure models. Preprints, 

12th Joint Conference on the Applications of Air Pollution Meteorology with the 

A&WMA, May 20–24, 2002, Norfolk, Virginia. American Meteorological Society, 

Boston, 97–98 (2002). 

Ching, J.K.S., T.L. Otte, S. Dupont, S. Burian, and A. Lacser. Urban morphology for Houston to 

drive Models-3/CMAQ at neighborhood scales. Preprints, 4th Symposium on the Urban 

Environment, May 20–24, 2002, Norfolk, Virginia. American Meteorological Society, 

Boston, 35–36 (2002). 

Cooter, E.J., and Y. Cohen. Model evaluation of dry deposition to vegetation for volatile and 

semi-volatile organic compounds in a multimedia environment. Water, Air, and Soil 

Pollution: Focus 1:258–294 (2001). 

Cohen, Y., and E.J. Cooter. Multimedia environmental distribution of toxics (Mend-Tox): Part I, 

Hybrid compartmental-spatial modeling framework. Practice Periodical Hazardous, 

Toxic, and Radioactive Waste Management 6:70–86 (2002). 

Cohen, Y., and E.J. Cooter. Multimedia environmental distribution of toxics (Mend-Tox): Part 

II, Software implementation and case studies. Practice Periodical Hazardous, Toxic, and 

Radioactive Waste Management 6:87–101 (2002). 

Cooter, E.J., and W.T. Hutzell. A regional atmospheric fate and transport model for atrazine: 

Part 1, Development and implementation. Environmental Science & Technology 

36:4091–4098 (2002). 

Fine, S.S. Finding a common data representation and interchange approach for multimedia 

models. Proceedings, Second Federal Interagency Hydrological Modeling Conference, 

Las Vegas, Nevada, July 28–August 1, 2002. Session 2B. Identifying Model Behavior. 

CD-ROM. ACWI (Advisory Committee on Water Information, Subcommittee on 

Hydrology (2002). 

Fine, S.S., C.C. Howard, A.M. Eyth, D.A. Herington, and K.J. Castleton. The EPA multimedia 

integrated modeling system software suite. Proceedings, Second Federal Interagency 

Hydrological Modeling Conference, Las Vegas, Nevada, July 28–August 1, 2002. 

Session 6. Demonstrations. CD-ROM. ACWI (Advisory Committee on Water 

Information, Subcommittee on Hydrology (2002). 

Finkelstein, P.L. Observations of similarity theory stability correction terms for momentum and 

temperature, Qm and Qh, over agricultural fields and forests. Preprints, 25th Conference 

on Agricultural and Forest Meteorology, May 20–24, 2002, Norfolk, Virginia. American 

Meteorological Society, Boston, 150–151 (2002). 

65
 
Gill, T.E., D.A. Gillette, T. Niemeyer, and R.T. Winn. Elemental geochemistry of wind-erodible 

playa sediments, Owens Lake, California. Nuclear Instruments and Methods in Physics 

Research B 189:209–213 (2002). 

Gillette, D.A. Windblown dust. In Encyclopedia of Soil Science. Rattan Lal (Ed.). Marcel 

Dekker, Inc., NY, 1443–1445 (2002). 

Gilliland, A.B., R.L. Dennis, S.J. Roselle, T.E. Pierce, and L.E. Bender. Developing seasonal 

ammonia emission estimates with an inverse modeling technique. Proceedings of the 2nd 

International Nitrogen Conference on Science and Policy: The ScientificWorld 

1:356–362 (2001). 

Godowitch, J.M. Photochemical and aerosol modeling with the CMAQ plume-in-grid approach. 

Preprints, 12th Joint Conference on the Applications of Air Pollution Meteorology with 

the A&WMA, May 20–24, 2002, Norfolk, Virginia. American Meteorological Society, 

Boston, 69–70 (2002). 

Hicks, B., R. Addis, W. Bach, T. Bauer, B. Beitler, P. Davidson, J. Ellis, D. Garvey, J. Irwin, J. 

McNitt, J. Mitchell, D. Payton, D. Randerson, J. Sarkisian, J. Stailey. Atmospheric 

Modeling for Releases from Weapons of Mass Destruction: Response by Federal 

Agencies in Support of Homeland Security. FCM-R17-2002. August, 2002, U.S. 

Department of Commerce, Office of the Federal Coordinator for Meteorological Services 

and Supporting Research, Silver Spring, MD (2002). 

Hogrefe, C., S.T. Rao, and K.L. Demerjian. Using the current-generation 

meteorological/photochemical modeling systems for real-time ozone forecasting. 

Preprints, 16th Conference on Probability and Statistics in the Atmospheric Sciences, 

January 13–17, 2002, Orlando, Florida. American Meteorological Society, Boston, 

Paper No. 3.17 (2002). 

Huber, A.H. Meteorological measurements and modeling in support of human exposure 

modeling: microenvironmental factors. Coordinating Research Council Air Toxics 

Modeling Workshop, February 25–26, 2002, The Woodland, Texas. Coordinating 

Research Council, Alpharetta, GA. Available at 

http://www.crcao.com/Air%20Toxics/airtoxicsworkshop.htm (accessed July 2002). 

Huber, A.H., R.C. Gilliam, H. Feingersh, and R. Kelly. Effects of meteorology on the transport 

and dispersion of emissions from the WTC recovery site. Proceedings of the 12th 

Conference of the International Society of Exposure Analysis (ISEA), August 11–15, 

2002, Vancouver, BC, Canada. International Society of Exposure Analysis, Boston, p. 

292 (2002). 

66
 
Irwin, J.S. A historical look at the development of regulatory air quality models for the United 

States Environmental Protection Agency. NOAA Technical Memorandum OAR 

ARL-244, Atmospheric Sciences Modeling Division, Research Triangle Park, NC, 67 pp. 

(2002). 

Irwin, J.S., and J.P. Notar. Long-range transport screening technique using CALPUFF. In 

Guildeline on Air Quality Models: A New Building, April 4-6, 2001, Newport, Rhode 

Island. Session 1 - CALPUFF Studies. CD-ROM. Air & Waste Management 

Association, Pittsburgh (2001). 

Irwin, J.S., D. Carruthers, J. Paumier, and J. Stocker. Application of ASTM D6589 to evaluate 

dispersion model performance to simulate average centerline concentration values. 

Proceedings of the 8th International Conference on Harmonisation Within Atmospheric 

Dispersion Modelling For Regulatory Purposes, Sofia, Bulgaria, October 14–17, 2002. 

53–57 (2002). 

Irwin, J.S., D. Carruthers, J. Paumier, and J. Stocker. Assessing dispersion model performance 

to simulate average centerline concentration values. Preprints, 12th Joint Conference on 

Applications of Air Pollution Meteorology with the A&WMA, May 20–24, 2002, Norfolk, 

Virginia. American Meteorological Society, Boston, 43–44 (2002). 

Irwin, J.S., K. Steinberg, C. Hakkarinen, and H. Feldman. Uncertainty in air quality modeling 

for risk calculations. In Guideline on Air Quality Models: A New Building, April 4–6, 

2001, Newport, Rhode Island. CD-ROM. Air & Waste Management Association, 

Pittsburgh (2001). 

Kapor, D., D.T. Mihailovic, T. Tosic, S.T. Rao, and C. Hogrefe. An approach for the 

aggregation of albedo in calculating the radiative fluxes over heterogeneous surfaces in 

atmospheric models. International Environmental Modelling and Software Society 

(IEMSS) 2002 Conference Integrated Assessment and Decision Support, Lugano, 

Switzerland, June 24–27, 2002. Swiss National Science Foundation, 389–394 (2002). 

Lacser, A., and T.L. Otte. Implementation of an urban canopy parameterization in MM5. 

Preprints, 4th Symposium on the Urban Environment, May 20–24, 2002, Norfolk, 

Virginia. American Meteorological Society, Boston, 153–154 (2002). 

Lazic, J., D.T. Mihailovic, S.T. Rao, and C. Hogrefe. Land air surface scheme (LAPS) for use in 

urban modeling. International Environmental Modelling and Software Society (IEMSS) 

2002 Conference, Lugano, Switzerland, June 24–27, 2002. Swiss National Science 

Foundation 448–453 (2002). 

67
 
Middleton, P., and W.G. Benjey. The climate-air quality scale continuum and the global 

emission inventory activity. In The Eleventh International Emission Inventory 

Conference: Emission Inventories - Partnering for the Future, Atlanta, Georgia, April 

15–18, 2002. Office of Air Quality Planning & Standards, U.S. Environmental 

Protection Agency, Research Triangle Park, NC. Available online from 

http://www.epa.gov/ttn/chief/conference/ei11/index.html#ses-10 (2002). 

Otte, T.L., and A. Lacser. Implementation of an urban canopy parameterization for fine-scale 

simulations. Preprints, Twelfth PSU/NCAR Mesoscale Model Users' Workshop, June 

24–28, 2002, Boulder, Colorado. National Center for Atmospheric Research, Boulder, 

CO, 138–140 (2002). 

Pierce, T., J. Pleim, E. Kinnee, and L. Joyce. Intercomparison of alternative vegetation databases 

for regional air quality modeling. Preprints, 12th Joint Conference on the Applications of 

Air Pollution Meteorology with the A&WMA, May 20–24, 2002, Norfolk, Virginia. 

American Meteorological Society, Boston, 67–67 (2002). 

Pierce, T., C. Geron, G. Pouliot, E. Kinnee, and J. Vukovich. Integration of the Biogenic 

Emissions Inventory System (BEIS3) into the Community Multiscale Air Quality 

modeling system. Preprints, 25th Conference on Agricultural and Forest Meteorology, 

12th Joint Conference on the Applications of Air Pollution Meteorology with the 

A&WMA, and Fourth Symposium on the Urban Environment, May 20–24, 2002, Norfolk, 

Virginia. Joint Sessions. American Meteorological Society, Boston, J85–J86 (2002). 

Pleim, J., 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(5/6):243–252 (2001). 

Pleim, J., F. Binkowski, R. Dennis, J. Godowitch, T. Otte, T. Pierce, S. Roselle, K. Schere, and J. 

Young. Application of the Models-3 Community Multiscale Air Quality (CMAQ) model 

system to SOS/Nashville 1999. Preprints, 12th Joint Conference on the Applications of 

Air Pollution Meteorology with the A&WMA, May 20–24, 2002, Norfolk, Virginia. 

American Meteorological Society, Boston, 95–96 (2002). 

Poole-Kober, E.M., and H.J. Viebrock (Editors). Fiscal year 2001 summary report of the NOAA 

Atmospheric Sciences Modeling Division to the U.S. Environmental Protection Agency. 

NOAA Technical Memorandum OAR ARL-245, Atmospheric Sciences Modeling 

Division, Research Triangle Park, NC, 109 pp. (2002). 

Porter, P.S., S.T. Rao, and C. Hogrefe. Discussion on linear trend analysis: a comparison of 

methods. Atmospheric Environment 36:3055–3056 (2002). 

68
 
Rao, S.T. and C. Hogrefe. Transforming deterministic air quality modeling results into 

probabilistic form for policy-making. Proceedings of the 25th NATO/CCMS International 

Technical Meeting on Air Pollution Modeling and Its Application, Louvain-la-Neuve, 

Belgium, October 15–19, 2001. NATO/CCMS International Technical Meeting on Air 

Pollution Modeling and Its Application, p. 5 (2002). 

Ryaboshapko, A., O.R. Bullock, Jr., R. Ebinghaus, I. Ilyin, K. Lohman, J. Munthe, G. Petersen, 

C. Seigneur, and I. Wangberg. Comparison of mercury chemistry models. Atmospheric 

Environment 36:3881–3898 (2002). 

Schell, B., I.J. Ackermann, H. Hass, F.S. Binkowski, and A. Ebel. 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). 

Schwede, D.B., S.K. LeDuc, and T.L. Otte. Using meteorological model output as a surrogate 

for on-site observations to predict deposition velocity. Water, Air, and Soil Pollution: 

Focus 1:59–66 (2001). 

Singh, R.B., and A.H. Huber. Application of a Microscale emission Factor model for 

Particulate Matter (MicroFacPM) in conjunction with the CALINE4 model. Proceedings 

of the 95th Annual Meeting of the Air & Waste Management Association, Baltimore, 

Maryland, June 23–27, 2002. CD-ROM. Session EE-1c. Mobile Source Hotspots 

Assessment—Identifying High-End Exposure. Air & Waste Management Association, 

Pittsburgh, Paper # 42816 (2002). 

Singh, R.B., A.H. Huber, and J.N. Braddock Application of a Microscale emission Factor 

model for Particulate Matter (MicroFacPM) to calculate vehicle generated contribution of 

PM2.5 emissions. Proceedings of the 95th Annual Meeting of the Air & Waste 

Management Association, Baltimore, Maryland, June 23–27, 2002. CD-ROM. Session 

ET-1a. Diesel Propulsion Emissions. Air & Waste Management Association, Pittsburgh, 

Paper # 42825 (2002). 

Snyder, W.H., R.E. Lawson, Jr., M.S. Shipman, and J. Lu. Fluid modelling of atmospheric 

dispersion in the convective boundary layer. Boundary-Layer Meteorology 102:335–366 

(2002). 

Teske, M.E., S.L. Bird, D.M. Esterly, T.B. Curbishley, S.L. Ray, and S.G. Perry. AgDRIFT®: A 

model for estimating near-field spray drift from aerial applications. Environmental 

Toxicology and Chemistry 21:659–671 (2002). 

69
 
Vukovich, J.M., and T.E. Pierce. The implementation of BEIS3 within the SMOKE modeling 

framework. In The Eleventh International Emission Inventory Conference: Emission 

Inventories - Partnering for the Future, Atlanta, Georgia, April 15–18, 2002. Office of 

Air Quality Planning & Standards, U.S. Environmental Protection Agency, Research 

Triangle Park, NC. Available online from 

http://www.epa.gov/ttn/chief/conference/ei11/index.html#ses-10 (2002). 

Weil, J.C, W.H. Snyder, R.E. Lawson, Jr., and M.S. Shipman. Experiments on buoyant plume 

dispersion in a laboratory convection tank. Boundary-Layer Meteorology 102:367–414 

(2002). 

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. S.-E. Gryning, and F.A. Schiermeier (Eds.). 

Kluwer Academic/Plenum Publishers, New York, 445–454 (2001). 

70
 
APPENDIX C: PRESENTATIONS
 

Benjey, W.G. Status and support: Community Multiscale Air Quality model. Presentation at the 

Department of Physical Chemistry, University of Waterloo, Ontario, Canada, November 

2, 2001. 

Benjey, W.G. Models-3/CMAQ and SMOKE status. Presentation to the NARSTO Reactivity 

Research Working Group, Research Triangle Park, NC, January 17, 2002. 

Benjey, W.G. CMAS status and emission modeling progress. Presentation to the Standing Air 

Emission Working Group, Coeur d’Alene, ID, April 3, 2002. 

Benjey, W.G. Community Analysis and Modeling System (CMAS) Center progress and support 

to the modeling community. Presentation at the U.S. Department of Agriculture, Forest 

Service Research and Development, Atmospheric Sciences Research Program Meeting, 

Fairfax, VA, August 14, 2002. 

Benjey, W.G. Overview of SMOKE updates. Presentation to the Standing Air Emission 

Working Group, Stowe, VT, September 25, 2002. 

Bullock, O.R., Jr. CMAQ-Hg model simulations of mercury in the closed cloud volume. 

Presentation at the 6th International Conference on Mercury as a Global Pollutant, 

Minamata, Japan, October 18, 2001. 

Bullock, O.R., Jr. Atmospheric mercury—Modeling the source media of a multi-media problem. 

Mercury Roundtable (monthly multi-agency conference call), November 20, 2001. 

Bullock, O.R., Jr. Eulerian-type atmospheric mercury model development at the U.S. EPA 

Office of Research and Development, CEC/IJC (North American Commission for 

Environmental Cooperation/International Joint Commission) Workshop on Atmospheric 

Mercury: Science and Policy, Research Triangle Park, NC, December 14, 2001. 

Bullock, O.R., Jr. Atmospheric mercury model development at the U.S. EPA, Office of 

Research and Development. Combustion Assistance Teleconference for Support 

(conference call organized by the Combustion Technical Assistance Center, January 17, 

2002. 

Bullock, O.R., Jr. Accuracy and sensitivity of atmospheric mercury simulations using CMAQ. 

Presentation at the Air & Waste Management Association Speciality Conference, 

Mercury: Consequences for Environment and Health, Montreal, Canada, March 5, 2002. 

71
 
Bullock, O.R., Jr. Modeling atmospheric mercury emission, transport, transformation and 

deposition. Presentation at the Committee on Environment and Natural Resources, Air 

Quality Research Subcommittee Meeting, White House Conference Center, Washington 

DC, April 18, 2002. 

Ching, J.K.S. Air toxics-PM modeling at neighborhood scales. Presentation at the FrenchAmerican Workshop on Air Quality Modeling, Rice University, Houston, TX, October 4, 

2001. 

Ching, J.K.S. Neighborhood scale air toxics and exposure modeling. Presentation at the ISEA 

2001 Symposium, Charleston, SC, November 7, 2001. 

Ching, J.K.S. Neighborhood scale modeling project. Seminar presented at the Atmospheric 

Sciences Modeling Division, Research Triangle Park, NC, November 27, 2001. 

Ching, J.K.S. Information issues for urban scale modeling. Presentation at the Office of Federal 

Coordinator for Meteorology Workshop on Strategy for Providing Atmospheric 

Information, Urban Air Quality Modeling, Crystal City, VA, December 3, 2001. 

Ching, J.K.S. Air toxics modeling at neighborhood scales to support human exposure. 

Presentation at the Coordinating Research Council Air Toxics Modeling Workshop, The 

Woodland, TX, February 27, 2002. 

Ching, J.K.S. Air quality modeling at neighborhood scales: A conceptual framework. 

Presentation at the University of Houston, Houston, TX, February 28, 2002. 

Ching, J.K.S. Neighborhood scale air toxics modeling (Prototype) for human exposure and 

community-based assessments. Poster presentation at the Environmental Protection 

Agency, Research Triangle Park, NC, June 11, 2002. 

Ching, J.K.S. Modeling sub-grid air pollution concentration distributions in air quality models. 

Presentation at George Mason University, Fairfax. VA, July 10, 2002. 

Ching, J.K.S. Neighborhood scale modeling. Briefing for the Air Toxics Modeling Group, 

Office of Air Quality Planning and Standards, Environmental Protection Agency, 

Research Triangle Park, NC, August 20, 2002. 

Ching, J.K.S. Neighborhood scale modeling project. Presentation at the University of Nantes 

(Ecole Centrale Nationale), France, September 19, 2002. 

Dennis, R.L. Ammonia deposition and airsheds and their relation to inorganic nitrogen 

deposition. Presentation at The Second International Nitrogen Conference: Optimizing 

Nitrogen Management in Food and Energy Production and Environmental 

Protection—N2001, Potomac, MD, October 15, 2001. 

72
 
Dennis, R.L. Atmospheric nitrogen deposition and coastal systems. Presentation at the EPRI Air 

Quality Area Council Meeting, Washington, DC, October 24, 2001. 

Dennis, R.L. Effects of the Clean Air Act, Phase 1, controls on sulfate air concentrations and the 

ability of CMAQ to replicate the changes. Presentation at the Lake Michigan Air 

Directors Consortium Regional Planning Organization Meeting, Chicago, IL, November 

8, 2001. 

Dennis, R.L. Preliminary evaluation of CMAQ on visibility. Presentation at the Lake Michigan 

Air Directors Consortium Regional Planning Organization Meeting, Chicago, IL, 

November 8, 2001. 

Dennis, R.L. Preliminary evaluation of CMAQ for fine particulate. Presentation at the Lake 

Michigan Air Directors Consortium Regional Planning Organization Meeting, Chicago, 

IL, November 8, 2001. 

Dennis, R.L. Preliminary results of the diagnostic evaluation of CMAQ for ozone. Presentation 

at the Lake Michigan Air Directors Consortium Regional Planning Organization Meeting, 

Chicago, IL, November 8, 2001. 

Dennis, R.L. Inverse modeling of ammonia emissions. Presentation at the Lake Michigan Air 

Directors Consortium Regional Planning Organization Meeting, Chicago, IL, November 

8, 2001. 

Dennis, R.L. Oxidized-nitrogen and reduced-nitrogen budgets and airsheds. Presentation at the 

Nitrogen Research Workshop, University of Maryland Center for Environmental Science, 

Frostburg, MD, December 11, 2001. 

Dennis. R.L. Atmospheric deposition of nitrogen as a contributor to coastal eutrophication: How 

far away it comes. Presentation at the 82nd Annual Meeting of the American 

Meteorological Society, Orlando, FL, January 15, 2002. 

Dennis, R.L. Background on MM5 and CMAQ for the BRACE study. Presentation at the 

BRACE (Bay Regional Atmospheric Chemistry Experiment) Workshop, University 

South Florida College of Public Health, Tampa Bay, FL, February 19, 2002. 

Dennis, R.L. PM model evaluation studies: Air quality model evaluation. Presentation at the 

National Research Council Implementation Panel Review, Research Triangle Park, 

March 12, 2002. 

Dennis, R.L. Model evaluation of CMAQ for acidic deposition and fine particles. Seminar at 

the U.S. EPA Clean Air Markets Division, Washington, DC, May 29, 2002. 

73
 
Dennis, R.L. Evaluation of the acidic wet deposition predictions of CMAQ. Presentation at the 

2002 Spring American Geophysical Union Meeting, Washington, DC, May 31, 2002. 

Dennis, R.L. Delineating the connection between airshed and watershed pathways. Presentation 

at the Air & Waste Management Association Section Meeting, Jupiter, FL, September 16, 

2002. 

Dolwick, P.D. CMAQ modeling at U.S. EPA Office of Air Quality Planning and Standards: 

Proof of concept modeling. Presentation at the Midwest Regional Planning Organization, 

Des Plaines, IL, November 8, 2001. 

Dolwick, P.D. OAQPS CMAQ modeling: Review, preliminary results, next steps. Briefing for 

the Director, Office of Air Quality Planning and Standards, Environmental Protection 

Agency, Research Triangle Park, NC, October 22, 2001. 

Dolwick, P.D. U.S. EPA meteorological modeling activities. Presentation at the Midwest 

Regional Planning Organization, Des Plaines, IL, November 8, 2001. 

Dolwick, P.D. U.S. EPA Office of Air Quality Planning and Standards meteorological modeling 

activities. Presentation at the Ad Hoc Meteorological Modeling Group, Des Plaines, IL, 

July 30, 2002. 

Dolwick, P.D. Summary of Clear Skies Initiative ozone performance evaluation. Presentation to 

the Regional Planning Organizations (by phone), August 20, 2002. 

Dolwick, P.D. U.S. EPA Office of Air Quality Planning and Standards meteorological modeling 

activities. Presentation at the U.S. EPA/OAQPS Regional/State/Local Modelers 

Workshop, Research Triangle Park, NC, August 22, 2002. 

Dolwick, P.D. Summary of VOC/NOx CAMx sensitivity modeling completed for the eight-hour 

ozone implemenation team. Briefing for the Director, Air Quality Strategies and 

Standards Division, Office of Air Quality Planning and Standards, Research Triangle 

Park, NC, August 27, 2002. 

Dolwick, P.D. Fine PM forecasting: Office of Air Quality Planning and Standards support to 

state/local agencies. Presentation at the Fall 2002 Standing Air Simulation Working 

Group Meeting (by phone), Stowe, VT, September 27, 2002. 

Eder, B.K. Lightning. Presentation at Kingswood Elementary School, Cary, NC, October 1, 

2001. 

Eder, B.K. Air quality forecasting evaluation. Presentation at the University of New Hampshire, 

Durham, NH, October 30, 2001. 

74
 
Eder, B.K. Rotated principal component analysis in environmental studies. Presentation at the 

Office of Air Quality Planning and Standards’ Spatial Analysis Workshop, Research 

Triangle Park, NC, December 3, 2001. 

Eder, B.K. Air quality forecasting evaluation. Presentation at the NOAA Aeronomy Laboratory, 

Boulder, CO, December 17, 2001. 

Eder, B.K. Preliminary evaluation of CMAQ for aerosols and visibility parameters. Presentation 

at the Mid-Atlantic Regional Air Management Association Meeting, Baltimore, MD, 

January 23, 2002. 

Gillette, D.A. The long-distance transportable fraction of the vertical flux of wind-transported 

dust. Presentation at the Fifth International Conference on Aeolian Research /Global 

Change and Terrestial Ecosystem—Soil Erosion Network Joint Meeting, Lubbock, TX, 

July 22, 2002. 

Gillette, D.A. Modeling of wind blown dust emissions at Owens Lake, California. Presentation 

at the Fifth International Conference on Aeolian Research /Global Change and Terrestial 

Ecosystem—Soil Erosion Network Joint Meeting, Lubbock, TX, July 24, 2002. 

Gilliland, A.B. Estimating seasonal NH3 emissions using inverse modeling. Presentation at the 

Second International Nitrogen Conference—N2001, Potomac, MD, October 15, 2001. 

Gilliland, A.B. Monthly and annual bias in weekly (NADP/NTN) versus daily (AIRMoN) 

precipitation chemistry data in the eastern USA. Presentation at The Second International 

Nitrogen Conference—N2001, Potamac, MD, October 15, 2001. 

Gilliland, A.B. Inverse modeling applications for air quality. Invited presentation to the Harvard 

Atmospheric Chemistry Modeling Group, Cambridge, MA, March 24, 2002. 

Gilliland, A.B. Evaluating air quality emissions with inverse modeling. Presentation at the 

Center for Integrating Statistical Environmental Sciences Inaugural Workshop, Chicago, 

IL, September 19, 2002. 

Huber, A.H. Meteorological measurements and modeling in support of human exposure 

modeling: Microenvironmental factors, Coordinating Research Council Air Toxics 

Modeling Workshop, The Woodland, Texas, February 25 2002. 

Huber, A.H. Characterizing meteorology and air pollution dispersion in urban environments: A 

case study in New York City post September 11, 2001. Presentation at the 2002 EPA 

Science Forum, Washington, DC, May 2, 2002. 

75
 
Huber, A.H. Cooperative research and development for application of CFD to estimating human 

exposures to environmental pollutants. Presentation at the 2002 Fluent Users’ Group 

Meeting, Manchester, NH, June 11, 2002. 

Huber, A.H. Summary of the meteorological measurements/modeling studies and the initial 

mapping of plumes from ground zero in support of the EPA initial World Trade Center 

exposure assessment studies. Presentation at the National Academy of Science 

Workshop Tools for Tracking Chemical/Biological/Nuclear Releases in the Atmosphere 

Implications for Homeland Security, Woods Hole, MA, July 22, 2002. 

Huber, A.H. Estimating the plume from the World Trade Center recovery site following 

September 11, 2001. Presentation at the New York State Health Department and New 

York City Health Department, New York City, NY, July 29, 2002. 

Huber, A.H. World Trade Center Studies and CFD update. Presentation at the U.S. 

EPA/OAQPS Regional/State/Local Modelers' Workshop, Research Triangle Park, NC, 

August 22, 2002. 

Irwin, J.S. Issues/concerns to be addressed in simulating atmospheric processes. U.S. Weather 

Research Program Prospectus Development Team (PDT-11) Meeting: Meteorological 

Research Needs for Improved Air Quality Forecasting, Palm Springs, CA , November 6, 

2001. 

Irwin, J.S. First define the information needed, then select a course of action. Presentation at the 

Workshop on Effective Emergency Response—Selecting a Suitable Dispersion Model for 

a Given Application, Washington, DC, December 5, 2001. Available at 

http://www.ofcm.gov/atd2/presentations/linking_list.htm (accessed October 2002). 

Irwin, J.S. Evaluate earth science models for what they are—cartoons of reality. Presentation at 

the Workshop on Effective Emergency Response—Selecting a Suitable Dispersion Model 

for a Given Application, Washington, DC, December 6, 2001. Available at 

http://www.ofcm.gov/atd2/presentations/linking_list.htm (accessed October 2002). 

Irwin, J.S. Issues/concerns to be addressed in dispersion model evaluations. California Energy 

Commission Short-Range Dispersion Modeling Workshop, Sacramento, California, 

January 24, 2002. 

Irwin, J.S. Uncertainty in air quality modeling for risk calculations. Coordinating Research 

Council Air Toxics Modeling Workshop, The Woodland, Texas, February 26, 2002. 

Irwin, J.S. Review of D6589 ASTM model evaluation procedure. Presentation at the U.S. 

EPA/OAQPS Regional/State/Local Modelers Workshop, Research Triangle Park, North 

Carolina, August 22, 2002. 

76
 
Otte, T.L. Preparing science fair projects. Presentations to 5th Grade (five sessions), Olive 

Chapel Elementary School, Apex, NC, February 13, 2002. 

Otte, T.L. Implementation of an urban canopy parameterization in MM5. Presented to 

representatives from the Japan Center for Air Pollution, Research Triangle Park, NC, 

March 5, 2002. 

Otte, T.L. Weather Jeopardy. Two sessions. Presented at the 10th Expanding Your Horizons 

Workshop, North Carolina State University, Raleigh, NC, March 12, 2002. 

Otte, T.L. Weather basics: Evaporation and condensation. Presentation to 2nd Grade (four 

sessions), Olive Chapel Elementary School, Apex, NC, April 12, 2002. 

Otte, T.L. Weather basics: Evaporation and condensation. Presentation to 2nd Grade (four 

sessions), West Lake Elementary School, Apex, NC, April 19, 2002. 

Otte, T.L. Weather basics: Evaporation and condensation. Presentation to 2nd Grade (four 

sessions), West Lake Elementary School, Apex, NC, May 9, 2002. 

Otte, T.L. Weather basics: Evaporation and condensation. Presentation to 2nd Grade (four 

sessions), Olive Chapel Elementary School, Apex, NC, May 15, 2002. 

Otte, T.L. Implementation of an urban canopy parameterization in MM5: Preliminary results. 

Presentation at the 6th George Mason University Workshop on Transport and Dispersion, 

George Mason University, Fairfax, VA, July 10, 2002. 

Otte, T.L. Neighborhood scale modeling. Presentation at the 3rd Ad Hoc Meteorological 

Modeling Workshop, Lake Michigan Air Directors Consortium, Des Plaines, IL, July 31, 

2002. 

Otte, T.L. MM5 overview. Presentation at the U.S. EPA/OAQPS Regional/State/Local 

Modelers' Workshop, Research Triangle Park, NC, August 22, 2002. 

Pierce, T.E. The wonders of weather. Presentation at the Morrisville Elementary School, 

Raleigh, NC, March 15, 2002. 

Pierce, T.E. The importance of biogenic emissions for air quality modeling, EPA Science 

Forum, Washington, DC, May 2, 2002. 

(http://www.epa.gov/asmdnerl/epa_science_forum2.PDF) 

Pierce, T.E. Intercomparison of alternative vegetation databases for regional air quality 

modeling, Gordon Research Conference on Biogenic Hydrocarbons and the Atmosphere, 

Oxford, England, September 2, 2002. 

77
 
Pleim, J.E. Modeling for air quality forecasting. U.S. Weather Research Program Prospectus 

Development Team (PDT-11) Meeting: Meteorological Research Needs for Improved Air 

Quality Forecasting, Palm Springs, CA , November 6, 2001. 

Pleim, J.E. Application of MM5 to air quality modeling of SOS/Nashville 1999. Presentation at 

the Twelfth PSU/NCAR Mesoscale Model Users' Workshop, Boulder, CO, June 25, 

2002. 

Pleim, J.E. The Models-3/Community Multiscale Air Quality (CMAQ) model: 2002 Release— 

Description and testing. Presentation at the U.S. EPA/OAQPS Regional/State/Local 

Modelers' Workshop, Research Triangle Park, NC, August 22, 2002. 

Pleim, J.E. Weather research and forecast (WRF) model. Presentation at the U.S. EPA/OAQPS 

Regional/State/Local Modelers' Workshop, Research Triangle Park, NC, August 22, 

2002. 

Pleim, J.E. Overview of the Models-3/Community Multiscale Air Quality (CMAQ) model. 

Presentation at the Environmental Modeling Center modelers meeting at the National 

Center for Environmental Prediction, Camp Springs, MD, September 17, 2002. 

Rao, S.T. Integrating observations and model predictions in designing emission control 

strategies. Invited talk at the EMEP/LOOP Workshop, Bern, Switzerland, December 4, 

2001. 

Rao, S.T. Using air quality models for emissions management decisions. Invited talk at the 

Uncertainty Treatment in Integrated Assessment Modelling Workshop of the UN/ECE 

Task Force on Integrated Assessment Modelling, IIASA, Laxenburg, Austria, January 24, 

2002. 

Rao, S.T. Probabilistic assessment of regional scale ozone pollution in the eastern United States. 

Invited presentation at the NATO-Advanced Research Workshop, Halkidi, Greece, June 

11, 2002. 

Rao, S.T. Examination of model predictions at different horizontal grid resolutions: Approaches, 

findings, and challenges. Invited presentation at the RAMS Workshop, University of 

Athens, Santorini Island, Greece, September 30, 2002. 

Schere, K.L. Environmental Protection Agency’s Community Multiscale Air Quality modeling 

system. Presentation at the American Meteorological Society-Central North Carolina 

Chapter, Research Triangle Park, NC, November 15, 2001. 

Schere, K.L. Preliminary model evaluation of CMAQ for particulate matter. Presentation at the 

Environmental Protection Agency, Office of Air and Radiation, Office of Research and 

Development Air Progress Review, Research Triangle Park, NC, November 30, 2001. 

78
 
Schere, K.L. Environmental Protection Agency’s multi-pollutant Models3/Community 

Multiscale Air Quality (CMAQ) model. Presentation at the Workshop on the Effects of 

Global Climate Change on Regional Air Quality, Research Triangle Park, NC, December 

5, 2001. 

Schere, K.L. NERL's modeling and chemistry program in particulate matter. Presentation at the 

EPA NRC Committee on Research Priorities for Airborne Particulate Matter, Research 

Triangle Park, NC, March 12, 2002. 

Schere, K.L. Environmental Protection Agency’s Community Multiscale Air Quality (CMAQ) 

model. Poster presentation at the Environmental Protection Agency Science Forum 2002: 

Meeting the Challenges, Washington, DC, May 1, 2002. 

Schere, K.L. NOAA/EPA-ASMD air quality forecasting program: The Models-3/CMAQ 

model—2002 Release: Description, testing, support. Presentation at the Standing Air 

Simulation Work Group Meeting, Stowe, VT, September 28, 2002. 

Schwede, D.B. Thinking like a scientist. Presentation at the Leadmine Elementary School, 

Raleigh, NC, March 1, 2002. 

Schwede, D.B. Thinking like a scientist. Presentation at the Olive Chapel Elementary School, 

Apex, NC, February 7, 2002. 

West, J.L. NARSTO/NERL. Poster presentation at the Environmental Protection Agency 

Building Dedication, Research Triangle Park, NC, May 29, 2002. 

79
 
APPENDIX D: WORKSHOPS AND MEETINGS
 

Texas Air Research Center Proposal Review Meeting, Houston, TX, October 2–3, 2001. 

R.L. Dennis 

NARSTO PM Assessment Meeting, Sacramento, CA, October 11–14, 2001. 

J.L. West 

Time Management, Planning and Prioritization for Special Librarians, GlaxoSmithKline, 

Research Triangle Park, NC, October 12, 2001. 

E.M. Poole-Kober 

Fall 2001 Standing Air Emissions Working Group, Nebraska City, NE, October 12–13, 2001. 

W.G. Benjey 

The Second International Nitrogen Conference: Optimizing Nitrogen Management in Food and 

Energy Production and Environmental Protection—N2001, Potomac, MD, October 14–18, 2001. 

R.L. Dennis 

NARSTO Quality Systems Science Center—Data Management, Knoxville, TN, October 24, 

2001. 

J.L. West 

EPRI Air Quality Area Council Meeting, Washington, DC, October 24, 2001. 

R.L. Dennis 

BRACE Planning Meeting (Bay Regional Atmospheric Chemistry Experiment), Research 

Triangle Park, NC, October 25, 2001. 

R.L. Dennis 

25th Anniversary of the North Carolina State Climate Office, North Carolina State University, 

Raleigh, NC, October 26, 2001. 

B.K. Eder 

80
 
New England Air Quality Study Planning Meeting, University of New Hampshire, Durham, NH, 

October 29–30, 2001. 

B.K. Eder 

Lake Michigan Air Directors Consortium Regional Planning Organization Meeting, Chicago, IL, 

November 7–8, 2001. 

R. Dennis 

Super Sites Workshop, Research Triangle Park, NC, November 13–14, 2001. 

B.K. Eder 

K.L. Schere 

United States-Canada Subcommittee 2 Workshop of PM Modeling, Research Triangle Park, NC, 

November 27–28, 2001. 

R.L. Dennis 

Office of Air Quality and Planning Standard's Spatial Analysis Workshop, Research Triangle 

Park, NC, December 3–5, 2001. 

B.K. Eder 

Office of Federal Coordinator for Meteorology Workshop on Effective Emergency Response, 

Crystal City, VA, December 3–5, 2001. 

J.K.S. Ching 

J.S. Irwin 

Office of Federal Coordinator for Meteorology, Workshop on Strategy for Providing 

Atmospheric Information, Urban Meteorology and Air Quality Session, Co-Chair, Crystal City, 

VA, December 3–5, 2001. 

J.K.S. Ching 

Workshop on Climate Change and Air Quality: Part I: Intercontinental Transport and Climatic 

Effects of Pollutants, Research Triangle Park, NC, December 3–5, 2001. 

J.M. Godowitch 

K.L. Schere 

81
 
Workshop on the Effects of Global Climate Change on Regional Air Quality, Research Triangle 

Park, NC, December 5–7, 2001. 

E.J. Cooter 

J.M. Godowitch 

J.E. Pleim 

K.L. Schere 

Nitrogen Research Workshop, sponsored by the University of Maryland, Center for 

Environmental Science, Frostburg, MD, December 11–12, 2001. 

R.L. Dennis 

CEC/IJC (North American Commission for Environmental Cooperation/International Joint 

Commission) Workshop on Atmospheric Mercury: Science and Policy, Research Triangle Park, 

NC, December 13–14, 2001. 

O.R. Bullock, Jr. 

Air Quality Forecasting Evaluation, NOAA Aeronomy Laboratory, Boulder, CO, December 

17–19, 2001. 

B.K. Eder 

K.L. Schere 

Environmental Protection Agency, Office of Research and Development Board of Scientific 

Counselors Review, Research Triangle Park, NC, December 18, 2001. 

T.L. Otte 

The 82nd Annual Meeting of the American Meteorological Society, Orlando, FL, January 13–17, 

2002. 

R.L. Dennis 

B.K. Eder 

E.M. Poole-Kober 

Fifth Annual Atmospheric Science Librarians International Conference, Orlando, FL, January 

16–17, 2002. 

E.M. Poole-Kober 

82
 
Mid-Atlantic Regional Air Management Association Modeling and Data Analysis Workshop, 

Baltimore, MD, January 22–23, 2001. 

B.K. Eder 

Short Range Dispersion Modeling Workshop, California Air Resources Board and California 

Energy Commission, Sacramento, CA, January 24–25, 2002. 

J.K.S. Ching 

The Technical Cooperation Program, Chemical, Biological and Radiological Defense Technical 

Panel 9 Meeting, Dispersion Modeling Workshop, Sacramento, CA, January 28–30, 2002. 

J.K.S. Ching 

14th Annual Alumni Student Career Connection, UNC General Alumni Association, University 

of North Carolina at Chapel Hill, Chapel Hill, NC, January 29, 2002. 

E.M. Poole-Kober 

U.S. EPA’s National Air Quality Conference: Forecasting and Public Outreach, San Francisco, 

CA., February 2–6, 2002. 

K.L. Schere 

International Mercury Model Intercomparison Meeting, Moscow, Russia, February 14–15, 2002. 

O.R. Bullock, Jr. 

BRACE Workshop (Bay Regional Atmospheric Chemistry Experiment), University of South 

Florida College of Public Health, Tampa Bay, FL, February 19–20, 2002. 

R.L. Dennis 

External Advisory Committee Meeting, Columbia University Global Climate STAR Grant, New 

York, NY, February 22, 2002. 

K.L. Schere 

Coordinating Research Council, Air Toxics Workshop, The Woodland, TX, February 26–27, 

2002. 

J.K.S. Ching 

83
 
Japan Center for Air Pollution Meeting, Environmental Protection Agency, Research Triangle 

Park, NC, March 5, 2002. 

T.L. Otte 

Japan Clean Air Program-II, Environmental Protection Agency, Research Triangle Park, NC, 

March 6, 2002. 

J.K.S. Ching 

10th Expanding Your Horizons Workshop, North Carolina State University, Raleigh, NC, March 

12, 2002. 

T.L. Otte 

National Research Council PM Implementation Panel Review, Environmental Protection 

Agency, Research Triangle Park, NC, March 12–13, 2002. 

R.L. Dennis 

Spring 2002 Standing Air Emissions Working Group, Coeurd’Alene, ID, April 3–4, 2002. 

W.G. Benjey 

PM2.5 and Electric Power Generation: Recent Findings and Implications, Pittsburgh, PA, April 

9–10, 2002. 

J.M. Godowitch 

American Society for Testing and Materials Committee Week, Philadelphia, Pennsylvania, April 

15–18, 2002. 

J.S. Irwin 

Eleventh International Emission Inventory Conference, Atlanta, GA, April 16–28, 2002. 

W.G. Benjey 

Environmental Protection Agency National PBT (Persistent Bioaccumulative Toxics) Monitoring 

Workshop, Raleigh, NC, April 22-24, 2002. 

O.R. Bullock, Jr. 

84
 
CASTNet Technical Advisory Committee, U.S. EPA Office of Air and Radiation, Office of Air 

Quality Planning and Standards, Research Triangle Park, NC, April 24–25, 2002 

D.B. Schwede 

Environmental Protection Agency Science Forum 2002: Meeting the Challenges, Washington, 

DC, May 1–2, 2002. 

K.L. Schere 

Workshop on Validation Data Sets for Modeling Mineral Aerosol in Global Climate Cycles, 

Max-Planck Institute for Biochemistry, Jena, Germany, May 2–4, 2002. 

D.A. Gillette 

BRACE (Bay Regional Atmospheric Chemistry Experiment) Field Study Visit, Tampa Bay, FL, 

May 7–12, 2002. 

R.L. Dennis 

12th Annual SAIL (Southeast Affiliate of IAMSLIC Libraries) Meeting, Gloucester Point, VA, 

May 14–17, 2002. 

E.M. Poole-Kober 

NARSTO Reactivity Research Workgroup Meeting: Atmospheric Chemistry, Research Triangle 

Park, NC, May 15–16, 2002. 

J.L. West 

Air Quality and Human Health Under Climate Change, Canadian Lung Association, Moncton, 

New Brunswick, Canada, May 15–17, 2002. 

E.J. Cooter 

American Meteorological Society’s Board of Urban Environment Annual Meeting, Norfolk, VA, 

May 20, 2002. 

J.K.S. Ching 

2002 Spring Meeting, American Geophysical Union, Washington, DC, May 28–31, 2002. 

R.L. Dennis 

85
 
U.S. EPA Clean Air Markets Division Seminar, Washington, DC, May 29, 2002. 

R.L. Dennis 

Air Toxics Implementation Planning Workshop, National Health and Environmental Effects 

Laboratory, Environmental Protection Agency, Research Triangle Park, NC, June 11–12, 2002. 

J.K.S. Ching 

95th Annual Conference of the Air & Waste Management Association, Baltimore, MD, June 

23–27, 2002. 

J.L. West 

Twelfth PSU/NCAR Mesoscale Model Users' Workshop, Boulder, CO, June 24–26, 2002. 

T.L. Otte 

J.E. Pleim 

2nd Annual Weather Research and Forecast Workshop, Boulder, CO, June 27–28, 2002. 

T.L. Otte 

J.E. Pleim 

Weather Research and Forecast Tutorial, Boulder, CO, June 27–28, 2002. 

T.L. Otte 

George Mason University Transport and Dispersion Modeling Workshop, Fairfax, VA, July 

9–12, 2002. 

J.K.S. Ching 

T.L. Otte 

12th Annual Jornada Symposium, New Mexico State University, Las Cruces, NM, July11, 2002. 

D.A. Gillette 

Fifth International Conference on Aeolian Research and the Global Change and Terrestrial 

Ecosystem-Soil Erosion Network, sponsored by U.S. Department of Agricultural Research 

Service and Texas Tech University, Lubbock, TX, July 22–25, 2002. 

D.A. Gillette 

86
 
3rd Ad Hoc Meteorological Modeling Workshop, Lake Michigan Air Directors Consortium, Des 

Plaines, IL, July 30–31, 2002. 

T.L. Otte 

Design Workshop for EPA's Causal Analysis Diagnosis Decision Information System (CADDIS) 

Environmental Protection Agency, Mt. Sterling, OH, August 26–28, 2002. 

S.S. Fine 

NARSTO Reactivity Research Work Group, Photochemical Reactivity, Research Triangle Park, 

NC, August 28–29, 2002. 

J.L. West 

U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards 

Regional/State/Local Modelers' Workshop, Research Triangle Park, NC, August 22, 2002. 

T.L. Otte 

EMEP Steering Body, Geneva, Switzerland, September 2-4, 2002. 

R.L. Dennis 

United Nations’ Environment Program, Global Mercury Assessment Work Group Meeting, 

Geneva, Switzerland, September 9–13, 2002. 

O.R. Bullock, Jr. 

Air & Waste Management Association Section Meeting, Jupiter, FL, September 15–17, 2002. 

R. Dennis 

National Academy of Science, Review of the NARSTO Assessment of the Atmospheric Science 

on Particulate Matter, Washington DC, September 25, 2002. 

J.L. West 

U.S. Forest Service Research Workshop, Fire Emission Modeling for CMAQ, George Mason 

University, Fairfax VA, August 13–14, 2002. 

J.K.S. Ching 

87
 
Air Quality Forecasting Meeting, National Oceanic and Atmospheric Administration, National 

Center for Environmental Prediction, Camp Springs, MD, September 17, 2002. 

T.L. Otte 

J.E. Pleim 

K.L. Schere 

J.O. Young 

Fall 2002 Standing Air Emissions Working Group, Stowe, VT, September 26–27, 2002. 

W.G. Benjey 

Standing Air Simulation Workgroup, Stowe, VT, September 27–28, 2002. 

K.L. Schere 

Models-3/CMAQ Annual Workshop, Research Triangle Park, NC, October 22–24, 2002. 

D.A. Atkinson 

P. D. Dolwick 

M.L. Evangelista 

J. S. Irwin 

B.L. Orndorff 

88
 
3 

APPENDIX E: VISITING SCIENTISTS 

9.	 Messrs. Mike Abraczinskas, George Bridgers, and John White 

North Carolina Division of Air Quality 

Raleigh, NC 

Messrs. Abraczinskas, Bridgers, and White visited the Division on December 11, 2001, to 

present a seminar on air quality forecasting for North Carolina. 

10.	 Dr. Kiran Alapaty 

MCNC Environmental Modeling Center 

Research Triangle Park, NC 

Dr. Alapaty visited the Division on October 31, 2001, and presented a seminar entitled The fluxadjusting surface data assimilation system (FASDAS) for meteorological applications. 

Dr. Nancy Brown 

Chief Scientist 

Lawrence Berkeley National Laboratory 

Berkeley, CA 

Dr. Brown visited ASMD to learn ASMD’s research in air quality model development and model 

performance evaluation in March 2002. 

4.	 Dr Steven Burian 

University of Arkansas 

Fayetteville, AR 

Dr. Burian visited the Division on January 4, 2002, for collaboration on urban morphology 

studies, and again on February 21, 2002, to assist in the development of urban morphological 

parameterizations. 

5.	 Mr. Satoru Chatani 

Toyota Central Research and Development Laboratories 

Nagakute, Japan 

Mr Chatani visited the Division on August 5, 2002, to discuss Japan Clean Air Program and to 

learn the latest status of the CMAQ model. 

6.	 Dr. Joshua Fu 

University of Tennessee 

Knoxville, TN 

Dr. Fu visited the Division from July 30-31, 2002, to obtain assistance with Models-3/CMAQ. 

89
 
7.	 Dr. Christian Hogrefe 

Division of Air Resources 

New York Department of Environmental Conservation 

Albany, NY 

Dr. Hogrefe visited the Division on April 11, 2002, to learn about emissions processing for air 

quality simulation models. 

8.	 Mr. Mike Moss 

U.S. Air Force Reserves
 

Tyndall Air Force Base, FL
 

Mr. Moss visited the Division on July 18, 2002, to learn about Models-3/CMAQ for potential Air 

Force applications. 

8.	 Dr. James Sloan 

Professor of Physical Chemistry 

Department of Chemistry 

University of Waterloo 

Waterloo, Ontario, Canada 

Dr. James Sloan visited the Division on July 18, 2002, to present and discuss the formation of a 

Models-3/CMAQ application center at the University of Waterloo, Ontario, Canada. 

9.	 Pam Tsai 

U.S. Environmental Protection Agency, Region 9
 

75 Hawthorne Street
 

San Francisco, CA
 

Pam Tsai visited the Division on April 12, 2002, to discuss CMAQ modeling in the United 

States. 

10.	 Dr. Satoshi Yamazaki (Toyota Research), Dr. Hitoshi Kunimi (Nissan Research), 

Dr. Shinji Kobayashi (National Institute for Environmental Sciences-Japan), 

Mr. Hayashi from Japan Clean Air Promotions Department, Mr. Shinichi Doki from 

Japan Clean Air Promotions Department, Japan Clean Air Program 

Tokyo, Japan 

Drs. Yamazaki, Kunimi, and Kobayashi, and Messrs. Hayashi, and Doki visited the Division on 

March 5 and 6, 2002, to discuss collaboration on CMAQ modeling in the United States and 

Japan. 

90
 
APPENDIX F: HIGH SCHOOL, UNDERGRADUATE, AND GRADUATE
 

STUDENTS, AND POSTDOCTORAL RESEARCHERS 

1.	 Dr. Jeffrey R. Arnold 

University Corporation for Atmospheric Research (UCAR) 

Boulder, Colorado 

Dr. Arnold, a postdoctoral researcher, is in his fourth 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 make weight of evidence approaches objective. 

2.	 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 2-year visit with the Division on August 21, 2000. 

91
 
APPENDIX G: ATMOSPHERIC SCIENCES MODELING DIVISION
 

STAFF AND AWARDS
 

All personnel are attached to the Environmental Protection Agency from the National Oceanic 

and Atmospheric Administration, except those designated EPA, who are employees of the 

Environmental Protection Agency, or SEEP, who are part of the EPA Senior Environmental 

Employment Program. 

Office of the Director 

Dr. S.T. Rao, Supervisory Meteorologist, Director 

Randy M. Brady (EPA), Deputy Director 

Herbert J. Viebrock, Meteorologist, Assistant to the Director 

Linda Green (EPA), Budget Analyst 

Dr. Jay Messer (EPA), Physical Scientist 

Evelyn M. Poole-Kober, Librarian 

Dr. Basil Dimitriades (SEEP), Physical Scientist 

Barbara Hinton (EPA), Secretary 

Atmospheric Model Development Branch 

Kenneth L. Schere, Supervisory Meteorologist, Chief
 

Dr. Francis S. Binkowski, Meteorologist (Until January 2002)
 

O. Russell Bullock, Jr., Meteorologist 

Gerald L. Gipson (EPA), Physical Scientist 

James M. Godowitch, Meteorologist 

Dr. Alan H. Huber, Physical Scientist 

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 

Dr. Jeffrey O. Young, Mathematician 

Patricia F. McGhee, Secretary 

92
 
Model Evaluation and Applications Research Branch 

William B. Petersen, Supervisory Physical Scientist, Chief 

Dr. Robin L. Dennis, Physical Scientist 

Dr. Brian K. Eder, Meteorologist 

Dr. Steven S. Fine, IT Specialist 

Dr. Peter L. Finkelstein, Physical Scientist 

Dr. Alice B. Gilliland, Physical Science Administrator 

Steven C. Howard, IT Specialist 

John H. Rudisill, III, Equipment Specialist 

Alfreida R. Torian, IT Specialist 

Gary L. Walter, Computer Scientist 

Jeffrey L. West, Physical Science Administrator 

Sherry A. Brown, Secretary 

Air-Surface Processes Modeling Branch 

Thomas E. Pierce, Supervisory Physical Scientist, Chief 

Dr. William G. Benjey, Physical Scientist 

Dr. Jason K.S. Ching, Meteorologist 

Dr. Ellen J. Cooter, Meteorologist 

Dr. Dale A. Gillette, Physical Scientist 

Dr. Steven G. Perry, Meteorologist 

Dr. George A. Pouliot, Physical Scientist 

Donna B. Schwede, Physical Scientist 

John J. Streicher, Physical Scientist 

Roger S. Thompson, Physical Scientist 

Lawrence E. Truppi, Meteorologist (Until January 2002) 

Bruce Pagnani (SEEP), Computer Programmer 

Ashok Patel (SEEP), Engineer 

John Rose (SEEP), Machinist/Model Maker 

Ruby S. Borden, (SEEP), 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 

93
 
Alan Huber received the EPA/ORD Science and Technology Award for 

exceptional/Outstanding Technical Assistance to the Regions or Program Offices in recognition 

of outstanding support provided to enhance the use of sound science in Agency decisions by 

playing a key role in developing exposure study requirements for emission and exposure studies 

for MMT and MTBE and for reviewing the study protocols submitted by industry in response to 

the requirements. 

Alan Huber received a special EPA commemorative medallion and a letter of appreciation from 

the EPA Administrator, Governor Whitman, for excellence in response to September 11, 2001, 

events as part of the EPA ORD Monitoring and Exposure Assessment Team. 

94