NOAA Technical Memorandum OAR ARL-252

FISCAL YEAR 2003 SUMMARY REPORT OF THE NOAA ATMOSPHERIC SCIENCES

MODELING DIVISION TO THE U.S. ENVIRONMENTAL PROTECTION AGENCY

Evelyn M. Poole-Kober

Herbert J. Viebrock

(Editors)

Atmospheric Sciences Modeling Division

Research Triangle Park, North Carolina

Air Resources Laboratory

Silver Spring, Maryland

June 2004
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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.
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PREFACE

This report summarizes the Fiscal Year 2003 research and operational activities of the

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

and Atmospheric Administration, working under the Memorandum of Understanding and

Memorandum of Agreement between the U.S. Department of Commerce (DoC) and U.S.

Environmental Protection Agency (EPA) through long-term Interagency Agreements

DW13938483 and DW13948634 between EPA and the National Oceanic and Atmospheric

Administration (NOAA), U.S. Department of Commerce. The summary includes descriptions of

research and operational efforts in air pollution meteorology, atmospheric modeling, air quality

model development and evaluation, and air pollution abatement and compliance programs aimed

at improving the Nation’s air quality.

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

collaboration on atmospheric research efforts. ASMD conducts research activities in-house and

through contract and cooperative agreements. ASMD also provides technical information,

forecasting support, and consultation on the meteorological and air quality modeling aspects of

air quality management to various EPA offices, including the Office of Air Quality Planning and

Standards, and to state/local agencies. To provide these services, the Division is organized into

three research Branches and an operational Branch: Atmospheric Model Development Branch,

Model Evaluation and Applications Research Branch, Air-Surface Processes Modeling Branch,

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

Division strategic plan and supporting NOAA’s mission.

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

the Director, NOAA, Atmospheric Sciences Modeling Division (E243-02), U.S. Environmental

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

Page

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

FIGURES .................................................................. vii

TABLES .................................................................... ix

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 for Research Applications . . 4

2.1.3 Land Surface and Planetary Boundary Layer Modeling ............... 4

2.1.4 High-Resolution Sea Surface Temperature Initialization for Meteorological

Models .................................................... 5

2.1.5 Anthropogenic Emissions ...................................... 6

2.1.6 Biogenic Emissions ........................................... 7

2.1.7 Modeling Smoke Emissions from Fires ............................ 8

2.1.8 Fugitive Dust Modeling ........................................ 9

2.1.9 Implementation and Testing of New and Refined Chemical Mechanisms

and Chemical Solvers in CMAQ ................................ 9

2.1.10 Aerosol Mechanism Improvements in CMAQ .................... 10

2.1.11 Plume-in-Grid Modeling ..................................... 11

2.1.12 CMAQ Code Integration and 2003 Release ....................... 12

2.1.13 Development and Testing of an Air Quality Forecast Model ......... 13

2.1.14 Linking Eta (Meteorological Model) with CMAQ for Air Quality

Forecasting ................................................ 14

2.1.15 Preliminary Evaluation of Eta-CMAQ Forecast Model System ....... 15

2.2 Atmospheric Model Evaluation and Application Activities ................... 16

2.2.1 Developing a Strategy for Assessing Performance of Regional-Scale Air

Quality Models ............................................. 16

2.2.2 Developing and Testing an ASTM Strategy for Assessing Performance of

Local-Scale Dispersion Models ................................ 18

2.2.3 Inverse Modeling for Ammonia ................................. 19

2.2.4 Diagnostic Metrics for Ozone and Particulate Matter ................ 20
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2.2.5 CMAQ Model Evaluation to Assess Readiness for State Implementation

Plans ..................................................... 20

2.2.6 Sensitivity of CMAQ Control Strategy Predictions to Model Input

Uncertainties for CMAQ and MM5 Configurations ................ 23

2.2.7 Model Evaluation Toolkit ..................................... 24

2.2.8 Meteorological Model Evaluation ............................... 24

2.2.9 Sub-Canopy Deposition Models ................................ 25

2.2.10 Modeling Studies in the Mid-Atlantic Region ..................... 27

2.2.11 Bay Regional Atmospheric Chemistry Experiment: Model 

Evaluation ................................................ 28

2.3 Air Toxics Modeling ................................................. 30

2.3.1 National Air Toxics Assessment ................................ 30

2.3.2 Fine-Scale Modeling of Air Toxics and Homeland Security ........... 30

2.3.3 Urban Canopy Parameterizations ................................ 31

2.3.4 Resolved Scale Modeling with CMAQ ........................... 32

2.3.5 Modeling Subgrid Concentration Variability ....................... 35

2.3.6 Wind Tunnel Modeling of the World Trade Center Disaster Site ....... 39

2.3.7 Numerical Modeling of the World Trade Center Site ................ 43

2.3.8 Development of Compartmental Modeling Tools for Toxics .......... 47

2.4 Multimedia Modeling ................................................ 48

2.4.1 Multimedia Integrated Modeling System .......................... 48

2.4.2 Urban Drainage Decision Support System Prototype ................ 48

2.4.3 MultiLayer BioChemical Model–Area Weighted ................... 49

2.4.4 Chesapeake Bay 2006 Re-Evaluation ............................ 49

2.4.5 Ammonia Budgets for Coastal Systems ........................... 50

2.4.6 Remote Sensing Image Processing: Pamlico Sound Study ............ 51

2.5 Climate Change Impacts on Regional Air Quality .......................... 52

2.6 Specialized Client Support ............................................ 54

2.6.1 Assistance to State/Local Air Quality Forecasters ................... 54

2.6.2 Support to the NOAA Office of the Federal Coordinator 

for Meteorology ............................................ 55

2.6.3 Sensitivity of PM2.5 Modeling to Grid Resolution ................... 56

2.6.4 Community Modeling and Analysis System Center ................. 56

2.6.5 Particle Deposition—Comparison of Aerodynamic and Mechanical

Resuspension .............................................. 57

2.6.6 NARSTO Program Support .................................... 58

2.6.7 European Monitoring and Evaluation Program ..................... 59

2.6.8 Modeling Human Exposure to Solar Ultraviolet Light Modeling Human

Exposure to Solar Ultraviolet Light ............................. 60

2.7 Regulatory Support .................................................. 60
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2.7.1 Ozone Modeling Completed in Support of the Interstate Transport 

Rule ..................................................... 60

2.7.2 Air Quality Modeling Completed in Support of the 8-Hour Ozone

Implementation ............................................ 61

2.7.3 Ozone Modeling Completed in Support of the Nonroad Rule ......... 62

2.7.4 Sensitivity Modeling Analyses for Regulatory Applications of CMAQ . . 62

2.7.5 Meteorological Classification to Augment Speciated Pollution Data .... 63

2.7.6 Support Center for Regulatory Air Models ........................ 63

REFERENCES .............................................................. 64

APPENDIX A: ACRONYMS, ABBREVIATIONS, AND DEFINITIONS ................ 69

APPENDIX B: PUBLICATIONS ................................................ 73

APPENDIX C: PRESENTATIONS .............................................. 78

APPENDIX D: WORKSHOPS AND MEETINGS .................................. 86

APPENDIX E: VISITING SCIENTISTS .......................................... 94

APPENDIX F: POSTDOCTORAL RESEARCHERS ................................ 96

APPENDIX G: ATMOSPHERIC SCIENCES MODELING DIVISION STAFF 

AND AWARDS .............................................................. 97
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Page

FIGURES

Figure 1. Gridded sea surface temperature ("F) in the Eastern Gulf of Mexico .............. 6

Figure 2. Estimated soil NO emissions using two versions of BEIS model ................. 7

Figure 3. Ozone concentrations (ppm) at 2 p.m. EDT from the CMAQ air quality 

 forecast model ....................................................... 14

Figure 4. Observed and modeled ozone ........................................... 26

Figure 5. Observed and modeled variance of the wind speed ........................... 26

Figure 6. Study location at forest edge behind the NC air monitoring station .............. 27

Figure 7. Sonic anemometer in canopy ............................................ 27

Figure 8. Ozone profile sampler ................................................. 27

Figure 9. Profile sampling tubes ................................................. 27

Figure 10. CMAQ simulations of CO at different spatial resolutions centered over Philadelphia. 

 Top (Left: 36 km, right: 12 km), Bottom (left: 4 km, right: 1.3 km) ............. 34

Figure 11. Ozone at 4 pm EDT (12 km). Top left (Mean from 1.3): Bottom left 

 (Parent @ 12 km): RHS: Mean-Parent ......................................... 34

Figure 12. CMAQ simulations of CO for July 12, 1995 ............................... 36

Figure 13. Skewness at 12-km grid resolution derived from 1.3 km simulations 

 for July 12, 1995 ...................................................... 36

Figure 14. Grid and range-to-mean derived from 1.3-km CMAQ simulations of formaldehyde 

 for July 12, 1995 ...................................................... 37

Figure 15. Concentration distribution histogram for 12 km dell in Central Philadelphia. 

 From left, CO, O3 NOx, acetaldehyde and formaldehyde. From top, 1700, 1800, 

 1900, and 2000 GMT ....................................................... 38

Figure 16. Smoke released from the scale model of the rubble pile at the WTC site enhanced by 

 horizontal sheet of laser light at elevation just above tops of tallest buildings. Wind 

 direction is westerly ................................................. 40
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Figure 17. Surface concentration pattern in the scale model of Lower Manhattan for the westerly 

 wind direction ....................................................... 40

Figure 18. Plume cross sections at downwind distances of: a) 300m, b) 600m, and c) 1200m 

 from the rubble pile for the westerly wind direction. The view is directly upstream 

 against skyline of the city. Colors indicate categories of building heights .............. 41

Figure 19. Flow patterns along Church Street for the westerly wind direction, three to four 

 blocks northeast of the WTC site, illustrating complex flows in street canyons .... 42

Figure 20. Simulated CALPUFF dilution of a volume source located at the WTC. Numbers are 

 hourly-averaged PM2.5 concentrations .................................... 45

Figure 21. Wind rose, dilution map, PM2.5 concentrations for average over the period 

 September 11-13, 2001. Wind rose for the grid point closest to the WTC ....... 45

Figure 22. CFD winds and streamlines through Manhattan street canyons ................ 46

Figure 23. CFD simulation of smoke and dust cloud following (Time=85 sec) the collapse of the 

 North Tower ....................................................... 46

Figure 24. The image on the left displays raw radiance from the AVIRIS spectrometer. The 

 same image on the right is shown as surface reflectance, following glint and 

 atmospheric correction .............................................. 52 

TABLES

Table 1. Summer 1999 evaluation statistics ........................................ 22

Table 2. Winter 2002 evaluation statistics ......................................... 22

Table 3. Threshold centerline tunnel speed vs. particle size and ratio of resuspension fluxes 

 for A to (A+M) ....................................................... 58
FISCAL YEAR 2003 SUMMARY REPORT OF THE NOAA

ATMOSPHERIC SCIENCES MODELING DIVISION TO THE

 U.S. ENVIRONMENTAL PROTECTION AGENCY

ABSTRACT. During Fiscal Year 2003, the NOAA Atmospheric Sciences

Modeling Division’s work on meteorological and air quality modeling, and policy

guidance was accomplished in accordance with the memorandum signed by the

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

This ranged from research studies and model applications to the provision of

advice and guidance in developing programs for improving the Nation’s air

quality. Research efforts emphasized the development, evaluation, and

application of meteorological and air quality models. Among the research studies

and results were the release in September 2003 of the latest version of the

Community Multiscale Air Quality (CMAQ) modeling system; continued

development and improvement of CMAQ and its modules; completion of the

wind tunnel modeling study of the estimation and characterization of the

dispersion of particulate matter from the World Trade Center recovery site after

September 11, 2001; development and evaluation of fine or neighborhood-scale

air quality models; development of techniques for model evaluation; development

of an updated version of the Biogenic Emissions Inventory System; initiation of a

study to model the smoke emissions from prescribed and wildfires; and

development of the Eta-CMAQ modeling system for use in air quality forecasting.

1. INTRODUCTION

In Fiscal Year 2003, the Atmospheric Sciences Modeling Division (ASMD) continued its

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

support of the missions of NOAA and EPA. Using an interdisciplinary approach emphasizing

integration and partnership 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
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management and analysis; and air quality forecasting. The Division is organized to respond

effectively to these research directions as more fully described in the following sections of the

report. A new version of the Community Multiscale Air Quality (CMAQ) modeling system,

incorporating the latest developments in state-of-science in modeling ozone and fine particles,

was released in August 2003. Research continued 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 National Weather Service and

Oceanic and Atmospheric Research, CMAQ was linked with the NOAA meteorological model,

Eta, for the preparation of national air quality forecasts by the National Weather Service. Initial

forecasts are for ozone. To improve the simulation of the transport and fate of airborne agents in

the near-field, a scale model of Lower Manhattan was used in the wind tunnel to study the impact

of pollutant release 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 and other near-field events.

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/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
1Copyright 1999 MCNC—North Carolina Supercomputing Center

3

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

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

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

visibility degradation, air toxins, and acidic and nutrient deposition and loadings to ecosystems at

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

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

provide science options through 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 September 2003 release,

as well as future releases, 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 case studies (episodic, seasonal, and annual) at a variety of

spatial scales using a series of one-way nested domains. MM5 is run retrospectively using fourdimensional 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 Sparse Matrix Operator Kernel Emission (SMOKE)©1 (emissions) and CMAQ

(chemistry) modules to describe the atmospheric state variables and the planetary boundary layer

characteristics.

Several projects were underway during FY-2003 using MM5 to support

Models-3/CMAQ applications. MM5 version 3 release 6 (MM5v3.6) was made available to the

modeling community by NCAR in December 2002. MM5v3.6 included updates to the PleimXiu land-surface model (Xiu and Pleim, 2001). During FY-2003, MM5v3.6 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-2003, 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. Evaluation
4

included comparison to surface observations, radar wind profilers, and surface flux

measurements. Some evaluation results are presented in Pleim and Xiu (2003).

A series of 32, 8, and 2 km grids of nested simulations for the spring and summer of 2002

were made for the Bay Regional Atmospheric Chemistry Experiment (BRACE) of Tampa ,

Florida. These runs will support CMAQ simulations that will be evaluated against a variety of

experimental field data for both meteorology and air chemistry. Continental-scale runs for

January and February of 2002 were also made to support development and evaluation of the

latest release of CMAQ. These runs focused on nitrate aerosols.

During the summer of FY-2003, the new version of the Weather Research and Forecast 

(WRF) model was run in-house on a daily basis. WRF is expected to be the next-generation

meteorology model that will include many of the features currently in MM5. It is also attractive

for air-quality modeling applications because it contains mass-conserving equations unlike MM5. 

The initial version of the model evaluation tool was used to assess the error and biases of the

model. Plans are to use the WRF data in the CMAQ model and compare against traditional

MM5-CMAQ applications.

2.1.2 Linking Meteorology and Chemistry Models for Research Applications

The Meteorology-Chemistry Interface Processor (MCIP) creates the off-line linkage

between meteorological models and CMAQ. It is essential that MCIP is compatible with

upgrades to the meteorological models that are used by CMAQ to preserve numerical and

physical consistency between the meteorology and chemistry models. In FY-2003, MCIP was

upgraded twice. For MCIP version 2.1 (MCIPv2.1), several important software errors were

identified and corrected; these errors were part of the original MCIP code. A major error was

identified in the layer collapsing section; the component wind fields that are used for the

chemistry transport were improperly set for the CMAQ vertical structure. The largest impacts to

the CMAQ simulation from that correction are expected when the number of vertical layers is

significantly reduced from the meteorology to the chemistry simulations, when layer collapsing

begins near the surface, when there is strong vertical shear, and when the wind speed is high. In

addition, algorithmic errors were corrected for the translation of winds from the Arakawa-B used

by MM5 to the Arakawa-C grid used by CMAQ, and for the hydrostatic and non-hydrostatic

calculations of the vertical velocity field used by CMAQ. MCIPv2.1 was released to the public

in March 2003.

The primary focus of MCIPv2.2 was the improvement of the calculation of dry deposition

velocities with an emphasis on winter simulations. Several changes were made to both the

M3Dry and RADMDry dry deposition schemes to include the effects of snow cover and the

addition of new chemical species. Also, several files that were routinely generated by MCIP but

never used were eliminated, and the overall output size was reduced by 17%. MCIPv2.2 was

designed to be a companion to the CMAQ summer 2003 release, and MCIPv2.2 was released to

the public in June 2003.
5

2.1.3 Land Surface and Planetary Boundary Layer Modeling

Realistic simulation of land surface and 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.

For many years, this effort 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 in public releases of both 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 was added to

CMAQ in the 2002 release.

In FY-2003, the data-assimilation scheme in PX LSM was analyzed and evaluated against

SOS 1999 field experiment data. This scheme involves Newtonian nudging of surface and rootzone soil moisture according to model biases in 2-m air temperature and relative humidity. The

coupling coefficients, nudging strength, are parameterized to nudge most strongly when the

influence of soil moisture on the surface layer air is strongest. Sensitivity tests confirm the value

of this scheme in improving temperature and surface flux simulations (Pleim and Xiu, 2003).

2.1.4 High-Resolution Sea Surface Temperature Initialization for Meteorological Models

The initial version of a sea surface temperature (SST) processing utility for MM5 was

developed in FY-2003. The main reasoning behind this development is that typically MM5 uses

coarse, 32 km or more, gridded SST data interpolated from larger scale models. In coastal areas,

the sea temperature is one of the most dominating factors influencing the boundary-layer

meteorology so it is import to resolve it to the grid scale of the model. The first MM5 simulation

in this study is focused over Tampa Bay, Florida (April 20 - June 7, 2002). It is expected that the

more detailed SST will improve the simulated coastal meteorology. A similar approach will be

applied to a 1-km grid-model simulation over the Houston area. Additionally, real-time

simulations with and without the high-resolution SST are being compiled during 2004. Figure 1

displays a sample of the SST data in the Eastern Gulf of Mexico, near Florida. All simulations

will be evaluated with observations to provide a better idea to what extent the more detail SST

improves the representation of the actual atmosphere.
6

Figure 1. Gridded sea surface temperature ("F) in the Eastern Gulf of Mexico. 

2.1.5 Anthropogenic Emissions

The SMOKE© modeling system (www.cep.unc.edu/empd/products/smoke/index.shtml)

was enhanced to version 2.0. Version 2.0 explicitly allows use of criteria and/or toxic volatile

organic compound (VOC) emission inventories from the EPA National Emission Inventory and

National Toxic Emission Inventory. Speciation files were created for the Carbon Bond-IV

(CB-IV) lumped-species mechanism, which allow substitution of toxic VOCs for VOC species

derived from the VOCs criteria inventory. The control strategy input format and design were

improved, e.g., control strategies can include changes in the reactivity of emitted species. This is

important when industrial processes are changed. In addition, a variety of software bugs were

fixed. Development of SMOKE©

 is continuing with the gradual development by several entities

for modeling of wild fire emissions (ASMD and U.S. Forest Service), and alternative land-cover

and wildfire emissions (EPA Office of Air Quality and Planning Standards (OAQPS)). Most

data input format features needed by SMOKE are included in the system, with the exception of

spatial gridding of input files and spatial surrogates. To meet this need, preparation and testing

of the spatial allocator tool of the Multimedia Integrated Modeling System (MIMS) is near
7

Figure 2. Estimated soil NO emissions using two versions of BEIS model.

completion. The spatial allocator requires only grid definitions and Geographic Information

System (GIS) shape files for spatial surrogates.

2.1.6 Biogenic Emissions

Introduced in 1988, the Biogenic Emissions Inventory System (BEIS) provides hourly,

gridded estimates of biogenic VOC and soil NO emissions to such regional air quality models as

CMAQ. During FY-2003, BEIS was upgraded from version 3.09 to version 3.12 (Pierce et al.,

2003). BEIS3.12 includes emission factors for 34 chemical species, including 14 specific

monoterpenes, methanol, and methyl-butenol (MBO). While previous versions used solar

radiation to modulate emissions of isoprene, BEIS3.12 has extended its use of solar radiation

adjustments to include methanol and MBO. The soil NO algorithm has been enhanced to

account for soil temperature, fertilizer application schedules, rainfall, and crop growth. The new

soil NO algorithm is the most significant change in BEIS compared to BEIS3.09. As shown

below in Figure 2, soil NO emissions with BEIS3.12 peak during the spring, soon after most

fertilizer applications and during periods of frequent rainfall. Soil NO emissions for this 2001

simulation averaged 30% higher than with BEIS3.09. The Division plans to assess this impact of

the new biogenic emission estimates with the CMAQ model during FY-2004. If testing with

BEIS3.12 proves successful, BEIS3.12 will be packaged as part of the SMOKE system and

released to the public..
2A fuel consumption model, which predicts total smoldering fuel consumption during

wildfires.

8

2.1.7 Modeling Smoke Emissions from Fires

A prototype, stand-alone emissions processor was developed to model smoke from fires

(prescribed and wildfires). 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.

This prototype system, consists of a set of processors based on state-of-science algorithms

developed primarily by the U.S. Forest Service. This development was a cooperative effort with

the National Park Service and included 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 Models-3/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 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 U.S. Forest Service Consume2 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 plan is to collaborate with the U.S. Forest Service and EPA’s OAQPS to update and refine

the prototype fire model and link it with the SMOKE© emission modeling system for use with

CMAQ.
9

2.1.8 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. Hourly

contributions from these sources are not incorporated in CMAQ because of a lack of an

acceptable emission processing system to models these fluxes. Algorithms for estimating the

emissions of windblown and fugitive dust 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

horizontal cell sizes was completed and work begun on its application to human-caused dust

generating activities, including dust from unpaved roads, construction sites, and agricultural

activities. The resulting applications will be included as modules in the SMOKE©

 emission

modeling system. The basis for the wind blown dust 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.

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. 

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 computationally

efficient. 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. Therefore, 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 atmospheric
10

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-2003, a variant of the Modified Euler Backward Iterative (MEBI) solver was

added to the array of gas-phase chemistry solvers in CMAQ. The new solver utilizes a

formulation similar to the Euler Backward Iterative (EBI) solver originally published by Hertel et

al. (1993). It is comparable to the existing MEBI approach, except that numerical solutions for

two groups of mechanism species are replaced by analytical expressions that provide

approximate solutions. Although this approach is somewhat less general than the numerical

scheme used in MEBI, computational efficiency is improved with minimal loss in accuracy. 

Thus far, the new EBI solver was developed for the CB-IV chemical mechanism where tests

conducted with CMAQ have shown it to be about two times faster than its MEBI analog. It was

included in the latest CMAQ public release, and it also is being used in the air quality forecasting

version of CMAQ. The use of the same approach for the SAPRC99 chemical mechanism is

being tested, with an anticipated release in FY-2004.

2.1.10 Aerosol Mechanism Improvements in CMAQ

The CMAQ aerosol module was revised to improve predictions of aerosol-phase nitrate

and secondary organic aerosol concentrations. Evaluations of the 2002 CMAQ release revealed

large overpredictions in the wintertime aerosol nitrate concentrations and year-round organic

carbon concentrations. To mitigate the nitrate overprediction, extensive analyses of the available

ambient data and relevant scientific literature were conducted. Ultimately, the heterogeneous

reaction probability of gaseous dinitrogen pentoxide with liquid water was modified to allow the

reaction probability to be computed as a function of the aerosol chemical composition instead of

using a fixed value. This modification is based on experimental evidence suggesting that the

heterogeneous reaction is inhibited by the presence of nitrate in the aerosol phase, and follows

the implementation of Riemer et al. (2003). In addition, the production pathway of nitric acid via

gas-phase hydrolysis of dinitrogen pentoxide has been removed, based on the work of Jacob

(2000). As a result of these modifications, the CMAQ overpredictions of wintertime nitrate were 

largely mitigated.

The overpredictions of organic carbon concentrations in the 2002 release of CMAQ were

largely due to a model assumption that restricted semi-volatile organic compounds (SVOC)

evaporation. Several changes were required to relax this assumption in the 2003 CMAQ public

release. In addition, various corrections were made to the SVOC production rates to account

more accurately for the specific compounds within lumped compound groups that yield

secondary organic aerosol (SOA). For example, the entire ARO1 lumped group in SAPRC99 is

treated as an SOA precursor although it includes benzene, which is not an SOA precursor. 

Therefore, the production rate of SVOCs arising from the oxidation of ARO1 must be scaled

down to account for the amount of benzene that is lumped with ARO1. Adjustments analogous

to this one were made to decrease the excess SOA production that arises artificially from

compound lumping.
11

2.1.11 Plume-in-Grid Modeling

The plume-in-grid (PinG) approach within the CMAQ modeling system provides for the

subgrid scale treatment of the physical and chemical processes governing gaseous pollutants and

aerosol species in isolated, major-point source plumes within an Eulerian grid framework. By

applying a Lagrangian approach, the CMAQ/PinG treats the horizontal and vertical growth of a

plume section in a gradual, real-world manner due to turbulence and wind shear processes, which

is in contrast to the instantaneous mixing of point emissions into the entire grid cell volume in

the traditional Eulerian modeling method. 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 model domains with a typical grid spacing above about 10 km. The PinG treatment was

designed to simulate multiple point-source plumes. A continuous plume is represented by a

series of plume sections released at 1-hour intervals and each horizontal plume cross-section is

internally resolved by a set of attached plume cells. The PinG module is fully integrated into the

CMAQ grid model, and it is exercised concurrently during a simulation in order to use grid cell

concentrations as boundary conditions at each edge of a plume section. An important feedback

occurs when a plume section reaches the model grid cell size as the subgrid plume treatment

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

description of the capabilities of the CMAQ/PinG modeling treatment and its technical

formulation were described by Gillani and Godowitch (1999). 

During FY-2003, an updated AE3 aerosol algorithm was included in the PinG module to

simulate aerosol species and PM2.5 along with gas-phase pollutant species in the subgrid plumes. 

Additional code revisions were also undertaken to permit a more frequent plume release rate than

the previous hourly default interval. CMAQ/PinG test simulations were successfully completed

on various computational platforms with single processors and multi-processors. Preliminary

results of modeling aerosols in PinG presented by Godowitch (2002) revealed differences in

aerosol sulfate (SO4) concentrations among the high NOx and SO2 point sources. For point

sources with comparable SO2 emissions, greater sulfate formation occurred in those plumes

exhibiting a lower NOx emission rate. These PinG results also appeared to agree with emerging

observed plume aerosol data. In addition, separate CMAQ model simulations were conducted

with PinG and excluding the PinG module using existing emission and meteorology data sets

from a summer period for a large eastern United States domain with a 36-km grid cell size. 

There were 77 high-emitting NOx

 and SO2

 point sources in the modeling domain simulated with

the PinG approach. Comparisons of modeled gaseous and aerosol species against surface

monitoring network data have commenced. So far, analyses of peak and hourly ozone

concentrations reveal that the CTM/PinG results displayed better agreement and less bias than

the CTM/NoPinG results, particularly in model areas where numerous large point sources exist. 

Additional model runs are planned for a different summer experimental period when observed

plume data collected by various airborne platforms are available. Evaluation results with the

CTM/PinG are expected during FY-2004. Further sensitivity test runs will be performed to

assess computational times and to investigate the impact on oxidant and aerosol species

concentrations using different chemical mechanisms (e.g., CB-IV, SAPRC) and different
3MPICH is a freely available, portable implementation of MPI, the Standard for messagepassing libraries.

12

chemistry solvers (e.g., Gear, Euler Backward Interative solver) available in the CMAQ

modeling system.

2.1.12 CMAQ Code Integration and 2003 Release

CMAQ was extensively revised in 2003. Changes include updated science, corrected

implementations, efficiency enhancements, and bug fixes. The biggest changes involve aerosol

modeling, particularly nitrate aerosols and SOA. Nitrate modeling was updated so it is consistent

with the most recent literature and the SOA implementation was corrected to allow for reversible

semi-volatility. These changes resulted in substantially lower concentrations of both aerosol

nitrates and SOA. Minor changes were made to aqueous processes and dry deposition. 

There were major modifications to improve model efficiency. A new fast gas phase

chemistry solver, known as the Euler Backward Iterative (EBI) scheme, was developed for the

CB-IV mechanism. Also, some of the fastest reacting species have been dropped from the

transport processes. The time step for operator splitting has been revised to allow different

advective time steps by vertical layer. 

Note that other components of the CMAQ system as SMOKE© and MCIP were also

revised recently. Therefore, MCIPv2.2 needs to be used for CMAQ modeling. The latest

version of CMAQ (September 2003) features several major changes:

a. Scripts to build and run CMAQ for MPICH

3

 Linux clusters; and

b. Incorporated the I/O API version 2.2.

c. Modified the treatment of secondary organic aerosol (SOA) formation:

i. The SOA algorithm was modified to make the gas-particle partitioning of

semi-volatiles reversible;

ii. Eliminated SOA production from anthropogenic alkenes;

iii. Adjusted yields of semi-volatiles from alkanes and aromatics to account

for emissions of non-SOA producers being lumped with those from SOAproducers; and

iv. Modified gas-phase monoterpene reactions rates in RADM2 and CB-IV.

d. Modified the heterogeneous N2O5 reaction probability;

e. Added an EBI gas-phase chemistry solver for the CB-IV family of chemical

mechanism;

f. Implemented layer-dependent horizontal advection time-stepping; and

g. Changed the order of the time splitting science processes to: vertical diffusion–>

advection–> mass-adjustment–> horizontal diffusion–> clouds–> chemistry–> 

aerosols.
13

h. Updated all gas-phase chemical mechanisms (CB-IV, RADM2, SAPRC):

i. New deposition velocity surrogates;

ii. New cloud scavenging surrogates;

iii. Zeroed reaction rate constant for N2O5

 + H2O -> 2HNO3

 in all AERO3

mechanisms; and

iv. For CB-IV, eliminated advection and diffusion of fast-reacting species.

i. Cloud enhancements/updates:

i. Modified to use effective Henry's Law constant for scavenging;

ii. Updated and added new coefficients to the Henry's Law constant table;

iii. Revised the timestep calculation in the aqueous chemistry subroutine; and

iv. Updated the coarse-mode aerosol number washout.

j. Other enhancements and bug fixes:

i. Modifications of the vertical diffusion module were made to improve data

locality to speed up computation;

ii. For the heterogeneous N2O5

 reaction in the aerosol module, an error was

corrected in the concentration units for calculating the HNO3 yield, and the

rate constant calculation for this reaction was changed to use effective

radius instead of diameter;

iii. An error in the contribution of N2O5

 to total initial HNO3

 was corrected in

the cloud module;

iv. Changed the units for aerosol for vertical diffusion to molar mixing ratio

instead of mass density concentration; and

v. Updated the minimum aerosol concentration array and bug fix in coarsemode aerosol standard deviation in the aerosol dry deposition subroutine.

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 has being linked with the NOAA/National Weather Service (NWS) mesoscale

Eta meteorological model. ASMD is collaborating with the NWS National Center for

Environmental Prediction (NCEP) to produce an initial air quality forecast capability for the

nation. Appropriate linkage software to drive CMAQ with the Eta model output has been

developed and tested. Other objectives of this work are to make significant improvements in the

computational performance of the CMAQ model so it can be used effectively in an operational

forecasting environment. 

During FY-2003, the initial Eta-CMAQ system was constructed and tested, and used to

make daily forecasts for ozone in the northeast United States from July through September. The

CMAQ model was run twice daily at NCEP to produce forecast guidance. The principal CMAQ

model run was driven by the 12Z Eta forecast. CMAQ simulations were made for 48-hour

duration, and were available by 1:30 p.m. EDT. A 30-hour update CMAQ simulation was run

using the 06Z Eta forecast, and those CMAQ results were available by 7:30 a.m. EDT. While

results were not made available to the public in this first test season, they were made available to
14

Figure 3. Ozone concentrations (ppm) at 2 p.m. EDT from the CMAQ

air quality forecast model.

the participating scientists and to a focus group of potential customers for feedback. The extent

of the modeling domain and example output for ozone are shown in Figure 3. Subsequent

versions of the Eta-CMAQ forecast system will be expanded across the United States, and will

add PM2.5 forecast capability.

2.1.14 Linking Eta (Meteorological Model) with CMAQ for Air Quality Forecasting 

As part of the development of the national air quality forecasting (AQF) system, a new

software program was required to couple NCEP’s Eta model with CMAQ. Before developing

the software program, there were several practical and technical differences between the Eta

model and CMAQ that needed to be considered. First, the Eta model uses a horizontal grid

(Arakawa-E staggering), a vertical coordinate (step-wise terrain-following “Eta”), and a

projection (rotated latitude-longitude) that CMAQ was not designed to handle. A major decision

was whether to undertake the challenge of modifying CMAQ to adapt to the native grid systems

used by the Eta model, or to interpolate Eta model output to a familiar grid structure and
15

projection and potentially jeopardize the mass conservation in CMAQ. Ultimately, the Eta

interpolation was selected for the AQF system. In addition, since CMAQ was run mostly with

retrospective meteorological data, it was unclear whether or not the forecast output from Eta

would contribute to reasonable ozone forecasts in CMAQ. Further, the Eta model creates a

different suite of meteorological variables often using different internal physics modules than

MM5, which had been coupled with CMAQ previously, so linking Eta and CMAQ required

considering ways to best use the Eta output.

To enable a linkage between Eta and CMAQ, NCEP made several changes to the

operational Eta system. For the AQF system, NCEP made hourly output files available (as

opposed to the three-hourly files that are typically generated for external customers). In addition,

NCEP added functionality to the Eta post-processor to vertically interpolate the Eta model output

from the 60 step-wise vertical (Eta) layers to 22 hydrostatic sigma-P vertical layers that are

compatible with the CMAQ system. NCEP added several output variables to the processing

stream for use in AQF (e.g., PBL height, canopy conductance, plant canopy water). Finally,

NCEP added new horizontal grids for the post-processed Eta model output that are specifically

designated for the AQF system.

A key ingredient in the Eta-CMAQ linkage is a new pre-processor for CMAQ

(PREMAQ) that was developed by ASMD and is largely equivalent to MCIP in the CMAQ

model system. PREMAQ places the post-processed Eta model output into the required

horizontal and vertical grids for CMAQ. Like MCIP, PREMAQ computes state variables and

other derived variables (e.g., air density, Jacobian, dry deposition velocities for chemical species)

that are required by CMAQ. Unlike MCIP, PREMAQ also includes calculations of the

meteorologically dependent emissions (i.e., biogenic and mobile sources) adapted from

SMOKE©

. The output from PREMAQ includes the full set of meteorology and emissions files

that are used by CMAQ.

There are several key differences between PREMAQ and MCIP. Of primary importance

for NCEP is that PREMAQ processes the World Meteorological Organization standard GRIdded

Binary (GRIB) files, which contain the data format used at NCEP and, by design, are unstaggered

two-dimensional fields. PREMAQ also provides a full suite of grid geometry calculations (i.e.,

latitude, longitude, map-scale factors) from grid definition information in the GRIB headers. 

There are several modifications in PREMAQ to account for the different output variables

generated by Eta compared to MM5. Notably, a new dry deposition routine, EtaDry, was

developed for PREMAQ to take advantage of the land-surface fields that are output by the Eta

model.

2.1.15 Preliminary Evaluation of Eta-CMAQ Forecast Model System

An operational evaluation of the coupled Eta-CMAQ forecast modeling system was

performed in which both discrete type forecasts (observed versus modeled concentrations) and

categorical type forecasts (observed versus modeled exceedances/non-exceedances) for both the
16

maximum 1-hour O3 concentrations (125 ppb) and 8-hour O3 concentrations (85 ppb) were

evaluated. The evaluation encompassed approximately three months (7 July–30 September

2003) and used hourly O3 concentration data obtained from the EPA AIRNow network. Overall,

the modeling system performed reasonably well in its first attempt at forecasting ozone

concentrations. Results from the discrete evaluation revealed that correlations ranged from 0.59

(max. 1-hour) to 0.62 (max. 8-hour), normalized mean biases (nmb) ranged from 28.2% (max.

1-hour) to 37.3% (max. 8-hour), and the normalized mean errors (nme) ranged from 32.2% (max.

1-hour) to 39.9% (max. 8-hour). Results from the categorical evaluation revealed accuracy

levels (which indicate the percent of forecasts that correctly predict an exceedance or nonexceedance) of 99.6% and 89.6% for the max. 1-hour. and max. 8-hour, respectively. Care must

be taken in interpretation of this metric, however, as it is greatly influenced by the overwhelming

number of non-exceedances. To circumvent this inflation, another metric called the Probability

of Detection was calculated. This metric ranged from 16.7% (max. 1-hour) to 41.0% (max.

8-hour), and measured the number of times a model predicted an exceedance when one actually

occurred.

An error was discovered midway through the forecast period in Eta’s post-processed

land-use designation that resulted in the underestimation of dry deposition, and, hence, led to the 

overestimation of ambient concentrations. To investigate the significance of the error, an 8-day

period (12–19 August) was re-simulated and re-evaluated. With the correction, the nmb fell

from 27.5% to 13.0% for max. 1-hour and from 37.2% to 20.1% for max. 8-hour. The nme were

also significantly reduced, falling from 31.7% to 21.7% for max. 1-hour and from 39.9% to

26.3% for max. 8-hour. The correlations between observation and modeled ozone values also

increased for both the max. 1-hour and max. 8-hour. The short duration and dearth of

exceedances precluded calculation of categorical evaluation metrics for the corrected simulation.

2.2 Atmospheric Model Evaluation and Application Activities

2.2.1 Developing a Strategy for Assessing Performance of Regional-Scale Air Quality

Models

Developing model performance metrics for regional-scale air quality models is a work in

progress. There are many sources of uncertainty, including boundary conditions, emissions,

chemistry, and transport. Complications arise because regional-scale models provide

volume-average concentrations, whereas the observations are daily averages on a one-in-three

day sampling interval at specific sparsely-spaced points. During 2003, investigations were

conducted of 1) regional-scale modeling to gain an understanding of whether the available

monitoring data is suitable to assess an increase in the model skill if smaller grid sizes are used,

and 2) monitoring data to gain an understanding of the extent observations collected by different

monitoring networks can be combined.

In the first investigation, Gego et al., (accepted for publication)(a) examined temperature

and ozone observations and model predictions for three high ozone episodes that occurred over
17

the northeastern United States during the summer of 1995. In the first set of simulations, the

meteorological model RAMS4a was run with three two-way nested grids of 108-, 36-, 12-km

grid spacing covering the United States and the photochemical model UAM-V was run with two

grids of 36-, 12-km grid spacing covering the eastern United States. In the second set of

simulations, RAMS4a was run with four two-way nested grids of 108-, 36-, 12-, 4-km grid

spacing and UAM-V was run with three grids of 36-, 12-, 4-km grid spacing with the finest

resolution covering the northeastern United States. The analysis focused on the comparison of

model predictions for the finest grid domain of the simulations, namely, the region overlapping

the 12 km and 4 km domains. Comparisons of 12-km versus 4-km temperature and ozone fields

showed that the increased grid resolution leads to greater texture in the model predictions;

however, comparisons of model predictions with observations did not reveal the expected

improvement in the predictions. Hence, while high-resolution modeling has scientific merit and

potential uses, it is uncertain how one would assess the accuracy of the high-resolution model

predictions with the currently available monitoring networks.

In the second investigation, Gego et al., (accepted for publication)(b) examined airborne

fine particulate matter in the United States as monitored by three different networks: the Clean

Air Status and Trend Network (CASTNet), the Interagency Monitoring of PROtected Visual

Environment Network (IMPROVE), and the Speciation and Trend Network (STN). If combined,

these three networks provide speciated fine particulate data at several hundred locations

throughout the United States. Differences in sampling protocols and samples handling might

preclude their joint use. With these concerns in mind, the objective of this study was to assess the

spatial and temporal comparability of the sulfate, nitrate, and ammonium concentrations reported

by each of these networks. One of the major differences between the networks is the sampling

frequency they adopted. While CASTNet measures pollution levels on 7-day integrated samples,

STN and IMPROVE data pertain to 24-hour samples collected every three days. STN and

IMPROVE data, therefore, exhibit much more short-term variability than their CASTNet

counterpart. Despite their apparent incongruity, averaging the data with a window size of four

weeks was sufficient to remove the effects of differences in sampling frequency and duration and

allow meaningful comparison of the sulfate and ammonium concentration values reported by the

three networks. After averaging, all the sulfate and, to a lesser measure, ammonium

concentrations reported by the three networks are fairly similar. Nitrate concentrations, on the

other hand, were still divergent. It was speculated that this divergence originates from the

different types of filters used to collect particulate nitrate. Finally, rotated principal component

analysis (RPCA) was used to determine if there were subregions where the temporal modes of

variation detected by each network were similar for the three pollutants of interest. For sulfate

and ammonium, the subregion boundaries established for each network and the modes of

variations within each cluster seemed to correspond. For the CASTNet and IMPROVE

networks, RPCA performed on nitrate concentrations revealed that the modes of variation did not

correspond to unified geographical regions but were found more sporadically. For STN, the

clustered areas were unified and easily definable. Hence, the possibility of jointly using the data

collected by CASTNet, IMPROVE, and STN has to be weighed pollutant by pollutant. While

sulfate and ammonium data show some potential for joint use when averaged over a 4-week

window at this point, combining the nitrate data may not be a judicious choice.
18

The results of these two investigations provide clues of how to conduct a first-order

evaluation of regional-scale model performance. The first investigation suggests the available

monitoring data and modeling results are best suited to assessing long-term variations in time and

larger spatial domain. The second investigation suggest there are local contiguous subregions

where monitoring results may have similar temporal behavior. These results suggest the

following strategy for assessing model performance. From a principle component analysis the

subregions in the monitoring data can be identified. Following the application of a 5-week

running average to the observed and modeled values, an assessment of the performance of the

regional-scale model can be conducted in each subregion. Assessing regional-scale model

performance on their ability to characterize the long-term seasonal time series in this manner is

seen to provide a first-order means for detecting gross sources of bias and would provide a basis

for a first-order quantitative comparison of the relative performance of several models.

2.2.2 Developing and Testing an ASTM Strategy for Assessing Performance of Local-Scale

Dispersion Models

During the development phase of an air quality dispersion model and in subsequent

upgrades, model performance is constantly evaluated. These evaluations generally compare

simulation results using simple methods that do not account for models only predicting a

portioning of the variability seen in the observations. To fill a part of this void, a standard was

developed and adopted by the American Society for Testing and Materials (ASTM, 2000),

designation D6589 Standard Guide for the Statistical Evaluation of Atmospheric Dispersion

Model Performance. In the annex to this standard is an example method that tests the ability of

dispersion models to simulate the average centerline concentration. The method involves

grouping observed data into groups or regimes, in which the dispersion is expected to be

somewhat similar. The average centerline concentration is then derived for each group using

bootstrap resampling. It is this average centerline concentration that is then compared with the

modeling results. By this means, the focus is on testing the ability of models to replicate the first

moment, the average, of the centerline concentration distribution, which for most operational

models is the only feature in the centerline concentration distribution they are capable of

simulating. Recent work (Irwin et al., 2003) was conducted to further test the ASTM example

test method. This work involved the application of the test method to the results from ADMS

version 3.1, AERMOD versions 98022 and 02161, HPDM version 4.3, level 920605, and

ISCST3 version 00101. Three atmospheric dispersion field studies are analyzed: Prairie Grass

1956 rural, low level release (Barad, 1958; Haugen,1959); Kincaid 1980 rural, elevated release

(Bowne et al., 1983); and Indianapolis 1985 urban, elevated release (Murray and Bowne, 1988). 

Following the ASTM method, the normalized mean square error (NMSE) was used in comparing

the performance of the five models over the three field experiments. The base models, those

having the lowest value of NMSE, were seen to be AERMOD version 02161 for Prairie Grass,

and HPDM for Kincaid and Indianapolis. Even though NMSE for ADMS was larger than that

for AERMOD, it was noticed that for Kincaid the results for AERMOD were found to be

significantly different from that found for HPDM the base model. This occurred because the

bootstrap-derived standard deviation of the NMSE for the ADMS results was sufficiently large to
19

encompass the HPMD results, whereas the bootstrap-derived standard deviation of the NMSE 

for the AERMOD results did not encompass the HPDM results. This result suggests there may

be inappropriately grouped data for some of the analyses, and that future investigations should

conduct a detailed investigation into each group to insure that questionable observations are not

unduly influencing the results. An underlying assumption in grouping data together is that they

will provide a representative average centerline concentration value. If the data are poorly

grouped, then the average centerline concentration for the regime would likely be biased too low,

concerns of which were expressed by Olesen (2001). 

2.2.3 Inverse Modeling for Ammonia

One of the key uncertainties affecting air quality model predictions is the uncertainty in

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

compared to other emissions, and they have an 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, which

extrapolated European emission factors 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 due to their seasonality and in their overall annual magnitude. 

Ammonia emission studies traditionally were 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 a Kalman Filter-based technique with CMAQ, inverse

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

were accounted for by introducing the standard error of the precipitation estimate into the

Kalman filter. Results suggest that the emissions should be highest during summer conditions

and lower during fall and winter (Gilliland et al., 2003).

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

United States continental domain where an annual simulation is already planned for CMAQ

model evaluation. The work will also be extended to examine such different sources of

emissions as cattle and 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 work of OAQPS, which is currently evaluating the NEI ammonia inventory and

developing an updated ammonia inventory based on newer research. This work also supports the
20

air quality modeling community whose modeling results can be detrimentally affected by large

uncertainties in ammonia emission inputs to the model.

2.2.4 Diagnostic Metrics for Ozone and Particulate Matter

Diagnostic metrics enable us to examine the process side of the model to better study 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 (Arnold et al., 2003). The model simulations examined for Nashville with 12-km

and 4-km horizontal grid cell sizes. The modeling results from 36-km grid spacing were not

examined because they were considered to be too coarse for ozone predictions for a moderatesized urban area. The diagnostic metrics suggest that the 4-km grid cell size produces slightly

better predictions relative to ozone process fidelity. Current work is examining differences

among the three chemical mechanisms in CMAQ with the help of sensitivity analyses that

include emission reductions. The metrics also highlighted sites that were being influenced by

local emission sources 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 test procedures for the quantitative comparison of ozone production efficiencies,

including uncertainty limits, are being 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 quantitative comparison of data partitioned

into bins will be developed next year. The metrics are now being applied to other time periods,

SOS Nashville 1999 data, and other cities, SOS Atlanta 1999, and South East Aerosol Research

and Characterization 1999, to test their robustness.

2.2.5 CMAQ Model Evaluation to Assess Readiness for State Implementation Plans

The 2003 release of CMAQ was made available during August. As part of this release,

an operational evaluation was performed involving two simulation periods (January 4–February

19, 2002, and June 15–July 16, 1999), using two chemical mechanisms, SAPRC and CB-IV. 

Only the SPARC simulations are presented here. Full evaluation results from the other

simulations as well as release notes documenting model changes and updates are available at

www.epa.gov/asmdnerl/models3/index.html. The summer simulation was performed using a

32-km resolution over the entire United States domain with a vertical resolution of 21 layers set

on a sigma coordinate; while the winter simulation used a 36-km grid resolution and 24 vertical

layers. For both simulations, the meteorological fields were derived by MM5 and were

processed through MCIPv2.2. Emissions of gas-phase SO2, CO, NO, NO2, NH3, and VOCs were
21

based on the 1999 EPA NEI. Primary anthropogenic PM2.5 emissions were separated into

different species, including particle SO4

, NO3

, OC, EC. Emissions of HC, CO, NOx

, and PM

from cars, trucks, and motorcycles are based on Mobile5b, while biogenic emissions were

obtained from BEIS3.12.

Operational data used in the evaluation were obtained from four nationwide networks.

Hourly O3 (ppb) data were obtained from over 700, mostly urban, Aerometric Information

Retrieval System (AIRS) stations over the 4-week summer evaluation period. Weekly average

concentrations of SO4

2-, NO3

-

, HNO3

 and NH4

+

 (:g m-3) were obtained from nearly 70, mostly

rural CASTNet stations. Four weekly collection periods coincided with the summer simulation

period; while six were available for the winter simulation. Daily average concentrations of SO4

2-

,

NO3

-

, PM2.5, OC, and EC (:g m-3) from 50 rural IMPROVE sites were also used. These data are

collected on every third day, (midnight to midnight, local time), limiting the number of days

available for comparison to 10 and 15 for the summer and winter simulations, respectively. 

Finally, data from the more recently established STN, which follows the protocol of the

IMPROVE network (i.e., every third day collection) were obtained from 60, mostly urban sites. 

The STN provided measurements of SO4

2-, NO3

-

, PM2.5, NH4

+

, OC and EC (:g m-3) for 15 days

during the winter simulation.

Examination of the tables reveals that CMAQ produces fairly unbiased Normalized Mean

Biases (NMB) <10%, and accurate Normalized Mean Errors (NME) ~ 20.0% simulations of both

the max.-1 hour and max.-8 hour ozone concentrations when compared against the AIRS data. 

Correlations are also fairly good (between 0.72 and 0.75) indicating that CMAQ is capturing

roughly 50% of the variability exhibited by the observations. CMAQ performs quite well in

simulating SO4 concentrations. Correlations are high, ranging from 0.90 during the summer

against both CASTNet and IMPROVE sites to 0.66 in the winter against STN sites. The NMBs

are, with one exception, positive and between 9.2 and 38.4%. The lone underprediction occurs

against STN data, NMB: -12.0%, and like the lower correlation, may be attributable to the urban

nature of the STN. The NMEs range from 25.7% (winter, CASTNet ) to 61.9% (summer,

IMPROVE). Of all of the species simulated by CMAQ, NO3 simulations are the worst. 

Correlations are lower for the winter simulations (0.27: CASTNet, 0.36: IMPROVE) when

compared to the summer simulations (0.39: STN, 0.54: IMPROVE, 0.76 (CASTNet). The

summer simulation produces negative biases (NMB: -30.8% for CASTNet, -39.1 for

IMPROVE), while NMBs for the winter simulation are mixed, ranging from 8.0% for STN to

46.8% for CASTNet. The errors associated with NO3

 simulations are generally the largest

produced by CMAQ and range from 66.9% (winter, STN) to 96.5% (winter, IMPROVE).

Results of NH4 simulations mirror those of SO4 in that CMAQ performs quite well and

consistently, especially when compared against CASTNet observations (correlations: 0.86

summer simulation, 0.85 winter). As seen with SO4

 and probably for the same reason, the

CMAQ correlation against the STN is considerably lower (0.41). The NMB against STN data is

however small (5.2% for the winter simulation) when compared against CASTNet (40.0% for the

winter and 22.7% summer). The NMEs range from 36.5% (summer CASTNet) to 57.7% (winter

STN). The results of the PM2.5 simulations are like PM2.5 itself, a composite of the other species. 
22

Correlations associated with the more rural IMPROVE network are considerably higher (0.71

summer, 0.68 winter) than those associated with the more urban STN network (0.37 winter). 

The NMB for the summer simulation is small and negative (-9.8%) against the IMPROVE

network, small but positive for the winter simulation against STN and large and positive for the

winter simulation against the IMPROVE network. The NME range from 40.3% (summer

simulation against IMPROVE) to 57.7% (winter simulation against STN).

The results associated with the HNO3 evaluation are consistent between the summer and

winter simulations. The correlation for the summer simulation is 0.79, while that for the winter

is 0.64. The NMBs are positive (49.0, 44.2 for summer, winter respectively) and the NMEs

average near 60%. Results for OC are mixed depending on season and network. The NMB

against IMPROVE is small and negative (-1.5%) in the summer yet it is positive and larger in the

winter (17.2%). Against the STN data, the winter CMAQ simulation significantly underpredicts

(NMB: -50.0%). The NMEs are more consistent, though large, ranging from 60.4% to 70.0%,

and the correlations ranging between 0.32 and 0.56. The summer CMAQ simulation of EC is

unbiased (NMB: 1.0%) and produces a correlation of 0.69 when compared against IMPROVE

data. Conversely, the winter simulation significantly overpredicts EC, resulting in large positive

biases (31.0% and 59.1% against IMPROVE and STN respectively) and large errors as well

(NME 81.0%, and 95.0%).

Table 1. Summer 1999 evaluation statistics

Species O3 (Max-1) O3 (Max-8) SO4 NO3 PM2.5 NH4 HNO3 OC EC

Network AIRS AIRS CAS IMP CAS IMP IMP CAS CAS IMP IMP

n 23,196 23,196 264 490 264 415 457 264 264 396 396

r 0.72 0.75 0.90 0.90 0.27 0.36 0.71 0.86 0.79 0.32 0.69

MB 2.3 4.3 1.74 0.75 -0.15 -0.11 -0.73 0.31 1.11 -0.02 0.00

NMB (%) 3.8 8.7 38.0 38.4 -30.8 -39.1 -9.8 22.7 49.0 -1.5 1.00

RMSE 14.5 12.8 2.89 2.37 0.57 0.51 4.70 0.70 1.77 1.35 0.24

NME (%) 18.8 20.2 46.4 61.9 75.7 95.0 40.3 36.5 58.7 67.2 52.6

Table 2. Winter 2002 evaluation statistics

Species SO4 NO3 PM2.5 NH4 HNO3 OC EC

Network CAS STN IMP CAS STN IMP STN IMP CAS STN CAS IMP STN IMP STN

n 407 1149 728 407 1044 688 927 714 407 1149 407 731 1106 731 1148

r 0.83 0.66 0.85 0.76 0.39 0.54 0.37 0.68 0.85 0.41 0.64 0.56 0.47 0.55 0.40

MB 0.15 -0.26 0.29 0.64 -0.27 0.20 0.51 1.49 0.33 0.07 0.55 0.14 -1.64 0.07 0.42

NMB (%) 9.2 -12.0 31.5 46.8 -8.0 28.9 4.1 40.3 40.0 5.2 44.2 17.2 -50.0 31.0 59.1

RMSE 0.60 1.16 0.72 1.36 4.37 1.26 10.45 3.81 0.51 1.46 1.07 0.93 3.32 0.48 1.17

NME (%) 25.7 35.9 53.1 70.4 66.9 96.5 50.0 68.9 47.2 57.7 61.3 70.0 60.4 81.0 95.0
23

2.2.6 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. Air

quality models are used in the State Implementation Plan (SIP) process to assess impacts of

potential emissions reduction strategies for the criteria pollutants, in particular ozone. These

predictions can be affected by model input uncertainties, model parameter uncertainties, and

structure of the model itself. One area of concern is the choice of vertical mixing algorithms

because they alter the species concentration mixing histories and, hence, the photochemical

processing, potentially altering the control strategy response from CMAQ. Two choices were

targeted for the sensitivity analysis because the first represents new science, ACM, offered within

CMAQ choices, and the second represents an important change made by the CMAQ developers

to the default configuration of the vertical eddy diffusivity option (KZ) in CMAQ to account for

severe nighttime overprediction of conservative species. The results were:

Change in base year O3

 simulation. The physics sensitivities had a noticeable effect on

the simulation of the base year ozone. For the ACM version at the nonurban sites, the change in

daytime O3

 spanned 40-45 percentage points and was predominately an increase, from a -5%

change to a +35% change. For the old KZ version, the change in daytime O3 spanned 30-40

percentage points with larger increases than decreases, from a -10% change to a +20% change. 

At the large urban site for both sensitivity versions the changes were more evenly divided

between negative and positive, spanning from a -30% to a +40% change in daytime O3.

Change in control strategy response. The physics sensitivities had only a small effect on

the simulation of the relative response of O3

 to a precursor emission reduction. The majority of

the differences in the predicted percent relative reduction factors were less than +/-0.5 percentage

points and most of the rest of the differences less than +/-1%. In contrast, the control strategy

response for the NOx emission reductions ranged from a 30% decrease to a 10% increase, and up

to a 130% increase for a few daytime hours for the large urban area. The control strategy

response for the VOC emission reductions ranged from zero to a 17% to 20% decrease.

Insignificant dependence on grid size. The dependence of the sensitivity response on grid

size for either the base year response or the control strategy response was very small.

Correlation between base simulation change and control strategy simulation change. The

base year O3

 changes due to the sensitivity were not at all correlated with the differences in the

relative control strategy response caused by the sensitivity.

An important observation is that the base year O3 changes in sensitivity is not to be

correlated with the differences in the relative control strategy response caused by the sensitivity. 

Sensitivity analyses investigate uncertainties by directly evaluating the effect of the uncertainty
24

on the control strategy prediction. Based on this analysis, a recommendation for CMAQ users is

that they stay close to the CMAQ default value and not be unduly influenced or concerned by the

nighttime performance for O3 in base year simulations. A paper on this sensitivity analysis is

being prepared.

The sensitivity analysis is being extended to the chemical mechanism choices in CMAQ

as a chemistry sensitivity. Based on completed chemical mechanism sensitivity runs, preliminary

analyses suggest that while differences in the type of vertical mixing (physics parameterizations)

used create comparatively much larger differences in predicted daily ozone for the base

simulation period, differences in chemical mechanisms, however, systematic differences among

different chemical mechanisms are seen in regard to the control strategy response.

2.2.7 Model Evaluation Toolkit

ASMD initiated the development of a prototype model evaluation toolkit. The intent of

the toolkit is to provide specialized data analysis capabilities for supporting the comparison of

model results with observations. For instance, Murphy et al. (1989) and Taylor (2001) describe

specialized data plots that provide better insight into how well a model's results corresponded

with observations than the types of plots that are typically available. The prototype toolkit has

been implemented in R, an open source statistical package. Users can easily combine R's

powerful data manipulation and statistical capabilities with the model evaluation toolkit's

features. The toolkit was designed to be used in a variety of applications. It provides general

data structures for gridded and point data, methods for converting between the data structures,

temporal averaging functions, standard performance measures, and specialized plots.

2.2.8 Meteorological Model Evaluation

Air quality modeling simulations are strongly dependent on the meteorology. Thus, the

performance of meteorological simulations must be assessed in conjunction with the

chemical-transport predictions. Traditional verification methods do not take advantage of the

increasing amounts of non-standard meteorological observations (e.g., wind profilers, aircraft,

satellite winds, etc.). During FY-2003, initial development was completed on a meteorological

evaluation system, which provides an efficient method to comprehensively assess modeled

meteorology using both traditional and non-standard observations. The core of the evaluation

system is automated to allow efficient evaluation of lengthy simulations.

The backbone of the model evaluation tool is flexibility, allowing more than one type of

model and many types of observations to be evaluated. Models that were tested include the

PSU/NCAR MM5, NCEP Eta model, and the WRF model. Additionally, many types of

observational data sets are compatible with the system. The core of the evaluation section of the

system currently consists of plug-in modules that compute traditional model statistics on surface

observations, temperature, moisture, wind, precipitation, and radiation, using specific criteria. 
25

Newer sophisticated techniques are planned for development. For example, one of these

techniques will use wind profiler data to estimate boundary-layer depth for comparison with

model values. Other techniques will take advantage of averaging and filtering of data to examine

whether or not temporal and spatial variability is properly represented. Several model data sets

have facilitated the development and testing of the model evaluation tool in FY-2003.

2.2.9 Sub-Canopy Deposition 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 concentration of pollutants in the air above or near the plant canopy is not the

same as the amount of pollutants 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 models 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 to access

the plant-damage relationship. 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. Higherorder closure models have been developed for this purpose. These models were developed for

use in tree canopies, but have received little evaluation. For use in herbaceous perennials, which

are frequently studied because of their sensitivity to pollutants, these models were not evaluated. 

ASMD has started a model evaluation and improvement program for this reason, adapting a

higher-order closure model developed by NOAA’s Atmospheric Turbulence and Diffusion

Division (ATDD) at Oak Ridge, Tennessee, and collaborating with ATDD 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.

A preliminary 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 National Park

Service 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 cutleaf coneflower (Rudbeckia laciniata), which has been shown to be

very sensitive to ozone. Populations of this plant are not uncommon in the park at higher

elevations.

Selected results from the 2002 study are shown below. Figure 4 displays measured and

modeled ozone concentrations at three levels in a stand of coneflowers that was growing under

the forest canopy. The model reproduced the observed values very well in the middle of the

canopy at 0.78 m, but overpredicted the ozone at the lower height near the ground. Figure 5

compares the predicted and observed turbulent energy above the canopy at 1.5 m. The model
26

Figure 4. Observed and modeled ozone. Figure 5. Observed and modeled variance

of the wind speed

overpredicted vertical (w’2) turbulent energy, but underpredicted the horizontal turbulent energy. 

The observed values of u’2

 and v’2

 are similar. This is not typical of a normal boundary layer,

and may be a result of the redistribution of energy by the tree trunks.

In August of 2003, a second field study was conducted to gather a more complete data set

for sub-canopy model evaluation. The study was conducted in the same location as the 2002

study, at Purchase Knob in the Great Smoky Mountains National Park (Figure 6). Some

differences between the 2002 preliminary study and the 2003 study were: (1) the study ran for

three weeks, instead of four days, and much more data were gathered, (2) a second sonic

anemometer was employed to measure turbulence within the plant canopy (Figure 7), and (3) a

new ozone profile measurement system was built, which used only one ozone monitor, sampling

different levels sequentially. This approach avoided bias between multiple ozone monitors

sampling at the same time. Figure 8 shows the sampler, and Figure 9 the sampling tubes and

filters located in the coneflower canopy. Finally, (4) the height and leaf area index (LAI) of the

coneflower stand were measured every two weeks from the emergence of the plants in May until

maturity in late August. This data will be used to model the total growing season exposure of the

plants, and relate it to observed plant injury.

The 2003 Purchase Knob field study resulted in an extensive data set of ozone profiles,

wind and turbulence measurements, and plant physiological data. The data are being analyzed 

and will be used to evaluate the NOAA-ATDD sub-canopy deposition models. Also, the data

will be used with Lagrangian near-field dispersion analysis to help understand the nature of the

ozone sinks in the sub-canopy environment.
27

Figure 6. Study location at forest edge

behind the NC air monitoring station. Figure 7. Sonic anemometer in canopy.

Figure 8. Ozone profile

sampler.

Figure 9. Profile sampling

tubes.
28

2.2.10 Modeling Studies in the Mid-Atlantic Region

Simulations with the Extended RADM were used in a joint effort with the National Park

Service to develop an environmental assessment for the SHENandoah National Park (SHEN). 

The SHEN assessment report was released in August 2003 (Sullivan et al., 2003). Such an

assessment is unique in National Park Service history in that its Natural Resource Protection

Program funds were allocated to a state-of-the-science assessment for a national park. This was

in part due to the documented loss of a fish species in the SHEN during the 1990's, indicating the

current reductions in sulfur and nitrogen deposition are inadequate.

The SHEN Assessment addresses air quality related values (AQRV’s) and key air

pollutants that threaten aquatic resources primarily from sulfur and nitrogen deposition,

terrestrial resources primarily from O3 exposure and nitrogen and sulfur deposition, and visibility

primarily from sulfate fine particles. The Extended RADM was used to create regional air

quality analyses and characterize the sources of air pollution affecting SHEN for 1990 emission

conditions and to estimate changes at the park in sulfur and nitrogen deposition and sulfate and

ozone air concentrations stemming from potential future reductions in SO2 and NOx emissions. 

To assess current contributions, airsheds were developed to indicate the principal source regions

responsible for the majority of acidic deposition and sulfate haze affecting the park. Also, the

relative contribution of airshed states to the acidic deposition and sulfate haze were defined and

the top-ranked source hot spots contributing to air pollution were identified. To estimate future

conditions, emission projections developed by the EPA Clean Air Markets Division and

combined with heavy duty diesel rule projections from EPA’s OAQPS were simulated with the

Extended RADM. Relative changes from the Extended RADM were provided to ecological

models for calculations of 1990 to 2020 changes to visibility and aquatic and forest resources. 

The assessment reveals that the park’s visibility and sensitive aquatic ecosystems have been

significantly impaired by human-caused air pollution, and that sensitive resource recovery would

fall substantially short of necessary improvements unless very aggressive emission reductions

occur, reductions well beyond Clean Air Act regulations as of mid-2003. The SHEN assessment

is posted at the web site for the National Park Service Air Resources Division

(www2.nature.nps.gov/ard).

2.2.11 Bay Regional Atmospheric Chemistry Experiment: Model Evaluation

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

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

Laboratory (ARL) to enter into a partnership to apply CMAQ to 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 multimedia problem

involving coarse particles and sea salt. Two of the largest power plants in the nation, in terms of
29

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 Air Chemistry Experiment (BRACE), designed for the above two

purposes, was conducted during May 2002. Division scientists, along with ARL colleagues,

were 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 professionals 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 flown by ARL.

The Wexler sectional aerosol model, Aerosol Inorganic Model (AIM), was adapted to

incorporate sea salt in its calculations. During FY-2003, this sectional model was implemented

into the 2002 release version of CMAQ. CMAQ-AIM was tested and successfully adapted to run

on multiple Linux CPU’s. The first calculations for the period of July 4–14, 1999, were

completed and compared to the CMAQ 2002 version. Results are to be presented in the BRACE

session at the Fall 2003 American Geophysical Union meeting. In early FY-2004, the Wexler

AIM sectional aerosol module is to be incorporated into the September 2003 release version of

CMAQ for a more current CMAQ-AIM version.

2.2.12 Recommendations for Observations-Based Methods Sherry delete this title and 

whole section

2.3 Air Toxics Modeling

2.3.1 National Air Toxics Assessment

The National Air Toxics Assessment (NATA) is designed to help EPA, state, local, and

tribal governments and the public better understand the air toxics problem in the United States.

The national-scale assessment includes four steps:

1. Compiling an inventory of air toxics emissions, 

2. Estimating the annual average outdoor air toxics concentrations, 

3. Estimating the exposure concentrations (what people are estimated to breathe),

and

4. Characterizating of potential public health risks. 
30

In general, larger urban areas appear to carry greater risk burdens than smaller urban and rural

areas, because the air toxic emissions tend to be higher in areas with more people. This trend is

not universal, and can vary from pollutant to pollutant, according to their sources. Additionally,

NATA has not focused on the identification of geographic areas or populations that have

significantly higher risks than others. Rather, it has focused on characterizing ranges of risks

across the country independent of their location. The highest localized inhalation exposures and

risks are not captured by this national-scale approach. It is important to use local-scale

assessments to characterize exposures and risks very close to specific sources. 

To address this issue, many regional and community scale modeling assessment projects

are now underway. For the Philadelphia regional scale assessment, the Division is collaborating

with EPA Region 3 to identify and correct problems in the emission inventory, which will be run

in a local scale dispersion model utilizing the improved particulate and gaseous deposition

algorithms recently implemented in the AERMOD dispersion model. Results will be used to

refine plans for regional and local air toxic control efforts. 

Although large uncertainties (e.g., emission levels, exposure, toxicity) are inherent in this

analysis, the modeling results can be interpreted in a relative rather than absolute manner. For

example, the results are appropriate to answer such questions as which pollutants or source

sectors may be associated with higher risks than others (e.g., priority setting for data collection),

but not for determining exactly how many people are exposed to certain levels of absolute risk

(e.g., to determine what's safe and what's not).

2.3.2 Fine-Scale Modeling of Air Toxics and Homeland Security

This effort seeks to develop and evaluate numerical and physical modeling tools for

simulating ground-level concentrations of airborne substances in urban settings at spatial scales

ranging from ~1-10 km. These tools will support modeling needs for air toxics and homeland

security. The air toxics tools will benefit EPA’s NATA program and contribute to the

improvement of human exposure models. The homeland security-related portion of this task will

help in developing tools to assess the threat posed by the release of airborne agents. Both sets of

tools will consider the effects induced by urban morphology on fine-scale concentration

distributions.

Due to source distributions and photochemical activity, airborne substances can exhibit 

different degrees of spatial and temporal variability, especially in urban areas and in different

geographical-climatic regimes. Because current observation networks for driving exposure

models are inadequate, human exposure assessments need to be driven by air quality simulations

performed at the scale of census tracts. Exposure models need ambient concentration fields at

neighborhood-scale resolution to address such issues as environmental justice, community-based

risk assessments, and for conducting hot spot analyses. The Models-3/CMAQ modeling system

is being applied with horizontal grid cell dimensions ranging from 36 km to 4 km to provide

concentration distribution at regional to urban scales respectively. There is a need to improve the
31

capability of current tools to perform model simulations that can provide accurate simulations at

neighborhood scale resolutions. Homeland security modeling requirements are closely related. 

Here, the need is to provide modeling tools and assessment capabilities for tracking the

dispersion of accidental or purposed release of toxic agents. Fluid modeling and computation

fluid dynamics modeling are tools that have been identified to provide a basis for this requisite

capability. These components provide synergism to the air quality modeling needs. Fluid

modeling activities focused on characterizing the dispersion and transport associated with the

collapse of the World Trade Center’s twin towers. The information from the FMF studies

provide an experimental bases for substantiating computational fluid dynamics (CFD) and

subgrid scale modeling studies.

2.3.3 Urban Canopy Parameterizations

Urban canopy parameterizations (UCPs) are being developed and implemented in MM5

to improve fine-scale (~1-km grid spacing) simulations. The UCPs involve changes to the

momentum and energy budgets in MM5, as well as acquisition and implementation of various

databases of the urban morphology of the modeled cities. The UCPs in MM5 leverage the work

of international scientists and contributions from visiting scientists and post-doctoral appointees. 

In FY-2003, the UCP based on the drag-force approach (Lacser and Otte, 2002; Otte and

Lacser, 2002) was applied to two July 1995 cases for Philadelphia. 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). The MM5 simulations with the UCP for Philadelphia in the 1.3-km domain generate

profiles of various fields that are more consistent with observations taken in urban areas, and

resulted in better comparisons with meteorological measurements than the simulations that use

the traditional methodology (roughness approach) in MM5 (Otte et al., accepted for publication). 

These cases were also used to evaluate the impact of UCP-based fields on the CMAQ

simulations, and special versions of MCIP were developed to include the effects of the UCPs on

the PBL fields.

In addition, an advanced UCP that includes coupling with an urban soil model, SM2-U

(Dupont et al., accepted for publication)(a), was implemented in MM5 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 test cases as the UCP described above, and those tests

showed improvements in the MM5 simulation when using the urban soil model (Dupont et al.,

accepted for publication)(b).

A more rigorous evaluation of the advanced UCP for Houston began in FY-2003. 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.,
32

building density and height, vegetation density and height, plan area density, and many more). 

The processing of the morphology database was completed in mid-FY-2003. Sensitivities are

planned for FY-2004 to determine the level of detail that will be required from an urban

morphology database to have a positive impact on the meteorology and chemistry simulations.

2.3.4 Resolved Scale Modeling with CMAQ

Consideration of urban air toxic pollution is moving towards a community, exposure and

risk-based modeling approach, with emphasis on assessing areas that experience elevated air

toxic concentration levels, the so-called hot spots. Making these assessments will require

characterizations of the spatial and temporal variability of toxic pollutant concentrations. Many 

toxic pollutants are active in photochemistry, and their ambient concentrations depend on

regional background as the concentrations produced from local emissions. The CMAQ modeling

system is being studied with the goal of producing pollutant features at high spatial resolution to

drive exposure models. The effort starts by setting the nesting of CMAQ for modeling from

regional to fine scales. Modeling results for various nests will be displayed and discussed. 

Given that exposure and risk assessments are typically focused on populations in urban and

industrial areas, a review is made of the requirements for modeling meteorological and air

pollution fields in urban areas at grid resolutions of the order 1 km. Subsequently, the 1.3-km

grid simulations in CMAQ is utilized as a basis for examining the inherent within-grid spatial

variability unresolved at native coarser scales. The examination reveals an additional sub-spatial

grid variability at less than 1.3 km. The methodology to attain information at grid scales smaller

than 1.3 km will require utilizing dispersion and transport modeled at a finer scale, and their

outputs will be in the form of distribution functions to compliment the 1.3-km CMAQ

simulations.

Simulations for this study were made on episodic bases and were focused on the

Philadelphia area. MM5 and CMAQ simulations were performed using nests at 36-, 12-, 4-, and

1.3-km horizontal cell sizes and results are shown for July 12 and 14, 1995. At 1.3 km, urban

canopy parameterizations (UCPs) were introduced into MM5 to account for the impact of urban

building structures on the meteorological fields (Lacser and Otte, 2002; Dupont, et al., 2003; and

Ching et al., 2003), based on Brown (2000) and Martelli et al. (2002). Sensitivity studies (not

shown here) have shown pronounced affects of the UCP on both the outputs of the MM5 and the

subsequent CMAQ simulations. The emissions were also spatially resolved at 1.3 km grids. Ten

additional vertical layers were introduced into both MM5 and CMAQ to provide vertical

resolution for implementing the UCP methodology. Studies showed some sensitivities to the

layer or layers nearest the surface in which small point, area, and mobile sources were

introduced. 

Figure 10 displays the carbon monoxide (CO) distribution resulting from four nested

CMAQ model grids. Using a smaller grid size enhances the spatial structure (gradients) and the

peak concentrations. The impact on such photochemically active compounds as ozone is even

more pronounced. To investigate the sensitivity of the simulations to grid resolution, a

simulation made at 1.3-km grid cells was established as a base case and ozone concentrations
33

Figure 10. CMAQ simulations of CO at different spatial

resolutions centered over Philadelphia. Top (left: 36km,

right: 12 km), Bottom (left: 4 km, right: 1.3km)

were aggregated to grids corresponding to coarser grid resolutions. Aggregating the model

results in this manner also enables study of the within-grid variability. Figure 11 compares the

1.3-km ozone concentration simulated at 4 p.m. (EDT) with the 12-km aggregated concentration. 

This limited set of model runs show:

• Introducing UCPs into the model changes the modeled flow and the resulting air quality

fields; 

• Resolving the flow and air quality at fine scales will significantly increase the level of

detail in the spatial features, increase the intensity of the concentration gradients, and

increase peak values; and 

• Compositing neighborhood scale simulations to coarser scales yields different results

when compared to coarse grid native simulations; the fine scale grid simulations provide

indications of variability in coarser grid solutions; and the character of these results

differs depending on the scale of the coarse grid mesh. 

During FY-2004, this effort will continue with runs of an air toxics version of CMAQ

with 12- and 4-km horizontal cell sizes and for subset of runs at 1-km grid spaciang focused on

the Philadelphia and Delaware areas on an annual basis. These sets of simulation will be used to

provide inputs for human exposure assessments that require annual ambient concentration values. 

Future studies will focus on detailed meteorological and air quality simulations using the high

resolution gridded UCP for Houston.
34

Figure 11. Ozone at 4 pm EDT (12 km). 

2.3.5 Modeling Subgrid Concentration Variability

 To improve human exposure and risk assessment modeling tools in urban settings,

within-grid concentration variability distribution functions in the form of probability density

functions (PDFs) are needed to complement finely-resolved scale (~1 km) model outputs. When

considered in the context of human exposure modeling and risk impacts, the within-grid spatial

variability in concentration fields arising from the distribution of sources in each grid can be as

important, and in many instances perhaps even more important, than the grid resolved fields. For

this research, the subgrid variability (SGV) from photochemistry and source dispersion processes

were considered. The goal is to develop PDFs that will incorporate contributions from (1)

multiple source dispersion and (2) from turbulence-induced photochemistry SGV derived from a

large-eddy simulation model with direct chemistry coupling using the LESChem model of

Herwehe (2000).

During FY-2003, efforts began to formulate distribution functions to represent the

subgrid variability fields. In an exploratory phase, results from 1.3 km grid-resolved fields

served as a surrogate for data at fine scales. These simulations were used to represent the subgrid

scale variability for grid meshes of 4 km and 12 km, recognizing that this approach will be

refined when modeling results from the source distributions and from subgrid chemistry become

available. The figures provide illustrative statistics to demonstrate the qualitative aspects of such

distributions. Figure 12 shows the standard deviation of the within grid variability at 4 km and

12 km. Figure 13 displays that the distributions for each of the pollutants exhibit a wide range in
35

Figure 12. CMAQ simulations of CO for July 12,

1995.

the value and sign of its skewness. Since exposure estimates depend on concentration and

dosage, the magnitude of the range of the within grid variability becomes an important measure

of risk. Figure 14 shows the range computed from the difference in the peak and minimum

values of the 1.3 km results for each cell of the 12- and 4- km simulations normalized by their

respective coarse scale aggregated grid mean. For the central Philadelphia area, the range

exceeds the mean by up to a factor of 2.

Figure 15 depicts concentration distribution histograms from CMAQ simulations for a

12 km grid from the 1.3-km grid simulations centered on Philadelphia for 17-20 GMT, July 14,

1995. Here the histograms can change rapidly in time, and their characteristics also differ

between the different pollutants. Several of the distributions exhibit multimode character and

such shapes changes in time.

From this limited set of model runs, a few noteworthy points emerge. The degree of

within grid variability varies as a function of grid resolution and pollutant species. The

characteristics of this variability depend on many factors, including the complexity of the urban

area and the distribution of emission sources. Even as the grid mesh resolution is refined to 1 km

or less, within-grid variability will generally occur due to within grid source configurations and

distribution and to chemistry and turbulent interactions (Ching et al., 2003). Methods to describe

these distributions will be a key focus during FY-2004. The fine-scale modeling results also

have implications in assessing model evaluation. In some areas, within grid air concentration

distributions may exhibit an inherently high degree of spatial variability throughout a model

domain; a comparison of model results with observations should factor-in such variability. In

general, since monitors will not be adequately sited to represent the grid resolved value, model

comparison and evaluation should introduce some measure of this subgrid scale variability.
36

Figure 13. Skewness at 12-km grid resolution derived from 1.3 km

simulations for July 12, 1995.
37

Figure 14. Grid and range-to-mean derived from

1.3-km CMAQ simulations of formaldehyde for July

12, 1995.
38

Figure 15. Concentration distribution histogram for 12 km cell in Central Philadelophia. From left, CO, 03, NOx,

acetaldehyde and formaldehyde. From top, 1700, 1800, 1900, and 2000 GMT.
39

2.3.6 Wind Tunnel Modeling of 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 in complex urban areas. To improve our

understanding of the transport and fate of pollutants emitted at the WTC disaster site, a

laboratory study was initiated in FY-2002 and continued through FY-2003 in the Fluid Modeling

Facility’s meteorological wind tunnel using a 1:600 scale model of Lower Manhattan. The

results of the study will be used to evaluate and enhance our numerical simulation capabilities for

Lower Manhattan and other urban areas and to support ongoing risk assessment and public health

studies of the WTC disaster.

The wind tunnel study included measurements and visualizations of flow and dispersion

for three wind directions (315°, 270° and 225°) relevant to the climatology of Lower Manhattan

in late autumn. Flow visualization was accomplished by illuminating theatrical smoke released

from the WTC site to develop a qualitative description of plume dispersion and highlight the

general flow features. Concentration measurements of a tracer released from the WTC site were

performed to quantitatively describe the spread of the plume as it interacted with the complex

building geometries of this highly urban area. Flow measurements were made using Laser

Doppler Velocimetry (LDV) to examine in detail both the mean flow and turbulence levels

within the street canyons. Selected results for the westerly (270°) wind direction are presented

here.

In the flow visualization phase of the experiments, a laser light sheet was used to reveal

the extent and structure of the cross section of the plume at various locations across the city. One

of the most prominent features observed was the entrainment of source material by the tallest

buildings around the WTC site. This upwash or ventilation of smoke along the lee side of these

buildings brought material up to and above the building tops providing initial vertical mixing and

elevated release of the WTC pollutants. This phenomenon is shown in Figure 16 in the lee of

three dominant Lower Manhattan buildings (the World Financial Center, Liberty Plaza, and

Chase Manhattan) where the horizontal laser sheet illuminates three elevated plumes. 

Additionally, large clusters of tall buildings, as found in the Wall Street area to the south and east

of the WTC site, function as single large obstructions to the flow. During the smoke

visualization, plume material moving eastward deflected toward the south around the Wall Street

area. The surface-level concentration distribution for the 270° wind is displayed in Figure 17. 

As observed qualitatively in the flow visualizations, the concentration measurements show the

plume moves initially toward the east, and eventually wraps around the Wall Street cluster of

buildings. Near the WTC site, the plume shows significant crosswind and even upwind spread

due to the mixing and updrafts caused by the buildings surrounding the site. The values of

concentration in Figure 17 and Figure 18 are non-dimensionalized as 100CUH2/Q, where C is

tracer concentration, U is free-stream speed, H is the urban height scale (90 m full scale)

indicative of the average building height, and Q is the volumetric source flow rate.
40

Figure 16. Smoke released from the scale model of the rubble pile at the

WTC site enhanced by horizontal sheet of laser light at elevation just

above tops of tallest buildings. Wind direction is westerly.

Figure 17. Surface concentration pattern in the scale

model of Lower Manhattan for the westerly wind direction.
41

Figure 18. Plume cross sections at downwind distances

of: a) 300 m, b) 600 m, and c) 1200 m from the rubble

pile for the westerly wind direction. The view is

directly upstream against skyline of the city. Colors

indicate categories of building heights.

(Tab) In addition to the surface measurements, vertical slices of plume concentration were

measured at 300, 600, and 1200 meters (full scale) downwind from the WTC site. Figure 18

depicts these cross sections against the background of the city skyline, as viewed from a

downwind position looking into the wind (looking westward). The measurements again support

the observations of the smoke visualization. At 300 meters (about 3 city blocks) downwind, the

plume cross section (Figure 18a) exhibits a double lobe in the 10 and 1 contours reflecting the

near source upwash of material from the Liberty Plaza building to the south and the World

Financial building to the northwest. At 600 meters (Figure 18b), the double lobe is still apparent

but the plume and its peak concentration have shifted toward the south. This is the beginning of

the blocking and deflection effect of the tall, dense cluster of buildings in the Wall Street area. 

At 1200 meters (Figure 18c), the plume continues to grow and shift toward the south. This

represents the plume distribution that is leaving Manhattan and is available for transport to

downwind locations (i.e. Brooklyn, Long Island, etc.).
42

Understanding the local dispersion of contaminants within the complex urban canopy of

Lower Manhattan requires flow characterization within and above the wide variety of street

canyon geometries. Velocity and turbulence fields were sampled using LDV, a non-invasive

technique where, in this study, laser beams were projected through optical quality windows in the

floor of the wind tunnel. The measurements show that the flow generally follows the street

canyons at elevations below the local roof lines, even in streets that are aligned as much as 60°

from the free-stream wind direction. As elevation increases, the flow tends to align with the

free-stream direction except in the vicinity of the taller buildings.

One of the street canyons that was examined in some detail is three to four blocks

northeast of the World Trade Center site on Church Street near the transition from areas of low

and medium rise buildings. Flow in planes perpendicular to Church Street is shown in Figure 19. 

These cross sections are located only 60 m (one block) apart along Church Street and are

separated by a cross street (Murray Street). In Figure 19a, with the upwind and downwind

buildings at about the same height, the flow appears to recirculate within the street canyon. 

There is also a velocity component along the street (not shown) such that the flow actually spirals

up the street. In contrast, only one block away a much taller building stands upwind and no

spiraling or recirculating flow is evident (Figure 19b); instead, upwash on the lee side of the

upwind building is very strong over the entire width of the street. This example of the

complexity of the flow fields in Lower Manhattan and the variability over very short distances

illustrates the challenge that modelers have in quantifying the transport and fate of airborne

pollutants in major urban areas.

Figure 19. Flow patterns along Church Street for the westerly wind direction,

three to four blocks northeast of the WTC site, illustrating complex flows in street

canyons.
43

Wind-tunnel measurements of velocities and dispersion have demonstrated the

complexity of the flow field in and above the street canyons of Lower Manhattan. However

complex, there are several generalities of the flow and pollutant dispersion that can be deduced

from this study and consequently considered for testing against numerical modeling approaches. 

The manner in which areas of densely-packed buildings act as obstructions to the flow and the

manner in which tall buildings can quickly move pollutants near the surface to high elevations

are just two examples. Aside from improving numerical modeling tools for general air pollution

and homeland security type applications, laboratory data can be useful for developing guidelines

for emergency responders making critical evacuation decisions. The model of Lower Manhattan

will be used in future studies to examine other release locations and release types in a variety of

street canyons and intersections.

2.3.7 Numerical Modeling of the World Trade Center Site

The scope of ongoing numerical modeling in support of the environmental assessment of

the events at the WTC during and subsequent to September 11, 2001, has three principal

components: (1) meteorology and pollutant plume in the New York Metropolitan Area and

impact on downwind locations, (2) fine-scale pollution impact in the local area of Lower

Manhattan south of Canal Street, and (3) assessment of human exposure to ambient pollution.

Actual emission rates of pollutants from “ground zero” are unknown; therefore, models cannot

be applied to estimate pollutant levels.

A general characterization was completed of the dispersion of particulate matter from the

WTC recovery site in the ambient air during the three months following the September 11, 2001,

events. 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, North Carolina State University, Raleigh, North Carolina. Figure

20 displays an example plume mapping, which is representative of a single 1-hour plume. Figure

21 displays the time average of the 1-hour plumes and the wind rose for the September 11–13

period. The plume color code represents the estimated dilution. Local wind flow patterns caused

by the sizable buildings in Lower Manhattan are not considered here. Hence, these plume

simulations can be considered appropriate for distances beyond 2 km downwind of the WTC site.

Computational Fluid Dynamics (CFD) simulation methods are being developed to better

estimate fine-scale flow patterns and pollutant dispersion. A digital model of the buildings of

Lower Manhattan are necessary to support the set up of a CFD simulation. A digital model

replication of all buildings south of Canal Street in Lower Manhattan was developed using

licensed digital information from Vexcel Corporation. A digital model of each building was

developed to support construction for wind tunnel model studies and the computation fluid

dynamics numerical simulations studies of the fine-scale pollution dispersion. Figure 22 shows a

CFD simulation of the winds and potential pollution transport pathways (streamlines) for
44

westerly winds. The wind tunnel model study of the same area is providing data on the winds

and pollution transport. Information from the wind tunnel model study will be used to develop

and evaluate the numerical simulation models for the buildings of Lower Manhattan. In addition,

the CFD simulation methods are being developed and evaluated by examining comparisons with

a range of past idealized wind tunnel model studies. Once these evaluation studies are

completed, the numerical simulations can be extended to a wider range of situations and

meteorological conditions. In addition, a CFD model was set up for simulating the collapse of

the North Tower. The collapsing tower was modeled by “pancaking” the building floors under

the force of gravity. The resulting simulated wind speeds exceeded 100 mph at the base of the

collapsed tower where vortices are generated and radiated outward carrying smoke and dust just

as was observed. Figure 23 depicts the outline of the resulting smoke/dust plume. Ongoing

developments include adding different mass (weight) particles to CFD simulation of particle

transport and dispersion for comparison with available information of the deposition pattern

around the WTC site.

A Division scientist participated in a workshop on environmental monitoring and

modeling associated with national emergencies to examine what was learned following the

events of September 11, 2001, at the New York City World Trade Center, and potential response

if a similar event occurred. Models exist to help general understanding of pollution transport on

a metropolitan to regional scale. On these scales, modeling information combined with air

monitoring information enables the assessment of where and when potential emissions may

affect people. Rapid responses using these models are possible when a real-time meteorological

measurements and demographics information are immediately available. New techniques are

needed to improve fine-scale pollution transport near the source and within the neighboring

building environments. Further research and development is needed to adapt existing exposure

models for emergency scenarios. For acute exposure assessments, models need to be developed

based on microenvironmental (e.g., outdoor and indoor) concentration fluctuations and exposure

variability for different media (e.g.,water, air, soil, dust, food, sediment). For chronic exposure,

models need to be developed based on microenvironmental average concentration and people’s

activities and locations relative to the source.
45

Figure 20. Simulated CALPUFF dilution of a volume source

located at the WTC. Numbers are hourly-averaged PM2.5

concentrations. Figure 21. Wind rose, dilution map, PM2.5 concentrations for

average over the period September 11-13, 2001. Wind rose for

the gridpoint closest to the WTC.
46

Figure 22. CFD winds and streamlines through Manhattan street

canyons.

Figure 23. CFD simulation of smoke and dust cloud

following (Tim=85 sec) the collapse of the North Tower.
4Copyright of Argonne National Laboratory. “Dynamic Information Architecture

System” by John H. Christiansen, and A. Peter Campbell. U.S. Patent # 6058387 (2 May 2000).

47

2.3.8 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-2003, multimedia research regarding fully coupled,

multimedia, dynamic, hybrid compartmental models, focused on the expansion of the 3cm pilot

compartmental modeling tool under the MIMS framework. Programming support was provided

by the Argonne National Laboratory (ANL) and consultative assistance was provided by

Multimedia Envirosoft Corporation, Los Angeles, California. The Four Compartment Hybrid

Model (4cm) application builds, solves, and displays the results of a system of coupled ordinary

differential equations (ODEs) and partial differential equations (PDEs), describing the dynamic

movement of chemical mass through multiple media. Like the 3cm, the 4cm was implemented

under the MIMS framework.

The 4cm multimedia system includes uniform air, water, and biota (fish) compartments as

well as a relatively simple non-uniform mixed-phase sediment compartment. The initial test case

developed for the 3cm was repeated for the 4cm and 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. Like the

3cm, the 4cm is not intended to accurately reproduce observed chemical concentrations, but to

1) 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)

demonstrate the practical, in-house, i.e., no proprietary code, implementation of the unique

PDE/ODE coupling approach developed by Cohen. In addition to development of the coupling

algorithm, the 4cm requires additional expressions accommodating aqueous as well as solid

phase chemical movement within and out of the sediment medium.

Implementation of the coupled hybrid compartmental approach proved more challenging

then originally anticipated. Tasks associated with model development included identification

and testing of a simple, fast PDE solution routine and modification of the ANL DIAS©h4

(Dynamic Information Architecture System) and MIMS frameworks. The resulting pilot model

remains unacceptably slow in its present form, but its development has facilitated in-house

understanding of uniform and non-uniform media coupling such that these lessons-learned can

now be applied, where appropriate, to future multimedia CMAQ model research and

development, e.g., regional-scale natural emission, transformation, transport, deposition and

re-emission of Hg.
48

2.4 Multimedia Modeling

2.4.1 Multimedia Integrated Modeling System

ASMD continued development of the Multimedia Integrated Modeling System (MIMS),

which includes the MIMS framework and the spatial allocator. The MIMS framework provides a

software infrastructure to support configuring, applying, and evaluating environmental models. 

ASMD enhanced the framework to make it easier to set up complex simulations, for instance,

where models are invoked repetitively or where there are many input or output parameters. A

Java scientific plotting library (based on the open source statistical package R) was also

significantly enhanced. OAQPS released its first version of the Total Risk Integrated

Methodology (TRIM) multimedia modeling system based on MIMS, which provides scientific

graphics and execution configuration and management support for TRIM. Another group used

MIMS as the basis for a prototype decision support system. The decision support system uses

nonlinear optimization to find promising alternatives that balance economic and environmental

objectives. In that system, MIMS provides the infrastructure for user interaction and managing

multiple model executions, which are used to explore the decision space.

The MIMS spatial allocator computes how spatial fields described on one set of polygons

on the Earth's surface would be distributed to a second set of polygons. Examples of this type of

operation include regridding and applying gridded model results and political boundaries. The

spatial allocator permits the performance of these calculations with common environmental file

formats without relying on a geographic information system. ASMD extended the spatial

allocator to provide more complete support for air quality emissions processing. A number of

groups were using the spatial allocator for that application.

2.4.2 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. During FY-2003, these uncertainty and

optimization tools were integrated into the MIMS framework for use with SWMM and other

models within MIMS. Integration of SWMM with the uncertainty and optimization tools was
49

also completed during FY-2003, along with tests of the coupled system in a case study of Rouge

River near Detroit, Michigan. During FY-2004, this work will be completed by developing

software interface tools for users less familiar with the decision support and analysis tools.

2.4.3 MultiLayer BioChemical Model—Area Weighted

CASTNet is operated by the EPA Clean Air Markets Division (CAMD) and the National

Park Service to monitor concentration and dry deposition at sites across the country to assess

long-term trends in air quality and environmental protection resulting from regulatory policies

and emission reductions required under the Clean Air Act. CASTNet estimates dry deposition

flux by combining measured concentrations of pollutants with modeled deposition velocities. In

FY-2000, an evaluation was performed of the deposition velocity model MultiLayer Model

(MLM) used by CASTNet, which identified several opportunities for improvement in the model. 

Over the last three years, a new deposition model was developed called the Multi-Layer

Biochemical Model (MLBC), which addressed many of the weaknesses in MLM. The MLBC

was described and evaluated in a series of published papers (Wu et al., 2003a; 2003b). In the

original design, MLBC treats all canopies as a mixture of the plant species. For CASTNet, the

canopy at a site is treated as spatially distinct species where the deposition velocity is determined

from area weighting the deposition velocities for each of the local species. MLBCv1.0 is being

modified to allow for this latter approach to develop a new version of the model, which will be

referred to as MLBC-AW (Area Weighted) to denote the area weighting algorithm. Additionally,

both the MLM and the MLBC will be integrated into the MIMS framework to facilitate this

comparison as well as create an easier framework for CAMD and its clients to run the models

with CASTNet or other data.

2.4.4 Chesapeake Bay 2006 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 source of reduced and oxidized nitrogen through deposition. 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 reevaluation is slated for 2006 or shortly thereafter.

Chesapeake Bay has been placed on EPA’s list of impaired waters. The Chesapeake

2000 agreement calls for preempting the need for a TMDL (Total Maximum Daily Load) plan by

cleaning up the Bay by 2010. The Bay 2006 re-evaluation is a critical step in this process

towards the 2010 cleanup and delisting, and ASMD is participating in the re-evaluation process. 

The best science is desired for the re-evaluations, and during the period between major reevaluations, 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
50

cases was 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 multimedia simulations, a 12-km nest over the Mid-Atlantic region covering most of the Chesapeake

Bay airshed was developed. This inner nest better resolves 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 CMAQ MCIP has new capabilities available. 

CMAQ dry deposition algorithms were revised in FY-2003, improving deposition

parameterizations for NH3, HNO3 and other nitrogen containing species. CMAQ now tracks wet

deposition of organic nitrates, which was omitted in the Extended RADM. Organic nitrates are

estimated to be 10-20% of the wet nitrogen deposition budget and it is expected that a significant

fraction of organic nitrates in rainwater could be products of photochemistry. An updated

calculation of 1990 nitrogen deposition was completed in FY-2003 and will be compared to the

previous estimates made using the Extended RADM. 

2.4.5 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

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 with MAQSIP, a development predecessor to CMAQ, 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 (Mathur and Dennis, 2003). 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.

The 1999 summer period was chosen for the next phase of analysis due to availability of

special 12-hour integrated gas and particle measurements at the Clinton site in the middle of the

hog farm area in North Carolina. The ammonia inverse was re-applied to July and August 1999. 
51

Test simulations were carried out at 32-km and 8-km resolutions with the newer CMAQ but with

the older M3Dry deposition algorithms. Preliminary comparisons showed very reasonable

agreement between modeled and measured NHX levels at Clinton and Atlanta, Georgia. With the

updated M3Dry deposition algorithms and with processes analysis turned on for budget studies,

CMAQ runs are underway at the 8-km resolution to support the analysis.

2.4.6 Remote Sensing Image Processing: Pamlico Sound Study

The AVIRIS (Airborne Visible and InfraRed Imaging Spectrometer) was flown over

Pamlico Sound, North Carolina, on May 15, 2002. In a coordinated effort involving EPA,

NASA, NOAA, the University of North Carolina at Chapel Hill, University of Maryland, and

Duke University, data were collected to characterize the spatial variation of chlorophyll A,

suspended sediment, and colored, dissolved organic matter (CDOM)) across the Pamlico Sound

using both hyperspectral remote sensing from 20 km above sea level, low altitude SeaWiFS

simulator imagery, submerged radiometery, and chemical/biological analysis of water samples. 

At the request of EPA, NASA offered the services of the ER-2 flight mission team to fly the

Pamlico Sound and Lower Neuse River Basin during a window from May 10 to June 5. Weather

conditions on May 15 provided near-perfect atmospheric and surface conditions for the imaging.

The objective of atmospheric correction of remotely sensed imagery is to derive surface

reflectance (i.e. spectral albedo) from measured upwelling radiance. The method employed here

used a look-up table approach, whereby MODTRAN (MODerate resolution radiative TRANsfer

model) is used to generate tables of radiances from known reflectance surfaces and model

atmospheres of known precipitable water vapor and aerosol optical depth. The inverse problem

(radiance-to-reflectance) is solved by referencing the appropriate tables. The correction

converted the measured upwelling radiance from 370 nm to 2510 nm to reflectance by removing

the degrading effects of atmospheric attenuation and scattering when viewing the ground from 20

km altitude (Figure 24). The spatial precision of the correction was scene-wise (approximately

50 km x 50 km) for atmospheric gasses, pixel-wise (20 m x 20 m) for water vapor, and

approximately 100 m x 100 m (variable) spatial precision for aerosol correction. 

A dark surface water vapor algorithm was developed and tested in the latest iteration of a

reflectance processor model. Dark surface aerosol algorithms from recently published literature

(Kahn et al., 2001; Holben et al., 1998) will be incorporated. These enhancements will enable

accurate determination of the atmospheric water vapor fields and aerosol optical depth over class

II waters such as the Pamlico, and yield improved accuracy in surface reflectance.
52

Figure 24. The image on the left displays raw radiance from

the AVIRIS spectrometer. The same image on the right is

shown as surface reflectance, following glint- and

atmospheric correction.

2.5 Climate Change Impacts on Regional Air Quality

The Climate Change on Regional Air Quality (CIRAQ) project was initiated in FY-2002

and will directly contribute to the EPA Global Change Research Program’s (EPA GCRP)

assessment reports of global climate change impacts on air quality. The Division’s role in the

assessment is to simulate air quality on a national domain under current and future climate

change conditions. 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. Current and
53

future (2050) 10-year regional climate simulations are under development during FY-2003-04

and will be followed by CMAQ air quality simulations during FY-2004-06. The primary goal of

these simulations is to develop future air quality modeling scenarios to compare against current

conditions to test the sensitivity of air quality to potential climate change. During FY-2003,

CIRAQ activities included:

Regional climate downscaling of meteorology. To support this project and ultimately the

air quality assessment, the 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 model (GCM) simulations, and the future GCM simulations will be rely on

Intergovernmental Panel on Climate Change (IPCC) future greenhouse gas scenarios. For

comparison against observationally-constrained boundary conditions, MM5 simulations using

NCEP reanalyzed meteorological fields as boundary conditions would also be performed.

During FY-2003, 10-year MM5 simulations using NCEP reanalysis fields as boundary

conditions were completed (1990-2000) and transferred to ASMD for archiving. PNNL is in the

process of completing two additional MM5 regional climate modeling simulations with boundary

condition links to the National Aeronautics and Space Administration (NASA) Goddard Institute

for Space Studies (GISS) GCM and the NCAR/PNNL Parallel Climate Model (PCM). These

MM5 simulations using GCM boundary conditions will be compared to the MM5-NCEP results

as a benchmark. MM5-GISS and MM5-PCM 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 regularly archives

the data and develops the model-ready meteorology fields needed for CMAQ and the emissions

processor SMOKE©.

Global climate and chemical transport simulations. During FY-2003, test simulations

were performed with the NASA GISS GCM under a future IPCC greenhouse gas emission

scenario. Ozone chemistry was also included in the simulation to provide chemical boundary

conditions for CMAQ. The GISS GCM output from the present through 2050 will be analyzed

to test how many years of CMAQ simulation are needed to separate out the influence of climate

change trends from interannual variability. The GISS simulations 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.

EPA grant collaborations. In FY-2003, EPA Science to Achieve Results (STAR) grants

and cooperative agreements were awarded to several groups focusing on global and regional air

quality impacts from climate change. ASMD has established collaborative plans with four

groups, including Harvard University, Carnegie Mellon University, and University of Illinois. 

Areas of collaboration include linkage of global chemical transport model results from different

global models with CMAQ and evaluation of regional versus global results over the United

States domain using methods such as empirical orthogonal functions. These activities will be
54

ongoing during FY-2004, followed by the development of model-ready emissions for CMAQ

based on 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.

Methods developed for analysis and evaluation of regional climate simulations. As MM5

regional climate simulation results become available for current time periods, it will be necessary

to evaluate historical time periods against meteorology to assess the model performance. While

it is not possible to evaluate future simulations against observations, the CIRAQ team developed

tools to screen the MM5-GISS and MM5-PCM future results for outliers or unstable, nonphysical results. The screening tools were tested and completed in FY-2003 and are being

applied to the regional climate simulation results as part of the model-ready meteorology

processing. Tools were developed to evaluate historical MM5 simulations against a suite of

observations and generate graphs using GraDs. 

In addition to direct evaluation of point observations at collocated MM5 grids, analysis

methods developed to consider the spatial and temporal variations in the MM5 were tested. A

cluster analysis method following Cohn et al. (2001) was implemented where major modes of

spatial variability are identified from meteorological simulations. This method is helpful in

comparison of spatial differences between simulations and against analyzed fields where

observational data were assimilated. Meteorological fields known to be related to air quality will

be considered using this method. Based on results from the cluster analyses, the KolmogorovZurbenko (KZ) filter that was tested during 2003 for time filtering can be applied to spatial areas

of interest. The KZ filter can separate out variations in meteorological variations into different

time scales. With a 10-year simulation, it will be possible to consider daily, synoptic, seasonal,

and interannual variations. Using time series analysis may help to identify differences between

periodic variations and actual trends in climate. Both the cluster analysis and KZ filter methods

can also be used to analyze air quality predictions once CMAQ simulations are completed.

2.6 Specialized Client Support

2.6.1 Assistance to State/Local Air Quality Forecasters

The EPA AIRNow program collects and disseminates Air Quality Index (AQI) data and

forecasts to inform the public about daily air pollution levels and the resultant health impacts in

their communities. During FY-2003, the AIRNow program was expanded to include daily

forecasts of PM2.5 levels in over 100 cities across the United States. To enable forecasts of

particle pollution from State/Local air pollution control agencies, ASMD helped develop the

EPA guidance document, Guidelines for Developing an Air Quality (Ozone and PM 2.5)

Forecasting Program (U.S. EPA, 2003). Division staff also worked with EPA on several other

activities designed to assist State/Local air quality forecasters in issuing accurate PM2.5 next-day

forecasts. Four training workshops were conducted to inform State and Local forecasters of the
55

latest tools and techniques associated with particle pollution forecasting. A series of statistical

and qualitative forecast tools were developed for 22 major metropolitan areas across the United

States. Finally, a variety of software products were developed to ease the day-to-day forecast

burden. The combination of the AIRNow infrastructure and the NOAA/EPA air quality forecast

model is expected to yield even greater amounts of air quality and health impact information.

2.6.2 Support to the NOAA Office of the Federal Coordinator for Meteorology

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 to

review the ATD modeling systems in use by the Federal agencies at operational modeling centers

and to conduct a preliminary analysis of gaps in understanding, and recommend areas for

research and development. The JAG/SEATD met approximately monthly through July 2002,

and summarized its findings in a report (Hicks et al., 2002). While there are over 140

documented ATD models 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 analysis of the gaps in the understanding and capabilities of 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. 

During the summer of 2003, the OFCM assisted Federal agencies having expertise in

ATD modeling systems to develop an interagency framework to provide tailored all-hazards

assessments for the Department of Homeland Security (DHS). The purpose of the framework is

to provide the best available information for atmospheric hazard predictions so that DHS can

make appropriate emergency response and consequence management decisions. The framework

designates the primary agency to be contacted, and specifies a progression of backup agencies to

provide redundancy. A communication protocol would be established so that in an emergency

those modeling centers with the appropriate expertise are alerted and a common understanding of

the emergency is established. Each contacted agency would provide an initial characterization,

which would be updated and refined as new information becomes available. The results from

each agency may differ due to differences in the underlying assumptions reflecting the

uncertainties of the situation, but summarizing all the results will provide a robust
5GRASS (Geographic Resources Analysis Support System) is Copyright, 1999-2002 by

the GRASS Development Team, and licensed under terms of the GNU General Public License.

6Copyright to Environmental Systems Research Institute, Inc. (ESRI); ArcGIS is a

trademark of ESRI.

56

characterization of the overall uncertainty. By this means, the primary agency can provide DHS

a tailored assessment of the probable impact of an airborne release and a summary of the

uncertainties associated with the assessment. To implement the framework, a common GIS

software (e.g., GRASS©5

, ArcGIS©TM6

) must be selected that can be used by each agency in

expressing its characterization of the impacts and uncertainties. Once this is selected, then the

cost of implementing the framework is whatever it takes for each agency to convert its ATD

predictions to the common format for dissemination and use.

2.6.3 Sensitivity of PM2.5 Modeling to Grid Resolution

As part of an exercise to develop EPA guidance to direct State/Local PM2.5 attainment

demonstrations, Division staff modeled a series of sensitivity runs using CMAQ to consider the

effects of 36-, 12-, 4-km grid cell size on PM control signal and signal strength. The modeling

analysis was completed for a limited summer case, July 7–15, 1995, using an older version of the

CMAQ model. 

The major conclusions from this short-term sensitivity modeling was that control ratios

can vary as a function of grid resolution, but the differences are generally small between 36 and

12 km in these simulations (e.g., only 21% of the simulated city/control cases show differences

of greater than 1%, and only 10% of the cases show differences of greater than 2%). The largest

base/control differences are generally seen in cases involving nitrate replacement. For example,

the effect of a 25% SOx cut in Baltimore was a 29% increase in nitrate in the 36 km grid, but only

a 18% increase in the 12 km grid. The control ratios in the 4 km grid were generally similar for

PM 2.5, but could vary widely for ozone based on the titration of ozone by NO emissions with the

urban core.

2.6.4 Community Modeling and Analysis System Center

FY-2003 was the second year of the cooperative agreement, begun at the initiative of

ASMD for the establishment and operation of Community Modeling and Analysis System Center

(CMAS) in support of the Models-3/CMAQ air quality modeling system. During FY-2003, the

cooperative agreement was transferred from the MCNC North Carolina Supercomputing Center

to the Carolina Environmental Program of the University of North Carolina at Chapel Hill

(UNC-CH). All CMAS Internet-based support functions were migrated to computers maintained

by UNC-CH and are located at www.cmascenter.org. CMAS strengthened its crucial role in the

growth and sustenance of the CMAQ user community by providing expanded collaboration in
7Copyright 1997-2000 MCNC-North Carolina Supercomputing Center, Research Triangle

Park, NC.

57

model improvements, regularly scheduled training, and support. The CMAS on-line support

functions were expanded to include on-line bulletin boards to allow users to share information on

specific Models-3/CMAQ topics, in addition to the automated bug-tracking system, users

listserv, and Frequently Asked Question section. Internet information and data clearinghouse

capabilities were also added to the CMAS web site. CMAS is now the portal for public release

of EPA air quality modeling products. The computer code and documentation for CMASsupported modeling products can be accessed through the CMAS web site. Current products

include the Models-3 IO/API, CMAQ, SMOKE©, MIMS, and PAVE©7 (Package for Analysis

and Visualization).

CMAS continued active outreach to the Models-3/CMAQ user community during

FY-2003. The Models-3 listserv reflected expanded use of the modeling in products in Europe

(England, Germany, Spain, Bulgaria) and Asia (China, Taiwan, Japan), as well as in North

America. The First CMAS User’s Workshop was held October 21–23, 2002, at the EPA

facilities in Research Triangle Park, North Carolina. There were over 110 participants and 40

presentations. At the end of FY-2003, the second workshop was being planned for October

2003, with increased registration and number of presentations expected. CMAS User’s

Workshop papers and presentations are available at www.cmascenter.org/workshop.shtml.

2.6.5 Particle Deposition—Comparison of Aerodynamic and Mechanical Resuspension

Mechanisms

Resuspension of uniform latex microspheres deposited on a single seed pod of field Rye

grass stalk and head was investigated experimentally in a wind tunnel. The experiment was

designed to answer the following questions: 

(1) Does the mechanical disturbance of grass (hereafter abbreviated by “M”) increase the

resuspension rate of particles deposited on grass when compared to the action of aerodynamic

mechanisms (hereafter abbreviated by “A”) alone? Examples of A are direct actions of the

turbulent air motions: vibration of vegetative surfaces, production of sweeping eddies that may

detach particles, and viscous forces that remove particles at the mean wind speeds. The

mechanical disturbance (M) considered in this experiment was the striking of the oscillating

grass stalk against a stationary object in response to turbulent air motions. 

(2) Does particle size affect the particle flux enhancement of the M mechanism?

The experiment was run for water-suspended spherical latex particles with diameters

from 2 to 10 :m painted onto seed pods of grass. Wind tunnel tests were run for wind speeds
58

from 2 to 18.5 ms-1 and a turbulence intensity (root-mean-square fluctuation wind speed /mean

wind speed) of 0.1. A locally available rye grass field (Secale Cercele) was chosen.

Experimental results for 2 :m microsphere tests suggest an exponential decrease of

resuspension with time. The data show that the flux of 2 :m particles is not sustained for wind

tunnel winds less than 12.4 m s-1. Above 12.4 m s-1, the flux is sustained. For wind speeds below

12.4 m s-1, particles are seen coming off the grass for a limited period of time. This is interpreted

as a small fraction of the particles being loosely adhering to the grass that are depleted soon

following the start of the test. The A threshold of 12.4 m s-1 was used based on our data. 

Likewise the threshold for the A mechanisms for the other particle sizes is given in Table 3. 

Table 3. Threshold centerline tunnel speed vs. particle size and ratio of resuspension fluxes

for A to (A+M). 

Diam, :m 2 3.2 4.5 8.1

Threshold, ms-1 12.4 13.5 13.5 16.7

Ratio of Fluxes

A to (A+M), %

15 44 43 47

Ratios of the fluxes for aerodynamic forces only (A) to fluxes for aerodynamic plus

mechanical (impacting grass) mechanisms (A + M) were calculated and are given in Table 3.

Whereas the resuspension of 2 :m microspheres was dominated by mechanical resuspension,

viscous/turbulent resuspension is almost equally effective for 3.2 and 4.5 :m microspheres. 

However, this ratio is probably underestimated because the tests for A + M resuspension were

always done following tests of A resuspension; that is, mechanical resuspension always operated

on an already depleted particle source. For 8.1 :m microspheres, the table shows that the

thresholds for A resuspension were at the upper part of the range of our wind tunnel speeds; the

A and M resuspension mechanisms were not fully developed at our highest wind tunnel speeds

compared to the 2, 3.2, and 4.5 :m microspheres.

The findings from these experiments suggest the following: (1) Resuspension particle

flux increases when mechanical impacts occur; and (2) mechanical resuspension dominates for 2

:m particles over purely aerodynamic resuspension, but for larger particles aerodynamic

mechanisms are roughly equally effective in resuspending particles. 

2.6.6 NARSTO Program Support

NARSTO, a multi-stakeholder entity, was organized in 1994 to sponsor cooperative,

public/private, policy relevant research on tropospheric ozone. In 1999, NARSTO’s charge was

expanded to include airborne particulate matter. Currently, its research programs focus on both

ozone and PM, including their combined atmospheric chemistry and physics. As a major part of
59

its charter, periodic policy relevant science assessments are commissioned. Its first assessment

was the 2000 Ozone Assessment and the second was the February 2003 Particulate Matter

Science Assessment. 

NARSTO is an umbrella organization with a bottom-up management structure where

each member controls its own resources, but contributes to a larger cause. NARSTO activities in

the PM arena fall into two general categories: direct NARSTO efforts, and internal efforts

conducted by NARSTO member organizations. Items in the first category are easy to list, while

those in the second are more difficult. A number of NARSTO's collective efforts, particularly

the field studies, deal with gaseous pollutants as well as particles. 

Direct NARSTO efforts:

• Production of the NARSTO PM Assessment

• Archival of the PM Super-Site data in the NARSTO data archive

• Organization and conduct of the NARSTO Workshop on Advanced Techniques for

Emission-Inventory Development and Verification (Austin, TX, October 2003)

• Organization and conduct of the meeting: Tropospheric Aerosols: Science and Decisions

in an International Community (Querétaro, Mexico, October 2000)

Collective NARSTO efforts:

• California Regional Particulate Air Quality Study (2000, San Joaquin Valley)

• Texas 2000 Field Study (East Texas)

• Pacific 2001 Field Study (Southwestern Canada)

• PNW 2001 Field Study (Northwestern United States)

• Mexico City Metropolitan Area 2003 Field Campaign

Individual NARSTO members contribute significantly to the advancement of the understanding

of atmospheric chemistry and physics, measurement technique development, computer modeling,

and emission characterization. More information can be found at:

http://www.cgenv.com/Narsto/

2.6.7 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
60

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. In FY-2002, the United States and Canadian

representatives offered to host a workshop on fine particulate matter measurement and modeling

to summarize the experiences of the EPA Supersite program for the benefit of the EMEP

program. The offer was enthusiastically accepted by EMEP. The workshop will be held in April

2004 in New Orleans, Louisiana.

2.6.8 Modeling Human Exposure to Solar Ultraviolet Light Modeling Human Exposure to

Solar Ultraviolet Light

Solar ultraviolet radiation is a known human carcinogen and a causal agent in cataract

induction. Experimental exposure assessment methods are currently limited to mannequin

studies or human volunteers performing scripted scenarios wearing UV-sensitive polysulfone

film badges. The modeling approach presented will enable calculation of the relative distribution

of sunlight exposure across the anatomy with unprecedented precision, providing dose estimates

used in the development of dose/response functions. The model will also be used to quantify

continuous duration acute human exposures and compare with controlled laboratory doses known

to induce basal cell and melanoma skin cancers in animals. An article will be published

describing a simulation model that calculates instantaneous irradiance or cumulative intercepted

energy at 40 anatomical locations for an arbitrary sun exposure scenario defined by latitude,

longitude, date, time, atmospheric transmissivity, and subject posture.

2.7 Regulatory Support

2.7.1 Ozone Modeling Completed in Support of the Interstate Transport Rule

Section 110(a)2(d) of the Clean Air Act (CAA) gives EPA the authority to require States

to develop plans to prohibit sources from emitting pollutants in amounts that will contribute

significantly to nonattainment in any other State. EPA can set emission budgets designed to

avoid significant amounts of interstate transport. In support of a proposed rulemaking designed

to reduce ozone and PM2.5 nonattainment in the future, Division staff completed an air quality

modeling analysis to consider whether significant State-to-State ozone transport affecting 8-hour

ozone nonattainment areas will exist in 2010.

The Comprehensive Air Quality Model with Extensions (CAMx) was used to assess

8-hour ozone concentrations as part of this proposed rulemaking. CAMx is a publicly available

Eulerian model that accounts for the processes that are involved in the production, transport, and

destruction of ozone over a specified three-dimensional domain and time period. The model

simulations were performed for a domain covering the eastern United States and adjacent

portions of Canada. Three episodes during the summer of 1995 were used for modeling ozone
61

and precursor pollutants: June 12–24, July 5–15, and August 10–21. An operational evaluation

was completed, which concluded that, on average, the model-predicted patterns and day-to-day

variations in regional ozone levels are similar to what was observed with measured data. Ozone

modeling was performed using 2001 emissions and for 2010 and 2015 base cases as part of the

approach for forecasting which counties are expected to be in nonattainment in these two future

years. In general, the approach involved using the model in a relative sense to estimate the

change in ozone between 2001 and each future base case.

The modeling approach used by EPA to quantify the downwind impact of emissions in

specific upwind States to projected downwind nonattainment for 8-hour ozone included two

different techniques, zero-out and source apportionment. The outputs of the two modeling

techniques were used to calculate metrics or measures of contribution. The metrics were

evaluated, for each State/State linkage, to determine which States make a significant air quality

contribution to downwind ozone nonattainment in other States. These two techniques provided

different technical approaches to quantifying the downwind impact of emissions in upwind

States. The zero-out modeling analysis provided an estimate of downwind impacts by comparing

the model predictions from a base case run to the predictions from a run in which the base case

man made emissions were removed from a specific State. In contrast to the zero-out approach,

the source apportionment modeling quantified downwind impacts by tracking the impacts of

ozone formed from emissions in an upwind source area. The EPA selected several metrics to

quantify the projected downwind contributions from emissions in upwind States. The metrics

were designed to provide information on three fundamental factors for evaluating whether

emissions in an upwind State make large and/or frequent contributions to downwind

nonattainment. These factors are: a) the magnitude of the contribution, b) the frequency of the

contribution, and c) the relative amount of the contribution. The magnitude of contribution

factor refers to the actual amount of ozone contributed by emissions in the upwind State to

nonattainment in the downwind area. The frequency of the contribution refers to how often

contributions above certain thresholds occur. The relative amount of the contribution is used to

compare the total ozone contributed by the upwind State to the total amount of nonattainment

ozone in the downwind area. The factors are the basis for several metrics that can be used to

assess the significance of a particular impact. The metrics used in this analysis were the same as

those used in the NOx

 SIP Call. The findings of this modeling analyses were that significant

ozone transport is likely to exist over broad portions of the eastern United States by 2010.

2.7.2 Air Quality Modeling Completed in Support of the 8-Hour Ozone Implementation

Rule

In June 2003, EPA proposed a rule to implement the 8-hour ozone NAAQS. As part of

the background information for the rulemaking, an economic analysis of the costs and benefits of

the 8-hour ozone NAAQS was initiated. To guide this cost-benefit analysis, a series of 34

CAMx modeling runs were completed to assess the emission reductions necessary to meet the air

quality standard by the attainment dates outlined in the rule. Each of the CAMx simulations

considered differing levels of NOx versus VOC control and various combinations of regional
62

versus local controls. Attainment targets were estimated for areas projected to be in

nonattainment of the 8-hour ozone NAAQS. 

As an example, the analysis indicated that the Providence, Rhode Island, metropolitan

statistical area would need either a) an additional 15% reduction in local NOx emissions, b) an

additional 10% reduction in local NOx plus VOC emissions, or c) an additional 10% reduction in

local NOx

 emissions in conjunction with a reduction in regional NOx

 emissions. These results

were input into an EPA economic model to determine the least cost set of controls that would

yield attainment. In general, the modeling concluded that large amounts of additional ozone

precursor control, beyond already promulgated measures, would be needed for several

metropolitan areas in the eastern United States to reach attainment by 2007, 2010, and/or 2013.

2.7.3 Ozone Modeling Completed in Support of the Nonroad Rule

In May 2003, EPA proposed to reduce emissions from nonroad diesel engines as a means

of reducing the population exposed to nonattainment levels of ozone and PM 2.5. To demonstrate

the need for, and the impact of the rule, ASMD performed CAMx modeling simulations of two

separate 36 and 12 km resolution domains, one covering the eastern United States and the other

covering the western United States. For the eastern United States domain, the model was applied

and evaluated over three episodes that occurred during the summer of 1995 base year. For the

western United States, two episodes that occurred during the summer of 1996 were modeled

using base year emissions. Subsequently, episodic ozone model runs were made for 2020 and

2030 base and control case scenarios for both domains and all episodes.

The model outputs from the 1996 base year and 2020 and 2030 base cases, combined

with current air quality data, were used to identify areas expected to exceed the ozone NAAQS in

2020 and 2030. These projected nonattainment areas will require additional emission reductions

to attain and maintain the ozone NAAQS. The costs, benefits, and expected impacts of the

proposed controls were determined by comparing the model results in the future year control runs

against the baseline simulations of the same year. Ultimately, the modeling supported the

conclusions that there would potentially be several metropolitan areas with predicted ozone

concentrations at or above the NAAQS in the 2020 and 2030 base case scenarios without

additional emission reductions; and that the proposed nonroad emissions reductions could be

expected to substantially improve ozone levels in the future.

2.7.4 Sensitivity Modeling Analyses for Regulatory Applications of CMAQ

ASMD conducted several model diagnostic tests to assess the status of the CMAQ model

and its suitability for regulatory support modeling. The results of some of these analyses were

used to support model developmental work. The effect of alternative roughness lengths and their

seasonality in MCIP were assessed. A series of runs investigating the effect of using pass-thru

options in MCIP versus recalculating PBL heights and radiation fields were completed. The
63

Division also assessed the effect of using revised versions of MCIP (v2.1 and v2.2). The

sensitivity of the model to the minimum Kv (Kv=Vertical diffusion coefficent) value within

CMAQ was considered, as was the effects of changes in CMAQ deposition velocities. Finally,

modeling was conducted to assess the effects of revisions to CMAQ horizontal diffusivities.

2.7.5 Meteorological Classification to Augment Speciated Pollution Data

A plan was developed to use meteorological data to assess seasonal speciation data to

determine source locations of high particulate matter (PM). Hourly meteorological data was

gathered for many sites within the United States by the NOAA. These sites tend to be in or near

metropolitan areas. An entire year of data is stored on a CD and part of the year 2001 and 2002

was used. Meteorological data from September 2001 through November 2001 will be used for

the fall season, December 2001 through February 2002 for the winter season, March through

May 2002 for the spring season, and June through August 2002 for the summer season. 

Meteorological data is being downloaded to a spreadsheet for 45 urban stations

throughout the United States. The meteorological data to be used include temperature, wind

direction and speed, relative humidity, and rainfall will be averaged over each of the four seasons

described above. This data will then be used to assess the speciated PM data that was collected

over the same time periods.

2.7.6 Support Center for Regulatory Air Models

SCRAM (Support Center for Regulatory Air Models) website continued to be updated to

reflect addition of models and data sets, including modifications to AERMOD deposition; these

updates comply with the official EPA web page template, as directed by the Office of

Environmental Information, and with EPA OAQPS guidelines for regulatory air models. 

Additional references and links were created to provide easier maneuvering to specific models

and related files. Updates also related to the 7th Modeling Conference for Air Quality Modeling

(http://www.epa.gov/scram001/tt26.htm) were made. SCRAM serves as the sole Internet source

for the EPA’s air quality dispersion models, guidance, and related programs and information.
64

REFERENCES

American Society for Testing and Materials. Standard Guide for Statistical Evaluation of

Atmospheric Dispersion Model Performance (D 6589). West Conshohocken, PA 19428,

17 pp. (2000). Available at http://www.astm.org.

Arnold, J.R., R.L. Dennis, and G.S. Tonnesen. Diagnostic evaluation of numerical air quality

models with specialized ambient observations: Testing the Community Multiscale Air

Quality (CMAQ) modeling system at selected SOS 95 ground sites. Atmospheric

Environment 37:1185–1198 (2003).

Barad, M.L. (Ed.). Project Prairie Grass, a Field Program in Diffusion. Geophysical Research

Papers, No. 59, Vols. I and II. Air Force Cambridge Research Center Report

AFCRC-TR-58-235, 479 pp. [NTIS PB 151 425 and PB 151 424] (1958).

Bowne, N.E., R.J. Londergan, D.R. Murray, and H.S Borenstein. Overview, results, and

conclusions for the EPRI plume model validation and development project: Plains site. 

EPRI EA-3074, Project 1616-1, Electric Power Research Institute, Palo Alto, CA, 234

pp. (1983).

Brown, M. Urban parameterizations for mesoscale meteorological models. In Mesoscale

Atmospheric Dispersion. Z Boybeyi (Ed.). WIT Press, Boston, 424 pp. (2000).

Ching, J., A. Lacser, T. Otte, and S. Dupont. Air quality simulations at neighborhood scales with

CMAQ. Proceedings of the 4th International Conference on Urban Air

Quality—Measuring, Modeling and Measurement, Charles University, Prague, Czech

Republic, March 25-28, 2003. Institute of Physics, the Charles University, and the

University of Hertfordshire in collaboration with SATURN and COST715, Charles

University, Prague, CZ, 47–50 (2003).

Cohn, R.D., B.K. Eder, S.K. LeDuc, and R.L. Dennis. Development of an aggregation and

episode selection scheme to support the Models3 Community Multiscale Air Quality

Model. Journal of Applied Meteorology 40:210–228 (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 (2002a).

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 (2002b).

Dennis, R.L., J.R. Arnold, and G.S. Tonnesen. On the need for better ambient observations of

important chemical species for air quality model evaluation. W.A. McClenny (Ed.). In 
65

Recommended methods for ambient air monitoring of NO, NO2, NYY, and individual NOZ

species. EPA/600/R-01/005, National Exposure Research Laboratory, U.S.

Environmental Protection Agency, Research Triangle Park, NC, 8–21 (2001).

Dupont, S., T.L. Otte, and J. S. Ching. Simulation of the meteorological fields within and above

urban and rural canopies with a mesoscale model (MM5). Boundary-Layer Meteorology

(accepted for publication)(a).

Dupont, S., I. Calmet, P. Mestayer, and S. Leroyer. Parameterisation of the urban energy budget

with the SM2-U model for the urban boundary layer simulation. Boundary-Layer

Meteorology (accepted for publication)(b).

Gego, E., C. Hogrefe, G. Kallos, A. Voudouri, P.S. Porter, J.S. Irwin, and S.T. Rao. 

Examination of model predictions at different horizontal grid resolutions. Environmental

Fluid Mechanics (accepted for publication)(a).

Gego, E., P.S. Porter, J. Irwin, C. Hogrefe and S.T. Rao. Assessing the comparability of

ammonium, nitrate and sulfate concentrations measured by three air quality monitoring

networks. Pure and Applied Geophysics (Accepted for publication)(b).

Gillani, N.V., and J.M. Godowitch. Plume-in-grid treatment of major point source emissions. In

Science algorithms of the EPA Models-3 Community Multiscale Air Quality modeling

system. Chapter 9. D.W. Byun, and J.K.S. Ching (Eds.). EPA-600/R-99/030, National

Exposure Research Laboratory, U.S. Environmental Protection Agency, Research

Triangle Park, NC, 1–40 (1999).

Gilliland, A.B., R.L. Dennis, S. Roselle, and T. Pierce. Seasonal NH3 emission estimates for the

eastern United States based on wet ammonium concentrations and an inverse modeling

method. Journal of Geographical Research-Atmospheres108(D15), 4477,

doi:10.1029/2002JD003063 (2003).

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

A&WMA, May 20–24, 2002, Norfolk, Virginia. American Meteorological Society,

Boston, 69–70 (2002).

Haugen, D.A. (Ed.). Project Prairie Grass, a Field Program in Diffusion. Geophysical Research

Papers. No. 59, Vol. III. Air Force Cambridge Research Center Report

AFCRC-TR-58-235, 673 pp. [NTIS PB 161 101] (1959).

Hertel O., R. Berkowicz, J. Christensen, and O. Hov. Test of two numerical schemes for use in

atmospheric transport-chemistry models. Atmospheric Environment 27A:2591-2611

(1993).
66

Hicks, B., R. Addis, W. Bach, T. Bauer, B. Beitler, P. Davidson, J. Ellis, D. Garvey, J. Irwin, J.

Mitchell, D. Payton, D. Randerson, and J. Sarkisian. 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, 160 pp. (2002).

Holben, B.N., T. Eck, I Slutsker, D. Tanre. AERONET—A federated instrument network and

data archive for aerosol characterization. Remote Sensing of the Environment 66:1–16

(1998).

Irwin, J., D. Carruthers, J. Paumier, and J. Stocker. Application of ASTM D6589 to evaluate

dispersion model performance. International Journal of Environment and Pollution. 

20(1–6):4–10 (2003).

Jacob, D.J. Heterogenous chemistry and tropospheric ozone. Atmospheric Environment

34:2131–2159 (2000).

Kahn, R., P. Banerjee, D. McDonald, and J. Martonchik. Aerosol properties derived from

aircraft multiangle imaging over Monterey Bay. Journal of Geophysical ResearchAtmospheres 106(D11):11977–11995 (2001).

Lacser, A., and T.L. Otte. Implementation of an urban canopy parameterization in MM5. 

Preprints, 4th Symposium of Urban Environment, May 20-24, 2002, Norfolk, Virginia. 

American Meteorological Society, Boston, 153–154 (2002).

Martelli, A., A. Clappier, and M. Rotach, 2002: An urban surface exchange parameterizations

for mesoscale models. Boundary-Layer Meteorology 104(2):261–304 (2002).

Mathur, R., and R.L. Dennis. Seasonal and annual modeling of reduced nitrogen compounds

over the Eastern United States: Emissions, ambient levels and deposition amounts. 

Journal of Geophysical Research-Atmospheres 108(D15), 4481

doi:10.1029.2002JD002794 (2003).

Murphy, A.H., B.G. Brown, and Y.-S. Chen. Diagnostic verification of temperature forecasts.

Weather and Forecasting 4:485–501 (1989).

Murray, D.R., and N.E. Bowne. Urban power plant plume studies. EPRI Report No. EA-5468,

Research Project 2736-1, Electric Power Research Institute, Palo Alto, CA (1988).

Olesen, H.R. A platform for model evaluation. International Journal of Environment and

Pollution 16(1–6):129–136 (2001).
67

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-

25, 2002, Boulder, Colorado. National Center for Atmospheric Research, Boulder,

Colorado 138–140 (2002).

Otte, T.L., A. Lacser, S. Dupont, and J.K.S. Ching. Implementation of an urban canopy

parameterization in a mesoscale meteorological model. Journal of Applied Meteorology

(Accepted for publication).

Pierce, T., W. Benjey, J. Ching, D. Gillette, A. Gilliland, S. He, M. Mebust, and G. Pouliot.

Advances in emissions modeling of airborne substances. Emission Inventories —

Applying New Technologies, 12th International Emission Inventory Conference, April

29–May 1, 2003, San Diego, California. Session 3 — Data Management. Office of Air

Quality Planning and Standards, Research Triangle Park, NC, (2003). Available at

http://www.epa.gov/ttn/chief/conference/ei12/index.html#ses-9

Pleim, J.E., and A. Xiu. Development of a land surface model. Part II: Data assimilation. 

Journal of Applied Meteorology 42:1811–1822 (2003).

Riemer, N., H. Vogel, B. Vogel, B. Schell, I. Ackermann, C. Kessler, and H. Hass. Impact of the

heterogeneous hydrolysis of N2O5

 on chemistry and nitrate aerosol formation in the lower

troposphere under photosmog conditions. Journal of Geophysical Research-Atmospheres

108(D4), 4144, doi:10.1029/2002JD002436 (2003).

Sullivan, T.J., B.J. Cosby, J.A Laurence, R.L. Dennis, K. Savig, J.R. Webb, A.J. Bulger, M

Scruggs, C. Gordon, J. Ray, E.H. Lee, W.E. Hogsett, H. Wayne, D. Miller, and J.S. Kern.

Assessment of Air Quality and Related Values in Shenandoah National Park, Technical

Report NPS/NERCHAL/NRTR-03/090, National Park Service, U.S. Department of the

Interior, Northeast Region, Philadelphia, Pennsylvania (2003).

Taylor, K.E. Summarizing multiple aspects of model performance in a single diagram. Journal

of Geophysical Research-Atmospheres 106(D7):7183–7192 (2001).

Tonnesen, G.S., and R.L. Dennis. Analysis of radical propagation efficiency to assess ozone

sensitivity to hydrocarbons and NOX

. Part 1: Local indicators of instantaneous odd

oxygen production sensitivity. Journal of Geophysical Research - Atmospheres

105(D7):9213-9225 (2000a).

Tonnesen, G.S., and R.L. Dennis. Analysis of radical propagation efficiency to assess ozone

sensitivity to hydrocarbons and NOX

. Part 2: Long-lived species as indicators of ozone

concentration sensitivity. Journal of Geophysical Research-Atmospheres

105(D7):9227–9241 (2000b).
68

U.S. Environmental Protection Agency. Guidelines for developing an air quality (ozone and

PM2.5) forecasting program. EPA-456/R-03-002, Office of Air Quality Planning and

Standards, Office of Air and Radiation, Research Triangle Park, NC, 96 pp. (2003).

Wu, Y., B. Brashers, P.L. Finkelstein, and J. E. Pleim. A multilayer biochemical dry deposition

model, Part 1: Model formulation. Journal of Geophysical Research-Atmospheres

108(D1), 4013, doi:10.1029/2002JD002293 (2003a).

Wu, Y., B. Brashers, P.L. Finkelstein, and J.E. Pleim. A multilayer biochemical dry deposition

model, Part 2: Model evaluation. Journal of Geophysical Research-Atmospheres

108(D1), 4014, doi:10.1029/2002JD002306 (2003b).

Xiu, A., and J. E. Pleim. Development of a land surface model. Part I: Application in a

mesoscale meteorology model. Journal of Applied Meteorology 40:192–209 (2001).
69

APPENDIX A: ACRONYMS, ABBREVIATIONS, AND DEFINITIONS

ACM Asymmetric Convective Model

ADMS Air Dispersion Modeling System

AE3 Aerosols component version 3

AERMET AERMOD Meteorological Preprocessor

AERMIC American Meteorological Society/Environmental Protection

Agency Regulatory Model Improvement Committee

AERMOD AMS/EPA Regulatory Model

AERSCREEN AERMOD screening model

ArcGIS A commercial GIS software package

ARL Air Resources Laboratory

ASMD Atmospheric Sciences Modeling Division

ASPEN Assessment System for Population Exposure Nationwide

ASTM American Society for Testing and Materials

ATD Atmospheric Transport and Diffusion 

ATDD Atmospheric Turbulence and Diffusion Division, NOAA

AVHRR Advanced Very High Resolution Radiometer

AVIRIS Airborne Visible and InfraRed Imaging Spectrometer

BCON Boundary Concentrations

BEIS-3 Biogenic Emissions Inventory System version 3

BELD Biogenic Emissions Land cover Database

BELD-3 Biogenic Emissions Land cover Database 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

CASTNet Clean Air Status and Trend Network

CB-IV Carbon Bond version 4

CCM Community Climate Model

CDOM Colored, Dissolved Organic Matter

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

DHS Department of Homeland Security 
70

DOE Department of Energy

EDAS Eta Data Assimilation System

EMEP European Monitoring and Evaluation Program

EPA Environmental Protection Agency

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

GCM Global Climate Models

GCRP Global Change Research Program

GCTM Global Chemical Transport Model

GIS Geographic Information System

GIS Graphical Information Software 

GISS Goddard Institute for Space Studies

GRASS A commercial GIS software package

GRIB GRIdded Binary files

HAP Hazardous Air Pollutant

HPDM Hybrid Plume Dispersion Model

HYSPLIT4 Hybrid Single-Particle Lagrangian Integrated Trajectory model

ICON Initial Concentrations

IMPROVE Interagency Monitoring of PROtected Visual Environment

Network

ISCST Industrial Source Complex Model (Short-Term)

ISORROPIA A computationally efficient thermodynamic model

JAG/SEATD Joint Action Group for Selection and Evaluation of Atmospheric

Transport and Diffusion Models

LADCO Lake Michigan Air Directors Consortium

LESchem Large-eddy simulation 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
71

MODTRAN MODerate resolution radiative TRANsfer 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

NMSE Normalized Mean Square Error

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

ODE Ordinary Differential Equation

OFCM Office of the Federal Coordinator for Meteorology

OTAQ EPA Office of Transportation and Air Quality

PBL Planetary Boundary Layer

PCDD’s Poly-Chlorinated Dibenzo-p-Dioxins

PCDF’s Poly-Chlorinated Dibenzo-Furans

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

RAMS Regional Atmospheric Modeling System

RFA Request for Assistance

RPCA Rotated Principal Component Analysis 

RPM Regional Particulate Model

SAPRC99 A gas-phase chemical mechanism
72

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

STN Speciation and Trend Network

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)

UAM-V Urban Airshed Model (Variable Grid)

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
73

APPENDIX B: PUBLICATIONS

Arnold, J.R., R.L. Dennis, and G.S. Tonnesen. Diagnostic evaluation of numerical air quality

models with specialized ambient observations: Testing the Community Multiscale Air

Quality (CMAQ) modeling system at selected SOS 95 ground sites. Atmospheric

Environment 37:1185–1198 (2003).

Athanassiadis, G.A., S.T. Rao, J.Y. Ku, and R.D. Clark. Boundary layer evolution and its

influence on ground-level ozone concentrations. Environmental Fluid Mechanics

2:339–357 (2003).

Athanassiadis, G.A., and S.T. Rao. Spatial and temporal variations in the trace elemental data

over the northeastern United States. Environmental Pollution 123:439–449 (2003).

Benjey, W.G., E. Cooter, A. Gilliland, A.E. Grambsch, E.L. Wright, C.D. Geron, C. Gage, and

D.A. Winner. Creating an emission inventory for modeling global climate change effects

on regional air quality. In The Twelfth International Emission Inventory Conference:

Applying New Technologies, San Diego, California, April 29-May 1, 2003. Office of Air

Quality Planning and Standards, U.S. Environmental Protection Agency, Research

Triangle Park, NC, (2003). Available at 

http://www.epa.gov/ttn/chief/conference/ei12/index.html#ses-10

Binkowski, F.S., and S.J. Roselle. Models-3 Community Multiscale Air Quality (CMAQ) model

aerosol component 1. Model description. Journal of Geophysical Research-Atmospheres

108(D6), 4183, doi:10.1029/2001JD001409 (2003).

Brankov, E., R.F. Henry, K.L. Civerolo, W. Hao, S.T. Rao, P.K. Misra, R. Bloxam, and N. Reid. 

Assessing the effects of transboundary pollution between Ontario, Canada and New York,

USA. Environmental Pollution 123:403–411 (2003).

Ching, J., A. Lacser, T. Otte, and S. Dupont. Air quality simulations at neighborhood scales with

CMAQ. Proceedings of the 4th International Conference on Urban Air Quality:

Measuring, Modeling and Measurement, Charles University, Prague, Czech Republic,

March 25-28, 2003. Institute of Physics, the Charles University, and the University of

Hertfordshire in collaboration with SATURN and COST715 and supported by AWMA

and IUAPPA, 47–50 (2003).

Civerolo, K.L., E. Brankov, S.T. Rao, K. Roy, G.P. Lewis, and P. Galvin. Analysis of ambient,

precipitation-weighted, and lake sulfur concentrations in the Adirondack Region of New

York. Environmental Pollution 123:337–345 (2003).
74

Civerolo, K., H. Mao, and S.T. Rao. The airshed for ozone and fine particulate pollution in the

eastern United States. Pure and Applied Geophysics 160:81–105 (2003).

Cooter, E., W. Hutzell, W. Foreman, and M. Majewski. A regional atmospheric fate and

transport model for atrazine 2 Evaluation. Environmental Science & Technology 36(21)

4593-4599 (2002).

Dupont, S., T. Otte, A. Lacser, and J. Ching: Using MM5 to simulation the meteorological fields

at neighborhood scales. Proceedings of the 4th International Conference on Urban Air

Quality: Measuring, Modeling and Measurement, Charles University, Prague, Czech

Republic, March 25-28, 2003. Institute of Physics, the Charles University, and the

University of Hertfordshire in collaboration with SATURN and COST715 and supported

by AWMA and IUAPPA, 428-431 (2003).

Gego, E., C. Hogrefe, S. T. Rao, and P. S. Porter. Probabilistic assessment of regional scale

ozone pollution in the eastern United States. In, Air Pollution in Regional Scale. 

Proceedings of the NATO Advanced Research Workshop, Kallithea, Halkidiki, Greece,

June 13–15, 2003. NATO Science Series: IV. Earth and Environmental Sciences. D.

Melas, and D. Syrakov (Eds.). Kluwer Academic Publishers, 87–96 (2003).

Gilliland, A.B., T.J. Butler, and G.E Likens. Monthly and annual bias in weekly (NADP/NTN)

versus daily (AIRMoN) precipitation chemistry data in the eastern USA. Atmospheric

Environment 36:5197–5206 (2002).

Gilliland, A.B., R.L. Dennis, S.J. Roselle, and T.E. Pierce. Seasonal NH3 emission estimates for

the eastern United States based on ammonium wet concentrations and an inverse

modeling method. Journal of Geophysical Research-Atmospheres 108(D15), 4477,

doi:10.1029/2002JD003063 (2003).

Hogrefe, C., S. Vempaty, S.T. Rao, and P.S. Porter. A comparison of four techniques for

separating different time scales in atmospheric variables. Atmospheric Environment

37:313–325 (2003).

Kang, D., B. Eder, and K. Schere. The evaluation of regional-scale air quality models as part of

NOAA’s air quality forecasting pilot program. Proceedings of the 26th NATO/CCMS

International Technical Meeting on Air Pollution Modeling and Its Applications,

Istanbul, Turkey, May 26–30, 2003. NATO/CCMS International Technical Meeting of

Air Pollution Modeling and Its Applications, 404–411 (2003). 

Liu, G., C. Hogrefe, and S.T. Rao. Evaluating the performance of regional-scale meteorological

models: Effect of clouds simulation on temperature prediction. Atmospheric

Environment 37:1425–1433 (2003).
75

Mathur, R., and R.L. Dennis. Seasonal and annual modeling of reduced nitrogen compounds

over the eastern United States: Emissions, ambient levels, and deposition amounts. 

Journal of Geophysical Research-Atmospheres 108(D15), 4481,

doi:10.1029/2002JD002794 (2003).

Mebust, M. R., B.K. Eder, F.S. Binkowski, and S.J. Roselle. Models-3 Community Multiscale

Air Quality (CMAQ) model aerosol component - 2. Model evaluation. Journal of

Geophysical Research, 108(D6), 4184, doi:10.1029/2001JD001410, (2003).

Mihailovic, D.T., S.T. Rao, C. Hogrefe, and R.D. Clark. An approach for the aggregation of

aerodynamic surface parameters in calculating the turbulent fluxes over heterogeneous

surfaces in atmospheric models. Environmental Fluid Mechanics 2(4):315–337 (2003).

Minville, F., B. Marticorena, D.A. Gillette, R.E. Lawson, R. Thompson, and G. Bergametti.

Relationship between the aerodynamic roughness length and roughness density for low

roughness density. Environmental Fluid Mechanics 3: 269–273 (2003).

NARSTO. Particulate Matter Science for Policy Makers—A NARSTO Assessment. Parts 1 and

2. NARSTO Management Office (Envair), Pasco, Washington. (2003).

http://www.cgenv.com/Narsto/.

New York State Department of Environmental Conservation. Assessing the effects of

transboundary pollution on New York’s Air Quality. S.T. Rao (Principal Investigator).

NYSERDA Final Report 03-02 (2003).

New York State Department of Environmental Conservation. Analysis of ozone and fine

particulate matter in the northeastern United States. S.T. Rao (Principal Investigator). 

NYSERDA Final Report 03-04 (2003).

Pastor, S.H., J.O. Allen, L.S. Hughes, P. Bhave, G.R. Cass, and K.A. Prather. Ambient single

particle analysis in Riverside, California, by aerosol time-of-flight mass spectrometry

during the SCOS97-NARSTO. Atmospheric Environment 37:S239–S258 (2003).

Pierce, T., W.G. Benjey, J. Ching, D. Gillette, A. Gilliland, S. He, M. Mebust, and G. Pouliot. 

Advances in emission modeling of airborne substances. In The Twelfth International

Emission Inventory Conference: Applying New Technologies, San Diego, California,

April 29–May 1, 2003. Office of Air Quality Planning and Standards. U.S.

Environmental Protection Agency, Research Triangle Park, NC (2003). Available online

at http://www.epa.gov/ttn/chief/conference/ei12/index.html#ses-10
76

Pouliot, G., and T. Pierce. Emissions processing for an air quality forecasting model. In The

Twelfth International Emission Inventory Conference: Applying New Technologies, San

Diego, California, April 29–May 1, 2003. Office of Air Quality Planning and Standards. 

U.S. Environmental Protection Agency, Research Triangle Park, NC (2003). Available

online at http://www.epa.gov/ttn/chief/conference/ei12/index.html#ses-10

Rao, S.T., J. Irwin, K. Schere, T. Pierce, R. Dennis, and J. Ching. Past, present, and future air

quality modeling and its applications in the United States. The 8th International

Atmospheric Sciences and Applications to Air Quality (AASAQ 2003) Conference, March

10-13, 2003, Tsukuba, Japan. (2003).

Rao, S.T., J.Y. Ku, S. Berman, K. Zhang, and H. Mao. Summertime characteristics of the

atmospheric boundary layer and relationships to ozone levels over the eastern United

States. Pure and Applied Geophysics 160:21-55 (2003).

Sestak, M, S. O’Neill, S. Ferguson, J. Ching, and D. G. Fox. Integration of Wildfire Emissions

into the Models-3/CMAQ with the Prototypes: Community Smoke Emissions Modeling

System (CSEM) and BLUESKY. In the 2002 Models-3 Users Workshop, Research

Triangle Park, North Carolina, October 21-23, 2002. Community Modeling and Analysis

System (CMAS) Center, Research Triangle Park, NC (2003). Available at

http://www.cmascenter.org/workshop/session5/strum_slides.ppt

Sistla, G., K. Civerolo, W. Hao, and S.T. Rao. An evaluation of the UAM-V predicted

concentrations of carbon monoxide and reactive nitrogen compounds over the eastern

United States. Journal of the Air & Waste Management 52:1324–1332 (2002).

Snyder, W.H., D.K. Heist, S.G. Perry, R. S. Thompson, and R.E. Lawson, Jr. Wind tunnel

simulations to assess dispersions around the World Trade Center. In Proceedings of

PHYSMOD2003 International Workshop on Physical Modeling of Flow and Dispersion

Phenomena, September 3–5, 2003, Prato, Italy.

Stockwell, W.R., R.S. Artz, J.F. Meagher, R.A. Petersen, K.L. Schere, G.A. Grell, S.E. Peckham,

A.F. Stein, R.V. Pierce, J.M. O’Sullivan, and P.Y. Whung. The scientific basis of

NOAA’s air quality forecasting program. EM December:20-27 (2002).

Streicher, J.J., and K. Endres. Modeling assessment of the biological and economic impact of

increased UV radiation on loblolly pine in the Middle Atlantic States. In Managing for

Healthy Ecosystems. D.J. Rapport et al. (Eds.). Lewis Publications CRC Press,

Washington DC, 513-527 (2003). 
77

Strum, M., L. Driver, G. Gipson, W.G. Benjey, R. Cook, M. Houyoux, C. Seppansen, and G.

Stella. The Use of SMOKE to Process Multipollutant Inventories - Integration of

Hazardous Air Pollutant and Volatile Organic Compound Emissions. In The Twelfth

International Emission Inventory Conference: Applying New Technologies, San Diego,

California, April 29-May 1, 2003. Office of Air Quality Planning and Standards. U.S.

Environmental Protection Agency, Research Triangle Park, NC (2003). Available at

http://www.epa.gov/ttn/chief/conference/ei12/index.html#ses-10.

Strum, M., M. Houxyoux, R. Ryan, G. Stella, W. Benjey, G. Gipson, and R. Cook. Integration of

criteria and toxic pollutants in SMOKE. In The 2002 Models-3 Users Workshop,

Research Triangle Park, North Carolina, October 21-23, 2002. Community Modeling

and Analysis System (CMAS) Center, Research Triangle Park, NC (2003). Available at

http://www.cmascenter.org/workshop/session5/strum_slides.ppt

Sullivan, T.J., B.J. Cosby, J.A Laurence, R.L. Dennis, K. Savig, J.R. Webb, A.J. Bulger, M.

Scruggs, C. Gordon, J. Ray, E.H. Lee, W.E. Hogsett, H. Wayne, D. Miller, and J.S. Kern.

Assessment of Air Quality and Related Values in Shenandoah National Park, Technical

Report NPS/NERCHAL/NRTR-03/090, National Park Service, U.S. Department of the

Interior, Northeast Region, Philadelphia, Pennsylvania, 2003.

Vickery, J. Conceptual models of PM for North American areas. R.L.Dennis (Contributing

Author) Chapter 10. In Particulate Matter Science for Policy Makers: a NARSTO

Assessment. EPRI Report No. 1007735 for NARSTO, February 2003 (2003).

Wu, Y., B. Brashers, P. L. Finkelstein and J. E. Pleim. A multilayer biochemical dry deposition

model, Part 1: Model formulation. Journal of Geophysical Research-Atmospheres

108(D1), 4013, doi:10.1029/2002JD002293 (2003).

Wu, Y., B. Brashers, P.L. Finkelstein, and J.E. Pleim. A multilayer biochemical dry deposition

model, Part 2: Model evaluation. Journal of Geophysical Research-Atmospheres

108(D1), 4014, doi:10.1029/2002JD002306 (2003).

Yu, S., P.S. Kasibhatla, D.L Wright, S.E. Schwartz, R. McGraw, and A. Deng. Moment-based

simulation of microphysical properties of sulfate aerosols in the eastern United States:

Model description, evaluation, and regional analysis. Journal of Geophysical ResearchAtmospheres 108, No. D21, 4353, doi:10.1029/2002JDJ002890 (2003).
78

APPENDIX C: PRESENTATIONS

Bhave, P.V. Air pollution at the single-particle level: Integrating atmospheric measurements

with mathematical models. Seminar presented at the Atmospheric Sciences Modeling

Division, Research Triangle Park, NC, June 19, 2003.

Bhave, P.V. EPA's vision for CMAQ. Presentation at the University of California, Davis, Air

Quality Research Group, Davis, CA, September 8, 2003.

Ching, J.K.S. Modeling PM2.5 with Models-3/CMAQ for exposure assessments. Presentation at

the Conference of the American Association for Aerosol Research, Charlotte, North

Carolina, October 9, 2002.

Ching, J.K.S. Subgrid air pollution concentration distributions as a complement to fine scale air

quality modeling. Presentation at the Seventh Annual Conference on Transport and

Dispersion Modeling, George Mason University, Fairfax, VA, June 17, 2003.

Cooter, E.J. Evaluation of CMAQ/atrazine results against 1995 field observations. Seminar

presented at the Atmospheric Sciences Modeling Division, Research Triangle Park, NC,

November 14, 2002.

Dennis. R.L. NOx versus VOC control for ozone attainment in the southeast United States: A

combined data and CMAQ model analysis with SOS 99 surface observations. 

Presentation at the US-German Workshop #2—Photochemistry, Bad Breisig, Germany,

October 10, 2002.

Dennis, R.L. Modeling and policy support: From RADM to CMAQ. Seminar presented at the

National Institute of Public Health and the Environment, The Hague, Netherlands,

October 17, 2002.

Dennis, R.L. From acidifying to eutrophying deposition: An evolving path and evolving model

(from NAPAP to N2001). Presentation at the National Institute of Public Health and the

Environment, The Hague, Netherlands, October 17, 2002.

Dennis, R.L. Modeling issues for PM. Presentation at the AWMA Information Exchange/ORD

PM Research Panel, Research Triangle Park, NC, December 3, 2002.

Dennis, R.L. Numerical modeling of regional air quality: Lining up model and measurements to

probe and address uncertainty. Invited presentation at the SAMSI/GSP Workshop on

Spatio-Temporal Modeling, National Center for Atmospheric Research, Boulder, CO,

June 2, 2003.
79

Dennis, R.L. CMAQ winter predictions of nitrate: The importance of N2O5 reactions in HNO3

production. Seminar presented at the NOAA Aeronomy Laboratory, Boulder, CO, June

4, 2003.

Dennis, R.L. Model evaluation activities. Presentation at the EPA Air Program Review,

Research Triangle Park, NC, September 30, 2003.

Dolwick, P.D. Plot that storm! Presentation to the Millbrook Elementary 5th grade classes (3),

Raleigh, NC, October 16, 2002.

Dolwick, P.D. Science spectacular: Plot that storm! Presentation to Apex Elementary 5th grade

classes (3), Apex, NC, November 22, 2002.

Dolwick, P.D. Science spectacular: Planet Polluto. Presentation to Apex Elementary 1st grade

classes (3), Apex, NC, February 7, 2003.

Dolwick, P.D. Plot that storm! Presentation to Chatham Charter School 5th grade classes (2),

Siler City, NC, March 28, 2003.

Dolwick, P.D. Weather safety. Presentation to Olive Chapel Elementary kindergarten classes

(4), Apex, NC, April 10, 2003.

Dolwick, P.D. EPA-funded activities designed to assist Regional/State/Local Agencies with

year-round air quality forecasting. Meeting on Year-Round/Multi-pollutant Air Quality

Forecasting and Outreach, Research Triangle Park, NC, April 24, 2003.

Dolwick, P.D. Air quality index (AQI): Year-round forecasting. Presentation at the Spring 2003

SASWG Meeting (by phone), Baltimore, MD, May 2, 2003.

Dolwick, P.D. Monitoring the weather. Presentation to Oak Grove Elementary 2nd grade classes

(4), Cary, NC, June 5, 2003.

Dolwick, P.D. Transport rule update: Ozone modeling results and issues. Presentation to the

EPA Assistant Administrator for the Office of Air and Radiation, Washington DC, June

30, 2003

Dolwick, P.D. Effect of revised MCIP on modeled air quality. Presentation at the Ad Hoc

Meteorological Modeling Group, Des Plaines, IL, July 29, 2003.

Dolwick, P.D. EPA-funded activities designed to assist Regional/State/Local Agencies with

year-round air quality forecasting. MARAMA PM2.5 Forecasting Meeting, Woodbridge,

VA, August 12, 2003.
80

Dupont, S. Modeling at the neighborhood scale. Presentation at the 2002 Models-3 Users’

Workshop, Research Triangle Park, NC, October 21, 2002.

Eder, B.K. Testing of the June 2002 version of the U.S. EPA’s CMAQ model. Presentation at

the US-German Workshop #2—Photochemistry, Bad Bresig, Germany, October 8, 2002.

Eder, B.K. An evaluation protocol for NOAA’s air quality forecasting pilot program. 

Presentation at NOAA, Silver Spring, MD, November 7, 2002.

Eder, B.K. Operational evaluation of air quality forecast models: Protocol development. 

Presentation at NOAA, Silver Spring, MD, March 14, 2003.

Eder, B.K. An evaluation of the Models-3/CMAQ aerosol module. Presentation at the 2003

AAAR PM Meeting: Atmospheric Sciences, Exposures and the Fourth Colloquium on PM

and Human Health, Pittsburgh, PA, April 4, 2003.

Eder, B.K. Preliminary evaluation of the June 2002 version of CMAQ. Presentation at the 2002

Models-3 Users’ Workshop, Research Triangle Park, NC, October 22, 2002.

Eder, B.K. Characterizing the spatiotemporal variability of environmental data. Presentation at

the Statistical and Applied Mathematical Sciences Institute, Research Triangle Park, NC,

January 24, 2003.

Fine, S.S. Introduction to EPA's Multimedia Integrated Modeling System software suite: A new

framework for Models-3. Presentation at the 2002 Models-3 Users’ Workshop, Research

Triangle Park, NC, October 22, 2002.

Gillette, D.A. Modeling of dust emissions. Presentation at the Sino-U.S. Workshop on Dust

Storm and Its Effect on Human Health, Raleigh, NC, November 25, 2002.

Gillette, D.A. Lessons on dust emissions derived from experimentation and development of a

model for Owens (dry) Lake, CA, dust emissions. Presentation at the 2002 Fall Meeting

of the American Geophysical Union, San Francisco, CA, December 2002.

Gillette, D.A. Dust emissions from mesquite-vegetated sites in the Chihuahuan Dessert. 

Presentation at the Jornada Symposium of the National Science Foundation’s Longterm

Ecological Research Project, Las Cruces, NM, June 11, 2003.

Gilliland, A.B. Community Multiscale Air Quality modeling: Global climate change and air

quality. Presentation at the EPA STAR Kickoff Meeting for 2002 Climate and Air

Quality Grants, Research Triangle Park, NC, March 23, 2002.
81

Gipson, G.L. Improvements to the computational efficiency of modeling gas-phase chemistry in

CMAQ. Seminar presented at the Atmospheric Sciences Modeling Division, Research

Triangle Park, NC, February 27, 2003.

Godowitch, J.M. Photochemical and aerosol modeling with the CMAQ Plume-in-Grid approach. 

Seminar presented at the Atmospheric Sciences Modeling Division, Research Triangle

Park, NC, September 25, 2003.

He, S. Modeling fugitive dust in the United States with the Models-3/Community Multiscale Air

Quality modeling system. Presentation at the Conference of the American Association for

Aerosol Research, Charlotte, North Carolina, October 10, 2002.

He, S. Development of a windblown fugitive dust emission model with the

Models-3/Community Multiscale Air Quality modeling system. Presentation at the 2002

Models-3 Users’ Workshop, Research Triangle Park, NC, October 22, 2002.

Heist, D.K. Wind tunnel simulations to assess dispersion around the World Trade Center. 

Presentation at the PHYSMOD2003: International Workshop on Physical Modeling of

Flow and Dispersion Phenomena, Prato, Italy, September 4, 2003.

Huber, A.H. Visualization and simulation of toxic air pollutants released in urban areas—

Including homeland security applications. Presentation at the 2002 SGI Homeland

Security Summit, Washington DC, November 13, 2002.

Huber, A.H. Environmental CFD simulation and visualization: Examples in support of the

reconstruction of the smoke/dust plume from the World Trade Center site following the

events of September 11, 2001. Poster presentation at the EPA Science Forum,

Washington, DC, May 5-6, 2003.

Huber, A.H. A New York World Trade Center project: Before, during, and after September 11,

2001. Seminar presented at the Atmospheric Sciences Modeling Division, Research

Triangle Park, NC, August 21, 2003.

Huber, A.H. Evaluation of potential human exposures and health impacts of airborne particulate

matter (PM) and its constituents following the collapse of the World Trade Center towers. 

Presentation at the American Chemical Society Meeting, New York, NY, September 11,

2003.

Hutzell, W.T. A regional model for PCDD/F's based on a photochemical model for air quality

and particulate matter. Presentation at the 2002 Fall Meeting of the American

Geophysical Union, San Francisco, CA, December 6, 2002.
82

Irwin, J.S. Application of ASTM D6589 to evaluate dispersion model performance to simulate

average centerline concentration values. Presentation at the 8th International Conference

on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes,

Sofia, Bulgaria, October 15, 2002.

Luecken, D.J. Regional scale assessments and the atmospheric fate and transport of air toxics: 

CMAQ. Presentation at the Risk Assessment Modeling Tools Symposium, Chicago,

Illinois, July 16, 2003.

Luecken, D.J. Predicting the atmospheric fate and transport of air toxics using CMAQ. Seminar

presented at the Atmospheric Sciences Modeling Division, Research Triangle Park, NC,

July 31, 2003.

Luecken, D.J. Modeling the atmospheric concentrations of toxic air pollutants. Presentation at

the U.S. EPA’s National Exposure Research Laboratory Air Research Program Peer

Review, Research Triangle Park, NC, September 30, 2003.

Otte, T.L. Science fair data presentation techniques. Presentation to 5th-grade classes (3) at

Olive Chapel Elementary School, Apex, NC, February 7, 2003.

Otte, T.L. Weather basics: Evaporation and condensation. Presentation to 2nd-grade class at

Morrisville Elementary School, Morrisville, NC, March 28, 2003.

Otte, T.L. Recent changes in MCIP2. Seminar presented at the Atmospheric Sciences Modeling

Division, Research Triangle Park, NC, May 8, 2003.

Perry, S.G. Simulation and visualization of the smoke/dust plume from the World Trade Center

site. Presentation at the U.S. Environmental Protection Agency Science Fair,

Washington, DC, May 6, 2003.

Pierce. T.E. Biogenic emissions: Is the uncertainty growing? Presentation at the U.S.-German

Workshop, Ozone and Particles: Policy and Science—Recent Developments and

Controversial Issues (Regional and Urban Air Pollution), Bad Breisig, Germany, October

10, 2002.

Pleim, J.E. Future enhancements of the CMAQ model. Presentation at the 2003 Models-3

Users’ Workshop, Research Triangle Park, NC, October 22, 2003.

Pouliot, G.A. Advances in emissions modeling of airborne substances. Poster presentation at

the 12th International Emission Inventory Conference, “Emission Inventories—Applying

New Technologies,” San Diego, California, April 28, 2003.
83

Pouliot, G.A. Creating an emission inventory for modeling global climate change effects on

regional air quality. Presentation at the 12th International Emission Inventory

Conference, “Emission Inventories—Applying New Technologies,” San Diego,

California, April 28, 2003.

Pouliot, G.A. Emissions processing for an air quality forecasting model. Poster presentation at

the 12th International Emission Inventory Conference, “Emission Inventories—Applying

New Technologies,” San Diego, California, April 28, 2003.

Rao, S.T. Using regional-scale photochemical models for guiding emissions management

decisions. Presentation at the U.S.-German Workshop, Ozone and Particles: Policy and

Science—Recent Developments and Controversial Issues (Regional and Urban Air

Pollution), Bad Breisig, Germany, October 9, 2002.

Rao, S.T. Past, present, and future air quality modeling and its applications in the United States. 

Presentation at the International Conference on Atmospheric Sciences and Applications

to Air Quality (ASAAQ), Tsukuba Science City, Japan, March 11, 2003.

Rao, S.T. Comparison of the space-time signatures of air quality data from different networks. 

Presentation at the NATO-CCMS International technical Meeting on Air Pollution

Modeling and Its Applications, Istanbul, Turkey, May 28, 2003.

Roselle, S.J. CMAQ briefing to the U.S. EPA’s Office of Air Quality Planning and Standards. 

Research Triangle Park, NC, March 27, 2003.

Russell, O.R., Jr. Mercury emissions and atmospheric transport, mercury TMDLs: Science and

model building, modeling mercury deposition. Western Mercury Workshop, EPA Region

8, Denver, CO, April 22, 2003.

Russell, O.R. Jr. Atmospheric mercury models: What can they tell us about air transport and

fate? Presentation at the Society of Environmental Toxicology and Chemistry Mercury

Workshop, Pensacola, FL, September 15, 2003.

Schere, K.L. The Models-3/CMAQ model: 2002 release - new features. Presentation at the 2002

Models-3 Users’ Workshop, Research Triangle Park, NC, October 21, 2002.

Schere, K.L. The EPA CMAQ modeling system. Presentation at the NOAA/NWS/OAR Air

Quality Forecasting Workshop, Silver Spring, MD, November 7, 2002.

Schere, K.L. NOAA air quality forecast modeling program. Presentation at the U.S.

EPA/AWMA 2003 National Air Quality Conference, San Antonio, TX, February 4, 2003.
84

Schere, K.L. NOAA air quality forecast modeling project. Seminar presented at the

Atmospheric Sciences Modeling Division, Research Triangle Park, NC, February 20,

2003.

Schere, K.L. The U.S. EPA Community Multiscale Air Quality modeling system - Current status

and applications. Presentation at the Atmospheric Sciences and Applications to Air

Quality (ASAAQ)-2003 Conference, Tsukuba, Japan, March 13, 2003.

Schere, K.L. NOAA/EPA operational air quality forecast model system. Presentation at the

Planning Meeting for Summer 2004 Climate and Air Quality Field Campaign, Durham,

NH, April 23, 2003.

Schere, K.L. CMAQ research update: Nitrate overprediction, optimization, secondary organic

aerosol. Presentation for the U.S. EPA, Office of Air Quality and Planning Standards,

Research Triangle Park, NC, July 8, 2003.

Schere, K.L. Meteorological and atmospheric modeling—Model development. Presentation at

the U.S. EPA’s National Exposure Research Laboratory Air Research Program Peer

Review, Research Triangle Park, NC, September 30, 2003.

Schwede, D.B. Use of magnets for navigation, Franciscan School, Raleigh, NC, October 21,

2002.

Young, J.O. Recent developments for parallel CMAQ. Seminar presented at the Atmospheric

Sciences Modeling Division, Research Triangle Park, NC, July 24, 2003.

Yu, S. Primary and secondary organic aerosols over the United States: Estimates on the basis of

observed organic carbon (OC) and elemental carbon (EC), and air quality modeled

primary OC/EC ratios. Presentation at the 2003 Models-3 Users' Workshop, Research

Triangle Park, NC, October 23, 2003.

. 

Yu, S. Simulation of primary and secondary (biogenic and anthropogenic) organic aerosols over

the United States by U.S. EPA Models-3/CMAQ: Evaluation and regional analysis. 

Presentation at the 2003 Models-3 Users' Workshop, Research Triangle Park, NC,

October 23, 2003. 

Yu, S. Relative contributions of primary and secondary (biogenic and anthropogenic) organic

aerosols at Nashville: Comparisons of observations and modeling results. Presentation at

the 2003 AAAR PM Meeting: Atmospheric Sciences, Exposure and the Fourth

Colloquium on PM and Human Health, Pittsburgh, PA, April 1, 2003.
85

Yu, S. Primary and secondary organic aerosols over the United States: Estimates on the basis of

observations and modeled primary OC/EC ratios. Presentation at the 2003 AAAR PM

Meeting: Atmospheric Sciences, Exposures and the Fourth Colloquium on PM and

Human Health, Pittsburgh, PA, April 2, 2003.

Yu, S. Simulation of primary and secondary (biogenic and anthropogenic) organic aerosol over

the Unites States by U.S. EPA Models-3/CMAQ: Evaluation and regional analysis. 

Presentation at the EGS-AGU-EUG Joint Assembly, Nice, France, April 10, 2003.

Yu, S. Can the thermodynamic model and 3-D air quality model predict the aerosol NO3

-

reasonably within a factor of 2? Presentation at the 2003 Models-3 Users' Workshop,

Research Triangle Park, NC, October 23, 2003. 

Yu, S. New unbiased symmetric metrics for evaluation of air quality model. Presentation at the

2003 Models-3 Users' Workshop, Research Triangle Park, NC, October 23, 2003. 
86

APPENDIX D: WORKSHOPS AND MEETINGS

Methyl Mercury Interagency Working Group, Washington, DC, October 2, 2002.

O.R. Bullock, Jr.

US-German Workshops on Photochemistry and Intercontinental Transport, Bad Breisig,

Germany, October 7–11, 2002.

R.L. Dennis T.E. Pierce

B.K. Eder S.T. Rao

VISTAS Fire Emission Workshop, Research Triangle Park, NC, October 17, 2002.

W.G. Benjey

2002 Models-3 User’s Workshop, Research Triangle Park, NC, October 21–23, 2002..

D.A. Atkinson B.K. Eder He, S G. Pouliot .

W.G. Benjey M.L. Evangelista J.S. Irwin S.J. Roselle

J.K.S. Ching S.S. Fine T.L. Otte K.L. Schere

R.L. Dennis G.L. Gipson T.E. Pierce J.L. Young

P.D. Dolwick J.M. Godowitch J.E. Pleim

NOAA Air Quality Forecast Workshop, Silver Spring, MD, November 7–8, 2002.

B.K. Eder S.T. Rao

T.E. Pierce K.L. Schere

J.E. Pleim

TARC Proposal Review Panel, Houston, TX, November 11–12, 2002.

R.L. Dennis

Sino-U.S. Workshop on Dust Storms and its Effect on Human Health, Raleigh, NC, November

25–26, 2002.

D.A. Gillette

AWMA Information Exchange/ORD PM Research Panel, Research Triangle Park, NC,

December 3–4, 2002.
87

R.L. Dennis

U.S. Climate Change Science Program: Planning Workshop for Scientists and Stakeholders,

Washington, DC, December 3–5, 2002.

K.L. Schere

Methyl Mercury Interagency Working Group, Washington, DC, December 6, 2002.

O.R. Bullock, Jr.

American Geophysical Union 2002 Fall Meeting, San Francisco, CA, December 6–10, 2002.

D.A. Gillette

W.T. Hutzell

Air Resource Laboratory Transport Dispersion Modeling Meeting, Washington, DC, December

10, 2002.

A.H. Huber

EPA’s 2003 National Air Quality Conference: Its Not Just About Ozone Anymore, San Antonia,

TX, February 2–5, 2003.

P.D. Dolwick

B.K. Eder

K.L. Schere

BRACE Workshop, Tampa Bay, FL, February 3–4, 2003. 

R.L. Dennis

BRACE Aerosol Meeting, University of South Florida, College of Public Health, Tampa Bay,

FL, February 5, 2003.

R.L. Dennis

Annual Meeting of the American Meteorological Society, Long Beach, CA, February 9–13,

2003.

E.M. Poole-Kober
88

Sixth Conference of Atmospheric Librarians International, Long Beach, CA, February 12–14,

2003.

E.M. Poole-Kober

Columbia University Climate and Health Project Meeting, New York, NY, February 26–27,

2003.

K.L. Schere

Second Workshop on Air Quality Modeling, Tsukuba, Japan, March 10, 2003.

S.T. Rao

K.L. Schere

EPA STAR Kickoff Meeting for 2002 Climate and Air Quality Grants, Research Triangle Park,

NC, March 23, 2002.

A.B. Gilliland

Great Lakes Regional Mercury Monitoring Workshop, East Lansing, MI, March 26–27, 2003.

O.R. Bullock, Jr.

Eleventh Jet Propulsion Lab Airborne Earth Science Workshop, Pasadena, CA, March 31–April

2, 2003.

J.J. Streicher

American Association for Aerosol Research Conference, PM Meeting: Atmospheric Sciences,

Exposures and the Fourth Colloquium on PM and Human Health, Pittsburgh, PA, March

31–April 4, 2003.

R.L. Dennis, Co-Chair

B.K. Eder

J.M. Godowitch

W.T. Hutzell

M.R. Mebust

NOAA/National Centers for Environmental Prediction Air Quality Forecast Meeting, Silver

Springs, MD, April 15, 2003.
89

K.L. Schere

Western Mercury Workshop, EPA Region 8, Denver, CO, April 21–22, 2003.

O.R. Bullock, Jr.

Planning Meeting for Summer 2004 Climate and Air Quality Field Campaign, Durham, NH,

April 22–23, 2003.

R.L. Dennis

K.L. Schere

Meeting on Year-Round/Multi-pollutant Air Quality Forecasting and Outreach, Research

Triangle Park, NC, April 24, 2003.

P.D. Dolwick

12th International Emission Inventory Conference, “Emission Inventories—Applying New

Technologies,” San Diego, California, April 28–May 1, 2003.

J.D. Mobley, Chair, Poster Session

G.A. Pouliot

Waterloo Centre for Atmospheric Sciences Meeting, University of Waterloo, Waterloo, Canada,

April 29–30, 2003.

K.L. Schere

S.T. Rao

Workshop on Air Quality Forecasting, U.S Weather Research Program Office, Houston, Texas,

April 29–May 2, 2003.

J.K.S. Ching

Technical Meeting on Air Quality Forecasting, National Centers for Environmental Predictions,

Camp Springs, MD, May 1, 2003.

T.L. Otte 

EPA 2003 Science Forum, Washington, DC, May 5–7, 2003.

A.H. Huber

K.L. Schere
90

SAIL (Southeast Affiliate of IAMSLIC Librarians) 2003 Annual Meeting, Harbor Branch

Oceanographic Institution, Fort Pierce, FL, May 13–16, 2003.

E.M. Poole-Kober

26th NATO/CCMS International Technical Meeting on Air Pollution Modeling and Its

Application, Istanbul, Turkey, May 19–23, 2003.

S. Dupont

SAMSI/GSP Workshop on Spatio-Temporal Modeling, National Center for Atmospheric

Research, Boulder, CO, June 1–6, 2003.

R.L. Dennis

MM5/WRF 3DVAR Tutorial, Boulder, CO, June 9, 2003.

R.C. Gilliam

J.E. Pleim

MM5/WRF Workshops, Boulder, CO, June 10–13, 2003.

R.C. Gilliam

J.E. Pleim

Workshop on Wildland Fire Emissions Modeling, U.S. Forest Service, Seattle, Washington, June

11–12, 2003.

T.E. Pierce

NOAA Data User's Workshop, Boulder, CO, June 11–13, 2003.

D.A. Atkinson

Air Quality Modeling Meeting of Chesapeake Bay Program, NOAA Chesapeake Bay Program

Office, Annapolis, MD, June 16, 2003.

R.L. Dennis

Seventh Annual George Mason University Transport and Dispersion Modeling Conference,

Fairfax, Virginia, June 17–19, 2003.

J.K.S. Ching

S.G. Perry
91

R.S. Thompson

1

st World Congress on Risk, Brussels, Belgium, June 20-27, 2003.

A.H. Huber

Air & Waste Management Association 96th Annual Conference, San Diego, California, June

22–26, 2003.

D.A. Gillette

Thirty First Annual Meeting of the American Society for Photobiology, Baltimore, MD, July 5–9,

2003.

J.J. Streicher

Air Toxic Risk Assessment Modeling Tools Symposium; Chicago, IL, July 15–17, 2003.

D.J. Luecken

NOAA/OAR Air Quality Forecast Planning Meeting, Boulder, CO, July 21, 2003.

S.S. Fine

J.E. Pleim

S.J. Roselle

K.L. Schere

Air Quality Meeting, National Center for Atmospheric Research, Boulder, CO, July 21, 2003.

S.J. Roselle

Ad Hoc Meteorological Modeling Group, Des Plaines, IL, July 29, 2003.

P.D. Dolwick

Joint Statistical Meetings 2003, Session, Applications in Spatio-Temporal Modelling: From the

F-22 to fMRI, San Francisco, CA, August 3, 2003. 

J.L. Swall, Chair
92

Managing Safety: Systems that Work for Senior Leader, Silver Spring, MD, August 3–6, 2003.

K.L. Schere

MARAMA PM2.5 Forecasting Meeting, Woodbridge, VA, August 12, 2003.

P.D. Dolwick

Workshop on Uncertainties in Emissions Modeling, American Petroleum Institute, Houston,

Texas, August 26–27, 2003.

W.G. Benjey

Air Resources Laboratory Climate Coordination Meeting, Silver Springs, Maryland, August 28,

2003.

E.J. Cooter

J.J. Streicher

PHYSMOD2003: International Workshop on Physical Modeling of Flow and Dispersion

Phenomena, Prato, Italy, September 3–5, 2003.

D.K. Heist

National Science Foundation Review of Long Term Ecological Research Project at Jornada

LTER, Las Cruses, NM, September 8–10, 2003.

D.A. Gillette

EMEP Steering Body Meeting, Geneva, Switzerland, September 8–11, 2003.

R.L. Dennis

PM2.5 Forecasting Workshop: Southeastern United States, Research Triangle Park, NC, September

11–12, 2003.

P.D. Dolwick

PM2.5 Forecasting Workshop: Midwestern United States, Des Plaines, IL, September 15–16, 2003.

P.D. Dolwick
93

International Standards Organization (ISO) Technical Committee 146 Subgroup Week, Bautahoj

Kursuscenter, Jaegerspris, Denmark, September 21–25, 2003.

J.S. Irwin

EPA Air Program Review, Research Triangle Park, NC, September 29–30, 2003.

R.L. Dennis

K.L. Schere

NOAA Air Quality Forecast Focus Group Meeting, Silver Spring, MD, September 9–10, 2003.

J.E. Pleim

K.L. Schere

Third Russian Mercury Emission Inventory Workshop of the Arctic Monitoring and Assessment

Program, Moscow, Russia, September 10–11, 2003.

O.R. Bullock, Jr.

Mercury Science Workshop, Pensacola, FL, September 14–17, 2003.

O.R. Bullock, Jr.

Air Quality IV Conference, Arlington, VA, September 23–24, 2003.

K.L. Schere
94

APPENDIX E: VISITING SCIENTISTS

Dr. Daewon Byun, Jiwen He, and Joshua Fu

Department of Geosciences

University of Houston

312 Science & Research, Building 1

Houston, TX 77204

Dr. Byun, He, and Fu visited the Division on January 10, 2003, to present seminars on air quality

research at the University of Houston.

Dr. David Rogers, Dr. Jack Hayes, Dr. Nelson Seaman, and Dr. Paula Davidson

NOAA/National Weather Service

Silver Spring, MD

Drs. Rogers, Hayes, Seaman, and Davidson visited the Division on January 30, 2003, to attend a

meeting on the NOAA/EPA collaboration on air quality forecasting.

Dr. Nelson Seaman

Department of Meteorology

Pennsylvania State University

503 Walker Building

University Park, PA 16802

Dr. Seaman visited the Division on January 7, 2003, to participate in discussions on the

NOAA/EPA collaboration on air quality forecasting.

Sue Stendebach

National Science Foundation

Arlington, Virginia

Ms. Stendebach visited the Division on May 1, 2003, to discuss the Digital Government Research

program of NSF.

Dr. Stephen E. Schwartz

Brookhaven National Laboratory

Upton, NY

Dr. Stephen E. Schwartz visited the Division on August 4, 2003, to present a seminar on aerosols

and climate change.
95

Jim Tuccillo

IBM Corporation

Atlanta, GA

Mr. Tuccillo visited the Division on February 20 and March 24, 2003, to discuss CMAQ model

code optimization for air quality forecasting.

Dr. Satoshi Yamazaki (Toyota Research)

Dr. Hitoshi Kunimi (Nissan Research),

Mr. Yoshiaki Shibata (Petroleum Energy Center)

Mr. Tatsuo Omata (Petroleum Energy Center)

Mr. Tetsuji Watanabe (Petroleum Energy Center)

Japan Clean Air Program

Tokyo, Japan

Drs. Yamazaki, and Kunimi, and Messrs. Shibata, Omata, and Watanabe visited the Division on

July 21–22, 2003, to discuss collaboration on CMAQ modeling in the United States and Japan.
96

APPENDIX F: POSTDOCTORAL RESEARCHERS

Dr. Sylvain Dupont performed research as an UCAR post-doc to integrate refined Urban Canopy

Parameterizations (UCPs) in the meteorological model MM5. This advanced version of MM5

should improve the simulation of turbulence and transport fields within and above the urban and

vegetative canopies for more accurate fine-scale simulations with the Community Multiscale Air

Quality (CMAQ) modeling system in urban areas.

Sylvain Dupont worked on an urban canopy parameterization for the MM5 meteorological model

to better simulate turbulence and energy exchanges within urban areas.

Shaocai Yu conducted research on CMAQ model diagnostic evaluation of PM2.5 and its

components, including comprehensive sensitivity and assessment testing of the aerosol

thermodynamic partitioning scheme.

Daiwen Kang worked on model evaluation procedures for the emerging NOAA air quality

forecast capability, including evaluation of several pilot model platforms for ozone forecasting.
97

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

J. David Mobley (EPA), Environmental Engineer, Associate Director

William B. Petersen, Physical Science Administrator, Assistant Director

Dr. Jay Messer (EPA), Physical Scientist

Jeffrey L. West, Physical Science Administrator

Barbara R. Hinton (EPA), Secretary

Program Operations Staff

Herbert J. Viebrock, Supervisory Physical Scientist, Chief

Linda W. Green, Administrative Specialist

Evelyn M. Poole-Kober, Librarian

John H. Rudisill, III, Equipment Specialist

Atmospheric Model Development Branch

Kenneth L. Schere, Supervisory Meteorologist, Chief

Dr. Prakash V. Bhave, Physical Scientist (Since March 2003)

O. Russell Bullock, Meteorologist

Gerald L. Gipson (EPA), Physical Scientist

Robert C. Gilliam, Meteorologist (Since April 2003)

James M. Godowitch, Meteorologist

Dr. Alan H. Huber, Physical Scientist

Dr. William T. Hutzell (EPA), Physical Scientist

Dr. Michelle R. Mebust (EPA), Physical Scientist (Until August 2003)

Tanya L. Otte, Meteorologist

Dr. Jonathan E. Pleim, Physical Scientist

Shawn J. Roselle, Meteorologist

Dr. Jeffrey O. Young, Mathematician

Patricia F. McGhee, Secretary
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Model Evaluation and Applications Research Branch

Dr. Steven S. Fine, Supervisory Physical Scientist, Chief

Dr. Robin L. Dennis, Physical Scientist

Dr. Brian K. Eder, Meteorologist

Dr. Peter L. Finkelstein, Physical Scientist

Dr. Alice B. Gilliland, Physical Science Administrator

Steven C. Howard, IT Specialist

Dr. Jenise L. Swall, Statistician (Since June 2003)

Alfreida R. Torian, IT Specialist

Gary L. Walter, Computer Scientist

Sherry A Brown, Secretary

Air-Surface Processes Modeling Branch

Thomas F. 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. David K. Heist, Physical Scientist (Since August 2003)

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 (Until September 2003)

Bruce Pagnani (SEEP), Computer Programmer

Ashok Patel (SEEP), Engineer

John Rose (SEEP), Machinist/Modeler

Ruby Borden (SEEP), Secretary (Until January 2003)

Jane Coleman (SEEP), Secretary (Since March 2003)

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
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Awards

Dennis Atkinson, Dr. Desmond Bailey, and John Irwin received EPA Bronze Medals for superior

customer service to the air quality community.

Mark Evangelista received an EPA Bronze Medal for initiative, creativity, and dedicated work in

community outreach.