Advances in Emissions Modeling of Airborne Substances
Thomas Pierce*, William Benjey*, Jason Ching*, Dale Gillette*,
Alice Gilliland*, Shan He*, Michelle Mebust, and George Pouliot*
Atmospheric Modeling Division/NERL
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, North Carolina 27711
Abstract
The U.S. Environmental Protection Agency's Atmospheric Modeling Division is engaged
in a number of research projects that are leading to advances in emissions modeling of
airborne substances. Two of these projects, air quality forecast modeling and global
climate change modeling, are presented in other papers at this conference. This paper
briefly highlights the advances in the following areas:
(1)	Ammonia emissions - development and application of an inversion technique for
refining seasonal and annual estimates of ammonia
(2)	Biogenic emissions - development and integration of the third generation of the
Biogenic Emissions Inventory System (BEIS3)
(3)	Fugitive dust emissions - development and testing of geographical databases and
a dynamic algorithm for making episodic estimates of wind blown fugitive dust
(4)	Sea salt emissions - development of an algorithm for estimating emissions
originating from oceans
(5)	SMOKE - support and refinements to the Sparse Matrix Operational Kernel
Emissions (SMOKE) modeling system
(6)	Wildfire and prescribed burn emissions - collaboration with the National Park
Service on development of the Community Smoke Emissions Model (CSEM)
Introduction
Since the 1950s, the primary mission of the Atmospheric Modeling Division has been to
develop and evaluate air quality simulation models. While the Division has traditionally
focused its research on the meteorological aspects of these models, this focus has
expanded in recent years to include emission processors, a critical but an inaccurate
component of air quality modeling. The need for emissions modeling research has been
prompted by the realization that many emission processes require dynamically-
responsive algorithms that account for the meteorological conditions and the need for
innovative ways to evaluate emission inventories.
*On assignment from Air Resources Laboratory, National Oceanic and Atmospheric Administration.
"Affiliated as a post-doc with the University Center for Atmospheric Research (UCAR).

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This paper briefly highlights research that is being performed in six areas: (1) ammonia
inverse modeling, (2) biogenic emissions modeling, (3) fugitive dust emissions, (4) sea
salt emissions, (5) support for the Sparse Matrix Operational Kernel Emissions (SMOKE)
system, and (6) wildfire and prescribed burn emissions.
Ammonia Inverse Modeling
Gilliland and Abbitt (2001) discuss the development of an inverse modeling method for
application with regional air quality modeling. Inverse modeling is an approach that has
been used in a "top-down" manner to estimate emissions for atmospheric chemical
transport models. In general, this approach compares modeled and observed
concentrations and applies a linear optimization technique to adjust emissions that
produce model results that better compare with observations. Inverse modeling has been
used for many years with global-scale models, but few studies have historically used
inverse modeling with regional-scale air quality models.
Because of the large uncertainties in ammonia (NH3) emission estimates, we are testing
the use of an inverse method for estimating NH3 emissions. Both the annual and seasonal
pattern of NH3 is uncertain, and we suspect strong seasonal variations due to the nature of
the sources. NH3 is an excellent candidate for inverse modeling, because the response of
NH4 wet concentrations to NH3 emissions adjustments is quite linear and because the
tropospheric lifetime of NII3 is much shorter than the monthly time scales of interest.
Adjustments to monthly estimates of NH3 emissions were derived for 1990 using wet
ammonium concentration data observed during the National Atmospheric Deposition
Program and simulated with the Community Multi scale Air Quality (CMAQ) model.
The results of Gilliland et al. (2003) suggest that strong seasonal adjustments to estimated
NH3 emissions are needed, such that NH3 emissions during the fall and winter should be
at least 75% lower than emissions during the summer. These results also suggest that the
annual estimate of the 1990 National Emission Inventory (NEI) for NH3 is too high by at
least 20%. This suggestion is supported by a recent USEPA study that proposes lower
emission factors for cattle and swine, two of the largest sources of NH3 emissions in the
national inventory.
Biogenic Emissions Modeling
The Biogenic Emissions Inventory System (BE1S) has been updated several times since
its introduction in 1988. BEIS estimates volatile organic compound (VOC) emissions
from vegetation and nitric oxide (NO) emissions from soils at a spatial resolution as fine
as 1 km. BEIS3.09 is currently formally imbedded in the Sparse Matrix Operation
Emission (SMOKE) modeling system (Vukovich and Pierce, 2002). However, two
research versions, BEIS3.10 and BEIS3.11, have been recently developed and are
undergoing tests.
Pierce et al. (2002) introduced BEIS3.10 as part of the 2002 upgrade of the CMAQ
modeling system. BEIS3.10 features a 1-km vegetation database for the contiguous
United States that resolves forest canopy coverage by tree species; normalized emission

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factors for 34 chemicals, including 14 monoterpenes and methanol; a soil nitric oxide
emissions algorithm that accounts for soil moisture, crop canopy coverage, and fertilizer
application; and, speciation factors for the CBIV, RADM2, and SAPRAC99 chemical
mechanisms.
BEIS3.11 involves two minor revisions to the soil NO algorithm in BEIS3.10. The soil
NO algorithm has been modified to more carefully distinguish between agricultural and
non-agricultural land use types. Adjustments due to temperature, precipitation, fertilizer
application, and crop canopy coverage are now limited to the growing season (assumed to
be April 1-October 31) and are restricted to areas of agriculture as defined by the
Biogenic Emissions Landuse Database. Outside of the growing season and for non-
agricultural areas throughout the year, soil NO emissions are assumed to depend only on
temperature and the base emission factor is limited to that for grasslands. Another
revision to BEIS3.11 is to incorporate leaf shading when estimating methanol emissions
from non-forested areas. This is accomplished by assigning a nominal leaf area index of
3 for non-forested areas. BEIS3.11 will be available on the EPA web site for testing at
www.epa.gov/asmdnerl/biogen.html.
To better characterize vegetation cover for estimating biogenic emissions, Pierce et al.
(2002) compared vegetation cover and the resulting isoprene emission estimates from
three databases: (1) the North American Land Cover Characteristics (NALCC) version 2
database, (2) the Biogenic Emissions Landcover Database (BELD3), and (3) the National
Land Cover Database (NLCD). The NALCC database consists of 1-km resolved land
cover classes derived from Advanced Very-High Resolution Radiometer (AVHRR)
satellite data (USGS, 2001). BELD3, which currently provides vegetation data to the
BEIS, combines the NALCC data with U.S. Forest Service and U.S. Department of
Agriculture databases so that tree and crop cover (by species) are resolved to 1 km
(USEPA, 2001). The NLCD is based on Landsat-TM data and is available at ~30 m
resolution (USGS, 2002). The relative distribution of forest and agriculture cover
contained in the NALCC database was found to differ considerably from the two other
databases across the mixed agricultural/forested region of the Tennessee Valley.
Isoprene emissions were found to vary by a factor of two depending on the source of
vegetation data. Caution is therefore urged when using broadly-defined vegetation
classes (such as in the NALCC data) to derive biogenic emissions. Pierce et al. (2002)
recommend that future work consider using more recent databases, such as the NLCD,
coupled with tree species distributions to simulate other meteorologically-related
processes dependent on vegetation data.
Fugitive Dust Emissions
Work on fugitive dust has been directed towards formulating a basic understanding of
fugitive dust emissions and on implementing an emissions algorithm for the CMAQ
modeling system. One of the first comprehensive models for estimating wind erosion
dust was given by Gillette and Passi (1988) for the National Acid Precipitation
Assessment Program. Gillette et al. (1992) estimated the combined emissions of dust by
wind erosion and road dust emissions, and dust devils for the 48 contiguous United

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States. Physical explanations for dust emissions by wind erosion were given by Gillette
(1999), Gillette and Chen (2001) explained that one challenge in estimating fugitive dust
and wind emitted dust emissions is the issue of "supply-limitation." Supply limitation is
simply a reduction of the emitted dust for a given meteorological condition by a lack of
the source of dust from the soil or road-way. In other work, a model of dust emissions by
the wind was constructed for Southwest Asia by Draxler et al. (2001). In this model, soil
properties were estimated from soil samples, soil maps, geomorphic maps, and
photography of locations in Northern Kuwait. An algorithm specified in the paper gave
emissions of dust driven by the wind. Concentrations derived from the NOAA/ARL
HY SPLIT model and observed after Desert Storm showed fair agreement. A summary of
the most important properties of the soil that relate to dust emissions was given by
Gillette (2002); these properties include soil texture, crusting, and soil roughness.
Gillette (2001) noted that when existing algorithms for estimating fugitive dust emissions
were put into transport models, predicted concentrations downwind were found to be
larger than observed concentrations at locations where fugitive dust emissions were
known to be important. An initial effort to reduce this discrepancy was made by Gillette
(2001). His model posited that deposition close to the source accounts for much of the
discrepancy. An adaptation of this model is described by He et al. (2002), who reported
on the development of an algorithm to be used in the CMAQ modeling system. Most
regional air quality models have either ignored emissions of windblown and fugitive dust
or have treated these emissions crudely, mainly because acceptable emission processing
systems have been lacking. Algorithms for simulating windblown and fugitive dust must
involve complex atmospheric processes and must link to spatially and temporally
variable land surfaces, soil types, and soil condition databases. Our prototype for
estimating windblown dust emissions is derived from the work of Gillette (2001). It uses
threshold friction velocities parameterizations and incorporates gridded databases
prepared from information on soil types, surface soil moisture content, weather,
vegetation type, and vegetation coverage. Due to the variability of vegetation coverage,
the non-homogeneities in the distribution of wind-erodible land use types, and the
interception of the uplifted dust particles by tree and vegetation canopies, numerical
studies have been performed to understand the sensitivity of the algorithm to sub grid-
scale variations in the distribution of wind erodible land use type and vegetative coverage
within 36 km grid cells.
Model development and testing continues on the windblown dust algorithm, and efforts
have been made to link the algorithm to the CMAQ modeling system. We are evaluating
a prototype in CMAQ with data from a multi-day windblown dust episode during April
2001. Development and testing of a prototype version for fugitive dust is expected to
continue through summer 2003, and we hope to have the algorithm implemented into
CMAQ during 2004.
Sea Salt Emissions
The aerosol module within the CMAQ model needs to account for sea salt emissions over
marine env ironments. We have examined several sea spray generation functions

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(Andreas, 1998; Monahan et al., 1986; Smith and Harrison, 1998; Smith et al., 1993) to
determine their relative merits and drawbacks. Functions of Smith and Harrison (1998)
appear best-suited for CMAQ's modal approach to aerosol modeling, and the necessary
equations to calculate sea salt emissions from these functions have been coded into a
stand-alone box model for testing. These equations calculate the number, volume and
mass of emissions based on vertical wind profiles and roughness length. Our tests have
included the following: (1) determining appropriate wind profile functions; (2)
performing sensitivity analysis with selected input terms, such as friction velocity; and,
(3) determining whether equilibrium exists between the gas and aerosol phases over
marine environments (Allen et al., 1989; Hildemann et al, 1984; Nenes et al., 1998;
Quinnet al., 1992).
Sea salt emissions, using the Smith and Harrison (1998) functions, have been generated
as a test case for a version of CMAQ that contains a sectional aerosol module. This test
case is based on a 32-km gridded national domain for a 15-day period in July 1999.
Generating the emissions required spatially delineating salt-water from the Biogenic
Emissions Landuse Database (BELD). Preliminary analysis of the gridded emission
estimates and CMAQ simulations is underway.
Support for the Sparse Matrix Operational Kernel Emissions System
Development of the Sparse Matrix Operator Emission Kernel (SMOKE) modeling system
began under the sponsorship of a cooperative research agreement between the
Atmospheric Modeling Division and the North Carolina Supercomputing Center.
SMOKE was designed to be applicable to any pollutant, computationally efficient, and
architecturally flexible relative to other emission modeling systems. SMOKE has
evolved into a community model and is being used with many air quality modeling
systems. It may be downloaded at www .cm ascenter/or g/.
Recently, we have worked to enhance SMOKE by allowing users to group major elevated
point sources by stack parameters, emissions, emission rank, plant identification number,
source identification number, and plume rise (Benjey et al. 2001). We have also fixed a
number of minor software "bugs" and installed a new version of BEIS (see the Biogenic
Emissions section). Current efforts include installing a capability to model emissions of
blowing dust and wildfires, and using alternative land cover data. We are collaborating
with EPA's Office of Air Quality Planning and Standards to define a methodology to
provide toxic emissions for CMAQ. The initial implementation of toxic emissions in
SMOKE is limited to mobile sources using the Carbon Bond 4 mechanism, although
application of the complete National Toxic Emission Inventory is planned in the near
future (Strum et al., 2003), Future work will eventually combine the criteria and toxic
emission inventories.
As with other emissions modeling systems, gridding raw emissions data is a challenge
when running SMOKE. This function was previously accomplished by SMOKE Tool,
which has been phased out along with the old Models-3 modeling framework and
graphical interface (Novak et al., 1998). The core Models-3 modeling components

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(CMAQ, MCIP, SMOKE) are often run independently and are unaffected by the phase-
out of the old framework and interface. SMOKE Tool was used to define major elevated
point sources and to grid emission data and related spatial surrogates. Point source
definition is now done within SMOKE and gridding of the raw input data may be
accomplished with a new Spatial Allocator tool from the Multimedia Modeling System
(MlMS) (Fine et al., 2002). Spatial Allocator may be downloaded from
www.epa.gov/AMD/mims/software/spatial allocator.html. Unlike SMOKE Tool, the
Spatial Allocator does not require the use of expensive 8 AS or Arc/Info software
licenses. Finally, SMOKE can be run either independently using scripts or with the new
MIMS graphical interface.
Wildfire and Prescribed Burn Emissions
In collaboration with the National Park Service and the Cooperative Institute for
Research in the Atmosphere, a stand-alone emissions processor is being developed that
will simulate smoke emissions from fires (prescribed and wildfires) in the CMAQ
modeling system. Our goal is to build a tool with the following characteristics: (1)
horizontal scale from regional to national with grid resolutions ranging from 1 to 36 km;
(2) temporal resolution from hourly to multi-year; (3) chemical speciation to include
criteria pollutants and their precursors; and, (4) an accuracy equivalent to other emissions
estimates. The prototype, Community Smoke Emission Model (CSEM), consists of
processors based on algorithms developed primarily by the US Forest Service (USFS).
These processors are designed to accomplish the following tasks:
(1)	Identify fire boundaries on an hourly or daily basis from various geographical data
sources. The National Fire Occurrence database, with a 1 km resolution, includes most
fires for the period 1986 - 1996 and is being used in the CSEM prototype.
(2)	Determine fuel loadings from fuel models that relate vegetation coverages and fuel
loading data. The CSEM prototype uses EPA vegetation maps and the USFS Risk
Analyses system.
(3)	Compute the fuel moisture content and compare this to the fuel moisture content
threshold for flammability. Fuel moisture content is being calculated from hourly
temperature, relative humidity and cloudiness data from the MM5 system.
(4)	Generate fires based on historical data or stochastic estimates. Historical data are
derived from satellite observations or individual fire records, while stochastic estimates
are based on precipitation, humidity, drought, lightning frequency, and the probability of
human-induced ignitions.
(5)	Determine fire behavior and biomass consumption using the CONSUME model.
(6)	Compute plume rise and chemical profiles using the Emissions Processing Model or a
comparable system.

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Initial results from the CSEM prototype have been presented by Sestak et al. (2002), who
describe the modeling prototypes from this effort and the BLUESKY project for the
USFS Fire Consortia for Advanced Modeling of Meteorology and Smoke (FCAMMS).
Plans are being made to evaluate CSEM with the fire emissions database for the western
states being assembled by the Western Regional Air Partners' Fire Emissions Forum.
Other plans are being developed by the USFS to integrate CSEM into SMOKE, to use
CSEM with the CMAQ model, and to distribute CSEM via the CMAS website.
Summary
Within the Atmospheric Modeling Division, emissions modeling research is advancing in
two areas. The first area features the development of innovative techniques such as
inverse modeling to evaluate emissions. Inverse modeling offers tremendous potential
and is leading to better seasonal profiles of ammonia emissions. The second area
includes the development and evaluation of stand-alone processors that account for
meteorology and simulate dynamically-varying emissions. With investment of resources
in these two areas, we can improve the estimation of emissions of airborne substances.
References
Allen, A., R. Harrison, and J. Erisman. "Field Measurements of the Dissociation of
Ammonium Nitrate and Ammonium Chloride Aerosols", Atmospheric Environment
23(7): 1591-1599 (1989).
Andreas, E. "A New Sea Spray Generation Function for Wind Speeds up to 32 m/s",
Journal of Physical Oceanography 28:2175-2184 (1998).
Benjey, W., M. Houyoux, and J. Susick. "Implementation of the SMOKE Emission Data
Processor and SMOKE Tool Input Data Processor in Models-3". In Proceedings of the
Tenth Emission Inventory Conference, U.S. Environmental Protection Agency, Office of
Air Quality Planning and Standards, Research Triangle Park, NC, 2001, 14 pp.
Draxler, R., D. Gillette, J. Kirkpatrick, and J. Heller, "Estimating PMi0 Concentrations
from Dust Storms in Iraq, Kuwait, and Saudi Arabia", Atmospheric Environment 35:
4315 4330 (2001).
Fine, S., S. Howard, A. Eyth, D. Herington, and K. Castleton, "The EPA Multimedia
Integrated Modeling System Software Suite". In Proceedings of the Second Federal
Interagency Hydrologic Modeling Conference, U.S. Environmental Protection Agency,
Las Vegas, NV, 2002, 10 pp.
Gillette, D., "A Qualitative Geophysical Explanation for 'Hot Spot' Dust Emitting Source
Regions", Contributions to Atmospheric Physics 72:67-77 (1999).

-------
Gillette, D., "Regional Scale Vertical Dust Flux is a Small Fraction of the Local Field-
Scale Horizontal Fugitive Dust Flux", Western Governors Association, Western Regional
Air Partners, Expert Panel on Fugitive Dust, Internet, WRAP website at
www.wrapair.org under reports-Final Report on Fugitive Dust also to be found under
R&D Forum Committee, April, 2001.
Gillette, D., "Windblown Dust", Encyclopedia of Soil Science, R. Lai (ed.), Marcel
Dekker, Inc., pp. 1443-1445, 2002.
Gilette, D and W. Chen, "Particle Production and Aeolian Transport from a Supply-
Limited Source Area in the Chihuahuan Desert", Journal Geophysical Res. 106(D6):
5267-5278 (2001).
Gillette, D.. and R. Passi, "Modeling Dust Emission Caused By Wind Erosion", Journal.
Geophysical Res. 93:14,233-14,242 (1988).
Gillette, D., G. Stensland, A. Williams, W. Barnard, D. Gatz, P. Sinclair, and T. Johnson,
"Emissions of Alkaline Elements Calcium, Magnesium, Potassium, and Sodium From
Open Sources in the Contiguous United States", Global Biogeochemical Cycles 6:437-
457 (1992).
Gilliland, A., R. Dennis, S. Roselle, and T. Pierce, "Seasonal Ammonia Emission
Estimates for the Eastern United States Based on Ammonium Wet Concentrations and an
Inverse Modeling Method", Journal Geophysical Res., in press (2003).
Gilliland, A. and P. Abbitt, "A Sensitivity Study of the Discrete Kalman Filter (DKF) to
Initial Condition Discrepancies", Journal Geophysical Res., 106 (D16): 17,939-17,952
(2001).
He, S., J. Ching, D. Gillette, W. Benjey, T. Pace, and T. Pierce." Modeling fugitive dust
in US with Models-3 Community Multiscale Air Quality (CMAQ) Modeling System",
Presented at the Annual Conference of the American Association for Aerosol Research,
Charlotte, North Carolina, 2002.
Hildemann, L.M., A.G. Russell and G.R. Cass. "Ammonia and Nitric Acid
Concentrations in Equilibrium with Atmospheric Aerosols: Experiment vs. Theory",
Atmospheric Environment 18(9): 1737-1750 (1984).
Monahan, E.C., D.E. Spiel and K.L. Davidson. "A Model of Marine Aerosol Generation
via Whitecaps and Wave Disruption", Oceanic Whitecaps. E.C. Monahan and G. Mac
Niocaill (Eds.). D. Reidel Publishing Company, 167-174 (1986).
Nenes, A., S.N. Pandis and C. Pilinis. "ISORROPIA: A New Thermodynamic
Equilibrium Model for Multiphase Multicomponent Inorganic Aerosols". Aquatic
Geochemistry 4:123-152 (1998).

-------
Novak, J.; Young, J.; Byun, D.; Coats, C.; Walter, G.; Benjey, W.; Gipson, G.; LeDuc, S.
"Models-3: A Unifying Framework for Environmental Modeling and Assessment". In
Proceedings of the 78th Annual Meeting of the American Meteorological Society,
American Meteorological Society, Boston, MA, 1998, 5 pp.
Pierce, T., C. Geron, G. Pouliot, E. Kinnee, and J, Vukovich (2002) "Integration of the
Biogenic Emissions Inventory System (BEIS3) into the Community Multiscale Air
Quality (CMAQ) Modeling System", In Proceedings of the AMS 4th Urban Environment
Symposium, Norfolk, Virginia, May 20-23,2002. (Available online:
ams.confex.com/ams/AFMAPUE/12AirPoll/abstraets/37962.htm)
Pierce, T., J. Pleim, E. Kinnee, and L. Joyce (2002) "Intercomparison of Alternative
Vegetation Cover Databases for Regional Air Quality Modeling", In Proceedings of the
AMS 12th Joint Conference with AWMA on Applications of Air Pollution Meteorology,
Norfolk, Virginia, May 20-23, 2002. (Available online:
ams.confex.com/ams/AFMAPUE/12AirPoll/abstraets/37984.htm)
Quinn, P., W. Asher and R. Charlson. "Equilibria of the Marine Multiphase Ammonia
System", Journal of Atmospheric Chemistry 14:11-30(1992).
Sestak, M., S. O'Neill, S. Ferguson, J. Ching, and D. Fox. "Integration of Wildfire
Emissions into Models-3/CMAQ with the Prototypes: Community Smoke Emissions
Modeling System (CSEM) and BLUESKY", In Proceedings of the CMAS workshop,
Research Triangle Park, North Carolina, October 22, 2002, (Available online:
www,cmascenter.org/workshop/session5/fox_abstract.pdf
Smith, M., and N. Harrison. "The Sea Spray Generation Function". Journal of Aerosol
Science 29(Supplement 1): S189-S190 (1998).
Smith, M., P. Park and I. Consterdine. "Marine Aerosol Concentrations and Estimated
Fluxes Over the Sea". Quarterly Journal of the Royal Meteorological Society 119:809-
824 (1993).
Strum, M., L. Driver, G. Gipson, W. Benjey, R. Cook, M. Houyoux, C. Seppanen,, and
G. Stella. "The Use of SMOKE to Process Multipollutant Inventories - Integration of
Hazardous Air Pollutant and Volatile Organic Compound Emissions". In Proceedings of
the Twelfth Emission Inventory Conference, U.S. Environmental Protection Agency,
Office of Air Quality Planning and Standards, San Diego, CA., 2003, 14 pp. (to be
available at http://www.epa.gov/ttn/chief/conferenee/)
U.S. Geological Survey (2001) North American Land Characteristics Database.
Available online: http://edcdaac.usgs.gov/glcc/na_int.html [July 16, 2001].
U.S. Geological Survey (2002) National Land Cover Database, Available online;
http://landeover.usgs.gov/natllandcover.html [February 7, 2002],

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U.S. Environmental Protection Agency (2001) Biogenic Emissions Landcover Database.
Available online: http://www.epa.gov/asmdnerl/biogen.html [December 5, 2001],
Vukovich, J. and T. Pierce (2002) "The Implementation of BEIS3 within the SMOKE
Modeling Framework", In Proceedings of the 11th International Emissions Inventory
Conference, Atlanta, Georgia, April 15-18, 2002. (Available online:
www.epa.gov/ttn/chief/conference/eil 1 /modeling/vukovich.pdf)
Disclaimer and Acknowledgements
This paper has been reviewed in accordance with the U.S. Environmental Protection
Agency's peer and administrative review policies. Mention of products or trade names
does not constitute endorsement or recommendation of their use.
The authors appreciate the collaboration and assistance of numerous colleagues from
EPA and other institutions, including T. Pace (EPA-OAQPS), C. Geron (EPA-ORD), M.
Strum (EPA-OAQPS), M. Hoyoux (CEP), J. Vukovich (CEP), A. Guenther (NCAR), and
B. Lamb (WSU). Editorial suggestions by W. Hutzell are also gratefully acknowledged.
Keywords
Emission modeling
Air quality modeling
Biogenic emissions
Sea salt emissions
Fugitive dust
Inverse modeling
Ammonia emissions
Wildfire emissions

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ADDRESSES
Thomas Pierce*, William Benjoy*, Jason Ching*, Dale Gillette*, Alice Gilliland*,
George Pouliot*
Atmospheric Sciences Modeling Division
Air Resources Laboratory
National Oceanic and Atmospheric Administration
Research Triangle Park, NC 27711
Shan He+ and Michelle Mebust
Atmospheric Modeling Division
National Exposure Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park NC 27711
*On assignment to the U. S. Environmental Protection Agency, National Exposure
Research Laboratory
+On assignment from the University Center for Atmospheric Research.

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