&EPA
United States
Environmental Protection
Agency
Summary Report of the
Atmospheric Modeling and
Analysis Division's Research
Activities for 2008
Regional scale
Office of
Research and Development
National Exposure
Research Laboratory
-------
EPA/600/R-10/014 February 2010 www.epa.gov/ord
Summary Report of the
Atmospheric Modeling and Analysis Division's
Research Activities for 2008
ST. Rao, Jesse Bash, Robert Gilliam, David Mobley,
Sergey Napelenok, Chris Nolle, Tom Pierce, and Rob Pinder
Atmospheric Modeling and Analysis Division
National Exposure Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
-------
Disclaimer
The information in this document has been funded by the U.S. Environmental Protection Agency. It has been
subjected to the Agency's peer and administrative review and has been approved for publication as an EPA
document. Mention of trade names or commercial products does not constitute endorsement or recommendation for
use.
-------
Foreword
The research presented here was performed partially under a Memorandum of Understanding and
Memorandum of Agreement between the U.S. Environmental Protection Agency (EPA) and the U.S. Department of
Commerce's National Oceanic and Atmospheric Administration (NOAA). These agreements were implemented
through Interagency Agreements DW13938483 and DW13948634 between EPA and NOAA. Under this arrangement,
most of the Division's employees were NOAA employees and worked in EPA facilities in Research Triangle Park, NC.
The NOAA employees were transferred to EPA on July 1, 2008, and the Interagency Agreement ended on
September 30, 2008. Under NOAA, the Division was known as the Atmospheric Sciences Modeling Division of the Air
Resources Laboratory. As a division within the EPA organizational structure, it was known as the Atmospheric
Modeling Division under the National Exposure Research Laboratory. In conjunction with this change in operating
structure, the Division's name was changed to the Atmospheric Modeling and Analysis Division under EPA. To avoid
confusion in organization structure, "the Division," usually is used in this report when referring to activities of this
unique group of employees engaged in air quality modeling research.
This report summarizes the research and operational activities of the Division for calendar year 2008. A
summary report of the Division's research activities has been published for many years under NOAA and/or EPA
auspices. The report this year is drawn largely from the Division's Web site (www.epa.gov/amad) as of 12/31/08.
-------
-------
Abstract
This report summarizes the air quality modeling research activities that are associated primarily with the
Community Multiscale Air Quality Model. In 2008, these activities were conducted by a division of employees
associated in various capacities with the U.S. Environmental Protection Agency (EPA) and the National Oceanic and
Atmospheric Administration (NOAA). The Division is responsible for providing a sound scientific and technical basis
for regulatory policies to improve ambient air quality. The models developed by the Division are being used by EPA,
NOAA, and the air quality community to understand and forecast the magnitude of the air pollution problem and also
to develop emission control policies and regulations. This report summarizes the research and operational activities of
the Division for calendar year 2008.
-------
Acknowledgments
Sherry Brown and Patricia McGhee of the Division provided technical editing and manuscript preparation.
VI
-------
Table of Contents
List of Figures viii
1. Introduction 1
2. Model Development and Diagnostic Testing 3
2.1 Introduction 3
2.2 CMAQ Aerosol Module 3
2.3 CMAQ Chemistry Mechanisms 4
2.4 Air Toxics Modeling 5
2.5 Mercury Modeling 7
2.6 Multiscale Meteorological Modeling for Air Quality 7
2.7 Planetary Boundary Layer Modeling for Meteorology and Air Quality 10
2.8 Coupled WRF-CMAQ Modeling System 11
2.9 Computational Science 11
3. Air Quality Model Evaluation 15
3.1 Introduction 15
3.2 Operational Performance Evaluation of Air Quality Model Simulations 15
3.3 Diagnostic Evaluation of the Oxidized Nitrogen Budget Using Space-Based, Aircraft, and Ground
Observations 16
3.4 Diagnostic Evaluation of the Carbonaceous Fine Particle System 17
3.5 Diagnostic Evaluation Using Inverse Modeling To Improve Emission Estimates 18
3.6 Dynamic Evaluation of a Regional Air Quality Model 18
3.7 Probabilistic Model Evaluation 20
3.8 Statistical Methodology for Model Evaluation 21
4. Climate and Air Quality Interactions 23
4.1 Introduction 23
4.2 CIRAQ Pilot Study 24
5. Linking Air Quality to Human Health 25
5.1 Introduction 25
5.2 Characterizing Spatial Variation of Air Quality Near Roadways 25
5.3 Evaluating the Effectiveness of Regional-Scale Air Quality Regulations 26
5.4 Linking Local-Scale and Regional-Scale Models for Exposure Assessments 28
5.5 National Urban Database and Access Portal Tool 29
6. Linking Air Quality and Ecosystems 31
6.1 Introduction 31
6.2 Research Description 31
6.3 Accomplishments 34
6.4 Next Steps 35
6.5 Impact and Transition of Research to Applications 36
References 37
Appendix A: Atmospheric Modeling and Analysis Division Staff Roster 39
Appendix B: Division and Branch Descriptions 40
Appendix C: Awards and Recognition for 2008 42
Appendix D: Publications for FY and CY2008 43
Appendix E: Acronyms and Abbreviations 48
VII
-------
List of Figures
1-1 The Division's role in the source-exposure-dose-effects continuum 1
1-2 The Division's structure and organization 2
2-1 A flowchart that outlines the various components of the CMAQ modeling system 4
2-2 Since 2002, efforts focused on reducing model overpredictions of total nitrate 5
2-3 CMAQ organic carbon (OC) predictions that include cloud-produced SOA agree better with water
soluble OC measurements made during ICARTT 6
2-4 Air quality modeling in the Philadelphia area in collaboration with Region 3 6
2-5a As the EMEP study was nearing completion, AMAD organized a second Hg model intercomparison
study, this time with a focus on North America 8
2-5b CMAQ Hg modeling capabilites have been applied to support the development of EPA's Clean Air
Mercury Rule 8
2-6 Above are the box plot distributions of absolute error as a function of model vertical level for potential
temperature, wind speed, and wind direction 9
2-7 The most direct measure of success for a PEL model for both meteorology and air quality is its ability to
accurately simulate the vertical structure of both meteorological and chemical species 10
2-8 Two sets of initial simulations have been conducted to test the evolving coupled WRF-CMAQ modeling
system and to systematically assess the impacts of coupling and feedbacks 11
2-9 A flowchart that outlines the various components of the CMAQ modeling system 14
3-1 Scatter AMAD model evaluation framework 15
3-2 Scatter plot of observed versus CMAQ-predicted sulfate for August 2006 created byAMET 16
3-3 Vertical profile of the ratio of nitric acid to total oxidized nitrogen, as sampled during the August 8, 2004,
ICARTT flight over the northeastern United States 17
3-4 Source contributions to the modeled concentrations of fine-particulate carbon in six U.S. cities 18
3-5 Comparison of modeled and observed NO2 column concentrations 19
3-6 Example of dynamic evaluation showing observed and air quality model-predicted changes from
differences between summer 2005 and summer 2002 ozone concentrations from Gilliland et al. (2008). . 20
3-7 Spatial plots of ozone and probability of exceeding the threshold concentration for July 8, 2002, at 5 p.m.
EOT 21
3-8 Assessment of CMAQ's performance in estimating maximum 8-h ozone in the northeastern United
States on June 14, 2001 22
4-1 Differences in mean and 95th percentile maximum daily 8-h average ozone concentrations 23
5-1 Linking local-scale and regional-scale models for exposure assessment characterizing special variation
of air quality near roadways assessing the effectiveness of regional-scale air quality regulations 25
5-2 Population density and modeled benzene concentrations in Houston, TX 26
5-3 Assessing the impact of regulations on ecosystems and human health end points showing the indicators
and process linkages associated with the NOX Budget Trading Program 27
5-4 Combined model results for multiple pollutants for all receptors 28
5-5 Urban canopy effects 30
6-1 A Venn diagram representing ecosystem exposure as the intersection of the atmosphere and
biosphere 36
6-2 CMAQ is a source of data for ecosystem managers that is not available in routine monitoring data, such
as complete dry and wet deposition estimates, and the "one atmosphere" concept of CMAQ is needed to
understand the balance between uncertainties in atmospheric reaction rates and deposition pathways. .. 36
VIM
-------
CHAPTER 1
Introduction
The research presented here was performed
partially under a Memorandum of Understanding and
Memorandum of Agreement between the U.S.
Environmental Protection Agency (EPA) and the U.S.
Department of Commerce's National Oceanic and
Atmospheric Administration (NOAA). These agreements
are implemented through Interagency Agreements
DW13938483 and DW13948634 between EPA and
NOAA. Under this arrangement, most of the Division's
employees were NOAA employees and worked in EPA
facilities in Research Triangle Park, NC. The NOAA
employees were transferred to EPA on July 1, 2008, and
the Interagency Agreement ended on September 30,
2008. Under NOAA, the Division was known as the
Atmospheric Sciences Modeling Division (ASMD) of the
Air Resources Laboratory. As a division within the EPA
organizational structure, the Division was known as the
Atmospheric Modeling Division (AMD) under the
National Exposure Research Laboratory (NERL). In
conjunction with this change in operating structure, the
Division's name was changed to the Atmospheric
Modeling and Analysis Division (AMAD) under EPA. To
avoid confusion in organization structure, "the Division",
usually is used in this report when referring to activities
of this unique group of employees engaged in air quality
modeling research. This report summarizes the research
and operational activities of the Division for calendar
year 2008.
At the end of 2008, the Division under EPA was
organized into four research branches:
(1) the Atmospheric Model Development Branch,
(2) the Emissions and Model Evaluation Branch,
(3) the Atmospheric Exposure Integration Branch, and
(4) the Applied Modeling Branch.
The appendixes to this report contain a list of
Division employees (Appendix A), descriptions of the
Division and its branches (Appendix B), lists of awards
earned by Division personnel (Appendix C), Division
publications (Appendix D), and acronyms and
abbreviations used in this report (Appendix E).
The Division's role within EPA's National Exposure
Research Laboratory's "Exposure Framework" and the
EPA Office of Research and Development's source-to-
outcome continuum is to conduct research that improves
the Agency's understanding of the linkages from source
to exposure (see Figure 1-1)1. Through its research
branches, the Division provides atmospheric sciences
expertise, air quality forecasting support, and technical
guidance 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 [OAQPS] and regional offices), other Federal
agencies, and State and local pollution control agencies.
The Division provides this technical support and
expertise using an interdisciplinary approach that
Adapted from "A Conceptional Framework for U.S. EPA's
National Exposure Research Laboratory," EPA/600/R-09/003,
January 2009.
Source-to-Outcome Continuum
Figure 1-1. The Division's role in the source-exposure-dose-effects continuum.
-------
emphasizes integration and partnership with EPA and
public and private research communities. Specific
research and development activities are conducted in-
house and externally via contracts and cooperative
agreements.
The Division's activities were subjected to a
comprehensive peer review in January 2009. (Additional
information from the peer review is available on the
Division's Web site [www.epa.gov/amad.]) To present
materials and programs to the peer review, the Division's
activities were summarized with the focuses on five
outcome-oriented theme areas:
(1) model development and diagnostic testing,
(2) air quality model evaluation,
(3) climate and air quality interactions,
(4) linking air quality to human health, and
(5) linking air quality and ecosystem health.
Research tasks were developed within each theme
area by considering the following questions.
• Over the next 2 to 3 years, who are the major clients
and what are their needs?
• What research investments are needed to further the
science in ways that help the clients? How will we
lead or influence the science in this area?
• What personnel expertise, resources, and partners
are needed to do this work?
• Does the proposed work fall within the current scope
and plans of existing projects, or would personnel
resources need to be shifted from other projects to
make this happen?
The result is a research strategy for meeting user
needs that is built around the five major theme areas
and supported by the four branches of the Division, as
depicted in Figure 1-2.
This report summarizes the research and
operational activities of the Division for calendar year
2008. It includes descriptions of research and
operational efforts in air pollution meteorology, in
meteorology and air quality model development, and in
model evaluation and applications. Chapters 2 through 6
of this report are organized according to the five major
program themes listed above (also shown in Figure 1-2).
/~
Sound Science for Environmental Decisions
Model Development and Diagnostic Testing
Model Evaluation: Establishing Model's Credibility
Linking Air Quality and Human Health
Climate Change and Air Quality Interactions
Linking Air Quality and Ecosystem Health
AMAD Structure and Organization
Figure 1-2. The Division's structure and organization.
-------
CHAPTER 2
Model Development and Diagnostic Testing
2.1 Introduction
EPA and the States are responsible for
implementing the National Ambient Air Quality
Standards (NAAQS) for ozone (O3) and particulate
matter (PM). New standards for 8-h average ozone and
daily average PM2 5 concentrations recently have been
implemented. Air quality simulation models, such as the
Community Multiscale Air Quality (CMAQ) modeling
system, are central components of the air quality
management process at the national, State, and local
levels. The CMAQ model, used for research and
regulatory applications by the EPA, States, and the
scientific community, must have up-to-date science to
ensure the highest level of credibility for the regulatory
decisionmaking process. The research goals under the
CMAQ model development and evaluation program are
as follows.
• To develop, evaluate, and refine scientifically credible
and computationally efficient process simulation and
numerical methods for the CMAQ air quality modeling
system;
• To develop the CMAQ model for a variety of spatial
(urban through continental) and temporal (days to
years) scales and fora multipollutant regime (ozone,
PM, air toxics, visibility, and acid deposition);
• To adapt and apply the CMAQ modeling system to
particular air quality/deposition/climate-related
problems of interest to EPA, and use the modeling
system as a numerical laboratory to study the major
science process or data sensitivities and uncertainties
related to the problem;
• To evaluate the CMAQ model using operational and
diagnostic methods and to identify needed model
improvements;
• To use CMAQ to study the interrelationships among
different chemical species as well as the impact of
uncertainties in meteorological predictions and
emission estimates on air quality predictions;
• To collaborate with research partners to maintain the
CMAQ model system; and
• To pursue computational science advancements (e.g.,
parallel processing techniques) to maintain the
efficiency of the CMAQ model.
The National Research Council (NRC, 2004)
recommended that air quality management strategies
transition from a pollutant-by-pollutant approach to an
integrated multipollutant strategy. In response to these
recommendations, CMAQ also contains the option to
simulate the atmospheric fate of mercury (Hg)
compounds and 40 other hazardous air pollutants
(HAPs). The selection of HAPs included in CMAQ was
based on consultation with the EPA OAQPS and
includes the 33 HAPs indentified under the Integrated
Urban Air Toxics Strategy as posing the greatest
potential public health concern in the largest number of
urban areas, as well as several additional HAPs that are
significant contributors to O3 and secondary PM
formation. This extended capability enables model-
based air quality assessment studies to transition from
the traditional pollutant-by-pollutant approach to an
integrated multipollutant air quality management
approach, wherein benefits/disbenefits of various control
strategies can be more robustly examined.
The CMAQ model initially was released to the
public by EPA in 1998. Annual updated releases to the
user community and the creation of a Community
Modeling and Analysis System (CMAS) center that
provides user support for the CMAQ system and holds
an annual CMAQ users conference have helped to
create a dynamic and diverse CMAQ user community of
over 1,000 users throughout the world. CMAQ has been
and continues to be used extensively by EPA and the
States for air quality management analyses (state
implementation plans, Clean Air Interstate Rule [CAIR],
Clean Air Mercury Rule, and Renewable Fuel Standard
Program), by the research community for studying
relevant atmospheric processes, and by the international
community in a diverse set of model applications. Future
research directions include development of an integrated
weather research and forecasting (WRF)-CMAQ model
for two-way feedbacks between meteorological and
chemical processes and models and extension of the
CMAQ system to hemispheric scales for global climate-
air quality linkage applications and to the neighborhood
scale for human exposure applications.
2.2 CMAQ Aerosol Module
Atmospheric PM is linked with acute and chronic
health effects, visibility degradation, acid and nutrient
deposition, and climate change. Accurate predictions of
the PM mass concentration, composition, and size
distribution are necessary for assessing the potential
impacts of future air quality regulations and future
climate on these health and environmental outcomes.
The objective of this research is to improve predictions
of PM mass concentrations and chemical composition,
by advancing the scientific algorithms, computational
efficiency, and numerical stability of the CMAQ aerosol
module.
-------
Meteorological Model
(WRF or MM5)
Met-Chem Interface
Processor (MCIP)
Met. Data Processing
t
I
CMAQ AQ Model
Chemical-Transport
Computations
Meteorological Data
EPA Emissions
Inventory
SMOKE
Anthropogenic and Biogenic
Emissions Processing
Hourly 3-D Gridded Chemical
Concentrations
Figure 2-1. A flowchart that outlines the various components of the CMAQ modeling system.
To achieve this objective, we have focused efforts
on five areas in which previous versions of the CMAQ
aerosol module were deficient. First, we doubled the
computational efficiency of the aerosol module by
improving the computations of coagulation coefficients
and secondary organic aerosol (SOA) partitioning.
Second, we worked with the developer of ISORROPIA,
CMAQ's thermodynamic partitioning module for
inorganic species, to smooth out discontinuities. Third,
we developed a new parameterization of the
heterogeneous hydrolysis of nitrogen pentoxide (N2O5)
as part of a larger effort to mitigate model
overpredictions of wintertime nitrate aerosol
concentrations. Fourth, we vastly improved the treatment
of SOA by incorporating several new SOA precursors
and formation pathways. Fifth, we implemented an
efficient scheme to treat the dynamic interactions
between inorganic gases and the coarse PM mode.
As a result of this research, the CMAQ aerosol
module has been enhanced greatly over the past 5
years. During that time, the aerosol module has been
used for regulatory and forecasting applications (e.g.,
EPA-CAIR, NOAA-NCEP) because it is scientifically
credible, computationally efficient, and numerically
stable. With the recent scientific enhancements, our
clients have increased confidence in the utility of CMAQ
predictions of PM for future regulatory applications (e.g.,
Renewable Fuel Standards rulemaking). Meanwhile, the
community of CMAQ users outside EPA continues to
grow rapidly.
2.3 CMAQ Chemistry Mechanisms
An accurate characterization of atmospheric
chemistry is essential for developing reliable predictions
of the response of air pollutants to emissions changes,
to predict spatial and temporal concentrations, and to
quantify pollutant deposition. In the past, air quality
modelers have focused largely on single-pollutant
issues, but it has since become clear that it is more
appropriate to treat chemistry in an integrated,
multiphase, multipollutant manner (NRC, 2004). For
example, both inorganic and organic aqueous-phase
chemistry can influence formation of SOA through cloud
processing (Carlton et al., 2006, 2007). High-NOx versus
low-NOx conditions influence both O3 and SOA formation
(Ng et al., 2007). In the past 5 years, our requirements
for air quality modeling also have changed: the new
NAAQS for O3 and fine PM (PM2 5) have shifted our
focus from urban-scale ozone episodes (~7 days) to
regional/continental-scale simulations over longer time
-------
CMAQ v4 2
Denteneri Crutzen. '
CMAQ v4.7
zwa
CMAQ v43
Rtem*r fit al., 2003
CMAQ v4,6
Evans & Jacob. 2005
0.10
0 38
0.06
- G.04
75-150m above surface
Average of nighttime hours: Jan. — Feb. 2001
0.02
0.00
Figure 2-2. Since 2002, efforts focused on reducing model overpredictions of total nitrate (gas-phase HNO3 plus
particulate NO's). Recently, a new parameterization of the heterogeneous reaction probability of IV^Os was developed in
house.
periods (1 mo to 1 year). In addition, our chemical
mechanisms must adapt quickly to address emerging
issues of high importance, such as changing climatic
conditions and the growing interest in biofuels.
The goal of our research in this area is to develop,
refine, and implement state-of-the-science chemical
mechanisms for use in the CMAQ model to
• ensure that CMAQ and other models that are used for
regulatory and research purposes have scientifically
justifiable chemical representations, are appropriate
for the application being studied, and are consistent
with our most up-to-date knowledge of atmospheric
chemistry;
• ensure that interactions between gas-, aqueous-, and
particle-phase chemistries are adequately accounted
for, so that we can predict accurately multimedia
chemical effects of emissions changes; and
• develop techniques, tools, and strategies so that we
are able to efficiently expand current mechanisms to
predict additional atmospheric pollutants that will
become important in the future.
Our efforts to improve the chemical mechanisms in
CMAQ have resulted in more complete and up-to-date
descriptions of the important chemical pathways that
influence concentrations of the criteria pollutants O3 and
PM. The inclusion of chlorine reactions and the explicit
chemistry for 43 HAPs has helped to expand the
applications for which CMAQ can be used. The inclusion
of additional chemical detail in the aqueous and aerosol
modules is providing pathways for more complete
descriptions of secondary organic aerosol formation and
decay.
2.4 Air Toxics Modeling
The Clean Air Act (CAA) of 1990 identified 188
individual compounds or mixtures of compounds as
HAPs that have the potential for causing adverse health
effects, such as cancer, reproductive and neurological
effects, immune system damage, and birth defects.
Toxins released into the atmosphere can disperse
across the country and be inhaled or be deposited on
the earth's surface, where they may be ingested directly
-------
-O- WSQC liMSMIBiKllt *
Sdlid IUKIS iiKcift jlliliHk reioiMlary y-asi*
-I
18
Time
-------
More background information on EPA's air toxics
program can be found at http://www.epa.gov/ttn/atw/.
2.5 Mercury Modeling
ASMD has been working on the development of
atmospheric Hg models since the early 1990s when the
Regional Lagranian Model of Air Pollution (RELMAP)
was adapted to simulate Hg in support of EPA's Mercury
Study Report to Congress. As the scientific
understanding of atmospheric Hg continued to develop
in the late 1990s, it became apparent that Lagrangian-
type models, also known as "puff models, would have
difficulties simulating the complex chemical and physical
interactions of Hg with other pollutants that were being
discovered. Thus, the AMAD's focus for atmospheric Hg
model development was moved to CMAQ. CMAQ
simulates atmospheric processes within a three-
dimensional array of predefined finite volume elements
and can model complex interactions among all of the
pollutants that might exist within each volume element.
CMAQ previously was developed to simulate
photochemical oxidants, acidic and nutrient pollutants,
and PM, all of which have been shown to interact with
Hg in air and cloud water and influence deposition to
sensitive aquatic ecosystems. The "multipollutant"
approach of CMAQ, where all pollutants are simulated
together just as they exist in the real atmosphere, is
applied in atmospheric Hg modeling.
A number of modifications were made to the
standard CMAQ model to allow it to simulate
atmospheric Hg; these are described in detail in Bullock
and Brehme (2002). Because new information about
chemical and physical processes affecting atmospheric
Hg continually is being published, refinement of the
model code is an ongoing process. The FORTRAN
subroutine for the CMAQ aqueous chemistry mechanism
is periodically optimized to efficiently calculate Hg
chemistry in concert with the standard CMAQ cloud
chemistry mechanism. Further modification of the CMAQ
chemical mechanisms for mercury in both the gaseous
and aqueous phases is expected as additional chemical
reactions are identified and studied. The latest public
release of CMAQ provides the ability to simulate
atmospheric Hg in the "multipollutant" version of the
model. We found this to be the most efficient way to
maintain and disseminate the Hg version of CMAQ
because of the increasing number of pollutants with
which Hg is known to react.
AMAD has participated in two major model
intercomparison studies for atmospheric Hg. The first
was the Intercomparison of Numerical Models for Long-
Range Atmospheric Transport of Mercury, sponsored by
the European Monitoring and Evaluation Programme
(EMEP) and organized by EMEP's Meteorological
Synthesizing Center-East in Moscow, Russia. The first
phase of this EMEP study involved the simulation of Hg
chemistry in a closed cloud volume given a variety of
initial conditions. Results obtained from the CMAQ Hg
model and the other participating models from Russia,
Germany, Sweden, and the United States were
compared to identify key scientific and modeling
uncertainties (Ryaboshapko et al., 2002). This study led
to some significant changes in some of the participating
models, including CMAQ. The second phase of the
EMEP study involved full-scale model simulations of the
emission, transport, transformation, and deposition of Hg
over Europe for two short periods (10 to 14 days). Model
simulations were compared to field measurements of
elemental Hg gas, reactive gaseous Hg, and particulate
Hg in air. The "phase 2" results were reported in
Ryaboshapko et al. (2007a). The third and final phase of
the EMEP intercomparison involved model simulations
for longer periods of time (up to 1 year) and comparisons
with observations of the wet deposition of Hg. Results
from "phase 3" of the EMEP study are reported in
Ryaboshapko et al. (2007b).
2.6 Multiscale Meteorological Modeling for Air
Quality
Air quality models require accurate representations
of airflow and dispersion, cloud properties, radiative
fluxes, temperature and humidity fields, boundary layer
evolution and mixing, and surface fluxes of both
meteorological quantities (heat, moisture, and
momentum) and chemical species (dry deposition and
evasion). Thus, meteorological models are critical
components of the air quality modeling system that
evolve with the state of science. Because of this
evolution, there is a need to frequently challenge our
established models and configurations; this includes
examining not only new physics schemes but also data
assimilation strategies, which serve to lower uncertainty
in model output. It is also necessary to develop and
refine physical process components in the models to
address new and emerging research issues. Each of
these research objectives has the overarching goal to
improve meteorological model simulations to ultimately
reduce uncertainty in air quality simulations. Our
meteorology modeling research program involves
several key projects that have led to improved
meteorological fields. The first is the transition from the
National Center for Atmospheric Research's (NCAR's)
mesoscale model (MM5) system to the (WRF) model
that represents the current state of science. Part of this
effort was to implement in WRF land-surface (Pleim-Xiu
[PX]), surface-layer (Pleim), and planetary boundary
layer (PEL; Asymmetric Convective Model version 2
[ACM2]) schemes that had been used in MM5 and are
designed for retrospective air quality simulations as
outlined by Pleim and Gilliam (in press) and Gilliam and
Pleim (2009). Part of this effort included improving the
PX land-surface physics that included a deep soil
-------
Figure 2-5a
Figure 2-5a. As the EMEP study was nearing completion, AMAD organized a second Hg model intercomparison study,
this time with a focus on North America. The North American Mercury Model Intercomparison Study (NAMMIS) took
advantage of standardized weekly wet deposition samples taken by the Mercury Deposition Network (MDN) as described
in Vermette et al. (1995) and separate event-based precipitation samples taken at Underhill, VT (Keeler et al., 2005). In
addition to CMAQ, two other regional models were tested in NAMMIS; the Regional Modeling System for Aerosols and
Deposition (REMSAD) and the Trace Element Analysis Model (TEAM). All three models were applied to simulate the entire
year of 2001 three times, each time using a different initial condition and boundary condition (IC/BC) datasets developed
from one of three global models. NAMMIS provided a comparison between regional atmospheric Hg models and also a
measure of the sensitivity of each regional model to uncertainties regarding intercontinental transport. NAMMIS
evaluated each regional model for its agreement to observations of wet deposition of Hg at 63 locations in the United
States and Canada (Figure 2-5a). Analysis of each model's average annual wet deposition (Figure 2-5b) found CMAQ to
be in best agreement with observations. Various other statistical comparisons were performed against annual, seasonal
and weekly observations. In nearly every case, CMAQ showed better performance than other models considered in this
study. NAMMIS results regarding model-to-model comparisons are reported in Bullock et al. (2008) and Bullock et al.
(2009).
Figure 2-5b. CMAQ Hg modeling capabilities have been applied to support the development of EPA's Clean Air Mercury
Rule. They also have been used to provide information regarding Hg deposition from global background concentrations
to tribal, State, and regional environmental authorities in the development of their water quality protection strategies.
AMAD plans to maintain and develop atmospheric Hg simulation capabilities in CMAQ to support ongoing environmental
assessment and future regulatory action.
-------
Theta
Wind Speed
Wind Dir
0
I
8"
o
0.
1s
*J
•C
-— §-
o-
^y
HH3~
i-CD
' rn
I_
• CD '
> ff~l «
• flU
• to
• CD
> CD
• ozi
• n I-
i
• rr
»-c
r"
r-
<\ug 2006
0.6 1.0
.-, , >
• •
(
H
..,.(
I-H
ZJ •
Tl <
-m •
•n — \
1.4 1.
0
0.
o
CM
0 "
OJ
o
T—
"
§'
•
T-
S
'••CO1
'•••O i
i-rjn i
i-{D '
•••CD <
'i rr
i-yj--
,'.,,rr}.(
, rn-<
ETjV
,..-PJ~H .
>-rn---<
f..{TJ"H
Aug 2006
T ' T ' 1
1 3 5
0
o"
0
o.
0
^~
"
o
0-
T—
>{D
-CD --<
-CD <•
i-UJ '
i iTl (
1 [ '
• 1
,,.£-[ ,
,..(T~I,. i
i--jl \
'••II i <
'••1 1 i--<
> -T V--t
'> oz
'•••I 1 f
Aug 2006
10 30 50 70
Absolute Error (K) Absolute Error (m s1) Absolute Error (Deg.)
5% 25 75 95%
median
Mean Model Wind Speed (m s )
Mean Observed Wind Speed (m s )
E o
—' O
->-• LO
C3) *~
ffl
8
oo
06
12
UTC
18
00
06
12
UTC
18
Figure 2-6. Above are the box plot distributions of absolute error (model compared to aircraft and wind profiler
observations hourly for August 2006) as a function of model vertical level for potential temperature (K), wind speed (mis)
and wind direction (degrees). Also provided is the mean wind speed profile from the model and observations (wind
profiler). Temperature is simulated with low error throughout the PBL. This error is close to the 2-m temperature error
near the surface (~1.5 K), but much lower in the middle part of the PBL. Wind speed errors are between 1.0 and 2.0 m s"1
throughout the PBL, and wind direction errors are between 15 and 30 degrees, which is generally the same as at 10 m.
nudging algorithm and snow cover physics that
dramatically improved temperature estimations in the
winter simulations and in areas with less vegetation
coverage. An additional effort was to work toward
implementing in WRF the nudging-based four-
dimensional data assimilation (FDDA) capability that had
been available in MM5. Another effort has been a
reexamination of FDDA techniques, including the use of
a developmental analysis package for WRF (OBSGRID)
to lower the error of analyses that are used to nudge the
model toward the observed state of the atmosphere.
-------
Current results of the implementation of new
physics in WRF show that our configuration is
comparable to or exceeds the level of MM5 in terms of
uncertainty or error in near-surface variables like 2-m
temperature, 2-m moisture, and 10-m wind. This is true
only when the new analysis package is used to improve
analyses used for FDDA and soil moisture and
temperature nudging in WRF. A new evaluation method
that utilizes both wind profiler and aircraft profile
measurements provides a routine method to examine
not only the uncertainty of simulated wind in the
planetary boundary layer but also the less examined
temperature structure. The WRF model has low error in
temperature (median absolute error of 1.0 to 1.5 K) in
the planetary boundary layer, which is generally less
than the error near the surface. The model also
simulates the evolution of the wind structure, including
features like nocturnal jets and the convective mixed
layer, with low error (<2.0 m s-1). Our current
configuration of WRF has met the requirements for the
transition from MM5.
2.7 Planetary Boundary Layer Modeling for
Meteorology and Air Quality
Air quality modeling systems are essential tools for
air quality regulation and research. These systems are
based on Eulerian grid models for both meteorology and
atmospheric chemistry and transport. They are used for
a range of scales from continental to urban. A key
process in both meteorology and air quality models is
the treatment of subgrid-scale turbulent vertical transport
and mixing of meteorological and chemical species. The
most turbulent part of the atmosphere is the PEL, which
extends from the ground up to 1 to 3 km during the
daytime but is only a few tens or hundreds of meters
deep at night.
The modeling of the atmospheric boundary layer,
particularly during convective conditions, has long been
a major source of uncertainty in numerical modeling of
meteorology and air quality. Much of the difficulty stems
from the large range of turbulent scales that are effective
in the convective boundary layer (CBL). Both small-scale
turbulence that is subgrid-scale in most mesoscale grid
models and large-scale turbulence extending to the
depth of the CBL are important for vertical transport of
atmospheric properties and chemical species. Eddy
diffusion schemes assume that all of the turbulence is
subgrid-scale and, therefore, realistically cannot simulate
convective conditions. Simple nonlocal-closure PEL
models, such as the Blackadar convective model that
has been a mainstay PEL option in MM5 for many years,
and the original ACM, also an option in MM5, represent
large-scale transport driven by convective plumes but
neglect small-scale, subgrid-scale turbulent mixing. A
new version of the ACM (ACM2) has been developed
that includes the nonlocal scheme of the original ACM
combined with an eddy diffusion scheme. Thus, the
ACM2 can represent both the supergrid-scale and
subgrid-scale components of turbulent transport in the
convective boundary layer. Testing the ACM2 in
1 2 S 4 S 6 7 296 300 SK 304 SOS 308 M 0
Qv (gflaj) Tlwla
Figure 2-7. The most direct measure of success for a PBL model for both meteorology and air quality is its ability to
accurately simulate the vertical structure of both meteorological and chemical species. Figure 2-7 shows an example of
WRF and CMAQ profiles (both use the ACM2 scheme) compared with aircraft measurements. The top of the PBL mixed
layer is well defined and modeled for both meteorology variables (water vapor mixing ratio Qv and potential temperature
theta) and chemical variables (total reactive oxides of nitrogen NOy). Although such simultaneous measurements of
vertical profiles of meteorology and chemistry are very rare, these limited results are encouraging.
10
-------
Case 1: Eastern U.S., August 2-11, 2006, 12 km resolution
Surface PM.H Aerosol Optical Depth
fl.W
Opfcal
properties of
aertraote.
Reduction in PEL
Reduction in shortwave radiation
reaching Hie surface in regigns of
aerosol loading
Reduction in SW
1000
Observed
• w/o Feedback
• with Feedback
Bondville - August 6, 2005
10 12 14 16 18 20 22 24
Time (UTC)
Figure 2-8. Two sets of initial simulations have been conducted to test the evolving coupled WRF-CMAQ modeling system
and to systematically assess the impacts of coupling and feedbacks. Key questions in application of the coupled
modeling system for assessment of air quality-climate interactions are, can aerosol radiative effects be detected in
available measurements, and can such measurements be used to verify the directionality and magnitude of simulated
effects? The upper panels in the figure above demonstrate the impact that aerosols estimated by CMAQ have on the
meteorological models estimates of PBL height and downward shortwave radiation. The lower panel of the figure above
is verification that the simulation, which includes these feedbacks, agrees better with the observed shortwave radiation.
one-dimensional form and comparing to large-eddy
simulations (LES) and field data from the second and
third GEWEX Atmospheric Boundary Layer Study,
known as the GABLS2 (CASES-99) and GABLS3
(Cabauw, NL) experiments demonstrates that the new
scheme accurately simulates PBL heights, profiles of
fluxes and mean quantities, and surface-level values.
The ACM2 performs equally well for both meteorological
11
-------
parameters (e.g., potential temperature, moisture
variables, winds) and trace chemical concentrations,
which is an advantage over eddy diffusion models that
include a nonlocal term in the form of a gradient
adjustment (Pleim 2007a, Pleim 2007b). Comparisons to
data from the TexAQS II field experiment show good
agreement with PEL heights derived from radar wind
profilers and vertical profiles of both meteorological and
chemical quantities measured by aircraft spirals.
ACM2 is in the latest releases of the WRF model and
CMAQ model and now is used extensively by the air
quality and research communities.
2.8 Coupled WRF-CMAQ Modeling System
Although the role of long-lived greenhouse gases in
modulating the Earth's radiative budget has long been
recognized, it now is acknowledged widely that the
increased tropospheric loading of aerosols also can
affect climate in multiple ways. Aerosols can provide a
cooling effect by enhancing reflection of solar radiation,
both directly (by scattering light in clear air) and indirectly
(by increasing the reflectivity of clouds). On the other
hand, organic aerosols and soot absorb radiation, thus
warming the atmosphere. Current estimates of aerosol
radiative forcing are quite uncertain. The major sources
of this uncertainty are related to the characterization of
atmospheric loading of aerosols, the chemical
composition and source attribution of which are highly
variable both spatially and temporally. Unlike
greenhouse gases, the aerosol radiative forcing is
spatially heterogeneous and estimated to play a
significant role in regional climate trends. The accurate
regional characterization of the aerosol composition and
size distribution is critical for estimating their optical and
radiative properties and, thus, for quantifying their
impacts on radiation budgets of the Earth-atmosphere
system.
Traditionally, atmospheric chemistry-transport and
meteorology models have been applied in an offline
paradigm, in which archived output describing the
atmosphere's dynamical state as simulated by the
meteorology model is used to drive the transport and
chemistry calculations of the atmospheric chemistry-
transport model. A modeling framework that facilitates
coupled online calculations is desirable because it (1)
provides consistent treatment of dynamical processes
and reduces redundant calculations; (2) provides the
ability to couple dynamical and chemical calculations at
finer time steps, facilitating consistent use of data; (3)
reduces the disk-storage requirements typically
associated with offline applications; and (4) provides
opportunities to represent and assess the potentially
important radiative effects of pollutant loading on
simulated dynamical features. To address the needs of
emerging assessments for air quality-climate interactions
and for finer scale air quality applications, AMAD
recently began developing a coupled atmospheric
dynamics-chemistry model, the two-way coupled WRF-
CMAQ modeling system. In the prototype of this system,
careful consideration has been given to its structural
attributes to ensure that it can evolve to address the
increasingly complex problems facing the Agency. The
system design is flexible regarding the frequency of data
communication between the two models and can
accommodate both coupled and uncoupled modeling
paradigms. This approach also mitigates the need to
maintain separate versions of the models for online and
offline modeling (i.e., with and without direct radiative
feedbacks that result from aerosols).
In the prototype coupled WRF-CMAQ system, the
simulated aerosol composition and size distribution are
used to estimate the optical properties of aerosols, which
are then used in the WRF radiation calculations. Thus,
the direct radiative effects of absorbing and scattering
tropospheric aerosols estimated from the spatially and
temporally varying simulated aerosol distribution can be
fed back to the WRF radiation calculations; this results in
a "two-way" coupling between the atmospheric
dynamical and chemical modeling components. This
extended capability provides unique opportunities to
systematically investigate how atmospheric loading of
radiatively important trace species affects the Earth's
radiation budget. Consequently, this modeling system is
expected to play a critical role in the Agency's evolving
research and regulatory applications exploring air
quality-climate interactions.
2.9 Computational Science
Distributed or so-called massively parallel
processing enables the efficient computation of
problems with very large domain sizes. However,
programming for effective distributed processing
requires careful code design and structuring. The basic
principle of parallel computing is divide-and-conquer.
This principle renders lower overall computational time
and potentially reduced local memory utilization
(storage) and is achieved by decomposing the problem
space into many smaller problems that are solved
concurrently.
In most applications, a processor must access data
that reside in the memory of other processors.
The following is an example to illustrate this.
DO J = 1, N
DO I = 1, M
DATA(I,J) = A(I+2,J) *
END DO
END DO
, 3-1)
Depending on the platform, there are two main data
access paradigms: (1) message passing and (2) shared
memory and, in some cases, a combination of both.
12
-------
Proc 2
Proc 0
X
•
Proc 3
Proc 1
Each paradigm has its pros and cons. It might be
convenient, for example, for programmers to see a
global memory model on a distributed processor system.
However, interprocessor communication bandwidth
might become a serious performance issue. For the
platforms we envision that will run the CMAQ models,
message passing seems the best choice, and,
consequently, we have developed some of the major
codes, in particular the CMAQ Chemistry-Transport
Model (CCTM), to use this paradigm.
The backbone of the interprocessor communication
within CMAQ is the Stencil Exchange (STENEX) library,
which was developed in house and based on Message
Passing Interface (MPI). STENEX library handles
domain decomposition details, as well as various types
of intercommunication schemes, for example, (a) interior
to ghost region, where the ghost region is indicated in
light blue; (b) interior to interior; (c) subsection data
redistribution; and (d) selective data collection.
ProcX
I
i
,
FrocY
(a) interior to ghost region
ProcX
\
i
i
ProcY
(b) interior to interior
13
-------
B"
(c) redistribution of a subsection of data
Proc2
ProcO
ProcS
Procl
(d) selective data collection
Figure 2-9. A flowchart that outlines the various components of the CMAQ modeling system.
14
-------
CHAPTER 3
Air Quality Model Evaluation
3.1 Introduction
AMAD's model evaluation research program has
been designed to assess CMAQ model performance for (2)
specific time periods and for specific uses of the model,
and to develop innovative model evaluation techniques.
Further, it has been a priority to identify improvements
needed in model processes or inputs and better
characterize and reduce model uncertainty. The Division (3)
has developed a framework (Figure 3-1) to describe
these different aspects of model evaluation under four
categories, as outlined and illustrated below.
(1) Operational evaluation, as defined here, is a (4)
comparison of model-predicted and routinely
measured concentrations of the end point
pollutant(s) of interest in an overall sense. This is the
first phase of any model evaluation study.
Diagnostic evaluation investigates the atmospheric
processes and input drivers that affect model
performance to guide CMAQ development and
improvements needed in emissions and
meteorological data.
Dynamic evaluation assesses a model's air quality
response to changes in meteorology or emissions,
which is a principal use of an air quality model for air
quality management.
Probabilistic evaluation strives to characterize
uncertainty in CMAQ model predictions for model
applications such as predicted concentration
changes in response to emission reductions.
Can v/e identify
improvements
for mooe!
processes or
inputs?
CMAQ-predicted
concentration and deposition
Model Inputs: meteorology ana emissions
Chemical transformation: gas, aerosol,
and aqueous phases
Transport: aspect/on and diffusion
Removal; dn an! wet o'epcs.'fon
Are we getting
the right answers?
Dynamic Evaluation
Can tiie model capture changes related to
meteorological events or variations?
Can the model capture changes related to
emission reductions?
Diagnostic Evaluation
Are model errors or biases caused by
model inputs or by modeled processes?
Can we identify tile specific modeled
process(es) responsible?
Operational Evaluation
How do trie model predicted concentrations
compare to observed concentration data?
Are there large temporal or spatial prediction
errors or biases?
Can vte capture
observed air /
quality changes?
Are vie getting the right
answers for the right (or
wrong) reuscr,s?
What is our
confidence in the
model predictions?
Probabilistic Evaluation
How should uncertainty in model
inputs and options be quantified?
What is the best way to propagate
uncertainty through the model?
What are the best ways to
communicate the confidence in
the model-predicted values?
•Applications
Figure 3-1. Scatter AMAD model evaluation framework.
3.2 Operational Performance Evaluation of Air
Quality Model Simulations
Two of the three main components of an air quality
model (e.g., CMAQ) simulation are the input
meteorology and the air quality model simulation itself,
with the third being the input emissions. Meteorological
data are provided by models, such as MM5 and WRF.
The quality of the meteorological data, specifically how
well the predicted values (e.g., temperature, wind speed,
etc.) compare with the observed state of the
atmosphere, is critical to the performance of the air
quality model, which is highly dependent on the
meteorological data to accurately simulate pollutants in
the atmosphere. As such, an important aspect of any air
quality simulation is the evaluation of the quality of the
predicted meteorological data. This is accomplished by
comparing model-simulated values with observed data
(Figure 3-2). This type of evaluation is referred to as
operational evaluation. An example of meteorological
evaluation can be found at
15
-------
Ml b_12hm_34L SO4 for AugiEt 2flM
. IW.PHOVr ;.U-b 1?krn .141)
£. STN (M1b_12km_34Lt
O _
FWSE '.'.•& hl
IMPROVE 1.79 -B.T M.1 -56 S7.B
ETN i.os -is sc.3 -oj 21.7
2A3TNOI 1.22 -11 7 17 6 -134 14 a
5 10
Observation
15
Figure 3-2. Scatter plot of observed versus CMAQ-predicted sulfate for August 2006 created by AMET.
http://www.epa.gov/asmdnerl/ModelDevelopment/
meteorologyModeling.html. A similar evaluation of the air
quality model simulation also is performed using
available observed air quality measurements.
As the developers of the CMAQ model, AMAD
frequently is evaluating CMAQ simulations as part of the
testing process as the model evolves with state-of-the-
art science. Examples of changes to the modeling
system that may require testing include
updates/corrections to the model code, changes in the
model inputs (e.g., meteorology, emissions), and any
other changes that may impact the model predictions. As
computing power has increased (and continues to
increase), the time required to run a simulation has
decreased. Additionally, the duration of model
simulations has increased from a week or several weeks
to multiple months and multiple years. With this increase
in the number and duration of air quality simulations
comes an increase in the time required to thoroughly
evaluate each simulation. To evaluate a simulation
within a reasonable amount of time, AMAD developed
the Atmospheric Model Evaluation Tool (AMET), which
aids researchers in evaluating the operational
performance of a meteorological or air quality simulation.
A brief description of AMET is given below, and a link to
AMET code can be found at
http://www.epa.gov/asmdnerl/tools.html.
AMET is a combination of an open source database
software (MYSQL), the R statistics software, and
FORTRAN and PERL scripts that together provide an
organized and powerful system for processing
meteorological and air quality model output and then
evaluating the performance of model predictions. AMET
uses FORTRAN and PERL scripts to pair observed
meteorological and air quality data with model
predictions, then populates a MYSQL relational
database with the paired data, and, finally, uses R
statistics scripts to create statistics and plots to show the
operational model performance. Many R scripts are
already available with the release version of AMET, but
users familiar with R can modify existing scripts or create
new scripts to suit their evaluation needs.
3.3 Diagnostic Evaluation of the Oxidized
Nitrogen Budget Using Space-Based, Aircraft,
and Ground Observations
Recent studies have shown that when compared
with field observations, chemical transport models make
significant errors in the simulated partitioning of nitrogen
compounds (NOy) between NO2, HNO3, and peroxyacyl
nitrates (PANs). This impacts the long-range transport of
ozone precursors, misrepresents the relative
effectiveness of local versus regional emission control
strategies, and distorts the spatial and temporal
distribution of nitrogen deposition. In this research, we
use a combination of modeling tools equipped with
process analysis, satellite data, aircraft observations
from the International Consortium for Atmospheric
Research on Transport and Transformation (ICARTT),
16
-------
HNO,: NO,
Jl—^
£
QJ
•g
+;
~ec
o
o
o ~
^
o
o
o ~
CO
o
0
o ~
(M
o
0
o "
o
/
o O'
\ f
\ j
rtn
1
000
J 1 \
0,00
HHn. -o
0.2 0.4
O O
fr"
observations
CMAQ-SAPRC
CMAQ-CB05
0.6 0.8 1.0
ratio
Figure 3-3. Vertical profile of the ratio of nitric acid (HNO3) to total oxidized nitrogen (NOy), as sampled during the August
8, 2004, ICARTT flight over the northeastern United States. When the observations are paired in time and space with the
CMAQ simulations, we find that the chemical mechanisms used in CMAQ overestimate the contribution of nitric acid to
total NOy, especially in the free troposphere.
INTEX-NA, and TexAQS 2006 field campaigns, and
surface observations to better understand and improve
the simulated fate and transport of oxidized nitrogen
species. We are applying this analysis to better quantify
the relative impact of local versus regional NOX emission
control strategies, the contribution of lightning NOX to
atmospheric chemistry, and the long-range transport and
deposition of NOyto remote ecosystems.
3.4 Diagnostic Evaluation of the Carbonaceous
Fine Particle System
Routine measurements of speciated PM25 (e.g.,
IMPROVE, STN) are often insufficient to diagnose the
causes of model errors in OC concentrations because
they cannot distinguish the origin of OC between primary
versus secondary, anthropogenic versus biogenic, or
mobile sources versus area sources. Through
identification of the sources and processes contributing
the OC, the necessary improvements in the modeled
processes or emission inputs can be identified. Current
diagnostic evaluation work is listed below that will
support better understanding of the carbonaceous
aerosol system.
Estimating how much OC observed is
secondary. Routine measurements of elemental carbon
(EC) and OC can be used in conjunction with model
predictions of EC and primary OC to estimate
concentrations of secondary OC (Yu et al., 2007). These
estimates can be used as a preliminary assessment of
model performance for secondary OC.
Primary OC predictions from different sources.
Measurements of individual organic compounds that are
specific to certain primary emission sources may be
used to evaluate model predictions of primary OC on a
source-by-source basis. Measurements of this type at
the SEARCH monitoring sites have been used to
evaluate model results during the July-August 1999
period in the southeastern United States (Bhave et al.,
2007).
Fossil-fuel versus modern carbon predictions.
Measurements of radiocarbon (14C isotope) allow one to
distinguish fossil-fuel carbon (e.g., motor vehicle
exhaust, coal and oil combustion) from modern carbon
(e.g., biomass combustion, biogenic SOA).
Measurements of this type at Nashville in summer 1999
(Lewis et al., 2004) are being used to evaluate model
predictions of these two types of carbon.
Tracers of anthropogenic and biogenic SOA.
Novel analytical techniques for quantifying individual
organic compounds that are unique tracers of
17
-------
anthropogenic and biogenic SOA have been developed
by EPA scientists. These compounds have been
measured at an RTP site throughout the 2003 calendar
year (Kleindienst et al., 2007) and have been used to
evaluate recent improvements to the CMAQ SOA
module (Bhave et al., 2007). A copy of the presentation
is located at http://iccpa.lbl.gov/presentations/iccpa-08-
bhave.pdf.
Many of these exploratory projects are in
collaboration with scientists in the NERL Human
Exposure and Atmospheric Sciences Division (HEASD).
The web address is http://www.epa.gov/heasd/.
D Brag Secondary
0 Anthrup Secondai>
r. other Pnmuy
• Misc i-ausfai
UCfuMai Material
• Paved Road Out!
• Food Cooking
D Natural Gas Comb
r Oil CofYib
D Caul Comb
• WaslftComb
• Wild Fires
• Anthrop BIG ma&s Comb
U Aircraft Exhautf
• Nonroad G«9OMne
• Gasoline Exhaust
• Nanroad Diesel
• D.WWI Extent)
Figure 3-4. Source contributions to the modeled concentrations of fine-particulate carbon in six U.S. cities.
3.5 Diagnostic Evaluation Using Inverse
Modeling To Improve Emission Estimates
Although continuously updated and improved,
emission inventories still are considered to be one of the
largest sources of uncertainty in air quality modeling. It is
often difficult to measure either the emission factors,
activity information, or both for various emitting
processes, such as forest fires, animal husbandry
practices, and motor vehicles. Therefore, bottom-up
inventories for such processes often are based on
estimates and averages.
To complement, evaluate, and better inform bottom-
up emission inventories, we develop and apply inverse
modeling methods. These types of "top-down"
approaches employ observational data from
continuously operating pollutant measurement networks,
intensive field campaigns, and remote sensing
technologies to infer emission inventories based on
current state-of-the-science understanding of physical
and chemical processes in the atmosphere.
In one specific application, we use the satellite-derived
NO2 column density to attempt to identify any possible
bias in the NOX emission inventories over several
regions in the southeastern United States. Figure 3-5
shows a model comparison of satellite observations
(from scanning imaging absorption spectrometer for
atmospheric cartography [SCIAMACHY] retrieval) and
CMAQ prediction. This application relies on the
adaptive-iterative Kalman filter as an inverse method
and decoupled direct method in 3D (DDM-3D) as a way
to quantify the relationship between emission rates of
NOX and atmospheric concentrations of NO2. We find
that urban emissions in Atlanta and Birmingham are
likely to be overestimated; more rural concentrations of
NO2 are likely to be low because of missing emissions
and chemical processes aloft in the CMAQ model.
3.6 Dynamic Evaluation of a Regional Air
Quality Model
A dynamic evaluation approach explicitly focuses
on the model-predicted pollutant responses stemming
from changes in emissions or meteorology. However,
the emergence of the dynamic evaluation approach
introduces new challenges. In particular, retrospective
case studies are needed that provide observable
changes in air quality that can be related closely to
known changes in emissions or meteorology. The NOX
SIP call has offered a very strong initial case study to
test model responses via dynamic evaluation.
The most direct example of a dynamic evaluation
study is described in Gilliland et al. (2008), where air
18
-------
Continental US
Southeast US
0 40-
o
o «-
co 3H
15
-2
<2
NO2(10 molecules cm )
2-4 4-6 6-8 8-10
Figure 3-5. Comparison of modeled and observed NO2 column concentrations.
quality model simulation results with the CMAQ model
were evaluated before and after major reductions in NOX
emissions. EPA's NOX SIP call required substantial
reductions in NOX emissions from power plants in the
eastern United States during summer O3 seasons with
the emission controls being implemented during 2003
through May 31, 2004. Gego et al. (2007) and USEPA
(2007) show examples of how observed O3 levels have
decreased noticeably after the NOX SIP call was
implemented. Because air quality models are applied to
estimate how ambient concentrations will change
because of possible emission control strategies, the NOX
SIP call was identified as an excellent opportunity to
evaluate a model's ability to simulate O3 response to
known and quantifiable observed O3 changes. Figure 3-
6 provides an example from this prototype modeling
study where changes in maximum 8-h O3 are compared
between the summer 2005 after the NOX controls and
the summer 2002 before these controls. The spatial
patterns of percentage decreases in O3 derived from
observations and the model exhibit strong similarities.
However, these results also revealed model
underestimation of O3 decreases as compared with
observations, especially in northeastern states at
extended downwind distances from the Ohio River
Valley source region. This may be attributed to an
underestimation of NOX emission reductions or a
dampened chemical response in the model to those
emission changes or to other factors. Analysis methods,
such as the e-folding distances (Gilliland et al., 2008,
Godowitch et al., 2008), have been used to show that
NOX emissions in these simulations are not impacting O3
levels as far downwind as observations suggest, which
could be a factor here. Next steps must involve further
diagnostic evaluation to identify what chemical, physical,
or emission estimation uncertainties are contributing to
these initial results from the model. Findings from
additional analysis of this case study ultimately can lead
to model improvements that are directly relevant to the
way air quality models are used for regulatory decisions.
19
-------
5
-5
-15
-20
-25
-30
(a) Obs
(b) CMAQ v4.6 CB05
Figure 3-6. Example of dynamic evaluation showing (a) observed and (b) air quality model-predicted changes (%) from
differences between summer 2005 and summer 2002 ozone concentrations from Gilliland et al. (2008). The results
illustrate the relative change in ozone when comparing the 95th percent daily 8-h maximum levels between the two
summers.
3.7 Probabilistic Model Evaluation
When weighing the societal benefits of different air
quality management strategies, policymakers need
quantitative information about the relative risks and
likelihood of success of different options to guide their
decisions. A key component in such a decision support
system is an air quality model that can estimate not only
a single "best-estimate" but also a credible range of
values to reflect uncertainty in the model predictions.
Probabilistic evaluation of CMAQ seeks to answer the
following questions.
• How do we quantify our uncertainty in model inputs
and parameterizations?
• How do we propagate this uncertainty to the predicted
model outputs?
• How do we communicate our level of confidence in
the model-predicted values in a way that is valuable
and useful to decisionmakers?
To address these questions, we've deployed a
combination of deterministic air quality models and
statistical methods to derive probabilistic estimates of air
quality. For example, an ensemble of deterministic
simulations frequently is used to account for different
sources of uncertainty in the modeling system (e.g.,
emissions or meteorological inputs, boundary conditions,
parameterization of chemical or physical processes). A
challenge with ensemble approaches is that chemical
transport models require significant input data and
computational resources to complete a single simulation.
We have applied the CMAQ-DDM-3D to generate large
member ensembles avoiding the major computational
cost of running the regional air quality model multiple
times. We also have used statistical methods to
postprocess the ensemble of model runs based on
observed pollutant levels. Maximum likelihood estimation
is used to fit a finite mixture statistical model to simulated
and observed pollutant concentrations. The final
predictive distribution is a weighted average of
probability densities, and the estimated weights can be
used to judge the performance of individual ensemble
members, relative to the observations.
These approaches provide an estimated probability
distribution of pollutant concentration at any given
location and time. The full probability distribution can be
used in several ways, such as estimating a range of
likely, or "highly probable," concentration values or
estimating the probability of exceeding a given threshold
value of a particular pollutant. For example, Figure 3-7
shows the estimated probability of exceeding an ozone
threshold concentration of 60 ppb over the southeastern
United States for current conditions (top) and with a 50%
reduction in NOX emissions (bottom). Compared to the
single-base CMAQ simulation (far left), the spatial
gradients provided by the ensemble-based estimates
(middle and right) more accurately reflect the observed
exceedances under current conditions.
3.8 Statistical Methodology for Model
Evaluation
Model evaluation efforts often include graphical
comparisons of monitoring data paired with the output
for the model grid cells in which the monitors lie and
statistical summaries of the differences that exist. If
certain differences or regions are of particular interest,
the investigator may narrow the evaluation's focus to a
limited area and time period. Advanced statistical
20
-------
Single Simulation
Ensemble Mean
Ensemble Prob.
160 00 0.2 0.4 0.6 06 10
Figure 3-7. Spatial plots of ozone and probability of exceeding the threshold concentration for July 8, 2002, at 5 p.m. EOT.
Observations are shown in white circles.
methods can aid the evaluator by making the best use of
the limited monitoring data available, accounting for the
differences between point-based measurements
(monitors) and grid cell averages (model output), and
assessing the model output for grid cells in which no
monitors are located.
Although a variety of approaches could reasonably
be utilized, we have focused on methods that allow us to
better understand and utilize the spatial correlation of
pollutant fields, such as kriging-based methods. For
example, we have used Bayesian kriging to investigate
the relationship between ammonium wet deposition and
precipitation and kriging with adjustments foranisotropy
to better understand O3 and PM2 5 concentrations in the
northeastern U.S. In addition, recent work has explored
the impact on model evaluation of incommensurability
(i.e., the mismatch between point-based measurements
and areal averages [model output]).
21
-------
£§
pB
1
40
14OO ICOO 1BOO 2000
Dicrtnoc(krn|
P
I
140D 1COD 19DD 20DO
Distance (km |
CO
(a) Observed concentrations
(b) Modeled concentrations
I
b
Dflto:2CO 1-06-14
1400 ICOO 1900 2000
,
1400 ICOO 1300 JOOO
Distnccikmi
(c) Block kriging estimates based on observations
(d) Grid cells of interest for further investigation
Figure 3-8. Assessment of CMAQ's performance in estimating maximum 8-h ozone in the northeastern United
States on June 14, 2001.
22
-------
CHAPTER 4
Climate and Air Quality Interactions
4.1 Introduction
Air quality is determined both by emissions of
pollutants, including volatile organic compounds, NOX,
sulfur dioxide (SO2), carbon monoxide, and Hg, and by
meteorological conditions, including temperature, wind
flow patterns, and the frequency of precipitation and
stagnation events. For air quality management
applications, regional-scale models are used to assess
whether various emission control strategies will result in
attainment of the NAAQS. These modeling applications
typically assume present meteorological conditions,
which means that potential changes in climate are not
included in the assessment. Because emission controls
are designed to be effective for several decades, future
climate trends could impact their adequacy in meeting
the NAAQS.
The first phase of the Climate Impact on Regional
Air Quality (CIRAQ) pilot study on the effect of climate
change on air quality has been completed. Future work
is proceeding in the following three broad areas.
(1) Developing methods to generate a range of future
regional-scale climate scenarios by downscaling
outputs from global climate models.
(2) Developing alternative scenarios for future U.S.
emissions of O3 precursors and species that form
atmospheric PM, taking into account current
regulations, technological change, and population
growth, and analyzing the impact of these emission
changes on air quality.
(3) Using the coupled WRF-CMAQ meteorology and
chemistry model to investigate feedbacks of future
emission scenarios on radiative budget.
1 Jun - 31 Aiig
1 Sep - 31 Oct
Figure 4-1. Differences in mean (top) and 95th percentile (bottom) maximum daily 8-h average (MDA8) ozone
concentrations. Results show summertime increases of 2 to 5 ppb in mean MDA8 concentrations in Texas and parts of
the eastern United States and even larger increases in 95th percentile concentrations, suggesting increased severity of
ozone episodes. Still larger increases are predicted for the September-October time period, suggesting a lengthening of
the ozone season (Nolte et al., 2008).
23
-------
4.2 CIRAQ Pilot Study
AMAD initiated the CIRAQ project in 2002 to
develop a pilot modeling study to incorporate regional-
scale climate effects into air quality modeling. It involved
collaboration across multiple Federal agencies and with
academic groups with global-scale modeling expertise,
who were supported through the EPA Science to
Achieve Results grant program.
The Goddard Institute for Space Studies global
climate model (GCM) version 2' was used to simulate
the period from 1950 to 2055 at 4° latitude x 5° longitude
resolution. Estimated historical values for greenhouse
gases (as carbon dioxide equivalents) were used for
1950 to 2000, with future greenhouse gas forcing
following the Intergovernmental Panel on Climate
Change's A1B (Special Report on Emissions Scenarios
[Nakicenovic et al., 2000]) scenario. Colleagues at the
Pacific Northwest National Laboratory downscaled the
GCM outputs using the Penn State/NCAR MM5 model to
simulate meteorology over the continental United States
at 36-km resolution for two 10-year periods centered on
2000 and 2050.
For the first phase of this project, the effect of
potential climate change alone was considered, without
attempting to account for changes in emissions of O3
and PM precursors. Hourly emissions were simulated
using the Sparse Matrix Operator Kernel Emissions
(SMOKE). Anthropogenic emissions were based on the
EPA 2001 modeling inventory, projected from the 1999
National Emission Inventory version 3. Biogenic
emissions were calculated using the simulated
meteorology. Air quality was simulated for two 5-year
periods (1999 to 2003 and 2048 to 2052) using CMAQ
v4.5.
As the next step, we are investigating the combined
effect of climate change together with emission changes
on air quality. Emission projections for different
scenarios of economic growth and technological
utilization are under development by colleagues at EPA/
Office of Research and Development's (ORD's) National
Risk Management Research Laboratory (NRMRL). Air
quality simulations using these emissions projections
and the climatological meteorology described above will
be conducted using CMAQ v4.7 in 2009.
24
-------
CHAPTER 5
Linking Air Quality to Human Health
5.1 Introduction
This research theme applies existing models and
tools and develops new tools and approaches to link air
quality to human exposure and human health. Typically,
epidemiological studies rely on ambient observations
from sparse monitoring networks to provide metrics of
exposure. Yet, for many pollutants in urban areas, large
spatial variations exist, particularly near roads and major
industrial sources. Further complicating the issue,
ambient concentrations do not necessarily represent
actual exposures, which are influenced by the infiltration
of ambient concentrations into indoor facilities (such as
automobiles, homes, schools, and work places) and the
activity of individuals (such as outdoor exercise, walking,
commuting, etc.). Finally, populations also are impacted
by the transport of pollutants. These multiple factors
affecting exposure require approaches that scale from
regional to local environments and to the individuals
experiencing the exposure. Thus, this research provides
analytical and physical modeling approaches that
provide the spatial and temporal detail of concentration
surfaces needed to understand the relationships among
pollutants emitted, the resulting air quality, and exposure
of humans to these pollutants.
Research conducted under this theme focuses on
developing analytical tools and methods based on
models and observations to improve the characterization
of human exposure, evaluate the effectiveness of control
strategies with respect to health outcomes, and address
critical exposure issues, such as exposure to multiple
pollutants for multiple scales.
Regional scale
Figure 5-1. Linking local-scale and regional-scale models for exposure assessment characterizing spatial variation of air
quality near roadways assessing the effectiveness of regional-scale air quality regulations (Source: Stein et al., 2007).
5.2 Characterizing Spatial Variation of
Air Quality Near Roadways
Recent studies have identified increased adverse
health effects in the large percentage of the population
that lives, works, and attends school near some major
roadways. EPA's Clean Air Research multiyear plan,
therefore, emphasizes air research to better understand
the linkages between traffic-pollutant sources and health
outcomes. The effort described here is to further
understand the atmospheric transport and dispersion of
emissions within the first few hundred meters of the
roadway, a region often characterized by complex flow
(e.g., sound barriers, road cuts, buildings, vegetation)
and where steep gradients of concentration have been
observed. Work within AMAD has focused on developing
and improving various numerical modeling tools
necessary for assessing potential human exposure in
near-road environments.
-------
Houston daytime population density Modeled benzene concentrations
in Houston, TX
-- •. •--. ,.- i
Figure 5-2. Population density and modeled benzene concentrations in Houston, TX.
Over the past decade, the Division has played a
central role in developing the AERMOD near-field
dispersion model, adopted by EPA as the preferred tool
for urban-scale analyses. Although the model can
simulate simple roadway configurations, it does not
specifically account for many complexities commonly
found near roads. However, after a thorough review of
publicly available modeling tools, AERMOD was
selected as the preferred platform on which
improvements could be made for local-scale dispersion
simulations of near-road applications and for inclusion in
hybrid modeling (with CMAQ) for urban-scale exposure
assessments. After initial wind tunnel studies, algorithms
for estimating the concentration gradients downwind of
roadways in the presence of noise barriers and
depressed roadways have been developed. Initial
evaluation of these enhancements are promising.
Additional wind tunnel studies with variations in wind
direction, barrier height, and surrounding surface
characteristics are in progress. Computational fluid
dynamics modeling of these and other scenarios is in
progress and is expected to yield a significant database
from which further improved parameterizations will
result. Ongoing and future field campaigns and tracer
studies will provide an excellent database for
development and evaluation efforts.
Once improved and evaluated, the new near-road
dispersion model will be used in the Air Quality Modeling
Study in Atlanta as a part of a Cooperative Research
Agreement between EPA/NERL and Emory University.
In this project, air quality estimates will be correlated with
a 10-year history of emergency room data and with the
experiences of over 800 patients with Implanted Cardiac
Defibrillators. Air quality estimates will be based on the
hybrid approach using combined regional (CMAQ) and
local-scale (AERMOD) modeling. Various source
configuration options will be tested in a sensitivity study
to estimate the impact of noise barriers on air quality and
exposure near roadways. Similar hybrid modeling
activities will be conducted for Baltimore as a part of
another cooperative agreement with the University of
Washington.
Finally, related urban research in AMAD prior to the
near-roadway program involved focus on homeland
security. Between 2002 and 2005, the Division's Fluid
Modeling Facility (FMF) examined flow and dispersion in
three actual urban settings. Using the meteorological
wind tunnel, AMAD provided critical modeling
information for EPA's response to the tragic events in
Manhattan in late 2001. As part of the Pentagon Shield
Program, FMF scientists examined the flow and potential
exposure to hazardous releases around the. In
collaboration with EPA's National Homeland Security
Research Center, wind tunnel measurements were
conducted for an examination of street canyon flows in
an urban neighborhood in Brooklyn, NY.
5.3 Evaluating the Effectiveness of Regional-
Scale Air Quality Regulations
A core objective of the Clean Air Act is to "protect
and enhance the quality of the Nation's air resources so
as to promote the public health and welfare and the
productive capacity of its population." To achieve this
goal, billions of dollars are spent annually by the
regulated community and Federal and State agencies on
promulgating and implementing regulations intended to
reduce air pollution and improve human and ecological
health. Historically, the impact of air pollution regulations
has been measured by tracking trends in emissions and
ambient air concentrations. Now, however, EPA is
26
-------
exploring the potential of extending the concept of
measuring impact to a more complete understanding of
the relationships along the entire source-to-outcome
continuum. Assessing whether air quality management
activities are achieving the originally anticipated results
from sources through outcomes requires (1) the
development of indicators that capture changes in
source emissions, ambient air concentrations,
exposures, and health outcomes; and (2) the ability to
characterize the processes that impact the relationships
among these indicators.
Power Industry N Ox Reductions
Ozone Season (2002 vs. 2004)
Linking ambient
concentrations to exposure
Exposure Estimates
for Ozone
Summer 2001 >
(99*1 percentile)
\
Linking exposure to human
or ecosystems health
Linking directly
between indicators
Monthly Rates of Respiratory
Admissions in NYS
\ A
Figure 5-3. Assessing the impact of regulations on ecosystems and human health end points showing
the indicators (boxes) and process linkages (arrows) associated with the NOX Budget Trading Program. (Source:
http://www.epa.gov/asmdnerl/HumanHealth/evaluatingRegulations.html).
The NOX SIP call recently was implemented by EPA
to reduce the emissions of NOX, and the secondarily
formed O3, to decrease the formation and transport of O3
across state boundaries. Over the past 3 years, AMAD's
research has demonstrated reductions in observed and
modeled ozone concentrations resulting from the NOX
SIP call. The CMAQ model was used to characterize air
quality before and after the implementation of the NOX
SIP call and to evaluate correlations between changes in
emissions and pollutant concentrations. Model
simulations were used to estimate the anthropogenic
contribution to total ambient concentrations and the
impact of not implementing the regulation. Methods were
developed to differentiate changes attributable to
emission reductions from those resulting from other
factors, such as weather and annual and seasonal
variations. Trajectory models were used to investigate
the transport of primary and secondary pollutants from
their sources to downwind regions.
We will continue to develop ways to systematically
track and periodically assess progress in attaining
national, State, and local air quality goals, particularly
those related to criteria pollutants regulated under the
NAAQS and related rules. Current research is focused
27
-------
on relating NOX emissions and ambient O3
concentrations to human exposure and health end
points. Improved air quality surfaces that combine
observed and modeled data are being generated for use
in exposure models, epidemiological health studies, and
risk assessments. These studies will examine the
benefits of using improved air quality surfaces versus
central monitoring approaches and of using exposure
probability factors versus ambient O3 concentrations in
health studies. In addition, these studies will evaluate
changes in predicted exposure and risk assessments
and actual changes in health end points (e.g., respiratory
diseases) between the pre- and post-NOx SIP call time
periods. Finally, research is moving beyond the NOX SIP
call to assess upcoming regulations. An approach for
evaluating a CAIR is being investigated to establish and
integrate "metrics" (predictions of changes associated
with the promulgation of CAIR) and "indicators" (actual
levels of the same or closely related parameters
observed during the implementation of CAIR).
5.4 Linking Local-Scale and Regional-Scale
Models for Exposure Assessments
Population-based human exposure models predict
the distribution of personal exposures to pollutants of
outdoor origin using a variety of inputs, including air
pollution concentrations; human activity patterns, such
as the amount of time spent outdoors versus indoors,
commuting, walking, and indoors at home;
microenvironmental infiltration rates; and pollutant
removal rates in indoor environments. Typically,
exposure models rely on ambient air concentration
inputs from a sparse network of monitoring stations.
The extent of variability in spatial and temporal
concentration gradients associated with large point
sources and roadways shown in this research is
Local impact from stationary sources
Near-road impact from mobile sources
Regional background from CMAQ
Combined model results
for multiple pollutants for
all receptors (census
block group centroids)
in the study area
Figure 5-4. Combined model results for multiple pollutants for all receptors (Source: Isakov et al., 2009).
28
-------
especially important, given the growing body of literature
on the potential adverse health effects associated with
elevated concentrations near such sources. A new
method to enhance air quality and exposure modeling
tools has been advanced to provide finer-scale air toxics
concentrations to exposure models. This hybrid
modeling approach combines the results from two types
of regional- and local-scale air quality models (the
CMAQ chemistry-transport model and the AERMOD
dispersion model). The resulting hourly concentrations
are used as inputs to population exposure models (the
Hazardous Air Pollutant Exposure Model [HAPEM] and
the Stochastic Human Exposure and Dose Simulation
[SHEDS] model) to enhance estimates of urban air
pollution exposures that vary temporally (annual and
seasonal) and spatially (at census block group
resolution). Thus, linkage between air quality and
exposure modeling will help improve health
assessments that include near-source impacts of
multiple ambient air pollutants.
Testing and demonstration of how this linked air
quality/exposure modeling approach may be used in
future community-level environmental health studies is
underway with an initial feasibility study providing
exposure estimates for residences near large industrial
facilities or major roadways in New Haven, CT. The
study objective is to examine the cumulative impact of
various air pollution reduction activities (at local, State,
and national levels) on changes in air quality
concentrations, human exposures, and potential health
outcomes in the community. In conjunction with local
data on emission sources, demographic and
socioeconomic characteristics, and indicators of
exposure and health, the methodology presented here
can serve as a prototype for providing high-resolution
exposure data in future community air pollution health
studies. For example, the methodology can be used to
provide the baseline air quality assessments of impacts
of regional- or local-scale air pollution control measures.
It also can be applied to estimate the likely impact of
future projected air pollution control measures or urban
and industrial growth on human exposures and health in
the community.
This hybrid approach provides deterministic
outcomes at local scales. The Division also is engaged
in studies of a complementary approach in which an air
quality (e.g., CMAQ) grid model's deterministically based
outputs operational at regional-urban grid size scales are
augmented with information on subgrid spatial variability
(SGV) provided a priori and in a stochastic framework as
parameterized distribution functions. In this paradigm,
the SGV for each grid would be determined uniquely,
and model parameterizations based on its emissions
distributions and relevant ventilation factors. For this
paradigm, the scope of this prototypic effort includes the
step of developing linkages with exposure models.
The hybrid modeling approach combines
concentrations from a grid-based chemical-transport
model and a local plume dispersion model to provide the
contribution from photochemical interactions and long-
range (regional) transport and local-scale dispersion. In
the New Haven feasibility study, we used the AERMOD
dispersion model, which treats individual road links as
area sources to simulate hourly concentrations of
various pollutants near the road. AERMOD also
simulates near-source impacts from stationary sources.
Contributions to photochemical interactions were
provided as background concentrations from CMAQ, a
regional grid model.
5.5 National Urban Database and Access
Portal Tool
Based on the need for advanced treatments of
high-resolution urban morphological features (e.g.,
buildings, trees) in meteorological, dispersion, air quality,
and human exposure modeling systems, a new project
was launched called the National Urban Database and
Access Portal Tool (NUDAPT). The prototype NUDAPT
was sponsored by EPA and involved collaborations and
contributions from many groups, including Federal and
State agencies and from private and academic
institutions here and in other countries. It is designed to
produce gridded fields of urban canopy parameters
(UCPs) to improve urban simulations given the
availability of new high-resolution data of buildings,
vegetation, and land use. Urbanization schemes have
been introduced into the Mesoscale Model, version 5
(MM5), the WRF, and other models and are being tested
and evaluated for grid sizes on an order of 1 km or so.
Additional information includes gridded anthropogenic
heating and population data, incorporated to further
improve urban simulations and to encourage and
facilitate decision support and application linkages to
human exposure models. An important core-design
feature is the utilization of Web portal technology to
enable NUDAPT to be a "community-based system.
This Web-based portal technology will facilitate
customizing of data handling and retrievals
(http://www.nudapt.org).
High-resolution building information is being
acquired by the National Geospatial Agency (NGA;
formerly the National Imagery and Mapping Agency).
When completed, NGA will have obtained data from as
many as 133 urban areas. Building data can be acquired
by extractions from paired stereographic aerial images
by photogrammetric analysis techniques or from digital
terrain models (DTMs) acquired by airborne light
detecting and ranging (LIDAR) data collection. LIDAR
data are acquired by flying an airborne laser scanner
over an urban area and collecting return signals from
pairs of rapidly emitted laser pulses and processed to
produce terrain elevation data products, including
29
-------
full-feature digital elevation models (OEMs) and bare-
earth DTMs. Subtracting the DIM from the DEM
produce data on building and vegetation heights above
ground level. Currently, NUDAPT has acquired datasets
and hosts 33 cities in the United States with different
degrees of coverage and completeness. Data are
presented in their original format, such as building
heights, day and night population, vegetation data, and
land-surface temperature and radiation, or in a "derived"
format such as the UCPs for urban meteorology and air
quality modeling applications.
Urban canopy effects
radiation
attenuation
turbulence production
anthropogenic
heating
radiation
trapping
urban thermal
properties
Modeling Requirement
To capture the grid
average effect of detailed
urban features in meso-
scale atmospheric models
Solution
Defined and implemented
Urban canopy parameterizations
such as height-to-width ratios and
sky view factors
into their model formulations
Houston
Sky View Fact or
0.451 - 075 ^|
0.751 - 0.85 ^|
0851-0.91 |
0.901 - 0.95 |^|
0.951 - 1 ^B
Figure 5-5. Urban canopy effects. (Source: Ching et al., 2009).
30
-------
CHAPTER 6
Linking Air Quality and Ecosystems
6.1 Introduction
A long-term goal of environmental management is
to achieve sustainable ecological resources. Ecological
resources are exposed to atmospheric pollutants
through wet and dry deposition processes (atmospheric
stressors) that can impact the means by which this goal
is met and maintained. Progress toward this goal rests
on a foundation of science-based methods and data
integrated into predictive multimedia, multidisciplinary,
multistressor open architecture modeling systems. The
strategic pathway described by the tasks presented here
progresses from the current approach that addresses
one stressor at a time to a comprehensive multimedia-
multiple stressor assessment capability for current and
projected ecosystem health.
The ecosystem exposure tasks in AMAD address a
number of issues that arise in multimedia modeling, with
an emphasis on interactions among the atmosphere and
other environmental media. The interaction between the
atmosphere and the underlying surface increasingly is
being recognized as an important factor in ecosystem
exposure and pollutant transport issues. However,
differences in functional scale are a fundamental
challenge to understanding and modeling these
interactions. For instance, the watershed is a
fundamental unit of ecosystem analysis, because
primarily of its containment of the hydrologic cycle and
related stresses, but the relevant atmospheric scale of
modeling and analysis is regional/continental in scope,
encompassing multiple States or watersheds. Targeted
development, evaluation, and application of state-of-the-
art, multipollutant atmospheric models of O3, sulfur,
nitrogen, and Hg to multimedia issues help determine
how to further improve the one-atmosphere models and
support ongoing ecological assessments by providing
ecosystem exposure estimates where monitoring data
are not available.
A through understanding of air quality model
sensitivities can guide the design of field campaigns so
that measurements of parameters needed for further
model development are collected, model uncertainties
can be reduced, and robust model evaluations can be
produced, which further the understanding of the
atmosphere-biosphere exchange. Software tools are
needed to support the linkage of models across media
and specialized multimedia data analysis applications.
Finally, this multimedia research brings the results
of air pollution control, that primarily stem from
addressing human health effects, into the management
purview for addressing multimedia or ecosystem
problems. Humankind benefits from a multitude of
resources and processes that are supplied by natural
ecosystems. Collectively, these benefits are known as
ecosystem services and include products like clean air
and clean water. Ecosystem services are distinct from
other ecosystem products and functions because there
is human demand for these natural assets.
Measurement of ecosystem services is the new strategic
focus for EPA's Ecological Services Research Program
(ESRP). It is believed that making the evaluation of
these services a routine part of decisionmaking will
transform the way we understand and respond to
environmental issues. The ESRP's mission is to conduct
innovative ecological research that provides the
information and methods needed by decisionmakers to
assess the benefits of ecosystem services to human
well-being and, in turn, to shape policy and management
actions at multiple spatial and temporal scales.
6.2 Research Description
The ecosystem research team has identified
several key research areas that have the potential to
reduce model uncertainties in deposition, to assess
model credibility, and to link the CMAQ model to
multimedia ecosystem exposure models.
Specific research tasks have been grouped under
the following more general research program elements.
• Linking air quality to aquatic and terrestrial
ecosystems
• Linking air quality to ecosystem services
• Improvements to CMAQ dry deposition algorithms
• CMAQ ecosystem exposure studies
• Multimedia tool development
Through the linking air quality to aquatic and
terrestrial ecosystems program element, the Division
develops and enhances dry deposition algorithms for
land cover specific subgrid cell ecological receptors to
facilitate improved interactions with ecosystem and
water quality models and to provide more relevant
information to ecosystem managers. Ecosystem
exposure occurs when stressors and receptors occur at
the same time and place. To model the exposure,
models for different media (e.g., air, water, land) must be
linked together. Linkages between models for air, water,
and land can occur through the use of consistent input
data, such as land use and meteorology, and through
the appropriate exchange of data at relevant spatial and
temporal scales.
31
-------
At present, mesoscale meteorological inputs (e.g.,
MM5) and CMAQ v4.7 rely on 1992 National Land Cover
Dataset (NLCD) classes to identify the location of
general land cover types (e.g., urban, row agriculture,
etc.). Most ESRP research employs more recent 2001
NLCD and 2001 to 2006 NOAA Coastal Change
Analysis Program (C-CAP) databases that also provide
higher resolution information. Meteorological and CMAQ
models are being updated to make use of these more
recent landcover databases. Vegetation species detail
currently is accessed by CMAQ only for the calculation
of bioemissions and is based on the Biogenic Emissions
Landcover Database, version 3 (BELD3). Agricultural
species distributions in BELD3 reflect 1995 U.S.
Department of Agriculture (USDA) National Agricultural
Statistics Service (NASS) surveys. To maintain
consistency with the 2001 NLCD vegetation classes,
these estimates are being updated to reflect 2001 crop
distributions. We anticipate more extensive use of the
updated BELD information in the estimation of
ecosystem-specific exposure to atmospheric nitrogen
and Hg deposition.
Watershed models are calibrated to multiple years
of observed hydrology and precipitation. Chemical
simulations are generated using the same inputs as well
as drawing on current monitored deposition fields from
the National Acid Deposition Program (NADP).
Scenarios of changes in deposition, however, are drawn
from CMAQ simulations (Sullivan et al., 2008).
Unfortunately, temporal and spatial agreement between
the modeled meteorological data used to drive CMAQ
deposition estimates and observed precipitation used to
drive the water quantity and quality simulations can be
poor, so that the base case and the futures case are not
consistent.
Focus areas of the linking air quality to aquatic and
terrestrial ecosystem program element include
• developing landcover-specific dry deposition
algorithms as opposed to a single grid value blended
across all landcover types;
• updating the current CMAQ landcover database with
more recent higher resolution databases;
• pdating the BELD3 crop distributions based on more
recent USDA crop data; and
• developing a methodology to rectify the differences in
CMAQ model input and output fields and water quality
model input fields.
Through the linking to ecosystem services program
element, the Division develops modeling tools, scenarios
and multidisciplinary collaborations to asses the impact
of air quality on the intrinsic value of natural and
agricultural ecosystems. The Future Midwestern
Landscapes (FML) Study is being undertaken as part of
EPA's ESRP. The FML goal is to quantify the current
magnitude of ecosystem contribution to human health
and to examine how ecosystem services in the Midwest
could change over the next 10 to 15 years, given the
growing demand for biofuels, as well as the growing
recognition that many different ecosystem services are
valuable to society and need to be encouraged. The
FML study will examine how the overall complement of
ecosystem services provided by the Midwest may be
affected. The study will characterize a variety of
ecosystem services for the 12-state area of the Midwest.
The significance of reactive nitrogen (Nr), which
includes oxidized, reduced, and organic forms, to the
environment stems from the duality of its environmental
impacts. On the one hand, Nr is one of life's essential
nutrient elements. It is required for the growth and
maintenance of all of earth's biological systems. For
humans, there are several sets of services provided by
natural and anthropogenic sources of reactive nitrogen,
including the production of plant and animal products
(food and fiber) for human consumption and the
combustion of fuels that support our energy and
transportation needs. Increasing demands for energy,
transportation and food lead to greater demand for Nr.
Although releases of nitrogen are associated with
societal benefits, Nr is a powerful environmental
pollutant. Over the past century, human intervention in
the nitrogen cycle and use of fossil fuels has led to
substantial increases in production of Nrand in human
and ecosystem exposure to Nr. The amount of Nr
applied to the nation's landscape and released to the
nation's air and water has reached unprecedented
levels, and projections show that Nr pollution will
continue to increase for the foreseeable future. These
increases in Nr pollution are accompanied by increased
environmental and human health problems. The ESRP
Nitrogen Team will address its broad goal of connecting
Nrto ecosystem services through a two-pronged effort,
with national work where possible and smaller scale,
regional studies tackling specific problems and
ecosystem types.
Focus areas of the linking to ecosystem services
program element include
• characterizing the role of atmospheric nitrogen
deposition in the achievement and maintenance of
ecosystem resources and services in the Midwestern
United States;
• developing the capacity to model air quality response
to future emission and land cover scenarios for
increased biofuel production to assess the impact of
hypothetical policy-driven changes in biofuels and the
impact of this response on ecosystem services;
• characterizing 12-km continental U.S. atmospheric
deposition of sulfur, oxidized nitrogen, reduced
nitrogen, and O3 for 2002, 2020, and 2030 using
CMAQ; and
• assessing regional ecosystem vulnerability to nitrogen
and sulfur deposition using CMAQ deposition output
32
-------
and NADP and Parameter-Elevation Regressions on
Independent Slopes Model (PRISM) data
Through the improvements to CMAQ dry deposition
algorithms program element, the Division develops
modeling algorithms, tools, and experiments to evaluate
and improve the modeling of the air-surface exchange of
pollutants. The interaction between the atmosphere and
the underlying surface increasingly is recognized as
important in ecosystem health and in air pollution
transport processes. Evasion of ammonia (NH3) and Hg
from vegetation, soil and water surfaces is important in
the long range transport of these pollutants and can act
as a vector of exposure to ecosystems remotely located
from anthropogenic sources. A key output of
atmospheric models for ecosystem health studies is the
dry deposition component of net ecosystem loading (wet
+ dry + evasion). There is an extreme paucity of
measured and monitored dry deposition estimates for
use with ecosystem management modeling. The
estimates from the atmospheric models fill a critical gap.
A targeted focus on creating state-of-the-science dry
deposition algorithms for the air quality models has
significant importance to ecosystem exposure to air
pollution. This is an area of strong collaboration between
CMAQ model development and ecosystem exposure
programs.
One of the ways EPA assesses the results of air
pollution control is through the Clean Air Status and
Trends Network (CASTNET). Dry deposition estimates
from CASTNET are inferred from measured atmospheric
concentrations and a dry deposition velocity estimated
from the physical characteristics of the ecosystem and
wind velocity measurements. The Multilayer Model
(MLM) (Meyers et al., 1998) is used to predict deposition
velocity, which then is paired with the measured
concentration to calculate the pollutant flux. Air-surface
exchange research will continue to develop better
models for predicting deposition velocity for network
operations. Providing better estimates of deposition flux
will improve our ability to forecast ecosystem
sustainability.
Excessive loading of nitrogen from atmospheric
nitrate and NH3 deposition to ecosystems can lead to
soil acidification, nutrient imbalances, and
eutrophication. Accurate net nitrogen flux estimates are
important for biogeochemical cycling calculations
performed by ecosystem models to simulate ecosystem
degradation and recovery. Because of the lack of
available monitoring data, these estimates are a high
priority for water and soil chemistry modeling of nutrient
loading, soil acidification, and eutrophication. A process-
level understanding of the biological, chemical, and
mechanical processes influencing the soil-vegetation-
atmosphere exchange of nitrogen over a variety of
managed and natural ecosystems is needed before
tenable mitigation strategies can be realized.
Atmospheric loadings of Hg to sensitive
ecosystems can lead to methylation and
bioaccumulation, adversely affecting wildlife and
becoming a vector for human exposure to
methylmercury. The transport of Hg in the environment
exhibits bidirectional surface exchange, similar to NH3,
and a bidirectional surface exchange model of Hg is
needed to estimate the net ecosystem loading of Hg
needed by ecosystem managers to assess the
vulnerability of ecosystems to Hg exposure.
Focus areas of the improvements to CMAQ dry
deposition algorithms program element include those
noted below.
• Develop better models for predicting deposition
velocities for monitoring network operations
• Collaborate with Federal and academic field scientists
to design and conduct experiments to measure air-
biosphere exchange parameters based on model
sensitivities and hypotheses proposed in the literature
• Develop and refine air-soil and air-vegetation NH3
exchange algorithms for agricultural and natural
ecosystems using the results from intensive
measurement campaigns
• Develop an air-biosphere exchange model for Hg
emissions from natural processes in CMAQ
Through the CMAQ ecosystem exposure studies
program element, the Division provides guidance and
advice to the ecosystem management community using
CMAQ as a laboratory. Atmospheric deposition of sulfur
and nitrogen is a key contributor to ecosystem exposure
and degradation, adding to the acidification of lakes and
streams and eutrophication of coastal systems.
Reductions in atmospheric deposition of sulfur and
oxidized nitrogen stemming from human-health-driven
regulations in the 1990 CAA amendments are expected
to significantly benefit efforts to improve water quality.
However, water quality managers are not taking
advantage of information on anticipated deposition
reductions in developing their management plans.
Managers need to understand what to expect from
atmospheric emissions and deposition. This
understanding must come from an air quality model
utilized as a laboratory; it cannot come from
measurements. The goal is to bring air quality into
ecosystem management through regional air quality
modeling and to facilitate the air-ecosystem linkage.
The Division's approach is to collaborate with
select, motivated air-water partners who are willing to
work together to provide a test laboratory with the
atmospheric model to explore, assess, and apply
improved techniques to advance water quality
management goals and test linkage approaches. The
Division is developing an understanding of the needs of
water quality managers through real-world experience
and participation with model applications. We then
design model analyses and sensitivity studies to identify
33
-------
and direct what atmospheric science needs to deliver.
Results help provide answers to nearly universal
questions uncovered in the course of the application
studies: How much is depositing? Who and where is the
deposition from? How much will deposition change
because of air quality regulations in the face of
population and economic growth?
Focus areas of the CMAQ ecosystem exposure
studies program element include those that follow.
• Evaluate CMAQ-UCD against the Bay Regional
Atmospheric Chemistry Experiment (BRACE) May
2002 data and make any model refinements that may
be required
• Assess the relative contributions from the different
emissions sectors, particularly mobile sources and
utilities, to the annual oxidized nitrogen deposition to
Tampa Bay
• Assess the change in annual deposition to Tampa
Bay that could be attributed solely to the NOX
emissions reductions by 2010 of the two power plants
on its shores
• Assess the change in annual deposition to Tampa
Bay that could be attributed to future mobile source
and utility reductions of SO2 and NOX in 2010
• Develop scenarios estimating the deposition
reductions to Chesapeake Bay expected by 2010 and
2020 stemming from Clean Air Act regulations
• Update the Chesapeake Bay scenarios with the
configuration of CMAQ that includes bidirectional flux
of NH3 still under development
• Estimate the relative contribution made by NOX
emissions from the six Bay States to the atmospheric
deposition of oxidized nitrogen to the Chesapeake
Bay watershed and Bay surface after implementation
of future reduction scenarios of mobile source and
utility emissions
Through the software tool development program
element the Division develops tools to analyze
observations and model results and provide them in a
form required to support management decisions. Most
off-the-shelf tools do not address the specialized needs
or applications encountered in analyzing data from a
multimedia perspective, making it more difficult than is
necessary to link elements of the multimedia
components together. The need for specialized tools is
especially pertinent to bringing atmospheric components
together with watershed components for multimedia
management analyses.
Focus areas of the software tool development
program element include support and development of
• the Visualization Environment for Rich Data
Interpretation (VERDI);
• the Watershed Deposition Tool (WDT); and
• the Spatial Allocator vector, raster, and surrogate
tools.
6.3 Accomplishments
Watershed modeled sensitivities to 2001 to 2003
precipitation data was explored through (1) the use of
daily cooperative station data to perform a monthly
calibration of the Grid-Based Mercury Model (Tetra
Tech, 2006), (2) calibrated model runoff volume
response to 36-km simulated daily precipitation and
mean daily temperature fields, (3) response to 12-km
simulated daily precipitation and mean daily temperature
fields, and (4) response to 4-km PRISM-generated
precipitation data.
Errors in the simulated meteorology related to
timing, spatial coverage, magnitude, and suppressed
interannual variability can be observed. The benefit of
higher scale meteorological simulations can be noted in
cases where model runoff volume driven by the 12-km
precipitation is much closer to the U.S. Geological
Survey observed runoff than that driven by the 36-km
simulation. Exceptions occur where there is little or no
runoff response difference between the two
meteorological datasets. This happens most often during
the fall months and has been traced to a failure of the
analysis model used to nudge the meteorological
simulation to capture the development of tropical storms
off the coast of North Carolina. The PRISM database
(Daly et al., 2002) contains 4-km gridded monthly
precipitation generated via a set of regression
expressions and cooperative station data and represents
a more spatially complete dataset that could, perhaps,
be used to calibrate the watershed model.
Scenario simulations for the Chesapeake Bay for
2002 and 2010 with CMAQ 4.7 using the new coarse-
mode sea salt dynamic mass transfer module were
completed. An analysis of the relative contributions of
NOX emissions from six sectors to the atmospheric
deposition of oxidized nitrogen to the Chesapeake Bay
watershed and Bay surface after the implementation of
CAIR was completed. The relative contribution the NOX
emissions from the six Bay states make to the
atmospheric deposition of oxidized nitrogen to the
Chesapeake Bay watershed and Bay surface after
implementation of CAIR was estimated.
The development of the preliminary bidirectional
NH3 exchange and coarse-nitrate model algorithms
improved the modeled oxidized and reduced nitrogen
budgets and the partitioning between gas and size-
segregated aerosol phases. Mechanistic model
algorithms developed in collaboration with measurement
groups enhances the credibility of the CMAQ nitrogen
budget for ecosystem assessments.
Results from the bidirectional NH3 exchange model
helped prioritize current and future measurement needs
in field experiments. Scientists from AMAD have
collaborated with scientists from EPA's National Risk
Management Research Laboratory (NRMRL), Duke
University, North Carolina State University, and the
34
-------
United Kingdom's Center for Hydrology and Ecology to
estimate in-canopy and soil NH3 exchange processes
based on field measurements and modeling theory. An
analytical in-canopy scalar transport closure model that
estimates in-canopy sources and sinks by using
measured concentration and wind speed profiles was
developed. The above-canopy ammonia flux, in-canopy
ammonia sources and sinks, soil chemistry, and leaf
chemistry measurements were collected in a fertilized
corn (Zea Mays) field in Lillington, NC, during the 2007
growing season. Estimates of in-canopy sources and
sinks were inferred using measured in-canopy
concentration profiles and a simple closure model.
VERDI was developed in 2007 for the EPA by
Argonne National Laboratory to improve the capability to
visualize data from the CMAQ model and associated
programs. In FY08, support for VERDI by the CMAS
Center was initiated. Improvements were made to the
VERDI software and a Web site was developed to
provide information and distribution. VERDI is an open
source program, which is licensed under the GPL
version 3. A SourceForge repository
(http://sourceforge.net) for VERDI was created to
facilitate distribution and source code version control.
WDT was developed for the EPA by Argonne
National Laboratory to provide an easy-to-use tool for
mapping the deposition estimates from CMAQ to
watersheds to provide the linkage between air and water
needed for total maximum daily load (TMDL) and related
nonpoint-source watershed analyses. Slight
modifications to the program were released in FY08.
This tool has been useful in performing analyses and
also in providing a means to communicate the strengths
of using CMAQ data rather than monitoring data in
watershed analyses.
6.4 Next Steps
Over the next several years, advancements are
planned for the multimedia theme area to investigate
more sophisticated futures scenarios for air-water
linkages, to adapt CMAQ to calculate bidirectional
exchange of NH3 and Hg, and to more closely couple to
ecosystems models. Some of the planned research
goals are as follows.
FY-2009
• Refinement of CMAQ air-canopy bidirectional NH3
exchange algorithms using the estimated sources and
sinks from the 2007 Lillington, NC, study
• Development of an air-soil biogeochemical nitrogen
model for fertilizer applications to agricultural
ecosystems using data from the 2007 Lillington, NC,
study
• Convert mosaic land-use interface to NLCD for
consistency with ecosystem models and test CMAQ
for land-use-change scenario analysis
• Complete preliminary air-water model linkage for N.C.
Albemarle-Pamlico estuarine system
• Collaboration with Federal laboratories and academic
institutions to design experiments to measure sources
and sinks of NH3 in forested ecosystems at Duke
Forest
FY-2010
• For ESRP place-based scenario analyses (Carolinas,
Midwest, Tampa), simulate nitrogen, sulfur, and O3
deposition futures incorporating land-use changes
• Incorporate into a science version of CMAQ a
generalized land-surface layer to support
multipollutant bidirectional flux calculations (already in
the Hg bidirectional exchange model)
• Adapt and implement an nitrogen fertilizer application
scenario tool to produce nationally consistent input
required by the improved bidirectional NH3 exchange
algorithms for agricultural ecosystems (see FY-2011)
• Collaboration with Federal laboratories and academic
institutions to estimate the sources and sinks of NH3
in a hardwood and coniferous forested ecosystem
• Annual simulation of CMAQ with bidirectional Hg
exchange for 2002 emissions to evaluate the
seasonality of the bidirectional and base model with
NADP monitoring Hg deposition data
• Simulate Chesapeake Bay futures scenarios with
CMAQ at 12-km grid cell size and incorporate NH3
bidirectional exchange influence for Chesapeake
sensitivity
• Improve meteorological precipitation simulations to
facilitate better hydrologic linkage with watershed
models using higher resolution simulations (4 km)
nudged using analyses that include more extensive
data assimilation or that employ more advanced data
assimilation techniques such as 3-D variational
analysis
• Couple VERDI with the watershed deposition tool
• Evaluate the NH3 bidirectional exchange algorithms
over grasslands with data collected at the 2008 Duke
Forest measurement campaign
• Annual simulation of CMAQ with bidirectional NH3
exchange in support of the ESRP FML
FY-2011
• Update CMAQ bidirectional NH3 exchange algorithms
for natural and managed ecosystems
• Development of an updated NH3 emission inventory
for use with CMAQ with bidirectional NH3 exchange
• Assess the impact of meteorological precipitation
errors on the deposition of nitrogen compounds from
• the atmosphere to underlying soils, vegetation, and
water surfaces and their subsequent transport to
downstream estuarine end points
• Evaluate the transport and fate of NH3 in CMAQ using
specially collected NASA Tropospheric Emission
35
-------
Spectrometer (TES) satellite observations and
collocated passive monitors
• Evaluate the bidirectional NH3 exchange algorithms
over forested ecosystem with data collected during
the 2009 Duke Forest measurement campaign
6.5 Impact and Transition of Research to
Applications
Air deposition reductions are now a vital component
of the Chesapeake Bay Program's restoration efforts.
Critical air deposition information also has been provided
to the Tampa Bay Estuary Program to address its TMDL
needs and assessment goals. Our efforts have opened
the door for water quality managers to include air
deposition and make their management plans more
efficient and effective. The work has paved the way for
using CMAQ in national NOx-sulfur oxides regulatory
assessments to protect ecosystems and for using
CMAQ in U.S. critical loads analyses.
An area where the "one atmosphere" approach of
CMAQ helped elucidate the connection between
modeled chemical mechanisms and ecosystem
exposure through dry deposition was heterogeneous
N2O5 conversion. The uncertainty in the heterogeneous
conversion of N2O5 to HNO3 was examined because it
impacts HNO3 concentrations and deposition. However,
this uncertainty has a minor impact on oxidized nitrogen
deposition because the deposition pathways among the
oxidized nitrogen species rebalance. Zeroing out this
conversion reduces HNO3and aNO3~ deposition by 18%
and 26%, respectively, whereas total oxidized nitrogen is
reduced by only 6%.
Atmospheric
Transformation
Transport
and Fate
of Stressor
In Space and Time
Aquatic and
Terrestrial
Receptor
Biogeochemical
Functioning
In Space and Time
Figure 6-1. A Venn diagram representing ecosystem exposure as the intersection of the atmosphere and biosphere
(http://www.epa.gov/amad/EcoExposure/index.html).
CMAQ Simulated Ratio of
Dry Deposition to Wet Deposition
of Total N: CMAQ 2002 Annual
' =• -'
g :. •
Total Oxidized-N Deposition to
Chesapeake Bay Watershed
I.,
••••=• •
•1.03E+M
1
i: -
OCr.,'i:C:
|Cf,FIC
B»«N,O, Conversion
Zwo N.,0- Conversion
Figure 6-2. CMAQ is a source of data for ecosystem managers that is not available in routine monitoring data, such as
(left panel) complete dry and wet deposition estimates, and (right panel) the "one atmosphere" concept of CMAQ is
needed to understand the balance between uncertainties in atmospheric reaction rates and deposition pathways
(http://www.epa.gov/amad/EcoExposure/ecoStudies.html).
36
-------
References
Bhave, P.V., G.A. Pouliot, and M. Zheng. Diagnostic Model
Evaluation for Carbonaceous PlVbs Using Organic
Markers Measured in the Southeastern U.S.
Environmental Science and Technology, 41:1577-1583
(2007).
Bullock, O.R., and K.A. Brehme. Atmospheric Mercury
Simulation Using the CMAQ Model: Formulated
Description and Analysis of Wet Desposition Results.
Atmos. Environ., 36:2135-2146 (2002).
Bullock, R., D. Atkinson, T. Braverman, K. Civerolo, A.
Dastoor, D. Davignon, J. Ku, K. Lohman, T.C. Meyer,
R.J. Park, C. Seigneur, N.E. Selin, G. Sistla, and K.
Vijayaraghavan. The North American Mercury Model
Intercomparison Study (NAMMIS). Study Description and
Model-to-Model Comparison. Mercury Fate and Transport
in the Global Atmosphere, Chapter 13. Journal of
Geophysical Research, American Geophysical Union,
Washington, DC, 113(D17310):1-17 (2008).
Bullock, O.R., Jr., D. Atkinson, T. Braverman, K. Civerolo, A.
Dastoor, D. Davignon, J-.Y. Ku, K. Lohman, T.C. Myers,
R.J. Park, C. Seigneur, N.E. Selin, G. Sistla, and K.
Vijayaraghavan. An Analysis of Simulated Wet Deposition
of Mercury from the North American Mercury Model
Intercomparison Study (NAMMIS). Journal of
Geophysical Research, 114, D08301,
doi:10.1029/2008JD011224 (2009).
Carlton, A.G., H-J. Lim, K. Altieri, S. Seitzinger, and B.J.
Turpin. Link Between Isoprene and Secondary Organic
Aerosol (SOA): Pyruvic Acid Oxidation Yields Low
Volatility Organic Acids in Clouds. Geophysical Research
Letters, 33: L06822, doi:10.1029/2005GL025374 (2006).
Carlton, A.G., B.J. Turpin, K. Altieri, S. Seitzinger, A. Reff, H.-J.
Lim, and B.E. Ervens. Atmospheric Oxalic Acid and SOA
Production from Glyoxal: Results of Aqueous
Photooxidation Experiments. Atmos. Environ., 41:7588-
7602 (2007).
Carlton, A.G., B.J. Turpin, K. Altieri, S. Seitzinger, R. Mathur,
S. Roselle, and R.J. Weber. "CMAQ Model Performance
Enhanced when In-Cloud SOA Is Included: Comparisons
of OC Predictions with Measurements." Environ. Sci.
Technol. 42(23):8798-8802 (2008).
Ching, J., M. Brown, S. Burian, F. Chen, R. Cionco, A. Hanna,
T. Hultgren, T. McPherson, D. Sailor, H. Taha, and D.
Williams. National Urban Database and Access Portal
Tool, NUDAPT. Bulletin of the American Meteorological
Society, 90(8): 1157-1168 (2009).
Daly, C., W.P. Gibson, G.H. Taylor, G.L. Johnson, and P.
Pasteris. A Knowledge-Based Approach to the Statistical
Mapping of Climate, dim. Res., 22: 99-113 (2002).
Dentener, F.J., and P.J. Crutzen. Reaction of N2O5 on
Tropospheric Aerosols: Impact on the Global Distributions
of NOX, O3 and OH. J. Geophys. Res., 98:7149-7163
(1993).
Evans, M.J. and D.J. Jacob. Impact of New Laboratory Studies
of N2Os Hydrolysis on Global Model Budgets of
Tropospheric Nitrogen Oxides, Ozone and OH. Geophys.
Res. Lett., 32, L09813 (2005).
Gego, E., P.S Porter, A. Gilliland, and ST. Rao. Observation-
Based Assessment of the Impact of Nitrogen Oxides
Emissions Reductions on O3 Air Quality over the Eastern
United States. Journal of Applied Meteorology and
Climatology, 46:994-1008 (2007).
Gilliam, R.C., and J.E. Pleim. Performance Assessment of
New Land-Surface and Planetary Boundary Layer
Physics in the WRF-ARW. J. Appl. Meteor, and dim., (in
press).
Gilliland, A.B., C. Hogrefe, R.W. Pinder, J.M. Godowitch, K.L
Foley, and ST. Rao. Dynamic Evaluation of Regional Air
Quality Models: Assessing Changes in O3 Stemming from
Changes in Emissions and Meteorology. Atmospheric
Environment, 42:5110-5123 (2008).
Godowitch, J.M., C. Hogrefe, and ST. Rao. Diagnostic
Analyses of Regional Air Quality Model: Changes in
Modeled Processes Affecting Ozone and Chemical-
Transport Indicators from NOX Point Source Emission
Reductions. Journal of Geophysical Research, 113:
D19303, doi:10.1029/2007JD009537 (2008).
Isakov, V., J. Touma, J. Burke, D. Lobdell, T. Palma, A.
Rosenbaum, and H. Ozkaynak. Combining Regional and
Local Scale Air Quality Models with Exposure Models for
Use in Environmental Health Studies. J. AandWMA,
59:461-472(2009).
Kleindienst, T.E., M. Jaoui, M. Lewandowski, J.H. Offenberg,
C.W. Lewis, P.V. Bhave, and E.O. Edney. Estimates of
the Contributions of Biogenic and Anthropogenic
Hydrocarbons to Secondary Organic Aerosol at a
Southeastern US Location, Atmospheric Environment,
41:8288-8300(2007).
Lewis, C.W., G.A. Klouda, and W.D. Ellenson. Radiocarbon
Measurement of the Biogenic Contribution to
Summertime PM25 Ambient Aerosol in Nashville, TN.
Atmosphereic Environment, 38:6053-6061 (2004).
Meyers, T.P., P. Finkelstein, J. Clarke, T.G. Ellestad, and P.F.
Sims. A Multilayer Model for Inferring Dry Deposition
Using Standard Meteorological Measurements. J.
Geophys. Res., 103(017):22645-22661 (1998).
Nakicenovic, N., J. Alcamo, G. Davis, B. de Vries, J. Fenhann,
S. Gaffin, K. Gregory, A. Grubler, T. Jung, T. Kram, E. La
Rovere, L. Michaelis, S. Mori, T. Morita, W. Pepper, H.
Pitcher, L. Price, K. Riahi, A. Roehrl, H. Rogner, A.
Sankovski, M. Schlesinger, P. Shukla, S. Smith, R. Swart,
S. van Rooijen, N. Victor, and Z. Dadi. Special Report on
Emissions Scenarios, Intergovernmental Panel on
Climate Change. Available at
http://www.qrida.no/publications/other/ipcc sr/ (2000).
Ng, N.L, P.S. Chhabra, A.W.H. Chan, J.D. Surratt, J.H. Kroll,
A.J. Kwan, D.C. McCabe, P.O. Wennberg, A.
Sorooshian, S.M. Murphy, N.F. Dalleska, R.C. Flagan,
and J.H. Seinfeld. Effect of NOX Level on Secondary
Organic Aerosol (SOA) Formation from the
Photooxidation of Terpenes. Atmos. Chem. Phys.,
7:5159-5174(2007).
Nolte.C.G., A.B. Gilliland, C. Hogrefe, and L.J. Mickley. Linking
Global to Regional Models To Assess Future Climate
Impacts on Surface Ozone Levels in the United States.
Journal of Geophysical Research, 113:014307 (2008).
37
-------
NRC. National Research Council of the National Academies.
Air Quality Management in the United States. The
National Academies Press: Washington, DC (2004).
Pleim, J.E. A Combined Local and Nonlocal Closure Model for
the Atmospheric Boundary Layer. Part I: Model
Description and Testing. J. Appl. Meteor. Clim., 46:1383-
1395(2007a).
Pleim, J.E. A Combined Local and Nonlocal Closure Model for
the Atmospheric Boundary Layer. Part II: Application and
Evaluation in a Mesoscale Meteorological Model. J. Appl.
Meteor. Clim., 4:1396-1409 (2007b).
Pleim, J.E., and R. Gilliam. An Indirect Data Assimilation
Scheme for Deep Soil Temperature in the Pleim-Xiu Land
Surface Model. J. Appl. Meteor. Clim., 48:1362-1376
(2010).
Riemer, N., H. Vogel, B. Vogel, B. Schell, I. Ackermann, C.
Kessler, and H. Mass, Impact of the Heterogeneous
Hydrolysis of ^Os on Chemistry and Nitrate Aerosol
Formation in the Lower Troposphere under Photosmog
Conditions, J. Geophys. Res., 108(04), 4144,
doi:10.1029/2002JD002436 (2003).
Ryaboshapko, A., O.R. Bullock, R. Ebinghaus, I. llyin, K.
Lohman, J. Munthe, G. Petersen, C. Seigneur, and I.
Wangberg. Comparison of Mercury Chemistry Models.
Atmos. Environ., 36:3881-98 (2002).
Ryaboshapko, A., R. Bullock, J. Christensen, M. Cohen, A.
Dastoor, I. llyin, G. Petersen, D. Syrakov, R.S. Artz, D.
Davignon, R.R. Draxler, and J. Munthe. Intercomparison
Study of Atmospheric Mercury Models: 1. Comparison of
Models with Short-Term Measurements. Science of the
Total Environment, 376(1-3):228-240 (2007a).
Ryaboshapko, A., R. Bullock, J. Christensen, M. Cohen, A.
Dastoor, I. llyin, G. Petersen, D. Styakov, R.S. Artz, D.
Davignon, R.R. Draxler, and J. Munthe. Intercomparison
Study of Atmospheric Mercury Models: 2. Modeling
Results Versus Long-Term Observations and
Comparison of Country Atmospheric Balances. Science
of the Total Environment. 377(2-3):319-333 (2007b).
Stein, A.F., V. Isakov, J. Godowitch, and R.R. Draxler. A
Hybrid Approach To Resolve Pollutant Concentrations in
an Urban Area. Atmospheric Environment, 41: 9410-9426
(2007).
Sullivan, T.J., B.J. Cosby, J.R. Webb, R.L Dennis, A.J. Bulger,
and F.A. Deviney, Jr. Streamwater Acid-Base Chemistry
and Critical Loads of Atmospheric Sulfur Deposition in
Shenandoah National Park, Virginia. Environ. Monit.
Assess., 137:85-99(2008).
Tetra Tech. Development of a Second-Generation of Mercury
Watershed Simulation Technology: Grid Based Mercury
Model, Users Manual, Version 2.0. Fairfax, VA (2006).
USEPA. U.S. Environmental Protection Agency. NOX Budget
Trading Program, EPA-430-R-07-009.
http://www.epa.gov/airmarkets (2007).
Yu, S., P.U. Behave, R.L. Dennis, and R. Mathur. Seasonal
and Regional Variations of Primary and Secondary
Organic Aerosols over Continental U.S.: Semi-emphirical
Estimates and Model Evaluation. Environmental Science
and Technology, 41:4690-4697 (2007).
38
-------
APPENDIX A
Atmospheric Modeling and Analysis Division Staff Roster
(as of December 31, 2008)
Office of the Director
S. T. Rao, Director
David Mobley Deputy Director
Patricia McGhee, Assistant to the Director
Sherry Brown
Veronica Freeman-Green
Linda Green
Ken Schere, Science Advisor
Gary Walter, IT Manager
Jeff West, QA Manager
Emissions and Model Evaluation Branch
Tom Pierce, Chief
Jane Coleman (SEEP1), Secretary
Wyat Appel
Brian Eder
Kristen Foley
Jim Godowitch
Steve Howard
Sergey Napelenok
George Pouliot
Alfreida Torian
Atmospheric Exposure Integration Branch
Val Garcia, Chief
Jesse Bash
Jason Ching
Ellen Cooter
Jim Crooks (postdoctoral fellow)
Robin Dennis
Vlad Isakov
Wilma Jackson (contractor)
Donna Schwede
Joe Touma
Adrienne Wootten (contractor)
David Heist, Fluid Modeling Facility
Ashok Patel (SEEP), Fluid Modeling Facility
Steve Perry, Fluid Modeling Facility
Bill Peterson (contractor), Fluid Modeling Facility
John Rose (SEEP), Fluid Modeling Facility
Atmospheric Model Development Branch
Rohit Mathur, Chief
Shirley Long (SEEP), Secretary
Prakash Bhave
Ann Marie Carlton
Tianfeng Chai (contractor)
Garnet Erdakos (NRC2 postdoctoral fellow)
Rob Gilliam
Bill Hutzell
Daiwen Kang (contractor)
Hsin-mu Lin (contractor)
Deborah Luecken
Harshal Parikk (contractor)
Jon Pleim
Shawn Roselle
Golam Sarwar
Heather Simon (postdoctoral fellow)
John Streicher
Daniel Tong (contractor)
David Wong
Jeff Young
Shaocai Yu (contractor)
Applied Modeling Branch
Alice Gilliland, Chief
Melanie Ratteray (SEEP), Secretary
Bill Benjey
Jared Bowden (NRC postdoctoral fellow)
Russ Bullock
Barren Henderson (ORISE3)
Jerry Herwehe
Chris Nolte
Tanya Otte
Rob Pinder
Jenise Swall
1SEEP—Senior Environmental Employee Program
2NRC—National Research Council
3ORISE—Oak Ridge Science and Education Program
39
-------
APPENDIX B
Division and Branch Descriptions
The Atmospheric Modeling Analysis Division leads
the development and evaluation of atmospheric models
on all spatial and temporal scales for assessing changes
in air quality and air pollutant exposures, as affected by
changes in ecosystem management and regulatory
decisions, and for forecasting the Nation's air quality.
AMAD is responsible for providing a sound scientific and
technical basis for regulatory policies to improve ambient
air quality. The models developed by AMAD are being
used by EPA, NOAA, and the air pollution community in
understanding and forecasting not only the magnitude of
the air pollution problem but also in developing emission
control policies and regulations for air quality
improvements. AMAD applies air quality models to
support key integrated, multidisciplinary science
research. This includes linking air quality models to other
models in the source to outcome continuum to effectively
address issues involving human health and ecosystem
exposure science.
The Atmospheric Model Development Branch
(AMDB) develops, tests, and refines analytical,
statistical, and numerical models used to describe and
assess relationships between air pollutant source
emissions and resultant air quality, deposition, and
pollutant exposures to humans and ecosystems. The
models are applicable to spatial scales ranging from
local/urban and mesoscale through continental, including
linkage with global models. AMDB adapts and extends
meteorological models to couple effectively with
chemical-transport models to create comprehensive air
quality modeling systems, including the capability for
two-way communication and feedback between the
models. The Branch conducts studies to describe the
atmospheric processes affecting the transport, diffusion,
transformation, and removal of pollutants in and from the
atmosphere using theoretical approaches, as well as
from analyses of monitoring and field study data. AMDB
converts these and other study results into models for
simulating the relevant physical and chemical processes
and for characterizing pollutant transport and fate in the
atmosphere. The Branch conducts model exercises to
assess the sensitivity and uncertainty associated with
model input databases and applications results. AMDB's
modeling research is designed to produce tools to serve
the nation's need for science-based air quality decision-
support systems.
The Emissions and Model Evaluation Branch
(EMEB) develops and applies advanced methods for
evaluating the performance of air quality simulation
models to establish their scientific credibility. Model
evaluation includes diagnostic assessments of modeled
atmospheric processes to guide the Division's research
in areas such as land use and land cover
characterization, emissions, meteorology, atmospheric
chemistry, and atmospheric deposition. The Branch also
advances the use of dynamic and probabilistic model
evaluation techniques to examine whether the predicted
changes in air quality are consistent with the
observations. By collaborating with other EPA offices
that provide data and algorithms on emissions
characterization and source apportionment and with the
scientific community, the Branch evaluates the quality of
emissions used for air quality modeling and, if
warranted, develops emission algorithms that properly
reflect the effects of changing meteorological conditions.
The Atmospheric Exposure Integration Branch
(AEIB) develops methods and tools to integrate air
quality process-based models with human health and
ecosystems exposure models and studies. The three
major focus areas of this Branch are (1) linkage of air
quality with human exposure, (2) deposition of ambient
pollutants onto sensitive ecosystems, and (3)
assessment of the impact of air quality regulations
(accountability). AEIB's research to link air quality to
human exposure includes urban-scale modeling,
atmospheric dispersion studies, and support of exposure
field studies and epidemiological studies. The urban-
scale modeling program (which includes collection and
integration of experimental data from its Fluid Modeling
Facility) is focused on building "hot-spot" air toxic
analysis algorithms and linkages to human exposure
models. The deposition research program develops tools
for assessing nutrient loadings and ecosystem
vulnerability, and the accountability program develops
techniques to evaluate the impact of regulatory
strategies that have been implemented on air quality and
conducts research to link emissions and ambient
pollutant concentrations with exposure and human and
ecological health end points.
The Applied Modeling Branch (AMB) uses
atmospheric modeling tools to address emerging issues
related to air quality and atmospheric influences on
ecosystems. Climate change, growing demand for
biofuels, and emission control programs and growth all
affect air quality and ecosystems in various ways that
require integrated assessment. Fundamental to these
40
-------
studies is the development of credible scenarios of development, and regulations will be used with regional
current and future conditions on a regional scale and atmospheric models to investigate potential changes in
careful consideration of global scale influences on air exposure risks related to air quality and meteorological
pollution and climate. Scenarios of climate, growth and conditions.
41
-------
APPENDIX C
Awards and Recognition for 2008
EPA Gold Medal
Annmarie Carlton, Brian Eder, Jerold Herwehe, Rohit
Mathur, Tanya Otte, Thomas Pierce, Jonathan Pleim,
George Pouliot, ST. Rao, Kenneth Schere, David
Wong, and Jeffrey Young — Air Quality Forecasting
Team
EPA Silver Medal
Valerie Garcia and Alice Gilliland—Public Health Air
Surveillance Valuation (PHASE) Team
EPA Bronze Medal
Russ Bullock, Bill Hutzell, Deb Luecken, Rohit Mathur,
Shawn Roselle, Golam Sarwar, and Ken Schere—
CMAQ Multipollutant Model Team
Kristen Foley, Val Garcia, Alice Gilliland, Jim
Godowitch, ST. Rao, and Jenise Swall—
Accountability: Evaluating the Effectiveness of the NOX
SIP Call Program in Improving Ozone Air Quality Over
the Eastern United States
Russ Bullock, Robin Dennis, and Donna Schwede—
Nitrogen and Mercury Watershed TMDL Assessment
Team
Vlad Isakov—Near Roadway Research Team
Jonathon Pleim—Exhaled Breath Condensate
Research
ORD Technical Assistance to the Regions or
Program Offices Award
David Mobley, Tom Pierce, and George Pouliot—
National Fire Emissions Inventory Team
Wyatt Appel, Jesse Bash, William Benjey, Prakash
Bhave, Russell Bullock, Annmarie Carlton, Robin
Dennis, Kristen Foley, Alice Gilliland, Bill Hutzell,
Deborah Luecken, Rohit Mathur, Sergey Napelenok,
Chris Nolte, Tanya Otte, Tom Pierce, Rob Pinder,
Jonathon Pleim, George Pouliot, Shawn Roselle,
Golam Sarwar, Kenneth Schere, Donna Schwede,
David Wong, and Jeff Young—CMAQ Model Team
ORD Scientific Communication Award
Robin Dennis, Valerie Garcia, Rohit Mathur, Patriacia
McGhee, David Mobley, and ST. Rao—EM Magazine
Team
Scientific and Technological Achievement
Awards (STAA) Winners
David Mobley—A Critical Overview of Air Emission
Inventories with Recommendations To Improve Their
Value to Air Quality Management
Recognition
Support to 2007 Nobel Peace Prize to
Intergovernmental Panel on Climate Change—David
Mobley
Embassy Science Fellow-New Zealand—David
Mobley (January-May 2008)
Embassy Science Fellow-Hong Kong—Golam Sarwar
(September-November 2008)
42
-------
APPENDIX D
Publications for FY and CY 2008
(Division authors are in bold.)
Journal Articles
Altieri, K. E., S. P. Seitzinger, A. G. Carlton, B. J.
Turpin, G. C. Klein, A. G. Marshall. Oligmers formed
through in-cloud methylglyoxal reactions: Chemical
composition, properties, and mechanisms investigated
by ultra-high resolution FTICR mass spectrometry.
Atmospheric Environment, 42 (7): 1476-1490, (2008).
Appel, W., A. Gilliland, G. Sarwar, and R.C. Gilliam.
Evaluation of the community multiscale air quality
(CMAQ) model version 4.5: Uncertainties and
sensitivities impacting model performance: Part I -
ozone. Atmospheric Environment, 41(40):9603-9613,
(2007).
Appel, K.W., P.V. Bhave, A. B. Gilliland, G. Sarwar,
and S.J. Roselle. Evaluation of the community multi-
scale air quality (CMAQ) model version 4.5:
Sensitivities impacting model performance; Part II -
particulate matter. Atmospheric Environment, 42(24):
6057-6066, (2008).
Boersma, K.F., D.J. Jacob, H. J. Eskes, R. W. Pinder,
J. Wang, and R. J. Van Der A. Intercomparison for
SCIAMACHY and OMI tropospheric NO2 columns:
Observing the diurnal evolution of chemistry and
emissions from space. Journal of Geophysical
Research, 113 (D16S26): 1-14, (2008).
Bowker, G., D. A. Gillette, G. Bergametti, , B.
Marticorena, and D. K. Heist. Fine-scale Simulations
of Aeolian sediment dispersion in a small area of the
northern Chihuahuan Desert. Journal of Geophysical
Research. 113(F02S11):1-28, (2008).
Bullock, R., D. Atkinson, T. Braverman, K. Civerolo,
A. Dastoor, D. Davignon, J. Y. Ku, K.Lohman, T.
Myers, R. Park, C. Seigneur, N. E. Selin, G. Sistla, and
K. Vijayaraghavan. The North American mercury
model intercomparison (NAMMIS). Study description
and model-to-model comparisons. Journal of
Geophysical Research, 113(017310): 1-17, (2008).
Carlton, A.G., B. Turpin, K.E. Altieri, S. Seitzinger,
A.M. Reff, H. Lim, and B. Ervens. Atmospheric oxalic
acid and SOA production from glyoxal: Results of
aqueous Photooxidation experiments. Atmospheric
Environment, 1(35):7588-7602, (2007).
Carlton, A. G., B. J. Turpin, K. E. Altieri, Sybil P.
Seitzinger, R. Mathur, S. Roselle, and R. J. Webber.
CMAQ Model performance enhanced when in-cloud
secondary organic aerosol is included: Comparisons of
organic carbon predictions with measurements.
Environmental Science and Technology, 42(23): 8798-
8802, (2008).
Chow, J.C., J.L. Watson, H.J. Feldman, J.E. Nolen, B.
Wallerstein, G. Hidy, P.J. Lioy, D. Mobley, K.
Baugues, and J.D. Bachmann. Will the circle be
unbroken: A history of the U.S. National Ambient Air
Quality Standards. Journal of Air and Waste
Management, 57(10):1151-1163, (2007).
Cook, R., V. Isakov, J. S. Touma, W. Benjey, J.
Thurman, E. Kinnee, and D. Ensley. Resolving local
scale emissions for near roads modeling assessments.
Journal of the Air and Waste Management
Association, 58(3):451-461, (2008).
Cooter, E., J. Swall, and R.C. Gilliam. Comparison of
700-hPa NCEP-R1 and AMIP-R2 wind Patterns over
the continental U.S. using the cluster analysis. Journal
of Applied Meteorology and Climatology, 46(11):1744-
1758, (2007).
Davis, J. M., P. V. Bhave, and K. M. Foley.
Parameterization of N2O5 reaction probabilities on the
surface of particles containing ammonium, sulfate, and
nitrate. Atmospheric Chemistry and Physics, 8(17):
5295-5311, (2008).
Dennis, R.L., R. Haeuber, T. Blett, J. Cosby, C.
Driscoll, J. Sickles, and J.M. Johnston. Sulfur and
nitrogen deposition on ecosystems in the United
States. EM: Air and Waste Management Associations
Magazine for Environmental Managers, 12-17, (2007).
43
-------
Dennis, R., P. V. Bhave, and R. W. Pinder.
Observable indicators of the sensitivity of PM2§ nitrate
to emission reductions, Part II: Sensitivity to error in
total ammonia and total nitrate of the CMAQ-predicted
nonlinear effect of SO2 emission reductions on PM25
nitrate. Atmospheric Environment, 42(6): 1287-1300,
(2008).
Ervens, B., A. G. Carlton, B.J. Turpin, K.E. Altieri, S.
M. Kreidenwies, and G. Feingold. Secondary organic
aerosol yields from cloud-processing of isoprene
oxidation products. Journal of Geophysical Research
Letters.35 L02816: 1 -20, (2008).
Garcia, V., N. Fann, R. Haeuber, and P. Lorang.
Assessing the public health impact of regional-scale
air quality regulations. EM, Air and Waste
Management Association Magazine for Environmental
Managers, 25-30, (2008).
Gego, E., S. Porter, A. Gilliland, C. Hogrefe, J.
Godowitch, and S. T. Rao. Modeling analysis of the
effects of changes in nitrogen oxides emission from
the electric power sector on ozone levels in the
eastern United States. Journal of Air and Waste
Management Association, 58(4): 580-588, (2008).
Gilliland, A.B., C. Hogrefe, R. W. Pinder, J. M.
Godowitch, K.M. Foley, and S.T. Rao. Dynamic
evaluation of regional air quality models: Assessing
changes in O3 stemming from changes in emissions
and meteorology. Atmospheric Environment, 42(20):
5110-5123(2008).
Godowitch, J., A. Gilliland, R. Draxler, and S.T. Rao.
Modeling assessment of point source NOX emission
reductions on ozone air quality in the eastern United
States. Atmospheric Environment, 42(1): 87-100,
(2008).
Godowitch J. M., C. Hogrefe, and S. T. Rao.
Diagnostic analyses of regional air quality model:
Changes in modeled processes affecting ozone and
chemical-transport indicators from NOX point source
emission reductions. Journal of Geophysical
Research, 113(019303): 1-15, (2008).
Hutzell, W. T., and D. Luecken. Fate and transport of
emissions fro several trace metals over the United
States. Science of the Total Environment. 396(2-
3):164-179, (2008).
Irwin, J. S., K. Civerolo, C. Hogrefe, W. Appel, K.
Foley, and J. Swall. A procedure for inter-comparing
the skill of regional-scale air-quality model
simulations of daily maximum 8-hour ozone values.
Atmospheric Environment, 42(21): 5403-5412, (2008).
Isakov, V., J. Touma, and A. Khlystov. A method of
assessing air toxics concentrations in urban areas
using mobile platform measurements. Journal of Air
and Waste Management, 57(11):1287 - 1295, (2007).
Kang, D., R. Mathur, S.T. Rao, and S. Yu. Bias-
adjustment techniques for improving ozone air quality
forecasts. Journal of geophysical Research,
113(023308): 1-17, (2008).
Liao, K., E. Tagaris, K. Manomaiphiboon, S.
Napelenok, J. Woo, S. He, P. Amar, and A. Russell.
Sensitivities of ozone and fine particulate matter
formation to emission under the impact of potential
future climate change. Environmental Science and
Technology, 41(24):8355-8361, (2007).
Liao, K.J., E. Tagaris, S. L. Napelenok, K.
Manomaiphiboon, J. H. Woo, P. Amar, S. He, and A.
G. Russell. Current and future linked responses of
ozone and PM2.5 to emissions controls.
Environmental Science and Technology, 42(13): 4670-
4675, (2008).
Lin, C., P. Pongprueksa, R. Bullock, S. Lindberg, S.O.
Pehkonen, C. Jang, T. Braverman, and T.C. Ho.
Scientific Uncertainties in atmospheric mercury models
II: Sensitivity analysis in the conus domain.
Atmospheric Environment. 41(31):6544-6560, (2007).
Luecken, D. J. and A. Cimorelli. CO-Dependencies of
Reactive Air Toxic and Criteria Pollutants on Emission
Reductions. The Journal of Air and Waste
Management Association, 58(5):693-701, (2008).
Luecken, D. L., and M. R. Mebust. Technical
challenges involved in implementation of VOC
reactivity-based control of ozone. Environmental
Science and Technology. 42(5): 1615-1622, (2008).
Luecken, D. J., S. Phillips, G. Sarwar, and C, Jang.
Effects of using the CB05 versus SAPRC99 versus
CB4 chemical mechanism on model predictions:
ozone and gas-phase photochemical precursor
concentrations. Atmospheric Environment, 42(23):
5805-5820, (2008).
Mathur, R., W.E. Frick, G. Lear, and R.L. Dennis.
Ecological Forecasting: Microbial Contamination and
Atmospheric Loadings of Nutrients to Land and Water.
EM: Air and Waste Management Associations
Magazine for Environmental Managers, 36-40, (2007).
44
-------
Mathur, R. Estimating the impact of the 2004 Alaskan
forest fires on episodic participate matter pollution over
the eastern United States through assimilation of
satellite-derived aerosol optical depths in a regional air
quality model. Journal of Geophysical Research,
113(017302): 1-14, (2008).
Mathur, R., S. Yu, D. Kang, and K.L. Schere.
Assessment of the winter-time performance of
developmental particulate matter forecasts with the
Eta-CMAQ modeling system. Journal of Geophysical
Research-Atmospheres (JGR-Atmospheres),
113(002303): 1 -15, (2008).
Mobley, D. and P. Gurnsey. New Zealand's innovative
approach to emissions trading for addressing global
climate change. EM, Air and Waste Management
Association Magazine for Environmental Managers,
14-19, (2008).
Napelenok, S.L., D. S. Cohan, M.T. Odman, and S.
Tonse. Extension and evaluation of sensitivity analysis
capabilities in a photochemical model. Environmental
Modeling and Software. 23(8):994-999, (2008).
Napelenok, S. L, R. Finder, A. B. Gilliland, and R.
V. Martin. A method of evaluating spatially-resolved
NO2 emissions using Kalman filter inversion, direct
sensitivities, and space-based NO2 observations.
Atmospheric Chemistry and Physics, 8: 5603-5614,
(2008).
Nolte, C.G., A. B. Gilliland, C. Hogrefe and L.J.
Mickley. Linking global to regional models to assess
future climate impacts on surface ozone levels in the
United States. Journal of Geophysical Research,
113(014307): 1-14, (2008).
Nolte, C.G., P.V. Bhave, J. R. Arnold, R. L. Dennis,
K. Max Zhang, and A. S. Wexler. Modeling urban and
regional aerosols -Application of the CMAQ-UCD
aerosol model to Tampa, a coastal urban site.
Atmospheric Environment, 42(13):3179-3191, (2008).
Otte, T.L. The Impact of nudging in the meteorological
model for retrospective air quality simulations. Part I:
Evaluation against national observation networks.
Journal of Applied Meteorology and Climatology,
47(7): 1853-1867, (2008).
Otte, T.L The Impact of nudging in the meteorological
model for retrospective air quality simulations. Part II:
Evaluating collocated meteorological and air quality
observations. Journal of Applied Meteorology and
Climatology, 47(7): 1868-1887, (2008).
Pinder, R., A. B. Gilliland, and R. Dennis.
Environmental impact of atmospheric NH3 emissions
under present and future conditions in the Eastern
United States. Geophysical Research Letter, 35(L I
2808): 1-6, (2008).
Pinder, R.W., R. L. Dennis, and P. V. Bhave.
Observable indicators of the sensitivity of PM25 nitrate
to emission reductions: Part I: Derivation of the
adjusted gas ratio and applicability at regulatory-
relevant time scales. Atmospheric Environment, 42(6):
1275-1286, (2008).
Pongprueksa, P. C-J, Lin, S. E. Lindberg, C. Jang, T.
Braverman, O.R. Bullock, Jr., T. C. Ho, and H-
W.Chu. Scientific uncertainties in atmospheric mercury
models III: Boundary and initial conditions, model grid
resolutions, and Hg (II) reduction mechanisms.
Atmospheric Environment. 42(8): 1828-1845, (2008).
Pouliot, G., T. Pace, B. Roy, T. Pierce, and D.
Mobley. Development of a biomass burning emissions
inventory by combining satellite and ground-based
information. Journal of Applied Remote Sensing, 2(1):
021501, (2008).
Pullen, Julie, J. Ching, W. Sailor, W. Thompson, B.
Bornstein, and D. Koracin. Progress toward meeting
the challenges of our coastal urban future. Bullentin of
the American Meteorological Society, 89(11): 1727-
1731, (2008).
Queen, A., Y. Zhang, R. Gilliam, and J. Pleim.
Examining the sensitivity of MM5-CMAQ predictions to
explicit, microphysics schemes, Part I - Database
description, evaluation protocol and precipitation
predictions. Atmospheric Environment, 42(16): 3842-
3855, (2008).
Rao, S. Linking air, land, and water pollution for
effective environmental management. EM: Air and
Waste Management Associations Magazine for
Environmental Managers, 5 (2007).
Rao, S. T. Exposure science and its applications for
effective environmental management. EM, Air and
Waste Management Association Magazine for
Environmental Managers, July 2008, 7, (2008).
Sarwar, G., D. Luecken, G. Yarwood, G. Z. Whitten,
S. Reyes, and W. P. L. Carter. Impact of an updated
carbon bond mechanism on predictions from the
Community Multi -scale Air Quality (CMAQ) modeling
system: Preliminary assessment. Journal of Applied
Meteorology and Climatology. 47(1): 3-14, (2008).
45
-------
Sarwar, G., S.J. Roselle, R. Mathur, W. Appel, R.L.
Dennis, and B. Vogel. A Comparison of CMAQ MONO
Predictions with Observations from the Northeast
Oxidant and Particle Study. Atmospheric Environment,
42(23):5760-5770, (2008).
Smolarkiewicz, P.K., R. Sharman, J. Weil, S.G. Perry,
D. Heist, and G.E. Bowker. Building resolving large-
eddy simulation and comparison with wind tunnel
experiments. Jounal of Computational Physics,
227(1):633-653, (2007).
Stein, A.F., V. Isakov, J.M. Godowitch, and R.R.
Draxler.
A hybrid modeling approach to resolve pollutant
concentrations in an urban area. Atmospheric
Environment. 41(40):9410-9426, (2007).
Sullivan, T. J., B. J. Cosby, J. R. Webb, R. L. Dennis,
A. J. Bulger, and F. A. Deviney. Streamwater acid-
base chemistry and critical loads of atmospheric sulfur
deposition in Shenandoah National Park, Virginia.
Journal of Environmental Monitoring and Assessment.
137(1-3): 85-99, (2008).
Tiwary, A., A. Reff, and J. J. Colls. Collection of
ambient particulate matter by porous vegetation
barrier: Sampling and characterization methods.
Journal of Aerosol Science, 39(1): 40-47, (2008).
long, D., R. Mathur, K.L. Schere, D. Kang, and S.
Yu. The use of air quality forecasts to assess impacts
of air Pollution on crops: Methodology and case study.
Atmospheric Environment, 41(38):8772-8784, (2007).
Venkatram, A., V. Isakov, E.D. Thoma, and R.W.
Baldauf. (AMD) Analysis of air quality data near
roadways using a dispersion model. Atmospheric
Environment. 41(40):9481-9497, (2007).
Book Chapters
Baklanov, A., J. Ching, C.S. B. Grimmmond, and A.
Martilli. Cost 728 Action Report: Urbanization of
meteorological and air quality models - Chapter 5 -
Model Urbanization strategy: Summaries,
recommendation, and requirements. Urbanization of
meteorological and air quality models, The Danish
Meteorological Institure, Copenhagen, Denmark, 118-
127, (2008).
Bullock, R. The Effect of Lateral Boundary Values on
Atmospheric Mercury Simulations with the CMAQ
Model. Chapter 2, Carlos Borrego; Ana Isabel Miranda
(ed.), Air Pollution Modeling and its Application XIX.
Springer, New York, NY, (Series C):173-181, (2008).
Davidson, P., K. L. Schere, R. Draxter, S.
Kondragunta, R. Wayland, J. F. Meagher,and R.
Mathur. Toward a US National Air Quality Forecast
Capability: Current and Planned Capabilities. Chapter
2, Carlos Borrego; Ana Isabel Miranda (ed.), Air
Pollution Modeling and its Application XIX. Springer,
New York, NY. 226-234, (2008).
Gego, E., P.S. Porter, V. Garcia, C. Hogrefe, and S.T.
Rao. Fusing Observations and Model Results for
Creation of Enhanced Ozone Spatial Fields:
Comparison of Three Techniques. Chapters, Carlos
Borrego; Ana Isabel Miranda (ed.), Air Pollution
Modeling and Its Application XIX. Springer, New York,
NY, 339-346, (2008).
Gilliland, A., J. M. Godowitch, C. Hogrefe, and S.T.
Rao. Evaluating Regional-Scale Air Quality Models.
Chapter 4, Carlos Borrego; Ana Isabel Miranda (ed.),
Air Pollution Modeling and Its Application XIX.
Springer, New York, NY. 412-419, (2008).
Hogrefe, C., J. Ku, G. Sistla, A. Gilliland, J. Irwin, P.
S. Porter, E. Gego, P. Kasibhatla, and S.T. Rao. Has
the Performance of Regional-Scale Photochemical
Modeling Systems Changed over the Past Decade?
Chapter 4, Carlos Borrego; Ana Isabel Miranda (ed.),
Air Pollution Modeling and Its Application
XlX.Springer, New York, NY. 394-403, (2008).
Isakov, V. and H. A. Ozkaynak. A Modeling
Methodology to Support Evaluation PublicHealth
Impacts on Air Pollution Reduction Programs. Chapter
7, Carlos Borrego; Ana Isabel Miranda (ed.), Air
Pollution Modeling and Its Application XIX. Springer,
New York, NY. 614-622, (2008).
Luecken, D. J., A. Cimorelli, C. Stahl, and D. Tong.
Evaluating the Effects of Emission Reductions on
Multiple Pollutants Simultaneously. Chapter 7, Carlos
Borrego; Ana Isabel Miranda (ed.),Air Pollution
Modeling and its Application XIX. Springer, New York,
NY. 623-631, (2008).
Luecken, D. J. Comparison of Atmospheric Chemical
Mechanisms for Regulatory and Research
Applications. NATO Advanced Research Workshop on
Simulation and Assessment of Chemical Processes in
Multiphase Environment, Alushta, Ukraine, October 01
- 04, 2007.Springer Science + Business Media, LLC,
New York, NY, 95-106, (2008).
46
-------
Mathur, R., S. J. Roselle, G. Pouliot, and G. Sarwar.
Diagnostic Analysis of the Three-Dimensional Sulfur
Distributions over the Eastern United States Using the
CMAQ Model and Measurements from the ICARTT
Field Experiment. Chapter 5, Carlos Borrego; Ana
Isabel Miranda (ed.). Air Pollution Modeling and its
Application XIX. Springer, New York, NY. 496-504,
(2008).
Mobley, J. D., L.L. Beck, G. Sarwar, A. Reff, and M.
Houyoux. SPECIATE - EPA's Databace of speciated
emission profiles. P2.4, Carlos Borrego; Ana Isabel
Miranda (ed.). Air Pollution Modeling and Its
Application XIX. Springer, New York, NY, 665-666
(2008).
Napelenok, S., R. W. Pinder, A. Gilliland, and R. V.
Marin. Developing a Method for Resolving NOx
Emission Inventory Biases Using Discrete Kalman
Filter Inversion, Direct Sensitivities, and Satellite-
Based Columns. Chapters, Carlos Borrego; Ana
Isabel Miranda (ed.). Air Pollution Modeling and its
Application XIX. Springer, New York, NY. 322-330,
(2008).
Nolte, C., A. Gilliland, and C. Hogrefe. Linking Global
and Regional Models to Simulate U.S. Air Quality in
the Year 2050. Chapter 6, Carlos Borrego; Ana Isabel
Miranda (ed.). Air Pollution Modeling and Its
Application XIX. Springer, New York, NY. 559-567,
(2008).
Pleim, J. A., J. O. Young, D. Wong, R. C. Gilliam, T.
L. Otte, and R. Mathur. Two-Way Coupled
Meteorology and Air Quality Modeling. Chapter 2,
Carlos Borrego; Ana Isabel Miranda (ed.). Air Pollution
Modeling and its Application XIX. Springer, New York,
NY. 235-242, (2008).
Pouliot, G., T. Pierce, X. Zhang, S. Kondragunta, C.
Wiedinmer, T. Pace and D. Mobley. The Impact of
satellite-derived biomass burning emission estimates
on air quality. SPIE/lnternational Society for Optical
Engineering, Vol. 7089(1): 7089F 1-12 (2008).
Rao, S.T., C. Hogrefe,, and G. Kallos. Long -range
transport of atmospheric pollutant's and transboundary
pollution. Anthem Press, World Atlas of Atmospheric
Pollution, Chapters, 35-45 (2008).
Roy, D., G. Pouliot, D. Mobley, G. Thompson, T. E.
Pierce, A. J. Soja,, J. J. Szykman, and J. Al-Saadi.
Development of Fire Emissions Inventory Using
Satellite Data. Chapter 2, Carlos Borrego; Ana Isabel
Miranda (ed.).Air Pollution Modeling and its Application
XIX. Springer, New York, NY, 217-225, (2008).
Sarwar, G., R. L. Dennis, and B. Vogel. The Effect of
Hetrogeneous Reactions on Model Performance for
Nitrous Acid. Chapter 4, Carlos Borrego; Ana Isabel
Miranda (ed.). Air Pollution Modeling and its
Application XIX. Springer, New York, NY. 349-357,
(2008).
Published Reports
Pierce, I.E., V. Isakov, B. Haneke, and J. Paumier.
Emission and Air Quality Modeling Tools for Near-
Roadway Applications. U.S. Environmental Protection
Agency, Washington, D.C., EPA/600/R-09/001 (NTIS
PB2009-103941), 2008.
Rao, S., R.L. Dennis, V. Garcia, A. Gilliland, R.
Mathur, D. Mobley, I.E. Pierce, and K.L. Schere.
Summary Report of Air Quality Modeling Research
Activities for 2006. U.S. Envirnmental Protection
Agency, Washington, D.C., EPA/600/R-07/103 (NTIS
PB2008-110094), 2008.
47
-------
APPENDIX E
Acronyms and Abbreviations
ACM Asymmetric Convective Model IC/BC
AERMOD AMS/EPA Regulatory Model IMPROVE
AMAD Atmospheric Modeling and Analysis
Division INTEX
AMB Applied Modeling Branch
AMD Atmospheric Modeling Division INTEX-NA
AMDB Atmospheric Model Development
Branch ISORROPIA
AMET Atmospheric Model Evaluation Tool LES
ASMD Atmospheric Sciences and Modeling LIDAR
Division MLM
BELD3 Biogenic Emissions Land Cover MM5
Database, v3
BRACE Bay Regional Atmospheric Chemistry MPI
Experiment MYSQL
CAA Clean Air Act NAAQS
CAIR Clean Air Interstate Rule NADP
CASTNET EPA's Clean Air Status and Trends NAMMIS
Network
CBL convective boundary layer MASS
C-CAP Coastal Change Analysis Program NCAR
CCTM CMAQ Chemistry-Transport Model
CIRAQ Climate Impacts on Regional Air Quality NCEP
CMAQ Community Multiscale Air Quality Model
CMAQ-UCD University of California Davis Aerosol NERL
Module coupled to the Community NGA
Multiscale Air Quality Model NH3
CMAS Community Modeling and Analysis NLCD
System NO2
DDM-3D Decoupled Direct Method—three- NO"3
dimensional N2O5
DEM digital elevation model NOAA
DTM digital terrain model
EC elemental carbon NOX
EMEP European Monitoring and Evaluation NOy
Programme Nr
EPA U.S. Environmental Protection Agency NRC
ESRP Ecological Services Research Program NRMRL
FDDA four-dimensional data assimilation
FMF fluid modeling facility NUDAPT
FML Future Midwestern Landscapes
FY fiscal year O3
GCM global climate model OAQPS
GPL Gnu public license
HAP hazardous air pollutant OC
HAPEM Hazardous Air Pollutant Exposure Model ORD
HNO3 nitric acid ORISE
ICARTT International Consortium for
Atmospheric Research on Transport and Ov
Transformation PAN
initial condition/boundary condition
Interagency Monitoring of Protected
Visual Environment Network
Intercontinental Chemical Transport
Experiment
Intercontinental Chemical Transport
Experiment—North America
thermodynamics module
large-eddy simulation
light detecting and ranging
multilayer model
fifth generation of the Penn State/UCAR
Mesoscale Model
message-passing interface
open-source database software
National Ambient Air Quality Standard
National Acid Deposition Program
North American Mercury Model
Intercomparison Study
National Agricultural Statistics Service
National Center for Atmospheric
Research
National Centers for Environmental
Prediction
National Exposure Research Laboratory
National Geospatial Agency
ammonia
National Land Cover Dataset
nitrogen dioxide
nitrate ion
nitrogen pentoxide
National Oceanic and Atmospheric
Administration
oxides of nitrogen
total reactive oxides of nitrogen
reactive nitrogen
National Research Council
National Risk Management Research
Laboratory
National Urban Database and Access
Portal Tool
ozone
Office of Air Quality Planning and
Standards
organic carbon
Office of Research and Development
Oak Ridge Science and Education
Program
water vapor mixing ratio
peroxyacyl nitrate
48
-------
PEL planetary boundary layer SIP
PM particulate matter SO2
PRISM Parameter-Elevation Regressions on SOA
Independent Slopes Model STENEX
PX LSM Pleim-Xiu Land Surface Model STN
RELMAP Regional Lagranian Model of Air TEAM
Pollution TES
REMSAD Regional Modeling System for Aerosols TexAQS
and Deposition TMDL
SCIAMACHY scanning imaging absorption UCP
spectrometer for atmospheric USDA
cartography VERDI
SEEP Senior Environmental Employee
Program WDT
SGV subgrid variability WRF
SHEDS Stochastic Human Exposure and Dose
Simulation
state implementation plan
sulfur dioxide
secondary organic aerosol
Stencil Exchange
Speciated Trends Network
Trace Element Analysis Model
Tropospheric Emission Spectrometer
Texas Air Quality Study
total maximum daily load
urban canopy parameter
U.S. Department of Agriculture
Visualization Environment for Rich Data
Interpretation
Watershed Deposition Tool
weather research and forecasting
49
-------
-------
-------
-------
&EPA
United States
Environmental Protection
Agency
PRESORTED STANDARD
POSTAGE & FEES PAID
EPA
PERMIT NO. G-35
Office of Research and Development (8101R)
Washington, DC 20460
Official Business
Penalty for Private Use
$300
Recycled/Recyclable Printed on paper that contains a minimum of
50% postconsumer fiber content processed chlorine free
------- |