The Effects of Climate and Electricity Emissions on Air Quality in the

The Effects of Climate and Electricity Emissions on
Air Quality in the United States
by
Steven D. Plachinski
A Master’s Thesis submitted in partial fulfillment of the requirements for the
degree of
Master of Science
Nelson Institute for Environmental Studies
at the
University of Wisconsin-Madison
January, 2011
i Table of Contents
Abstract
Acknowledgements
iii
v
Chapter 1 – Introduction and Motivation
1
A. Background – Ozone and Fine Particulate Matter
B. The National Ambient Air Quality Standards
C. Improving Future Air Quality
D. Research Overview
Figures
References
Chapter 2 – Ozone and PM2.5 Response to Changing Electricity Emissions
2
4
6
8
10
15
17
A. Introduction
B. Data and Methods
B.i. MyPower Electricity Model
B.ii. The 2002 National Emissions Inventory
B.iii. Air Quality Modeling
B.iv. Meteorology Modeling
B.v. SMOKE Emissions Processing
C. Model Evaluation
D. Results – 2002 BC Scenario
E. Results – Sensitivity Simulations with MyPower Scenarios
E.i. NOx and SO2 Emissions
E.ii. Ozone
E.iii. Nitrate
E.iv. Sulfate
F. Discussion
17
20
21
25
27
30
33
36
37
40
41
41
42
42
47
Appendix 2.A – Comparison of 2008 RC and 2002 BC
50
Figures
References
52
96
Chapter 3 – Ozone Response to Meteorology and Climate
A. Introduction
B. Data and Methods
B.i. Ozone Observations and Meteorological Reanalysis
B.ii. Air Quality and Meteorology Models
C. Observed Variability in Ozone-Temperature Relationships
D. Model Performance
100
100
108
108
110
112
114
ii E. From Diagnosis to Understanding – July 2002 Episode
F. Discussion
120
124
Figures
References
127
146
Chapter 4 – Conclusions and Future Work
A. Research Findings
B. Broader Conclusions
149
149
152
iii Abstract
Many areas across the United States experience high levels of air pollution that
negatively impact human health and the environment.
Ground-level ozone (O3) and fine
particulate matter (PM2.5) are the two major pollutants of concern, and both are regulated under
the U.S. Clean Air Act. While emissions from fossil fuel combustion are a major cause of O3
and PM2.5 pollution, meteorological conditions and transport are also important factors
controlling these two pollutants. Therefore, understanding how air quality is affected by both
energy sector emissions and by meteorology is a necessary step in designing effective strategies
for future air quality improvement. This research examines the air quality impacts of changing
pollution emissions, meteorological variability, and their complex interactions relevant for both
science and policy.
The first section of this thesis assesses the regional air quality impacts of lower-carbon
electricity generation in Wisconsin. The MyPower electricity dispatch model is used to quantify
the NOx and SO2 emissions reductions resulting from two future carbon reduction policies – an
enhanced renewable electricity portfolio standard (RPS) and increased energy efficiency. These
future electricity policies reduce NOx and SO2 emissions by more than 50% compared to current
levels and more than 30% compared to future business-as-usual levels.
The Community
Multiscale Air Quality (CMAQ) model is then used to quantify the impacts of these emission
changes on concentrations of O3 and two aerosol species, sulfate and nitrate. Of the pollutants
examined, sulfate aerosol concentrations exhibit the largest response to electricity emission
reductions, with decreases of 6-12% compared to current concentrations and 3-6% compared to
business-as-usual levels dependent on the spatial distribution of emissions reductions. We find
that, in certain cases, sulfate decreases are amplified over Lake Michigan compared to the
iv adjacent land. Our analysis suggests that air quality co-benefits associated with CO2 emission
reductions could be significant for Wisconsin and can be maximized if both air quality and
carbon are included in energy decision-making.
The second section of this thesis explores the response of O3 concentrations to
meteorology and climate, focusing primarily on temperature, over the continental United States.
Many studies have shown a positive relationship between O3 and temperature (more O3 at higher
temperatures) but do not assess the spatial and temporal variability of this relationship. EPA
AQS O3 observations and NARR meteorological reanalysis for the summer months of 19952005 are used here to identify the spatial patterns of O3-temperature relationships and assess the
variability of these patterns. Results show that the response of O3 to temperature can vary from
month to month and year to year: many regions (Northeast, Upper Midwest, West Coast) exhibit
consistently positive correlations as expected, while other regions (Ohio River Valley, Southern
Great Plains) exhibit negative correlations during individual months. We also evaluate the
ability of the CMAQ model to accurately represent these observed relationships.
For the
summer of 2002, CMAQ captures much of the spatial and temporal variability seen in observed
relationships.
A closer analysis of one episode with negative O3-temperature relationships
(Illinois during July 2002) shows that humidity has a greater impact on O3 than temperature
during this episode, suggesting that the covariance between temperature and specific humidity
partially explains the negative O3-temperature relationships. Our results show that the response
of O3 to temperature varies with space and time and that air quality models used in decisionmaking are accurately capturing this response.
v Acknowledgements
Many individuals have played a role in the work presented here and deserve to be
acknowledged. First and foremost, I wish to wholeheartedly thank my advisor Prof. Tracey
Holloway for her guidance and encouragement throughout my graduate career. I am convinced
that few advisors offer the combination of research expertise and professional mentorship that I
received from Prof. Holloway, and much of my academic success can be attributed to her
support. A great thanks also goes to the other members of my thesis committee, Prof. Greg
Nemet and Prof. Chris Kucharik, for improving the quality of this work through their
encouragement and thoughtful suggestions.
In addition to my committee members, several researchers made significant contributions
to the design of this study. The research presented in Chapter 2 builds off the electricity
modeling work performed by Dr. Paul Meier, and it has been a pleasure to work with him
throughout the development and implementation of this research. Jacob Oberman provided
invaluable technical assistance at several stages, and I am grateful for his efforts.
Model
simulations performed by Caitlin Littlefield and Dr. Scott Spak were also utilized in this study,
and the scope of this thesis would not have been possible without their use. A special thanks
goes to Caitlin Littlefield as she graciously offered her assistance during model troubleshooting.
I must also acknowledge the organizations that funded this research and my graduate education:
the University of Wisconsin-Madison Graduate School, Wisconsin Focus on Energy, and the
CHANGE certificate through the program’s NSF-IGERT grant.
Furthermore, I wish to thank all the members of the Holloway research group, both past
and present: Erica Bickford, Keith Cronin, Phil Duran, Dr. Meiyun Lin, Caitlin Littlefield, Matt
Luedke, Claus Moberg, Jami Morton, Jacob Oberman, and Dr. Scott Spak.
This research
vi community has not only provided scientific and technical assistance but has also been a source of
support and companionship throughout my studies. You will be greatly missed.
Finally, I could not have accomplished all that I have in the past two and a half years
without the love and support of my family and friends. You know who you are. This thesis
belongs to you.
1 Chapter 1 – Introduction and Motivation
Despite significant improvements in the past decades, air quality is still a concern in the
United States. Many urban and rural areas across the country experience high levels of air
pollution that negatively impact human health and the environment. This poor air quality stems
largely from the pollution emitted from fossil fuel combustion for electricity generation,
industry, and transportation. To reduce high pollution levels, air quality improvement strategies
have focused on reducing pollutant emissions from these anthropogenic (human) sources.
However, meteorological conditions and transport are also important factors controlling air
quality; weather plays an important role in creating, transporting, and removing air pollution in
the atmosphere. Therefore, understanding how air quality is affected by both emissions and
meteorology is a necessary step in designing effective strategies for air quality improvement.
This thesis examines the air quality impacts of both changing pollutant emissions and
changing meteorology. First, we assess the air quality impacts of changing electricity sector
emissions in Wisconsin and the Great Lakes region. We then explore the response of air quality
to climate variability over the entire continental United States. This complementary research
uses an air quality model as the primary assessment tool and highlights some of the many
complex interactions between air quality, energy, and climate change: energy from fossil fuels is
the primary emission source of health-relevant air pollution and greenhouse gases, changing
climatological conditions affects air pollution processes and can increase energy demand for
space heating and cooling, air pollutants can affect the radiative balance of the Earth’s climate,
and policies to reduce air pollution and/or mitigate climate change directly affect how energy is
generated and used.
2 Our work aims to improve understanding of the links between air quality, energy, and
climate change. In the context of this study, we focus on links identified by two related research
questions:
•
What air quality impacts can be expected from energy policies aimed at reducing
greenhouse gas emissions?
•
How do relationships between climate and air quality vary in time and space?
These questions are the motivations for this work. To address this first question, we quantify the
impact of lower-carbon electricity generation in Wisconsin on ozone and particulate matter
concentrations in the Great Lakes region, thus assessing the impact of changing emissions on air
quality. To address this second question, we examine the impacts of climate variability on ozone
over the continental United States and focus on the variability of ozone-temperature
relationships, thus assessing the impact of changing meteorology on air quality. The findings of
this research have important implications for air quality, climate, and energy policies.
A. Background – Ozone and Fine Particulate Matter
Ground-level ozone and fine particulate matter are regulated under the Clean Air Act and
are the two major pollutants of concern in the United States [Jacob and Winner, 2009; EPA,
2010a]. Ozone (O3) is a gaseous pollutant formed in the atmosphere and not directly emitted
from any source (i.e. a secondary pollutant). Not to be confused with stratospheric ozone that
exists at high altitudes and protects the Earth from harmful UV radiation, tropospheric or
ground-level ozone is formed through chemical reactions between nitrogen oxides (NOx) and
hydrocarbons (or volatile organic compounds, VOCs) in the presence of sunlight. In the United
States, ozone formation is highest during the summer months due to high amounts of solar
3 radiation and atmospheric conditions favorable for formation [Holloway et al., 2008; Weaver et
al., 2009].
Particulate matter (PM) is a complex mixture of microscopic particles and liquid droplets
in the atmosphere.
PM, also called aerosols, can be comprised of a number of chemical
components such as nitrates, sulfates, organics, metals, dust, or sea salt and can be formed in the
atmosphere (secondary) or emitted directly from sources (primary). It can come in a wide range
of sizes: some PM species are large enough to be seen with the naked eye while others are so
small that they can only be detected using specialized equipment. Particulate matter is usually
divided into two categories based on size: PM10 or coarse PM for particles smaller than 10
micrometers (µm) in diameter, and PM2.5 or fine PM for particles 2.5 µm in diameter and
smaller. While levels of individual chemical constituents vary with location and season, high
concentrations of total PM2.5 can occur throughout the year [Spak and Holloway, 2009; EPA,
2010a].
Concentrations of nitrate (NO3-), and sulfate (SO42-), two of the most important
secondary PM2.5 species, are assessed in this work.
The formation and removal/destruction of ozone and PM2.5 are controlled by both
pollutant emissions and weather [Jacob, 1999]. Emissions of NOx, carbon monoxide (CO), and
hydrocarbons from natural and anthropogenic sources lead to higher ozone concentrations. In
addition, weather conditions such as sunlight and temperature can enhance or inhibit ozone
formation. The principal sinks of ozone include photochemistry in the presence of water vapor
and reaction with the Earth’s surface or vegetation (dry deposition), and both of these sinks are
strongly influenced by meteorology [Jacob and Winner, 2009]. PM2.5 is usually formed from the
oxidation of NOx, sulfur dioxide (SO2), and VOC emissions but can also be directly emitted from
combustion sources. Meteorological conditions largely control the partitioning between the
4 particle and gas phases of multiple species of PM2.5, and scavenging by precipitation (wet
deposition) is the main removal mechanism for PM2.5 in the atmosphere [Jacob and Winner,
2009; Spak and Holloway, 2009].
Ozone and PM2.5 are important pollutants because of their negative impacts on humans
and the environment.
The negative impacts of these pollutants on human health are well
documented [EPA, 2004; EPA, 2006]. Exposure to ozone can decrease lung function, causing
respiratory symptoms such as coughing and airway inflammation. Ozone also can aggravate
preexisting conditions such as asthma and bronchitis. When inhaled, PM2.5 can penetrate deep
into lung tissue and enter the blood stream, causing a variety of respiratory and cardiovascular
problems. Figure 1-1 shows that more than a third of the U.S. population lives in areas with
unhealthy levels of either ozone or PM2.5. Ozone and PM2.5 also have negative environmental
impacts [EPA, 2004; EPA, 2006]. Ozone can damage the leaves of trees and other vegetation,
reducing forest growth and crops yields. PM2.5, when it is deposited to the Earth’s surface, can
negatively affect the nutrient balances of ecosystems and is the source of acid rain that damages
vegetation and increases the acidity of lakes and rivers. PM2.5 is also the primary cause of
reduced visibility in many areas of the country.
B. The National Ambient Air Quality Standards
Because of these negative impacts, ozone and PM2.5 are regulated by the National
Ambient Air Quality Standards (NAAQS) as criteria pollutants under the Clean Air Act to
protect public health and welfare. Based on the known health effects, the standards set for each
pollutant reflect the concentration and exposure time above which an endangerment to public
health is anticipated. The U.S. EPA must periodically review the scientific bases (or criteria) for
5 each criteria pollutant’s NAAQS by assessing the newest scientific information [EPA, 2006].
The NAAQS for ozone and PM2.5 are shown in Table 1-1. The current 8-hour ozone standard is
set at 85 parts per billion (ppb), while the current PM2.5 standards are set at 35 µg/m3 for a 24hour averaging time and 15 µg/m3 on an annual average basis.
When concentrations at a location in the U.S. exceed the NAAQS levels for one of these
pollutants, then this location is considered ‘nonattainment’ for that pollutant.1
States with
nonattainment areas must develop a state implementation plan (SIP) to reduce pollutant
concentrations in these areas and comply with the NAAQS. Areas previously designated as
nonattainment that have reduced pollution concentrations and been recently redesignated as
attainment are classified as maintenance areas. Nonattainment areas across the United States for
the current (1997) ozone standard are shown in Figure 1-2.
Most counties designated as
nonattainment or maintenance are in urban areas of the eastern United States, though many
counties in California, both urban and rural, are also designated as nonattainment. Figure 1-3
shows the nonattainment areas for the current (2006) 24-hour PM2.5 standard.2 Compared with
ozone, fewer areas across the U.S. are designated nonattainment for PM2.5, though most
nonattainment areas are again in the eastern U.S. and California. For our work focused on the air
quality impacts of changing electricity sector emissions in Wisconsin and the Great Lakes
region, it is important to note that Milwaukee, Waukesha, and Racine counties in southeastern
Wisconsin are designated nonattainment for both ozone and PM2.5.
Overall, concentrations of ozone and PM2.5 and the number of nonattainment areas have
decreased over the past decades [Bachmann, 2007; EPA, 2010a].
However, the EPA has
periodically lowered standards for these two pollutants [Bachmann, 2007]. For example, the
1
2
For specific information on attainment for each pollutant, see Table 1-1. No counties, outside of 24-hour nonattainment areas, failed to meet the annual PM2.5 standard. 6 1997 PM2.5 24-hour standard of 65 µg/m3 was lowered to 35 µg/m3 in 2006. This lowering of
standards can cause new areas to be designated as nonattainment.
The future revision of
standards, resulting in new nonattainment areas that must develop plans to reduce pollutant
levels, is also likely. In January of 2010, the EPA announced plans to revise the 8-hour ozone
standard to a lower level within the range of 60-70 ppb [EPA, 2010b]. The potential impacts on
attainment of this new 8-hour ozone standard throughout the U.S. are shown in Figure 1-4.
Under a 60 ppb 8-hour standard, the number of counties designated as nonattainment drastically
increases, and all states except Montana would have at least one area exceeding this standard.
While no revision of PM2.5 standards has been announced by the EPA, many areas are close to
exceeding current standards. For example, EPA documentation [EPA, 2010c] shows that the
nine areas redesignated as attainment in 2010 for the 24-hour PM2.5 standard have an average
design value3 of 33.4 µg/m3, less than 2 µg/m3 from the standard. This demonstrates that the
attainment status of many areas in the U.S. for ozone and PM2.5 would be affected by new
revisions of the NAAQS.
C. Improving Future Air Quality
Because air quality standards for ozone and PM2.5 are likely to be tightened in the future
and many areas already experience air quality levels near current standards, new air quality
improvement strategies will need to be developed to meet future standards. As in the past,
reducing emissions will likely be the focus of these air quality improvement plans [Bachmann,
2007]. However, because meteorology is also an important driver of air quality, and global
3
A design value is a statistic that describes the air quality status of a given area relative to the National
Ambient Air Quality Standards (NAAQS). Design values are helpful because they are expressed as a
concentration, thereby allowing a direct comparison to the level of the standard. 7 climate change is expected to alter regional meteorology, the consideration of meteorology will
be of increasing importance in these improvement strategies.
Changing meteorological
conditions under climate change is likely to have an impact on regional air quality. Since ozone
formation is highly sensitive to certain meteorological conditions such as high temperatures and
stagnation events, and climate change is expected to increase the prevalence of these
meteorological conditions, ozone pollution is likely to worsen under future climate change
[Dawson et al., 2007; Nolte et al., 2008; Jacob and Winner, 2009]. This likelihood of increased
ozone concentrations is highlighted by the IPCC as one of the top human health impacts of
climate change [IPCC, 2007]. The effects of climate change on PM2.5 are uncertain but could be
potentially significant due to future changes in precipitation and stagnation [Jacob and Winner,
2009].
In addition, it is possible that the effects of climate change on air quality could partly
offset the air quality benefits of emissions reductions [Nolte et al., 2008; Wu et al., 2008;
Bloomer et al., 2009; Jacob and Winner, 2009]. These studies highlight the notion of a ‘climate
penalty’ as the need for stronger emission controls in order to achieve a given air quality
standard under future climate change. Furthermore, Hogrefe et al. [2004] suggests that the
effects of a changing climate may be of equal importance compared to other factors such as
emissions and global pollution transport when planning for future air quality standards.
Therefore, the complex interactions between air quality, emissions from the energy sector, and
meteorology under future climate change must be better understood in order to design strategies
and policies that improve future air quality. The work presented here on the links between air
quality, energy, and climate helps to meet this need.
8 D. Research Overview
In order to address the two research questions presented above, we employ an air quality
model as our primary assessment tool. Emissions, meteorology, and atmospheric chemistry
represent many complex and interrelated processes, and these processes are variable over space
and time. While essential in air quality assessment, in-situ measurements and satellite estimates
of emissions and air pollution are not continuous in time and space, and many important
chemical species or processes are difficult or even impossible to measure.
Furthermore,
measurements cannot inform the assessment of air quality with changing emissions or under
changing meteorological conditions, which are critical to air quality management.
Three-
dimensional air quality models fill these information gaps and are therefore important analysis
tools for studying the link between air quality and its drivers.
In this study, we use an air quality model, in concert with measurements and other
datasets, to assess the response of air quality to changing emissions and changing meteorology in
the United States. The work presented here addresses two specific questions within the larger
field of air quality, energy, and climate research. This work expands the boundaries of this
emerging research field and has important implications for both science and policy. In Chapter
2, we explore the air quality impacts of energy policies aimed at mitigating climate change. An
electricity dispatch model is used to quantify the pollution emission reductions of lower-carbon
electricity generation in Wisconsin. Then, an air quality model is used to evaluate the air quality
impacts of these changes in the Great Lakes region. In Chapter 3, we explore the relationships
between climate and air quality. Ozone observations and meteorological reanalysis are used to
identify the spatial patterns of ozone-meteorology relationships over the continental U.S. and
assess the temporal variability of these patterns. Then, the observed relationships are used to
9 evaluate the ability of air quality models to accurately represent the variable response of ozone to
meteorology. Finally, in Chapter 4, we provide our overall research conclusions and identify
areas for future work.
Figure 1-1 – U.S. Population Affected by Unhealthy Levels of Air Pollution
Number of people (in millions) living in U.S. counties with air quality concentrations above
regulatory standards (NAAQS) in 2008, presented by pollutant. [EPA, 2010a]
10 11 Table 1-1 – National Ambient Air Quality Standards for Ozone and PM2.5
Pollutant
Standard1
Averaging Time
Ozone
85 ppb2
60-70 ppb
35 µg/m3
15.0 µg/m3
8-hour
8-hour
24-hour
Annual average
PM2.5
Date
Implemented
1997
Future
2006
1997
1
In the NAAQS, primary standards are set to protect human health and secondary (less stringent)
standards are set to protect human welfare such as visibility and crops. (These primary and
secondary standards should not to be confused with primary and secondary pollutants.) For
ozone and PM2.5, the secondary standards are equal to the primary standards.
2
A newer ozone standard of 75 ppb was implemented in 2008 but is in the process of revision.
Therefore, the 1997 standard currently remains in effect.
•
•
•
To attain the 8-hour ozone standard, the 3-year average of the fourth-highest daily maximum 8hour average ozone concentrations measured at each monitor within an area over each year must
not exceed 0.08 ppm (which rounds up to 0.085 ppm).
To attain the 24-hour PM2.5 standard, the 3-year average of the 98th percentile of 24-hour
concentrations at each population-oriented monitor within an area must not exceed 35 µg/m3.
To attain the annual PM2.5 standard, the 3-year average of the weighted annual mean PM2.5
concentrations from single or multiple community-oriented monitors must not exceed 15.0
µg/m3.
12 Figure 1-2 – Current Ozone Nonattainment Areas
8-Hour Ozone Nonattainment and
Maintenance Areas (1997 Standard)
When only a portion of a county is shown in color,
it indicates that only that part of the county is within
an area boundary.
9/2010
Nonattainment Areas
Maintenance Areas
The following multi-state nonattainment area, Chicago-Gary-Lake County, IL-IN 8-hr Ozone area, has some
states in the area that have been redesignated, but it is not considered a maintenance area until all states
in the area are redesignated. The counties for this area are displayed as nonattainment areas:
13 Figure 1-3 – Current PM2.5 Nonattainment Areas
6/2010
PM-2.5 Nonattainment Areas (2006 Standard)
Nonattainment areas are indicated by color.
When only a portion of a county is shown in color,
it indicates that only that part of the county is within
a nonattainment area boundary.
14 Figure 1-4 – Potential Impacts of Proposed Ozone Standard Revision on Attainment
-
Dark brown = areas that would violate the 2008 8-hour ozone standard of 75 ppb. (Because
this 2008 standard is currently under revision, the 1997 standard remains in effect.)
Light brown = areas that would violate a 60 ppb (or 0.060 ppm) 8-hour standard.
Light blue = areas that would meet a 60 ppb 8-hour standard.
Obtained from:
http://www.nytimes.com/2010/01/08/science/earth/08smog.html?_r=1&scp=3&sq=ozone+stand
ards&st=nyt
15 References
Bachmann, J. (2007), Will the circle be unbroken: A history of the US national ambient air
quality standards, Journal of the Air & Waste Management Association, 57(6), 652-697.
Bloomer, B. J., J. W. Stehr, C. A. Piety, R. J. Salawitch, and R. R. Dickerson (2009), Observed
relationships of ozone air pollution with temperature and emissions, Geophysical Research
Letters, 36, 5.
Dawson, J. P., P. J. Adams, and S. N. Pandis (2007), Sensitivity of ozone to summertime climate
in the eastern USA: A modeling case study, Atmospheric Environment, 41(7), 1494-1511.
Hogrefe, C., B. Lynn, K. Civerolo, J. Y. Ku, J. Rosenthal, C. Rosenzweig, R. Goldberg, S.
Gaffin, K. Knowlton, and P. L. Kinney (2004), Simulating changes in regional air pollution
over the eastern United States due to changes in global and regional climate and emissions,
Journal of Geophysical Research-Atmospheres, 109(D22).
Holloway, T., S. N. Spak, D. Barker, M. Bretl, C. Moberg, K. Hayhoe, J. Van Dorn, and D.
Wuebbles (2008), Change in ozone air pollution over Chicago associated with global climate
change, Journal of Geophysical Research-Atmospheres, 113(D22), 14.
Intergovernmental Panel on Climate Change (2007), Climate Change 2007 - Synthesis Report,
Geneva, Switzerland. http://www.ipcc.ch/publications_and_data/ar4/syr/en/contents.html
Jacob, D. J. (1999), Introduction to Atmospheric Chemistry, Harvard University Press,
Cambridge, MA.
Jacob, D. J., and D. A. Winner (2009), Effect of climate change on air quality, Atmospheric
Environment, 43(1), 51-63.
Nolte, C. G., A. B. Gilliland, C. Hogrefe, and L. J. Mickley (2008), Linking global to regional
models to assess future climate impacts on surface ozone levels in the United States, Journal
of Geophysical Research-Atmospheres, 113(D14), 14.
Spak, S. N., and T. Holloway (2009), Seasonality of speciated aerosol transport over the Great
Lakes region, Journal of Geophysical Research-Atmospheres, 114, 18.
U.S. Environmental Protection Agency (2004), Air Quality Criteria for Particulate Matter (Final
Report), Washington, DC. EPA/600/P-99/002aF-bF.
U.S. Environmental Protection Agency (2006), Air Quality Criteria for Ozone and Related
Photochemical Oxidants (Final Report), Washington, DC. EPA/600/R-05/004aF-cF.
U.S. Environmental Protection Agency (2010a), Our Nation's Air: Status and Trends Through
2008, Office of Air Quality Planning and Standards, Research Triangle Park, North Carolina.
EPA-454/R-09-002.
U.S. Environmental Protection Agency (2010b), National Ambient Air Quality Standards for
Ozone, Proposed Rule, 75 Federal Register 11 (19 January 2010), pp. 2938-3052.
U.S. Environmental Protection Agency (2010c), PM2.5 Air Quality Data Update: 2007-2009
Design Values, Air Quality Trends and Analysis Group, Research Triangle Park, NC.
http://www.epa.gov/airtrends/values.html
Weaver, C. P., et al. (2009), A PRELIMINARY SYNTHESIS OF MODELED CLIMATE
CHANGE IMPACTS ON US REGIONAL OZONE CONCENTRATIONS, Bulletin of the
American Meteorological Society, 90(12), 1843-1863.
Wu, S. L., L. J. Mickley, D. J. Jacob, D. Rind, and D. G. Streets (2008), Effects of 2000-2050
changes in climate and emissions on global tropospheric ozone and the policy-relevant
background surface ozone in the United States, Journal of Geophysical ResearchAtmospheres, 113(D18), 12.
16 17 Chapter 2 - Ozone and PM2.5 Response to Changing Electricity Emissions
A. Introduction
This chapter examines the air quality impact of one important driver:
changing
electricity emissions. Here we use a regional air quality modeling system to quantify the affects
of changing NOx and SO2 emissions from Wisconsin electricity generating units (EGUs) on O3
and PM2.5 concentrations over the Great Lakes region. This research builds on the work of other
air quality-electricity studies and employs an innovative methodology linking climate policy,
electricity generation, and air quality.
Studies using air quality models to quantify the effect of changing electricity emissions
on local and regional air quality in the United States commonly apply emission growth/reduction
factors to the entire power sector in order to represent changing emissions [Hogrefe et al., 2004;
Steiner et al., 2006; Odman et al., 2007; Tagaris et al., 2007]. For example, Odman et al. [2007]
perform sensitivity simulations to quantify the impacts of emission controls for several
pollutants, including NOx and SO2, on regional particulate concentrations for the year 2018 in the
southeastern U.S., primarily focusing on visibility in national parks and wilderness areas. In this
study, the effects of each emission source category, including EGUs, are evaluated using a
uniform 30% reduction for the entire source category. While this methodology is transparent
and a valuable approximation, it does not account for the spatial variability of emissions changes
in the power sector.
In order to better capture this spatial variability, some air quality modeling studies
examine changes in electricity emissions on a plant-by-plant basis [Abdel-Aziz and Frey, 2004;
Frost et al., 2006; Kim et al., 2006; Cho et al., 2009]. For example, Frost et al. [2006] model the
impacts of changing NOx emissions from power plants on O3 concentrations in the eastern
18 United States. In this proof-of-concept paper, 1999 emissions from 53 high-emitting power
plants are updated to 2003 levels using Continuous Emissions Monitoring System (CEMS)
measurements. The emission changes (mostly reductions) from these updates, processed through
a chemical transport model, cause O3 concentrations to decrease in some regions and increase in
others. Kim et al. [2006] also use this ‘updating’ method, as well as NO2 satellite measurements,
to validate modeled NOx concentrations.
In another study, Abdel-Aziz and Frey [2004] develop a suite of hourly NOx emission
inventories for nine coal-fired power plants in the Charlotte, NC area based on CEMS plant data
and used these inventories to model O3 air quality impacts. This analysis focused on the effects
of air quality model emissions input uncertainty. Plant emissions were modified to represent the
full range of emission uncertainty, and it was found that 1-hour O3 variance between emissions
inventories was as large as 25 parts per billion (ppb). Similarly, Cho et al. [2009] examine the
effects of power plant emissions data accuracy on particulate matter concentrations near
Edmonton, Canada through air quality model sensitivity simulations.
Base NOx and SO2
emissions from Environment Canada for three coal-fired power plants are corrected using CEMS
data, and primary PM2.5 emissions are corrected using at-source measurements.
While examining the changes in electricity emissions on a plant-by-plant basis better
captures the spatial and plant-specific impacts on air quality, these studies require CEMS data or
some other plant-specific emissions measurement data. While useful, these observations-based
methodologies cannot be used to quantify future changes in emissions on a plant-by-plant basis
since they do include information on electricity generation. Estimating future plant-by-plant
emissions requires detailed information such as electricity demand, generation costs, pollution
controls, and new generation sources.
19 Only two studies, by the same lead author, use this detailed power sector information to
estimate future plant-by-plant changes in electricity emissions and the resulting air quality
impacts: Carreras-Sospedra et al. [2008] and [2010]. Carreras-Sospedra et al. [2008] use an air
quality model to evaluate the O3 and PM2.5 impacts of replacing central generation from current
utility-scale EGUs with distributed energy generation (DEG) in the northeastern U.S. The
authors use a detailed methodology for predicting potential penetration of DEG (and thus
replacement of the highest-emitting EGUs) which considers fuel and electricity prices, air
emissions regulations, interconnection and other institutional factors, maximum potential growth,
and technology costs and performance. Then, the resulting emissions of NOx, SO2, and other
criteria pollutants from the new DEG are calculated. For the air quality sensitivity tests, the
emissions of the replaced EGUs are removed and the DEG emissions are added. Simulations are
performed over a 14-day July period for a 2010 baseline case and a 2010 DEG implementation
scenario.
This emissions replacement results in a domain-wide decrease in NOx and SO2
emissions by about 20%, resulting in regional decreases in O3 and PM2.5 concentrations,
especially downwind from the replaced EGUs. Carreras-Sospedra et al. [2010] employ a similar
methodology to compare the air quality impacts of central electricity generation to distributed
generation in Southern California.
However, these methods employed by Carreras-Sospedra et al. [2008, 2010] do not
represent a truly realistic way of quantifying the emissions changes from changes in electricity
generation. In these methods, selection of the EGUs that are replaced by DEG is determined
based only on EGU emission levels, which is only one of many factors (cost, location,
efficiency, etc.) used to determine EGU dispatch and decommissioning. Using an electricity
dispatch model is a more realistic way to quantify emissions changes due to changes in
20 electricity generation. Electricity dispatch models are commonly employed to examine the
economic and environmental impacts of climate policies and increased renewable energy on the
electricity sector [Johnson and Keith, 2004; Musgens, 2006; Goransson and Johnsson, 2009].
However, no published studies have used a statewide electricity dispatch model to calculate
changes of future pollutant emissions on a plant-by-plant basis, and then used a chemical
transport model to evaluate the air quality impacts of these emission changes.
In this study, we quantify the air quality co-benefits of lower-carbon electricity
generation. It is well established that strategies to reduce CO2 emissions from the power sector,
such as increased energy efficiency and more renewable electricity generation, can also reduce
emissions of health-relevant pollution [Nemet et al., 2010]. Here, we employ the MyPower
electricity dispatch model to simulate the complex, interconnected electricity system of
Wisconsin.
We use this electricity model to create detailed, realistic scenarios of future
Wisconsin EGU NOx and SO2 emissions under CO2-reduction policies, and then use a threedimensional chemical transport model to quantify the effect of these emission changes on both
O3 and aerosol species in the Great Lakes region.
B. Data and Methods
To quantify the emissions changes from lower-CO2 electricity generation, we use the
MyPower model to evaluate three Wisconsin EGU emission scenarios developed by Dr. Paul
Meier. First, the model is used to calculate historical EGU emissions for the year 2008 (2008
Reference Case, or 2008 RC). A future business-as-usual scenario is then calculated for the year
2024 absent any climate policies (2024 BAU). Lastly, the model is used to calculate a scenario
for the year 2024 with two greenhouse gas emission reduction policies (2024 RPS): a Wisconsin
21 renewable electricity portfolio standard (RPS) of 24% renewable electricity by 2024, as well as
energy efficiency programs to reduce electricity demand. These climate policies (24% RPS and
energy efficiency programs) were chosen for this study following the recommendations of the
Governor’s Task Force on Global Warming [WGTFGW, 2008].
Then, we employ the Community Multiscale Air Quality (CMAQ) model to quantify the
air quality impacts of these emissions reductions. This air quality model calculates spatially and
temporally varying ambient air pollution concentrations needed for air quality assessment based
on the relationships between pollutant emissions, meteorology, and atmospheric chemistry. The
Wisconsin EGU emissions from each MyPower scenario, along with meteorology and non-EGU
emissions, are used to drive air quality sensitivity simulations for the month of July 2003 over
the Great Lakes region.
B.i. MyPower Electricity Model
This study utilizes the MyPower electricity dispatch model developed by Dr. Paul Meier,
director of the UW Energy Institute, to calculate Wisconsin electricity emissions. MyPower
produces a bottom-up pollutant emissions inventory and represents an innovative emissions data
source unique to this study. The MyPower model includes all emitting and non-emitting EGUs
in Wisconsin: emitting units include both large plants (coal, natural gas, and biomass power
plants) and smaller or distributed units (diesel generators, manure digesters, and waste energy
recovery units), while non-emitting units include nuclear and hydroelectric power plants as well
as wind turbines. The model simulates individual power plant operation using a dispatch routine
to satisfy electricity demand with the least-cost combination of power plants as determined by
each plant’s thermal efficiency, fuel price, variable operation and maintenance cost, production
22 credits, and operational constraints. Plant-specific emissions from emitting EGUs are then
estimated as a function of electricity generated, heat rate (fuel required per unit electricity
generated), emission rate (emission mass per unit fuel input), and air pollution control efficiency
[Meier et. al., 2005].
In this way, the MyPower model is able to calculate electricity output and pollutant
emissions for an entire sector or region on a plant-by-plant basis. In this study, MyPower is used
to calculate total annual nitrogen oxide (NOx) and sulfur dioxide (SO2) emissions from all
emitting EGUs in Wisconsin. For EGUs, SO2 is emitted when trace amounts of sulfur are
present in the fuel source, whereas NOx is formed both from diatomic oxygen and nitrogen (O2
and N2) in air under the heat of combustion and from nitrogen in the fuel. Therefore, SO2
emissions are dependent on fuel type whereas NOx emissions are more dependent on the
specifics of the fuel combustion process. These two pollutants are regulated as criteria pollutants
under the Clean Air Act and lead to the formation of other criteria pollutants, namely
tropospheric O3 and PM2.5. A significant portion of U.S. NOx and SO2 emissions originate from
the power sector, making the power sector a major contributor to O3 and PM2.5 in the United
States [Frost et al., 2006; Kim et al., 2006; Cho et al., 2009; Vijayaraghavan et al., 2010].
The power sector is also an important source of primary PM2.5 emissions. Primary PM2.5
emissions are comprised of many different chemical species, and their emission from the
combustion process is based on a variety of factors including fuel type, combustion
characteristics, and pollution controls. While EGU PM2.5 emissions would provide an additional
layer of analysis in our study, power-plant specific emissions rates for PM2.5 are not readily
available or easily estimated. EPA PM emissions data are largely based on estimates that
employ emission factors generated from empirical and engineering studies, rather than on actual
23 measurements of PM emissions [EPA, 2010a]. Estimating emissions on a plant-by-plant basis
would be highly uncertain, and therefore we did not include EGU PM2.5 emissions in our
analysis.
One important benefit of MyPower modeling is the ability to estimate EGU emissions
under future economic, policy, and technological changes.
emissions requires three general components:
Modeling future power sector
data describing the electric power sector
infrastructure and fuels, a model to simulate the operation and emissions of individual power
plants, and characterization of future scenarios which represent anticipated or possible changes
to the electric sector over time. To simulate future years, seasonal load duration curves (LDCs)
are scaled to match future demand projections accounting for growth in energy consumption and,
specifically for this study, increases in energy efficiency and renewable electricity generation.
These future scenarios consider fuel price changes, planned additions of new power plants,
retirements of existing power plants, and mandated additions of air pollution control equipment.
For our study, only EGUs in the state of Wisconsin were simulated. A quantitative
description of the three Wisconsin EGU scenarios generated by MyPower (2008 RC, 2024 BAU,
and 2024 RPS) can be found in Table 2-1.1 For these future MyPower scenarios, it is assumed
that electricity demand increases from 2008 to 2024. Thus, electricity generation increases by
about 35% from the 2008 RC (77 TWh) to the 2024 BAU (105 TWh). However, improved
energy efficiency limits the electricity generation increase in the 2024 RPS to 12% (86 TWh in
2024 RPS). Renewable generation increases from 3% in 2008 to 12% in the 2024 BAU without
any policy, and CO2 emissions increase by about 19% from 2008 (59 million tons) to the 2024
BAU (71 million tons), largely due to the increase in electricity generation. For the 2024 RPS,
1
In addition to the three MyPower scenarios, this table also includes the 2002 BC case that will be
presented in the next section. 24 renewable generation increases to 24% to meet the RPS requirement, and CO2 emissions (54
million tons) actually decrease by more than 9% compared to 2008 levels because of both
increases in energy efficiency and renewable generation.
EGU emissions of NOx and SO2 decrease substantially in both the 2024 BAU and 2024
RPS compared to the 2008 RC. The two main factors driving these emissions decreases are
changes in electricity generation/dispatch (i.e. more electricity being generated from loweremitting EGUs and less from higher-emitting EGUs) and the installation of emissions control
technologies (pollution controls such as wet or dry scrubbing for SO2 and Selective Catalytic
Reduction for NOx) on a plant-by-plant basis. Emissions of NOx in the 2024 BAU (34,279 tons)
decrease 33% compared to 2008 RC levels, while emissions of SO2 in the 2024 BAU (85,683
tons) decrease 38% compared to 2008 RC levels. Emission reductions in this case are primarily
due to pollution controls. Each EGU is divided into units, and pollution controls are applied to
individual EGU units. In MyPower calculations, NOx controls ranged from 47% to 90% for each
plant unit, while SO2 controls ranged from 81% to 92%.2 In the 2024 BAU, NOx controls are
added to 8 of the highest-emitting plants (17 total plant units), and new SO2 controls are added to
4 plants (6 total units). These new pollution controls are added in order to achieve statewide
emission reduction targets set by CAIR.3 Selection of the specific plants and plant units to
receive controls was based on personal consultation (by Dr. Paul Meier) with the Lake Michigan
Air Directors Consortium (LADCO) and the Wisconsin Department of Natural Resources
(WDNR). While the installation of pollution controls at a power plant decreases plant efficiency
and increases the cost of generating electricity (thus potentially changing dispatch order), these
2
For example, a 90% control reduces a unit’s emissions by 90%. 3
The Clean Air Interstate Rule (CAIR), issued by the EPA in 2005, permanently capped power plant NOx
and SO2 emissions in 28 eastern states (including Wisconsin) and mandated state-specific emissions
reductions for these pollutants. CAIR has since been vacated by a legal ruling, and a new rule to replace
CAIR is being developed by the EPA. More information can be found at http://www.epa.gov/cair/.
25 effects are not included in MyPower calculations. These estimates of future pollution controls
used here are a major assumption in this study.
In the 2024 RPS, NOx emissions (23,372 tons) and SO2 emissions (57,527 tons) decrease
55% and 59% compared to 2008 RC levels. Emission reductions in the 2024 RPS are driven
both by pollution controls, assumed identical to the 2024 BAU, and changes in electricity
generation and dispatch from increased renewable generation and decreased total electricity
demand. Therefore, the difference in emissions from the 2024 BAU to the 2024 RPS (32% and
33% for NOx and SO2) are attributable to increased energy efficiency and renewable generation.
Thus, assessment of the two cases for the year 2024 shows that both pollution controls and
changes in generation/dispatch due to energy and climate policy can significantly reduce NOx
and SO2 emissions.
B.ii. The 2002 National Emissions Inventory
The three MyPower electricity scenarios include emissions for all Wisconsin EGUs.
However, EGUs represent only one of many sources of pollutant emissions. For realistic air
quality simulations, the pollutant emissions from all emission sources need to be included.
Furthermore, emissions from EGUs outside of Wisconsin are not included in MyPower
scenarios. Therefore, an additional source of emissions data is needed to supplement MyPower
emissions for our air quality sensitivity simulations.
In this study, the source of non-EGU emissions and emissions of EGUs outside
Wisconsin is the U.S. EPA National Emissions Inventory (NEI) for the year 2002.4 The NEI
represents emissions data from the entire continental U.S. (as well as Canadian and Mexican
4
More information on the 2002 NEI and how it is created can be found at
http://www.epa.gov/ttn/chief/net/2002inventory.html. 26 regions bordering the U.S.) from all relevant emission sectors. The NEI is one of the most
commonly employed emissions data sources for air quality modeling studies over the United
States [Frost et al., 2006; Sarwar et al., 2008; Spak and Holloway, 2009; Yu et al., 2010]. The
2002 version of the NEI is used to drive CMAQ simulations for July 2003. Although some
changes in emissions are expected from 2002 to 2003, the 2002 NEI is the available version
closest to 2003 and is appropriate for the emission sensitivity analysis performed in this study.
The NEI combines a variety of emissions databases to produce a comprehensive
inventory and is organized by pollutant type and emission sector. First, emissions are divided
into two pollutant groups: criteria air pollutants (CAPs) and hazardous air pollutants (HAPs).
The CAP dataset includes the EPA-defined criteria pollutants and their chemical precursors5,
while the HAP dataset includes a variety of air toxics, volatile organic compounds, and heavy
metals [EPA, 2008]. Only the CAPs emissions dataset is used in this study. Second, emissions
are further organized by source categories. The NEI includes all available point, area, mobile,
and biogenic source emissions. This allows for the assessment of all emission sources and how
each source contributes to air pollution. All emissions source categories and their descriptions
are presented in Table 2-2. For each emission source, the NEI includes: the spatial location
(exact latitude and longitude for point sources, county and state for area sources, etc.), the total
annual emissions for each pollutant, chemical speciation information for classes of pollutants6,
and a temporal profile used for temporal allocation of the annual emissions. This information is
5
This includes NO, NO2, SO2, CO, various categories of PM, and a number of hydrocarbons (or VOCs).
The precursors of ozone, a criteria pollutant that is not directly emitted, are included here. While not a
criteria pollutant, NH3 from agriculture is included. Lead, while a criteria pollutant, is also considered a
HAP and is included in the NEI’s HAP group. 6
For many sources, total annual emissions are specified from a pollutant class instead of a single
pollutant. For example, EGU emissions of NO and NO2 in the NEI are listed together as the NOx
pollutant class. Thus, the chemical speciation information provided by the NEI is used to allocate some
NOx emissions as NO and the rest as NO2, specifically 90% NO and 10% NO2. 27 used to process emissions for use in CMAQ.
In addition to the three MyPower electricity scenarios presented above, we consider an
additional ‘baseline’ emissions scenario (shown in Table 2-1) that employs Wisconsin EGU
emissions from the 2002 NEI instead of MyPower. This scenario will be referred to as the 2002
BC (BaseCase) and is compared to the 2008 RC for quality control purposes in Appendix 2.A.
In addition, results from the air quality simulation driven by the 2002 BC are used to evaluate
CMAQ performance.
B.iii. Air Quality Modeling
The chemical transport model utilized in this study is the Community Multiscale Air
Quality (CMAQ) model, version 4.6. This model requires two data inputs: meteorology and
emissions. Meteorological measurement data are not continuous in time and space, and many
important parameters such as boundary layer height cannot be easily measured. Likewise, the
emissions of pollutants from a multitude of non-EGU sources cannot be continuously measured
and are calculated using typical activity data and emission factors. Therefore, the spatially and
temporally continuous meteorology and emissions data required by the air quality model are also
computed using sophisticated computational models. CMAQ, together with the meteorological
and emissions models, allows for a thorough analysis of the interactions between air quality and
its drivers.7
CMAQ is an open-source air quality model developed by the U.S. EPA for use in air
quality research over in the United States. CMAQ is an Eulerian model of the atmosphere that
7
This section presents the CMAQ modeling process specific to our assessment of air quality under
changing electricity emissions in the Great Lakes region. While the ozone-climate analysis presented in
Chapter 3 utilizes the same CMAQ modeling structure presented here in Chapter 2, there are some
differences in inputs and methods. The data and methods specific to the ozone-climate analysis are
presented at the beginning of Chapter 3. 28 calculates time-varying ambient concentrations and deposition rates for a variety of healthrelevant air pollutants. An Eulerian model is a three-dimensional model where the atmosphere is
divided into three-dimensional grids or boxes of uniform size. In this type of model, mass is
tracked as it is transported over time from grid to grid. CMAQ was designed as a “state-of-thescience” tool for a variety of user groups and is employed by many researchers in the
atmospheric chemistry and air quality policy communities [Byun and Schere, 2006]. CMAQ
was chosen for this study because of its ability to capture transport and chemical processes at
local and regional scales and because of its wide use in the peer-reviewed literature assessing
ground-level ozone and particulate matter over the continental United States [Hogrefe et al.,
2004; Steiner et al., 2006; Marmur et al., 2006; Odman et al., 2007; Tagaris et al., 2007; Spak
and Holloway, 2009].
In this study, simulations for two model domains were performed: a domain covering the
entire continental United States (CONUS) with a 36 km x 36 km horizontal resolution, and a
smaller domain covering the Great Lakes region (GL) with a 12 km x 12 km resolution. These
two geographic domains are shown in Figure 2-1. In contrast to the horizontal dimensions, the
grid or layer size in the vertical dimension changes with space and time. CMAQ uses a sigma
vertical coordinate system that defines dynamic layers based on the air pressure at a level
normalized by the surface air pressure [Skamarock et al., 2005; Eckermann, 2008]. Thus, this
coordinate system (see Figure 2-2) follows surface terrain: an air parcel at sea level and an air
parcel at a mountain peak are in the same vertical layer because both parcels are at the Earth’s
surface. Both domains in this study have a vertical resolution of 15 layers, with shallower
(higher resolution) layers near the surface (see Table 2-3).
29 For this study, CMAQ was run over both the CONUS and GL domains for the month of
July 2003.
These domains and this time period were chosen in order to utilize available
meteorology modeling simulations by Caitlin Littlefield as part of her work on atmospheric
mercury in the Great Lakes region [Littlefield, 2009]. These meteorology simulations were run
over the CONUS and GL domains for the entire calendar year of 2003. The month of July in
2003 was chosen for this study because it represents a period where high temperatures and
stagnant air masses lead to high levels of both O3 and PM2.5, the pollutants of primary concern in
this study. In addition, a 12 km x 12 km resolution for the GL domain is able to capture local
and regional scale processes as well as the behavior of large point source emission plumes
[Gilliani and Pleim, 1995].
Certain modeling techniques are used to account for (or manage) the fact that the CMAQ
simulations only cover a small region of the globe for a limited time period.
First, the
simulations are initialized using the CMAQ default pollutant concentration profiles for both the
CONUS and GL domain runs. To eliminate the influence of these three-dimensional initial
concentrations on model results, a 10-day spin-up period is applied for both runs. Thus, CMAQ
was run from June 21 to July 31 with the June 21-30 period not used for analysis. Second,
because air parcels are transported in and out of the defined domain at the boundaries, boundary
conditions must also be applied. The CMAQ default boundary concentration profiles were used
for the CONUS run. However, since the GL domain lies fully within (or is nested in) the
CONUS domain, the GL run uses the CONUS output pollutant concentrations at the GL domain
boundary as boundary conditions in a technique called ‘nesting.’ Finally, to account for solar
radiation entering the top of the atmosphere and its effect on atmospheric chemistry, standard
photolysis rates for each domain are calculated using incoming radiation levels and the chemical
30 properties of each molecular species. These rates are interpolated for each grid cell and are
attenuated when clouds are present.
In these CMAQ simulations, model chemistry and transport is calculated to produce
hourly output files of concentrations for all modeled chemical species. CMAQ utilizes a variety
of chemical and transport mechanisms in order to manage two competing concerns: an accurate
simulation of complex atmospheric processes, and the limits of computational resources. The
CMAQ chemical transport solver has a number of different mechanisms, each employing a
different parameterization of atmospheric and chemical processes.
This study utilizes the
Carbon Bond Five (CB05) gas phase chemistry mechanism, the AERO4 aerosol mechanism, the
Yamartino advection scheme, vertical diffusion, eddy diffusion, and the Asymmetrical
Convective Model (ACM2) for cloud formation. This configuration of CMAQ utilizes the
newest set of mechanisms, and these mechanisms have been extensively evaluated for O3 and
PM modeling with CMAQ [Luecken et al., 2008; Sarwar et al., 2008; Yu et al., 2010].
Like all chemical transport models, CMAQ requires emissions and meteorology data as
input. The geographical and temporal characteristics of these input data must match CMAQ
resolution. Therefore, two supporting models are utilized to produce hourly inputs for July 2003
over the CONUS and GL domains. The WRF model is used to generate the needed meteorology
data, while the SMOKE model is used to generate the needed emissions data. These two models
and their inputs are described below.
B.iv. Meteorology Modeling
The meteorology for this study was generated from the Advanced Research Weather
Forecasting Model (ARW-WRF, or WRF), version 3.0. The WRF simulations used in this study
31 were produced by Caitlin Littlefield as part of her work on atmospheric mercury. A detailed
description of WRF meteorology modeling and inputs can be found in her M.S. thesis
[Littlefield, 2009]. Like CMAQ, WRF is a three-dimensional Eulerian model designed for
regional and continental scales and also uses the sigma vertical coordinate system. WRF is a
nonhydrostatic model: geophysical fluid dynamics are calculated without assuming that the
pressure gradient force is equal and opposite to gravity (hydrostatic equilibrium). This allows
the model to solve for small-scale vertical motions that are important in local and regional
atmospheric processes. In addition, this meteorology model has a variety of user-defined physics
options, based on the scale and focus of model simulations, for the land surface, the planetary
boundary layer, atmospheric and surface radiation, microphysics, and cloud formation. WRF
was designed for a broad range of meteorological applications and is also employed in regional
air quality modeling studies [Appel et al., 2009; Matsui et al., 2009; Im et al., 2010; Lin et al.,
2010].
This meteorology model incorporates processed measurement data through a process
called four-dimensional data assimilation (FDDA), or ‘nudging’. Nudging compares the modelcalculated values for a meteorological variable with the measured value for every grid cell and
adjusts the modeled value towards the measured value by a predefined coefficient, thus ensuring
a general agreement between the model and measurements on large-scale weather patterns.
Using the North American Regional Reanalysis (NARR) meteorological dataset, nudging was
performed every three hours for winds, temperature, and moisture throughout the entire WRF
simulation. The NARR dataset is meteorological reanalysis, the assimilation of meteorological
observations from a variety of sources into one comprehensive dataset, and is described in
greater detail in Chapter 3.
32 WRF was run with 30 sigma vertical model layers, a higher vertical resolution than
CMAQ, to capture smaller-scale vertical motions. The WRF meteorology outputs are processed
through the CMAQ Meteorology-Chemistry Interface Processor (MCIP), version 3.4. MCIP
translates WRF output into CMAQ-ready form: it interpolates the WRF vertical model layers to
the required CMAQ layers, trims the horizontal domain if necessary, and calculates a number of
new variables needed for chemical processing.
The 2003 July WRF meteorology simulations used in this study are thoroughly evaluated
in Caitlin Littlefield’s M.S. thesis [Littlefield, 2009].
Evaluation focuses on temperature,
humidity, and precipitation as these variables are important in accurately calculating many
chemical processes in the atmosphere. WRF was compared with site observation data from the
National Climatic Data Center (NCDC) and with the NARR meteorological dataset. Modeled
July temperatures for the GL domain were consistently within two degrees Celsius of NCDC
measurements and compare well with NARR temperatures. WRF generally captures monthly
total precipitation at NCDC sites, with varied performance from site to site, and moderately
under-predicts precipitation compared to NARR. Furthermore, WRF slightly under-predicts
water vapor mixing ratios (used as a proxy for precipitation) compared to NARR values. In
addition, WRF was found to consistently under-predict air temperature and water vapor mixing
ratio over the region’s water bodies. These under-predictions are likely linked in that colder
temperatures would cause more water to condense out of the atmosphere. This finding is
important when considering the effect of lake meteorology on O3, nitrate, and sulfate chemistry
as pollutant formation and deposition will be affected by inaccuracy in temperature and
precipitation.
Overall, the WRF model captures temperatures well and under-predicts
precipitation over the GL domain for July 2003.
33 B.v. SMOKE Emissions Processing
The emissions for input into CMAQ are generated by the Spatial Matrix Operator Kernel
Emissions (SMOKE) Model, version 2.4. SMOKE, designed by the U.S. EPA for use with
CMAQ, creates gridded CMAQ-ready emissions from calculated or measurement emissions
data. The purpose of any emissions processor is to convert the resolution of emissions source
data to the resolution required by the air quality model. SMOKE processes the information
contained in the emissions data, allocating source emissions in time and geographical space. The
two emissions data sources used here, the National Emissions Inventory (NEI) and the MyPower
electricity model (discussed above), are comprised of different sectors and scales but are used in
conjunction for this study.
Emissions from the NEI and the MyPower model are merged and processed through
SMOKE. SMOKE processes each source category separately and then merges the emissions
from each source together, producing gridded, fully speciated, hourly emissions from all
emission sources. Because point and biogenic source emissions are dependent on meteorology,
SMOKE uses MCIP meteorology output as an input to calculate these emissions. Emissions
processing through SMOKE for the 2002 BC emissions used the standard process as only NEI
emissions were used.
However, the remaining three scenarios required special processing
because MyPower emissions are not in SMOKE-ready format. For these scenarios, Wisconsin
EGU NOx and SO2 emission values in the NEI EGU annual emissions file were replaced with
MyPower NOx and SO2 emission values. Emissions of all other chemical species for Wisconsin
EGUs were left unchanged. A detailed description of the process of incorporating the MyPower
emissions into the NEI EGU file is presented below.
34 For each MyPower emissions scenario, MyPower emission values replaced NEI values in
the NEI EGU file for all Wisconsin power plants. Then, the NEI EGU file with the MyPower
data for Wisconsin EGUs was processed normally through SMOKE. However, not all of the
power plants included in the MyPower model of Wisconsin are included in the 2002 NEI: some
plants in the MyPower model were commissioned after 2002 and are thus not included in the
2002 version of the NEI, whereas other MyPower plants were omitted from the NEI because of
their small size. Therefore, a system of matching was employed in connecting the MyPower
emissions to the NEI.
Of the 66 pollutant-emitting power plants in the MyPower model, 50 were found in the
NEI (seen in Figure 2-3). The emissions of the 16 plants missing from the NEI were each
matched to the nearest (geographically) NEI plant based available information: the missing
plants with a known location (13) were matched to the closest NEI plant based on latitude and
longitude.
Plants planned for future construction (3), thus having limited geographic
information, were matched to NEI plants in the same or closest county.
Furthermore, a small number of hypothetical plants and distributed generation units (such
as manure digesters and waste energy recovery units) are included in the MyPower model in
addition to the 66 plants discussed above. Because these units have no geographic information,
they could not be matched to power plants in the NEI. Therefore, the emissions from these
hypothetical units were totaled and distributed evenly among the 10 largest power plants in the
MyPower model based on electricity generation (MWh) in the 2008 RC. Thus, 10% of the total
hypothetical emissions were matched to each of the 10 highest generating plants.
The
geographic bias with the hypothetical matching is minimal because the 10 largest plants are
geographically distributed across southern and central Wisconsin.
35 Our overall emissions processing results from SMOKE are not affected by the fact that
certain MyPower EGUs are not in the 2002 NEI (see Table 2-4). Over 94% of NOx emissions
and over 99% of SO2 emissions in each of the MyPower scenarios are produced by EGUs that
are included in the NEI – only a very small portion of the total NOx and SO2 emissions are
produced by missing units.
As noted, this study utilizes annual emission values for all EGUs. While EGU emissions
with a higher time resolution are available (such as the CEMS data), we use annual emissions for
two main reasons. First, the MyPower dispatch model simulates electricity generation and
emissions on an annual basis, so utilizing MyPower requires the use of annual emissions for
SMOKE processing.
In addition, the emissions sensitivity analysis aspect of this work
necessitates a relatively simple and reproducible emissions methodology. SMOKE allows for
EGU processing with daily or hourly inputs, but altering emissions on these timescales on a
plant-by-plant basis for several scenarios would not be feasible for a regional study.
As with any modeling methodology, there are limitations to the use of annual EGU
emissions. SMOKE allocates annual emissions by hour using a typical daily profile. Figure 2-4
shows the NOx and SO2 EGU emissions in three Wisconsin grid cells. EGU emissions in each
grid cell follow the same daily profile: low emissions overnight, a peak in emissions during the
morning hours, a slight midday decrease, and the highest peak in emissions during the
afternoon/evening. The profile amplitude for each EGU varies with the magnitude of emissions
(larger magnitude, larger amplitude), but the profile shape is identical. This same profile shape
is used for both pollutants for all EGUs (for both NEI and MyPower data sources) and does not
vary by day or by month. In reality, EGU emissions vary from day to day and month to month
based on a number of factors, and these variations can have significant impacts on air quality that
36 are not captured by our modeling methodology.
Thus, the need for sensitivity analysis
methodologies employing higher time resolution EGU emissions is evident.
C. Model Evaluation
To evaluate the air quality modeling system described above, in-situ ambient air pollution
observations are also used in our analysis. These observations, obtained from the U.S. EPA Air
Quality System (AQS) database [EPA, 2010b], are widely used for model evaluation and quality
control purposes.
Hourly observations of NO2, NOx, and O3 are compared with hourly
concentrations of these pollutants from the 2002 BC 12km-GL simulation.8 Only observation
data for these pollutants at monitoring sites within a domain covering the entire state of
Wisconsin and portions of the surrounding states (from 41°N to 48°N latitude and 82°W to
94°W longitude) were utilized. This observational domain was chosen because it captures our
primary area of focus within the GL domain for this study and includes both rural and urban
sites. In addition, analytical and computational costs make using all hourly observations from a
larger domain impractical.
For comparison with the model, these observations were gridded on the GL horizontal
grid. Each observation at each time step was binned into the appropriate grid cell based on the
latitude and longitude of its corresponding monitoring site. If more than one observation was
binned into a grid cell at any time step, then the grid value is simply the mean of the multiple
observations. As these AQS observations are spatially discrete, many grid cells have no value.
These gridded AQS observations are then compared with gridded model concentrations to ensure
proper model functioning and prevent the propagation of errors. The evaluation also compares
AQS observations of NOx are the sum of NO and NO2 measurements. Both NOx and NO2 observations
8
are utilized to assess model performance.
37 modeled values to measurements in order to assess modeling system performance.
After
assuring proper functioning and assessing performance, sensitivity test simulations can be
performed.
Hourly modeled concentrations of NO2, NOx, and O3 are evaluated against gridded
hourly AQS observation (Figure 2-5). Scatter plots for NOx and NO2 exhibit significant scatter,
and NO2 is slightly over predicted by the model. The model’s moderate ability to capture hourly
observations of NOx and NO2 are not surprising given the localized nature of these primary
pollutants.
Furthermore, many AQS monitoring sites for these pollutants are located near
emission sources and can experience significant variability in concentrations based on local
meteorological conditions. The model may not be able to resolve these local processes. Further
evaluation of CMAQ’s ability to capture the trends of these pollutants using daily observation
values is needed. On the other hand, the O3 scatter plot shows that the model captures hourly
observed O3 well but consistently over-predicts concentrations.
Other studies evaluating
CMAQ with the same chemical mechanism used here also find that CMAQ generally overpredicts summertime O3 concentrations [Sarwar et al., 2008; Yu et al., 2010].
D. Results – 2002 BC Scenario
Emissions of NOx and SO2 from the 2002 BC are carefully evaluated since these two
pollutants are the focus of our sensitivity analysis. Because this analysis focuses on Wisconsin
and the Great Lakes region, we present results for the GL model domain. The spatial distribution
of NOx emissions from all sources (presented in Figure 2-6) is as expected: high levels of
emissions are observed in urban areas (e.g. Chicago and Minneapolis-St. Paul) with high
emissions in individual grid cells scattered along the Ohio River and in other more remote areas
38 throughout the region.
In Wisconsin, NOx emissions are highest over the urban areas of
Milwaukee, Green Bay, and Madison and at several locations away from these cities. For both
Wisconsin and the Great Lakes region in general, lower emission levels (green colors in Figure
2-6) are present throughout large portions of the rural areas and along major roadways (e.g. I-94
in western Wisconsin). These spatial patterns are better understood by examining how NOx
emissions are broken down by source category (Figure 2-7 and Table 2-5). For Wisconsin, onroad vehicles are the largest source of NOx emissions followed by EGUs and non-road vehicles,
respectively. Thus, one would expect to see high NOx emission levels in urban areas with many
on-road and non-road vehicles (automobiles, buses, trucks, construction vehicles) and at the
locations of large EGUs, both within and away from urban areas. The observed patterns of
remote area emissions likely represent large contributions from on-road (vehicles on major and
local roadways) and non-road (farm or construction vehicles) sources.
The spatial distribution of SO2 emissions from all sources (presented in Figure 2-8) is
similar to the distribution of NOx: high levels of emissions are observed in urban areas and in
most of the same remote grid cells as with NOx. However, in contrast to the NOx spatial
distribution, urban areas are much less defined by SO2, and most rural areas are devoid of any
SO2 emissions (blue color in Figure 2-8). This distinction is evident in Wisconsin as urban areas
are less distinct. Examining Wisconsin SO2 emissions separated by source category (Figure 2-9
and Table 2-6) shows that EGUs are by far the largest source of SO2 with non-EGU point
sources as second largest. Thus, one would expect to see high SO2 emission levels at EGU
locations (same as with NOx) and in urban areas with industrial facilities, while we would expect
very small emissions in areas without power plants or heavy industry. The low levels of SO2
emissions that are present in certain rural areas not near an EGU are likely from on-road (diesel
39 vehicles on major highways), non-road (farm or construction vehicles), and other mobile
sources.
The spatial distribution of EGU NOx and SO2 emissions is shown in Figure 2-10. For
Wisconsin, both figures show a number of EGUs along Lake Michigan in the east, and number
along the Mississippi River in the west, and a few units in the central part of the state. Some
EGU grid cells with low levels of NOx emissions do not have any SO2 emissions. This is
expected since large coal-fired power plants emit relatively large amounts of NOx and SO2,
whereas smaller natural gas power plants typically emit only NOx.
As Wisconsin and the Great Lakes region are the focus of this study, CMAQ evaluation
will focus on the GL domain.
All modeled pollutant concentrations in this study, unless
otherwise noted, are ground-level (the lowest model layer). Monthly mean concentrations for
the 2002 BC are shown in Figure 2-11. Spatial distributions of NOx and SO2 concentrations
follow closely the spatial distributions of emissions shown earlier as these primary pollutants
have relatively short lifetimes and are chemically transformed soon after their emission.
On the other hand, O3, nitrate, and sulfate are secondary pollutants and exhibit spatial
patterns less connected to emission sources.9 Concentrations of O3 are much more uniform
across the domain with the highest concentrations in the eastern Ohio River region and over
Lake Erie and Lake Michigan. Concentrations of nitrate aerosol are highest in Indiana and Ohio,
while other areas such as northern Wisconsin and West Virginia have very low concentrations.
In contrast, sulfate concentrations are highest along the Ohio River and are lowest in the
northwestern part of the domain. In the eastern United States, nitrate and sulfate make up
significant portions of total PM2.5 concentrations (Figure 2-12). Total PM2.5 concentrations in
Nitrate and sulfate are two common PM2.5 species. 9
40 CMAQ were calculated using the method presented in the EPA’s model evaluation
documentation [EPA, 2005]. In most areas in Wisconsin and the surrounding states, sulfate
makes up a larger portion of PM2.5 than does nitrate. This pattern is expected since, due to
aerosol thermodynamics, nitrate formation is lowest during the summer months whereas sulfate
formation is greatest in the summer months [Spak and Holloway, 2009; EPA, 2010c].
E. Results – Sensitivity Simulations with MyPower Scenarios
To validate the 2008 RC emissions generated by MyPower, these emissions are
compared with measured emissions from the U.S. EPA Clean Air Markets database (CAMD) on
a plant-by-plant and sector-total basis [EPA, 2010d]. The CAMD contains annual electricity and
emissions data for most large power plants in the U.S., and CAMD data is available for 30 EGUs
in the MyPower model of Wisconsin. Here, we compare MyPower NOx and SO2 emission
values for these 30 EGUs to their corresponding CAMD values. Overall, MyPower emissions
compare well with CAMD values. The total NOx and SO2 emissions from these 30 plants in the
MyPower simulations are very close to CAMD values, only 1.68% and 1.67% higher than
CAMD emissions, respectively. For individual EGU comparison, 14 of 30 MyPower plants are
within +/- 30% of CAMD values for both NOx and SO2, with the largest differences at the
smaller plants. For most plants, discrepancies between MyPower and CAMD emission values
are primarily due to differences in electricity generation. In addition, the percentage difference
in NOx and SO2 emissions scale together for these plants, implying that most EGU emission
factors used in MyPower emissions calculations are relatively accurate.
Finally, we use the 2008 RC to assess the emissions reductions and resulting air quality
impacts of the 2024 BAU and the 2024 RPS. Results are presented by pollutant.
41 E.i. NOx and SO2 Emissions
Figure 2-13 shows the emissions changes from the 2008 RC to the 2024 BAU.
Emissions of NOx and SO2 decrease at several locations in southern and central Wisconsin.
Certain locations show emission reductions in one pollutant but not the other, such as the grid
cell containing the Columbia power plant in south-central Wisconsin (significant SO2 reductions
and no change in NOx) and the grid cell containing the Weston plant in north-central Wisconsin
(significant NOx reductions and no change in SO2). This is due to pollution controls being added
for one pollutant but not the other. The emissions changes from the 2008 RC to the 2024 RPS
are shown in Figure 2-14. Emissions of NOx and SO2 decrease at a number of additional
locations compared to the 2024 BAU due to decreases in electricity generation for these EGUs.
E.ii. Ozone
Figure 2-15 shows the changes in ozone concentrations from the 2008 RC to the 2024
BAU. Impacts of these emission changes on O3 concentrations are minimal, with small increases
at the locations with the highest NOx emissions reductions.
Though counterintuitive, it is
expected to see increases in monthly mean O3 concentrations resulting from lower NOx
concentrations in a polluted area such as the Weston plant in north-central Wisconsin. While
daytime maximum O3 does decrease at the Weston plant location with lower NOx concentrations,
nighttime O3 actually increases with lower NOx due to less NOx titration. Thus, these nighttime
O3 increases are larger than the daytime decreases, resulting in an increase in monthly mean
concentration. Ozone impacts of the 2024 RPS (Figure 2-16) exhibit similar patterns, though
42 with an additional small regional decrease over northern Lake Michigan. For these two cases, O3
concentrations are only slightly affected by the changes in NOx emissions.
E.iii. Nitrate
Figure 2-17 shows the changes in nitrate concentrations from the 2008 RC to the 2024
BAU. As with ozone, decreases in nitrate are also minimal, with the largest decreases on the
Door Peninsula. Nitrate impacts from the 2008 RC to the 2024 RPS (Figure 2-18) show similar
patterns, with the largest decreases in northeastern Wisconsin. While the overall impacts of
emission reductions on nitrate are minimal, two intriguing patterns are observed. First, we see
that nitrate decreases are lowest over Lake Michigan, suggesting that lake processes are
suppressing the impacts of emissions reductions.
Second, we see that while the absolute
decreases in nitrate over northern Wisconsin are small, the percentage decreases are relatively
large. This suggests that the impacts of emission reductions on nitrate during periods with higher
nitrate concentrations, such as the winter months, could be significant.
E.iv. Sulfate
Sulfate exhibits the largest response to emission reductions of the three pollutants
examined here. From the 2008 RC to the 2024 BAU (Figure 2-19), sulfate concentrations
exhibit moderate decreases in southeastern and south-central Wisconsin with the largest
decreases (+12%) near the Columbia power plant in south-central Wisconsin. Sulfate decreases
are even more pronounced in the 2008 RC – 2024 RPS comparison (Figure 2-20): moderate
sulfate decreases extend over Lake Michigan into western Michigan, again with the largest
decreases near the Columbia power plant in Wisconsin. Because these emissions reductions
produce a significant decrease in sulfate concentrations, we compare sulfate in the two 2024
43 cases. Figures 2-21 and 2-22 show the difference in SO2 emissions and sulfate concentrations
between the 2024 BAU and the 2024 RPS. The differences in emissions between the two cases
occur at locations throughout central and southern Wisconsin, while the difference in sulfate
concentrations is centered over northeastern Wisconsin and northern Lake Michigan.
A
comparison of sulfate concentrations in the three MyPower scenarios over the Milwaukee grid
cell is shown in Figure 2-23. This time series clearly shows that the differences in sulfate
concentrations between the three scenarios are not uniform throughout the simulation period but
occur only during certain episodes, such as the July 5-7 period. Furthermore, the decreases in
sulfate are often during the middle or medium peaks in sulfate concentrations, highlighting the
significance of these emission reductions on high sulfate episodes and potential NAAQS
exceedences for PM2.5.
The Effects of Lake Michigan on Sulfate
Here, we explore the impact of lake processes on sulfate concentrations. While Lake
Michigan has been shown to enhance ozone concentrations, no studies have shown that sulfate
aerosol concentrations are affected by lake processes. Because no lake effects have been linked
to sulfate enhancement, we were surprised to see the spatial patterns of sulfate exhibited in these
scenarios: sulfate concentrations are elevated over the lake compared to the adjacent land, and
the largest decreases in sulfate concentrations from the 2002 BC to the 2008 RC are centered
over the lake. (While the above discussion has focused on comparing the MyPower scenarios,
the 2002 BC – 2008 RC comparison directly informs our analysis here because the lake
influence on sulfate seen in this comparison is more pronounced than in any other comparison.
See Figure 2A-2.)
44 To better understand sulfate over Lake Michigan and identify the lake processes
responsible for these spatial distributions, we examine the three potential mechanisms by which
Lake Michigan may enhance or suppress sulfate concentrations: chemical formation, deposition,
and transport. In addition, we analyze SO2 and sulfate concentrations at two lake locations: a
southern lake site (42.70°N, 86.97°W – location of a NOAA weather buoy) and a northern lake
site (44.06°N, 87.04°W). These sites are located within the spatial pattern of reduced sulfate
observed in Figure 2A-2 and provide a representation of both southern and northern lake
processes.
Chemical Formation:
Sulfate aerosols are secondary pollutants formed from the
atmospheric oxidation of SO2. There are two main pathways for SO2 oxidation: gas phase and
aqueous phase [Unger et al., 2006]. In the gas phase, SO2 is oxidized by the hydroxyl radical
(OH). In the aqueous phase, where chemistry takes place with water (such as in clouds), SO2
oxidation is dominated by hydrogen peroxide (H2O2) with some oxidation by O3. Generally,
more sulfate is formed by the aqueous-phase pathway than the slower gas-phase oxidation
[Unger et al., 2006]. A different chemical environment from lake to land could cause differences
in sulfate formation. Thus, greater concentrations of these oxidants would suggest greater SO2
oxidation and sulfate formation, thus increasing sulfate concentrations. In model simulations for
this study, concentrations of OH over the lake are slightly higher than concentrations over the
adjacent land (seen in Figure 2-24), especially in the northern part of the lake, and concentrations
of O3 are significantly higher over the lake (seen in Figure 2-11). Conversely, modeled H2O2
concentrations are significantly lower over the lake than over the land, as seen in Figure 2-25.
Here, we see competing factors as concentrations of H2O2, the most important oxidant identified
by Unger et al. [2006], decrease whereas the concentrations of the other two oxidants increase.
45 Deposition:
Sulfate aerosols are prone to wet deposition in clouds (scavenging by
precipitation), and wet deposition is the major sink of sulfate [Koch et al., 2003].
Thus,
differences in precipitation from the land to the lake could cause differences in sulfate removal.
In model simulations, sulfate wet deposition over Lake Michigan is lower than over the adjacent
land (Figure 2-26). This is primarily due to lower amounts of precipitation over the lake
compared to the land during the July 2003 simulation period. Because less sulfate is being wet
deposited, more remains in the atmosphere, enhancing sulfate concentrations over the lake. Dry
deposition of sulfate is negligible over both the land and the lake. Thus, we see that lower wet
deposition over the lake enhances sulfate concentrations.
Transport: Wind patterns and mixing over the lake can affect the transport of sulfate and
its precursors.
In the model, high concentrations of SO2 from the high-emitting areas of
Chicago, Illinois and Gary, Indiana at the southern tip of Lake Michigan are transported out over
the lake: Figure 2-27 shows that SO2 concentrations are highest over the lake during periods
with a southerly wind. Sulfate concentrations exhibit this same pattern with wind direction,
showing that SO2 is converted to sulfate during transport. Therefore, transport of SO2 from onland sources is enhancing lake sulfate concentrations. Furthermore, Figure 2-28 shows that the
planetary boundary layer (PBL) height (or mixing height) over Lake Michigan is a fraction of the
PBL height over the adjacent land. This suggests that the shallow above-lake boundary layer
inhibits mixing and traps SO2 and its oxidants close to the lake surface, enhancing chemical
reactions and sulfate formation. Spak and Holloway [2009] have explored the importance of
above-lake PBL height on pollutant concentrations; however, the suggestion that sulfate
formation is enhanced by a lower PBL has not previously been examined. Thus, we see that lake
46 transport is enhancing sulfate concentrations by transporting SO2 emissions over the lake and by
inhibiting above-lake mixing.
By evaluating each of these mechanisms, we see that most lake factors favor sulfate
enhancement. The effects of each mechanism of sulfate concentrations are shown in Table 2-7.
We see that deposition and transport processes favor sulfate enhancement, whereas the abovelake concentrations of SO2 oxidants have a mixed effect on sulfate. While each of these factors
plays a role in the higher levels of sulfate over Lake Michigan, the relative importance of these
factors is unknown and should be examined in future work.
How do these mechanisms affect the response of sulfate over Lake Michigan to
emissions perturbations, in particular the SO2 reductions from the 2002 BC to the 2008 RC
(quantified in Table 2-1 and shown in Figure 2A-2)? We focus on the 2002 BC - 2008 RC
emissions reductions since this comparison exhibits the largest decreases in sulfate
concentrations of the scenarios evaluated above. Figure 2-29 shows that differences in sulfate
concentrations at both Lake Michigan sites are not uniform throughout the simulation period but
occur largely during certain episodes (e.g. July 7, July 16-17, July 24-25, July 29-30 for southern
lake site). These differences in sulfate correspond with differences in SO2: sulfate differences
are concurrent or slightly following differences in SO2 (also shown in Figure 2-29). This pattern
suggests that episodes of lower sulfate concentrations are due to lower sulfate formation because
less SO2 is available for oxidation during these episodes. This suggests that a significant portion
of the SO2 during these episodes originate from Wisconsin EGUs where emissions are reduced,
whereas very little SO2 during other periods originate from these EGUs. Thus, the emissions
reductions strongly affect above-lake SO2 concentrations during certain periods but not others.
47 F. Discussion
In this chapter, we assess the air quality impacts of lower-carbon electricity generation in
Wisconsin.
We use the CMAQ regional air quality modeling system and the MyPower
electricity dispatch model to quantify the effects of changing electricity NOx and SO2 emissions
in Wisconsin on concentrations of O3 and two PM2.5 species in the Great Lakes region. We find
that significant emissions reductions in the Wisconsin power sector have a moderate impact on
air quality in Wisconsin and the surrounding states. Sulfate aerosol concentrations exhibit the
largest decreases from EGU emission reductions of the pollutants examined, with sulfate
decreases of 6-12% over many areas of south-central Wisconsin from the 2008 RC to the 2024
RPS. We also see sulfate decreases are centered over Lake Michigan for the 2002 BC – 2008
RC comparison, prompting an exploration of lake processes on sulfate. As highlighted earlier,
this analysis focuses on one summer month, and it is expected that spatial and temporal trends in
air quality impacts would differ during different seasons.
In addition, this study illuminates that the spatial distribution of emissions changes has a
significant impact on the magnitude and spatial distribution of air quality impacts.
The
significance of emissions distribution is illustrated by the relationship between SO2 emissions
and sulfate concentrations. From the 2002 BC to the 2008 RC, SO2 emissions from Wisconsin
EGUs decrease by 53,720 tons, resulting in sulfate concentration decreases mainly over Lake
Michigan and in western Michigan. In this case, a majority of the emissions reductions occur
from EGUs in the eastern part of the state along Lake Michigan. From the 2008 RC to the 2024
BAU, total SO2 emissions decrease by 53,540 tons, nearly identical to the emission reductions
from the 2002 BC to the 2008 RC. However, the resulting sulfate decreases from the 2008 RC to
the 2024 BAU are much lower than in the previous case. This is primarily because the majority
48 of the emissions reductions occur in central and western Wisconsin, further away from the
amplifying effects of Lake Michigan.
Furthermore, total SO2 emissions reductions from the 2008 RC to the 2024 RPS total
81,696 tons, over 50% higher than in the 2002 BC – 2008 RC comparison. The resulting sulfate
decreases have a larger distribution than in the 2002 BC – 2008 RC comparison, covering eastern
Wisconsin as well as Lake Michigan and western Michigan, but a lower magnitude in most
areas, especially over the lake. Once again, the majority of the emissions reductions occur in the
central and western parts of Wisconsin. Therefore, the spatial distribution of emission reductions
can be as important as the magnitude of emission reductions when seeking air quality
improvements.
This finding is also a testament to the complexity and nonlinearity of
atmospheric chemistry and how lakes affect this chemistry. Further research is needed to
examine how changes in air quality are dependent on the plant-specific distribution of emission
reductions.
In this study, we also find that power sector policies aimed at reducing CO2 emissions
also significantly reduce emissions of NOx and SO2 and improve air quality.
Emission
reductions of 32% for NOx and 33% for SO2 (from the 2024 BAU to the 2024 RPS) are
attributable to increased renewable generation and energy efficiency. The air quality impacts of
these emission reductions, as for other scenario comparisons, are most pronounced for sulfate
with decreases of 3-6% in areas near the largest emissions decreases. However, the assumptions
regarding the addition of pollution controls at many of the highest-emitting EGUs in the 2024
BAU diminish the impacts of the two climate policies explored here. Larger emission reductions
and air quality improvements are seen from the 2008 RC to the 2024 BAU due to these pollution
controls than from the 2024 BAU to the 2024 RPS. This approach to quantifying the air quality
49 impact of climate policies is unique in that it uses a future BAU case of emissions and air quality
to compare with a future policy case, rather than simply evaluating the future policy case against
current emissions and air quality.
Finally, these findings highlight the importance of using electricity models to calculate
the emission changes of energy and climate policies. In this study linking climate, energy, and
air quality, we utilize an advanced methodology to quantify changes in electricity emissions from
a multi-pollutant perspective and assess the air quality impact of these emissions changes. While
our work uses Wisconsin as a case study and evaluates the air quality impacts of future climate
policies, this methodology used here can be applied to any state or region and can be used to
evaluate the multi-pollutant impacts of a variety of power sector policies or market shifts.
Further research is being conducted at UW-Madison using this methodology to assess the air
quality of changes in electricity emissions on a regional and national scale.
50 Appendix 2.A – Comparison of 2008 RC and 2002 BC
The 2008 RC is our sensitivity analysis baseline, and we use the 2002 BC to compare
with the 2008 RC for model evaluation purposes. Figure 2A-1 shows the spatial differences in
emissions between the two cases, and Figure 2A-2 shows the differences in ambient
concentrations. Overall, emissions of NOx decrease from the 2002 BC (91,087 tons) to the 2008
RC (51,483 tons) by 43%, while emissions of SO2 decrease from the 2002 BC (192,943 tons) to
the 2008 RC (139,223 tons) by 28%. The specific causes of this decrease in emissions are
largely unknown because of limited information on electricity generation and pollution controls
in the NEI. Compared to the 2002 BC, NOx emissions for the 2008 RC are significantly lower at
several locations in southern and central Wisconsin with small increases for only a few locations.
For SO2, 2008 RC emissions at many of the same locations are significantly lower and but are
significantly higher at three locations. These differences in emissions result in few significant
differences in NOx and SO2 ambient concentrations except at the locations with the largest
emissions changes. For example, notable NOx and SO2 concentration decreases are observed in
the southeastern Wisconsin grid cell of the Pleasant Prairie power plant, which correspond to the
plant’s large emissions decreases.
While the overall difference in emissions between the 2008 RC and the 2002 BC is large,
it is expected that emissions at most EGUs would be lower in 2008 compared to 2002. On the
whole, emissions from the power sector have shown a downward trend in the past decades [Frost
et al., 2006; Bloomer et al., 2010]. Thus, the locations that see significant increases in emissions
and thus increased concentrations from the 2002 BC to the 2008 RC are unexpected. One
example is the Menasha plant power plant north of Lake Winnebago. Annual SO2 emissions
from the Menasha plant increase from 40 tons/yr in the 2002 BC to 2,462 tons/yr in the 2008 RC,
51 resulting in a 10-40% increase in SO2 concentrations at and downwind of the plant shown in
Figure 2A-2. A closer examination of this plant in the MyPower model shows that the WDNR’s
maximum permitted SO2 emission rate10 for this plant was used to calculate emissions because
no data on the actual emission rate was available. It is likely that this actual emission rate is
lower than the permitted rate and that the SO2 emissions from this plant are artificially high. As
a result of the artificially high or lower emissions of plants like Menasha in the 2008 RC, some
of the emissions changes and the resulting air quality impacts seen in the 2024 cases could be
over-estimated.
Ambient concentrations of O3, nitrate aerosol, and sulfate aerosol for the 2008 RC are
also compared with 2002 BC levels. Overall, O3 concentrations are only slightly affected by the
changes in NOx emissions: local increases at the locations of the largest NOx emissions changes
with a small regional decrease over northern Lake Michigan. Though counterintuitive, it is
expected to see increases in monthly mean O3 concentrations resulting from lower NOx
concentrations in a polluted area like the Pleasant Prairie location (chemistry discussed in main
text). Decreases in nitrate concentrations from the 2002 BC to the 2008 RC are greatest in
northeastern Wisconsin along Lake Michigan, though overall changes in nitrate concentrations
are minor. Of the three secondary pollutants discussed here, sulfate exhibits the most significant
concentration decreases from the 2002 BC to the 2008 RC. Sulfate decreases of 6-12% are seen
and are geographically centered over Lake Michigan.
10
As part of Wisconsin’s State Implementation Plan (SIP) to comply with the Clean Air Act, the DNR
issues air permits to each plant. The permitted emission rate for the Menasha plan is reported in the
National Electric Energy Data System (NEEDS) database.
52 Table 2-1 – Wisconsin Emissions Scenarios
2002 BC
2008 RC
2024 BAU
2024 RPS
Generation (GWh)
-
77,037
104,780
86,193
Renewable (%)
-
3%
12%
24%
CO2 Emissions
-
59,107,000
70,545,000
53,556,000
NOx Emissions
91,087
51,483
34,279
23,372
SO2 Emissions
192,943
139,223
85,683
57,527
Shown are the annual scenario values from the Wisconsin electricity sector for electricity
generation, percent of electricity generated from renewable sources, and emissions. Emissions
are given in tons/year. Because the 2002 BC uses NEI emissions for Wisconsin EGUs, there is
no information on electricity generation and CO2 emissions for this scenario.
53 Table 2-2 – NEI Emissions Source Categories
1
Source Category
Type
Description
Agriculture
Area
Livestock and fertilizer ammonia.
Average Fire
Area
Fugitive Dust
Area
Nonpoint
Area
Other Area1
Area
Biogenic2
Biogenic
Air, Locomotive, Marine
Mobile
Non-road
Mobile
On-road
Mobile
Other On-road1
Mobile
Point IPM
Point
Point Non-IPM
Point
Average-year wildfires and prescribed burning.
Fugitive dust sources such as building and road
construction, unpaved roads, agricultural dust,
and mining dust.
All area sources not otherwise included in other
categories. Includes agricultural burning and
open burning.
Canadian and Mexican area sources.
Natural emissions from vegetation. Created by
SMOKE using the BEIS program.
Aircraft, locomotives, and commercial marine
vessels.
All non-road vehicles such as agricultural,
construction, and recreational vehicles.
All on-road vehicles such as cars, light trucks,
heavy trucks, and buses.
Canadian and Mexican on-road mobile sources.
All EGUs in the EPA's Integrated Planning
Model (IPM) of the electric power sector
All point sources not in the IPM such as
chemical plants, refineries, and other industrial
facilities.
Canadian and Mexican point sources were not available during the time of this study due to
proprietary reasons.
2
Biogenic emissions used in this study were produced by Caitlin Littlefield as part of her work
on atmospheric mercury.
Figure 2-1 – Model Domains and Resolution
54 55 Figure 2-2 – Physical Representation of Sigma Coordinate Layers
Lines of constant pressure (hPa) are shown on left, while the sigma layers (σ) are in red on right.
Sigma layers follow terrain very closely at surface and follow pressure levels near the top of the
modeled atmosphere. Figure obtained from
http://atmo.tamu.edu/class/metr452/models/2001/vertres.html
56 Table 2-3 – CMAQ Vertical Resolution
Layer
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
Surface
Sigma Value
0.000
0.019
0.200
0.400
0.550
0.650
0.740
0.800
0.860
0.910
0.940
0.960
0.980
0.990
0.993
1.000
Layer Top (m)
16311
15368
9845
6309
4331
3196
2279
1708
1169
736
486
321
159
79
55
0
Layer Depth (m)
943
5523
3536
1978
1134
918
571
539
432
251
164
162
80
24
55
-
Pressure (hPa)
108
196
363
517
627
711
777
830
879
914
936
954
967
973
978
-
Sigma values are predefined for each simulation. Layer and pressure values are July 2003
averages for the grid cell containing Madison, WI in the GL simulation.
57 Figure 2-3 – MyPower Wisconsin EGUs in the NEI
-
The ten largest power plants are indicated by the * symbol.
The + symbol indicates a special case: one of the ten largest plants (Riverside Energy
Center in Rock County) was recently constructed and is not included in the 2002 NEI.
Thus, this plant was matched to the Rock River plant.
Alma and Madgett are two separate but co-located plants.
58 Table 2-4 – Breakdown of MyPower Emissions
MyPower EGUs in NEI
MyPower EGUs not in NEI
- Hypothetical / Distributed
- Matched by Location
2008 RC
NOx
SO2
98.52% 99.97%
1.48%
0.03%
0.18%
0.00%
1.30%
0.03%
2024 BAU
NOx
SO2
94.79% 99.83%
5.21%
0.17%
3.35%
0.11%
1.86%
0.05%
2024 RPS
NOx
SO2
96.81% 99.98%
3.19%
0.02%
2.45%
0.00%
0.75%
0.02%
Each table value shows the percentage of total emissions. For example, 0.18% of NOx emissions
in the 2008 RC originate from hypothetical/distributed units.
Figure 2-4 – Time series of 2002 BC EGU Emissions
Hourly emission rates (moles/s) of NOx and SO2 from EGUs in the Milwaukee, Madison, and
Green Bay grid cells. 59 Figure 2-5 – 2002 BC Concentrations Comparison with AQS Observations for July 2003 a) NOx
60 b) NO2
61 c) Ozone
62 Figure 2-6 – 2002 BC Emission Rates for NOx
63 64 Figure 2-7 & Table 2-5 – 2002 BC Wisconsin NOx Emissions by Source Category
Source Category
Air, Locomotive, Marine
Nonpoint
Non-road
On-road
Point Non-IPM
Point IPM (EGUs)
Biogenic
Total
Emissions
(tons/day)
83.1
29.2
166.7
452.2
104.0
249.7
75.0
1160.0
Percent
of Total
7.16%
2.52%
14.37%
38.99%
8.97%
21.52%
6.47%
100.00%
*These emissions were taken from SMOKE for an average weekday in July 2003. While there
are small fluctuations in emissions from day to day for each sector, the overall contribution
percentages for each sector are robust.
Figure 2-8 – 2002 BC Emission Rates for SO2 (GL domain and WI region subset)
65 66 Figure 2-9 & Table 2-6 – 2002 BC Wisconsin SO2 Emissions by Source Category
Source Category
Air, Locomotive, Marine
Nonpoint
Non-road
On-road
Point Non-IPM
Point IPM (EGUs)
Total
Emissions
(tons/day)
13.1
7.5
15.1
20.4
173.1
528.6
757.8
Percent
of Total
1.73%
0.98%
1.99%
2.69%
22.85%
69.76%
100.00%
*These emissions were taken from SMOKE for an average weekday in July 2003. While there
are small fluctuations in emissions from day to day for each sector, the overall contribution
percentages for each sector are robust.
Figure 2-10 – 2002 BC EGU Emission Rates for NOx and SO2
NOx
SO2
67 Figure 2-11 – 2002 BC Ambient Concentrations of a) NOx, b) SO2, c) O3, d) Nitrate, and e)
Sulfate
a) NOx (ppb)
b) SO2 (ppb)
68 c) Ozone (ppb)
d) Nitrate (µg/m3)
69 e) Sulfate (µg/m3)
70 Figure 2-12 – Contributions of Nitrate and Sulfate to Total PM2.5 Concentrations (2002 BC
monthly mean concentrations)
Nitrate
Sulfate
71 Figure 2-13 – Emissions Difference between 2024 BAU and 2008 RC
NOx
SO2
72 Figure 2-14 – Emissions Difference between 2024 RPS and 2008 RC
NOx
SO2
73 Figure 2-15 – Ozone Concentration Difference between 2024 BAU and 2008 RC
Absolute Difference
Percent Difference
74 Figure 2-16 – Ozone Concentration Difference between 2024 RPS and 2008 RC
Absolute Difference
Percent Difference
75 Figure 2-17 – Nitrate Concentration Difference between 2024 BAU and 2008 RC
Absolute Difference
Percent Difference
76 Figure 2-18 – Nitrate Concentration Difference between 2024 RPS and 2008 RC
Absolute Difference
Percent Difference
77 Figure 2-19 – Sulfate Concentration Difference between 2024 BAU and 2008 RC
Absolute Difference
Percent Difference
78 Figure 2-20 – Sulfate Concentration Difference between 2024 RPS and 2008 RC
Absolute Difference
Percent Difference
79 Figure 2-21 – SO2 Emissions Difference between 2024 RPS and 2024 BAU
80 Figure 2-22 – Sulfate Concentration Difference between 2024 RPS and 2024 BAU
Absolute difference
Percent difference
81 Figure 2-23 – Time series of Sulfate Concentrations for the three MyPower Scenarios over the
Milwaukee grid cell
82 Figure 2-24 – Monthly Mean OH Concentrations
83 Figure 2-25 – Monthly Mean H2O2 Concentrations
84 Figure 2-26 – Monthly Total Sulfate Wet Deposition
85 Figure 2-27 – Scatter Plot of Hourly SO2 Concentrations and Wind Direction
Wind directions: from the east = 90°; from the south = 180°; from the west = 270°; from the
north = 0°/360°.
86 Figure 2-28 – Monthly Mean Planetary Boundary Layer Heights
87 88 Table 2-7 – Effects of Lake Michigan on Sulfate
Mechanism
Factor
Chemical formation OH concentrations
H2O2 concentrations
O3 concentrations
Deposition
Wet Deposition
Transport
Transport from high emission
area
PBL height
Compared to
Land
+/=
+
n/a
Effect on
Sulfate
+/=
+
+
+
-
+
Table shows how each factor differs over the lake compared to the land and the resulting effects
on sulfate. For example, H2O2 concentrations are lower over the lake compared to the adjacent
land, which would likely decrease sulfate formation and thus lower sulfate concentrations.
Figure 2-29 – 2002 BC to 2008 RC Comparison of SO2 and Sulfate at both Lake Sites
Southern Lake Site
Northern Lake Site
89 Figure 2A-1 – Emissions Difference between 2008 RC and 2002 BC
NOx
SO2
90 Figure 2A-2 – Concentration Difference between 2008 RC and 2002 BC
a) NOx – Absolute difference
b) NOx – Percent difference
91 c) SO2 – Absolute difference
d) SO2 – Percent difference
92 e) O3 – Absolute difference
f) O3 – Percent difference
93 g) Nitrate – Absolute difference
h) Nitrate – Percent difference
94 i) Sulfate – Absolute difference
j) Sulfate – Percent difference
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100 Chapter 3 – Ozone Response to Meteorology and Climate
A. Introduction
In order to develop effective strategies for air quality improvement, a detailed
understanding of how meteorology and climate affect air pollution is also needed. Many studies
have examined the effects of climate on ozone pollution over the continental United States, a
number of which are highlighted in a recent review by Jacob and Winner [2009]. The two most
commonly employed methods for assessing the relationships between ozone and meteorology
are observed correlations with meteorological variables [Jacob et al., 1993; Sillman and Samson,
1995; Camalier et al., 2007; Holloway et al., 2008; Bloomer et al., 2009; Chan, 2009] and
sensitivity simulations using chemical transport models [Aw and Kleeman, 2003; Hogrefe et al.,
2004; Murazaki and Hess, 2006; Steiner et al., 2006; Dawson et al., 2007].
Much of the focus in these studies has been on temperature. Observational studies
consistently show that temperature is one of the most important meteorological factors
controlling ozone concentrations. Jacob et al. [1993] and Sillman and Samson [1995] were some
of the first to explore the relationship between ozone photochemistry and temperature. Jacob et
al. [1993] compare ozone and temperature observations at nine sites in the eastern U.S. and find
that ozone concentrations exhibit a significant positive dependence on temperature. Model
simulations are then used to show that most of this temperature dependence is due to the
sensitivity of ozone chemistry to temperature. Sillman and Samson [1995] calculate ozonetemperature sensitivities for 12 sites, both urban and rural, throughout the U.S. using a simple
model and observations for the summer of 1988, finding a strong positive relationship between
temperature and ozone also due to the temperature-dependent photochemistry of ozone
precursors.
The authors also determine that the slope of ozone-temperature relationships
101 increase at higher temperatures. This change in slope is also found by Holloway et al. [2008]
using observation data for Chicago and by Chan [2009] using rural observations in the U.S. and
Canada. More recent studies find similar ozone-temperature relationships. Bloomer et al. [2009]
analyze summer ozone and meteorological observations from rural sites in four U.S. regions,
showing that higher temperatures lead to higher ozone in all regions. Camalier et al. [2007]
examine ozone trends at urban sites in the eastern U.S. and develop a statistical methodology to
characterize the relationships between ozone and individual meteorological parameters. Using
observations from May to September during the 1997-2005 period, increasing temperature is
found to increase ozone across the study area.
Modeling sensitivity studies also show the primacy of this positive ozone-temperature
relationship. Aw and Kleeman [2003] examine the impacts of temperature variability on ozone
in the polluted urban regions of southern California using a high-resolution model, finding that
the increase in ozone concentrations from higher temperatures is due to increased gas-phase
reaction rates and is greatest downwind of the area of highest NOx emissions. Steiner et al.
[2006] examine the impacts of climate change on ozone in central California for a five-day
summer 2000 period. Temperature increases, isolated from all other factors, cause increases in
ozone concentrations throughout the model domain. Dawson et al. [2007] use a regional air
quality model to investigate the sensitivities of ozone to a number of meteorological variables
(on an individual basis) in the eastern U.S., showing that temperature has the largest impact on
ozone of any variable with a positive ozone-temperature relationship across the model domain.
These observational and modeling studies also identify meteorological parameters and
processes in addition to temperature that have an effect on ozone, though these factors do not
exhibit as clear a relationship with ozone. One of these is atmospheric water vapor. Generally,
102 water vapor promotes ozone loss by interrupting the photolysis-driven chemical cycling of ozone
[Jacob, 1999]. However, atmospheric water vapor is also important for the formation of the
hydroxyl radical (OH). Depending on chemical conditions in the atmosphere, OH can react with
hydrocarbons to increase ozone concentrations, can convert NOx to other species to decrease
ozone concentrations, or can react directly with ozone to decrease ozone concentrations [Jacob,
1999]. As a result of these competing chemical processes involving water vapor, increasing
humidity generally decreases ozone in rural areas but has a mixed effect on ozone in polluted
areas [Jacob and Winner, 2009].
When determining the impacts of humidity on ozone, a distinction must be made between
specific and relative humidity: specific humidity1 represents the total amount of water vapor in a
parcel of air (expressed as a ratio of water vapor mass to the total mass of the air parcel), whereas
relative humidity represents the amount of water vapor in a parcel compared to the maximum
possible amount in the parcel at a given temperature and pressure (expressed as a percentage).
Because warmer air can hold more water vapor, atmospheric humidity is partially dependent on
air temperature.
Specific humidity is a measure of humidity independent of temperature,
whereas relative humidity takes this temperature dependence into account. Therefore, if both
temperature and atmospheric humidity increase in an air parcel, then specific humidity will
increase while relatively humidity will stay nearly constant.
Studies generally show that increases in relative humidity decrease ozone. Camalier et
al. [2007] find that increasing relative humidity decreases ozone in almost all locations, while
sensitivity simulations by Aw and Kleeman et al. [2003] show that increasing relative humidity
has a small negative effect on ozone. However, studies show that specific humidity has a
Specific humidity, which measures the mass of water vapor, is similar to absolute humidity, which
measures the volume of water vapor. 1
103 variable impact on ozone based on an area’s atmospheric chemical environment. Both Murazaki
and Hess [2006] and Dawson et al. [2007] find that increasing specific humidity generally
decreases ozone but leads to ozone increases in some polluted areas. Steiner et al. [2006] also
show that higher specific humidity leads to increased ozone near regions of high pollutant
emissions.
Winds are another factor affecting ozone. Low wind speeds associated with large-scale
stagnation events generally cause increases in ozone due to less mixing and longer times for
chemical reactions [Jacob and Winner, 2009]. Dawson et al. [2007] find that these effects are
greatest in polluted areas. Camalier et al. [2007] and Holloway et al. [2008] also highlight wind
direction and transport distance as important factors for certain locations. Similar to the wind
effects, the effects of mixing depth on ozone are also found to be locally dependent with no
generalizable trend [Dawson et al., 2007; Jacob and Winner, 2009].
As is expected given ozone chemistry, increased solar radiation has a positive effect on
ozone, but this effect is found by Sillman and Samson [1995] and Dawson et al. [2007] to be
much smaller compared to the factors discussed above and could reflect the association between
clear skies and high temperatures during the summer months [Jacob and Winner, 2009].
Likewise, the relationship between ozone and precipitation is weak: ozone and its precursors
have low solubility in water and are not greatly affected by precipitation [Dawson et al., 2007;
Jacob and Winner, 2009].
These relationships between ozone and meteorology are often used to remove the effects
of meteorological variability when assessing long-term trends in observed ozone concentrations
[Jacob and Winner, 2009]. Because high ozone events are typically associated with hot, stagnant
conditions (and vice versa), meteorological data can be used to smooth out some of the
104 variability in observed ozone values, allowing for a better understanding of long-term ozone
trends and the impacts of emission changes in polluted areas [Camalier et al., 2007; EPA, 2010].
Many scientific studies and regulatory reports employ meteorological adjustment when
examining ozone trends and use a variety of statistical and regression techniques [Fiore et al.,
1998; Gego et al., 2007; Zheng et al., 2007; Camalier et al., 2007; Chan, 2009; Bloomer et al.,
2009; EPA, 2010]. Fiore et al. [1998] diagnose U.S. ozone trends by sorting daily ozone
concentrations using only their corresponding daily temperature values, whereas both Chan
[2009] and Camalier et al. [2007] use a generalized linear model based on several meteorological
parameters including temperature, relative humidity, and wind speed to adjust ozone values.
Zheng et al. [2007] show that different methods for meteorological adjustment can result in
significantly different ozone trends for some sites.
Temporal Variability of Ozone-Meteorology Relationships
In this study, we examine the response of ozone to meteorology and climate over the
continental United States. Ozone observations and meteorological reanalysis during an 11-year
period are used to identify the spatial patterns of ozone-meteorology relationships and assess the
temporal variability of these patterns. Although some of the studies presented above discuss the
spatial variability of ozone-meteorology relationships, none perform an in-depth examination of
the temporal variability of these relationships.
Steiner et al. [2006] highlight the spatial
variability of changes in ozone in central California due to changes in meteorology, showing that
the positive effects of temperature and specific humidity on ozone are highest in urban areas.
However, model simulations for this study cover only a five-day period during the summer of
2000 for a small region, precluding an assessment of ozone-meteorology relationships for a
105 longer time period or larger region. Dawson et al. [2007] evaluate the spatial variability of
ozone-meteorology relationships over the entire eastern U.S., finding that temperature and
specific humidity are the two most important meteorological variables controlling ozone
concentrations. Ozone-temperature sensitivities are positive throughout the domain but are
strongest over major urban areas and are weakest in rural areas. Conversely, ozone-humidity
sensitivities are highly variable over the domain with negative sensitivities in most areas and
positive sensitivities in a few locations south of the Ohio River, in the Northeast, and over
Chicago, with no clear pattern between urban and rural locations. However, like Steiner et al.,
the generally applicability of these results are limited since the length of the model simulation is
very short (nine days during July 2001).
In these modeling studies, any effort to assess
variability in ozone-meteorology relationships is hampered by short simulations over limited
geographical domains.
Camalier et al. [2007] use summertime observations in the eastern U.S. over a nine-year
period to determine the spatial gradients of ozone-meteorology sensitivities. Based on the spatial
patterns of these sensitivities, the authors define six geographic zones of ozone-meteorology
relationships, from north to south, based on each zone’s most important meteorological factors.
These spatial patterns are shown in Figure 3-1. Patterns show that the positive sensitivity of
ozone to temperature is lowest along the Gulf Coast and highest in the North, whereas the
negative sensitivity of ozone to relative humidity is lowest in the Northeast and Great Lakes
regions and highest along the Gulf Coast. Transport is also shown to be an important factor.
However, despite the detailed assessment of these spatial patterns, no consideration is given to
the monthly and interannual variability of these patterns. The statistical analysis performed for
106 each observation site covers the entire nine-year period and therefore does not capture the
variability of ozone-meteorology sensitivities within the period.
Similarly, Bloomer et al. [2009], while showing the spatial variability of ozonetemperature sensitivities by U.S. regions, do not explore monthly or interannual variability.
Ozone-temperature sensitivities from 1987-2002 are compared with sensitivities from 2003-2007
to assess long-term trends, showing that increases in ozone per degree Celsius decrease from the
first period to the second in three of four regions examined. However, the statistics performed
for each of these periods do not capture the variability within each period. Thus, the temporal
variability of ozone-meteorology relationships, and the meteorological processes responsible for
this variability, has yet to be fully examined.
The potential of temporal variability in observed ozone-meteorology relationships has
significant implications for meteorologically-adjusted ozone trends.
Ozone-meteorology
relationships in the many methodologies used for meteorological adjustment are considered
constant throughout a multi-year period.
Therefore, meteorology-adjusted ozone without
consideration of the variability of these relationships from month to month or year to year could
result in an inaccurate adjustment. For example, if ozone values during a summer are lowered
because of above-average temperatures, but the ozone-temperature relationships during this
summer are weaker or of opposite sign compared to aggregate relationships, then a lowering of
ozone values for this summer would be an erroneous adjustment. Thus, a better understanding of
the temporal variability in ozone-meteorology relationships could improve meteorological
adjustments of ozone trends and provide air quality managers with better information.
107 Ozone-Meteorology Relationships in Regional Models
In addition to examining the spatial and temporal variability of historical ozonemeteorology relationships, we evaluate the ability of air quality models to accurately capture the
patterns and variability of observed ozone-meteorology relationships over the continental U.S.
While the response of ozone to meteorology strongly affects the ability of air quality models to
capture the variability of ozone concentrations, modeled ozone-temperature relationships have
not been fully evaluated. Air quality models are often used to estimate the impacts of future
climatic changes on ozone and other pollutants, but different models are producing divergent
projections. Jacob and Winner [2009] and Weaver et al. [2009] highlight the lack of agreement
between modeling studies as to the sign and magnitude of ozone changes due under future
climate change.
By better understanding how models represent current ozone-meteorology relationships,
the ability of these models to predict the impacts of future climate change on ozone can be better
assessed.
Observed ozone and meteorology data and the resulting ozone-meteorology
relationships are valuable tools for evaluating the ability of air quality models to capture these
relationships in the past and present. As discussed by Jacob and Winner [2009], observed daily
correlations between ozone and meteorological variables is an underused but recommended way
to evaluate model skill. In practice, model evaluations compare absolute values of ozone and
temperature with observations but do not evaluate model ability to capture the relationships
between the two [Hogrefe et al., 2004; Murazaki and Hess, 2006; Bell et al., 2007; Tagaris et al.,
2007; Weaver et al., 2009]. In the many studies using models to assess the impact of climate on
ozone, only two studies [Jacob et al., 1993 and Steiner et al., 2006] use observed ozone-
108 meteorology relationships to evaluate model performance, and these two studies are limited by
the number of observations sites (less than ten for both) used for model comparison.
B. Data and Methods
In this study, we combine several observational and modeling tools to assess the response
of ozone to meteorology over the entire continental United States. Ambient ozone observations
and meteorological reanalysis data are used to assess observed ozone-meteorology relationships,
and the Community Multiscale Air Quality (CMAQ) modeling system is employed to allow for
assessment of modeled relationships. We focus our analysis of ozone-meteorology relationships
on the summer months of June, July, and August because these months constitute the ‘summer
ozone season’ where ozone concentrations are highest during the year [Holloway et al., 2008;
Jacob and Winner, 2009; Weaver et al., 2009].
B.i. Ozone Observations and Meteorological Reanalysis
The ambient ozone observations utilized in this study were obtained from the U.S. EPA
Air Quality System (AQS) database [EPA, 2009]. Daily maximum 8-hour average (MDA8)
ozone observations from all measurement sites in the continental U.S. were obtained for each
June, July, and August throughout the 1995-2005 period.2 MDA8 ozone is found by calculating
average ozone values for each 8-hour time span during a day (e.g. 8 AM to 4 PM, 9 AM to 5
PM, etc.) and taking the maximum of these 8-hour averages. The MDA8 metric was chosen as
opposed to daily averages or daily maximums because it represents the best measure of daytime
ozone concentrations, it is the metric used to assess compliance with National Ambient Air
Data files were graciously provided by Nick Mangus of the U.S. EPA Air Quality System office. 2
109 Quality Standards (NAAQS), and it is a common metric used to assess long-term changes in
ozone [Weaver et al., 2009].
The North American Regional Reanalysis (NARR) meteorological dataset, developed by
the National Centers for Environmental Prediction (NCEP), is the ‘measured’ meteorology data
source used in this study.
Meteorological reanalysis is the assimilation of meteorological
observations from a variety of sources into one comprehensive dataset and is commonly used
with meteorological models for comparison and nudging (see Chapter 2). Assimilation is the
process of combining observations, which are discrete in time and space, with a meteorological
prediction model forecast to produce a uniform, continuous output. Thus, the NARR dataset
does not contain true measurements or observations but is observational data interpolated and
smoothed using the Eta atmospheric model. This dataset contains values for a multitude of
variables in three-hour increments over a 32 km x 32 km horizontal domain (covering North and
Central America as well as large portions of the Atlantic and Pacific Oceans) with 45 vertical
layers from the surface up to 100 hPa [Mesinger et al., 2006]. Studies have shown that NARR
meteorology agrees well with measurements and generally captures regional weather patterns
[Mesinger et al., 2006; Bukovsky and Karoly, 2007]. In addition, the NARR dataset has been
used for evaluation of meteorological models in air quality modeling studies [Kim et al., 2008]
and has been used to investigate the long-term trends and variability of various meteorological
relationships [Lu and Takle, 2010]. The NARR dataset was chosen for this study because the
data is continuous, its output is gridded, and it is available for our entire study period.
110 B.ii. Air Quality and Meteorology Models
As in Chapter 2, the CMAQ modeling system is employed to create gridded air pollution
concentrations. The simulations used in this analysis were produced by Scott Spak as part of his
work on the chemical transport of ozone and fine particles in the Great Lakes region, and a
detailed description of model configuration and all inputs can be found in Spak’s Ph.D.
dissertation [Spak, 2008] and Spak and Holloway [2009]. While the basic modeling structure
used here is identical to the model structure presented in Chapter 2, there are some differences in
the models employed as well as the data inputs and model configurations. These differences are
described below.
Simulations using CMAQ, version 4.6, were produced over the 36 km x 36 km CONUS
domain (same as in Chapter 2) for the summer months of 2002. Model vertical resolution is 14
layers with higher resolution near the surface to resolve boundary layer transport and chemistry.
Model initial conditions were generated from Regional Acid Deposition Model Version 2
(RADM2) concentration profiles, representing ‘clean’ atmospheric conditions in the midlatitudes of the Northern Hemisphere, and a 10-day spin-up period was applied. Boundary
conditions were derived from the Model of Ozone and Related Tracers (MOZART), a global
CTM jointly developed by the National Center for Atmospheric Research (NCAR), the
Geophysical Fluid Dynamics Laboratory (GFDL), and the Max Planck Institute for Meteorology
(MPI-Met) and designed for the simulation of intercontinental pollutant transport.
Here,
MOZART 2002 monthly mean concentrations fields for fourteen gaseous and aerosol species are
interpolated to the CMAQ horizontal and vertical grids.
Chemical and transport mechanisms utilized in these CMAQ simulations include the
Carbon Bond Four (CB04) gas phase chemistry, the AERO3 aerosol mechanism, RADM
111 aqueous chemistry, piecewise parabolic advection, and eddy diffusion. This configuration, while
not employing the newest mechanisms, was chosen because it was the most tested and reliable
set of mechanisms available at the time of model simulations. Emissions for CMAQ were
processed by SMOKE, version 2.1, using the EPA 1999 National Emissions Inventory (NEI),
updated to 2001 for analysis of the Clean Air Interstate Rule (CAIR). This inventory was used
by the EPA for analyses of CAIR and was the newest inventory available at the time of CMAQ
simulations.
The modeled meteorology to drive CMAQ simulations was generated from the Penn
State University and National Center for Atmospheric Research Mesoscale Meteorology model
(MM5), version 3.6.1. MM5 is one of the most commonly employed meteorology models in
regional air quality modeling studies [Hogrefe et al., 2004; Steiner et al., 2006; Dawson et al.,
2007; Spak and Holloway, 2009; Weaver et al., 2009]. MM5 simulations, provided here by the
Lake Michigan Air Directors Consortium (LADCO), were run over the CONUS domain with 34
vertical layers and processed through MCIP, version 3.2. Model evaluation of MM5 shows good
performance for temperature, humidity, and wind speed [Spak and Holloway, 2009]. We do find
that precipitation is significantly overpredicted by MM5, but this is generally due to increased
rainfall during each precipitation event rather than an increase in overall precipitation events.
This overprediction in precipitation is of minimal significance for this work since ozone and its
major precursors have low solubility in water and are not prone to wet deposition [Jacob and
Winner, 2009].
112 C. Observed Variability in Ozone-Temperature Relationships
To quantify the relationships between ozone and meteorological variables, we calculate
the correlation coefficient between MDA8 ozone and daily values of each meteorological
variable for the entire summer period (JJA) and for each individual month.
Correlation
coefficients represent a simple but informative way to characterize the relationship between
ozone and meteorology and are utilized by Jacob et al. [1993] to compare observed and modeled
ozone-temperature relationships. Observed correlations are calculated with AQS observations
and NARR meteorology, while modeled ozone-meteorology correlations are calculated with
CMAQ concentrations and MM5 meteorology. The same basic method for calculating both
modeled and observed correlations is utilized to allow for comparison.
In order to best compare discrete AQS ozone observations with gridded, continuous
NARR meteorology, we grid the ozone observations on the horizontal NARR grid. Each AQS
MDA8 observation was spatially binned into the appropriate NARR grid cell based on the
latitude and longitude of its corresponding monitoring site. If more than one observation was
binned into a grid cell at any time step, then the ozone value for that time step is the mean value
of the multiple observations. As ozone observations are spatially discrete, many grid cells have
no value. For NARR meteorology, daily averages and daily maximums (single timestep) for
each variable were calculated. However, since NARR data is only in 3-hour increments, the
calculation time range used here is from 06 UTC to 06 UTC (of the following day) with 9 time
steps in total, equivalent to 1 AM to 1 AM EST and 10 PM to 10 PM PST. The calculation
biases for these daily average and daily maximum values are of minimal significance to our
study since we are most concerned with the day-to-day value variance and not the absolute
113 values for each day. Lastly, correlation coefficients are calculated for each grid cell with ozone
data.
For this study, we focus on the relationship between ozone and temperature because it is
the most studied of all ozone-meteorology relationships and because temperature is shown to be
one of the most important meteorological factors controlling ozone concentrations.
While
correlations were performed for many meteorological variables, we present here only the
correlations between MDA8 ozone and daily maximum surface temperature.3
To assess the general spatial patterns and temporal variability of observed ozonetemperature relationships, the correlation between ozone and temperature is calculated for each
month during the JJA 1995-2005 period.
As a sample, Figure 3-2 shows the observed
correlations between MDA8 ozone and daily maximum temperature for each July from 1995 to
2005.4 Upon comparison, the vast majority of months exhibit the same spatial patterns: positive
ozone-temperature relationships (red in figures) for nearly all grids throughout the United States,
with the strongest positive correlations in the Northeast, Upper Midwest, and the West Coast and
weaker positive correlations in the Southeast.
increase with increasing temperature.
Thus, in these areas, ozone concentrations
However, a few months exhibit noticeably different
patterns. First, an area of strong negative correlation (blue in figures) over the Ohio River Valley
appears during June 2004 (shown in Figure 3-3), whereas this area exhibits positive correlations
in all other years during the 1995-2005 period. Second, an area of negative correlation centered
over Illinois and Indiana appears during July 2002 (shown in Figure 3-2) and is also not seen
during any other year.
Third, very weak and/or negative correlations in areas of Texas,
Correlations with daily average temperature we also examined and exhibit the same general patterns and
variability as daily maximum correlations. 4
The number of AQS monitoring sites for ozone changes from year to year as new sites are added and
some older sites are retired. Therefore, certain grid cells may have data for some years but not others. 3
114 Oklahoma, Arkansas, and Missouri appear in months scattered throughout the JJA 1995-2005
period, with varying magnitude and spatial extent. The largest extent of negative correlations in
this area occurs in August 2002 (shown in Figure 3-4). These observed patterns, as they relate to
modeled patterns, are discussed in greater detail in the next section.
D. Model Performance
Before assessing CMAQ’s ability to capture observed ozone-temperature relationships
during the summer of 2002, simulated ozone concentrations are evaluated against the
observations for quality control purposes. Figure 3-5 shows the modeled and observed mean
MDA8 ozone concentrations over the June-August period (JJA).5 CMAQ captures the general
spatial trends in the observations, with the highest concentrations in the Ohio River valley, the
southern Appalachians, the Mid-Atlantic, and central and southern California with the lowest
concentrations along the Gulf Coast and in the Pacific Northwest. The model exhibits a large
area of high ozone concentrations in Colorado and the surrounding states that is not seen in the
observations, though the limited number of observation sites in this region preclude a full
comparison. This region of high ozone, centered over the highest elevations in the country, is
likely caused by anomalously high amounts of stratospheric ozone import due to the coarse
vertical resolution of the air quality model. In most other regions, ozone concentrations are
generally overpredicted by CMAQ with the exception of California. An evaluation of CMAQ
with the same chemical mechanisms used here also finds that CMAQ generally over-predicts
summertime ozone concentrations [Yu et al., 2010].
Unless otherwise mentioned, all pollutant concentrations or meteorological values presented here are at
surface level (or the lowest model layer). 5
115 Modeled
ozone-temperature
correlations
are
calculated
using
CMAQ
ozone
concentrations and MM5 temperatures. First, ground-level MDA8 ozone was calculated from
CMAQ hourly ozone concentrations for each grid cell. Because the highest ozone levels at any
location usually occur during the daylight and/or evening hours, the differences in time zones
(and thus sunlight hours) across the continental U.S. must be accounted for. Therefore, MDA8
concentrations were calculated for each day using a 28-hour time range starting at midnight EST
(5:00 UTC). This 28-hour range captures the daylight and evening hours of all four U.S. time
zones and will accurately represent modeled MDA8 ozone, assuming that maximum ozone
concentrations occur during this period and not overnight. For modeled meteorology, daily
average and daily 1-hour maximum values for each meteorological variable in MM5 were
calculated over this same 28-hour period. While the daily average values will be slightly biased
towards nightly values due to this 28-hour averaging time, this bias is of minimal significance to
our study since we are concerned with the day-to-day variance and not the absolute values for
each day. Finally, as with the observed correlations, correlation coefficients for modeled ozone
and meteorology were calculated for each grid cell.
Here, we present modeled correlations between MDA8 ozone and daily maximum
surface temperature and compare modeled patterns and variability with the patterns and
variability seen in the observations. Figure 3-6 shows the modeled and observed correlations of
daily ozone and temperature for the JJA period of 2002.
The model exhibits a positive
relationship between ozone and temperature in most areas, while the observations exhibit a
positive relationship in all areas with data. Both modeled and observed relationships show that
the strongest positive correlations occur in the Northeast, Midwest, and along the West Coast.
This spatial distribution of ozone-temperature relationships compares well with the results from
116 Camalier et al. [2007] showing strong positive ozone-temperature relationships in the Northeast
and Midwest with much weaker relationships to the south.
In Figure 3-6, the model also exhibits two main areas of negative correlation: one large
area in the Mountain West with stronger negative correlations and a smaller area in Missouri and
Arkansas with weaker negative correlations. While the observed relationships do not exhibit
these patterns of negative correlation, they do not fundamentally disagree with modeled patterns.
For the Missouri/Arkansas area of negative correlation in the model, most of the observations
exhibit weak positive correlations. For the Mountain West area of negative correlation, there are
very few observation sites in this region. In the model, there are two locations within this
negatively correlated area that show positive correlations – Boise, ID and Salt Lake City, UT –
and the positive relationships at these locations agree with the observations.
The other
observation sites within the model’s negatively correlated area (northwestern Wyoming, southcentral Idaho, eastern Nevada, and southeastern Utah) all show very weak correlations.
For the aggregated June-August period, modeled ozone-temperature relationships
generally agree with observed relationships. In order to explore the temporal variability of these
relationships during this summer, we examine ozone-temperature correlations for each individual
month. Because of the lack of monitoring sites in the Mountain West, and because of anomalous
model performance over this region, we will focus our attention on the Eastern U.S. and the
West Coast. Figure 3-7 shows the modeled and observed correlations of MDA8 ozone and daily
maximum surface temperatures for June 2002. Spatial patterns in modeled relationships in the
eastern U.S. are similar to patterns for the JJA period: positive correlations are strongest in the
Midwest and Northeast with weaker correlations to the south. The largest area of negative
correlation for the model is in Virginia and North Carolina. The model compares well to
117 observed ozone-temperature relationships in the areas with positive correlations. While negative
correlations in Virginia/North Carolina are not seen for the observations, the observed positive
correlations in this area are noticeably weaker compared to correlations for the JJA period. For
the West Coast, both modeled and observed relationships exhibit positive correlations in the
Pacific Northwest, while modeled correlations in California are on the whole more negative than
for the observations.
In addition to examining the variability of ozone-temperature relationships within the
summer period by considering each individual month, we use observed ozone-temperature
relationships for each summer month from 1995 to 2005 to examine the interannual variability of
these relationships. By comparing the observed relationships during the same summer month for
every year, we can see how the distinct ozone-temperature patterns in each month vary from year
to year. For the month of June, the observed relationships between MDA8 ozone and daily
maximum temperature from 1995-2005 show that observed June 2002 relationships are typical
for most years but that certain years exhibit distinctly different patterns. For example, Figure 3-3
shows the June ozone-temperature relationships for three consecutive years (2002-2004) during
this period. Observed ozone-temperature relationships are consistently positive in the Northeast,
Upper Midwest, and West Coast. However, an area of strong negative correlation over the Ohio
River Valley appears during June 2004, whereas this area exhibits strong positive correlations
during the other two years. This area of negative correlation is not observed in any other year
during the 1995-2005 period. Variable ozone-temperature correlations over these three years are
also seen in Texas and Arizona, and correlations in these two areas are variable throughout the
entire 1995-2005 period.
118 Figure 3-8 shows the modeled and observed correlations of MDA8 ozone and daily
maximum surface temperatures for July 2002. As with June 2002, both modeled and observed
relationships for July are positive in the Northeast, Upper Midwest, and West Coast. However,
an area of modeled negative correlation centered over Illinois and Missouri not seen in June
appears in July. This area of negative correlations is also seen in the observations, though
slightly displaced to the east (centered over Illinois and Indiana) compared to the model. The
July ozone-temperature relationships in the 1995-2005 observations illustrate that these July
2002 relationships are unique. Figure 3-2 shows that ozone-temperature relationships in Illinois
and the surrounding areas are negative in July 2002 but are moderately or strongly positive for
all other years. While the correlations in this area for July do vary from year to year, most years
during the 1995-2005 period exhibit strong positive correlations with 2002 as the only July
where correlations are distinctly negative.
Lastly, we compare modeled and observed relationships between MDA8 ozone and daily
maximum temperature for August.
Figure 3-9 shows that modeled ozone-temperature
relationships for August 2002, like in June and July, are positive in the Northeast, Midwest, and
West Coast. However, modeled relationships are strongly negative in areas of Texas, Oklahoma,
Arkansas, and Missouri and weakly negative in Florida. For this Texas region, while there are a
limited number of observation sites, most grid cells with data for the observed relationships show
very weak or negative correlations. Multi-year observed ozone-temperature relationships for
August (Figure 3-4) show strongly positive correlations over most of this Texas region for 2001
and 2003 in contrast to the mostly weak and negative correlations in 2002, suggesting that the
modeled relationships are capturing this difference in observed relationships for August 2002.
Correlations in this Texas region vary throughout the 1995-2005 period, and the spatial extent of
119 negative correlations in this region, when present, vary from year to year. The largest extent of
negative correlations of any year occurs in 2002. Observed relationships in Florida are mostly
positive for August 2002, in contrast to the model, and are mostly positive throughout the 19952005 period.
By examining each summer month individually, we illustrate the variability of ozonetemperature relationships and highlight the model’s ability to capture these relationships. We see
significant variability in the observed ozone-temperature relationships of several regions both
within the summer of 2002 and from summer to summer in the 1995-2005 period. While
positive correlations are the norm in the areas of focus here, large areas of negative correlations
appear over the Illinois region in July and over the Texas region in August. These areas of
negative correlation appear for only one of the three summer months in 2002, highlighting the
variability of ozone-temperature relationships within the summer ozone season. Examining only
the JJA period as a whole would miss the distinct patterns seen in individual months. In
addition, the observed relationships from 1995-2005 show that the large areas of negative
correlation seen in July and August are not seen every year. In fact, these patterns are unique:
the negative correlation in the Illinois region for July is not seen in any other July from 19952005, whereas the negative correlation in the Texas region for August is seen in other years but
is at its largest spatial extent in 2002. Furthermore, an area of strong negative correlation over
the Ohio River Valley is seen in the June 2004 observations but does not appear for any other
year.
This variability in ozone-temperature relationships, both on an intra-summer and
interannual basis, is a surprising finding. Despite generally overpredicting ozone concentrations,
the CMAQ model accurately captures much of the observed variability in ozone-temperature
120 relationships. Modeled relationships in most areas for each month are positive, which agree with
observed relationships. The Northeast, Upper Midwest, and West Coast, which exhibit strong
positive correlations for all months during the 1995-2005 period, are well represented by
CMAQ. In addition, the model captures the two main episodes of negative correlations (Illinois
region in July; Texas region in August) seen in 2002. While there are other minor areas of
negative correlations in the observations that are not well represented by CMAQ (e.g. Florida in
August 2002), and while the spatial distribution of the two main negative episodes are not
exactly matched by the model, the CMAQ model performs surprisingly well in capturing the
observed ozone-temperature relationships.
E. From Diagnosis to Understanding – July 2002 Episode
To better understand this variability in ozone-temperature relationships, we focus on one
region and one month where ozone-temperature relationships are negative: the area of negative
correlation centered on Illinois during July 2002 (Figure 3-10). We chose this episode for
several reasons: this negative relationship episode does not appear in this region during any
other year in the 1995-2005 period, this episode occurs in an area where temperature is typically
positively correlated with ozone and is one of the most important meteorological variables
controlling ozone concentrations [Camalier et al., 2007], and this area it is not strongly
influenced by marine or orographic meteorological processes.
Because the model captures this episode of negative ozone-temperature relationships, we
use predominantly use the model to examine this episode. Two subareas within the larger
episode area are used for this analysis: the tri-state area of Illinois, Missouri, and Iowa (Zone 1),
and central Illinois and Indiana (Zone 2). Each of these zones, shown in Figure 3-10, contains
121 three observation grid cells (with data). For this process analysis, the ozone and meteorology
values for these three grid cells and their corresponding grid cells in the model are averaged,
producing one value for each zone at each time step. Using these two zones allows for a
representation of the eastern and western boundaries of this episode and prevents the data at one
grid cell from unduly influencing results.
To evaluate whether the negative correlations between ozone and temperature during this
month could be due to one or two events that dominate the monthly statistics, we examine
timeseries of modeled and observed MDA8 ozone and daily maximum temperature values for
Zone 1 and Zone 2 during July 2002 (Figure 3-11). In Zone 1, we see that modeled ozone
increases with lower temperatures during several periods (July 10-15, 22-26) and decreases with
higher temperatures (July 19-22, 25-28). The July 1-8 period shows little correlation between
ozone and temperature. A similar pattern for the model is seen for Zone 2, though the negative
correlation for Zone 2 is weaker than in Zone 1. For both zones, the trends in modeled ozone
and temperature generally compare well with observed (AQS/NARR) trends. Thus, we find that
the anti-correlation between ozone and temperature for these zones, occurring primarily during
the last 20 days of the month, reflects more than a few multi-day events.
The two meteorological variables, in addition to temperature, with the strongest
correlation to ozone during this episode are specific humidity and wind speed. As discussed in
the introduction, wind speed generally has a negative relationship with ozone, while specific
humidity generally has a positive relationship with ozone in polluted areas. However, studies
show that the relationships between ozone and these two variables are complex and site specific
[Dawson et al., 2007; Camalier et al., 2007; Jacob and Winner, 2009]. Figure 3-12 shows the
relationships between ozone and the three meteorological variables (temperature, specific
122 humidity6, wind speed) for Zone 1 during the July 2002 episode. All three of these variables
exhibit negative correlations with ozone. Based on the correlation coefficients, wind speed has
the strongest relationship with ozone (r = -0.562), followed by humidity (r = -0.464), with
temperature having the weakest relationship of the three (r = -0.372). This suggests that during
July 2002, winds and humidity are more important that temperature in driving MDA8 ozone
concentrations. From Figure 3-13, we see that temperature and humidity track closely together
(higher temperatures leads to higher humidity, r = 0.662) while temperature and wind speed
exhibit a weak relationship. The positive relationship between humidity and temperature is
expected since warmer air can hold more water vapor. Similar correlations are seen for Zone 2,
with wind speed (r = -0.533) and specific humidity (r = -0.541) exhibiting a much stronger
relationship with ozone than temperature (r = -0.123), and a positive relationship between
temperature and humidity (r = 0.470).
This evidence suggests that for July 2002, conditions with low wind speeds and low
humidity promote higher ozone concentrations instead of higher temperatures promoting higher
ozone. In addition, we see that temperature and humidity have a strong positive relationship
during this period. Therefore, this suggests that the negative relationship between ozone and
temperature during this episode is attributable to the covariance between temperature and
humidity. When humidity is a stronger driver of ozone than temperature, and humidity levels
closely follow temperature, then we would expect the counterintuitive result of temperature
having a negative relationship with ozone.
To help confirm this explanation, we compare July 2002 (where ozone-temperature
relationships in the episode area are negative) with August 2002 (where ozone-temperature
6
Water vapor mixing ratio (mass of water vapor / mass of dry air) is the metric of specific humidity used
in the model. In most cases, these two metrics can are nearly equal. 123 relationships are positive). Figure 3-14 shows the relationships between ozone and the three
meteorological variables of interest in Zone 1 for August 2002. Here, we see that temperature
has the strongest relationship with ozone (r = 0.455), followed by wind speed (r = -0.276) and
humidity (r = -0.254). For August, temperature has a positive relationship with ozone and is a
stronger driver of ozone than humidity. In addition, humidity levels are positively correlated
with temperature in August (Figure 3-15, r = 0.354) but much less strongly than in July.
Therefore, we see a strong positive ozone-temperature relationship and a weaker negative ozonehumidity relationship in August because temperature is a stronger driver of ozone and the
covariance between temperature and humidity is weaker. We see this same distinction between
July and August for Zone 2. Thus, this examination of August 2002 supports our suggestion that
the negative ozone-temperature relationships during July 2002 in the Illinois area are due at least
in part to covariance between temperature and humidity.
To further validate this finding, we examine the observed ozone-meteorology
relationships for July throughout the 1995-2005 time period in Table 3-1. As expected based on
historical ozone-temperature relationships shown in Figure 3-2, we see negative correlations
between ozone and temperature in both zones only during 2002, with positive correlations in all
other years. Likewise, 2002 is the year with the strongest negative correlation between ozone
and humidity in both zones, with significantly weaker or positive correlations for all other years.
Table 3-1 shows that July 2002 is unique for both ozone-temperature relationships and ozonehumidity relationships, further supporting our suggestion that negative ozone-temperature
relationships during this episode are due to the covariance between temperature and humidity.
Based on these results, high ozone concentrations during July 2002 in the Illinois area
occur under dry, cool conditions with low winds. It is possible that these conditions and/or the
124 covariance of temperature and humidity are the primary cause of negative ozone-temperature
correlations in other areas and months. If ozone concentrations are more sensitive to specific
humidity than temperature in during certain episodes, and specific humidity levels closely follow
temperature, then we might expect to see weak or negative ozone-temperature relationships. An
examination of relative humidity in Zone 1 and Zone 2 could further elucidate the relationships
between ozone and humidity during these episodes.
Furthermore, covariances between
temperature and other meteorological parameters could be important during certain episodes.
More work is needed to determine the chemical processes responsible for these changing ozonemeteorology sensitivities.
F. Discussion
In this study, we use ozone observations, meteorological reanalysis, and an air quality
model to explore the relationships between climate and ozone. Our work, using a simple but
effective method for representing ozone-meteorology relationships, shows that ozone response to
temperature over the continental U.S. varies from month to month and year to year: ozonetemperature relationships are consistently positive in most regions, but certain areas exhibit
negative relationships during individual months.
In addition, CMAQ, though generally
overestimating ozone concentrations, captures much of the spatial and temporal variability seen
in observed ozone-temperature relationships. However, as seen in Chapter 2, emissions and
concentrations of ozone precursors such as NOx and VOCs are also important factors controlling
ozone. In this study, we only consider the impacts of meteorology on ozone concentrations and
do not examine the spatial and temporal variability in ozone precursor emissions and
concentrations. The emissions of many ozone precursors have decreased over the time period
125 examined in this study [Bloomer et al., 2009], so accounting for the effects of variability and
trends in these ozone precursors would allow for a better assessment of ozone-meteorology
relationships. In addition, there are important links between meteorology and ozone precursor
emissions that affect ozone-meteorology relationships. Two examples are NOx and isoprene:
higher temperatures can increase emissions of NOx due to increased electricity generation for air
conditioning, while the emission of isoprene (a biogenic VOC) from plants is strongly dependent
on air temperature [Murazaki and Hess, 2006; Jacob and Winner, 2009; Weaver et al., 2009]. A
better understanding of these links between climate, emissions, and ozone would help validate
model performance in simulating future air quality.
Furthermore, a closer analysis of one episode with negative ozone-temperature
relationships in this study suggests that humidity has a greater impact on ozone than temperature
during this episode, and that the covariance between temperature and specific humidity at least
partially explains the negative ozone-temperature relationships. It is possible that the covariance
of temperature and humidity are the primary cause of negative ozone-temperature correlations
seen in other areas and months. However, we do not quantify the relative importance of
meteorological variables on ozone concentrations in this study. The statistical metric used here,
correlation coefficient, does not isolate the effects of each meteorological variable on ozone.
While calculating the sensitivity of ozone to each individual meteorological variable requires a
statistical model as in Camalier et al. [2007], or many model sensitivity simulations as in
Dawson et al. [2007], quantifying the relative importance of each meteorological variable on
ozone during episodes of negative ozone-temperature correlation would provide further insight
into the meteorological processes response for these negative correlations.
126 Before models can be used with confidence to quantify the air quality impacts of future
climate change, a better understanding of these impacts and of model ability to accurately
represent these impacts is needed. Therefore, more research focused on the past and current
impacts of climate on ozone is needed in addition to the large body of research that has focused
on future climate and ozone. Our results address two understudied issues in the larger area of
climate and ozone – the variability of ozone-meteorology relationships, and the evaluation of air
quality models to assess the impacts of climate on ozone. Including the effects of future climate
change on ozone in air quality and climate policies will require increased confidence in model
ability to capture these effects [Jacob and Winner, 2009], which requires a better understanding
of ozone-meteorology relationships in general. This study is one of the first to address these
needs.
127 Figure 3-1 – Spatial Patterns of Ozone-Meteorology Relationships from Camalier et al. [2007]
This map synthesizes the dominant meteorological parameters driving ozone concentrations in
each city as found in Camalier et al. [2007]. Geographical zones of meteorological influence
(bands of color) represent the two most important parameters driving ozone concentrations for
each city.
128 Figure 3-2 – July 1995-2005 Observed Correlations between MDA8 Ozone and Daily Maximum
Temperature
a) 1995
b) 1996
c) 1997
129 Figure 3-2 continued
d) 1998
e) 1999
f) 2000
130 Figure 3-2 continued
g) 2001
h) 2002
i) 2003
131 Figure 3-2 continued
j) 2004
k) 2005
132 Figure 3-3 – June 2002-2004 Observed Correlations between MDA8 Ozone and Daily Maximum
Temperature
2002
2003
2004
133 Figure 3-4 – August 2001-2003 Observed Correlations between MDA8 Ozone and Daily
Maximum Temperature
2001
2002
2003
Figure 3-5 – JJA 2002 Mean MDA8 Ozone Concentrations from CMAQ and AQS
CMAQ
AQS
134 Figure 3-6 – JJA 2002 Modeled and Observed Correlations between MDA8 Ozone and Daily
Maximum Temperature
Modeled
Observed
135 136 Figure 3-7 – June 2002 Modeled and Observed Correlations between MDA8 Ozone and Daily
Maximum Temperature
Modeled
Observed
137 Figure 3-8 – July 2002 Modeled and Observed Correlations between MDA8 Ozone and Daily
Maximum Temperature
Modeled
Observed
138 Figure 3-9 – August 2002 Modeled and Observed Correlations between MDA8 Ozone and Daily
Maximum Temperature
Modeled
Observed
139 Figure 3-10 – July 2002 Area of Negative Correlation in Model and Observations
The three grid cells in Zone 1 are shown within the green oval, whereas Zone 2 sites are shown
within the purple oval.
Figure 3-11 – Timeseries of MDA8 Ozone and Daily Maximum Temperature at Zone 1 and
Zone 2 for July 2002.
Zone 1
Zone 2
140 141 Figure 3-12 – Scatter Plots of MDA8 Ozone and Meteorological Variables for Zone 1 in July
2002
Upper left – daily maximum temperature (TEMP2); upper right – daily average humidity (QV);
lower left – daily average wind speed (WSPD10). The ‘r’ value for each plot represents the
correlation coefficient with the line of best fit drawn in black.
142 Figure 3-13 – Scatter Plots of Temperature with Humidity and Wind Speed for Zone 1 in July
2002
143 Figure 3-14 – Scatter Plots of MDA8 Ozone and Meteorological Variables for Zone 1 in August
2002
Upper left – daily maximum temperature (TEMP2); upper right – daily average humidity (QV);
lower left – daily average wind speed (WSPD10). The ‘r’ value for each plot represents the
correlation coefficient with the line of best fit drawn in black.
144 Figure 3-15 – Scatter Plots of Temperature with Humidity and Wind Speed for Zone 1 in August
2002
145 Table 3-1 – July Observed Correlations between Ozone and Meteorological Variables
a) Zone 1
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Temperature
0.390
0.352
0.708
0.388
0.302
0.359
0.396
-0.181
0.457
0.505
0.637
Specific Humidity
0.289
-0.008
0.381
0.160
0.029
0.088
0.091
-0.453
0.140
-0.090
0.157
Wind Speed
-0.100
-0.131
-0.137
0.492
-0.436
0.143
-0.032
-0.495
0.095
-0.450
0.071
Temperature
0.494
0.373
0.647
0.449
0.071
0.365
0.619
-0.274
0.333
0.367
0.344
Specific Humidity
0.227
-0.030
0.397
0.005
-0.197
0.008
0.347
-0.618
-0.030
0.118
-0.317
Wind Speed
-0.464
-0.180
-0.368
0.284
-0.515
-0.034
-0.167
-0.563
-0.349
-0.442
-0.424
b) Zone 2
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
*The number of AQS ozone monitoring sites change from year to year as new sites are added.
One of the sites in Zone 2 (the middle site from west to east in Figure 3-10) was not installed
until 2001. Therefore, Zone 2 correlations from 1995 to 2000 are calculated using data from the
two existing sites.
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149 Chapter 4 – Conclusions and Future Work
In this study, we explore the links between air quality, energy, and climate change by
examining the response of air quality to both emissions and meteorology. First, we assess the air
quality impacts of changing electricity sector emissions in Wisconsin over the Great Lakes
region.
We then explore the response of air quality to climate variability over the entire
continental United States. This complementary research uses an air quality model as the primary
assessment tool and highlights the complex interactions between emissions, meteorology, and
atmospheric chemistry. Here, we discuss our general conclusions from this work and how these
conclusions can better inform future research and policy.
A. Research Findings
First, our work shows that climate change policy can reduce emissions of health-relevant
pollutants and improve air quality. In Chapter 2, we use an electricity dispatch model to simulate
the Wisconsin electricity sector in the year 2024 under a business-as-usual assumption and under
CO2 reduction policies – a renewable electricity portfolio standard (RPS) and energy efficiency
programs. While these policies do not target air quality, we find that increased renewable
generation and decreased electricity demand are effective at reducing pollutant emissions:
emission reductions of more than 30% for NOx and SO2, compared to business-as-usual, are
attributable to these policies. These emissions reductions result in small but non-negligible air
quality improvements in the region. In addition, these policies are also effective at reducing CO2
emissions: CO2 emissions from electricity generation under these policies decrease by 24%
compared to business-as-usual and by 9% compared to 2008 levels. Thus, our research suggests
150 that renewable portfolio standards and energy efficiency are effective at reducing CO2 emissions
while also producing air quality co-benefits.
However, this work also suggests that climate change policy may not be the best way to
reduce air pollution. Pollution controls are shown to be a more effective way to improve air
quality. The emission reductions and air quality improvements attributable to the two climate
policies in our study are significantly smaller than the emission reductions and air quality
improvements attributable to the addition of pollution controls on several of the highest-emitting
EGUs. The assumption of which EGUs receive pollution controls in the 2024 business-as-usual
case is an important factor driving this conclusion. If our study had assumed a lower number of
additional pollution controls in the 2024 BAU, then the emissions and air quality improvements
of the climate policies would likely be much greater. Even though the air quality benefits of
climate policies are less than those of the pollution controls, the ‘free’ emission reductions from
the climate policies could partially offset the need for some of these expensive pollution controls.
How to appropriately value greenhouse gas emission reductions compared to reductions in air
pollution emissions when choosing between energy options is an area of ongoing study.
The work presented in Chapter 2 also suggests that the Great Lakes have an important
effect on secondary air pollution. Air quality simulations over the Great Lakes show that ozone
and sulfate concentrations are elevated over Lake Michigan compared to the adjacent land.
Moreover, sensitivity simulations driven by the electricity emissions scenarios show that, in
certain cases, the decreases in ozone and sulfate concentrations from NOx and SO2 emissions
reductions are amplified over Lake Michigan compared to the land, whereas nitrate
concentration decreases from emission reductions are instead suppressed over the lake. A
detailed examination of sulfate over Lake Michigan suggests that lake processes have a
151 pronounced effect on sulfate chemical formation, deposition, and transport. While we do not
examine these effects for ozone and nitrate, it is possible that many of these same lake processes
are responsible for the ozone and nitrate patterns seen in air quality simulations. The effect of
the Great Lakes on secondary air pollution represents an area for future work.
Our work presented in Chapter 3 shows that the response of ozone to meteorology can
vary significantly in time and space. Using ozone observations and meteorological reanalysis,
we show that ozone-temperature relationships over the continental U.S. are consistently positive
in most regions, with the strongest positive relationships in the Northeast, Upper Midwest, and
West Coast. However, observations also show that these ozone-temperature relationships vary
from month to month and year to year in certain areas, and some areas even exhibit negative
ozone-temperature relationships during certain months. In addition, CMAQ, though generally
overestimating ozone concentrations, captures much of the spatial and temporal variability seen
in observed ozone-temperature relationships, including the episodes of negative ozonetemperature correlations. These results suggest that this regional air quality model is capturing
the meteorological and chemical processes responsible for the variability in ozone-meteorology
relationships.
Furthermore, this assessment of ozone-meteorology relationships demonstrates that
meteorological parameters other than temperature play an important role in controlling ozone
concentrations. A closer analysis of one episode with negative ozone-temperature relationships
shows that specific humidity and wind speed have stronger correlations with ozone than does
temperature. Our analysis suggests that humidity has a greater impact on ozone than temperature
during this episode, and that the covariance between temperature and specific humidity at least
partially explains the negative ozone-temperature relationships. These results show that the
152 relative importance of meteorological variables on ozone varies with time, and more work is
needed to identify the large-scale meteorological processes responsible for this variability. In
addition, given the connections between atmospheric humidity and the biosphere through
processes such as evapotranspiration, changes in land cover or vegetation could also play a role
in the variability of ozone-meteorology relationships. More work is needed to identify and
quantify the possible links between the land surface and ozone air pollution.
B. Broader Conclusions
In addition to the conclusions of the research presented in Chapter 2 and Chapter 3, some
broader conclusions can be drawn from this complementary work. First, we see that examining
the specific spatial patterns or variability of emissions, meteorology, and atmospheric chemistry
are important for understanding the links between energy, climate, and air quality. In Chapter 2,
the spatial distribution of emissions changes has a significant impact on the magnitude and
spatial distribution of air quality impacts:
distributing the same magnitude of emission
reductions to different EGUs across the state of Wisconsin can result in significantly different
distributions and magnitudes of air quality improvements. Likewise, representing the spatial
patterns of ozone-temperature relationships shown in Chapter 3 and the variability of these
patterns is important for accuracy in meteorological-adjusted ozone trends and for assessing the
impacts of a changing climate on ozone concentrations. Furthermore, this importance of spatial
patterns or distributions necessitates a local or regional approach to air quality research: largescale assessments or modeling simulations could miss these smaller-scale patterns. Thus, the
spatial distribution of emissions reductions and air quality-meteorology relationships on local
153 and regional scales needs to be considered when developing local and regional air quality
improvement strategies.
The spatial distribution of emissions is particularly important when comparing
greenhouse gases and air pollutants. Because greenhouse gases are stable with long atmospheric
lifetimes and contribute to global climate change, the spatial distribution of greenhouse gas
emissions is of little significance. However, because air pollutants are often highly reactive with
much shorter atmospheric lifetimes, the specific location or region where these air pollutants are
emitted is likely to receive the largest impact, be it unhealthy levels of pollution, ecosystemdamaging acid rain, or reduced visibility. The impacts of greenhouse gases are not connected to
the spatial distribution of their emission, whereas the impacts of air pollution are directly
connected to the spatial distribution of emissions. Air quality, energy, and climate policies need
to account for this distinction.
This thesis expands the boundaries of linked air quality, energy, and climate research.
We focus our efforts on two relatively unstudied areas: the air quality impacts of lower-carbon
electricity generation, and the ability of air quality models to capture the variability of observed
ozone-meteorology relationships. To address these questions in the most comprehensive way
possible, a combination of models, measurements, and other datasets are required. In this thesis,
we employ air quality, meteorology, and electricity models as well as AQS air quality
observations, NARR meteorological reanalysis, and NEI emissions datasets. Data from these
varied sources are combined in multiple ways and allow for innovative approaches to research
questions. Lastly, this policy-relevant research has led to advances in basic science. The climate
policy co-benefits work in Chapter 2 has lead to the advancement of sulfate chemistry over the
Great Lakes. Similarly, the ozone-meteorology work in Chapter 3, which was motivated by a
154 need to evaluate the models that are currently used in decision-making to assess ozone under a
future climate, has led to a better understanding of basic ozone-meteorology relationships.