Analysis of CAMx and UAM-V Modeling in an Indirect Technique to Assess Contribution to Ozone Exceedances in the Eastern United States
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Analysis of CAMx and UAM-V Modeling in an Indirect
Technique to Assess Contribution to Ozone Exceedances
in the Eastern United States
Paper presented at the 93rd Annual Meeting of the Air and Waste Management
Association, Salt Lake City, UT, June 2000.
Saravanan Arunachalam
Environmental Programs
MCNC–North Carolina Supercomputing Center
P.O. Box 12889, 3021 Cornwallis Road
Research Triangle Park, NC 27709-2889
ABSTRACT
Regional-scale photochemical air quality simulation models (PAQSMs) are increasingly
being used to address the complex nature of the tropospheric ozone problem. The U.S.
Environmental Protection Agency recently promulgated a NOx State Implementation Plan
(SIP) Call to address the problem of transport of ozone and its precursors across the
eastern United States. The NOx SIP Call includes emissions budgets that are designed to
eliminate specific amounts of NOx, one of the precursors to ozone. In developing the NOx
SIP Call, EPA performed numerous air quality simulations using the Variable-grid Urban
Airshed Model(UAM-V) and the Comprehensive Air Quality Model with
Extensions(CAMx) to identify the amounts of NOx emissions that contribute significantly
to nonattainment of the National Ambient Air Quality Standard (NAAQS) for ozone in
downwind areas. The analysis described here, which focuses on the “zero-out” technique,
and the “source-apportionment” technique, specifically compares the quantification of
state-by-state contributions to ozone exceedances using two modeling systems. The zero-
out method estimates the downwind impacts on ozone nonattainment by comparing the
model predictions from a base case simulation to the predictions from a simulation in
which all anthropogenic emissions are removed from a specific state. EPA had applied
this “zero-out” technique for three different states using both UAM-V and CAMx for
four different episodes in which high levels of ozone were observed in various parts of
the eastern United States. To assess the results from each of these simulations, we
identified various metrics designed to provide information on the fundamental factors for
evaluating whether emissions in an upwind state can contribute to downwind
nonattainment. To further corroborate these results, the outputs from CAMx simulations
with source apportionment were also analyzed and compared. The results from this
analysis should assist the air quality modeling community in the scientific and regulatory
world in comparing the performance of two different modeling systems, and identify
advantages, or even potential limitations, in the choice of a given PAQSM for addressing
regional-scale air quality issues.
1INTRODUCTION
The problem of regional transport of ozone and its precursors is being studied for more
than two decades1 . The scales of such transport could be inter-city, inter-state, or even
inter-regional. The movement of elevated regional ozone concentrations may reflect
impact of large-scale weather systems. Exceedances of the ozone standard often occur
when the ozone precursor emissions from major metropolitan areas interact with the
elevated regional concentrations resulting from transport. Emission areas that are
potentially responsible for the violations of the air quality standard at downwind Areas of
Violations (AOV) are termed Areas of Influence (AOI). Various tools that could help in
establishing the domains of AOIs and AOVs are: Analysis of Monitor Data, Air Parcel
Trajectory Modeling and Photochemical Air Quality Modeling. Though each have their
own advantages and limitations, grid-based Photochemical Air Quality Simulation
Modeling (PAQSM) has the potential to be a most comprehensive tool for source
attribution processes. Of course, this requires that multi-scale grid is designed in such a
way that it is able to “catch” the transport phenomena of importance; unfortunately, this
is not the case yet with most existing models. Major metropolitan areas like New York,
Chicago, and Atlanta are often dominated by overwhelming transport of ozone and its
precursors from upwind sources. While local emissions within the domain are expected
to be more important with respect to 1-h concentrations of ozone, larger areas and longer
distance scales can affect the system when longer averaging times are considered for
reducing the regional pollutant levels.
In developing the NOx State Implementation Plan (SIP) Call2 , the U.S. EPA performed
various air quality analyses3 to support the multifactor approach used to identify amounts
of NOx emissions that contribute significantly to nonattainment in downwind areas.
These analyses include subregional and state-by-state modeling intended to
1. quantify the air quality contributions from emissions in upwind states to both 1-h and
8-h nonattainment in downwind areas, and
2. determine whether these contributions are significant.
The modeling for assessing contributions consisted of state-by-state zero-out modeling
using the Variable-grid Urban Airshed Model (UAM-V)4 and Comprehensive Air Quality
Model with Extensions (CAMx)5 , and state-by-state source apportionment modeling
using the CAMx Anthropogenic Precursor Culpability Assessment (APCA)6 technique.
For assessing regional strategies, EPA performed a series of additional UAM-V model
runs. This study focuses on the state-by-state zero-out modeling performed with UAM-V
and CAMx, and source apportionment modeling performed with CAMx. The motivation
for this study is to see how two very different modeling systems behave in assessing air
quality contributions when the models are subject to similar emissions perturbations.
APPROACH
To assess the effect of transport of ozone and its precursors, various geographical
sensitivity simulations have been designed and performed: source-apportionment
technique, rollout simulations, inverse-rollout7 simulations, zero-out technique, direct
2emissions reductions approach, etc. While all of them have their limitations, it is
important to understand the differences between them. The zero-out modeling is quite
different from the source apportionment modeling for evaluating upwind contributions to
downwind receptors. This is because the zero-out modeling is an indirect technique to
quantify the contributions by the differences of two model-runs (i.e. base case versus
zero-out), while the source apportionment technique directly estimates the contributions
of upwind sources to receptor areas from a single simulation. This might lead to
differences in both magnitude and frequency of contributions for individual upwind-to-
downwind linkages. From the various modeling runs, EPA found that the source
apportionment modeling tended to show greater contribution magnitudes than the zero-
out modeling. While both modeling techniques have their usefulness, they also have their
limitations and there is no strong technical evidence to show that one is clearly superior
to the other.
The two models used by EPA, UAM-V (Version 1.24) and CAMx (Version 1.13), are
three-dimensional photochemical air quality simulation models (PAQSMs) designed for
integrated assessment of photochemical air pollution over regional and urban scales. Both
the models simulate the emission, dispersion, and removal of inert and chemically
reactive pollutants in the lower troposphere by solving a set of species mass continuity
equations that represent the relevant processes for each chemical species on a system of
nested three-dimensional grids. While there are many similarities between the two
modeling systems, there are major differences between them: the treatment of plume-in-
grid, the numerical methods for solving chemistry and vertical diffusion, and the
calculations of dry deposition. A key feature of CAMx is its ability to perform source
apportionment to assess the contributions of multiple source regions and source
categories to specific receptor areas from a single simulation. While giving a clear picture
of the likely distribution of ozone and its precursors by source category, this also
indicates whether the ozone at the selected time and location would be more likely
respond to upwind NOx or VOC controls. Both models are suitable for evaluating the air
quality effects of emission control scenarios because they account for spatial and
temporal variations as well as differences in the reactivity of emissions. Traditionally, the
modeling applications involve first replicating historical ozone episodes to establish a
base year simulation. Once the model results have been evaluated and the model
determined to perform within prescribed levels, the same base year meteorological inputs
for each episode are combined with projected emissions inventories to simulate a
projected future base case and alternative emission control strategies as well.
In support of the NOx SIP Call, for purposes of evaluating air quality impacts, EPA
modeled all four episodes that were used by the Ozone Transport and Assessment Group
(OTAG)8 . The OTAG effort is one of the most comprehensive three-dimensional gridded
regional ozone modeling projects to date, and the results from OTAG are likely to be
used as boundary conditions for future urban-scale ozone modeling studies. These
episodes correspond to periods of elevated ozone concentrations in the eastern United
States. The first period was July 1-15, 1988, which had elevated ozone in the Midwest,
Northeast, and Southeastern United States. The second period was July 13-21, 1991,
which had high ozone in the Midwest and Northeastern areas. The third period was July
320-30, 1993, which had elevated ozone levels throughout the Southeast and in portions of
the Southwest, Midwest, and Northeast. The fourth period was July 7-18, 1995, which
had elevated ozone in the Midwest, Northeast, and Southeast.
Each of the above episodes included a 2- or 3-day “ramp-up” period to initialize the
model (the first three simulation days for the 1988, 1991, and 1995 simulations and the
first two days for the 1993 simulations). Predictions from these the ramp-up periods were
excluded from the evaluation and analyses.
After projecting the emissions from these episodic years to the future year 2007 and
establishing a 2007 base case for each episode, EPA performed the state-by-state zero-out
modeling with UAM-V for the following states: Alabama, Georgia, Illinois, Indiana,
Kentucky, Massachusetts, Michigan, Missouri, North Carolina, Ohio, South Carolina,
Tennessee, Virginia, West Virginia, and Wisconsin, for the combined states of Illinois
and Wisconsin, and the combined Southeastern states of Alabama, Georgia, North
Carolina, South Carolina, and Tennessee. CAMx, on the other hand, was used to perform
zero-out modeling only for the states of Illinois, Michigan, and North Carolina.
Therefore, this study focuses on analyses of the zero-out CAMx and UAM-V simulations
for Illinois, Michigan, and North Carolina only; these are referred to as zeroIL, zeroMI,
and zeroNC.
CAMx simulations with “source-apportionment” technique were performed by EPA for
the 2007 base year for all the four episodes. The analyses of model comparisons from
UAM-V and CAMx zero-out simulations are then corroborated with outputs from CAMx
simulations performed with source apportionment.
MODEL SET-UP
The modeling domain used in all these simulations was the same as that used for OTAG.
It includes portions or all of 37 states in the eastern United States, the District of
Columbia, and part of Canada. EPA used the same OTAG configuration here for initial
and boundary conditions, meteorological inputs, and grid configuration in both the
horizontal and vertical resolutions. Horizontally, the modeling domain consisted of a
coarse grid with 64 x 63 cells at a 36 km grid resolution and a fine grid with 137 x 110
cells at a 12 km grid resolution. The meteorological inputs for the 1991 and 1995
episodes were derived from applications of the Regional Atmospheric Modeling System–
Version 3a (RAMS)9 . For the 1988 and 1993 episodes, the meteorological inputs were
derived from the Systems Application International Mesoscale Meteorological model
(SAIMM)10 .
The base year emissions inventory was a combination of 1995 and 1996 data, intended to
be as representative of the 1994-1996 time period as possible. To produce the 2007 base
case inventory, the 1995/1996 base year inventory was projected to 2007 and certain
controls were applied. The 2007 base case emissions reflect Clean Air Act mandated
controls as well as certain Federal measures that EPA has promulgated. Across the entire
modeling domain, the 2007 base case NOx emissions and VOC emissions are 1% and
19% less than the 1995/1996 base year emissions, respectively.
4MODEL PERFORMANCE EVALUATION
As part of the OTAG study, an objective evaluation of UAM-V model performance11 was
conducted for each of the four episodes to determine how well the modeling system
represented regional ozone concentration levels. Various statistical metrics comparing
predicted ozone to observed ground-level ozone concentrations were computed. While
the results indicated generally good agreement between observed and predicted values, a
few relatively minor concerns were found. One of them was that the model tended to
underestimate ozone aloft, especially overnight into the early morning hours. Thus, the
contribution of upwind source regions to ozone levels in downwind areas may actually be
greater than that estimated by the model.
Several modeling comparative studies have evaluated the model performance of CAMx
and compared it with that of UAM-V. Two such studies independently focused on the
1991 and 1995 episodes.12,13 In general, these comparative studies found that the
performance of CAMx for ozone was similar to the performance of UAM-V. CAMx,
with a lower mean normalized error, performed slightly better on average for 1995 than
did UAM-V. Comparisons with aircraft data14,15 revealed that both models may
somewhat underestimate the average ozone concentrations aloft. In support of the NOx
SIP Call, EPA also compared the two models in terms of the predictions of ozone and its
precursors for the 1995 episode alone. In addition, the comparisons by EPA found that
both models tend to underpredict the mean ozone concentration above 80 and 120 ppb,
and that CAMx tends to predict higher ozone domain-wide than UAM-V does (especially
in the high-ozone hours) by 2-5 ppb.
The model performance evaluations and comparisons discussed above show that the two
models are technically equivalent based on various statistical measures.
ANALYSES
In using the two models to assess the relative contribution from zeroing out individual
states, we focused on the 1-h metrics alone for nine nonattainment regions. The major
urban areas included in these regions are Atlanta, Baltimore, New York, Philadelphia,
Chicago, Milwaukee, Western Massachusetts (Springfield), Greater Connecticut
(Hartford), and Washington, D.C.
We analyzed the model-predicted ozone concentrations using the following three metrics,
calculated by EPA for the 2007 base case and then the three zero-out scenarios from both
models.
• Metric 1: Maximum 1-h Concentration, which is the maximum concentration
predicted by the model for the entire episode
• Metric 2: Exceedances, which is the number of 12-km grid cells with predictions of
1-h ozone =125 ppb
• Metric 3: Ozone reduced (ppb), which is the magnitude and frequency of the “ppb”
contributions in ozone concentrations =125 ppb for the 1-h NAAQS. These ozone
5impacts were quantified by calculating the difference in predicted ozone between
each scenario and the 2007 base case. Contributions are found in terms of the
frequency of impacts from the zero-out simulation on ozone concentrations in each
downwind area for six concentration ranges: >2 to 5 ppb, >5 to 10 ppb, >10 to
15 ppb, >15 to 20 ppb, >20 to 25 ppb, and >25 ppb.
These three metrics are each calculated twice, first for all the grid cells within a state and
then for just the designated 1-h-exceedance grid cells within each state. The latter is
intended to assess the impacts seen only in the nonattainment counties, as opposed to the
entire state.
To assess the outputs from the CAMx simulations with source apportionment, the
following metrics alone calculated by EPA are used in the analyses:
• Metric 4: Total ppb 1-h ozone, contributed by a single source state to various
downwind receptor states
• Metric 5: Total ppb 1-h ozone, in a single downwind receptor state contributed by
various upwind source states
• Metric 6: Total ppb 8-h ozone, contributed by a single source state to various
downwind receptor states
• Metric 7: Total ppb 8-h ozone, in a single downwind receptor state contributed by
various upwind source states
These four metrics are averaged across all the four episodes mentioned earlier. It should
be noted that while the calculation of Metrics 4 and 5 was done only in the nonattainment
counties of each state, Metrics 6 and 7 were calculated in all grid-cells in each state.
RESULTS
The metrics as defined above are presented for both CAMx and UAM-V simulations for
the 2007 base case, and then for the three zero-out simulations for all four episodes.
Metric 1:
Figure 1 shows in the form of bar charts, the episodic maximum 1-h ozone concentration
predicted within the nonattainment counties in each state for each of the four episodes
from CAMx and UAM-V simulations. The top portion of Figure 3 shows the maximum
1-h ozone concentration from all four episodes, calculated within nonattainment counties.
The only significant contribution to this metric seems to be from the zeroIL simulation,
and the effects are seen in the maxima predicted within the states of both IL and WI. The
decreases range from 10% with respect to the base case in the 1998 episode to 37% in the
case of the 1995 episode. The other two cases, zeroMI and zeroNC, seem to have almost
negligible impacts on all the states considered. Both CAMx and UAM-V show similar
behavior for all four episodes.
6Metric 2:
Figure 2 shows metric 2 predicted within nonattainment counties in each state for each of
the four episodes from CAMx and UAM-V simulations. The bottom portion of Figure 3
shows the same metric calculated cumulatively for all four episodes.
The zeroIL case seems to show almost 100 % reduction in the number of exceedances in
IL itself in 1988 and 1995 from both CAMx and UAM-V. While most of the other
reductions are comparable between the two models, the zeroMI case seems to show a
100% reduction in NY (CAMx 1993) and the zeroNC case seems to show a 100%
reduction in PA (UAM-V 1991) cases. These reductions are not shown by the other
models in the corresponding cases.
Metric 3:
This metric is presented in Figures 4-7 as stacked bar charts representing the various bins
of the “ppb” contributions to ozone concentrations =125 ppb at different threshold ranges
for each state.
Figures 4-6 show the contributions in the nonattainment counties of each state from
zeroIL, zeroMI, and zeroNC, from both CAMx and UAM-V modeling for each of the
four episodes. Figure 4 shows that the zeroIL case for the 1988 episode, while exhibiting
impacts in just the 2-5 ppb range in both CT and NY, has impacts in the 20-25 ppb range
in IL from both CAMx and UAM-V simulations. In the case of the zeroMI case (Figure
5), both CAMx and UAM-V seem to show reductions in the states of MD and NY in the
2-5 ppb range. The zeroNC case in Figure 6 shows impacts in MD in ranges up to 10-
15 ppb as predicted by CAMx for the 1988 episode. Marginal impacts are predicted by
both models for the other three episodes
Figure 7 shows the same metric plotted for the composite of the four episodes for zeroIL,
zeroMI, and zeroNC, calculated in the nonattainment counties of each state. While these
plots do not show very large bias from either model, it should be noted that the
percentage reductions predicted by CAMx are somewhat higher than those predicted by
UAM-V in most cases.
Metric 4 and 6:
The percent contribution of ozone from each of the three source states – IL, MI, and NC
– to downwind states are ranked, and the top 10 states affected are shown as pie charts in
Figure 8 (1-h ozone on the left and 8-h ozone on the right).
Metric 5 and 7:
The percent contribution of ozone in six downwind receptor states – CT, DE, MD, NJ,
NY, and PA – from various source states are ranked, and the top 10 contributing states
are shown as pie charts in Figure 9 (for 1-h ozone) and Figure 10 (for 8-h ozone).
There are some very interesting results seen from these pie charts. While sources in IL
contribute 10-12% of 1-h ozone in both receptors NJ and NY, of all the 1-h ozone in NJ
7and NY, sources in IL constitute less than 5 %. Also, while sources in MI contribute
around 17% to 1-h ozone in NJ and NY, sources in MI constitute only less than 2% in the
NJ and NY receptor charts. However, the zeroIL and zeroMI CAMx simulations in
Figure 2 for the 1993 and 1995 episodes seem to show almost 50 to 65% reductions in
the number of 1-h exceedances in NJ and NY.
Similarly, sources in IL, MI and NC contribute anywhere between 14.6 to 32.5% to 1-h
ozone in MD. However, of all the 1-h ozone in MD, these three combined contribute by
less than 1%. From Figure 2, there was only less than 10% reduction in the number of 1-
h exceedances from any of the three zero-out cases from both models.
8Figure 1. Episodic maximum 1-h ozone concentration (ppb) predicted within non-
attainment counties from CAMx (left) and UAM-V (right) for 1988, 1991, 1993, and
1995 episodes (top to bottom).
9Figure 2. Number of exceedances of 1-h ozone concentration (ppb) predicted within non-
attainment counties from CAMx (left) and UAM-V (right) for 1988, 1991, 1993, and
1995 episodes (top to bottom).
10Figure 3. Maximum 1-h ozone concentration (top) and total number of exceedances of 1-
h ozone concentration (ppb) (bottom) predicted within non-attainment counties from all
four episodes from CAMx (left) and UAM-V (right)
11Figure 4. Magnitude and frequency of “ppb” contributions to ozone concentrations
=125 ppb in nonattainment counties due to Illinois zero-out from CAMx (left) and
UAM-V (right) for 1988, 1991, 1993, and 1995 episodes (top to bottom).
12Figure 5. Magnitude and frequency of “ppb” contributions to ozone concentrations
=125 ppb in nonattainment counties due to Michigan zero-out from CAMx (left) and
UAM-V (right) for 1988, 1991, 1993, and 1995 episodes (top to bottom).
13Figure 6. Magnitude and frequency of “ppb” contributions to ozone concentrations
=125 ppb in nonattainment counties due to North Carolina zero-out from CAMx (left)
and UAM-V (right) for 1988, 1991, 1993, and 1995 episodes (top to bottom).
14Figure 7. Magnitude and frequency of “ppb” contributions to ozone concentrations
=125 ppb in nonattainment counties for composite of the four episodes from CAMx (left)
and UAM-V (right) for Illinois, Michigan, and North Carolina zero-out simulations (top
to bottom).
15Figure 8. Percent of the ozone contribution from each of the source states (IL, MI, and
NC) to downwind states for 1-h ozone concentrations (left) and 8-h ozone concentrations
(right) from CAMx Source Apportionment modeling (composite of all the four episodes).
16Figure 9. Percent of the 1-h ozone contribution in receptor states (CT, DE, MD, NJ, NY,
and PA) from various source states from CAMx Source Apportionment modeling
(composite of all the four episodes).
17Figure 10. Percent of the 8-h ozone contribution in receptor states (CT, DE, MD, NJ,
NY, and PA) from various source states from CAMx Source Apportionment modeling
(composite of all the four episodes).
18CONCLUSIONS
We used the zero-out technique to analyze state-by-state contributions to ozone
exceedances, based on outputs from two different modeling systems, CAMx and
UAM-V. The results from CAMx simulations with source apportionment were also used
to compare with findings from the zero-out simulations. Various metrics calculated from
zero-out simulations for the states of Illinois, Michigan, and North Carolina for four
different episodes were used to assess downwind impacts on ozone nonattainment. The
model performance evaluations that were performed as part of earlier studies for both
CAMx and UAM-V indicated that both models are technically equivalent, and that both
models may underestimate the average ozone concentrations aloft. The results of the
zero-out simulations analyzed for all the grid cells seem to show similar patterns to those
analyzed for the nonattainment counties alone within each state, for both CAMx and
UAM-V. Both models predicted very small changes in the peak 1-h ozone concentration
within each downwind area for all cases, except in IL and WI due to zeroIL. In most
cases, the other two metrics behaved fairly similarly in assessing upwind contribution to
downwind ozone nonattainment. Comparisons with assessment from source-
apportionment seemed to show somewhat different contributions, especially to some
states in the Northeastern U.S. Given the nature of the two techniques used here (direct
versus indirect approach to assessing ozone nonattainment), some of these differences
can be placed in perspective. However, the results from this study still present an
opportunity to compare the behavior of two different modeling systems with similar
inputs in addressing regional air quality issues. The results presented here are limited to
the extent that the zero-out simulations were available only for three states for both
modeling systems. Additional zero-out simulations performed for other states might help
to validate the findings discussed here.
ACKNOWLEDGMENTS
The author gratefully acknowledges the U.S. EPA’s Office of Air Quality Planning and
Standards, which provided the results from all the model simulations that were used for
these analyses, and the immense contributions from the various modeling centers that
participated in the OTAG study. The author would also like to acknowledge the editorial
assistance provided by Jeanne Eichinger in reviewing this manuscript and providing
useful comments.
DISCLAIMER
The opinions presented in this paper are those of the author alone and do not reflect those
of any other agency or organization. Mention of model names does not constitute
endorsement or recommendation for use in any manner.
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