Attachment 15 UMD WOE Section.pdf
The Relationship between Urban Tree Cover and Ground Level Ozone 

March 15, 2007 

Charles Piety 

Department of Atmospheric and Oceanic Sciences 

University of Maryland 

College Park, MD 

Abstract 

Tree cover in urban areas plays an important role in the complex system of 

ground level ozone production. Numerical modeling studies predict a decrease in surface 

temperatures (on the order of 1-2 deg C) will occur when more (estimates range from 10-

40% over urban areas) trees are planted in an urban environment. Corresponding 

changes in wind speed will occur in modified areas as well. These changes are the result 

of the lessening of the urban “heat island” effect. Heat islands form as cities replace 

natural land cover with pavement, buildings, and other infrastructure. The heat in a heat 

island is the result of the different radiative properties (i.e. the ability of objects to absorb 

and/or reflect the sun’s energy) between urban and rural areas. Urban areas are more 

efficient at absorbing sunlight than rural areas. The excess energy absorbed by urban 

areas is eventually reradiated back to the atmosphere in the form of heat (aka infrared 

radiation). Weakening of the heat island effect results in a transfer of energy from the 

urban areas to downwind areas which causes some locations to observe a slight increase 

in surface temperatures (1-2 °C) and in wind speeds (1-2 ms

-1). The modeled 

temperature changes are accompanied by changes in ground level ozone concentrations. 

Owing to the complexity of the system, changes in ozone, like temperature, are not 

confined to locations where trees are planted. Different locations downwind of modified 

areas observed either increased or decreased ozone levels depending on a variety of 

factors. The reduction of surface temperature (specifically, cooling in urban areas) 

obtained from the numerical modeling studies was used as the basis for predicting 

changes in daily mean peak 8-hour ozone levels derived from a multiple linear regression 

model for the Baltimore Non-Attainment Area. Results from both methods of study 

(numerical and multiple linear regression) suggest that decreases in ground level ozone 

concentrations on the order of 1-3 ppbv could be realized with an increase in urban tree 

cover ranging from 20 - 40%. Averaged over the entire modeling domain of the 

Northeastern and Mid-Atlantic U.S., this change would amount to ~1-2% increase in tree 

cover. It is believed that the genera of trees used to reforest is important, but what 

species of tree offers the most benefit is not clear and may differ from locale to locale. 

Other significant, logistical questions also need to be considered when planning a largescale tree planting program. The general consensus from the studies mentioned above 

suggests that increased tree cover has a beneficial effect on local air quality by reducing 

ozone in and around urban areas. The major decreases in ozone occur near urban areas 

1 Appendix G Attachment 15 - Page Number:

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where the 8-hour ozone NAAQS is not met, while the minor increases in ozone occur in 

upwind areas where the NAAQS is met. Thus, the net impact is favorable for reaching

the standards across entire Baltimore Non-Attainment Areas. It should be noted that any 

plan involving tree planting to improve air quality should be viewed as a long term plan 

(e.g. 10-20 years) given the growth rate of many of the trees considered. However, in the 

short term, tree cover reduction should be avoided or temperatures will rise, as will 

ozone. 

Introduction

The Roman poet Caecilius Statius more than 2000 years ago wrote “we plant trees 

not for ourselves, but for future generations.” The words are as true today as they were 

then. The role that trees play in the air quality of an urban environment for several years 

has been a subject of keen interest to air quality planners both from a policy and scientific 

perspective. The interest has been motivated by the principal desire to improve air 

quality. Trees have important functions in the urban environment that are easily 

observed: Trees provide shade, create habitats for flora and fauna, limit soil erosion and 

are often esthetically appealing. However, trees may also have a subtle, more complex 

connection to air quality that is challenging for scientists to observe and understand. For 

instance, through a series of complex relationships, trees have a connection to ground 

level ozone and PM2.5. The degree to which trees add to pollutant levels is dependent on 

the amount and type of biogenic volatile organic compounds (BVOC) emitted by the 

trees (Lee et al., 2006, Geron, et al., 2001, Moukhtar et al., 2005). Trees may affect air 

quality by changing meteorological parameters such as surface temperatures, boundary 

layer winds, and vertical mixing heights. These changes occur because energy transfer 

between the Earth’s surface and the atmosphere is altered though factors such as surface 

roughness, albedo and soil moisture (1988, Taha et al., 1996, Rossa et al., 2005). Trees 

can also impact air quality indirectly through changes to dry deposition rates (Chameides 

et al.). Dry deposition is a removal process for atmospheric species that denotes direct 

transfer of species to the Earth’s surface. Wet deposition is a similar process, but one that 

involves precipitation. Understanding the connection between trees and air quality in an 

urban area, as well as considering some of the challenges associated with a large-scale 

tree-planting program are important issues that need to be addressed prior to the planning 

and implementation of such a plan. 

Background 

Several studies have been undertaken both nationally and internationally to study 

how trees influence air quality. Perhaps the most relevant investigation was a recent joint 

research effort undertaken by the New York Department of Environmental Conservation, 

the USDA and the Davey Resource Group (Bond, 2006). The work addressed important 

considerations of large-scale tree planting for a state implementation plan. The overall 

goal of Bond’s work was to examine the feasibility of a large scale tree planting program. 

Others studies have focused on particular aspects of the problem of wide scale tree 

planting in urban areas such as: 

What genera of trees emit the least BVOC’s? 

What is the effect on meteorology from large scale tree planting? 

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What are the diurnal and seasonal variations of BVOC’s emissions from trees? 

(Nowak et al., 2000, Civerolo et al., 2000, et al., Geron, 2001, Donovan, 2005). The 

tools used to answer these questions were state-of-the-art meteorological (i.e., MM5, 

WRF) and photochemical (i.e., CMAQ and CAMx) 3-dimensional gridded airshed 

models. The changes associated with large scale tree planting were assayed by 

performing meteorological and photochemical simulations using existing land use 

inventories and comparing those results to model runs with increased tree cover in urban 

areas. The simulations were typically performed on historical time periods (i.e. the ozone 

seasons of 1988, 1995, and 2003) when high ozone occurred (Cardelino and Chamedies, 

1990, Civerolo et al., 2000, Taha, 2005). 

Results from a study of a high ozone period in Atlanta, GA. on June 4th, 1984 

suggested that a reduction in tree cover of 20% would increase maximum peak 1-hour 

ozone concentrations by ~14% (Cardelino and Chamedies, 1990). Increase in 

temperature was the principal cause of the increased ozone. Investigations of other cities 

such as New York City and Los Angeles concluded that in order to achieve a reduction in 

peak 8-hour ozone on the order of 2-3 ppbv, eight-to-ten million trees would need to be 

planted in these large urban areas (Luley and Bond 2002, Taha, 2005). More recent work 

investigated the diurnal and seasonal variation of isoprene concentrations and its effect on 

peak 1-hour ozone levels in Taiwan (Lee and Wang, 2006). It was determined that the 

timing of isoprene flux from trees, which is different from season to season, is important 

in shaping daily ozone profiles and may in some cases be more significant than the 

reactivity of isoprene. Another study of air quality in Taiwan used remote sensing and 

numerical modeling to assess the impact of biogenic emissions on ground level ozone 

concentrations (Jeng and Chang, 2004). The conclusion was that the majority of biogenic 

emissions occurred in farmland and mountain areas downwind from the major urban 

areas in South Taiwan. Therefore, compared to anthropogenic emissions, biogenic 

emissions have a lower impact on air quality than anthropogenic sources. The work by 

Jeng and Chang highlights the important point that regions with different geographic 

patterns (i.e. land use and topography) will not have the same relative contributions from

biogenic and anthropogenic emissions and therefore will likely require different tree 

planting strategies. Recent simulations of a high ozone period in Birmingham, England 

produced results of reductions in urban ozone comparable to the work by Luley, Bond, 

and Taha when 10% of the available planting space was filled with low-VOC emitting

trees (Donovan et al., 2005). The Donovan study stressed the importance of planting 

trees that are low-VOC emitters. In that work, trees were ranked based on observations

and simulations for their potential to impact air quality. The least favorable trees were 

the genera poplar, willow and oak, while the most beneficial were maples, hawthorns and 

pines. It should be noted that there is some disagreement within the scientific community 

regarding what genera of trees are most suitable for planting. For instance, the Donovan 

study ranked oak trees as some of the worst trees to select for planting because of their 

comparably high levels of isoprene emission. The Davey group on the other hand, 

favored the inclusion of oak trees, sighting the high variation in isoprene emissions 

among oak tree species as well as other considerations such as the drought tolerant nature 

of oaks and the endemic nature of oaks in many parts of the country. The divergence 

between the two investigations underscores the complexity and evolving nature of this 

3 Appendix G Attachment 15 - Page Number:

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research. Efforts to better understand this system continue with both the Donovan and 

Davey groups. 

One of the most important ways that trees can alter air quality is believed to be 

surface modifications, which lessen the “heat island” effect, which in turn reduces surface 

temperatures. Other secondary effects include changes in wind fields and planetary 

boundary layer heights. The modeling study by Nowak et al. (2000) demonstrated that 

increased tree cover lowered surface temperatures in areas where tree cover was 

increased and in some cases also in locations downwind. The sensitivity of MM5 to 

changes in tree cover was determined by comparing simulations using an existing land 

use data base and simulations using a ~ 40% increased tree cover of deciduous trees in 

grid cells containing the urban areas of New York, NY., Philadelphia, PA., and 

Baltimore, MD. In extreme cases modeled temperature differences were as much as 6°C 

with more typical differences on the order of ~1-2°C (Nowak et al., 2000, Civerolo et al., 

2000). Considering the complex nature of ground-level ozone production, it is perhaps 

not surprising that the work by Nowak et al. (2000) presented results that showed a slight 

increase in ozone over the entire model domain including the northeastern U.S. urban 

corridor and extending from northern Virginia to Massachusetts. A 0.26 ppbv increase 

over the entire domain was less than the average 1 ppbv decrease in ozone observed over 

urban areas. The peak decrease over urban areas was 2.4 ppbv. Part of the net increase 

was the result of higher nighttime ozone levels (~ 1 ppbv), caused by weaker wind speeds 

and the increased loss of NOx (a scavenging agent of ozone) through higher NOx

deposition rates. The general consensus from the studies mentioned above suggests that 

increased tree cover has a beneficial effect on local air quality by reducing ozone in and 

around urban areas. The major decreases in ozone occur near urban areas where the 8-

hour ozone NAAQS is not met, while the minor increases in ozone occur in upwind areas 

where the NAAQS is met. The net impact is favorable in terms of entire regions meeting 

the NAAQS. 

It is worth noting that there are other questions relevant to the planning aspects of 

large-scale tree planting which are more logistical in nature but nonetheless are an 

important consideration. Some of these issues include: 

• Where will all of the trees come from? 

• How long will it take to grow a stand of trees? 

• What work force will be used to plant the trees? 

• How will the work force be trained? 

• Is there a plan to deal with problems such as disease or extreme drought 

that could lead to failure of the tree planting program? 

• How much of the existing tree population will be lost to land

development?

Methodology and Results 

Another method to assay potential improvements in ozone from increased tree 

planting is to review historical correlations between ozone and temperature. Multiple 

linear regression models have been used for several decades to forecast daily maximum 

ozone (Wolff and Lioy, 1978; Ryan et al., 2000). In order to develop a new statistical 

correlation between ozone and temperature, an estimate of changes in peak 8-hour ozone 

levels can be made by applying adjusted temperature data which reflect cooler 

4 Appendix G Attachment 15 - Page Number:

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temperatures resulting from increased tree cover. While temperature is the strongest 

predictor of ozone it is not the only parameter important to ozone formation. Wind 

speed, cloud cover, and boundary layer heights are among a number of other important 

factors. However, for this study, only temperature adjustments are considered. All other 

parameters used in the linear regression model (i.e. minimum temperature, wind speed, 

relative humidity) are held constant. The next portion of this section attempts to 

correlate, using statistical techniques, changes in ozone over a region associated with 

temperature changes that might be expected from a large-scale tree planting program. 

Figure 1, a plot of daily peak 8-hour ozone for the Baltimore Non Attainment Area versus 

maximum temperatures for Baltimore Washington International Airport, illustrates the 

general correlation between temperature and ozone. In light of the modeling work 

discussed above, the University of Maryland used a multiple linear regression model to 

calculate peak 8-hour ozone levels. Adjusted temperature data used in the model were

derived by Nowak et al. (2000). Using ozone and meteorological data from the 

Baltimore Non-Attainment Area covering the time period 1994-2005, a multiple linear 

regression model was developed to predict daily mean peak 8-hour ozone values for the 

Baltimore Non-Attainment Area. The model was created with the aid of the analytical

software program SPSS®. Using a forward, stepwise regression technique, the 

correlation coefficients in Equation 1 in Appendix A for the meteorological variables 

were determined. From this regression model, a sensitivity of ozone to reductions in 

temperature was calculated (see Equation 1 in Appendix A).. It was determined that a 

decrease in maximum temperature of 1°F corresponded to ~ 2 ppbv reduction in the mean 

peak ozone levels (see Table 1 and Figure 2). When considering a subset of high ozone 

days when the daily 8-hour peak ozone ≥ 85 ppbv, the slope increased so that a cooling of 

1°F resulted in a ~ 3 ppbv decrease in the daily peak 8-hour ozone levels (Table 1). The 

change in slope, which is a result of the inherent non-linearity of the ozone-meteorology 

relationship, implies that at higher temperatures ozone production is more sensitive to 

temperature changes than at lower temperature. Some of the causes of the nonlinearity 

are rates of chemical reactions, precursor emissions and planetary boundary layer 

processes. The work by Nowak et al. (2000) estimated a temperature decrease of 1-2°C 

(2-4°F) as a direct result of a 40% increase in tree cover. From Figure 2, this implies a 

change in the mean peak 8-hour ozone of from 4-8 ppbv. Given that only temperature 

was considered here, it is reasonable to assume that the range of 4-8 ppbv is perhaps the 

upper limit, as many of the other meteorological predictors (e.g. minimum temperature) 

are negatively correlated with ozone. Civerolo et al. (2000) reported a slight lowering of 

both minimum temperatures and maximum temperatures from increased tree cover. 

Thus, it is reasonable to conclude that a change in maximum temperature will be 

associated with other subtle changes in weather that may slightly offset maximum 

temperature changes. For instance, inspection of Equation 1 in Appendix A suggests that 

the slight lowering of minimum temperatures would counter some of the benefit of cooler 

maximum temperatures. Considering all of the ancillary weather changes associated with 

an increase in tree cover, perhaps a more conservative estimate of changes in daily peak 

8-hour ozone as reflected by a multiple linear regression model would be closer to 2-4 

ppbv. 

5 Appendix G Attachment 15 - Page Number:

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0

20

40

60

80

100

120

140

60 65 70 75 80 85 90 95 100 105

Tmax (1⁄4F)

Figure 1. A scatter plot of the daily 8-hour peak ozone over the Baltimore Non 

Attainment Area versus the daily maximum temperature at BWI Airport for the 

year 2002 (May-September, N = 138, Tmax ≥ 70°F). 

6 Appendix G Attachment 15 - Page Number:

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Table 1. Observed and predicted ozone in parts per billion (ppbv) for the summer of 2002 over 

the Baltimore Non-Attainment Area.

Observed Tmax0 Tmax0-0.5 Tmax0-1.0 Tmax0-1.5 Tmax0-2.0

Average 

Maximum 8-hour 

Ozone for153 

cases (ppbv) 

71 69 68 67 66 65

Average Peak 8-

hour Ozone for 

subset of 39 High 

Ozone cases (≥ 85 

ppbv) 

101 92 90 89 88 87

These results come from the regression analysis for adjusted maximum temperature values for 

the summer of 2002 (May-September). The subset of high ozone cases shown in row two are 

defined as 8-hour maximum ozone ≥ 85 ppbv. 

50

55

60

65

70

75

Observed T(-0) T(-0.5 1⁄4F) T(-1.0 1⁄4F) T(-1.5 1⁄4F) T(-2.0 1⁄4F) T(-2.5 1⁄4F)

Mean Peak 8-hour ozone (ppbv)

Observed

T(-0)

T(-0.5 1⁄4F)

T(-1.0 1⁄4F)

T(-1.5 1⁄4F)

T(-2.0 1⁄4F)

T(-2.5 1⁄4F)

Figure 2. Observed and predicted mean daily maximum 8-hour ozone over the Baltimore 

Non-Attainment Area. Predicted data was determined from a regression analysis 

using meteorological and ozone data from 1994-2005. The maximum 

temperature was reduced by 0.5oF increments, and mean daily maximum 8-hour

ozone was recalculated using the adjusted temperature data.

7 Appendix G Attachment 15 - Page Number:

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Conclusions 

 This analysis presents results of studies performed by the University of Maryland 

and other research institutions that support the theory that increased tree cover in urban 

areas results in a decrease in peak ground level ozone concentrations. A review of 

current literature suggests that the changes in meteorology (i.e. surface temperatures, and 

boundary layer winds), dry deposition rates and BVOC levels that are associated with 

increased tree cover over urban areas, could lead to a decrease of 1-2 ppbv in daily mean 

peak 8-hour ozone. Land use modifications to 10-40% of the urban area land surface, or 

to 1-2% of the model domain (e.g. the Baltimore Non-Attainment Area) are needed to 

create the change in peak 8-hour ozone concentrations. The change in ozone (and 

meteorological parameters) is typically observed in and downwind of the urban areas. 

Some studies determined that over the entire modeling domain there is a net increase in 

ozone when more trees are planted. However, this increase is smaller in magnitude than 

the improvements observed in urban areas. The small increases in ozone over non-urban 

areas (where regional daily peak 8-hour ozone levels rarely occur) is perhaps less 

significant in terms of air quality planning than reductions observed in and just 

downwind of an urban area. It is the latter locations that historically have had the poorest 

air quality and thus stand to benefit the most. 

Using historical ozone and meteorological data from the Baltimore NonAttainment Area and BWI airport, a statistical correlation determined the sensitivity of

ozone to changes in temperature for the Baltimore Non-Attainment Area. From the 

results of the work by Civerolo et al. (2000), and Nowak et al. (2000), an estimate of 

modeled temperature changes that would result from large scale tree planting was 

obtained. Derived temperature changes from the photochemical modeling exercises were 

used as a starting point for what might be a reasonable temperature change associated 

with tree planting. These modeled temperature changes were then applied to the 

statistical relationship between ozone and maximum temperature. The relationship was 

derived from peak 8-hour ozone data for the BNAA and the maximum temperature 

values from BWI. The correlation was used to form the estimate that a cooling of 1-2°F 

would lead to a 2-4 ppbv decrease in peak 8-hour ozone levels. , thereby demonstrating 

evidence that trees reduce ozone indirectly. The numerical models cited in the report 

focused on the benefits to ozone levels from an increased tree canopy. While ozone is 

often correlated with haze in the summer, and some ozone precursors also contribute to 

summertime PM2.5 formation, it is unclear what effect large scale tree planting in urban 

areas will have on urban PM2.5 levels. 

Future Work

Future work should focus in part on improving existing land use databases, 

specifically, the inclusion of up to date land use inventories. This is important because

land use is constantly changing so that any predicted change to tree cover distribution 

must consider land use changes that have occurred since the last collection of land use 

information. Efforts to improve estimates of changes in air quality from large scale tree 

planting should also focus on improving meteorological modeling to take advantage of 

better land use estimates. Refined emissions inventories that better describe BVOC 

emissions from particular tree genera would provide policy makers with additional 

8 Appendix G Attachment 15 - Page Number:

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information to determine which species of tree are most suitable for planting. Future 

work at the University of Maryland involves a plan to run NCAR's Coupled 

WRF/Noah/Urban-Canopy Model in a nested-grid (27/9/3/1 km) mode. A nested grid 

has successively smaller domains (with successively better grid resolutions) nested 

together. One major advantage of a nested grid model is that the finer-scale models 

employ data from the larger domains as input (e.g. boundary conditions), which, in 

theory, creates a more stable modeling environment. This version of the Coupled 

WRF/Noah/Urban-Canopy Model will be run for the Washington-Baltimore metropolitan 

region for a period of one week (at the finest grid size of 1km) when ozone 

concentrations were elevated. 

After evaluating the model-simulated surface meteorology by comparison against 

observations, three urban tree scenarios will be examined over the region to assay the 

sensitivity of the model-simulated surface conditions to varying tree canopies: 

unchanged base tree population, increased tree canopy of 10%, and increased tree canopy 

of 30%. 

9 Appendix G Attachment 15 - Page Number:

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Appendix A: A Regression equation used to determine sensitivity of ozone to changes 

in temperature. 

The variables listed below were used to create the multiple linear regression equation 

used to test the sensitivity of ozone to temperature changes. From the input variables 

listed below, Equation 1 was created using the analytical software program SPSS®.. 

SPSS® is a modular, integrated, software program used for analytical processing such as 

data management and preparation, data analysis, reporting, and deployment. The 

program used a forward, stepwise regression algorithm to select the input variables (from

a larger set of meteorological input variables) below. The meteorological data was 

obtained from the National Climate Data Center (NCDC). The response of ozone to the 

adjusted temperatures was determined by feeding the new temperatures into Equation 1, 

along with the other meteorological variables (which were unchanged) for each case in 

2002. From those values the average responses for each temperature range was obtained. 

Input Variables: 

Tmax = Maximum Temperature (oF) 

Tmin = Minimum Temperature (oF) 

RH19-21UTC = Average Relative Humidity between 19-21 UTC (%) 

WS18UTC = Wind Speed at 18 UTC (ms

-1) 

WS85012UTC = 12 UTC Wind Speed at 850 MB (ms

-1) 

SZA = Solar Zenith Angle 

LAG8hr = The previous day’s peak 8-hour ozone (ppbv) 

Output: 

[O3]8hr = Daily maximum 8-hour ozone 

Equation 1: 

[ ]

2

12 8

max min 19 21 18

3 8 0.026 850 030 0.019

2.774 0.113 0.062 0.003 0.015

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

∗ − ∗ + ∗

+ ∗ − ∗ − ∗ − ∗ − = −

UTC hr

UTC UTC

hr WS SZA LAG

T T RH WS

O

10 Appendix G Attachment 15 - Page Number:

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References: 

Bond, J., 2006 The Inclusion of Large Scale Planting in a State Implementation (A 

Feasibility Study). Davey Resource Group., Prepared for the National Tree Trust by the: 

Davey Resource Group, 3904 Willowdale Lane, Geneva, New York 14456. 

jbond@davey.com 315.585.9145. 

Cardelino, C.A. and Chameides, W.L., 1990. Natural hydrocarbons, urbanization and 

urban ozone. J Geophys. Res., 95(D9). 13971-13979. 

Chameides, W.L., Lindsay, R.W., Richardson, J. and Kiang, C.S., 1988. The role of 

biogenic hydrocarbons in urban photochemical smog: Atlanta as a case study. Science, 

241, #4872, 1473-1475. 

Civerolo, K., Sistla, G., Rao, S.T. and Nowak, D.J. 2000. The effects of land use in 

meteorological modeling: implications for assessment of future air quality scenarios. 

Atmos. Environ., 34, 1615-1621. 

Geron, C., Harley, P. and Guenther, A., 2001. Isoprene emission capacity for US tree 

species. Atmos. Environ., 35, 3341-3352. 

Jeng, K. Y. and Chang, N. B., 2004. Assessing Biogenic Emission Impact on the 

Ground-level Ozone Concentration by Remote Sensing and Numerical Model. IEEE 

International Geoscience and Remote Sensing Symposium, Anchorage, Alaska 

(Interactive presentation), Sep. 22-24, 2004. 

Lee, B.S. and Wang, J.L., 2006. Concentration variation of isoprene and its implications 

for peak ozone concentration. Atmos. Environ., 40, 5486-5495. 

Luley, C.J. and Bond, J., 2002. A plan to integrate management of urban trees into air 

quality planning. Report to Northeast State Foresters Association. Davey Resource 

Group, Naples, NY. 

Moukhtar, S., Bessagnet, B., Rouil, L. and Simon, V., 2005. Monoterpene emissions 

from Beech (Fagus sylvatica) in a French forecast on secondary pollutants formation at 

region scale Atmos. Environ., 39, 3535-3547. 

Nowak, D.J., Civerolo, K., Sistla, S.T., G., Rao, Luley, C.J. and Crane, D.E., 2000. A 

modeling study of the impact of urban trees on ozone. Atmos. Environ., 34, 1601-1613. 

Rossa, G. D., Hope, E. S., Owen, S.M., Mackenzie, A.R. and Hewitt, C. N., 2005. 

Development and Application of an Urban Tree Air Quality Score for Photochemical 

Pollution Episodes Using the Birmingham, United Kingdom, Area as a Case Study. 

Environ. Sci. Technol., 39, 6730-6738. 

11 Appendix G Attachment 15 - Page Number:

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Taha, H., 1996. Modeling impacts of increased urban vegetation on ozone air quality in 

the South Coast Air Basin. Atmos. Environ., 30 (20), 3423-3420. 

Taha, H., 2005. Urban Surface Modification as a Potential Ozone Air-quality 

Improvement Strategy in California- Phase One: Initial Mesoscale Modeling. Altostratus 

Inc. for the California Energy Commission, PIER Energy-Related Environmental 

Research. CEC-500-2005-128. 

Ryan, W.F., Piety, C.A. and Luebehusen, E.D., 2000. Air Quality Forecasts in the Mid 

Atlantic Region: Current Practice and Benchmark Skill. Wea. and Fore., 15, 46-60. 

Taha H, Konopacki, S. and Gabersek S, 1999. Impacts of large-scale surface 

modifications on meteorological conditions and energy use: A 10-region modeling study. 

Theo. and App. Climo. 62 (3-4): 175-185. 

Tewari1, M., Chen, F. and Kusaka, H., 2006. Coupled WRF/Unified Noah/UrbanCanopy Modeling System, UCAR Research Document. 

http://www.rap.ucar.edu/research/land/technology/urban/WRF-LSM-Urban.pdf

Wolff, G.T. and Lioy, P.J., 1978. An empirical model for forecasting maximum daily 

ozone levels in the northeastern United States. Atmos. Environ., 11, 967-983. 

12 Appendix G Attachment 15 - Page Number:

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Comparison of CMAQ-calculated ozone to surface and aloft measurements 

March 16, 2007 

Dale Allen, Charles Piety, Jeffrey Stehr, and Jennifer Hains 

Department of Atmospheric and Oceanic Sciences 

University of Maryland 

College Park, MD 

Abstract 

In an effort to assess the ability of the Community Multi-scale Air Quality model 

(CMAQ) to replicate ozone patterns, particularly high ozone events over the Ozone 

Transport Region (OTR), comparisons are performed between surface and aircraft ozone 

measurements and CMAQ ozone simulations using the 2002 base case B1 emissions 

inventory. Overall, CMAQ does an excellent job of capturing the mean distribution of 

surface layer ozone during the ozone season. However, the success is somewhat 

misleading. EPA performance criteria may appear to be independent or offer different 

information, but in reality, nearly all criteria are strongly geared toward average 

performance at the surface. In this analysis, UMD explores several other means of 

evaluating the CMAQ model by examining its performance only on high ozone days, by 

separating performance at rural, suburban, and urban sites, and by comparing CMAQ to 

aloft ozone data from aircraft campaigns. The mixed results of these comparisons show 

that CMAQ has critical shortcomings (e.g., transport appears to be underrepresented) that 

appear to be magnified during periods when high ground level ozone concentrations are a 

concern. 

Ozone levels from before the NOx SIP Call were also compared with ozone 

concentrations after the NOx SIP Call. When corrected for meteorological variations, the 

analysis indicates that ozone after the NOx SIP Call improved significantly, suggesting 

that future control programs similar to those implemented over this time period should be 

highly effective as well. 

Comparison with aircraft profiles from 136 Regional Atmospheric Measurement 

Modeling and Prediction Program (RAMMPP) flights reveals that CMAQ has an overall 

high bias of ~15% from the surface to ~500 meters above sea level (ASL) and a low bias 

aloft (600-2600 meters ASL) of ~10%. Agreement between CMAQ-calculated and 

aircraft-measured ozone varies substantially from flight to flight. 

Inspection of the surface maps for the OTR reveals that CMAQ, in general, 

replicates the spatial pattern of high ozone events but often does not capture the full 

spatial extent or magnitude of the high ozone patterns. Mean CMAQ-calculated and 

measured 8-hour ozone values from 66 surface ozone monitors in the Baltimore, 

Washington, D.C., and Philadelphia nonattainment areas are highly correlated 

(correlation coefficient, R, of 0.92) over the ozone season (May 15 – September 15) and 

1 Appendix G Attachment 15 - Page Number:

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well correlated (R=0.81) when a subset of 38 high ozone days (i.e. days when the peak 

daily 8-hour average ozone in Maryland exceeded 85 ppbv) are compared. Biases 

between CMAQ-calculated and observed 8-hour ozone mixing ratios are minimal (-1.6 

ppbv) when averaged over the entire ozone season. However, larger negative biases are 

seen during high ozone days (-2.2 ppbv at urban sites and -7.7 ppbv at rural/suburban 

sites). 

The high bias near the surface and low bias aloft is indicative of an 

underestimation of transport by CMAQ. Aloft is where most transport occurs; groundlevel air does not move as readily. On the highest ozone days, CMAQ’s performance is 

not as good as on lower ozone days. This is a statistical reflection of CMAQ’s inability 

to capture large-scale deviations from average or median conditions. These deviations 

occur on days with poor air quality. CMAQ performs better at urban sites than at 

suburban and rural areas. This bias provides more evidence that CMAQ is missing 

incoming ozone, possibly transport. In some instances, these rural/suburban areas are 

dominated by power-plant emissions more than they are dominated by motor vehicle 

emissions. The bias may also indicate that CMAQ’s relatively coarse vertical resolution

is unable to resolve the transport of point source (i.e. power plant) emissions. In 

particular, performance at upwind sites with fewer nearby sources is poorer on the whole 

than it is at other sites (see Chapter 9). 

None of these shortcomings are reflected in EPA’s traditional ozone model 

performance measures. However, these shortcomings make it necessary to consider 

CMAQ output and other evidence when evaluating the probability of success of State 

Implementation Plans (SIPs). This Chapter goes with Chapter 9 on model performance, 

uncertainty, and responsiveness. 

Introduction

The following analysis details comparisons between ozone observations and 

CMAQ ozone simulations using the 2002 “base case B1” emissions inventory. This 

analysis is important in understanding the limitations of CMAQ and its strengths and 

weaknesses in simulating air quality over the Mid-Atlantic in particular and the Ozone 

Transport Region in general. Understanding basic CMAQ model performance, compared 

to surface and aloft observations, will provide a foundation for additional commentary on 

CMAQ simulations related to future emissions strategies. Some key questions that will 

be addressed by this analysis include: 

• How well does CMAQ replicate the mean transport of ozone and its precursors? 

• How well does CMAQ capture temporal fluctuations in ozone concentrations both 

at the surface and aloft? 

• What do identified CMAQ limitations imply for modeled attainment 

demonstrations using future emissions strategies?

Many of the images of surface comparisons presented in this study employ a map 

which covers a large portion of the Ozone Transport Region (OTR) (see Figure 1). The 

domain in Figure 1 was selected in an effort to present as much of the OTR modeling 

domain as possible while keeping the focus on the Baltimore, Washington, D.C. and 

Philadelphia nonattainment areas (NAA). Additional comparative images were derived 

2 Appendix G Attachment 15 - Page Number:

14
from CMAQ ozone vertical profiles and aircraft ozone vertical profiles collected by the 

University of Maryland research aircraft. Other ozone images include comparisons for 

ozone monitors within the Baltimore, Washington D.C., and Philadelphia NAAs. Time 

series plots were derived from CMAQ simulations of the entire summer of 2002. 

Because there is keen interest in CMAQ model performance when high ozone was 

observed over Maryland, as a subset of the model evaluation, the 38 days when the 

observed peak daily 8-hour average ozone exceeded 85 ppbv (i.e. the NAAQS for 8-hour 

average ozone) somewhere in Maryland were analyzed separately. 

Figure 1. A plot of the correlation coefficients (R) for daily maximum 8-hour ozone over 

the OTC modeling domain (base case B1 vs observations). Time period for the 

plot is the summer of 2002 (May-September). 

Methods 

As mentioned previously, the evaluation of the CMAQ base case B1 simulation 

was performed using surface and aloft observations. Comparisons with surface data were 

conducted using surface ozone maps covering much of the OTR modeling domain, while 

time series plots were produced for ozone monitors within the Baltimore, Washington 

D.C., and Philadelphia NAAs. For surface maps (i.e. 2002 ozone observations, base case 

B1 simulations and difference plots), Delaunay Triangulation was used to map irregularly 

spaced observations and model output onto the desired grid. The surface plots present 

the peak daily 8-hour average ozone and do not represent one particular time of day. The 

3 Appendix G Attachment 15 - Page Number:

15
timing of peak 8-hour ozone concentrations varies with location. For comparisons of 

time series of surface ozone, CMAQ-simulated surface ozone was sampled at locations 

corresponding to the latitude and longitude of surface ozone monitors. 

For CMAQ comparisons to aloft data, the lowest 16 layers of the CMAQ model 

were assumed to be 10, 24, 68, 116, 185, 282, 398, 544, 727, 949, 1212, 1523, 1886, 

2312, 2820, and 3393 meters above ground level (typical altitudes for summertime 

conditions in the northeastern U.S.). These elevations were then converted to meters 

above sea level by adding the surface elevation of each grid point. CMAQ modeled 

ozone was linearly interpolated in altitude and time to best match the location and time of 

aircraft measurements. 

The differences between aircraft and model profiles were calculated, accounting 

for differences in shape (the locations of the minima and maxima in the profiles) and 

magnitude (absolute differences in mixing ratio). All aircraft spiral measurements and 

corresponding CMAQ-calculated ozone fields were initially averaged into 100 m altitude 

bins. This allowed for consistent comparisons between pairs of modeled and measured 

ozone. To calculate the difference between modeled and measured ozone, UMD 

accounted for the shape and morphology of the profiles by looking at the absolute 

difference in the mixing ratio as well as the slope and correlation coefficient between 

modeled and measured ozone in four altitude bins (250-650 m, 651-1150 m, 1151-1650 

m, and 1651-2150 m). The altitude bins were selected in an effort to obtain a statistically 

significant number of data points in each bin while also allowing for some partitioning 

between the planetary boundary layer and the lower free troposphere. Equation 1 

(Taubman et al., 2006, Hains et al., 2007) was used to calculate the difference (Dij) 

between each pair of modeled (i) and measured (j) ozone profiles: 

∑ ∑( ) ( ) =

=

=

=

⎪

⎭

⎪

⎬

⎫

⎪

⎩

⎪

⎨

⎧

= − + − + − − − 4

1

_ 2

2

_1

2

( ) * 1 [1 ] [1 exp( ( 1) ] k

k

Part Part

a m

a n ij ia ja D abs c c R s 14 2 44 4 3 44 14 2 4444 444443 Equation 1 

Here k represents the four different altitude bins, a is an index that can take on values 

between 1 and the total number of data points between 250 and 2150 meters, n (m) is an 

index that point to the first (last) data point in each bin. The number of data points within 

each altitude bin (m-n+1) varies with altitude bin (k). The ozone mixing ratio is 

represented by c for the i

th

 (modeled) and j

th (measured) profiles. A regression between 

CMAQ calculations and observations is calculated, giving the slope, s, and correlation

coefficient, R, for each of the four layers. The first part of Equation 1 calculates the 

square of the sum of the differences between values (from observations and CMAQ) at 

each altitude level, k. The second part of the equation multiplies the difference by one 

plus differences associated with the correlation and slope. When the correlation is small 

or negative, the profiles are very different and the 1- R portion increases, which increases 

the total difference Dij. The exponent of the slope portion is used to account for the slope 

of the best-fit line between CMAQ and observations in each of the profile altitude bins. 

A slope near one suggests that the profiles are similar and should therefore add little to 

the total difference. The exponent of the slope was used to guarantee that slopes much 

different from one would make the exponential term small, thereby increasing the (1-

4 Appendix G Attachment 15 - Page Number:

16
exponent) term and increasing the total difference. Taken as a whole, Equation 1 

becomes large when absolute differences between predicted and observed concentration 

are large, 

Results 

Figure 1 is a plot of R, the correlation coefficient, between peak 8-hour ozone 

surface observations and peak 8-hour ozone values from CMAQ. The correlation 

coefficient indicates the strength and direction of a linear relationship between two 

variables. These correlations were calculated, site-by-site over the ozone season, May 

15- September 15, 2002. Figure 1 shows values of R ranging from 0.55 – 0.90, with the 

poorest correlation occurring over northwestern OH, northern VA and northwestern PA. 

Correlations are better over much of the Interstate-95 corridor from Washington, D.C. to 

Boston, MA with values ranging from 0.75 - 0.90. Over the Mid Atlantic region, R

ranges from 0.60 – 0.90, with the poorest correlations occurring over central-western VA. 

Model performance is poorer over the Ohio River Valley (R=0.65-0.80). Figure 1 

demonstrates that on average, CMAQ simulates peak 8-hour ozone reasonably well. The 

relatively low correlations over some portions of the Ohio River Valley and central VA

are interesting. These regions are often upwind of the Mid-Atlantic region during major 

pollution events. The poorer performance in these regions could indicate that CMAQ is 

not accurately representing ozone pollutant transport into the Mid Atlantic region from

these two regions. More conclusive statements must await comparisons with aircraft

measurements taken downwind of the Mid-Atlantic region. 

Table 1 lists the surface monitors used in the surface comparisons presented in

Figures 2 and 3. Information on how CMAQ performs at individual monitors is also 

shown in Figure 1. For example, CMAQ-calculated and measured 8-hour ozone at 

Edgewood is well correlated (R=0.84). The mean bias (-2 ppbv) is small but larger on 

high ozone days (-9 ppbv). The best fit line to the relationship between CMAQcalculated (x) and measured (y) 8-hour ozone at Edgewood is given by y = 10.0 + 0.88x. 

Figure 2 is a time series of the average, peak 8-hour ozone values, from surface 

comparisons of 66 ozone monitors located within Virginia; Maryland; Washington, D.C.; 

and the Philadelphia non-attainment area. Figure 3 shows surface ozone monitor data 

categorized by EPA’s classification of monitor location (urban, suburban and rural). 

Table 2 presents summary average statistics associated with Figure 2 and Figure 3. 

Considering 66 monitors, CMAQ displays a 1.6 ppbv low bias and a 7.0 ppbv centered 

root mean square error (RMSc) for the entire ozone season and a low bias of 7.0 ppbv and 

an RMSc of 5.8 ppbv when only the 38 high ozone days are included. The correlation 

coefficients, R, are 0.92 and 0.81 for the entire 2002 ozone season and the high ozone 

days respectively. The lower value for R on high ozone days is a consequence of 

subsampling the data set and does not by itself imply poorer model performance on high 

ozone days. Figure 3 shows that biases are small when averaged over the ozone season

ranging from a 2.4 ppbv low bias at suburban sites to a 1.5 ppbv low bias at urban sites. 

Biases are larger on high ozone days ranging from a 2.2 ppbv low bias at urban sites to a 

7.7 ppbv low bias at suburban and rural locations. Differences in biases between urban 

and suburban/rural locations should not be over interpreted as the site classification by 

EPA appears to be dated. Several sites (e.g., Rockville, MD or Greenbelt, MD) 

designated as “rural” are now suburban. The larger biases at suburban/rural locations 

5 Appendix G Attachment 15 - Page Number:

17
exist because CMAQ tends to underestimate the spatial extent of high ozone events. i.e., 

it tends to underestimate the regional character of ozone episodes. Both deficiencies in 

the chemical algorithm (see Chapter 4) and transport (it is often underestimated) may be 

responsible for these differences in biases. Correlation coefficients are insensitive to 

monitor location. Overall, urban performance is better than suburban, which is in turn 

better than rural performance. The poorer performance at more rural locations could be 

caused by CMAQ underestimating the relative importance of ozone and precursor 

transport in determining ozone amounts. 

Figure 2. Comparison of the average peak 8-hour ozone from 66 surface ozone monitors in 

Virginia, Maryland, Washington D.C., and the Philadelphia NAA. Out of the 123 days, 38 

days are considered “high ozone days” when 8-hour peak ozone is greater than or equal to 85 

ppbv at one or more monitors within the Baltimore NAA. CMAQ is generally lower than the 

peak observations and higher than the nighttime minima. 

6 Appendix G Attachment 15 - Page Number:

18
Figure 3. Surface ozone comparisons for (a) urban, (b) suburban, and (c) rural surface

sites in Virginia, Maryland, Washington D.C., and the Philadelphia non-attainment area

(n = the number of monitors).

7 Appendix G Attachment 15 - Page Number:

19
Table 1. CMAQ performance at individual monitors 

Site ID Monitor Name Classificationa Latitude Longitude Rb RMSc Bias (all 

days) 

Bias (high 

O3 days) 

100010002 Killens Pond R -75.56 38.98 0.82 10.2 -2.1 -8.7 100031007 Lums Pond R -75.73 39.55 0.84 10.7 5.3 -0.9 100031010 Brandywine R -75.56 39.82 0.85 11.7 -2.3 -8.7 100031013 Bellefonte S -75.5 39.77 0.85 10.6 3 -0.2 100051002 Seaford S -75.61 38.64 0.82 10.5 -3.4 -10.1 100051003 Lewes R -75.16 38.78 0.85 9.6 -0.3 -6 110010025 Takoma Park U -77.02 38.98 0.89 9.2 4 1.5 110010041 River Terrace U -76.95 38.9 0.89 9.4 -2.2 -6.9 110010043 McMillan Reser U -77.01 38.92 0.9 9.2 -8.1 -13.8 240030014 Davidsonville R -76.65 38.9 0.88 10.3 -4.5 -10.4 240030019 Fort Meade S -76.73 39.1 0.88 9.8 -2.4 -5.8 

a Monitor classification (Urban, Suburban, or Rural) from http://www.epa.gov/air/data/index.htmlb Correlation coefficient (R) c Centered root mean square error after removing bias (a positive bias indicates that CMAQ-calculated 8-hour ozone exceeds measured Appendix G Attachment 15 - Page Number:20
Table 1 (continued) CMAQ performance at individual monitors 

Site ID Monitor Name Classificationa Latitude Longitude Rb RMSc Bias (all 

days) 

Bias (high 

O3 days) 

240051007 Padonia S -76.63 39.46 0.83 11.5 -1.2 -6.7 240053001 Essex S -76.47 39.31 0.82 13.6 2.4 1.9 240130001 South Carroll R -77.04 39.44 0.82 9.9 0.3 -4.6 240150003 Fair Hill R -75.86 39.7 0.85 12 -1.6 -10.5 240170010 S. Md

(Hughesville) 

R -76.81 38.5 0.84 10.3 0.1 -6.6 

240210037 Frederick Apt S -77.38 39.41 0.85 9.6 -0.4 -5 240251001 Edgewood R -76.3 39.41 0.84 12.4 -2 -9 240259001 Aldino S -76.2 39.56 0.87 11.1 -3.2 -11 240290002 Millington R -75.8 39.31 0.83 11.3 -2.8 -11.5 240313001 Rockville R -77.11 39.11 0.86 9.9 5.3 2.5 

a Monitor classification (Urban, Suburban, or Rural) from http://www.epa.gov/air/data/index.htmlb Correlation coefficient (R) c Centered root mean square error after removing bias (a positive bias indicates that CMAQ-calculated 8-hour ozone exceeds measured Appendix G Attachment 15 - Page Number:21
Table 1 (continued) CMAQ performance at individual monitors 

Site ID Monitor 

Name

Classificationa Latitude Longitude Rb RMSc Bias (all 

days) 

Bias (high 

O3 days) 

240330002 Greenbelt R -76.83 39.02 0.87 10.3 1.2 -2.4 240338003 PG Equest Ctr R -76.74 38.81 0.83 12.1 2.7 -4.7 240430009 Hagerstown R -77.72 39.57 0.87 9 -2.7 -6.7 245100053 Baltimore

County 

U -76.55 39.29 0.78 14.7 5.9 5.7 

340010005 Nacote Creek R -74.46 39.53 0.85 9.6 3 -1.5 340070003 Camden Lab S -75.1 39.92 0.84 12.2 -3.3 -7.5 340071001 Ancora

Hospital 

R -74.86 39.67 0.88 10.7 -3.5 -8.7 

340110007 Millville R -75.03 39.42 0.87 10.3 -1.6 -8.4 340210005 Rider U S -74.75 40.28 0.86 11.6 -2.6 -8.5 340290006 Colliers Mills R -74.45 40.07 0.85 13.2 -4.6 -12.7 

a Monitor classification (Urban, Suburban, or Rural) from http://www.epa.gov/air/data/index.htmlb Correlation coefficient (R) c Centered root mean square error after removing bias (a positive bias indicates that CMAQ-calculated 8-hour ozone exceeds measured Appendix G Attachment 15 - Page Number:22
Table 1 (continued) CMAQ performance at individual monitors 

Site ID Monitor Name Classificationa Latitude Longitude Rb RMSc Bias (all 

days) 

Bias (high 

O3 days) 

420170012 Bristol S -74.88 40.11 0.87 11.6 -0.5 -4.8 420290050 West Chester S -75.6 39.94 0.87 10.1 -5.7 -11.7 420290100 New Garden

(Ai 

R -75.77 39.83 0.85 11.8 -7.7 -17.3 

420450002 Chester U -75.37 39.84 0.86 11.4 -4.1 -10.1 420910013 Norristown S -75.31 40.11 0.87 10.4 0.1 -3.7 421010004 Frankford (Lab U -75.1 40.01 0.84 11.2 13.6 13.3 421010014 Northwest (Rox S -75.24 40.05 0.84 11 -1.9 -4.3 421010024 Northeast (Air S -75.01 40.08 0.88 11.2 -6.6 -12.9 421010136 Southwest (Elm U -75.22 39.93 0.86 11 5.3 1.9 510130020 Arlington Co. U -77.06 38.86 0.9 9.7 -4.5 -11.3 

a Monitor classification (Urban, Suburban, or Rural) from http://www.epa.gov/air/data/index.htmlb Correlation coefficient (R) c Centered root mean square error after removing bias (a positive bias indicates that CMAQ-calculated 8-hour ozone exceeds measured Appendix G Attachment 15 - Page Number:23
Table 1 (continued) CMAQ performance at individual monitors 

Site ID Monitor Name Classificationa Latitude Longitude Rb RMSc Bias (all 

days) 

Bias (high 

O3 days) 

510330001 Caroline Co. R -77.38 38.2 0.76 10.1 2.1 1.3 510360002 Charles City S -77.26 37.34 0.84 12.1 -4.6 -11.9 510410004 Chesterfield R -77.59 37.36 0.76 11.2 -4.6 -12.7 510590005 Chantilly R -77.47 38.89 0.86 9.3 -5.6 -10.1 510590018 Mt. Vernon S -77.08 38.74 0.85 11.7 2.1 -4.4 510590030 Lee Park S -77.11 38.77 0.88 10.3 -3.6 -10.3 510591005 Annandale S -77.16 38.84 0.83 12.1 -1.8 -7.1 510595001 McLean S -77.2 38.93 0.79 12.3 6.4 3.7 510610002 Fauquier Co. R -77.77 38.47 0.77 9.7 -0.2 -7 510690010 Frederick Co. R -78.08 39.28 0.8 10.6 -0.9 -7 510850003 Hanover Co. S -77.22 37.61 0.8 11.6 -5.3 -14.3 510870014 Henrico Co. S -77.4 37.56 0.85 9.9 -5.1 -9.1 

a Monitor classification (Urban, Suburban, or Rural) from http://www.epa.gov/air/data/index.htmlb Correlation coefficient (R) c Centered root mean square error after removing bias (a positive bias indicates that CMAQ-calculated 8-hour ozone exceeds measured Appendix G Attachment 15 - Page Number:24
Table 1 (continued) CMAQ performance at individual monitors 

Site ID Monitor Name Classificationa Latitude Longitude Rb RMSc Bias (all 

days) 

Bias (high 

O3 days) 

511071005 Loudoun Co. S -77.49 39.02 0.85 10.3 -6.6 -13.7 511130003 Madison Co. - R -78.44 38.52 0.75 9.3 -9.1 -15.1 511390004 Page Co. R -78.5 38.66 0.66 10.4 -2 -7.4 511530009 Prince William S -77.64 38.86 0.81 10.3 -3.4 -12.1 511611004 Roanoke Co. S -79.88 37.29 0.8 9.2 -4.9 -9.8 511630003 Rockbridge Co. R -79.51 37.63 0.61 11.2 -2.2 -7.8 511790001 Stafford Co. S -77.37 38.48 0.87 9.2 -0.3 -4.6 511970002 Wythe Co. R -81.25 36.89 0.72 9.8 -6.6 -12.4 515100009 Alexandria U -77.04 38.81 0.88 9.7 4.9 -0.3 516500004 Hampton S -76.4 37 0.72 14.8 -9.9 -11.7 518000004 Suffolk - TCC S -76.44 36.9 0.86 9.4 0 -5.1 518000005 Suffolk - Holl S -76.73 36.67 0.79 11.9 -3.4 -12.6 

a Monitor classification (Urban, Suburban, or Rural) from http://www.epa.gov/air/data/index.htmlb Correlation coefficient (R) c Centered root mean square error after removing bias (a positive bias indicates that CMAQ-calculated 8-hour ozone exceeds measured Appendix G Attachment 15 - Page Number:25
Table 2. Comparison of 2002 Base B1 Simulation and 2002 Observations 

Bias (ppbv) RMS error* R**

All 66 Sites

Entire ozone

Season 

-1.6 7.0 0.92

High ozone Days -7.0 5.8 0.81 

Urban Sites

Entire ozone

Season 

-1.5 7.7 0.92

High ozone Days -2.2 7.1 0.86 

Suburban Sites 

Entire ozone

Season 

-2.4 7.0 0.92

High ozone Days -7.7 6.3 0.78 

Rural Sites 

Entire ozone

Season 

-1.7 7.5 0.90

High ozone Days -7.7 6.1 0.76 

* Centered root mean square error in ppbv 

** R is the correlation coefficient 

Figure 4 shows the median, 25th and 75th percentiles for all aircraft-measured 

ozone (136 profiles) and matching CMAQ ozone predictions for 2002. While differences 

between the model-calculated and observed profiles are substantial, the model-calculated

profile always remains between the 25th and 75th percentile of the observed profile. The 

large width of the observed profile (roughly 25 ppbv between the 25th and 75th percentile) 

shows that lower tropospheric ozone amounts vary substantially even on days when 

observed ozone amounts were expected to be large (most flights were made on days 

when high ozone concentrations were forecast). The large variability in observed ozone 

concentrations is also a function of the large spatial and temporal range of the data. The 

aircraft measurements were taken at locations extending from North Carolina to Maine, 

and therefore sometimes sampled very different chemical regimes. Another factor that 

can create variability is the occasional natural variability in ozone with altitude that

produces scattered data at the high time resolution of the ozone instrument onboard the 

aircraft. Longer averaging periods would produce smoother data sets, but at the expense 

of vertical resolution in the boundary layer (the aircraft is ascending or descending when 

collecting data so that a longer sampling period implies more vertical distance will be 

covered per sampling period). The aircraft ozone instrument collects a running 1 minute 

average ozone value, which is obtained from 10-second sampling intervals. The data in 

Figure 4 suggests CMAQ has a high bias of ~15 % from the near surface to ~500 m

above ground, and the aircraft profiles have on average 10% more ozone than the CMAQ 

profiles aloft, from 600 – 2600 m. 

14 Appendix G Attachment 15 - Page Number:

26
Figure 4. Median CMAQ and aircraft O3 profiles from 2002 (June–August, 136 profiles). The ends of 

the horizontal bars represent the 25th and 75th percentiles. 

Grand statistical averaging (i.e. over an entire year or ozone season) is helpful in 

identifying major problems associated with performance, (e.g. a large error in an 

emissions inventory or improper land surface use) but less helpful from a forensic 

perspective when subtle errors or compensating errors are present that may change from

ozone episode to ozone episode. Types of error that may evade inspection by grand 

statistical averaging, include biases in the timing and spatial coverage of convection 

and/or biases in the timing and extent of planetary boundary layer ventilation. To further 

elucidate CMAQ performance during specific ozone episodes, several case studies were 

performed. For organizational purposes, selected case studies include periods when 

CMAQ performance was characterized, based on aircraft data, as below-median (the 95th

percentile differences as calculated by equation 1), average (the 50th percentile 

differences) and above-median (the 5th percentile differences). For each case, surface 

layer ozone distributions and vertical profiles are compared to measurements. In 

addition, 24-hour back trajectories were calculated from 40 km North American Weather 

Model (NAM) data using modeled wind velocities from HYSPLIT (HYbrid SingleParticle Lagrangian Integrated Trajectory, [Draxler, 1991]). To complement this 

analysis, general weather conditions will be discussed to elaborate on the history of air 

parcels in the context of air quality. Through presentation of CMAQ performance during 

individual ozone episodes (occurring over the Baltimore-Washington, D.C. non15 Appendix G Attachment 15 - Page Number:

27
attainment areas), the ability of CMAQ to replicate conditions during “base case” 

emissions conditions is evaluated. 

Stoeckenius and Kemball-Cook (2005) developed a classification scheme and 

applied it to all OTR ozone episodes during the summer of 2002. They used a clustering 

technique to classify ozone episodes based on spatial ozone patterns and weather 

conditions (wind direction, temperature, etc.). Table 3 below is a guide to episode types 

and composite ozone patterns determined in their work. To better identify the episodes

discussed, the episode type as classified by Stoeckenius and Kemball-Cook are reported 

in this study. 

Table 4 is a summary table of the ten flights selected for closer inspection in the 

following section. The ten flights contain three instances each of occasions when the 

CMAQ performance (based on the definition of Hains et al., 2006) is classified as above 

median and median, and four flights for which ozone performance was below median. 

Twice, multiple flights were performed on the same day (June 25 and August 2). CMAQ 

performance during each flight is evaluated separately. Table 5 and Figure 5 provide 

summary data regarding aircraft spiral and airport locations. 

Table 3. Composite Pattern and Episode Type determined by Stoeckenius and 

Kemball-Cook (2005).

Composite Pattern Episode Type 

3 Type A: High ozone throughout the OTR 

2 

Type B: High ozone confined to extreme 

southeastern OTR 

5 

Type C: High ozone along I-95 corridor and northern 

New England 

4 Type D: High ozone in the western OTR 

1 Type E: Generally low ozone throughout the OTR 

16 Appendix G Attachment 15 - Page Number:

28
Table 4. Summary of CMAQ Comparisons of ozone vertical profiles to aircraft ozone 

vertical profiles 

CMAQ 

Performance 

Flight 

# 

Date/Time Spiral 

Location 

Composite 

ozone 

Pattern 

Above Median RF09 06/11/02 13:00 UTC Louisa, VA 5 

Above Median RF15 06/25/02 19:00 UTC Churchville, MD 4 

Above Median RF41 08/02/02 15:00 UTC Cumberland, MD 4 

Median RF29 07/16/02 20:00 UTC Tappahannock, VA 2 

Median RF30 07/17/02 14:00 UTC Crewe, VA 3 

Median RF10 06/11/02 20:00 UTC Ashland, VA 5 

Below Median RF42 08/02/02 19:00 UTC Fort Meade, MD 5 

Below Median RF14 06/25/02 14:00 UTC Winchester, VA 4 

Below Median RF17 08/12/02 18:00 UTC Bennington, VT 4 

Below Median RF49 08/13/02 14:00 UTC Morrisville, VT 3 

17 Appendix G Attachment 15 - Page Number:

29
Table 5. Reference Guide for Aircraft Spiral Locations 

ID # Spiral Airport Town Latitude Longitude Elevation*

1 LKU Louisa Co. Freeman Louisa, VA 38.01oN 77.97oW 150 

2 0W3 Harford Co. Churchville, MD 39.56oN 76.20oW 121 

3 CBE Cumberland Regl. Cumberland, MD 39.62oN 78.76oW 237 

4 W79 Tappahannock Mun. Tappahannock, VA 37.92oN 76.87oW 10 

5 W81 Crewe Mun. Crewe, VA 37.18oN 78.10oW 131 

6 OFP Hanover Co. Mun. Ashland, VA 37.71oN 77.44oW 62 

7 FME Tipton AFB Fort Meade, MD 39.09oN 76.76oW 42 

8 OKV Winchester Regl Winchester, VA 39.14oN 78.14oW 222 

9 DDH William Morse St. Bennington, VT 42.89oN 73.25oW 252 

10 MVL Morrisville-Stowe Morrisville, VT 44.54oN 72.61oW 223 

Figure 5. Airport spiral locations of ozone profiles collected by the UMD aircraft 

presented in this analysis. * Elevation in meters above Mean Sea Level. 

18 Appendix G Attachment 15 - Page Number:

30
Figure 6 is a legend for HYSPLIT trajectories and aircraft/CMAQ ozone profiles 

presented in the subsequent pages. For each case study, a back trajectory is calculated 

using the HYSPLIT program to present a history of the air entering the area where an 

aircraft spiral was performed. Back trajectories for all cases extend 24-hours, and are 

performed at 500, 1000 and 1500 meters. These levels were selected to obtain 

information about the boundary layer (500, 1000 meters) and lower free troposphere 

(1500 meters). The 24-hour duration was selected to minimize model (HYSPLIT) 

uncertainties that can result from random errors contained in the numerical weather 

model used to determine the back trajectories, which may propagate when longer backtrajectories are employed. Figure 6 also provides a legend called “Ozone Data” for 

panels showing a comparison of aircraft profiles (pink stars) against CMAQ profiles 

(blue diamonds). Occasionally, CMAQ profiles will show discontinuities of 5-10ppbv 

that repeat back and forth throughout the profile. These discontinuities usually occur at 

sampling locations near the edges of CMAQ grid boxes. At these locations, slight 

changes in horizontal location result in sampling from a different CMAQ grid box. , 

Figure 6. Guide to viewing HYSPLIT back trajectories, aircraft profiles and surface ozone 

difference plots. 

Figure 7 is a sample 4-panel surface plot to familiarize the reader with subsequent 

figures that will be presented. The upper left panel is a map of observed 8-hour maximum

ozone over the OTR, the upper right panel shows CMAQ-calculated 8-hour maximum 

19 Appendix G Attachment 15 - Page Number:

31
ozone interpolated to ozone measurement sites within the OTR, and the lower left panel 

is a difference plot created by subtracting the CMAQ-calculated ozone (upper right) from

measured ozone (upper left). The lower right panel shows CMAQ-calculated 8-hour 

ozone on the original model grid (meaning that the ozone values have not been 

interpolated to the measurement sites). Comparison of the upper-right and lower-right 

hand plots shows that interpolation of model output onto the OTR domain causes 

minimal loss of information. For simplicity, subsequent figures will exclude the plot 

showing model output on the original grid. 

Figure 7. Sample plots for June 5, 2002. Latitude and longitude coordinates are provided 

on the x axis and y axis of each plot. The upper left plot shows the observed daily 

8-hour ozone maxima (ppbv). Ozone (ppbv) from the 2002 base B1 simulation 

interpolated to the locations of ozone monitors is shown in the upper right panel. 

The difference between measurements and model outputs is in the lower left panel 

in ppbv, and the 2002 base B1 simulation on the original model grid is shown in 

the lower right panel, also in ppbv. Since the upper right plot is almost entirely 

the same as the lower right plot, the upper right plot is not be included in 

subsequent surface ozone comparison plots. 

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Above Median CMAQ Performance Cases in 2002 

Tuesday, 06/11/2002, 13:00 UTC Case Study 

June 11 was classified as a type 5 episode (high ozone along the I-95 corridor and 

northern New England). Figures 8A-B show back trajectories ending on June 11 over 

northern VA (Louisa County) and a corresponding aircraft profile collected on the 

morning (08:00 EST) of the 11th, respectively. The back trajectory showed that winds

were light and that the air mass was over southwestern VA and eastern TN 24-hours 

earlier. CMAQ performance is excellent on this occasion, with CMAQ accurately 

replicating the boundary layer ozone morphology as near-surface minima of ~ 55 ppbv 

increase to near 85 ppbv by 300 m. It appears in this case, based on the vertical ozone 

profiles and back trajectories, that the boundary layer has yet to vent. Thus, the origin of 

the observed pollution aloft was likely from transport rather than from local emissions. 

The 500- and 1000m back trajectories show a shift from a westerly flow to a flow along 

the I-95 corridor. Figure 8C-E compares surface ozone data on the 11th to a CMAQ 

simulation. CMAQ accurately represents ozone over much of PA and MD but generally 

under underpredicts ozone concentrations elsewhere with peak underpredictions (~ 30 

ppbv) occurring over central OH and the southern extreme of the modeling domain. 

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Figure 8. Tuesday, June 11, 2002, 08:00 AM EST, Louisa, VA Case Study 

A. 24-hour HYSPLIT back trajectories terminating over Louisa, VA. 

 Blue = 500m, Green = 1000m, Red = 1500m 

B. Aircraft (pink stars) and CMAQ (blue diamonds) ozone profiles. 

C. OTR surface ozone monitor data. 

D. 2002 base B1 CMAQ simulation averaged for OTR monitor locations. 

E. Difference plot. Negative values indicate model under-prediction. 

22 Appendix G Attachment 15 - Page Number:

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Tuesday, 06/25/2002, 19:00 UTC Case Study

 June 25 was classified as a type 4 episode (high ozone in the western OTR). Like 

June 24, the highest ozone concentrations on June 25th were confined to a box south and 

west of New York City and north of VA, with widespread exceedances in the Ohio River 

Valley and scattered Code Red concentrations along the Washington-Philadelphia 

Corridor. Figure 9A shows back-trajectories terminating over northeastern MD in the 

early afternoon (15:00 EST) of June 25 and Figure 9B shows a corresponding aircraft 

profile. The back trajectory originates from eastern OH at the lowest levels and extends 

to near Detroit, MI farther aloft. Like the vertical profile from June 11th (Figure 8B) the 

boundary layer appears to be stratified, with minimum ozone values at the surface of ~70 

ppbv increasing to a well-mixed layer of ~95 ppbv from 500 m to 2500 m. The 

stratification is surprising as this is an afternoon profile. CMAQ does not capture this 

stratification; however, CMAQ performance was still defined as good since as noted 

previously, for consistency (and to accommodate for changes in elevations of ozone 

monitors), CMAQ and aircraft data were compared from 250 m to 2150 m, and therefore, 

the lowest portion of the boundary layer was not included in this comparison. To further 

complicate this analysis, it appears, based on surface ozone maps, that ozone was 

spatially highly variable in the area surrounding the aircraft profile (Churchville, MD). 

Thus CMAQ performance at the lowest levels, which initially did not seem favorable, is 

actually (on a slightly larger scale) reflective of the variable spatial ozone pattern in the 

area. This notion is supported when comparing modeled and observed surface ozone 

over the entire OTR (Figure 9C-E) and underscores some of the challenges encountered 

when making aircraft comparisons. 

CMAQ continued to underpredict peak values, but model performance improved 

compared to earlier days in this episode when underpredictions were severe. Differences 

between the model and observations are less than 15 ppbv over much of PA, OH and NJ, 

with differences in VA of ±5 ppbv. There are multiple areas of model overprediction 

(~15-25 ppbv) found in western MD-eastern WV-northern VA, southwestern and central 

OH and northern NJ-Long Island-Boston, MA. 

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Figure 9. Tuesday, June 25, 2002, 14:00 EST, Churchville, MD. Case Study 

A. 24-hour HYSPLIT back trajectories terminating over , Churchville, MD. 

 Blue = 500m, Green = 1000m, Red = 1500m 

B. Aircraft (pink stars) and CMAQ (blue diamonds) ozone profiles. 

C. OTR surface ozone monitor data. 

D. 2002 base B1 CMAQ simulation averaged for OTR monitor locations. 

E. Difference plot. Negative values indicate model under-prediction.

24 Appendix G Attachment 15 - Page Number:

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Friday, 08/02/2002, 19:00 UTC Case Study 

August 2 was classified as a type 4 episode (high ozone in the western OTR). 

Figure 10A indicates a 24-hour back trajectory terminating over western MD, passing 

through the clean corridor in eastern Pennsylvania, and originating over western NY and 

southern Ontario. Figure 10B shows good air quality and excellent agreement between 

CMAQ and aircraft observations. Vertical profiles show relatively constant, low ozone 

with values ranging from ~40 to 60 ppbv. Peak ozone values are located over southern 

OH, central PA and along the I-95 Corridor. CMAQ does not capture the very low ozone 

values over eastern KY, registering a high bias of 40 ppbv. To the north, CMAQ 

overpredicts (by 15-25 ppbv) over much of the southern New England Region. In 

general, CMAQ replicates the pattern, but underpredicts the magnitude of the area of 

high ozone over eastern OH, western PA, and along the I-95 corridor (Figure 10C-E). 

However, at individual sites within a region, CMAQ sometimes performs differently. 

For example, on August 2, CMAQ overpredicts ozone amounts near Edgewood, MD (a 

site within the I-95 corridor). These small-scale differences in model performance are 

not surprising given small-scale variations in terrain and circulation (e.g., Edgewood is 

located near the Chesapeake Bay and is often subject to land-sea breezes). However, 

these differences do indicate that no single site should be given extra focus when 

evaluating model performance. For most applications, this is not a problem, but it is a 

concern for SIP modeling, since model performance at the worst air quality sites within a 

nonattainment area is relevant when determining if emission reductions are sufficient to 

bring a non-attainment area into compliance with the 8-hour ozone standard. 

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Figure 10. Friday, August 02, 2002, 10:00 EST, Cumberland, MD. Case Study 

A. 24-hour HYSPLIT back trajectories terminating over Cumberland, MD. 

 Blue = 500m, Green = 1000m, Red = 1500m 

B. Aircraft (pink stars) and CMAQ (blue diamonds) ozone profiles. 

C. OTR surface ozone monitor data. 

D. 2002 base B1 CMAQ simulation averaged for OTR monitor locations. 

E. Difference plot. Negative values indicate model under-prediction.

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Median CMAQ Performance Cases in 2002 

Tuesday, 07/16/2002, 20:00 UTC Case Study 

July 16 was classified as a type 2 episode (high ozone confined to the extreme 

southeastern OTR). Figure 11A shows relatively fast 24-hour back trajectories (ending 

over Tappahannock, VA at 15:00 EST) extending northwest into northern MI and central 

Ontario. Starting at 500 m and looking upwards throughout the rest of the profile, 

CMAQ agrees with observations within 10 ppbv, with CMAQ showing consistently 

lower concentrations. From Figure 13 it can be seen that peak ozone levels on the 16th

occurred along a line from the OH-KY border east-southeast across WV, VA and NC. 

Most of those values were in excess of 85 ppbv, with levels reaching 105 ppbv near 

Cincinnati, OH. On average the model performance appears to be good with differences 

over the domain mostly below 15 ppbv. The largest discrepancy is associated with the 

peak value near Cincinnati, OH; the local maximum that CMAQ places in that region is 

too low and displaced south of the actual peak. The result is an area of underprediction 

over southwestern OH and a small area of overprediction in northern KY. The spatial 

extent of the underestimation is probably exaggerated as only a few monitors exist in 

southeastern Ohio and northern Kentucky. As has been noted, in other comparisons, 

CMAQ tends to overpredict (15-25 ppbv) surface layer ozone along the Ohio River and 

over portions of WV. 

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Figure 11. Tuesday, July 16, 2002, 15:00 EST, Tappahannock, VA. Case Study 

A. 24-hour HYSPLIT back trajectories terminating over Tappahannock, VA. 

 Blue = 500m, Green = 1000m, Red = 1500m 

B. Aircraft (pink stars) and CMAQ (blue diamonds) ozone profiles. 

C. OTR surface ozone monitor data. 

D. 2002 base B1 CMAQ simulation averaged for OTR monitor locations. 

E. Difference plot. Negative values indicate model under-prediction. 

28 Appendix G Attachment 15 - Page Number:

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Wednesday, 07/17/2002, 14:00 UTC Case Study

July 17 was classified as a type 3 episode (high ozone throughout the OTR). Like 

the previous day, 24-hour back trajectories from central VA ending at 10:00 EST were 

fast (Figure 12A), but more northerly in nature, passing over central PA, western NY, and 

Lake Ontario. Figure 12B shows that ozone values begin fairly low (~40 ppbv) near the 

surface and increase to just above 50 ppbv by 600 m. Observations increase to ~80 ppbv 

by 1100 m and then tail off to a constant value of ~ 60 ppbv by 1500 m. On this morning 

CMAQ does an excellent job of replicating relatively low ozone levels near the surface, 

but it struggles to simulate the structure of ozone between 900m and 1200m. Instead, the 

ozone remains constant above 900 m. This is a case when it appears that CMAQ is not 

capturing a regional transport signal. Figure 12C-E shows that 8-hour ozone mixing 

ratios exceeding 85 ppbv were reported from SC to as far north as CT and westward into 

OH. Scattered peak 8-hour ozone concentrations exceeding 105 ppbv occurred along and 

east of the I-95 Corridor as well as near Cleveland and Buffalo. Surface model 

performance appears to be slightly less accurate than on the prior case study of July 16th, 

with the majority of differences in the range of 15 to 25 ppbv. Of note is the large area of 

underprediction covering much of the northern two thirds of OH and portions of southern 

NY, especially near the Canadian border. 

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Figure 12. Wednesday, July 17, 2002, 09:00 EST, Crewe, VA. Case Study 

A. 24-hour HYSPLIT back trajectories terminating over Crewe, VA. 

 Blue = 500m, Green = 1000m, Red = 1500m 

B. Aircraft (pink stars) and CMAQ (blue diamonds) ozone profiles. 

C. OTR surface ozone monitor data. 

D. 2002 base B1 CMAQ simulation averaged for OTR monitor locations. 

 E. Difference plot. Negative values indicate model under-prediction.

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Tuesday, 06/11/2002, 20:00 UTC Case Study 

June 11 was classified as a type 5 episode (high ozone along the I-95 corridor and 

in northern New England). Recall that CMAQ-calculated and observed ozone agreed 

well during a morning flight on June 11th. The flight addressed here is an afternoon flight 

at a different location (see Table 4). Figure 13A shows weak westerly winds that 

advected air parcels originating over southwestern VA and the central VA/NC border. 

Figure 13B indicates that CMAQ captures the general shape of the ozone profile nicely 

(i.e. fairly flat throughout the boundary layer) but underpredicts ozone throughout the 

profile by 10-20 ppbv. Air quality is quite poor with ozone mixing ratios of ~100 ppbv to 

~ 2000m. Given that this is an afternoon (15:00 EST) profile it is possible that CMAQ

has vented the boundary layer too early or too vigorously which could account for the 

differences between CMAQ and observations. However, the low-bias also illustrates the 

tendency of CMAQ to underestimate ozone amounts during particularly poor air quality 

days. . Figure 13C-E shows high ozone concentrations reported along the I-95 

Corridor, with scattered locations showing values exceeding 105 ppbv from central NC to 

northern CT. The most concentrated area of high ozone is in a band from just northeast of 

Baltimore into southeastern PA between New Castle, DE and Lancaster, PA. The back 

trajectories show a shift from a west-northwest flow to one along the I-95 corridor. 

CMAQ underpredicts ozone concentrations on June 11 over much of the model 

domain, with the greatest underpredictions (~ 30 ppbv) occurring over portions of 

western PA and southwestern VA. Along the I-95 corridor, just north of Baltimore, MD 

extending to near Boston, MA CMAQ overpredicts ozone levels by 5-15 ppbv. 

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Figure 13. Tuesday, June 11, 2002, 15:00 EST, Ashland, VA Case Study 

A. 24-hour HYSPLIT back trajectories terminating over Ashland, VA. 

 Blue = 500m, Green = 1000m, Red = 1500m 

B. Aircraft (pink stars) and CMAQ (blue diamonds) ozone profiles. 

C. OTR surface ozone monitor data. 

D. 2002 base B1 CMAQ simulation averaged for OTR monitor locations. 

E. Difference plot. Negative values indicate model under-prediction. 

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Below Median CMAQ Performance Cases in 2002 

Friday, 08/02/2002, 19:00 UTC Case Study 

August 2 was classified as a type 4 episode (high ozone in the western OTR). 

Figure 24A shows that 24-hour back trajectories ending over Fort Meade, Maryland in 

the afternoon (15:00 EST) show a high degree of variability. Recall that CMAQcalculated and observed ozone agreed well during a morning flight on August 2. The 

flight addressed here is an afternoon flight at a different location (see Table 4). At the 

upper levels, air parcels originated over southern Ontario, Canada but back trajectories 

only extend to southeastern PA at the lowest levels. Figure 14B shows CMAQ greatly 

overpredicted ozone, with simulated values (~115 ppbv) well mixed throughout the 

boundary layer. Aircraft observations show slightly more variability, but only range from

~80 ppbv at the lowest levels to ~60 ppbv by 1500 m, before falling off to less than 40

ppbv. Figure 14C-E show that CMAQ is actually doing a reasonable job replicating the 

spatial pattern of ozone, especially along the I-95 corridor. The high variability in the 

observed surface maxima creates pockets of relatively large disagreement over small 

areas. The aircraft spiral was performed over one of those pockets. It is possible, given 

the proximity of the spiral to the Chesapeake Bay, that land-sea interactions may have

affected the simulation locally. Peak ozone values appear over southern OH, central PA, 

and throughout the Interstate-95 Corridor. Isolated areas of CMAQ overprediction occur 

over eastern KY (~40-50 ppbv), the Philadelphia-Baltimore-Washington region, and 

much of southern New England (≤ 25 ppbv). 

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Figure 14. Tuesday, August 2, 2002, 14:00 EST, Fort Meade, MD. Case Study 

A. 24-hour HYSPLIT back trajectories terminating over Fort Meade, MD. 

 Blue = 500m, Green = 1000m, Red = 1500m 

B. Aircraft (pink stars) and CMAQ (blue diamonds) ozone profiles. 

C. OTR surface ozone monitor data. 

D. 2002 base B1 CMAQ simulation averaged for OTR monitor locations. 

E. Difference plot. Negative values indicate model under-prediction. 

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Tuesday, 06/25/2002, 14:00 UTC Case Study 

June 25 was classified as a type 4 episode (high ozone in the western OTR). 

Figure 15A is a back trajectory for the morning of June 25 (10:00 EST) over Winchester 

VA. The trajectory is weak from the northwest reaching only southwestern PA and 

eastern OH. Recall that an afternoon (15:00 EST) flight on the 25th (see Figures 9A and

9B) was classified as a good CMAQ performance day. Analysis of the morning flight 

presented in this case study reveals that CMAQ overpredicted ozone aloft (~200 m

through 700 m) by ~20 ppbv (Figure 15B). At 700 m, aircraft observations sharply 

increase to match CMAQ simulations of ~90 ppbv in a thin layer at 900m; farther aloft, 

the two measurements diverge again, with CMAQ still higher than observations (by as 

much as 50 ppbv). Based on surface comparisons from Figure 23 (also Figure 7) over 

that region, one conclusion is that CMAQ is just missing the location of a local ozone 

plume (which the aircraft interacts with briefly at ~700 m). Thus, spatially, CMAQ 

appears to be representing existing conditions with reasonable accuracy. However, 

because of the sharp ozone gradient over the measurement location and resolution 

limitations of the model, CMAQ does not compare favorably with aircraft observations. 

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Figure 15. Tuesday, June 25, 2002, 14:00 EST, Winchester, VA. Case Study 

A. 24-hour HYSPLIT back trajectories terminating over Winchester, VA. 

 Blue = 500m, Green = 1000m, Red = 1500m 

B. Aircraft (pink stars) and CMAQ (blue diamonds) ozone profiles. 

C. OTR surface ozone monitor data. 

D. 2002 base B1 CMAQ simulation averaged for OTR monitor locations. 

E. Difference plot. Negative values indicate model under-prediction. 

36 Appendix G Attachment 15 - Page Number:

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Monday, 08/12/2002, 18:00 UTC Case Study 

 August 12 was classified as a type 3 episode (high ozone throughout the OTR). A 

24-hour back trajectory shows fast westerly flow ending over Bennington, VT on the 12th

of August, originating from over MI and southern Ontario (Figure 16A). A vertical 

comparison from the early afternoon reveals CMAQ is underpredicting ozone compared 

to aircraft observations by as much as 40 ppbv in the layer between 200 m and 1000 m 

(Figure 16B). CMAQ reports a near-constant value of ~60 ppbv from the surface to 

~2000 m, while aircraft observations show values closer to 100 ppbv from the ground to 

~1000 m. This appears to be a case where CMAQ, rather than nearly missing an ozone 

plume, is not fully describing the spatial extent of the ozone episode. One possible 

reason why CMAQ underpredicted ozone is a failure to accurately simulate boundary 

layer mixing. Inspection of the August 12 synoptic conditions reveals strong, widespread 

convection developing late in the day. The underprediction of ozone and the apparent 

well-mixed boundary layer from CMAQ, suggest that convection was initiated earlier in 

CMAQ (in the parent MM5 simulation) than actually occurred. Another cause of CMAQ 

underprediction could be a failure to capture westerly transport of ozone and ozone 

precursors that originated over southern Ontario. Precursor emissions in Canada are less 

certain than they are over the U.S. Surface comparisons in Figure 16C-E are consistent 

with the aloft observations in that CMAQ underpredicts ozone over much of the eastern 

half of PA, NJ and portions of the southern New England area. In contrast, CMAQ 

overpredicts ozone over much of Long Island, Coastal MA and southern CT. The 

locations of the overpredictions (e.g., Long Island, NY) suggest that CMAQ fails to 

develop the boundary layer fully over water, which can lead to relatively high ozone 

values just offshore. This is a potential problem over the Chesapeake Bay. CMAQ is 

known to have problems with coastal locations, since CMAQ apportions emissions 

according to multiple land uses within a grid cell, while MM5 assumes that the dominant 

land use in the grid cell dictates boundary layer development. In a coastal grid cell, MM5 

often produces a very tight boundary layer, while CMAQ often puts a lot of emissions 

(say from a small coastal city) into the very tight boundary layer that MM5 produces. 

Under these artificial conditions, ozone concentrations can soar unrealistically in the 

model. It is possible that biases at coastal locations are exaggerated. Ozone monitors are 

underrepresented at coastal locations, so it is difficult to judge. Biases offshore must be 

interpreted cautiously as offshore measurements were not available for this comparison. 

37 Appendix G Attachment 15 - Page Number:

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Figure 16. Monday, August 12, 2002, 14:00 EST, Bennington, VT. Case Study 

A. 24-hour HYSPLIT back trajectories terminating over Bennington, VT. 

Blue = 500m, Green = 1000m, Red = 1500m

B. Aircraft (pink stars) and CMAQ (blue diamonds) ozone profiles. 

C. OTR surface ozone monitor data. 

D. 2002 base B1 CMAQ simulation averaged for OTR monitor locations. 

E. Difference plot. Negative values indicate model under-prediction. 

38 Appendix G Attachment 15 - Page Number:

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Tuesday, 08/13/2002, 14:00 UTC Case Study 

 August 13 was classified as a type 3 episode (high ozone throughout the OTR). A 

24-hour back trajectory shows west-southwest flow ending over Morrisville, VT on the 

13th of August, passing over Buffalo, NY and southern Ontario, Canada (Figure 17A). 

Figure 17B is a late morning (10:00 AM EST) aircraft profile over Morrisville, VT. As

on the 12th, CMAQ underpredicts ozone in the lower boundary layer. Aircraft 

observations show a sharp ozone gradient with near-surface values of ~ 40 ppbv rapidly 

increasing to over 100 ppbv by ~400 m. The peak ozone levels persist until ~1100 m and 

then gradually fall off to a constant value of 60 ppbv by 1500 m. The CMAQ simulation 

is marginally improved from the previous day. Even though CMAQ underpredicts ozone 

throughout the entire profile it does mimic the vertical morphology of the observations. 

CMAQ performance on August 13 is similar to the 12th in that it is generally 

characterized by widespread underpredictions (Figure 17C-E). Underpredictions ranged 

from 15-25 ppbv over much of the domain with extreme excursions of over 50 ppbv 

occurring in isolated areas of central PA, eastern NY, and portions of the New England 

region.Unlike August 12th, this profile occurs at ~ 10:00 AM EST, so biases in the timing 

of convection on the 13th cannot be blamed; however, it is possible that convection on the 

12th was too strong in MM5 leading to an underestimation of the amount of residual layer 

ozone available to mix down in the morning. However, inspection of satellite imagery

showed widespread convective activity over much of the MM5 domain on both the 12th

and 13th of August. 

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Figure 17. Tuesday, August 13, 2002, 09:00 EST, Morrisville, VT. Case Study 

A. 24-hour HYSPLIT back trajectories terminating over Morrisville, VT. 

 Blue = 500m, Green = 1000m, Red = 1500m 

B. Aircraft (pink stars) and CMAQ (blue diamonds) ozone profiles. 

C. OTR surface ozone monitor data. 

D. 2002 base B1 CMAQ simulation averaged for OTR monitor locations. 

E. Difference plot. Negative values indicate model under-prediction. 

40 Appendix G Attachment 15 - Page Number:

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Effect of the NOx SIP Call on Maryland Ozone 

When considering the question of whether NOx reductions related to the NOx SIP 

Call (i.e. reductions beginning in 2002) have had a beneficial effect on peak 8-hour ozone 

levels over Maryland, consideration should be given to fluctuations in seasonal ozone 

levels attributed to meteorology. It is generally accepted that meteorological processes 

strongly influence tropospheric ozone levels (National Research Council, 1991, Biswas 

and Rao, 2001, EPA, 2006). Meteorology directly determines whether ozone precursors 

remain local or are transported downwind along with the resulting ozone. Ozone levels 

build when temperatures are high, enhancing the rate of ozone formation, and air 

stagnates, limiting vertical mixing of pollutants. These weather processes can vary 

spatially and temporally from small-scale features such as clouds covering only tens of 

kilometers to large tropical systems spanning several thousand kilometers. Of all the 

meteorological variables, temperature displays the strongest correlation with peak ozone 

concentrations (Wolff and Lioy, 1978; Ryan, et al., 2000). This analysis attempts to 

examine the effect of NOx reductions on peak 8-hour ozone levels over the Baltimore 

Non-Attainment Area (BNAA) by analyzing ozone and weather before and after the NOx

SIP Call went into effect. In order to account for year-to-year weather differences, 

conditional mean ozone values were calculated as outlined below. 

Peak ozone data were binned according to temperature for the time periods before 

(1997-2002) and after (2003-2006) the NOx SIP Call (i.e. 2002). From the two groups of 

ozone values, statistical data were calculated (i.e. conditional mean, standard deviation, 

etc.) and plotted to determine the probability that differences between the two data sets 

were the result of random fluctuations. In this study, ozone observations were grouped 

into 5°F temperature bins before and after the NOx SIP Call (e.g. 1997-2002 and 2003-

2006). Daily maximum temperature data from Baltimore-Washington International 

Airport (BWI) were used. When calculating statistics for the binned ozone data, the 

degree of autocorrelation of maximum temperature was also determined. Autocorrelation 

is a mathematical tool used for analyzing functions or series of values (e.g. daily 

maximum temperature values). Informally, it is a measure of how well a signal matches 

a time-shifted version of itself, as a function of the amount of time shift. The 

autocorrelation is important in determining the number of degrees of freedom, which 

strongly affects the statistical significance of a calculation. The degree of autocorrelation 

for maximum temperature was determined to be three days using the SPSS® statistical 

program (see Appendix A in Chapter 1 for a description of SPSS®). A three-day 

autocorrelation means that after three days, the temperature is independent of prior 

temperatures, or, put another way, synoptic conditions at BWI change every three days 

on average. The autocorrelation is needed to calculate the standard deviation of the mean 

of the binned ozone data (Equation 2). 

41 Appendix G Attachment 15 - Page Number:

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σ mean = σ

n

N

 Equation 2 

σmean = standard deviation of the conditional mean

σ = standard deviation 

n = total number of (daily) observations 

N = degree of auto correlation (days)

The effect of autocorrelation is to reduce the number of independent observations, in this 

case meaning the number of independent airmasses. If autocorrelation were not taken 

into account, the number of independent observations could balloon if observations were 

taken more frequently and considered entirely independent. The data in Figure 18 and 

Table 6 suggest that, even when taking into account seasonal differences in temperature 

between the two time periods (pre and post NOx SIP Call), the mean of peak 8-hour 

ozone observations over the BNAA is significantly lower for the time period 2003-2006 

than it was for 1997-2002. 

Table 6. Summary Binned Ozone Statistics used in Figure 18. 

1997-2002 

Temperature Bin 80-84°F 85-89°F 90-94°F >95°F 

Ozone (ppbv) 

Minimum 28 41 49 40

Mean 66.8 80.7 93.7 99.3

Maximum 106 123 131 149

First quartile 58.6 71.0 82.7 85.3 

Third quartile 76.0 91.0 104.1 116.1 

Mean +1σ mean 68.2 83.1 96.2 104.9

Mean -1σmean 65.4 78.4 91.2 93.7

σ 13.0 16.0 15.6 22.9

σmean 1.4 2.3 2.5 5.6

n 248 140 119 51

2003-2006 

Temperature Bin 80-84°F 85-89°F 90-94°F >95°F 

Ozone (ppbv) 

Minimum 21.0 37.8 53.0 50.0

Mean 60.6 70.1 83.2 84.9 

Maximum 95.1 104.0 130.1 113.0

First quartile 54.0 61.9 72.7 77.0 

Third quartile 69.0 77.8 92.2 95.0 

Mean +1σ mean 62.6 72.3 86.8 93.4

Mean -1σmean 58.7 67.9 79.7 76.3

σ 13.8 13.2 16.8 17.1

σmean 1.9 2.2 3.5 8.5

42 Appendix G Attachment 15 - Page Number:

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n 153 109 67 12

43 Appendix G Attachment 15 - Page Number:

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Figure 18. A box and whisker plot of the conditional mean peak 8-hour ozone over the 

BNAA for the years 1997-2002. The red asterisk corresponds to the mean peak 

8-hour ozone over the BNAA for the years 2003-2006. Inspection of the plot 

shows that when temperature is accounted for, the mean peak 8-hour ozone for 

the years following NOx controls (2003-2006) is more than two standard 

deviations lower than the mean peak for the years before the NOx controls 

occurred. The difference between the two time periods increases at higher 

temperatures, suggesting that the reductions are most beneficial during weather 

conditions most conducive to elevated ozone. 

Maximum temperature is the best single predictor of ozone (e.g. Ryan et al., 

2000), and accounts for most of the meteorological variability in ozone, so a correction 

for peak temperature corrects for most of the meteorological variability, and allows a 

trend in ozone to be seen. After correcting for the effects of peak temperature, ozone 

after the NOx SIP Call has decreased significantly when compared with that before the 

NOx SIP Call. The average ozone in each temperature bin is now located where the 

lower quartile (25th percentile) in each temperature bin had been previously. These data 

suggest that the NOx reductions at power plants implemented as a result of the NOx SIP 

Call, and those through other coincident programs, were highly effective at reducing 

ozone under all meteorological conditions, and even more effective when ozone was at its 

highest. 

44 Appendix G Attachment 15 - Page Number:

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Conclusions 

CMAQ model performance was evaluated for the base year 2002 by comparing 

surface and aloft CMAQ simulations to corresponding observations from the summer of 

2002. Based on these comparisons, it is concluded that CMAQ replicates the spatial 

patterns of high ozone events. The ability of CMAQ to capture the spatial morphology of 

ozone events suggests that from a meteorological perspective certain key processes such 

as synoptic-scale winds and temperature fields are being modeled correctly. In most 

cases CMAQ does replicate the spatial patterns of an ozone event, but does not fully

capture the magnitude or spatial extent of peak ozone values. A comparison of CMAQ to 

observations from selected surface monitors shows that CMAQ has a low bias of 

approximately 7 ppbv during pollution events but only ~1 ppbv when averaged over the 

entire ozone season. Clearly, high-biases during low ozone periods compensate for lowbiases during pollution events. As discussed later, in chapter 9, the compensating biases 

are part of a general tendency of CMAQ to capture the mean ozone concentrations and 

miss the extreme ozone concentrations. Or to put it another way, CMAQ is adroit at 

capturing temporal fluctuations in 8-hour ozone (correlation coefficients of 0.8-0.9 are 

common); however, the amplitude of these fluctuations is generally underestimated. 

Biases also vary between urban and suburban/rural locations. With the caveat that the 

classifications appear to be dated, CMAQ has smaller biases at urban locations than it 

does at more rural locations. 

The comparisons of CMAQ with aircraft observations taken on 136 spirals flown 

in the summer of 2002 (May-September), show CMAQ underpredicts ozone aloft (i.e. 

between 600-2200 m) by ~10 %. As seen in case studies performed in this report, 

comparisons between aircraft observations and model simulations can be problematic 

because of rapidly changing temporal and spatial scales. Comparisons of individual 

cases highlight the complex nature of ozone predictions and measurements. More 

generally, the underprediction of aloft ozone by CMAQ suggests that CMAQ is not fully 

capturing all of the transport and/or boundary layer processes associated with ground 

level ozone and the formation, transport and destruction of its precursors. This may help 

explain some of the ozone underprediction at some surface monitors. Some possible 

reasons as to why CMAQ is underpredicting ozone in the lower boundary layer or aloft 

are as follows: 

• Errors in mesoscale aerometric data (MM5 wind fields) which could compromise 

the advection of ozone and precursors by the mean wind components 

• Errors in CMAQ’s handling of turbulent diffusion or parameterized convection 

• Incomplete chemical reaction schemes (e.g. see Chapter 4 of this Appendix)

• Improper accounting of actinic flux aloft where actinic flux is defined as the 

quantity of light available to molecules at a particular point in the atmosphere 

and which, on absorption, drives photochemical processes in the atmosphere 

(See Chapters 4 and 9 of this Appendix for details) 

• Unaccounted sources of ozone precursors 

The relatively poor performance of CMAQ at rural/suburban sites during 

pollution events provides information on the shortcomings of CMAQ. In some instances, 

rural/suburban sites are more power-plant dominated than motor vehicle dominated. The 

underestimation of ozone fluctuations at these sites may indicate that variations in power 

plant emissions are not fully felt at these sites. Consequently, CMAQ is unlikely to 

45 Appendix G Attachment 15 - Page Number:

57
reproduce the full benefit of power plant emission reductions at these sites and also sites 

downwind from these sites. These biases add uncertainty to future ozone predictions 

from CMAQ (see also Chapter 9). 

Analysis of the impacts of the NOx SIP Call on Maryland ozone levels show that 

ozone dropped significantly after implementation of the NOx SIP Call. The significant 

reduction suggests that similar measures should be expected to have similarly large 

impacts in the future. 

Future Work 

Future efforts to compare CMAQ simulations (or other deterministic 

photochemical models) to observations will remain vital to better understanding the 

complex nature of ground-level ozone. Some of the methods that could facilitate future 

efforts include: 

• Simulations with higher vertical resolution might do a better job of capturing the 

initial dispersion and transport of ozone plumes from power plants. A denser 

network of observations would be useful for evaluating the representativeness of 

individual ozone monitors. Increased, more-coordinated measurements aloft (e.g. 

hourly ozonesonde launches during high-ozone periods coupled with nighttime 

aircraft flights) would help explain the diurnal cycle of ozone in rural, urban and 

elevated surface sites. 

• CMAQ-calculated CO profile concentrations at rural locations were typically 50-

150 ppbv lower than concentrations observed on RAMMPP flights. The 

implications of the effect of this bias on upwind ozone need to be investigated. 

• Aircraft observation remains one of the best tools to collect aloft data because of 

the ability to cover vast distances (both vertically and horizontally) in a relatively 

short period of time and, perhaps more importantly, the ability to collect ancillary 

measurements (e.g., VOC’s, NOX, SO2, CO, PM2.5), especially NOx. 

• Ozonesondes provide another measurement tool that over time, if collected 

regularly, can produce an ozone climatology in the lower boundary layer, stable 

nocturnal boundary layer, and lower free troposphere. 

• A rigorous evaluation of input parameters from MM5 would also be useful. For 

example, comparison of MM5 meteorological parameters with balloon soundings, 

FAA on-board aircraft data, and most importantly satellite cloud fields. 

Increased use of all the tools detailed in this analysis will help produce a better 

comparative product which will increase confidence in the robustness of future ozone

simulations. 

46 Appendix G Attachment 15 - Page Number:

58
References

Biswas J. and Rao., S. T., 2001. Uncertainties in Episodic Ozone Modeling Stemming 

from Uncertainties in the Meteorological Fields. J. App. Met., 40 #2, pp 117–136. 

Draxler, R. A., Stunder, B., Rolph, G. and Taylor, A., 2006. HYSPLIT4 USER's GUIDE 

Version 4.8 - Last Revision: June 2006. NOAA Air Resources Laboratory, 327 E West 

Hwy, Silver Spring, 20910. 

http://www.arl.noaa.gov/data/web/models/hysplit4/win95/user_guide.pdf

EPA, 2006. Weather Makes a Difference: 8-hour Ozone Trends for 1997-2005. U.S. 

Environmental Protection Agency, Office of Air and Radiation, Office of Air Quality 

Planning and Standards. (http://www.epa.gov/airtrends/2006/weather/region03.pdf) 

Hains, J.C., Taubman, B.F., Thompson, A.M., Marufu, L.T., Doddridge, B.G. and Stehr, 

J.W., Dickerson, R.R., 2007. A clustering methodology to identify distinct chemical 

events in the troposphere using aircraft trace gas and aerosol vertical profiles, manuscript. 

National Research Council, 1991: Rethinking the Ozone Problem in Urban and Regional 

Air Pollution. 1st. National Academy Press, pp. 93-107. 

Ryan, W.F., Piety C.A., Luebehusen E.D., “Air quality forecasts in the mid-Atlantic 

region: Current practice and Benchmark skill”, Weather And Forecasting 15 (1): 46-60 

Feb 2000. 

Stoeckenius, T. and Kemball-Cook, S., 2005. Determination of representativeness of 

2002 ozone season for ozone transport region SIP modeling. Environ International 

Corporation. 

Taubman, B.F., Hains, J.C., Thompson, A.M., Marufu, L.T., Doddridge, B.G., Stehr, 

J.W., Piety, C.A. and Dickerson, R.R., 2006. Aircraft vertical profiles of trace gas 

and aerosol pollution over the mid-Atlantic United States: Statistics and meteorological 

cluster analysis, J. Geo. Res., 111, D10S07. 

Wolff G.T., Lioy P.J., “Empirical Model For Forecasting Maximum Daily Ozone Levels 

In Northeastern United-States”, Journal Of The Air Pollution Control Association 28 

(10): 1034-1038, 1978.

47 Appendix G Attachment 15 - Page Number:

59
Acronym List 

CMAQ- Community Multi-scale Air Quality 

RAMMPP- Regional Atmospheric Measurement Modeling and Prediction Program 

AGL- Above Ground Level 

OTR- Ozone Transport Region 

UMD- University of Maryland 

EPA- Environmental Protection Agency 

NAA- Non Attainment Area 

R- Rural

S- Suburban 

U- Urban 

HYSPLIT- HYbrid Single-Particle Lagrangian Integrated Trajectory 

MM5- The PSU/NCAR mesoscale model (5th generation) 

48 Appendix G Attachment 15 - Page Number:

60
Analysis of the Details of CMAQ 4.5.1 Chemistry 

March 16, 2007 

R. R. Dickerson, J. C. Hains, D. J. Allen, J. W. Stehr, C. A. Piety 

Department of Atmospheric and Oceanic Science 

The University of Maryland 

College Park, MD 

Abstract 

In order to accurately predict changes in ozone resulting from changes in emissions, 

CMAQ must accurately represent the chemistry of the lower atmosphere in both urban 

and rural locations and during both daytime and nighttime conditions. Several studies 

suggest that CMAQ underestimates the benefit from reduced emissions of NOx from

elevated sources. Comparison of aircraft profiles to CMAQ-generated ozone profiles 

show that the model calculates too much ozone in the lowest few hundred meters and too 

little between 600 and 2500 m altitude. This analysis addresses the questions: 

• Does CMAQ capture all the relevant reactions that sequester or remove NOx in 

urban plumes?

• Would consideration of the impact of aerosols on the rate of photolysis of NO2

change the column content and shape of the ozone profile calculated by CMAQ?

The take away message from this study is that the CB4 mechanism and photochemical 

processor used in the version of CMAQ run for this SIP are simplified and missing 

reactions that were thought to be inconsequential, but are now known or in some

instances suspected to play a major role. All higher aldehydes are treated as acetaldehyde 

(C2), but other higher aldehydes (such as C3 and C4) are certainly formed and they react 

faster with NO3 radicals to form HNO3 at night, representing an irreversible removal of 

NOx. CB4 also neglects the small fraction of alkanes that react directly with NO3 radicals 

to form HNO3 at night, as well as a fraction of higher alkanes that rearrange to form alkyl 

nitrates in daytime reactions with OH and NO. Altogether, these reactions probably 

sequester at least 1.5 ppbv NOx, and unless there are compensating errors, CMAQ may be 

overestimating the mixing ratio of ozone formed in the Baltimore urban plume by about 6 

ppbv at the surface. Scattering of radiation by aerosols can accelerate ozone formation in 

the lower free troposphere and inhibit it closer to the Earth’s surface. Model simulations 

of the impact of aerosols on jNO2 indicates that CMAQ should calculate 1-18 ppbv less 

ozone in the lowest few hundred meters and 1-3 ppbv more ozone above - this moves the 

model closer to aircraft observations, but not into agreement. Indirect evidence suggests 

that MM5/CMAQ is underestimating low level cloud cover, and this could contribute 

substantially to the disagreement between measurement and model. 

1 Appendix G Attachment 15 - Page Number:

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Maryland’s attainment demonstration model runs may well overestimate the rate 

of formation and concentration of ozone, especially in VOC-rich urban plumes. The 

overall chemistry may be more NOx-limited than CMAQ would suggest. In comparison 

to aircraft observations, the base-case model run underestimates the rate of 

photochemical smog production above about 500 m and overestimates it below this 

altitude. Comparison of the details of the chemical processes simulated in CMAQ to 

observations shows that the model may still underestimate the importance of 

photochemistry in large-scale, multi-day processes involving transport and processing at 

higher altitudes, thus the simulations may underestimate the benefit of decreasing NOx

emissions, especially from elevated emissions sources such as power plants. 

4.1 Introduction 

UMD has investigated the chemical mechanism of CMAQ to see if it accurately 

represents observed chemistry of smog events in the Northeast, with an emphasis on 

reasons that the model might overestimate ozone production and under-represent the 

benefit of reduced NOx emissions from elevated non-urban sources such as power plants. 

These studies also help quantify the uncertainty in CMAQ calculations of ozone. 

Observed concentrations of ozone in the eastern United States have fallen substantially in 

the past few years during a time when the major change in emissions was the NOx SIPCall for diminished NOx emissions from power plants e.g., (Gégo et al. 2007). This 

section deals with the CB4 chemical mechanism of CMAQ and where it might miss 

details of NOx and volatile organic compound (VOC) photochemistry that enhance the 

efficacy of emissions reductions. 

4.2 NO3-VOC Reactions 

Recent measurements of ozone formation during the 2003 electrical blackout and 

investigations of nighttime boundary layer chemistry suggest that some of the NOx from

urban areas may be lost or sequestered. This reduces the rate of ozone formation in areas 

of high concentrations of VOC’s and implies lesser ozone formation potential for urban 

plumes (Brown et al. 2006a; Brown et al. 2006b; EPA 2006; Marufu et al. 2004). 

NO2 can react with ozone to form highly reactive nitrate radicals, NO3. During 

daylight hours, the nitrate radical NO3 is easily returned to NOx by photolysis or reaction 

with NO, but at night NO3 concentrations can build up and these radicals attack some 

VOC’s, as shown schematically in Figure 4.1. Organo-nitrates sequester NOx for several 

days, and HNO3 is basically inactive in smog photochemistry. If NO3 reacts with VOC’s 

to form HNO3 quickly at night, it could make NOx in urban plumes relatively less 

important for O3 formation than NOx in power plant plumes, where VOC concentrations 

are lower. 

2 Appendix G Attachment 15 - Page Number:

62
 

 

Figure 4.1 Schematic diagram of nighttime VOC/NO3 chemistry in the urban boundary 

layer. VOC’s can react with NO3 radicals to form organo-nitrates such as 

alkyl nitrates that sequester NOx, or to form nitric acid, which is lost 

permanently from the system. 

CMAQ 4.5.1, as configured for this SIP, employs an abridged set of NO3 reactions. 

Different treatments of VOC reactions are known to lead to substantial differences in 

calculated ozone (Kuhn et al. 1998). This version of CMAQ uses CB-4, which explicitly 

includes formaldehyde, cresols (CRES) and reaction products of isoprene (ISPD). 

H2CO + NO3 → HNO3 + HCO·

CRES + NO3 → HNO3 + CRO 

ISPD + NO3 → HNO3 + PROD (7.5% yield) 

CB4 also contains a variable called XO2N which is an alkene adduct species. 

OLE + NO3 →...→ RO2 + 0.09*XO2N + products 

Although this reaction does not make HNO3, it sequesters NOx; CB4 uses a rate constant 

of 7.7x10-15

 cm3 s

-1 that is appropriate for propene, but butenes react about 50 times 

faster. The Regional Acid Deposition Model (RADM2), no longer an option for CMAQ, 

has more NO3 + ORG reactions including GLY, (glyoxal) MGLY (methyl glyoxal; 

H3CC(O)C(O)H), DCB (unsaturated dicarbonyl), CSL (cresol), and MACR 

(methacrolein). 

In CB4, higher aldehydes are grouped as ALD2 and assumed to be composed entirely of 

acetaldehyde for reaction with the nitrate radical; this proceeds with a rate constant of 

2.5x10-15

 cm3 s

-1. 

Surface

NO3

NO3

HNO VOC’s 3

Organo-nitrate 

Reservoir

3 Appendix G Attachment 15 - Page Number:

63
ALD2 + NO3 → HNO3 + C2O3

The rate constants for reactions of higher aldehydes with NO3 radicals are faster; 6x10-15

cm3 s-1 for propanal and 11.0x10-15 for butanal, so the lifetime of these carbonyls in the 

polluted nighttime environment is hours (Bossmeyer et al. 2006); see also

http://www.iupac-kinetic.ch.cam.ac.uk/. 

CMAQ estimated the concentration of ALD2 as about 3 ppbv at Essex during high ozone 

days of 2002. Because the rate constant used is appropriate for acetaldehyde, the rate of 

reaction of higher aldehydes with NO3 radicals is underestimated. Table 4.1 shows that 

carbonyl concentrations can be substantial even at remote sites in the Northeast, but 

measurements of aldehydes longer than formaldehyde are rare, and there are no wellaccepted detection and calibration techniques. The concentration of higher aldehydes can 

be estimated from the concentration of alkenes. For reactions with OH, simple alkenes 

(methy-substituted ethenes, and 1-alkenes) form aldehydes (two per alkene) with high

yield, essentially 100% for butenes and decreasing for the larger 1-alkenes to about 50-

30% for 1-hexene through 1-octene. The O3 reactions form roughly 50% carbonyls per 

alkene reacted. The aldehydes are less reactive than the larger alkenes and have longer 

lifetimes in the atmosphere. Table 4.2 gives the mean concentrations of higher alkenes 

(up to C5) measured at Essex, MD during high ozone days in 2002. The total of 

measured C3 to C5 alkenes is about 1 ppbv with little diurnal variation, and the lifetimes 

can be short (e.g., a few hours for trans-2-butene in the presence of 20 ppbv ozone), 

indicating that these unsaturated hydrocarbons are roughly in steady state. 

For a concentration of 5x109

 cm-3 (200 ppt) NO3, the lifetime of propanal is 9 hr 

and the lifetime of butanal is 5 hr. The steady state total of higher aldehydes (greater 

than or equal to C2) is thus at least 1 ppbv. Over the course of a night, reactions of 

aldehydes C3 and greater, not included in CB4, generate a mixing ratio of HNO3 of at 

least 1 ppbv. 

A small fraction (about 2%) of alkanes reacts directly with NO3 radicals at night, 

and this leads to alkyl radicals and HNO3 production. 

RH + NO3 → HNO3 + R· 

Table 4.3 shows the nighttime mean alkane concentrations, relative rate constants, and 

the amount of HNO3 formed. The total for all alkanes is about 0.2 ppbv NOx converted 

to HNO3 by this process every night. Together these mechanisms account for in excess 

of 1.2 ppbv conversion of NO3 to HNO3 each night, and one can reasonably expect that 

CMAQ missed the irreversible loss of at least 1.2 ppbv NOx. 

4 Appendix G Attachment 15 - Page Number:

64
Table 4.1 Carbonyl Measurements1

Minimum Maximum Mean Median σ N

PSP2

HCHO 45 6215 2038 2027 1079 1791

CH2OHCHO 5 966 301 323 187 673

CH2OHCOCH3 30 1650 372 332 256 1740

WFM3

HCHO 30 6170 1340 1260 821 3178

All units pptv (parts per trillion by volume). 

1

Personal Communication from Xianliang Zhou 

Research Scientist, Wadsworth Center, 

New York State Dept. Health Phone (518) 474-6693 

Associate Professor, School of Public Health Fax (518) 473-8117 

University at Albany, SUNY E-mail: zhoux@wadsworth.org

2 Pinnacle State Park in Addison, NY, from June 26 to July 14, 1998 

3 Summit of Whiteface Mountain, from June 14 to July 20, 1999 

Table 4.2 Mean alkene concentrations for high ozone days in 2002 at Essex, MD. 

VOC Mean ppbC Cn* Mean (ppbv) 

Propene 1.443 3 0.481

1-butene 0.374 4 0.093

trans-2-butene 0.451 4 0.113

cis-2-butene 0.314 4 0.078

1-pentene 0.413 5 0.083

trans-2-pentene 0.637 5 0.127

cis-2-pentene 0.336 5 0.067

Total 1.043 

*Carbon number. 

Notes: VOC concentrations as measured at Essex, MD on high ozone days in 2002. 

Concentration units are ppbv or parts per billion substance derived from ppbC. The total 

of 1.043 ppbv represents the lower limit mixing ratio of unsaturated VOC’s that could 

form carbonyls in reactions not appropriately represented in the CB4 ozone formation 

mechanism. 

5 Appendix G Attachment 15 - Page Number:

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Table 4.3 Alkane reactions to produce HNO3 for high ozone days, 2002 Essex, MD. 

VOC Mean (ppbv) (kOH/kNO3)*103 HNO3 formed 

(ppbv) 

Propane 2.82 0.015 0.043

n-butane 1.44 0.027 0.038

Isobutane 0.87 0.033 0.028

n-pentane 1.70 0.020 0.034

n-hexane 0.48 0.019 0.009

Cyclohexane 0.11 0.018 0.002

2-me-pentane 0.62 0.018 0.011

3-me-hexane 0.23 0.020 0.005

2-me-hexane 0.18 0.020 0.004

n-heptane 0.21 0.019 0.004

n-octane 0.08 0.021 0.002

2-me-heptane 0.05 0.023 0.001

3-me-heptane 0.07 0.036 0.002

n-nonane 0.07 0.024 0.002

n-decane 0.08 0.023 0.002

n-undecane 0.07 0.002 0.000

Total 0.187 

Notes: VOC concentrations measured between 0:00 to 05:00 EST as measured at Essex, 

MD on high ozone days in 2002 and calculated formation of nitric acid. Concentration 

units are ppbv or parts per billion substance, not ppbC. The fraction reacting with the 

nitrate radical is computed with the ratio of rate constants for reaction with OH to that 

with NO3, and assuming 1000 times more NO3 than OH. The total of 0.187 ppbv 

represents the mixing ratio of NOx that would be irreversibly removed from the 

possibility of ozone formation. The reactions considered here are not part of the CB4 

mechanism. 

6 Appendix G Attachment 15 - Page Number:

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4.3 Alkyl Nitrates 

Most alkanes react with OH radicals in the daylight hours to form alkyl radicals 

that quickly form alkyl peroxy radicals: 

RH + OH (+O2) → H2O + RO2· 

The reaction of alkyl peroxy radicals with NO usually leads to NO2 and rapid ozone 

formation. 

RO2· + NO → RO· + NO2 

Some fraction of the alkyl peroxy radicals, however, rearranges to form not NO2, but 

alkyl nitrates. 

RO2· + NO + M → RONO2 + M 

The reactions producing alkyl nitrates are not included in the CB-4 mechanism for the 

CMAQ runs performed. For the alkyl nitrates that have been investigated, the lifetime 

with respect to photolysis or OH attack is several days or longer; alkyl nitrates thus 

sequester NOx to slow the rate of formation of ozone ((JPL 2006; Luke et al. 1989), R. 

Atkinson personal communication, 2007). In the mid Atlantic region, the residence time

of an air mass is typically only a few days, so formation of alkyl nitrates may effectively 

delay the formation of ozone until air parcels have been advected over the Atlantic 

Ocean. 

Alkane concentrations measured at Essex, MD on high ozone days in 2002 are 

listed on Table 4.4; this list is not exhaustive. The mixing ratio of alkyl nitrate formed in 

OH attack is the product of the mean alkane mixing ratio, the fraction reacting, and the 

yield. The fraction reacting is computed with the rate constant for reaction with OH times 

the assumed mean OH concentration of 5 x 106

 cm-3 for an eight hour period. The yield 

is the rate of formation of all alkyl nitrates divided by the total product yield (Arey et al. 

2001) R. Atkinson personal communication 2007]. The total of 0.24 ppbv represents the 

mixing ratio of NOx that would be sequestered against ozone formation for a period of 

several days. This is a few percent of the total NOx of 5-10 ppbv; the reactions 

considered here are not part of the CB4 mechanism, although they are included in other 

mechanisms. 

7 Appendix G Attachment 15 - Page Number:

67
Table 4.4 Alkyl nitrate formation for high ozone days at Essex, MD. 

VOC Mean (ppbC) 

Fraction 

Reacting Yield Cn

*

Alkyl Nitrate 

formed (ppbv) 

propane 3.567 0.17 0.036 3 0.007

n-butane 2.918 0.36 0.077 4 0.020

isobutane 1.543 0.36 0.077 4 0.011

n-pentane 2.501 0.6 0.105 5 0.032

n-hexane 1.210 0.8 0.141 6 0.023

cyclohexane 0.261 0.8 0.17 6 0.006

2-me-pentane 1.644 1 0.14 6 0.038

3-me hexane 0.873 1 0.178 7 0.022

2-me-hexane 0.627 1 0.178 7 0.016

n-heptane 0.809 1 0.178 7 0.021

n-octane 0.318 1 0.226 8 0.009

2-me-heptane 0.211 1 0.226 8 0.006

3-me-heptane 0.226 1 0.226 8 0.006

n-nonane 0.322 1 0.25 9 0.009

n-decane 0.412 1 0.25 10 0.010

n-undecane 0.364 1 0.25 11 0.008

Total 0.244

* Carbon Number

Notes: VOC concentrations are means for 8:00 to 16:00 EST as measured at Essex, MD 

on high ozone days in 2002. Concentration units are ppbC or parts per billion of the 

substance multiplied by the number of carbon atoms in the molecule, Cn. The fraction 

reacting is computed with the rate constant for reaction with OH times the assumed mean 

OH concentration of 5 x 105

 cm-3 for an eight hour period. The yield is the rate of 

formation of all alkyl nitrates divided by the total product yield (Arey et al. 2001; R. 

Atkinson personal communication 2007). The total alkyl nitrate formed is the product of 

the mean alkane mixing ratio (ppbC/Cn), the fraction reacting, and the yield. The total of 

0.244 ppbv represents the mixing ratio of NOx that would be removed, sequestered 

against ozone formation for a period of several days. This is a few percent of the total 

NOx of 5-10 ppbv, and the reactions considered here are not represented in the CB4 

mechanism. If each ppbv NOx produces 4 ppbv ozone, then formation of alkyl nitrates 

accounts for about 1 ppbv O3. 

8 Appendix G Attachment 15 - Page Number:

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4.5 NOx/VOC Chemistry Conclusions 

High concentrations of VOC’s can sequester or remove NOx from the atmosphere, and

many of these processes are missing from the CB4 mechanism employed in CMAQ as 

used for these studies; CB5 attempts to correct some of these shortcomings (Luecken and 

Sarwar 2006). NOx removal due to reactions with higher aldehydes can be expected to 

exceed 1 ppbv; about 2% of alkanes are removed by direct attack by NO3 radicals for 

about 0.2 ppbv NOx loss; OH attack on alkanes will produce about 0.24 ppbv alkyl 

nitrates. The total is thus about 1.5 ppbv NOx – a fair fraction of the mean 5-15 ppbv NOx

present. CMAQ showed a high bias for NOx of 1.3 ppbv (G. Sistla, personal 

communication 2006). CB4 may however have compensating errors; early versions of 

CB5 calculate a few ppbv more ozone (Luecken and Sarwar 2006). For the typical ozone 

production efficiency of 4 ppbv O3 per ppbv NOx, this represents about 6 ppbv of ozone 

loss unaccounted for in CMAQ. The 6 ppbv estimated is a lower limit for model 

uncertainty. The loss of NOx in urban plumes may also explain the apparent greater 

efficiency of ozone production in plumes from elevated NOx sources and help to explain 

why ozone in the mid-Atlantic has decreased substantially in response to power plant 

NOx emissions reductions. 

4.6 Photolysis Rates 

We have identified the impact of aerosols on photolysis rates as a process 

occurring in the atmosphere and not represented in the runs of CMAQ for this SIP. 

CMAQ generates ozone mixing ratios similar to or slightly greater than observations near 

the Earth’s surface, but underestimates ozone aloft. The impact of highly scattering 

aerosols on the rate of NO2 photolysis is to inhibit ozone formation near the Earth’s 

surface and to accelerate ozone formation aloft with an overall impact of more ozone 

(Dickerson et al. 1997). Hains (2007) used an improved profile of photolysis rates to 

bring CMAQ output into closer agreement with aircraft observations. 

4.6a Recalculated NO2 photolysis rate coefficients 

Figure 4 of Chapter 2 shows the median aircraft measured and CMAQ ozone

profilesfor 2002. The aircraft ozone is on average 10% (6 ppbv) larger than in the 

CMAQ profiles between 600 and 2600 m, while about 6 ppbv smaller than CMAQ below 

500 m in the PBL. CMAQ under-predicts ozone in the lower free troposphere and overpredicts ozone near the surface, with the model integrated ozone column content 4%

smaller than observed. 

Reasons for measurement/model differences may include problems with 

emissions inventories, advection, vertical mixing, cloud cover, and chemistry. The NO2

photolysis rates that CMAQ uses impact how much ozone is produced by the model. The 

rate coefficient for the photolysis of NO2 (hereafter referred to as jNO2 value) used by the 

default version of CMAQ assumes no aerosol loading. Earlier UMD work (Dickerson et 

al. 1997; Park 2001; Park et al. 2001) shows that an increase of aerosols from an optical 

depth of 0 to 2 increases the jNO2 values by 30% above the boundary layer and increases 

the total mass of ozone formed. Park et al. [2001] performed a sensitivity test of CMAQ 

9 Appendix G Attachment 15 - Page Number:

69
using jNO2 values associated with CMAQ aerosols. UMD (Hains 2007) performed a

sensitivity study using jNO2 values associated with typical aerosols measured in the MidAtlantic from July 15-18, 2002 using the (Park 2001) program. The optical properties of 

the aerosols must be specified in the model. These include the Angstrom exponent (also 

called Angstrom slope) and intercept related to the size of the particles (slope of 2.0, 

intercept of 0.1 were used); the single scattering albedo, which gives the probability that a 

photon will be scattered rather than absorbed (0.96); and the asymmetry parameter that 

indicates the fraction scattered in the forward direction (0.8). Based on aircraft 

measurements, aerosols were assumed to be confined to the lowest 1.5 km of the 

atmosphere. The observed mean aerosol optical depth was 0.3 at 550 nm, which 

corresponds to 0.6 at 380 nm, the wavelength most relevant for NO2 photolysis. Results 

(Figure 4.2) show diminution in jNO2 near the Earth’s surface but substantial increase 

aloft and at all times of the day. 

10 Appendix G Attachment 15 - Page Number:

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Figure 4.2 Standard (without aerosols, green) and revised (with aerosols, red) jNO2

values used in CMAQ for several daylight hours. Aerosols reduce photolysis 

rate coefficients in the first few hundred meters of the PBL, but increase them

aloft. Altitude is in m-AGL. 

j-NO2 values with and without aerosols

0

2000

4000

6000

8000

10000

12000

0.000 0.100 0.200 0.300 0.400 0.500 0.600 0.700 0.800

j-NO2 value (min-1)

A ltitu

d

e (m )

12 noon without

1pm without

2 pm without

3 pm without

4 pm without

12 noon with

1 pm with

2 pm with

3 pm with

4 pm with

11 Appendix G Attachment 15 - Page Number:

71
4.6b CMAQ runs with revised photolysis rates 

UMD ran CMAQ from July 15-18, 2002 with the standard jNO2 values (assuming 

no aerosol) and revised jNO2 values, using aerosol concentrations and properties typical 

for the episode. The same aerosol values were used throughout the domain. The aircraft 

flies over rural and suburban areas, but also downwind of urban areas with large optical 

depths, so using the average optical depth from all of the flights should be a reasonable 

approximation of the Mid-Atlantic average optical depth. 

Two emissions scenarios were used; one with 2002 emissions and one with 

substantially lower emissions for 2018. This resulted in four model runs for comparison: 

• 2002 emissions with standard jNO2 values (2002, j-values no aerosols) 

• 2002 emissions with revised jNO2 values (2002, j-values with aerosols) 

• 2018 emissions with standard jNO2 values (2018, j-values no aerosols) 

• 2018 emissions with revised jNO2 values (2018, j-values with aerosols). 

Figures 4.3 – 4.7 show ozone generated by CMAQ using the standard jNO2 values, ozone 

generated using the modified jNO2 values, and ozone measured from the aircraft; note 

Universal Time (UT) is 5 hr ahead of Eastern Standard Time (EST), which is close to 

local solar time (LST). When the jNO2 values were changed, ozone at altitudes above 

~1000 m increased by about 1-2 ppbv, and below this altitude decreased by a few ppbv 

on average, but by as much as 15 ppbv near the Earth’s surface. Using improved 

photolysis rates did not eliminate measurement/model differences, but brought the 

CMAQ output closer to observations. Aircraft observations for the 2002 season are 

compared to CMAQ output for the same time and in the grid box. The spatial resolution 

of the observations is greater than that of the CMAQ (12 km); sometimes the aircraft flew 

out of one model box into another, and this can result in a discontinuity of 5-10 ppbv 

ozone. 

12 Appendix G Attachment 15 - Page Number:

72
a b

c

Aircraft

CMAQ (no

aerosols)

CMAQ (with

aerosols

* Aircraft

CMAQ (no

aerosols)

CMAQ (with

aerosols

*

Figure 4.3 Ozone profiles from the aircraft (pink stars), CMAQ using standard jNO2 values 

(without aerosols, shown in blue open squares), and CMAQ using revised jNO2 valu

(with aerosols, shown in green closed squares) for July 15, 2002 at a) Fort Meade, MD

14:00 EST, b) Easton, MD 16:00 EST, c) Churchville, MD 15:00 EST. Above 1000 m

the revised CMAQ profiles (with revised jNO

es

2 values, shown in green) are about 1 ppbv

larger than the standard CMAQ profiles shown in blue. Below 1000 m the standard 

CMAQ profiles are up to 5 ppbv larger than the revised CMAQ profiles. Note the 

discontinuity in observed ozone at 500 m altitude over Churchville, MD – the model also 

shows a strong gradient there. 

13 Appendix G Attachment 15 - Page Number:

73
 

Figure 4.4 Ozone profiles from the aircraft (pink stars), CMAQ using standard jNO2 values (without 

aerosols, and shown in blue open squares), and CMAQ using revised jNO2 values (with 

aerosols, shown in green closed squares) for the morning of July 16, 2002 at a) Louisa, VA 

9:00 EST, b) Crewe, VA 10:00 EST, and c) Richmond, VA 10:00 EST. Above 1000 m the 

revised CMAQ profiles (with revised jNO2 values, shown in green) are about 1 ppbv larger 

than the standard CMAQ profiles shown in blue. Below 1000 m the standard CMAQ 

profiles are 1-10 ppbv larger than the revised CMAQ profiles.

a b

c

Aircraft

CMAQ (no 

aerosols)

CMAQ (with 

aerosols

* Aircraft

CMAQ (no 

aerosols)

CMAQ (with 

aerosols

*

14 Appendix G Attachment 15 - Page Number:

74
Figure 4.5 Ozone profiles from the aircraft (pink stars), CMAQ using standard jNO2

values (without aerosols, and shown in blue open squares), and CMAQ using 

revised jNO2 values (with aerosols, shown in green closed squares) for the 

afternoon of July 16, 2002 at a) Wakefield, VA 13:00 EST, b) Quinton, VA 

14:00 EST, c) Tappahannock, VA 15:00 EST, d) Richmond, VA 15:00 EST. 

Above 1000 m, the revised CMAQ profiles (with revised jNO2 values, shown 

in green) are about 1 ppbv larger than the standard CMAQ profiles shown in 

blue. Below 1000 m the standard CMAQ profiles are about 1 ppbv larger 

than the revised CMAQ profiles. 

a b

d

Aircraft

CMAQ (no 

aerosols)

CMAQ (with

aerosols

* Aircraft

CMAQ (no 

aerosols)

CMAQ (with

aerosols

*

c

15 Appendix G Attachment 15 - Page Number:

75
a b

c

Aircraft

CMAQ (no

aerosols)

CMAQ (with

aerosols

* Aircraft

CMAQ (no

aerosols)

CMAQ (with

aerosols

*

Figure 4.6 Ozone profiles from the aircraft (pink stars), CMAQ using standard jNO2 values 

(without aerosols, and shown in blue open squares), and CMAQ using revised jNO2

values (with aerosols, shown in green closed squares) for July 17, 2002 at a) Louisa, 

VA 8:00 EST, b) Richmond, VA 10:00 EST, and c) Crewe, VA 9:00 EST. Above 1000 

m the revised CMAQ profiles (with revised jNO2 values, shown in green) are about 1 

ppbv larger than the standard CMAQ profiles shown in blue. Below 1000 m the 

standard CMAQ profiles are as much as 18 ppbv larger than the revised CMAQ 

profiles. 

16 Appendix G Attachment 15 - Page Number:

76
Figure 4.7 Ozone profiles from the aircraft (pink stars), CMAQ using standard jNO2 values (without 

aerosols, and shown in blue open squares), and CMAQ using revised jNO2 values (with 

aerosols, shown in green closed squares) for July 18 2002 at a) Richmond, VA 7:00 EST, 

b) Fort Meade, MD 9:00 EST, and c) Ashland, VA 8:00 EST. Above 1000 m the revised 

CMAQ profiles (with revised jNO2 values, shown in green) are about 1 ppbv larger than 

the standard CMAQ profiles shown in blue. Below 1000 m the standard CMAQ profiles 

are larger than the revised CMAQ profiles.

a b

c

Aircraft

CMAQ (no

aerosols)

CMAQ (with

aerosols

* Aircraft

CMAQ (no

aerosols)

CMAQ (with

aerosols

*

17 Appendix G Attachment 15 - Page Number:

77
Differences in CMAQ runs with and without revised photolysis rate coefficients are seen 

in Figure 4.8 for model levels 1, 8, and 16 (approximately at the surface, 500 m, and 2000 

m altitude) at 9:00 EST (14 UT) when the largest differences occurred. Values from the 

revised run are subtracted from the standard run so that negative numbers mean the 

standard CMAQ overestimated ozone (generally at low altitudes) and positive numbers 

mean that the standard CMAQ underestimated ozone (generally in the free troposphere). 

There are positive changes of 10 ppbv or more near the surface and small negative 

changes (mean of 1 ppbv) above 500 m. 

A curtain plot (Figures 4.9 and 4.10) was used to examine the diurnal variation in 

the first 2000 m of CMAQ showing the differences in ozone generated from:

2002, j-no aerosols − 2002, j-with aerosols 

Positive numbers (green, yellow, orange, red) indicate that standard CMAQ 

overestimates ozone; negative numbers (gray, blue, dark blue, indigo) indicate that 

CMAQ underestimates ozone. The x-axis represents a one grid cell swath at 77o W

longitude running north-south through the Washington/Baltimore urban area and 

extending from 31o to 47o N over the eastern United States. The y-axis represents the 

first 16 layers of CMAQ reaching from the surface to about 2000 m altitude. Six time 

periods of 21:00, 2:00, 6:00, 10:00, 14:00, 18:00 EST (3, 7, 11, 15, 19 and 23 UT) are 

shown for July 17, 2002. In the early morning, there are negative changes (where the 

standard version of CMAQ underestimates ozone) above 500 m that are mixed down to 

the surface. At 10:00 to 17:00 EST (15 and 23 UT) there is a positive change (where the 

standard version of CMAQ overestimates ozone) near the surface. 

The altitude dependence of the changes in ozone due to changes in photolysis 

rates is summarized in Figure 4.11. The median change is a little more than 2 ppbv near 

the surface and about 1 ppbv aloft with the standard version of the model overestimating 

ozone at lower altitudes and underestimating at higher altitudes. 

18 Appendix G Attachment 15 - Page Number:

78
Figure 4.8 Differences between standard and revised CMAQ ozone (standard – revised). The 

standard CMAQ used jNO2 values that did not account for aerosols, while the revised 

CMAQ used jNO2 values that did account for aerosols measured for a July 2002 smog and 

haze episode. These plots are for 9:00 EST; a) surface, b) 500 m, c) 3400 m AGL. The 

differences are positive at the surface (a) meaning that the standard CMAQ overestimated 

ozone there. Above 500 m, the differences are negative and the revised CMAQ run 

produces more ozone than the standard CMAQ, generally in better agreement with 

observations. 

a b

c 

19 Appendix G Attachment 15 - Page Number:

79
Figure 4.9. Map of eastern United States with swath used for curtain plot (Figure 4.10) 

shown as a thick, red line through the Washington/Baltimore area at 77◦

W 

longitude.

20 Appendix G Attachment 15 - Page Number:

80
21

Figure 4.10. Ozone differences (standard – revised) for a single swath in the CMAQ grid. The yaxis represents the 16 altitude layers used in CMAQ. The 16th altitude layer is around 2000 m and 

the 8th altitude layer is around 500 m. The x-axis represents the grid cells examined, where 1 is 

the southernmost grid cell and 172 is the northernmost grid cell. Here positive differences mean 

that the standard CMAQ run generates more ozone than the revised CMAQ run, and these positive 

differences are seen closer to the surface. 

Appendix G Attachment 15 - Page Number:

81
 

Figure 4.11 Differences in ozone mixing ratios generated with CMAQ with standard 

photolysis rates minus – that calculated with revised photolysis rates) for 16 

profiles generated between July 15-18, 2002. Diamonds represent medians; 

error bars represent the 25th and 75th percentiles. Near the surface the standard 

CMAQ run generates more ozone than the revised CMAQ run, and above 500 

m the standard CMAQ run generates less ozone than the revised CMAQ run.

0

500

1000

1500

-3 -2 -1 0 1 2 3 4 5

2002 ozone differences, ppbv 

(j standard - j revised)

2000

2500

Altitude (m)

22 Appendix G Attachment 15 - Page Number:

82
4.6c Photolysis rate conclusions 

Compared to aircraft measurements of ozone, CMAQ under-predicts ozone by 

10% between 600 and 2600 m-AGL and over-predicts by ~15% at lower altitudes, with 

the CMAQ ozone column content 4% small raft ozone column content. 

UMD improved the simulation of photochemistry in CMAQ to account changes in the 

UV flux due to aerosol scattering. The photolysis rate coefficient for NO2 increased aloft 

and decreased near the Earth’s surface. Ozone mixing ratios increased aloft (above 500 

m) by 1-2 ppbv and decreased below 500 m with a median change of ~2 ppbv and a 

range of 1-16 ppbv. Maximum dif s were observed in late afternoon. The impact 

of including aerosol radiative effects was not sufficient to bring model output into 

agreement, but it did improve the si n and show that the standard CMAQ runs 

underestimate the role of ozone production aloft. This may contribute to the simulations’ 

underestimating the benefit from reduced emissions of NOx from tall stacks. 

4.7 Future wo .

 To more rigorously quantif CB4 mechanism

UMD recommends comparing exis th alternative 

l mechanisms that explicitly g longer VOC’s. It would 

strative to compare CB4 to model with detailed chemistry such as the Master 

observatio

at night, a

compariso

Night flig

of NOx rem

The impact of changes in photolysis rates was tested for only one episode, and a 

more thorough study is called for. The entire summer should be run with well 

characterized aerosol loading. This will entail compiling data from the aircraft, MODIS 

(Levy 2007) and GASP (Knapp et al. 2005) satellite instruments as well as AERONET 

sun photometer observations. CMAQ appears to underestimate substantially aerosol 

optical depth, so self-generated aerosols would not be appropriate (Levy 2007). Low 

level clouds, such as the fair weather cumulus common on hot summer days in the MidAtlantic region, have an effect on UV flux similar to that of aerosols. They may also 

affect mixing. CMAQ CO fields should be improved so that they can be used to evaluate 

mixing. Clouds accelerate NO2 photolysis above and slow it below. CMAQ does 

account for radiative properties of clouds as generated by MM5. Several lines of 

reasoning, however, suggest that MM5 may underestimate the cloud cover. In prior 

investigations of simulated clouds (Mueller et al. 2006), a serious bias was discovered in 

MM5/CMAQ – the model consistently under-estimated spatially averaged clouds below 

3.7 km altitude. Over the Mid-Atlantic region, CMAQ overestimates SO2

concentrations (Hains 2007) and underestimates aerosol loading, dominated by sulfate 

(Levy 2007). Insufficient low-level cloud cover would explain both of these 

observations. Clouds in MM5 and CMAQ should be evaluated for the summer of 2002 

against National Weather Service surface observations as well as satellite observations. 

The models should be modified to better match cloud observations, and then evaluated 

for agreement with SO2, sulfate, and aerosol optical depth measurements. 

er than the airc

ference

tuatio

rk

y the impact of simplifications in the

ting CMAQ runs to CMAQ runs wi

chemica include reactions involvin

also be illu

Mechanism for a reduced domain such as Essex, MD, where a variety of direct 

ns are available. Direct measurements of NOx, NOy, and nitric acid formation

s well as VOC’s and, if possible, higher aldehydes are called for. Further 

ns to observations of surface and high altitude trace gases must be made. 

hts in the Baltimore or Washington urban plumes would help determine the role

oval in the efficiency of ozone formation. 

23 Appendix G Attachment 15 - Page Number:

83
Acknowledgments: MDE’s Ambient Air Monitoring Program supplied the VOC data 

used for this analysis; we thank R. Atkinson of UCR and S. T. Rao of NOAA for helpful

discuss

:

.

rocessing

and its role in regional air quality. Science, 311, 67-70. 

authors, 2006b: Nocturnal odd-oxygen budget and its implications 

nd

ent 

er

Hains,

l

l

over

y of

Alkyl

ions.

References

Arey, J., S. M. Aschmann, E. S. C. Kwok, and R. Atkinson, 2001: Alkyl nitrate, 

hydroxyalkyl nitrate, and hydroxycarbonyl formation from the NOx-air 

photooxidations of C-5-C-8 n-alkanes. Journal of Physical Chemistry A, 105,

1020-1027. 

Bossmeyer, J., T. Brauers, C. Richter, F. Rohrer, R. Wegener, and A. Wahner, 2006

Simulation chamber studies on the NO3 chemistry of atmospheric aldehydes

Geophysical Research Letters, 33. 

Brown, S. S., and Coauthors, 2006a: Variability in nocturnal nitrogen oxide p

Brown, S. S. and Co

for ozone loss in the lower troposphere. Geophysical Research Letters, 33. 

Dickerson, R. R., S. Kondragunta, G. Stenchikov, K. L. Civerolo, B. G. Doddridge, a

B. Holben, 1997: The impact of aerosols on solar ultraviolet radiation and 

photochemical smog. Science, 278, 827-830. 

EPA, 2006: Air Quality Criteria for Ozone and Related Photochemical Oxidants

EPA/600/R-05/0004aA. 

Gégo, E., P. S. Porter, A. Gilliland, and S. T. Rao, 2007: Observation-based assessm

of the impact of nitrogen oxides emissions reductions on ozone air quality ov

the eastern United States. Journal of Applied Meteorology and Climatology, in 

press. 

J. C., 2007: Measurements and Models of Pollutants over the Mid Atlantic: A 

Chemical Climatology. Ph.D., Chemistry, The University of Maryland.

JPL, 2006: Chemical kinetics and photochemical data for use in atmospheric studies 

Evaluation No. 15. 

Knapp, K. R., R. Frouin, S. Kondragunta, and A. Prados, 2005: Toward aerosol optica

depth retrievals over land from GOES visible radiances: determining surface 

reflectance. International Journal of Remote Sensing, 26, 4097-4116. 

Kuhn, M., and Coauthors, 1998: Intercomparison of the gas-phase chemistry in severa

chemistry and transport models. Atmos Environ, 32, 693-709. 

Levy, R. C., 2007: Second Generation Retrieval of Tropospheric Aerosol Properties

Land from Inversion of Visible and Near-Infrared Spectral Reflectance: 

Application over Maryland, Atmospheric and Oceanic Science, The Universit

Maryland. 

Luecken, D., and G. Sarwar, 2006: Effects of using the CB05 versus the CB4 chemical 

mechanisms on model predictions CMAS Conference. 

Luke, W. T., R. R. Dickerson, and L. J. Nunnermacker, 1989: Direct Measurements of 

the Photolysis Rate Coefficients and Henry Law Constants of Several

Nitrates. Journal of Geophysical Research-Atmospheres, 94, 14905-14921. 

Marufu, L. T., B. F. Taubman, B. Bloomer, C. A. Piety, B. G. Doddridge, J. W. Stehr, 

and R. R. Dickerson, 2004: The 2003 North American electrical blackout: An 

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accidental experiment in atmospheric chemistry. Geophysical Research Letters, 

31. 

Mueller, S. F., E. M. Bailey, T. M. Cook, and Q. Mao, 2006: Treatment of clouds and the 

associated response of atmospheric sulfur in the Community Multiscale Air 

ty (CMAQ) modeling system. Atmos Environ, 40, 6804-6820. 

ark, R. J., 2001: The Interaction of Regional and Global-Scale Atmospheric Chemistry, 

Park, R

, 2001: Regional air pollution and its radiative forcing: Studies with a 

Quali

P

Transport, and Climate Processes. Ph.D., Meteorology, The University of

Maryland. 

. J., G. L. Stenchikov, K. E. Pickering, R. R. Dickerson, D. J. Allen, and S.

Kondragunta

single-column chemical and radiative transport model. J. Geophy. Res., 106,

28751-28770. 

25 Appendix G Attachment 15 - Page Number:

85
Ozone Sensitivity to NOx Emissions 

March 16, 2007 

J. C. Hains 

Deptartment of Chemistry 

University of Maryland 

College Park, MD 

Abstract 

Upwind power plant emission sources of NOx and SO2 from West Virginia, Ohio, 

and Pennylvania along the Ohio River Valley play a crucial role in the amount of ozone 

and aerosol measured in the lower troposphere in the Mid-Atlantic region. Greater NOx

emissions along back trajectories were positively correlated with greater ozone mixing 

ratios measured by 232 aircraft vertical profiles performed in the Mid-Atlantic and 

Northeast U.S. between 1997 and 2003. Ozone column contents during the flights are 

strongly influenced by point source emissions with a slope of 61.6 ppbv ozone / (g NOx

m-2 day-1), an intercept of 1.3 ppbv, and a correlation (r2

) of 0.997 (e.g. Hains et al., 

2007). 

Introduction

In this analysis a hierarchical clustering method was used to separate distinct 

chemical and meteorological events from 232 lower tropospheric aircraft vertical profiles 

measuring ozone in the Mid-Atlantic and Northeast U.S. between 1997 and 2003. 

NOAA HYSPLIT 48-hour back-trajectories ending at 1 km (above ground level), using 

EDAS 80 km meteorology and modeled vertical velocity, were run for each profile to 

determine the most influential NOx sources. When examining individual episodes, 24-

hour back-trajectories are better than 48-hour back-trajectories because there are fewer 

fluctuations in the meteorology. However, when examining more than one episode, 48-

hour back-trajectories are more suitable. This is especially true for large sets of ozone 

episodes, because ozone has a lifetime longer than a single day. For this reason 48-hour 

back-trajectories were used in this analysis. The 2-day integrated NOx emissions 

encountered by each trajectory were calculated using the EPA Clean Air Market Division 

point source emissions database. A regression was made between the median integrated 

NOx emissions and the median ozone column contents for each cluster to determine the 

relationship between NOx emissions and ozone. Only point source integrated NOx

emissions were examined. There is a positive correlation between point and area source 

emissions, and the relationship between point source NOx emissions and ozone should be 

extended to include area source NOx emissions as well. 

Methods 

A statistical cluster analysis generally involves determining the differences 

between the objects being analyzed, and grouping those objects with the smallest 

1 Appendix G Attachment 15 - Page Number:

86
differences. Taubman et al., (2006) examined statistics for University of Maryland 

research aircraft profiles of ozone and showed median profiles and the statistical spread

around those profiles. Ozone showed distinctive profiles (Taubman et al., 2006); for 

example, ozone was most concentrated above 500 m in the morning and was more 

uniform from the surface to 2000 m in the afternoon (the largest values in the profiles 

were found near the 1100 m level). The differences between each pair of profiles were 

calculated in a manner similar to that described in Hains et al., 2007. 

The raw data were averaged into altitude layers of 100 m, and then the averages 

were grouped into bins shown in Table 1. The slope and correlation of the points in each 

pair of profiles under comparison were considered as well as the total difference in 

mixing ratio between the two profiles within each altitude bin. The following equation 

was used to calculate the differences among the aircraft profiles: 

∑ ∑( ) ( ) =

=

=

=

⎪

⎭

⎪

⎬

⎫

⎪

⎩

⎪

⎨

⎧

= − + − + − − − 4

1

_ 2

2

_1

2

( ) * 1 [1 ] [1 exp( ( 1) ] k

k

Part Part

a m

a n ij ia ja D abs c c R s 14 2 44 4 3 44 14 2 4444 444443 Equation 1 

Here, k denotes four different altitude bins for the trace gas profiles, and a denotes the 

100 m layers (n through m within each altitude bin (Table 1). The species value is 

represented by c for the i

th and jth profile. In each altitude bin, k, there are at least four 

100 m layers of ozone data. A regression between the ozone mixing ratios for each pair 

of profiles at the four layers in each of the bins was used to obtain the slope, s, and the 

correlation coefficient, R. The first part of Equation 1 determines the square of the sum

of the differences between values at each altitude level, k. The second part of the 

equation multiplies the difference by one plus differences associated with the correlation 

and slope. When the correlation is small or negative, the profiles are very different and 

the 1- R portion increases, which increases the total difference Dij. The exponent of the 

slope portion is used to account for the slope between the pairs of profiles. A slope near 

unity suggests that the profiles are similar and thus should add little to the total 

difference. The exponent of the slope was used to guarantee that slopes much different 

from unity will make the exponential term small and thus increase the total difference. 

Once the difference between each pair of profiles was calculated, the profiles with the 

smallest differences were clustered together using a standard hierarchical agglomerative 

clustering algorithm in Matlab. 

Table 1. Altitude bins used in Equation 1. 

k level Altitude bins for ozone 

1 151-650 m 

2 651-1150 m 

3 1151-1650 m

4 1651-2450 m

Results and Discussion

Figure 1 shows the median profiles for each of the six ozone clusters calculated in 

the above manner. Figure 2 shows back trajectory density plots for five of the clusters. 

These were generated from back trajectories that terminated at the location and time the 

profile was made. Flights were made from North Carolina to Maine between 1997 and 

2003, and these are shown in Figure 3. Cluster 6 has very large values of ozone 

2 Appendix G Attachment 15 - Page Number:

87
measured above 2000 m. There are only two profiles in this cluster, and they were made 

on 8 and 9 July 2002 when smoke from Canadian forest fires impacted the Mid-Atlantic 

region (Colarco et al., 2004; Taubman et al., 2004). The transported ozone can be seen in 

the peak (up to 150 ppbv) above 2 km. 

Profiles from clusters 3, 4 and 5 had large ozone values (Figure 1) and the back 

trajectory density plots (Figure 2) show passage over the Northern Ohio River Valley, 

where there is a high concentration of NOx sources. Taubman et al. (2006) found a 

similar relationship between back trajectories concentrated over the Northern Ohio River 

Valley and large values of ozone and suggested that the high concentration of power 

plants in this region contribute to ozone in the Mid-Atlantic region. The back trajectory 

density plots for clusters 3, 4 and 5 also show large densities around the I-95 corridor, 

which is suggestive of weak winds that lead to higher ozone values. Cluster 2 has the 

smallest ozone values and back trajectories that cross over the Atlantic Ocean, which may 

explain the small ozone values associated with this cluster. Cluster 1 has the second 

smallest ozone values (cluster 1 column content is 19% less than clusters 3), however, the 

back trajectories associated with cluster 1 are similar to those of cluster 3. To address 

this discrepancy, the integrated NOx point source emissions along the back trajectories 

were examined. 

0

500

1000

1500

2000

2500

0 50 100 150 200

Median O3 (ppb)

Altitu

d

e (m M

S

L)

Cluster 1 (80)

Cluster 2 (52)

Cluster 3 (66)

Cluster 4 (28)

Cluster 5 (4)

Cluster 6 (2)

3 Appendix G Attachment 15 - Page Number:

88
Figure 2. Back trajectory density plots for ozone clusters 1-5. The largest 0.3% NOx

sources are shown with a + symbol. Clusters 3, 4, and 5, associated with 

larger ozone values, show larger densities near point sources as well as along 

the I-95 corridor. Clusters 1 and 2, with low ozone mixing ratios, are 

associated with more variable winds.

4 Appendix G Attachment 15 - Page Number:

89
Figure 3. Locations of University of Maryland research aircraft flight spirals made in 

the eastern United States between 1997 and 2003 

To understand the influence of emissions upwind of the spiral locations, the 

integrated NOx emissions along each back trajectory were calculated. Emissions from

the daily EPA Clean Air Market unit level emissions database 

(http://cfpub.epa.gov/gdm/index.cfm?fuseaction=emissions.wizard) were used in this 

analysis. A circle was drawn, centered at each back trajectory position for each hour of

the two day back trajectory (Figure 4). The radius of the circle was 80 km to account for 

uncertainties associated with the back trajectory position and the influence of eddy 

diffusion and mixing processes. The emissions within each circle were summed. The 

sum of the emissions for each circle was divided by the area of the circle. The emissions 

from the day on which the back trajectory crossed a circle were used for the date of each

back trajectory position. The summed emissions will be referred to as integrated 

emissions. Statistics (median, 5th, 25th, 75th and 95th percentiles) for the integrated NOx

emissions are shown in Figure 5. 

5 Appendix G Attachment 15 - Page Number:

90
Figure 4. Circles drawn around an example back trajectory. The emissions contained in 

each circle were summed and divided by the area of the circle. Then 

emissions from each circle were summed. Each asterisk represents a point 

source location (all point sources in the domain are shown). 

6 Appendix G Attachment 15 - Page Number:

91
Figure 5. Statistics for NOx emissions encountered by back trajectories for each ozone 

cluster. The NOx emissions are sums of all emissions (g day-1) encountered 

by a back trajectory divided by the area of the circle drawn around the back 

trajectory point (m2

). Clusters 1 and 2, with the smallest ozone values are also 

associated with the smallest NOx emissions. Clusters 3, 4 and 5, with the 

largest ozone values, are associated with the largest NOx emissions. 

0

1

2

3

4

Cluster 1 

(80)

Cluster 2 

(52)

Cluster 3 

(66)

Cluster 4 

(28)

Cluster 5 (4)

O3 Cluster

Inte

grate

d N

O x emissi

o

n

s

(g m-2 da

y-1)

Q25

Q5

median

Q95

Q75

Ozone column contents were calculated by integrating the profiles from 100 m to 

2500 m; these column contents were converted to average column contents in ppbv, 

assuming standard temperature and pressure (273.15 K and 1 atm). Note that this is not 

a measurement of ozone at the surface but what the ozone mixing ratio would be in a 

profile with a constant mixing ratio. Clusters 3, 4 and 5 have the largest ozone values 

and the largest NOx emissions, while clusters 1 and 2 have the smallest ozone values and 

the smallest NOx emissions. The median ozone column content was regressed against the 

integrated NOx emissions (Figure 6). The correlation coefficient, r2

, of 0.997 is very 

high. The slope is 61.6 ppbv ozone /(g NOx m-2 day-1) with an intercept of 1.3 ppbv 

ozone. The slope of the line in Figure 6 does not provide a quantitative evaluation of the 

relationship of ozone mixing ratio with NOx emissions, but it does indicate that ozone is 

related to point source NOx emissions and a reduction in NOx corresponds to roughly the 

same reduction in ozone. Part of the reason for positive correlation between ozone and 

point source NOx emissions is that point sources are correlated with area sources of NOx. 

Figure 7 shows a positive relationship between area and point source emissions of NOx

from eight states in the Mid-Atlantic region. 

7 Appendix G Attachment 15 - Page Number:

92
y = 61.6x + 1.3

R2

 = 0.997

0

20

40

60

80

100

0 0.5 1 1.5

NOx emissions (g m

-2 day-1)

C

olu

m

n averageO 3 (p

p

b)

Figure 6. Median ozone column average and median integrated NOx emissions (from

point sources) for ozone clusters 1-5. The ozone is positively correlated with 

integrated NOx emissions. 

y = 0.32x + 0.46x108

R2 = 0.32

0.E+00

1.E+08

2.E+08

3.E+08

4.E+08

5.E+08

6.E+08

0.E+00 1.E+08 2.E+08 3.E+08 4.E+08 5.E+08 6.E+08

NOx area source emissions (kg year-1)

N

O

x point s

our

c

e e

mis

sions (

kg y

e

ar-1)

Figure 7. A regression between the sum of NOx area sources and NOx point sources for 

eight locations in the Mid-Atlantic region (District of Columbia, Delaware,

Maryland, New Jersey, New York, Pennsylvania, Virginia, and West 

Virginia). There is a positive correlation between point and area sources of

NOx. 

8 Appendix G Attachment 15 - Page Number:

93
Profiles were also grouped by time of day, where morning profiles are defined as 

those made before 12:00 noon EST and afternoon profiles are defined as after 12:00 noon 

EST. Sixty-one and sixty-eight percent of the profiles in clusters 3 and 4 were measured 

in the afternoon, whereas only 38% of the profiles in clusters 1 and 2 were measured in 

the afternoon. Greater ozone values in clusters 3 and 4 may be partly explained by the 

increased number of afternoon profiles, which were generally made downwind of urban 

centers, and had a longer time period for ozone production. 

Conclusions 

A cluster analysis of ozone profiles was performed to determine meteorological 

and point source influences on the profiles. Profiles with elevated levels of ozone are 

positively correlated with NOx point source emissions encountered by a 48-hour backtrajectory. The linear regression of ozone column content and integrated NOx emissions 

(from a 48-hour back-trajectory) gives a slope of 61.6 ppbv ozone / (g NOx m-2 day-1), 

intercept of 1.3 ppbv and a correlation coefficient, r2

, of 0.997. Point source emissions of 

NOx are positively correlated with area source emissions and both point and area source 

emissions affect ozone production. The linear relationship and strong correlation 

suggests that reductions of NOx emissions (from both point and area sources) should 

reduce ozone levels. Chapter 2 shows that CMAQ generally underestimates ozone aloft, 

and because ozone, as shown in this analysis, is positively correlated with NOx point 

source emissions, CMAQ may not adequately demonstrate or capture the efficacy of NOx

point source emissions controls, and may underestimate the role of transported ozone in 

Maryland. 

Future Work 

This study has laid the groundwork for a more rigorous study relating both point 

and area sources (on, for example, the county scale) of NOx to vertical ozone profiles. 

Because EPA regulates surface ozone, an additional examination should focus on the 

relationship between surface ozone and the integrated NOx emissions (from point and 

area sources) encountered over a two day period. This study examined data from 1997-

2003. In 2003, large scale reductions of point source NOx emissions were implemented. 

It would be enlightening to determine how these reductions impacted vertical profiles and 

surface measurements of ozone in years subsequent to the NOx SIP Call. 

9 Appendix G Attachment 15 - Page Number:

94
References

Colarco, P.R., M.R. Schoeberl, B.G. Doddridge, L.T. Marufu, O. Torres, and E.J. 

Welton, 2004. Transport of smoke from Canadian forest fires to the surface near 

Washington, D.C.: Injection height, entrainment, and optical properties, Journal of 

Geophysical. Research, 109, D06203. 

Taubman, B.F., Hains, J.C., Thompson, A.M., Marufu, L.T., Doddridge, B.G., Stehr, 

J.W., Piety, C.A., Dickerson, R.R., 2006. Aircraft vertical profiles of trace gas and 

aerosol pollution over the mid-Atlantic United States: Statistics and meteorological 

cluster analysis, Journal of Geophysical Research, 111, D10S07. 

Hains, J.C., Taubman, B.F., Thompson, A.M., Marufu, L.T., Doddridge, B.G., Stehr, 

J.W., Dickerson, R.R., 2007. A clustering methodology to identify distinct chemical 

events in the troposphere using aircraft trace gas and aerosol vertical profiles, manuscript 

in preparation. 

10 Appendix G Attachment 15 - Page Number:

95
Regional Nature of Ozone Transport 

March 7, 2007 

R. Hudson and J. Hains. 

Department of Atmospheric and Oceanic Science 

University of Maryland 

College Park, MD 

Abstract 

The transport of ozone into and within the State of Maryland above the nocturnal 

boundary layer has been examined using a combination of aircraft and ground-based 

measurements. The evidence from both the aircraft and the station ozone data clearly 

points to the importance of transport in the overall quality of Maryland’s air. Ozone 

introduced to the surface after the breakdown of the nocturnal inversion can either be 

transported or derived from locally produced ozone, depending on nighttime residual 

layer wind speeds. The contribution of regional transport to afternoon boundary layer 

ozone has been quantified using ozone profile data obtained on aircraft flights. When the 

source regions lay over the Ohio River Valley (~59% of the profiles), transport accounted 

for 69-82% of the afternoon boundary layer ozone. When winds were weak (~27% of the 

profiles), transport only accounted for 58% of the afternoon boundary layer ozone. The 

ground level ozone data obtained from MDE monitoring stations has also been examined 

for evidence of downward mixing. On days when the transported ozone is low, peak 

ozone occurs at about 15:00 EST. However when the transported ozone is large, an 

earlier peak occurs at about 10:00 EST, corresponding to the breakdown of the nocturnal 

boundary layer. The rate of increase of ozone within this peak is about four times greater 

than that due to pure photochemistry. A study was made of the relative contribution of 

transported and local photochemistry to the ozone data for six 8-hour exceedance days in 

August, 2002. The results show that if local photochemistry were the only source of 

ozone, none of the 6 days examined would have exceeded the 8-hour ozone standard. The 

effect of the transported ozone is to add ozone early in the day and hence to expand the 

time interval over which the ozone level exceeds 85 ppbv. This directly affects the 8-

hour ozone averages. 

Introduction

Land surfaces are far more efficient at radiating heat than the atmosphere above 

them, hence on a cloudless night, the Earth’s surface cools more rapidly than the air. That 

temperature drop is then conveyed to the lowest few hundred feet of the atmosphere. The 

air above this altitude cools more slowly, and a temperature inversion forms only a few 

hundred feet above the ground. The inversion, known as the nocturnal inversion, divides 

the atmosphere into two layers which do not mix. Below the nocturnal inversion the 

surface winds are weak, and any pollutants emitted overnight accumulate beneath the 

1 Appendix G Attachment 15 - Page Number:

96
inversion. Above the nocturnal inversion, winds continue throughout the night and can 

become even stronger as the inversion isolates the winds from the friction of the rough 

ground surface. This situation is especially prevalent when a high pressure system exists, 

which is the hallmark of high pollution events along the East Coast. It should be noted 

that the time used throughout this section is Eastern Standard Time (EST, or UTC-5). 

In the morning, the sun warms the Earth’s surface, and conduction and convection 

transfer heat upward. This warms the air near the surface more effectively than higher up, 

and by about 10:00 EST the temperature of the air above the surface has risen sufficiently 

to remove the nocturnal inversion. As a result, the ozone and its precursors that were 

above the inversion mix down to the surface. This mixing can either lower or increase the 

air pollution levels at the surface, depending on whether the air above the nocturnal 

inversion is cleaner or more polluted than the air at the surface. When wind speeds above 

the nocturnal boundary layer are brisk overnight, and the area where the air mass 

originated had high ozone levels the previous day, the ozone and precursors that are 

mixed down in the central Mid-Atlantic region can be referred to as transported ozone. 

However, when wind speeds are weak above the nocturnal boundary layer overnight, the 

ozone produced in the central Mid-Atlantic area during the previous day can become

decoupled from the surface with the formation of the nighttime inversion, remain in place 

over the region, and be mixed down the following day after the breakdown of the 

nocturnal boundary layer. This added ozone can be referred to as yesterday’s local 

recirculated ozone. Vukovich and Scarborough (2005) have modeled the effect of this 

transported ozone and precursors using the aircraft data obtained by the University of 

Maryland (Doddridge et al, 1995). They found a mixed response. On some days there 

was a large increase in the local recirculated ozone, indicating an increase in the chemical 

production of ozone, but on other days, the increase in local ozone was due only to the 

transported ozone mixed down from above the nocturnal inversion. 

Results from aircraft measurements.

From 1997 to 2003, aircraft measurements of ozone were made, from the surface 

to 3 km, during summertime air pollution episodes over the Mid-Atlantic U.S. as part of 

the University of Maryland’s Regional Atmospheric Measurement, Modeling, and 

Prediction Program (RAMMPP). Morning flights (before 12 noon EST) were generally 

made upwind of urban centers over locations such as Luray, VA, Winchester, VA, and 

Cumberland, MD (Figure 1). Afternoon flights (after 12:00 EST) over locations such as 

Hartford, MD and Easton, MD were made downwind of urban centers (Figure 1). 

Boundary layer ozone measured in the afternoon was generally greater than that 

measured in the morning, though the lower free tropospheric ozone showed little variance 

(Taubman et al., 2006). The increased afternoon ozone is explained by three factors: (1) 

longer time periods of daylight that allow for photochemical buildup, (2) afternoon 

meteorology that allows for mixing of transported ozone, and (3) the location of the 

flights. 

2 Appendix G Attachment 15 - Page Number:

97
Cumberland

Winchester

Luray

Easton

Hartford

Figure 1. A typical flight path made by University of Maryland research aircraft. 

Morning spirals were made in Cumberland, Winchester, and Luray, all 

upwind of the Baltimore-Washington corridor. Afternoon spirals were made 

in Hartford and Easton, downwind of the Baltimore-Washington corridor. 

Taubman et al. (2006) used a cluster analysis of back trajectories in conjunction 

with the vertical profile data to identify source regions and characteristic transport 

patterns during summertime pollution episodes. Four of those clusters representing 

different meteorological regimes are discussed below. 

Cluster 1, labeled as Moderate Northwesterly flow (Figure 2a), has large amounts 

of ozone (Figure 3), a large SO2/CO ratio, large, highly scattering particles, and a large 

aerosol optical depth (AOD). These values are indicative of aged point source pollution. 

The greatest trajectory density lies over the northern Ohio River Valley where there are 

several large NOx and SO2 sources. Fresh NOx and SO2 emissions from these sources 

have had ample opportunity under a moderate flow regime to produce ozone and 

secondary aerosols en route to the Mid-Atlantic. 

Cluster 2 shows wind directions similar to cluster 1 (Figure 2b), but with faster 

wind speeds (Fast Northwesterly flow). The greatest trajectory density also lies mainly 

over the northern Ohio River Valley and extends into the Great Lakes region. The 

particles are also large and highly scattering, but the AOD is smaller. The ozone values 

associated with this cluster are smaller than those in Cluster 1 (Figure 3). Clusters 1 and 2 

are both associated with northwesterly flow bringing in point source pollution from the 

northern Ohio River Valley. The smaller ozone values associated with cluster 2 likely 

result from the faster wind speeds associated with this cluster. Faster winds inhibit local 

photochemical ozone production and increase ventilation of urban mobile source 

pollution. 

Cluster 3 is typified by Slow Southerly flow (Figure 2c). The greatest air parcel 

density is found over the central Mid-Atlantic region. Ozone values, particularly in the 

afternoon, are large (Figure 3). These large ozone values likely result from the previous 

day’s ozone being brought down from the nocturnal boundary layer where it combines 

with the current day’s ozone. The previous day’s ozone, which was decoupled from the 

surface with the formation of the nighttime inversion, remained in place over the central 

3 Appendix G Attachment 15 - Page Number:

98
Mid-Atlantic region until being reintroduced to the surface layer the next day upon 

breakdown of the nocturnal boundary layer. The previous day’s ozone could have been 

transported ozone or locally grown ozone. Figure 2c shows that there are few large NOx

and SO2 sources in the area of greatest trajectory density. The particle property values are 

moderate and the CO values are large. These values, together with the weak winds 

associated with cluster 3, indicate local, urban pollution dominated by mobile sources. 

The transport pattern identified by cluster 4 is characterized as Moderate 

Southwesterly flow and the greatest trajectory density lies over the southern Ohio River 

Valley. For the most part, this cluster is associated with little pollution loading, which 

suggests that there are fewer point sources located farther south along the Ohio River. 

Figure 2d shows no large NOx or SO2 sources in the area of greatest trajectory density, 

although many do encircle this area. Also, the afternoon ozone values are particularly 

small (Figure 3), and not much larger than the morning values, indicating there was little 

photochemical production during transport of the air parcels. 

The contribution of regional transport to afternoon boundary layer ozone is 

quantified as follows. The percent of the afternoon ozone boundary layer column content 

for each cluster that can be accounted for by regional transport was estimated with the 

following equation:

 % Ozone transported = ⎟×100 ⎠

⎞ ⎜

⎝

⎛ ⎟ ×

⎠

⎞ ⎜

⎝

⎛

ABL

MBL

MBL

RL (1) 

RL: The residual layer column content, defined as the layer between 500 m

and 2000 m in the morning profiles 

MBL: The morning boundary layer column content, defined as the layer between 

100 m and 2000 m in the morning profiles 

ABL The afternoon boundary layer column content, defined as the layer 

between 100 m and 2000 m in the afternoon profiles 

The equation simplifies to the ratio of RL/ABL after the MBLs cancel out. 

Assuming the RL ozone was transported to the morning spiral location from an upwind 

regional source and subsequently transported to the afternoon spiral location where it 

mixed with the locally produced ozone to give the ABL ozone column content, the 

equation should provide the relative contribution of regionally transported ozone to the 

afternoon boundary layer column content. The accuracy of this method depends upon the 

Lagrangian nature of the morning and afternoon profiles from each cluster. Because 

flight plans were designed in a Lagrangian manner, where morning flights were upwind 

of afternoon flights (Figure 1), the estimate should be accurate. When the greatest cluster 

trajectory density lay over the Ohio River Valley (~59% of the profiles), transport 

accounted for 69-82% of the afternoon boundary layer ozone. When winds were weak 

(~27% of the profiles), transport only accounted for 58% of the afternoon boundary layer 

ozone. 

4 Appendix G Attachment 15 - Page Number:

99
Figure 2. Trajectory density maps for back trajectory clusters with a) Moderate Northwesterly 

flow (Cluster 1), b) Fast Northwesterly flow (Cluster 2), c) Slow Southerly flow 

(Cluster 3), and d) Moderate Southwesterly flow (Cluster 4). The plots were generated 

using a linear interpolation method between the trajectory end points. They indicate the 

relative density (%) of air parcels over the total area described by the spaghetti plots. 

Also pictured are the locations of the top 0.3% emitters annually of NOx (diamonds) 

and SO2 (crosses) in the eastern United States. 

5 Appendix G Attachment 15 - Page Number:

100
0

0.5

1

1.5

2

2.5

3

3.5

0 20 40 60 80

Figure 3. The median morning (a) and afternoon (b) profiles for back trajectory clusters 

with Moderate Northwesterly flow (Cluster 1), Fast Northwesterly flow 

(Cluster 2), Slow Southerly flow (Cluster 3), and Moderate Southwesterly 

flow (Cluster 4). 

100

Median O3 (ppb)

Altitude (

k

m)

Moderate

Northwesterly

(45)

Fast

Northwesterly

(34)

Slow Southerly

(56)

Moderate

Southwesterly

(20)

0

0.5

1

1.5

2

2.5

3

3.5

0 20 40 60 80

Median O3 (ppb)

Altitude (

k

m)

Moderate

Northwesterly

(62)

Fast

Norhwesterly

(43)

Slow Southerly

(52)

Moderate

Southwesterly

(18)

100

6 Appendix G Attachment 15 - Page Number:

101
Results from Ground level Ozone Measurements 

Figure 4 shows the hourly ozone measurements made at the Fort Meade air 

monitoring site for the first 9 days of August 2002, a month when there were many ozone 

episodes that led to 8-hour ozone exceedances. The hourly ozone data for August 2nd and 

August 8th show different slopes between 10:00 EST and 15:00 EST. This is shown more 

clearly in Figure 5 where the two days are plotted on top of one another. 

Figure 4. Ground based ozone measurements made at the Fort Meade station for August 

1 to August 9, 2002. 

Figure 5. Ozone data for August 2nd (blue asterisks) and August 8th (green 

crosses) as a function of time (EST, UTC-5). The red diamonds are the difference 

between the two ozone curves.

7 Appendix G Attachment 15 - Page Number:

102
The data for August 8 are typical for local photochemical production of ozone. Emissions 

occur early in the morning, and as the solar radiation and ground temperature increase, 

the ozone climbs to a maximum at about 15:00 EST. The surface temperature maximum

occurs simultaneously. The red curve on Figure 5 is the difference between the data for 

August 2nd and August 8th. The most striking feature of the difference plot is the peak of 

ozone centered at about 11:00 EST. Figure 6 is the same as Figure 5 except that the ozone 

data for August 8th (O3(8)) have been multiplied by a normalization factor of 1.3 (the 

ratio of the measured ozone at 15:00 EST on August 2nd (O3(2,1500))and August 8th

(O3(8,1500), that is: 

O3rev(8) = O3(8) * O3(2,1500)//O3(8,1500) 

This factor accounts for the fact that the ground temperature on August 8th is 

lower than the ground temperature on August 2nd. The difference curve (red) now shows 

a more pronounced peak. The modified August 8 data in Figure 6 can be interpreted as 

the local photochemical curve for August 2nd. The sudden increase in the rate of 

production of ozone starting at about 8:00 EST on August 2 is about a factor of four 

larger than the rate due solely to photochemistry. This remarkable increase is the product 

of ozone and its precursors being brought down from aloft as the nocturnal inversion 

slowly erodes and eventually breaks down at about 10:00 EST (Vukovich and 

Scarborough, 2005). The timing of the 11:00 EST peak in Figure 6 and the 10:00 EST 

peak found by Vukovich is remarkably similar given the limited data employed for the 

single case study presented here. 

Figure 6. Ozone data for August 2nd (blue asterisks) and August 8th (green crosses)

normalized to the August 2nd data at 17:00 EST (UTC-5). The red diamonds 

are the difference between these two curves. The solid black curve is a 

representative Gaussian curve. 

8 Appendix G Attachment 15 - Page Number:

103
Another inference of the difference between the ozone data on August 2nd and August 8th

is that there is no transported ozone evident on August 8th. The reason for this can be seen 

if one compares the back trajectories of air parcels for these two days that reached 

Baltimore at 10:00 EST. These are shown in Figure 7. On August 2nd the upper level 

parcels of air come from eastern Pennsylvania. The air that reaches Baltimore at the 500 

meter level has been lifted from an altitude of 200m 15 hours earlier, and one can expect 

that lower altitudes will have come from close to the ground. This transported air will be 

polluted. However, on August 8th, the air parcels arrived from Canada accompanied by 

strong subsidence. These air parcels will have picked up little pollution. 

Figure 7. 24 hour back trajectories for August 2nd and August 8th ending at 10:00 EST. 

Red asterisks are for back-trajectories that begin at Baltimore at an altitude of 500 m

AGL. The green asterisks are for 1000 m AGL, and the blue asterisks are for 1500 m

AGL. 

9 Appendix G Attachment 15 - Page Number:

104
Figure 8. Calculated results for the transported and local photochemistry for August 2nd,

2002 with time in EST (UTC-5). The blue asterisks are the measured ozone 

data. The orange crosses are the best fit to the local photochemistry. The green 

diamonds are the transported ozone, and the red triangles are the best fit to the 

overall data. 

If one assumes that the effect of the transported ozone shown in Figure 6 can be 

approximated by a Gaussian curve, then one can fit the overall ozone data from 6:00 EST 

to 18:00 EST by a combination of a Gaussian curve and the ozone data for August 8th

multiplied by a constant. The Gaussian curve is a function of three constants, the 

amplitude, the width, and the time of the peak. The overall data are fit to these four 

constants using a non-linear least squares procedure (Press et al., 1986). The result of 

such a fit for August 2nd is shown in Figure 8. The green curve represents the effect of the 

transported ozone, the blue curve is the local photochemical curve, and the red curve is 

the sum of these two. The best fits for six days in August 2002 (2, 4, 11, 12, 13, and 19) 

when Maryland exceeded the 8-hour ozone standard are shown in Figure 9. The back 

trajectories for these same days are given in Figure 10. They cover a wide range of wind 

directions. 

 

10 Appendix G Attachment 15 - Page Number:

105
Figure 9. Best fit for the transported and local photochemical ozone for selected days 

(days that exceeded the 8 hour ozone standard) in August 2002. The color 

scheme is the same as for Figure 8, and time is in EST (UTC-5). 

11 Appendix G Attachment 15 - Page Number:

106
Figure 10. 24-hour back-trajectories for the days shown in Figure 9. Red asterisks are 

for back-trajectories that end at 10:00 EST at Baltimore at an altitude of 500 m

AGL. The green asterisks are for 1000 m AGL, and the blue asterisks for 1500 

m AGL. 

12 Appendix G Attachment 15 - Page Number:

107
Conclusions

The evidence from both the University of Maryland aircraft and the Fort Meade 

ozone data clearly highlight the importance of transport to the overall air quality in the 

State of Maryland. However, one point that comes out of the study is that the transport 

need not be fast, and that ozone introduced to the surface after the breakdown of the 

nocturnal inversion can either be transported or derived from the previous day’s locally 

produced ozone, dependent on nighttime residual layer wind speeds. The contribution of 

regional transport to afternoon boundary layer ozone has been quantified using ozone 

profile data obtained on aircraft flights. When the source regions lay over the Ohio River 

Valley (~59% of the profiles), transport accounted for 69-82% of the afternoon boundary 

layer ozone. When winds were weak (~27% of the profiles), transport only accounted for 

58% of the afternoon boundary layer ozone. The study of the relative contribution of 

transported and local photochemistry to the surface ozone data for six exceedence days in 

August 2002 suggests that if local photochemistry were the only source of ozone, none of 

the 6 days examined would have exceeded the 8-hour ozone standard. The effect of the 

transported ozone is to add ozone early in the day and hence to expand the time interval 

over which the ozone levels may exceed 85 ppbv. Clearly, if the transported portion of

the daily ozone content were removed from Maryland, all monitors within the state would 

be well below the 8-hour ozone NAAQS. 

Future Work 

The analysis of the station ozone data would have benefited from ozone data taken at 

shorter intervals than one hour. It is recommended that data be archived at every 5 

minutes for this purpose. In this study only a few days were analyzed. It is clear that a 

more extensive analysis needs to be implemented, and that other species be considered 

for detailed monitoring. 

13 Appendix G Attachment 15 - Page Number:

108
References

Dodderidge, B.G., W.F. Ryan, R.R. Dickerson, R.M. Morales, R.M. Kelley, P.T. Roberts, 

D.L. Blumenthal, J.A. Anderson, K.L. Civerolo, 1993, ‘Observations of summertime air 

quality over the U.S. mid-Atlantic region’, Transactions of the AGU, #76 

Press, W.H., B.R.Flannery, S.A.Teukolsky, and W.T.Vetterling, 1986, ‘Numerical 

Recipes’, Cambridge University Press. 

Taubman, B.F., J.C. Hains, A.M. Thompson, L.T. Marufu, B.G. Doddridge, J.W. Stehr, 

C.A. Piety, and R.R. Dickerson (2006), 2006, ‘Aircraft vertical profiles of trace gas and 

aerosol pollution over the mid-Atlantic United States: Statistics and meteorological 

cluster analysis’, J. Geophys. Res., 111(D10), D10S07 MAR 29 2006. 

Vukovich, F.M. and J. Scarborough. 2005, ‘Aspects of ozone transport, mixing, and 

chemistry in the greater Maryland area’, Atmos. Env., 39, 7008-7019, 2005. 

14 Appendix G Attachment 15 - Page Number:

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15 Appendix G Attachment 15 - Page Number:

110
16 Appendix G Attachment 15 - Page Number:

111
Air Quality Benefits of an Aggressive Telecommute Strategy 

March 16, 2007 

Jeffrey Stehr, Charles Piety, Dale Allen, Patricia Castellanos*

Department of Atmospheric and Oceanic Science 

*Department of Chemical and Biomolecular Engineering 

University of Maryland 

College Park, MD

Abstract 

To simulate the effects of an aggressive telecommute program, the University of 

Maryland modeled the air quality change that would result if 40% of all light duty 

vehicles were taken off the road in the non-attainment areas of Baltimore, Philadelphia, 

and Washington, D.C. on 38 high ozone days in the summer of 2002. Changes in 

emissions were implemented as a flat 40% reduction in vehicle miles traveled in each 

county of the three non-attainment areas. The effects of implementing such a program 

were modeled using version 4.4 of the CMAQ model. The model results showed that 

across the three non-attainment areas tested, an aggressive telecommute program has the 

potential for considerable benefit to air quality, with fairly uniform benefits across all 

three areas. The highest monitoring locations in the Philadelphia and Washington, D.C. 

non-attainment areas would see the largest benefits from this program, suggesting that 

reductions in mobile emissions would have the greatest impact at the most troublesome

monitoring locations on the worst ozone days. Benefits in all three non-attainment areas 

averaged a reduction in ozone of over 2 ppbv. 

1 Appendix G Attachment 15 - Page Number:

112
Introduction

Ozone levels are episodic, and high ozone concentrations are largely influenced 

by meteorology, so a forecast-driven program of emissions reductions makes a lot of 

sense. To this end, it has been suggested that telecommuting be strongly encouraged on 

high ozone days in the summer to take vehicles off of the roads and vehicle emissions out 

of the air. To simulate the effects of a very aggressive telecommute program, the 

University of Maryland performed a sensitivity test, assuming that 40% of all light duty 

vehicles were taken off the road in the non-attainment areas of Baltimore, Philadelphia, 

and Washington, D.C. on the 38 days in the summer of 2002 when 8-hour ozone was ≥

85 ppbv (AQI of code orange or higher). Changes in emissions were implemented as a 

flat 40% reduction in vehicle miles traveled (VMT) in each county of the three nonattainment areas. No attempt was made to determine areas where workers were more or 

less likely to telecommute. 

Air quality was modeled for the summer of 2002, using the 2009 base A1 

emissions inventory as a starting point, and reducing the light duty vehicle VMT in that 

inventory only on high ozone days and only within the three non-attainment areas 

mentioned. The model started on May 22, and ran through the end of September, though 

benefits were only calculated from June 1 to September 30 to allow concentrations in the 

model time to reach realistic levels, also known as “spin up” time. Since the model starts 

initially with clean conditions, it must be run for a few days to allow pollution levels to 

build up to representative levels. In keeping with other base A1 modeling, CMAQ 

version 4.4 was used so that the same 2002 and 2009 base case values determined from

other modeling runs could be used for comparisons. Relative reduction factors were 

calculated as per EPA guidance, using 2002 design values as a base. 

Results 

As might be expected for an aggressive local program targeted to the worst air 

pollution days in a region, the results indicated significant benefits at all monitoring 

locations throughout the region. As pointed out in Chapter 2, CMAQ appears to be most 

sensitive to local emissions changes, and model performance is at its best in urban and 

suburban areas. These are the same areas covered by this scenario, so the strong model 

response of lower ozone concentrations is not surprising. In all three areas, the average 

benefit across all monitoring locations exceeds 2 ppbv. Throughout the Baltimore nonattainment area, the improvements are substantial (Table 1), with an average benefit of 

2.4 ppbv. The smallest benefit is a 1.3 ppbv drop at the Essex monitoring location, while 

the largest is a 3.3 ppbv benefit at the South Carroll monitoring location. In the 

Washington, D.C. non-attainment area, results are likewise rather striking (Table 2), with 

an average benefit of 2.6 ppbv, ranging from only 0.3 ppbv in Frederick County, Virginia 

to 3.5 ppbv in Chantilly, Virginia. To the north, in the Philadelphia non-attainment area 

(Table 3), the average benefit is 2.0 ppbv, ranging from 1.1 to 3 ppbv. 

2 Appendix G Attachment 15 - Page Number:

113
Table 1. CMAQ Results for Monitoring Locations in the Baltimore, Maryland 

Non-Attainment Area 

Monitoring 

Location

EPA AQS 

Site Code

2002 

Base 

Design 

Value 

(ppbv)

2009 

OTB/OTW 

(ppbv)

Telecommute 

Scenario 

(ppbv)

Change due to 

Telecommuting 

(ppbv)

Davidsonville 240030014 98.0 83.4 80.5 -2.9

Fort Meade 240030019 97.0 83.1 80.1 -3.0

Padonia 240051007 88.7 77.1 74.6 -2.5

Essex 240053001 91.3 79.8 78.5 -1.3

South Carroll 240130001 88.7 75.0 71.8 -3.3

Edgewood 240251001 100.3 85.1 83.3 -1.8

Aldino 240259001 97 82.1 80.0 -2.0

Table 2. CMAQ Results for Monitoring Locations in the Washington, D.C. NonAttainment Area 

Monitoring 

Location

EPA AQS 

Site Code

2002 

Base 

Design 

Value 

(ppbv)

2009-A1 

OTB/OTW 

(ppbv)

Telecommute 

Scenario 

(ppbv)

Change due to 

Telecommuting 

(ppbv)

Takoma Park 110010025 88.7 78.5 75.8 -2.7

River Terrace 110010041 89.0 78.1 75.3 -2.8

McMillan 

Reservoir 110010043 92.7 81.4 78.4 -3.0

Southern MD 

(Hughesville) 240170010 93.0 74.9 72.6 -2.2

Frederick Apt 240210037 87.3 73.9 71.8 -2.2

Rockville 240313001 86.7 75.5 72.6 -2.9

Greenbelt 240330002 94.0 80.9 78.0 -2.9

Prince 

Georges 

Equestrian 

240338003 94.0 81.0 78.3 -2.7

Arlington Co. 510130020 96.7 85.6 82.6 -3.0

Chantilly 510590005 87.0 74.9 71.4 -3.5

Mt. Vernon 510590018 96.7 84.8 81.9 -2.9

Lee Park 510590030 95.0 83.3 80.4 -2.9

Annandale 510591005 94.0 82.4 79.5 -2.9

McLean 510595001 88.0 77.7 74.8 -2.9

Frederick Co. 510690010 82.7 71.9 71.6 -0.3

Loudoun Co. 511071005 90.0 77.6 75.3 -2.2

Prince 

William 511530009 85.0 73.6 71.7 -1.9

Alexandria 515100009 90.0 78.9 76.2 -2.7

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Table 3. CMAQ Results for Monitoring Locations in the Philadelphia Non-Attainment 

Area 

Monitoring 

Location

EPA AQS 

Site Code

2002 

Base 

Design 

Value 

(ppbv)

2009-A1 

OTB/OTW 

(ppbv)

Telecommute 

Scenario 

(ppbv)

Change due to 

Telecommuting 

(ppbv)

Fair Hill 240150003 97.7 80.7 78.1 -2.6

Brandywine 100031010 92.7 81.7 79.2 -2.5

Bellefonte 100031013 90.3 78.4 76.0 -2.3

Killens Pond 100010002 88.3 77.8 76.4 -1.4

Lewes 100051003 87.0 77.5 76.4 -1.1

Lums Pond 100031007 94.5 79.3 76.9 -2.4

Seaford 100051002 90.0 75.1 73.7 -1.3

Colliers Mills 340290006 106.0 92.9 89.9 -3.0

Rider U 340210005 97.0 86.8 84.5 -2.3

Ancora Hospital 340071001 100.7 89.1 86.5 -2.6

Camden Lab 340070003 98.3 89.0 87.0 -2.0

Clarksboro 340155001 98.3 87.8 86.2 -1.6

Millville 340110007 95.7 81.7 79.3 -2.4

Nacote Creek 340010005 89.0 77.8 76.4 -1.3

Bristol 420170012 99.0 89.6 87.0 -2.6

West Chester 420290050 95.0 81.4 79.2 -2.2

New Garden 420290100 94.7 78.5 76.1 -2.4

Chester 420450002 91.7 81.5 79.6 -1.9

Norristown 420910013 92.3 81.9 80.1 -1.7

Southwest 421010136 83.0 74.7 73.2 -1.5

Frankford 421010004 71.3 64.9 63.4 -1.6

Northwest 421010014 90.7 82.0 80.5 -1.4

Northeast 421010024 96.7 88.0 85.7 -2.3

In some areas, the biggest benefits occurred at the monitoring locations that have 

the leading design values for the non-attainment area (e.g. the Colliers Mills monitoring 

location in the Philadelphia non-attainment area). This was not uniformly the case, but 

all monitoring locations did see benefit. For example, the Edgewood monitoring location 

would be brought well into attainment by implementing this telecommute scenario, 

dropping 1.8 ppbv from a projected 2009 design value of 85.1 ppbv to 83.3 ppbv. For the 

Baltimore area, this is especially important, since Edgewood is the lone remaining 

Maryland monitoring location with a design value above 85 ppbv. Benefits in the 

Washington, D.C. area are likewise fortuitous, with the high monitoring location in 

Arlington County, Virginia dropping from a projected 2009 design value of 85.6 to 82.6. 

4 Appendix G Attachment 15 - Page Number:

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Conclusions 

Across the three non-attainment areas that are tested, an aggressive telecommute 

program has the potential for considerable benefit to air quality, with fairly uniform

benefits across all three areas. The highest monitoring locations in the Philadelphia and 

Washington, D.C. non-attainment areas see the largest benefits from this program, 

suggesting that it is targeting the most troublesome monitoring locations on the worst 

ozone days. 

Future Work 

The simulation might be updated in the future when newer versions of the 

emissions inventories are available. The simulation might also benefit from a different 

chemical mechanism such as SAPRC99. As outlined in Chapter 4, there are solid 

scientific reasons why CMAQ’s CB4 chemistry does not entirely represent the chemistry 

of the atmosphere. As outlined in Chapter 9, CMAQ underpredicts the changes in ozone 

due to changes in NOx emissions, so a future model run might be expected to more 

accurately project the benefits of a telecommuting program. 

Acronyms 

AQI Air Quality Index 

CB4 Carbon Bond IV chemical mechanism 

CMAQ Community Multiscale Air Quality model 

EPA United States Environmental Protection Agency 

NOx Reactive oxides of nitrogen, the sum of only NO and NO2. 

ppbv Parts of ozone (or any other substance) per billion parts of air, by volume

SAPRC99 Statewide Air Pollution Research Center (1999) chemical mechanism

VMT Vehicle Miles Traveled 

OTB All regulations on the books 

OTW All regulations on the way 

 

5 Appendix G Attachment 15 - Page Number:

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Uncertainty in CMAQ and Over-predictions of Future Year Ozone Design Values 

March 15, 2007 

Jeffrey Stehr, Charles Piety, Dale Allen 

Department of Atmospheric and Oceanic Science 

University of Maryland 

College Park, MD 

Abstract 

Several different methods have been used to compare the measured effects from

historical changes in emissions to those forecast by the CMAQ model, and all affirm the 

idea that the response of CMAQ is less than that seen in nature. A study of the August 

2003 Northeast Blackout shows that the electrical blackout caused a change of at least 7 

ppbv ozone, and likely more, while a CMAQ simulation of the same event predicted only 

a 2.2 ppbv change. Other studies suggest similar CMAQ underpredictions of ozone 

changes in response to emissions changes. An ongoing study by EPA reveals that the 

NOx SIP call likely produced double the benefit predicted by CMAQ. Meanwhile, the 

State of New Jersey reports that its ozone monitors appear to have reached their projected 

2009 ozone concentrations already, three years ahead of schedule. Even when compared 

to results from within the 2002 ozone season, CMAQ underpredicts daily ozone 

variability, and shows important performance shortcomings in areas just upwind of 

Maryland on high ozone days, namely in the Ohio River Valley and central Virginia. 

CMAQ is therefore likely underpredicting changes in ozone due to emissions changes 

and underpredicting ozone transport, so its results should be viewed with such an 

understanding. 

In an effort to intelligently represent CMAQ results, uncertainties have been 

estimated for two types of errors in CMAQ modeling. One source of uncertainty is the 

likely meteorological variability of future years. This is not to say that 2002 was not 

representative, but instead that meteorological variability from year to year is well 

known, and any future projections must account for this to achieve a reasonable margin 

of safety so a particularly bad future year will not result in numerous exceedances of the 

standard. A second type of uncertainty comes from the model and its emissions. This 

uncertainty was partially estimated by examining several different 2009 scenarios and 

determining the range of possible 2009 ozone values from those scenarios. These two 

sources of uncertainty do not cover all the possible sources of uncertainty in CMAQ 

projections, since errors in the inventory, meteorology, and model formulation all play a 

role, but are significantly more difficult to estimate. Most of the remaining errors are 

systematic, not random, in nature, so they should not be accounted for by expanding the 

uncertainty in the future year projection, but by altering CMAQ’s prediction of future 

year ozone accordingly. As demonstrated earlier, CMAQ is known to underpredict 

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change, likely underpredicting change by 100%. To account for CMAQ’s 

underprediction, CMAQ changes were increased by a conservative 50%, and probable 

future ozone levels were calculated, along with probable ranges to account for 

uncertainty in representativeness and some model errors. The picture for the future is 

that the best estimates of 2009 ozone concentrations are very much like the 2012 

concentrations modeled directly by CMAQ. This is in line with current observations 

from New Jersey (home to the highest ozone in the Northeast) that show modeled 2009 

ozone concentrations turning up in 2006. 

Introduction

Air quality models have generally been evaluated by their ability to reproduce 

average concentrations. Statistical measures such as mean error and bias show good 

performance when the average or median is well captured. Some performance measures 

that emphasize peak pollution performance have also been used, but seldom if ever has 

the model’s ability to reproduce a change in pollution in response to a change in 

emissions been evaluated. Rapid changes in emissions are rare, so relatively few 

opportunites for such comparisons have existed. Most of the changes in ozone from day 

to day are caused by meteorology, with emissions remaining relatively constant. Because 

static performance measures have been used in the past, CMAQ’s development has been 

geared toward improving these performance measures instead of toward improving 

CMAQ’s ability to respond to changes in emissions. Therefore, it is entirely possible, 

indeed likely, that CMAQ would pass traditional performance criteria while 

underpredicting changes in air pollution in response to a change in emissions. In this 

section, we use several independent evaluations to demonstrate, and to some extent 

quantify, CMAQ’s response to changes in emissions. Since CMAQ has been evaluated 

against, and therefore is developed with an eye toward, performance measures that 

emphasize the mean (e.g. mean bias, error, median, etc.) its dynamic response and its 

ability to respond to a change in emissions have not been evaluated, nor have they been 

incorporated into the development of the model. CMAQ, therefore, can do a good job of 

meeting traditional performance criteria that emphasize average performance while still 

failing to capture ozone extremes and ozone in certain areas. 

Several studies combine to suggest that CMAQ, and likely photochemical models 

in general, underpredict the change in ozone concentrations that result from a change in 

NOx emissions, particularly those from power plants. In this chapter, the reasons behind 

the idea that CMAQ underpredicts change are explored, and then a plausible range of 

future design values is determined, examining several sources of variability and 

uncertainty. 

The 2003 Northeast Blackout is examined from three observational perspectives 

and compared with the lone air quality modeling study to date to show that CMAQ falls 

short of simulating the changes expected on this day. Next, an upcoming paper (in 

preparation) is explored that models the effects of the NOx SIP call on air quality, and 

finds that CMAQ captures roughly half the benefits that were actually observed between 

2002 and 2004. CMAQ predictions from the attainment modeling in this SIP are then 

examined against current trends at ozone monitors in New Jersey (home to the highest 

design values in the Northeast), showing that the projected 2009 future year design values 

have already been achieved. CMAQ’s performance is then examined in more detail, and 

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its inability to follow the daily range of ozone values is explored. CMAQ also does not 

perform as well in rural and upwind areas as in cities and suburban areas, especially in

central Virginia and the Ohio River Valley, two known source regions for ozone on high 

ozone days in Maryland. This rural underperformance is likely a reflection of CMAQ’s 

difficulty in handling ozone transport, as discussed in chapter 2. Finally, CMAQ’s 

performance is analyzed as a function of NOx emissions from nearby sources, revealing 

that CMAQ does best when nearby sources are abundant, and worst when few sources 

are nearby. This again points to CMAQ’s tendency to underpredict the magnitude of 

transported ozone. 

Having laid the basis for why it appears that CMAQ will under represent changes 

in ozone, a second section explores sources of uncertainty in future year design values, 

and lays out ranges of probable design values. In light of these issues, a single future 

year design value cannot be accurately predicted. Instead, a range of values is given. 

1. Dynamic Evaluation of CMAQ Leads to the Conclusion that CMAQ Ozone 

Predictions Lack the Dynamic Range Found in Nature 

Air Quality Changes Due to the August 14, 2003 Northeast Blackout 

On August 14, 2003, a chain reaction triggered the shutdown of much of the generating 

capacity in the northeastern U.S. and southeastern Canada. On August 14, 2003, just after 

4:00 PM Eastern Daylight Time, the power grid of the United States and Canada 

experienced the largest blackout ever. The blackout affected an estimated 50 million 

people, and more than 61,800 megawatts (MW) of electrical load was lost in parts of 

Ohio, Michigan, New York, Pennsylvania, New Jersey, Connecticut, Massachusetts, 

Vermont and the province of Ontario. The North American Electric Reliability Council 

(NERC) reported that many units shut down completely at the start of the blackout, and 

that a cascade of events continued for several minutes until maximum impact. This 

resulted in over 263 power plants with 531 units in the U.S. and Canada (NERC reports 

that all of the fossil-fuel units in Ontario were blacked out) being shut down. 

Investigation of hourly CEM (Continuous Emissions Monitoring) data from the power 

plant smokestacks indicate that many of the units affected by the blackout reported no 

heat input at all during this 24-hour period until attempting to start up. 

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Figure 1. Visible satellite photos twenty hours before the blackout (left) and seven hours 

after the blackout (right), indicating near total loss of power around Lake Erie and 

Lake Ontario, and substantial power loss in Detroit and Long Island, NY, among 

others. 

The obvious problem with studying air quality due to a blackout is that many groundbased monitoring stations were without power as well. Fortunately, the UMD aircraft 

was flying on this day (having been forecast as a high ozone day—a forecast that did not 

pan out), and was rerouted to Selinsgrove, PA, downwind from the heart of the blackout. 

Airborne measurements were taken over Maryland and Virginia (outside the blackout 

area) and Pennsylvania (in the center of the area affected by the blackout) on August 15, 

2003, 24 hours into the blackout [Marufu et al., 2004]. 

This flight, in conjunction with others, provides an excellent opportunity to test the 

response of air quality models to a large, sudden drop in emissions. Several different 

comparisons based on observations suggest that the 2003 Northeast Blackout produced a 

large change in ozone, while the lone modeling study performed to date [Hu et al, 2006] 

shows much smaller changes in ozone. 

Airborne measurements on August 15, 2003, the day after the 2003 Northeast Blackout 

began, show that ozone was 30 ppbv lower throughout the lowest 1.5 km of the 

atmosphere on that day, and 38 ppbv lower at ground level, than on a meteorologically 

similar day, August 4, 2002 (Figure 2). 

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a b c

Figure 2. Back trajectories ending at 1000 m over Selinsgrove, PA, in the heart of the 

area affected by the blackout on a) August 15, 2003, one day into the blackout, b) 

August 4, 2002, the first reference day, and c) August 3, 2005, the second 

reference day. The newer reference day is meteorologically more like the day of 

the blackout, but it occurs after controls mandated by the NOx SIP Call were 

installed. 

Reference Days for Comparison with the Day of the 2003 Northeast Blackout. 

A subsequent comparison to August 3, 2005, which was, from a transport standpoint 

(Figure 3), more like the blackout day than the day in 2002 was, found smaller changes in 

ozone. Unfortunately, the August 3, 2005 flight took place after the large reductions 

from the NOx SIP Call had been put in place, so the comparison is not as direct as one 

would like. Comparisons with the August 3, 2005 reference day therefore represent 

changes that are smaller because the NOx SIP Call and other emissions reduction 

programs reduced ozone on that day, so changes are less dramatic. Regardless, on the 

2005 reference day, ozone concentrations were 7 ppbv higher than on the blackout day. 

Since this flight took place after NOx SIP Call controls were installed, the actual change

in ozone due to the blackout must have been substantially larger. Therefore, the actual 

baseline must be higher than the ozone profile measured on August 3, 2005. 

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Figure 3. Ozone profiles taken from the UMD aircraft on the day of the blackout 

(leftmost, in black), the 2005 control day (middle, in blue), and the 2002 control 

day (rightmost, in red). As shown in Figure 2, the meteorology on the reference 

day in 2005 was more like that on the day of the blackout, but 2005 was also after 

the installation of controls from the NOx SIP Call, so the smaller effect is in part

due to controls installed between 2002 and 2005. 

Comparisons with Air Quality in Areas not Affected by the Blackout. 

Parts of the Northeast were not affected by the 2003 blackout. On the day of the 

blackout, the University of Maryland aircraft flew through those areas in addition to areas 

(e.g. near Selinsgrove, PA) that were affected by the blackout. When compared with 

observations taken outside the area of the blackout, ozone was minimally 10 ppbv lower, 

and more likely 20-25 ppbv lower in the area around Selinsgrove. The areas around 

Selinsgrove are indicated in Figure 4 by dashed lines leading from “SEG”, the symbol for 

Selinsgrove’s airport. 

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Figure 4. Ozone (diamonds, in red) and aircraft altitude (black line) over the course of 

the flight from Cumberland, MD to the airport in Selinsgrove, PA (SEG), and on 

to Ft. Meade, MD one day into the August, 2003 blackout. The area indicated by 

the lower ends of the dashed lines is within the area affected by the blackout, 

where ozone was approximately 50 ppbv. Outside the area affected by the 

blackout, though not at the same altitude, ozone is roughly 60 ppbv, and well 

outside, ozone is roughly 75 ppbv. The effect of the blackout therefore ranges 

from a conservative estimate of 10 ppbv to 20 or 25 ppbv. 

Comparisons Between the Blackout and Climatological Ozone from Past Flights. 

To examine the possibility that the day of the blackout was a low-ozone day, but one well 

within normal ranges, the data from the blackout flights have been compared to the 

climatological data set of all UMD aircraft flights. The University of Maryland has 

flown hundreds of flights throughout the Northeast on high ozone days. Back-trajectories 

(48 hour) were calculated for all the spirals flown by the UMD aircraft and grouped into 

clusters, with each cluster therefore representing similar meteorological conditions. 

When compared with flights falling within the same cluster, the ozone observations taken 

below ~2000 m over Selinsgrove during the blackout are the lowest of all past 

observations. Air quality observations on the blackout flight therefore represent a highly 

unusual day. The climatology of past ozone flights is indicated below in Figure 5, with 

the ozone profile also superimposed. 

7 Appendix G Attachment 15 - Page Number:

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0

500

1000

1500

2000

2500

3000

3500

0 20 40 60 80 100 120

Ozone (ppb)

Figure 5. Climatology of ozone flights. Back trajectories were clustered, and all 62 

flights in this cluster are compared against the blackout profiles (solid lines in 

color) of ozone taken over Selinsgrove on August 15, 2003. Data from all 62 

profiles were grouped into 100 m altitude bins, and analyzed statistically. The 

median is shown by the solid vertical black line, and the 10th and 90th percentiles 

for all flights in this cluster are shown as horizontal error bars. The blackout 

flight (one spiral upward and one downward) stands out as distinct, and those data 

points below 2000 m represent the lowest ozone of all flights in this cluster. 

Comparison with the CMAQ Model. 

These three comparisons (ozone on the blackout day vs. two reference days, ozone inside 

the area affected by the blackout vs. outside, and blackout profiles over Selinsgrove vs. 

climatology) suggest that the blackout produced a substantial change in ozone over 

central Pennsylvania. Unfortunately, these changes are not reflected in CMAQ modeling. 

Hu et al. [2006] used the Direct Decoupled Method (DDM), which their group embedded 

in CMAQ, to calculate changes in ozone on the blackout due to a variety of sources from

several regions. DDM calculates sensitivities to emissions changes as well as the usual 

pollutant concentrations that are calculated in CMAQ. Using this method, Hu et al. 

calculated the sensitivity of ozone to changes in power plant emissions and another 

sensitivity to changes in mobile source emissions, calculating an ozone reduction of only 

4.2% (2.2 ppbv) to power plant NOx, and very little change due to changes in mobile 

source NOx, which is far less than the response calculated by any of the above 

observation-based methods. The mobile source NOx emissions changes used in this 

modeling exercise were not calculated by any direct means, but determined by assuming 

sensitivity to changes in mobile source emissions, calculating an ozone reduction of only 

4.2% (2.2 ppbv) to power plant NOx, and very little change due to changes in mobile 

source NOx, which is far less than the response calculated by any of the above 

observation-based methods. The mobile source NOx emissions changes used in this 

modeling exercise were not calculated by any direct means, but determined by assuming 

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that weekend travel patterns existed on the day of the blackout. This simulation was 

performed using an updated version of CMAQ 4.3 with the SAPRC99 chemical 

mechanism, which is thought to be more responsive to changes in emissions than the 

CB4 chemical mechanism that has been used for OTC/MANE-VU modeling, owing to its 

more detailed chemistry. Since even the smallest calculated change in ozone (from

observational methods) was seven ppbv (14%), and the changes were likely considerably 

higher (e.g. exceeding 30%), it appears that the model is underrepresenting the change in 

ozone due to changes in emissions. The comparison of changes simulated in the model to 

changes deduced from a number of observation-based analyses suggests that the model is 

not appropriately capturing the response in ozone due to changes in power plant 

emissions. 

EPA Modeling Study of the Effects of the NOx SIP call. 

EPA, in association with several university colleagues, is performing a CMAQ 

simulation of 2002 and 2004 summertime air quality to determine the benefits of the NOx

SIP call [Gilliland et al., manuscript in preparation, 2007]. Since this represents a large 

change in emissions over a relatively short time, the benefits should be noticeable. 

Emissions and meteorology were simulated for both 2002 and 2004. The final results are 

not out yet, and the paper is currently in preparation, but preliminary results suggest that 

though observed median 8-hour ozone levels changed by about 18 ppbv, the CMAQ 

model only simulates a change of 8 ppbv. It is possible that the model’s lack of 

responsiveness might be due to some quirk of the meteorological simulation, whereby the 

meteorological simulation was in error in such a way as to render the photochemical 

simulation unresponsive to emissions changes or that the meteorological simulation could 

have produced an artificial environment that would have yielded higher ozone in reality, 

but these results suggest that the CMAQ model underpredicts changes in ozone, 

especially where power plant emissions are concerned. The only mobile emissions 

changes that would have occurred from 2002 to 2004 would have been from the modest 

vehicle fleet turnover in those years. 

Critical monitors in New Jersey have already reached their 2009 design values in 2006. 

Analysis of the latest 8-hour ozone design values (based on observations from the 

summers of 2004, 2005, and 2006) for the Colliers Mill ozone monitoring location in 

central New Jersey show that this monitor has already reached its projected 2009 design 

value in 2006 [R. Papalski, NJDEP, personal communication]. These calculations use a 

base year of 2002, so 2006 is only four years into the seven-year period, and New Jersey 

DEP calculates that automobile fleet turnover alone will still generate some substantial 

reductions in NOx emissions over the rest of the time period. This is a critical location, 

since its 2002 design value was a colossal 106 ppbv. Its projected 2009 design value is 

93, and its current design value is only 92. Furthermore, this is not a fluke result at an 

isolated monitor. All other New Jersey ozone monitoring locations are now close to their 

projected 2009 design values (Table 1), and the average value at all of the ozone 

monitoring locations is the same as the 2009 average projected value. Lastly, none have 

2004-2006 design values that are higher than Colliers Mills, so that is still the high ozone 

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monitoring location for the state of New Jersey, and indeed the entire Mid-Atlantic and 

Northeast. 

Table 1. Predicted and Measured Design Values in New Jersey

CCOUNTY OZONE MONITOR LOCATION 

2004-2006 

DESIGN

VALUE

Modeled 

Predicted O3

Concentration 

(BOTW-v.3)

(ppbv) (ppbv) 

Atlantic Nacote Creek 78 75 

Passaic Ramapo 79 79 

Monmouth West Long Beach (Monmouth Univ) 81 83 

Morris Chester 82 85 

Camden Camden Lab 83 89 

Bergen Teaneck 85 87 

Cumberland Millville 85 82 

Gloucester Clarksboro 85 88 

Hudson Bayonne 85 80 

Mercer Rider U 87 87 

Middlesex Rutgers U 88 85 

Hunterdon Flemington 88 84 

Camden Ancora 88 90 

Ocean Colliers Mills 92 93 

Average (ppbv) 85 85 

CMAQ Does Not Capture the Diurnal Cycle, and Therefore the Dynamic Range of 

Ozone Values. 

CMAQ’s response lacks the diurnal and longer-time scale cycles that are in the 

observations. CMAQ generally falls short of peak ozone and overpredicts low ozone at 

night. These shortcomings likely have specific causes (e.g. NOx titration has been 

blamed in the past for failures in predicting ozone at night) but those specific causes 

generally add up to a lack of response to change in the model. Figure 6 below compares 

CMAQ predictions with surface ozone measurements taken across Maryland, 

Washington, D.C., Virginia, and the Philadelphia nonattainment area. CMAQ 

consistently falls short of peak ozone values, especially on the highest ozone days. 

CMAQ’s performance is therefore at its worst when it is needed the most. As will be 

shown in the following section, CMAQ’s performance is also worse in the geographic 

locations where it is most needed. 

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a) 

b) 

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c) 

Figure 6. Ozone CMAQ simulations (dashed line) and measurements (solid line) across 

Washington, D.C., Maryland, Virginia and the Philadelphia nonattainment area. 

Observations were averaged across all monitor locations in Washington, D.C., 

Maryland, Virginia, and the Philadelphia non-attainment area. Model outputs 

were sampled from the grid cell containing each monitored location and also 

averaged. Ozone is consistently overpredicted by CMAQ (dashed line) at night 

and underpredicted during the day. CMAQ’s low bias is especially pronounced 

on high ozone days. a) June 15-30, 2002 b) July 1-14, 2002 c) August 1-15, 

2002. 

Performance in Upwind and Rural Areas is Worse than in Urban and Suburban Areas. 

As mentioned above, CMAQ’s low bias on high ozone days is larger (more negative) at 

rural sites than in suburban or urban areas. This topic has already been covered in chapter 

2, so only a brief review will be given here. Suburban performance is somewhat poorer 

than that in urban areas, though not as bad as in rural areas. Since these rural areas are 

generally upwind of sites with high design values, CMAQ is underpredicting transport in 

crucial areas at crucial times. Furthermore, CMAQ’s performance is poor in the Ohio 

River Valley and central Virginia, both of which are often upwind of Maryland on high 

ozone days. To further illustrate this point, the correlation coefficient, R, was calculated 

by performing a least squares fit between ozone observations and CMAQ predictions for 

each monitoring location. These correlation coefficients were then compared with 

emissions data for point sources within an 80 km radius of each observing site. In this 

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part of the country, and on this scale, point source emissions are correlated with 

emissions from automobiles and other sources, so areas with fewer point source 

emissions also tend to have fewer emissions of other types. The results are presented in 

Figure 7, and show that CMAQ performs poorly in areas with few local emissions. As 

has been found earlier, CMAQ has some trouble in properly capturing transported ozone, 

and this is likely another reflection of that problem. 

Figure 7. Correlation coefficient between surface ozone observations and CMAQ ozone 

predictions vs. ozone season point source emissions of NOx within an 80 km

radius of the ozone monitor. Point source emissions in this region tend to be colocated with other emissions, and serve as a proxy for total emissions. 

Performance at sites with lower nearby NOx emissions is notably poorer than in 

areas where NOx is relatively abundant. Since areas with lower local nearby NOx

tend to be upwind rural areas, CMAQ is underperforming in upwind areas. 

2. Uncertainties and Probability in Future Year Design Values. 

Two sources of uncertainty in projecting future year design values are relatively 

straightforward to quantify: representativeness of 2002 design values as indicators of 

current air quality, and uncertainty in the range of future year design values as calculated 

from several different simulations of base year and future year design values (e.g. similar 

runs with different versions of the model and different versions of the inventory as well 

as a run with small changes in the future year emissions would all produce somewhat 

different results, but would not represent the changes expected from the implementation 

of a major control program). Other sources of uncertainty include model formulation and 

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the degree to which the meteorological fields represent actual conditions in 2002. These 

additional sources of uncertainty are extremely difficult to quantify, since they represent 

unknowns in the formulation of the model or sensitivity to differences between modeled 

and actual meteorology that cannot easily be determined. Therefore, the approach taken 

here is to generate reasonable estimates of uncertainty (note that this is different from

model error) due to the things that are readily quantified and to modify them only 

slightly based on the preceding discussion. 

Range of Base Year Design Values. 

EPA’s recommended procedure for calculating design values calls for creating 3-year 

averages of the fourth highest 8-hour average ozone reading for the individual years. 

Since there is some variability in these 3-year averages, EPA further recommends 

averaging three such values from successive years to obtain a design value that is 

centered on the base year (e.g., for 2002, one would take the 3-year averages from 2000-

2002, 2001-2003 and 2002-2004, thereby giving 2000 and 2004 single weight, 2001 and 

2003 double weight, and 2002 triple weight in a 5-year average). Since variations in 

meteorology lead to substantive variations in year-to-year peak ozone values, the degree 

to which the base year, or any of these 3-year periods, is representative of overall 

conditions in the area is one source of uncertainty in determining whether or not an area 

will come into attainment in the future. 

Currently, most ozone monitoring locations throughout the Mid-Atlantic and Northeast 

show improving trends in ozone concentrations over the years that went into the 5-year 

weighted average, though the design values at some have risen modestly. The average 

difference between the highest and lowest 3-year design values is 6 ppbv. Similarly, the 

average standard deviation for each site over this time period is +/- 3 ppbv (using 

standard deviation only as an estimate of variability and not suggesting that a 3-data point 

standard deviation is adequate for any individual station). Both these measures suggest 

that variations in meteorology can reasonably be expected to produce substantial 

variability in the design values themselves. 

It appears reasonable that the representativeness of the current year can therefore produce 

a variation of 3 ppbv about some central value. Further analysis of these design values 

shows that the vast majority of sites show substantial improvements in ozone design 

values over this time period, likely due to the combination of NOx reductions from the 

NOx SIP call and meteorological conditions in 2003 and 2004 that were less conducive to 

ozone production. 

The largest improvement was a 14 ppbv reduction at Colliers Mills, the ozone monitor 

location with the highest current and future year design value in the entire Northeast, and 

the worst was a 5 ppbv increase in the Bronx, NY. No Maryland monitors show any 

increases in design values, with all showing decreases, as shown in Table 2. Beyond the 

scope of the analysis time period, the decreases continued in Maryland through 2005, and 

experienced a slight increase in 2006. The summer of 2006 was a very warm summer, 

with conditions highly conducive to making ozone, so the slight increase from 2005 

14 Appendix G Attachment 15 - Page Number:

130
likely represents progress, since ozone levels would have risen somewhat higher if not for 

the control programs that were going into place. 

Table 2. Trends in Design Values at Maryland Monitors 

Design Values by Year

Site Name AIRS ID 2002 

(ppbv)

2003 

(ppbv)

2004 

(ppbv)

Change from 2002 to 

2004 (ppbv)

Davidsonville 240030014 102 98 94 -8

Ft. Meade 240030019 101 97 93 -8

Padonia 240051007 92 89 85 -7

Essex 240053001 93 93 88 -5

South Carroll 240130001 92 89 85 -7

Fair Hill 240150003 104 98 91 -13

S Maryland 240170010 94 94 91 -3

Frederick Airport 240210037 91 88 83 -8

Edgewood 240251001 104 103 94 -10

Aldino 240259001 100 98 93 -7

Millington 240290002 102 95 89 -13

Rockville 240313001 89 88 83 -6

Greenbelt* 240330002 95 93 *

PG Equestrian** 240338003 94 * 

Hagerstown 240430009 87 86 83 -4

*Monitor discontinued in 2003 (Greenbelt) due to loss of permission to use location. 

**Monitoring began in 2002 at this location.

Ranges of Future Year Predictions. 

Examining the results from similar 2009 scenarios gives insight into how the model 

responds to changes in emissions. Different future year scenarios have been modeled to 

support the Maryland SIP modeling effort. Taken together, the variations among these 

attempts at representing future year emissions give some idea as to the precision, though 

not the total error in the model, since the largest errors in the modeling are systematic, 

and will not show up in tests of model and emissions inventory precision. As discussed 

above, the CMAQ model likely does not respond as much to changes in emissions as the 

atmosphere does. Therefore, it is not surprising that its future year predictions do not 

vary much between different emissions scenarios. The scenarios used to examine this 

source of uncertainty were: OTC base A and base B modeling, VISTAS model outputs at 

overlapping monitors, and OTC’s “beyond on the books on the way” modeling run. The 

VISTAS modeling represents a different, partially independent attempt at modeling 

future year design values using somewhat different emissions, different meteorology, and 

a slightly different modeling platform. The OTC base A and base B cases represent two 

different versions of the CMAQ model (4.4 and 4.5) and different versions of the base 

year inventory. The “beyond on the books on the way” run was also examined because 

its results are similar to the other 2009 future base scenarios, except for a few additional 

emissions control strategies, so in this context it represents perturbations to the emissions 

15 Appendix G Attachment 15 - Page Number:

131
projected for 2009. Examining this run allows one to gain some insight into the 

sensitivity of the model to errors and uncertainties in the emissions inventories. There 

are not enough scenarios to generate a proper standard deviation at each site, or to give 

much meaning to a range generated from those individual predictions, but the average

range and the average standard deviation give an estimate of variability between runs at 

sites across the modeling domain. The average range was +/- 0.83 ppbv, and the average 

standard deviation was +/- 0.75 ppbv. Splitting the difference gives roughly +/-0.8 ppbv. 

This represents the variability to be expected from different attempts at modeling future 

year air quality, and some of the variability expected from small errors in the emissions 

inventory. The range of 2009 projections does not represent the full uncertainty in future 

year results, but the sensitivity of the model to small variations in emissions. Therefore, 

it represents only part of the uncertainty in the modeled result. Emissions are likely more 

uncertain than these simple estimates would suggest, with errors in some emissions 

inventory categories as high as 50% [e.g. Choi et al., 2006]. 

The two measures above can be combined to give a conservative estimate of the 

uncertainty in future year projections. Since the representativeness of the base year and 

variations in future year projections are uncorrelated, standard error propagation 

techniques can be used, namely by squaring and adding the uncertainties, and taking the 

square root of the sum to get the combined uncertainty. The uncertainties do not add 

because they are not correlated, so one is as likely to be positive as the other is to be 

negative. The combination gives an uncertainty in future year design values of 3.1 ppbv. 

The sizes of other sources of uncertainty are considerably more difficult, if not 

impossible (since they represent unknown aspects of the science of air pollution), to 

estimate reasonably. Many, such as CMAQ’s lack of responsiveness to changes in 

emissions, are systematic, and therefore cannot reasonably be represented with error bars, 

but should instead be represented with a shift of the projected value. The discussion in 

the previous part of this chapter centered on several comparisons between modeled 

values and measurements around times of change. As discussed, CMAQ underrepresents 

change, and should be corrected for that tendency. The previous discussion hints that the 

benefits are likely to be double (or more) what CMAQ predicts, so any benefit from 

CMAQ should be increased 100%. To allow for considerable margin for error, the likely 

100% increase in benefit is cut in half, and changes predicted by CMAQ will be 

increased by only 50% to account for its lack of sensitivity. Doubling the effects 

predicted by CMAQ more closely represents the previous discussion, but the more 

conservative route has been chosen. 

Results are presented below (Table 3, Figures 8 and 9) as base year (2002) values and 

CMAQ predictions for 2009 and 2012, along with a projection of the likely 2009 design 

value, based on the conservative 50% underresponse estimate, and the upper and lower 

bounds of that 2009 design value, combining the estimates of uncertainty established 

before with the estimate of CMAQ’s lack of responsiveness. 

16 Appendix G Attachment 15 - Page Number:

132
Table 3. Current and Projected Design Values and Their Uncertainties for Maryland 

Monitors 

Observed Modeled Probable

Monitor 

Name AIRS-ID 2002 

Design 

Value

2009 

Design 

Value

2012 

Design 

Value

2009 

Design 

Value

2009 

Lower 

Bound

2009 

Upper 

Bound

Davidsonville 240030014 98.0 84 78 77.0 73.9 80.1

Ft. Meade 240030019 97.0 84 78 77.5 74.4 80.6

Padonia 240051007 88.7 77 72 71.2 68.1 74.3

Essex 240053001 91.3 80 76 74.4 71.3 77.5

South Carroll 240130001 88.7 75 69 68.2 65.1 71.3

Fair Hill 240150003 97.7 81 75 72.7 69.6 75.8

S. Maryland 240170010 93.0 76 70 67.5 64.4 70.6

Frederick 

Airport 240210037 87.3 74 68 67.4 64.3 70.5

Edgewood 240251001 100.3 85 80 77.4 74.3 80.5

Aldino 240259001 97.0 82 76 74.5 71.4 77.6

Millington 240290002 95.3 80 74 72.4 69.3 75.5

Rockville 240313001 86.7 76 71 70.7 67.6 73.8

Greenbelt 240330002 94.0 82 76 76.0 72.9 79.1

P. G. County 

Equestrian 240338003 94.0 81 76 74.5 71.4 77.6

Hagerstown 240430009 85.3 73 67 66.9 63.8 70.0

Table 3 illustrates the best estimates for future design values and their uncertainties. 

Even with a broad range of uncertainty due to representativeness issues and some model 

uncertainty, given CMAQ’s under-response to changes in emissions, it is highly probable 

that Maryland will attain the 8-hour ozone standard in 2009, even allowing for adverse 

weather conditions. The upper bounds are all below 81 ppbv, with most well below. 

Probable 2009 design values are also very similar to calculated design values for 2012. 

Therefore, much like the current (2006) New Jersey design values presented in Table 1, 

which showed that 2009 values had arrived three years early, these projections suggest 

that 2012 design values will arrive early for Maryland monitors as well. Regardless, by 

2012, CMAQ predicts that Maryland will be well in attainment at all of its monitors, with 

only Edgewood remaining as high as 80 ppbv. 

17 Appendix G Attachment 15 - Page Number:

133
84 84

77

80

75

81

76 74

85

82

80

76

82 81

73

77 77.5

71.2

74.4

68.2

72.7

67.5 67.4

77.4

74.5 72.4 70.7

76 74.5

66.9

80.1 80.6

74.3

77.5

71.3

75.8

70.6 70.5

80.5

77.6 75.5 73.8

79.1 77.6

70

73.9 74.4

68.1

71.3

65.1

69.6

64.4 64.3

74.3

71.4 69.3 67.6

72.9 71.4

63.8

60

65

70

75

80

85

90

95

100

105

Davidsonville 

Ft. Meade

Padonia 

Essex 

South Carroll

Fair Hill

S. Maryland

Frederick Airport

Edgewood 

Aldino 

Millington

Rockville 

Greenbelt 

P. G. County Equestrian

Hagerstown 

Monitoring Location

Observed 2002 Design Value

Modeled 2009 Design Value

Probable 2009 Design Value

Probable 2009 Design Value Upper Bound

Probable 2009 Design Value Lower Bound 

Figure 8. A graphical depiction of the data from Table 3, depicting 2002 base year 

design values (blue columns), modeled 2009 design values (black diamonds), and 

the most probable future year design values along with the upper and lower 

bounds for those future year values (round circles and associated error bars, 

respectively). All Maryland monitoring locations are shown. 

18 Appendix G Attachment 15 - Page Number:

134
60

65

70

75

80

85

90

Davidsonville

Ft. Meade

Padonia 

Essex 

South Carroll

Fair Hill

S. Maryland

Frederick Airport

Edgewood 

Aldino 

Millington 

Rockville 

Greenbelt 

P. G. County Equestrian

Hagerstown 

Monitoring Location

All sites attain the 85ppb NAAQS for 8-Hour Ozone

 

 Range of 2009 8-Hour Ozone Design Values from Weight of Evidence

Figure 9. The ranges of probable future year design values from Table 3, given in a 

graphical format. The lower end of each bar represents the lower bound of the 

most likely future year design value, while the upper end of each bar represents an 

upper bound. 

Conclusions 

Several different methods have been used to compare the measured effects from

changes in emissions to those predicted by the CMAQ model, and all affirm the idea that 

the changes in ozone will be larger (e.g. ozone will be better) than predicted by CMAQ. 

A study of the 2003 Northeast Blackout [Marufu et al., 2004] shows that the blackout 

caused a drop of at least 7 ppbv ozone, and likely considerably more, while a modeling 

study of the same event [Hu et al., 2006] used CMAQ to predict only a 2.2 ppbv change. 

An ongoing study by EPA reveals that the NOx SIP call likely produced double the 

benefit that CMAQ predicted. Meanwhile, the State of New Jersey reports that its ozone 

monitor locations appear to have reached their 2009 design values in 2006, three years 

ahead of time. When compared to observations from the 2002 ozone season, CMAQ 

underpredicts diurnal variability, and shows important performance shortcomings in areas 

just upwind of Maryland on high ozone days, namely the Ohio River Valley and the state 

of Virginia. Furthermore, performance on high ozone days tends to be best in urban 

areas, next best in suburban areas, and worst in rural areas, so CMAQ is underpredicting 

ozone in upwind areas from which it would enter the largely urban and suburban 

nonattainment areas. 

Uncertainties have been estimated for two types of errors in CMAQ modeling. 

One source of uncertainty is the range of possible meteorological conditions that might 

be encountered in future years. This is not to say that 2002 was not representative, but 

19 Appendix G Attachment 15 - Page Number:

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instead that meteorological variability from year to year is well known, and any future 

projections must account for this to achieve a reasonable margin of safety, so particularly 

bad future year meteorology will not result in numerous exceedances of the standard. 

Some of the uncertainty arising from the model and its emissions was estimated by 

examining several different 2009 scenarios and determining the range of possible 2009 

ozone design values from those scenarios. These two sources of uncertainty do not cover 

all the possible sources of uncertainty in CMAQ projections; errors in the inventory, 

meteorology, and model formulation all play a role, but are significantly more difficult to 

estimate. The error estimate and the future year meteorological variability estimate were 

combined to generate an estimate of future year uncertainty in ozone design values. 

To account for CMAQ’s resistance to change, CMAQ changes were increased by 

50%, and probable future ozone design values were calculated, along with probable 

ranges of ozone concentrations to account for meteorological variability and some model 

errors. The resulting picture of future ozone is that likely 2009 ozone design values 

correspond to 2012 design values calculated directly by CMAQ. This is in line with 

current observations from New Jersey (home to the ozone monitoring location with the 

highest design value in the Northeast) that show projected 2009 ozone concentrations 

turning up in 2006. 

Future Work 

A considerable ongoing effort is underway throughout the numerical modeling 

community to determine the proper way to calculate the uncertainty from a computer 

models. Currently, proper calculations of model uncertainties are difficult to determine, 

especially when the model is used in a relative sense by calculating changes from a 

baseline instead of absolute predictions of pollutant concentrations. For photochemical 

modeling, the largest errors are thought to be systematic, and should be correctable. 

Meteorological modeling, in contrast, often suffers from random errors and nonlinear 

behavior, so small changes are amplified. Since the two systems are different (though 

coupled), methods different from those that have worked so well in meteorological 

modeling (e.g. ensemble modeling techniques) will have to be developed to assess

photochemical modeling uncertainties. 

In Chapter 4 of this Appendix, some of the shortcomings of the CMAQ chemical 

mechanism were laid out, along with some possible suggestions for remedies. One 

possible course of action would be to perform some sensitivity tests with another model 

or another chemical mechanism such as SAPRC99, which is more detailed and therefore 

should be more representative of atmospheric chemistry. Some of the lack of response in 

CMAQ is likely due to problems with emissions inventories and their processing. 

Emissions from point sources are likely underestimated on high energy demand days, 

since peaking units come on line for only a short time in the heat of the day in response to 

peak demand. Scattering of light by aerosols is known to increase photolysis rates aloft 

and decrease them near the surface, thereby altering photochemistry. Likewise, clouds 

alter photochemistry, and remain a very difficult forecasting problem, so they too are 

likely poorly represented in both MM5 and CMAQ. If clouds and the effects of aerosols 

are misrepresented, then CMAQ’s photochemistry will not perform properly, 

overpredicting ozone at the surface and underpredicting ozone, and therefore transport, 

20 Appendix G Attachment 15 - Page Number:

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aloft. Finally, the meteorology by itself is another source of uncertainty, and almost 

certainly plays a role in CMAQ’s lack of response. 

CMAQ is a state-of-the-art air quality modeling tool, and has proven its utility 

with impressive capabilities. The purpose of this chapter is not to disparage CMAQ, but 

to advocate using a tool responsibly, with a proper acknowledgement of its abilities and 

shortcomings. By assessing the model’s performance and examining the implications of 

the model’s shortcomings one can improve upon its predictions. A comparable approach 

is used in generating the MOS (Model Output Statistics) by comparing observations to 

outputs from National Weather Service forecast models. The skill of weather predictions 

increases substantially in going from raw model output to the MOS because MOS takes 

into account (through a more complex statistical procedure) model tendencies and 

corrects them by comparing the model to observations. Furthermore, the comparisons 

made in this and other chapters likely were not possible in the past, since large, sudden 

reductions in emissions like those from the NOx SIP Call and the 2003 blackout are 

relatively rare. The prospect of dynamic model evaluation holds great promise for rapid 

model development in the coming years. 

21 Appendix G Attachment 15 - Page Number:

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References

Choi, Y.-J., S. H. Ehrman, R. V. Calabrese, J. W. Stehr, R. R. Dickerson, A Combined

Approach for the Evaluation of a VOC Emissions Inventory, J. Air Waste Manage. 

Assoc., 56:169–178, February, 2006. 

Gilliland, A., Personal communication, January 12, 2007; manuscript in preparation.

Hu, Y. M. T. Odman, and A. G. Russell, Re-examination of the 2003 North American 

electrical blackout impacts on regional air quality, Geophysical Research Letters, Vol. 

33, L22810, doi:10.1029/2006GL027252, 2006. 

 

Marufu, L.T., B. F. Taubman, B. Bloomer, C. A. Piety, B. G. Doddridge, J. W. Stehr, 

and R. R. Dickerson, The 2003 North American electrical blackout: An accidental 

experiment in atmospheric chemistry, Geophysical Research Letters, Vol. 31, L13106, 

doi:10.1029/2004GL019771, 2004. 

22 Appendix G Attachment 15 - Page Number:

138
Attachment 16_OTC_2007_SpecialMtg_ 

HEDDMOU_Final_070302.pdf
Memorandum of Understanding Among the States of the Ozone

Transport Commission Concerning the Incorporation of High Electrical 

Demand Day Emission Reduction Strategies into Ozone Attainment State 

Implementation Planning 

Whereas the Ozone Transport Commission (OTC) was established under

Sections 176A and 184 of the federal Clean Air Act (CAA) to ensure the

development and implementation of regional strategies to reduce ground-level

ozone to healthful levels; and 

Whereas the adverse health effects of ground-level ozone are well 

documented, and in spite of significant reductions of ozone precursor 

emissions achieved to date as a result of our NOx MOU of 1994, the US 

Environmental Protection Agency (EPA) NOx SIP call effective in 2003, and 

expected reductions to be further achieved by federal and state programs over 

the next decade, a significant portion of the ozone problem continues to be 

caused by nitrogen oxides (NOX) transported into and generated within our 

region by electrical generating units (EGUs); and 

Whereas, high electrical demand day (HEDD) operation of EGUs generally

have not been addressed under existing air quality control requirements, and 

these units are called into services on the very hot days of summer when air

pollution levels are highest, and 

Whereas, HEDD unit operations are a significant contributor to NOx emissions

on high ozone days; and 

Whereas, the NOx cap and trade program, although effective generally has, 

by its very nature, had limited success in reducing emissions from HEDD units

on HEDDs; and 

Whereas, OTC staff, state environmental and utility regulators, EPA staff, 

EGU owners and operators and the independent regional systems operators

have been meeting to assess emissions associated with HEDD during the 

ozone season and to address excess NOx emissions on HEDDs, and 

Whereas, OTC is guided by its precepts to seek reductions in the most 

comprehensive, cost effective manner possible in order to maximize public

health, environmental and economic benefits while ensuring an adequate 

electrical capacity and reliability for the region; and

___________

Connecticut 

Delaware 

District of Columbia 

Maine 

Maryland 

Massachusetts 

New Hampshire 

New Jersey

New York 

Pennsylvania 

Rhode Island 

Vermont 

Virginia 

___________

Christopher Recchia 

Executive Director 

___________

444 N. Capitol St. NW

Suite 638 

Washington, DC 20001 

(202) 508-3840 

FAX (202) 508-3841 

Email: ozone@otcair.org

___________

Appendix G Attachment 16 - Page Number:

1
Whereas, our investigations have found that NOx emissions are much higher on 

a high electrical demand day than on a typical summer day and there is the 

potential to reduce HEDD emissions by approximately 25% in the short term 

through the application of known control technologies to HEDD combustion 

turbine, coal and residual oil burning units; and 

Whereas, installing typically used NOx control technologies may not be available 

to, or be the most cost effective method of, controlling HEDD NOx emissions 

from specific units; and 

Whereas, energy efficiency is the most cost effective method to reduce HEDD 

NOx emissions, but cannot alone, nor in the short term, achieve sufficient 

emission reductions to achieve attainment of the ozone standard in many areas; 

and 

Whereas, demand response programs can be a very cost effective mechanism 

to reduce emissions if they result in clean behind the meter generation and are 

supported by appropriate market devices, including but not limited to dynamic 

pricing; and 

Whereas, any strategy to address HEDD emissions must recognize and address 

the issue of high emitting behind the meter units; and 

Whereas, EPA and State workgroups estimate that using a cap and trade 

mechanism alone to provide sufficient financial incentives to cause the clean up 

of HEDD units would need an 18:1 retirement ratio and such a strategy would 

consume 74% of all available CAIR allowances for 12 HEDD days; 

Therefore, be it RESOLVED that 

The OTC States identified in the following table commit to pursue the following 

reductions in NOx emissions associated with HEDD units on high electrical 

demand days during the ozone season; such reductions to be achieved 

beginning with the 2009 ozone season or as soon as feasible thereafter, but no 

later than 2012: 

Appendix G Attachment 16 - Page Number:

2
State NOx 

(tons per day) 

Percent Reduction from 

HEDD Units 

CT 11.7 25% 

DE 7.3 20% 

MD 23.5 32% 

NJ 19.8 28% 

NY 50.8 27% 

PA 21.8 32% 

Total 134.9 

Furthermore, that such reduction commitment will be included in each of the 

several states’ 8-hour ozone attainment State Implementation Plan submissions 

to EPA due in June 2007; and 

Furthermore, that each state shall select the strategy or combination of 

strategies that provides both maximum certainty and appropriate flexibility for that 

state and its electric generators. Such mechanisms for achieving the reductions 

may include but are not limited to: 

• regulatory caps for emissions from HEDD units on HEDDs; 

• performance standards; 

• State/generator HEDD partnership agreements; 

• energy efficiency programs; 

• demand response programs, provided that such programs reduce and/or 

preclude the installation or use of distributed generation with 

unacceptably high emissions; 

• regulatory standards or controls for behind-the-meter generators; 

• effective adjustment of the NOx retirement ratio to provide reductions on 

HEDDs; and 

Furthermore, the undersigned states for whom no state-specific target emission 

reduction is specified above sign this MOU in support and appreciation of the 

listed states making this commitment, will continue to evaluate the HEDD issue in 

their state and, as necessary and appropriate, may choose to pursue additional 

emission reductions from the HEDD sector in their state. 

Be it FURTHER RESOLVED that the OTC states will continue their work to 

establish long-term standards and programs to address emissions on HEDDs, 

such programs and standards to include: 

• continued work with state energy and utility regulators as well as the 

regional transmission operators regarding energy efficiency, dynamic 

pricing and other market oriented incentives toward significant demand 

reduction and clean new or repowered supply 

Appendix G Attachment 16 - Page Number:

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• development of long-term performance standards that will ensure 

reliable, clean future generation. 

• development of emissions portfolio standards applicable to load serving 

entities, distribution companies, “aggregators” and generators, according 

to the structure of the energy supply market 

Executed by the undersigned States this 2nd day of March, 2007: 

_______________________________ 

Connecticut 

_______________________________

Delaware 

_______________________________ 

District of Columbia 

_______________________________

Maine 

______//s//_________________________

Maryland 

_______________________________

Massachusetts 

______________________________ 

New Hampshire 

_______________________________

New Jersey

_______________________________ 

New York 

_______________________________

Pennsylvania 

_______________________________ 

Rhode Island 

_______________________________

Vermont 

_______________________________ 

Virginia 

Appendix G Attachment 16 - Page Number:

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