# Attachment 15 UMD WOE Section .pdfThe Relationship betw e e n Urban Tree Cover and Ground Level Oz one

March 15, 2007

Charles Piety Departm e nt of At m o spheri c and Oceanic Sciences University o f Maryland College Park, MD

Abstract

Tree cover in urban areas plays an impor tant role in the complex system of ground level ozone production. Num e rical m o deli ng studies predict a decrease in surface

tem p erature s (on the o r d e r of 1-2 de g C) will oc cur when more (es tim ates range f r o m 10- 40% over urban areas) trees are planted in an urban environm ent. Corresponding changes in wind speed will occu r in m odif i ed ar eas as well. These chang e s are the r e sult of the lessening of the urban “heat island” eff ect. Heat is land s for m as cities rep l ace natural land cover with pavem e nt, buildings, and other infrastructure. Th e heat in a h eat
island is th e result of the dif f e rent radiative properties (i.e. th e ability of objects to abso rb and/or reflect the sun’s energy) between urba n and rural areas. Urban areas are m o re efficient at absorbing su nlight than rura l areas. The excess energy absorbed by urban

areas is even tually reradiated back to the atm o sphere in the form of heat (aka infrared radiation). Weakening of the heat island eff ect results in a transf er of energy from the

urban areas to downwind areas which causes som e locations to observe a slight increase in surface temperatures (1-2 ° C) and in wind speeds (1-2 m s -1 ). The m odel e d

tem p erature changes are accom p anied by change s in ground level ozone concentration s

. Owing to the com p lexity of the system , changes in ozone, like tem p erature, are not
confined to location s where trees are plante d. Dif f erent locations downwind of m odifi ed

areas obs erv e d eith er in creased o r decreas ed ozone levels depending on a variety of

factors. The reduction of surface tem p eratu r e (sp ecifically, cooling in u r ban areas)

obtained from the num er ical m odeling studies was used as the basis for predicting changes in d a ily m ean peak 8-hour ozone levels d e rived from a m u ltiple linear regress i on model for the Baltim ore Non-Attain m e nt Area. Results fro m both m e thods of study (num erical and m u ltiple linear regress i on) s ugges t that decreas e s in ground level o z one concentrations on the order of 1-3 ppbv could be realized with an in crease in urban tree cover ranging from 20 - 40%. Averaged over the entire m ode ling dom ain of the Northeastern and Mid-Atlantic U.S., this ch ange would amount to ~1-2% increase in tree

cover. It is believed that th e genera of trees used to re forest is im portant, but what species of tree offers the m o st benefit is not clear and m a y differ from locale to locale. Other sign if icant, log i stic al questions also need to be c onsidered when planning a largescale tree planting program . The general c onsensus from the studies m e ntioned above suggests that increased tree cover has a benefi cial effect on local air quality by reducing

ozone in and around urban areas. The m a jor d ecreases in ozone occur near urban areas

1where the 8-hour ozone NAAQS is not m e t, wh ile the m i nor increases in ozone occur in upwind areas where the NAAQS is m e t. Thus, the net im pact is favorab le for reach i n g the standards across entire Balt im ore Non-Attainm e nt Areas. It should be noted that any plan involving tree planting to im prove air qu ality should be viewed as a long term pl an (e.g. 10-20 years) given the growth rate of m a ny of the trees considered. However, in the

short term , tree cover reduction should be a voided or tem p eratures will rise, as will

ozone.

Introductio n The Rom a n poet Caecilius Statiu s more th an 200 0 years ago wrote “we p l ant trees not for ourselves, but for future generations.” The words are as true today as they were

then. The ro le tha t trees play in the a i r qua lity of an urban en vironm ent for sever a l ye ars has been a subject of keen inte rest to air quality planners bo th from a policy and scientific perspective. The interest has been motivat ed by the principal desire to im prove air quality. Trees have im portant functions in the u r ban environ m ent that are easily

observed: T r ees provide shade, create habita ts for flora and f a una, lim it soil erosion and are often esthetically appealing. However, trees m a y al so have a subtle, more com p l e x connection to air quality that is challenging for scientists to observe and understand. F o r instance, through a series of com p lex relationships, trees have a connection to ground level ozone and PM2.5. The degree to which tree s add to pollutant le vels is dependent on

the am ount and type of biogenic volatile organic com pounds (BVOC) e m itted by the trees (Lee et al., 2006, Geron, et al., 2001, Moukhtar et al., 2005) . Trees m a y a ffect air quality by changing m e teorological param e te rs such as surface tem p eratures, boundary layer winds, and vertical m i xing heig hts. These changes occu r becaus e en ergy transfer

between the Earth’s surface and th e atm o sphere is altered th ough factors such as surf ace roughness, albedo and soil m o isture (1988, Taha et al., 1996, Rossa et al., 2005). Trees

can also im pact air quality i ndirectly through changes to dr y deposition rates (Cham e ides et al.). Dry deposition is a rem oval process for atmospheric s p ecies that d e notes d i rect
transfer of s p ecies to the Eart h’s surface. W e t deposition is a sim ilar proc ess, but one that involves p r ecipitation. Understanding the conne ction between trees a nd air quality in an urban area, as well as co nsider ing so m e of the challeng es as s o ciated with a larg e-scale

tree -plan ting program are im portant is sues tha t need to be addressed prior to the planning

and im ple m entation of such a plan.

Background

Several studies have been undertaken both nationally and internationally to study

how trees in f l uence air q u ality. Perh aps the m o st relev a nt inv e stig ation w a s a re cent joint research effort undertaken by the New York Departm e nt of Environm ental Conservation,

the USDA a nd the Davey Resource Group (Bond, 2006). The work addressed im portant
consideratio ns of large-s cale tree p l anting for a state im plem e n tation plan. The overall goal of Bond’s work was to exam ine the feasibili ty 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?

2What are the diurnal and seasonal varia tions 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 thes e questions were state-of-the-a rt m e teorological (i.e., MM5, WRF) and photochem i cal (i.e., CMAQ and CAM x ) 3-dim e nsional gridded airshed models. The changes as sociated with large s cale tree p l antin g were assay e d by perform i ng m e teorological and photochem i cal simulations using existing land use

inventories and com p aring those results to m ode l runs with increas ed tree cover in urban

areas. The sim u lations were typically perform e d on historical tim e periods (i.e. the ozone seasons of 1988, 1995, and 2003) when high ozon e occurred (Cardelino and Cha m edies, 1990, Civerolo et al., 2000, Taha, 2005). Results from a study of a high ozone period in Atlanta, GA. on June 4 th , 1984 suggested that a reduction in tree cover of 20% would increase m a xi m u m peak 1-hour ozone concentrations by ~14% (Cardeli no and Cham edies, 1990). Increase in

tem p erature was the prin cipal cause of the incr eased ozone. Investigations of other cities such as New York City and Los Angeles conclu ded that in order to achieve a reduction in peak 8-hour ozone on the order of 2-3 ppbv, ei ght-to-ten m illio n trees would need to be planted in these large urban areas (Luley and Bond 2002, Taha, 2005). More recent w o rk investigated the diurnal and s easonal variation of isoprene con centr ation s a nd its ef f e ct on peak 1-hour ozone levels in Taiwan (Lee a nd W a ng, 2006). It was determined that the

tim ing of isoprene flux from trees, which is diffe rent from season to season, is im portant in shaping daily ozone profiles and m a y in som e cases be m o re sign ifican t than the reactivity of isoprene. A nother study of air quality in Taiwan used rem o te sensing and

num e rical m odeling to assess the im pact of biogenic em issions on ground level ozone concentrations (Jeng and Chang, 2004). The conc lusion was that the m a jority of biogenic

em issions occurred in farm land and mount ain areas downwind from the m a jor urban

areas in South Taiwan. Th erefore, com p ared to anth ropogenic em issions, biogenic

em issions have a lower im pact on air quality than anthropogenic sources. The work by Jeng and Chang highlights the im portant point that regions with different geographic patterns (i.e. land use an d topograph y ) will no t h a ve the s a me relativ e co ntribu tions f r om biogenic an d anthropog enic em issio n s and ther efore will lik e ly requ ire d i fferent tree planting strategies. Recent sim u lations of a high ozone period in Birm ingham

, England produced results of reductions in urban oz one comparable to the work by Luley, Bond,

and Taha when 10% of the ava ilab l e planting spa ce was f illed with low-VOC em itting trees (Donovan et al., 2005). The Donovan st udy stressed the im portance of planting

trees tha t are low-VOC em itters. In that wo rk, tr e e s were rank ed based on observations

and sim u lations for their potential to im pact air q u ality. The least favorab le trees were the genera poplar, willow and oak, while the m o st beneficial were m a ples, hawthorns and pines. It should be noted that there is so m e disagreem ent within the s c ien t if ic community regarding what genera of trees are most su itable for planting. Fo r instance, the Donovan study ranked oak trees as som e of the worst tr ees to select for planting because of their

com p arably high levels of isoprene em ission. The Davey group on the other hand, favored the inclusion of oak trees, sighti ng the high variation in isoprene em issions

am ong oak t r ee species as well as other consider ations such as the dr ought tolerant nature
of oaks and the endem i c nature of oaks in m a ny parts of the count ry. The divergence between the two investig ations underscores the com p lexity and evolving nature of this

3research. Efforts to better understand this system continue with both the D onovan and Davey groups.

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

surface m o difications, which lessen the “heat isla nd” effect, which in tu rn reduces su rface

tem p eratures. Other secondary effects incl ude changes in wind fields and planetary boundary layer heights. The m odeling study by Nowak et al. (2000) demonstrated that

increased tree cover lowered surface tem p eratures in areas where tree cov e r was increased and in som e cases also in locatio ns downwind. The sensitivity of MM5 to changes in tree cover was determ ined by com p ar ing sim u lations using an existing land

use data base and sim u lations using a ~ 40% increased tree cover of deciduous trees in grid cells containing the urban areas of New York, NY., Philadelphia, PA., and

Baltim ore, MD. In extr em e cases modeled te m p eratu r e dif f e rences wer e as m u ch as 6 ° C

with m o re typical differences on the order of ~1-2 ° C (Nowak et al., 2000, Civerolo et al., 2000). Considering the com p lex nature of ground-level ozone production, it is perhaps not surprising that the work by Nowak et al. (2 000) presented results that showed a slight

increase in ozone over the entire m o del dom ain including the north eastern U.S. urban corridor and extending from northern Virginia to Massachusetts. A 0.26 ppbv increase

over the entire dom ain was less than the aver age 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 nighttim e ozone le vels (~ 1 ppbv), caused by weaker wind speeds

and the in creased los s of NO

x (a scavenging agent of ozone) through higher NO

x deposition rates. The general consensus from the studies m e ntioned above suggests that

increased tree cover has a bene ficial effect on local air qua lity by reducing ozone in and

around urban areas. The m a jor decreases in ozone occur near urban areas where the 8- hour ozone NAAQS is not m e t, while the m i nor increases in ozone occur in upwind areas

where the N AAQS is m e t. The net impact is favorable in term s of entire regions m eeting the NAAQS

. It is worth noting that there are other ques tions relevant to th e planning aspects of large-scale tree planting which are m o re logi stical in nature but nonetheless are an

im portant consideration. Som e of these issues include:

• W h ere will all of the tre e s com e f r om ?

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

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

• How will the work f o rce be tra i ned ?

• Is there a plan to deal with proble m s such as disease or extrem e drought

that cou l d le ad to f a ilu re of the tree p l anting p r ogr am ?

• How m u ch of the existing tree popu lation will b e lost to lan d developm ent?

Methodology and Results

Another m e thod to assay potential impr ovem e nts in ozone from increased tree planting is to review histori cal correlations betw een ozone and tem p erature. Multiple linear reg r es sion m odels have been u s ed for several decad es to forecast daily m a xi m u m ozone (W olff and Lioy, 1978; Ryan et al., 2000). In order to develop a new statistical

correlation between ozone and tem p erature, an estim ate of changes in peak 8-hour ozone

levels can be m a de by a pplying adjusted tem p erature data which reflect cooler

4tem p eratures resulting from increased tree c over. W h ile temperature is the strongest predictor of ozone it is not the only param e ter important to ozone for m ation. W i nd speed, cloud cover, and boundary layer height s are am ong a num b er of other im portant factors. However, for this study, only tem p er ature adjustm e nts are considered. All other

param e ters used in the linear regression m odel (i.e. m i ni m u m tem p erature, wind speed,

relative hum idity) are held constant. The ne xt portion of this section attempts to

corre late, us ing sta tis tic a l t echniques, changes in ozone ove r a region associated with

tem p erature changes th at m i ght be ex pected from a larg e-scale tree p l antin g program

. Figure 1, a p l ot of daily peak 8-hour ozone for th e Baltim ore Non Attain m e nt Area versus

m a xi m u m temperatures for Baltim ore W a shingt on Intern ationa l Airpor t, illustra tes the general correlation betw een tem p erature and ozone. In light of the m odel i ng work

discussed ab ove, the University of Maryland us ed a m u ltiple linear r e gress i on m odel to calculate peak 8-hour ozone le vels. Adjusted temperature data used in the m odel we re derived by Nowak et al. (2000). Using ozone and m e teorological data from the

Baltim ore Non-Attainm e nt Area covering th e tim e period 1994 -2005, a m u ltiple lin ear regression model was developed to predict daily m ean peak 8-hour ozone values for the

Baltim ore Non-Atta inm e nt Area. Th e m odel was crea ted with the aid of the analy tica l software pro g ram SPSS ® . Using a forward, stepwise regression technique, the correlation coefficients in Equation 1 in Appendix A for the m e teorological variables

were determined. From this regression m odel, a sensitivit y of ozone to reductions in

tem p erature was calcu lated (see Equation 1 in Appendix A).. It was determ ined that a

decrease in m a xi m u m temperature o f 1 ° F corresponded to ~ 2 ppbv re duction in the m ean peak ozone levels (see T a ble 1 and F i gure 2). W h en 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 dail y peak 8-hour ozone levels (Table 1). T h e change in slope, which is a result of the i nherent non-linearity of the ozone-m eteorology

rela tionsh i p, im plies that at higher temperatures ozone produ ction is m o re sensitive to

tem p erature changes th an at lower temperatur e. Som e of the causes of the nonlinearity

are rates of chem ical reactions, precurs or em issions and planetary boundary layer processes. The work by Nowak et al. (2000) estim a ted 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 im plies a change in the m ean peak 8-hour ozone of from 4- 8 ppbv. Given that only tem p erature was considered here, it is reasonable to assu m e that the range of 4-8 ppbv is perhaps the upper lim it, as m a ny of the other m e teorolog ical predictors (e.g. m i nim u m tem p erature) are negatively correlated with ozone. Civerolo et al. (2000) reported a slight lowering of

both m i ni mum tem p eratures and m a xim u m te m p eratu r es fro m increased tree cover. Thus, it is re asonable to conclude tha t a change in m a xim u m tem p erature will be

associated w ith other subtle changes in weather that m a y slightly offset m a xim u m

tem p erature changes. For in stance, inspection of Equation 1 in Appendix A suggests that the slight lowering of minim u m te mperatures w ould counter som e of the benefit of cooler

m a xi m u m temperatures. Considering all of th e ancillary weather ch anges associated with an increase in tree cover, perhaps a m o re c onservative estim ate of changes in daily peak

8-hour ozon e as reflected by a m u ltiple linear regression m odel would b e closer to 2

- 4 ppbv.

50 20 40 60 80 100 120 140

60 65 70 75 80 85 90 95 100 105

Tmax (1⁄4F )

Figure 1. A scatter p l ot of the daily 8-hour peak ozone over the Baltim ore Non

Attainm e nt Area versus the daily m a xim u m tem p eratu r e at BW I Airport for the year 2002 (May-September, N = 138, Tm ax ≥ 70 ° F).

6Table 1. Observed and predicted ozone in pa rts per billion (ppbv) for the summer of 2002 over

the Baltim ore Non-Attainm e nt Area.

O b s e r v e d T m ax 0 Tm ax 0- 0. 5 Tm ax 0- 1. 0 Tm ax 0- 1. 5 Tm ax 0- 2. 0 Average

Maxim u m 8-hour

Ozone for153 cases (ppbv) 7 1 6 9 6 8 6 7 6 6 6 5

Average Peak 8-

hour Ozone for subset of 39 High Ozone cases ( ≥ 85 ppbv) 1 0 1 9 2 9 0 8 9 8 8 8 7

These resu lts com e from the reg r ess i on analys is for adjus t ed m a xi m u m temperature v a lues for

the summ er of 2002 (May-Septem b er). The subs et of high ozone cases shown in row two are

defined as 8-hour m a ximum 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 dail y m a xim u m 8-hour ozone over the Baltimore

Non-Attainm e nt Area. Predicted data wa s determ ined from a regression analysis using m e teorological and ozone data from 1994-2005. The m a xi m u m

tem p erature was reduced by 0.5oF increm ents, and m ean daily m a xi m u m 8

- h o u r ozone was recalcu lated using th e adjusted tem p e r a t u r e d a t a

.

7Conclusions This analysis presents results of stud ies perform e d 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

curren t literature sugg ests that the ch ange s in m e teorology (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 le ad to a decrease of 1-2 ppbv in daily m e an peak 8-hour ozone. Lan d use m odifi cation s to 10 -40% of the urban area land surface, or

to 1-2% of the m odel dom a in (e.g. the Baltim ore Non-Attain m e nt Area) are need ed to create the change in peak 8-hour ozone concentrations. The change in ozone (and

m e teorological param e ters) is typically obser ved in and downwind of the urban areas.

Som e studies determ ined that over the entire m o deling dom ain there is a net increase in ozone when more trees are planted. However, this inc r eas e is sm aller in m a gnitude than

the im provem e nts observed in urban areas. The sm all increases in ozone over non-urban areas (where regional daily p eak 8-hour ozone levels ra re ly oc cur) is perh a p s less significant in term s of air quality planni ng than reductions observed in and just

downwind of an urban area. It is the latter locations that hi storically have had the poorest air quality and thus stand to benefit the m o st. Using histo r ical o z one a nd m e teorological d a ta f r om the Baltim ore NonAttainm e nt Area and BW I airport, a sta tis tica l corre lation d e term ined th e sensitiv ity of

ozone to cha nges in tem p eratu r e f o r the Baltim ore Non-Attainm e nt Area. From the results of the work by Civerolo et al. ( 2000), and Nowak et al. (2000), an estim ate of modeled temperature changes that would re sult from large s cale tree p l anting was

obtained. D e rived tem p erature changes from the photochem i cal m odeling exercises were

used as a s t arting po int f o r what m i ght be a reaso n able tem p erature chang e associated with tree planting. These m odeled temperat ure changes were then applied to the

statistica l re lationsh i p be tw een ozone and m a xi mum tem p erature. The relationship was derived from peak 8-hour ozone data fo r the BNAA and the m a xi m u m temperature
values from BW I. The correlation was used to fo rm the estimate th at a co oling of 1-2 ° F would lead to a 2-4 ppbv decrease in peak 8-hour ozone levels. , thereby dem onstrating

evidence that trees reduce ozone ind i rectly. The num e rical m odels cited in the report

focused on the benefits to ozone levels fr om an increased tree canopy. W h ile ozone is

of

ten corre la ted with ha z e in the summ er, a nd som e ozone precursors also contribute to

summertim e PM2.5 form ation, it is unclear what effect large scale tr ee 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 inclusio n of up to date land u s e inventories. This is im portan t becaus e land use is constantly changi ng so that any predicted chan ge to tree cover distribution must consider land use changes that have o ccurred since the last collection of land use

inform ation. Efforts to improve estim a tes of cha nges in air quality from l a rge scale tree planting should also focus on im proving m e te orological m o deling to take advantage of

better land use estim a tes. Refined em issions inventories that be tter describe BVOC

em issions f r om particu l a r tre e gene ra would provide policy m a kers with a ddition a l

8inform ation to determ ine which species of tree are m o st suitable for planting. Future work at the University of Maryland i nvolves a plan to run N C AR' s Coupled

WRF/Noah/ U rban-Canopy Model in a nested -grid (27/9/3/1 km ) m ode. A nested grid

has succes sively sm aller dom a ins (with success i v e ly better g r id resolu tion s ) nested together. One m a jor advantage of a nested gr id model is tha t the f i ne r-sc a le m odels

em ploy data from the larger dom ains as input (e.g. boundary c onditions), which, in theory, creates a m o re stable m odeling envi ronm ent. This version of the Coupled

WRF/Noah/ U rban-Canopy Model will be run fo r the W a shington-Baltim ore m e tropolitan region for a period of one week (at the finest grid size of 1km ) when ozone

concentratio ns were elev ated. After evaluating th e m o del-sim u lated surface m e teorology by com p arison against

observations , three u r ban tree s cena r ios will be ex am ined over the reg i on to assay the sensitivity o f the m odel-sim u lated su rf ace conditions to vary ing tree cano p ies: unchanged base tree population, increased tr ee canopy of 10%, and increased tree canopy

of 30%.

9Appendix A: A Regression equa tion used to d e term ine sen s itiv ity of oz one to chang e s in tem p eratu r e.

The variab le s lis ted be lo w were used to cre a te th e m u ltiple linear r e gress i on equation used to test the sensitivity of ozone to te m p erature changes. From the input variables listed below, Equation 1 was created usi ng the an alytical so ftware program SPSS ®

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

data m a nagem e nt and preparation, data analysis, reporting, and deployment. The

program used a forward, stepwise regression algorithm to sele ct the input variables (from a larger set of m e te orological input variab les) below. The m e teorological data was

obtained from the National Clim ate Data Cent er (NCDC). The response of ozone to the adjusted tem p eratu r es was determ ined by f eeding the new temperatures into Equation 1, along with the other m e teorolog ical variables (which were unchanged) f o r each case in 2002. From those values the averag e responses for each tem p eratu r e ran g e was obtained.

Input Variables:

Tm ax = Maxim u m Tem p erature ( o F)

T m i n = M i n i mu m T e mp e r a t u r e ( o F)

RH 19- 21UT C = Average Relative Hum i dity be tween 19-21 UTC (%)

WS 18UT C = W i nd Speed at 18 UTC (m s -1 )

WS850 12UT C = 12 UTC W i nd Speed at 850 MB (m s -1 )

SZA = Solar Zenith Angle

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

Output: [O3 ] 8hr = Daily m a ximum 8-hour ozone

Equation 1:



## [ ] 2

8 12 18 21 19 min max

8 3 019

. 0 030 850 026

. 0 015

. 0 003

. 0 062

. 0 113

. 0 774

. 2 ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

∗ + ∗

− ∗ − ∗

− ∗

− ∗

− ∗ +

= −

hr UTC UTC UTC

hr LAG SZA WS WS RH T T O

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the South C o ast Air Basin. Atmos. Environ., 30 (20), 3423-3420.

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12Comparison of CMAQ-calcu lated oz one to surface and aloft measurements

March 16, 2007

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

Departm e nt of At m o spheri c and Oceanic Sciences University o f Maryland College Park, MD

Abstract

In an ef f o rt to assess the ability of th e Community Multi-sc a le Air Quality model (CMAQ) to replicate ozone patterns, part icularly high ozone events over the Ozone Transport R e gion (OTR), com p arisons are pe rform e d between surface an d aircraft ozone

m easure m ents and CMAQ ozone simulations using the 2002 base case B1 em issions inventory. Overall, CMAQ does an excellent jo b of capturin g the m ean distribution of surface lay e r ozone during the ozone season. However, the success is so m e what

m i sleading. EPA perform a nce criteria m a y app ear to be independent or offer different

inform ation, but in reality, nearly all crit eria are strongly geared toward average perform a nce at the surface. In th is anal ysis, UMD explores se veral other m e ans of evaluating the CMAQ model by exam ining its perform a nce only on high ozone days, by separating perform a nce at rural, suburban, and urban sites, and by com p aring CMAQ to

aloft ozone data from aircraft cam p a i gns. The mixed results of these comparisons show that CMAQ has critical shortcom ings (e.g., tran sport appears to be underrepresented) that appear to be m a gnified during periods when high ground level ozone concentrations are a

concern. Ozone levels from befor e the NO

x SIP Call were also com p ared with ozo n e concentratio ns after th e NO

x SIP Cal l . W h en corrected fo r meteorological variations, the analys is ind i cates that o z one after th e NO

x SIP Ca ll im proved significantly, suggesting

that f u tur e c ontrol p r ogr am s sim ilar to those im plem ented over this tim e period should be highly effective as well.

Com p arison with aircraf t profiles from 136 Regional Atm o s pheric Measurem ent Modeling and Prediction Program (RAMMPP) flights reveals that CMAQ has an overall high bias of ~15% from the surface to ~500 m e ters above sea level (ASL) and a low bias aloft (600-2600 m e ters ASL) of ~10%. Agreement between CMAQ-calculated and

aircraft-m easured ozone varies substa ntially from flight to flight. Inspection o f the surface m a ps for the OTR reveals that CMAQ, in general, replicates th e spatial pattern of high ozone events but often does not capture the full spatial extent or m a gnitude of the high ozone patterns. Mean CMAQ-calculated and

m easured 8-hour ozone values from 66 surface o z one m onitors in th e Baltim ore, Washington, D.C., and Philadelphia nona ttainm e nt areas ar e highly correlated (correlation coefficient, R, of 0.92) over the ozone season (May 15 – Septem ber 15) and

1well correlated (R=0.81) when a subset of 38 high ozone days (i.e. days when the peak daily 8

- hour average o z o n e in Mary land exceeded 85 ppbv) are com p ared. Biases between CMAQ-calculated and observed 8-hou r ozone m i xing ratios are m i nim a l (-1.6 ppbv) when averaged over the entire ozone s eason. However, larger negative biases are seen during high ozone days (-2.2 ppbv at ur ban sites and -7.7 ppbv at rural/suburban sites). The high bias near th e su rface and lo w bias aloft is indicative of an

underestim ation of transport by CMAQ. Alof t i s where m o s t transport occurs; groundlevel air does not m ove as readily. On th e highest ozone days, CMAQ’s perform a nce is not as good as on lower ozone days. This is a statistical reflection of CMAQ’s inability
to captu re large-scale d e viations fro m aver age or m e dian conditions. These deviations occur on days with poor air quality. CMAQ perform s better at urban sites than at suburban and rural areas. This bias pr ovides m o re evidence that CMAQ is m i ssing

incom i ng ozone, possibly transport. In som e instances, these rural/suburban areas are

dom i nated by power-plant em issions m o re than they are dom inated by m o tor veh i cle

em issions. The bias m a y also indicate that CM AQ’s re latively coarse v e rtic al reso lution

is unable to resolve the tran sport of point source (i.e. power plant) em issions. In particular, perform a nce at upwind sites with fewer nearby sources is poorer on the whole than it is at other sites (see Chapter 9).

None of these shortcom ings are reflected in EPA ’ s traditional ozone m ode l perform a nce m easures. However, these shor tcom ings m a ke it necessary to consider CMAQ output and other evidence wh en evaluatin g the probability of success of State

Im ple m entation Plans (S IPs). This C h apter goes with Chapter 9 on m odel perform a nc e, uncertainty, and responsiveness.

Introductio n The following analys is details com p aris ons betw een ozone observations and CMAQ ozone sim u lations using the 2002 “bas e case B1” emissions inventory. This analysis is important in unde rstanding the lim itations of CMAQ and its strengths and

weaknesses in sim u latin g air qua lity over the M i d-Atlan tic in part icular and the Ozone Transport R e gion in general. Understandi ng basic CMAQ model perform a nce, compared

to surface an d aloft observations, will provi de a fo undation for additional comm entary on CMAQ si mulations related to future em issions strategies. Som e key ques tions that will be addressed by this analysis include:

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

• How well does CMAQ capture temporal fluct uations in ozone concentrations both at the surface and aloft?

• What do id e n tified CMA Q limitation s imply for modeled attainment demonstrations using futu re emission s strategies?

Many of the im ages of s u rface com p arisons p r esented in this study em ploy a m a p which covers a large portion of the O z one Transport Region (OTR) (see Figure 1). The

dom a in in Figure 1 was selected in an effo rt to present as m u ch of the OTR m odeling

dom a in as p o ssible while keeping th e focus on the Baltim ore, W a shington, D.C. and Philadelphia nonattainment areas (N AA). Add itional com p arative im ages were derived

2fro m CMA Q ozone vertical profiles and aircraft ozone vertical profiles collected by the University of Maryland research aircraft. Other ozone im ages include co mparisons for

ozone m onitors within the Baltim ore, W a sh ington D.C., and Philadelphia NAAs. Ti me series plots were derived from CM AQ si m u lations of the entire summ e r of 2002.

Because there is keen in teres t in CM AQ m odel perform a nce when high ozone was observed over Maryland, as a subset of the m odel eval uation, the 38 days when the observed peak daily 8-hour average ozone exceeded 85 ppbv (i.e. the NAAQS for 8-hour

average o z o n e) som e where in M a ry land were an alyzed s e parately.

Figure 1. A plot of the corre lation coefficients (R) for da ily m a xim u m 8-hour ozone over

the OTC m o deling dom ain (base case B1 vs observations). Tim e period for the plot is the summer of 2002 (May-Septem b er).

Methods

As m e ntioned previously, the evaluati on of the CMAQ base case B1 sim u lation was performed using surface and alo f t observati o n s. Com p arisons with s u rface data were

conducted u s ing surface ozone m a ps covering m u ch of the OTR m odeling dom a in, while

tim e series p l ots were p r oduced f o r ozone m onitors within the Baltim ore, W a shington D.C., and Philadelphia NAAs. For surface m a p s (i.e. 2002 ozone observations, base case

B1 sim u lations and difference plots), Delauna y Triangulation was used to m a p irregularly spaced observations and m odel outp u t onto th e d e sired g r id. The surface plots presen t the peak daily 8-hour average ozone and do not represent one particul ar tim e of day. The

3tim ing of pe ak 8-hour ozone concentrations varies with location. For comparisons of

tim e series o f surface ozone, CMAQ-sim ulated s u rface ozon e was sam p l e d at lo catio ns correspond ing to the latitude and lon g itude of surface ozone monitors. For CMAQ com p arisons to aloft data, th e 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 summertim e conditions in the northeastern U.S.). These elevations were then converted to m e ters above sea level by addin g the surface elev ation of each grid p o int. CMAQ m odeled ozone was linearly interpolated in altitude and time to best m a tch the location and tim e of

aircraft m easurem ents.
The differences between aircraft and mode l profiles were ca lculated, accounting for differences in shape (the locations of the m i ni m a and m a xim a in the profiles) and

m a gnitude (absolute differences in mixing ratio ). All aircraft spiral m e asurem ents and correspond ing CMAQ-calculated ozone fields we re in itially averaged into 100 m altitude bins. This allowed for consistent comparis ons between pairs of m odeled and m easured

ozone. To calculate the difference betw een m o deled and m easured ozone, UMD

accounted f o r the shape and m o rphology of the profiles by looking at the absolute

difference in the m i xing ratio as well as th e s l ope and correla t ion coefficient between

modeled an d m easured ozone in four altitud e bin s (250-650 m, 651-1150 m

, 1151-1650 m, and 1651-2150 m ) . The altitude bins were select ed in an effort to obtain a statistically signif i can t n u m b er of data points in e ach bin while also a llowing f o r som e partition i ng between the planetary boundary layer and the low e r free troposphere. Equation 1 (Taubm an et al., 2006, Hains et al., 2007) was used to calculate the difference (D ij ) between each pair of m o deled ( i ) and m easured ( j ) ozone prof iles:



# ( ) ( ) ∑ ∑ =

= =

= ⎪ ⎭ ⎪ ⎬ ⎫

⎪ ⎩ ⎪ ⎨ ⎧

− − − +

− +

− = 4

1

2

_ 2

1

_ 2 ] ) 1 ( exp( 1 [ ] 1 [ 1 * ) ( k

k

Part Part m a

n a ja ia ij s R c c abs D

4 4 4 4 4 4 4 4 4 4 2 1

4 4 4 3

4 4 4 2 1 3 Equation 1

Her e k rep r e s ents th e f our dif f e rent a l titud e bins, a is an ind e x that c a n tak e on values

between 1 and the total num b er of data points between 250 and 2150 m e ters, n (m ) is an

index tha t p o int to the f i rst ( l as t) da ta point in ea ch bin. T he num b er of data points within each altitude bin ( m

- n +1) varies with altitude bin ( k ). The ozone m i xing ratio is represented by c for t h e i t h (m odeled) and j th (m ea sured) profiles. A regression between CMAQ calculations and observations is calculated, giving the slope, s , an d correlation coefficient, R , for each of the four layers. The fi rst part of Equation 1 calculates the square of the sum of the di fferences between values (fro m observations and CMAQ) at each altitude level, k . The second pa rt of the equa tion m u ltiplies the d i fference by one plus differen ces associated with the correlati on and slope. W h en the co rrelation is sm all or negative, the profiles ar e very different and the 1- R portio n increases, which increases

the to tal d i fference D ij . The exponent of the slope portion is used to account for the slope

of the best-fit lin e between CMAQ and observati o n s in each of the prof ile altitude bin s

. A slope near one suggests that the profiles ar e sim ilar and should therefore add little to the total difference. The exponent of the sl ope was used to guarantee that slopes m u ch

different from one would m a ke the exponentia l term s m all, thereby increasing the (1-

4exponent) term and increasing the total diffe rence. Taken as a whole, Equation 1

becom e s large when absolute d i fferences be tween predicted and obs erved 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 streng th and dire ction of a linear relationship between two

variab les. T h ese correlations were calcula ted, site-by-site over the ozone season, May 15- Septem ber 15, 2002. Figure 1 shows values of R ranging from 0.55 – 0.90, with the poorest correlation occurring ove r northwestern OH, northern VA and northwestern PA.

C o rrela tions are be tte r o v er m u ch of the In ters tate-95 corridor from W a shington, 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 th e poo rest co rrelations occurr in g over cen tral-western VA. Model perform a nce is poorer over the Ohio River Valley ( R =0.65-0.80). F i gure 1

dem onstrates that on average, CMAQ sim u late s peak 8-hour ozone reasonably well. The relatively low correlations over some portions of the Ohio River Valley and central V A are interesting. These regions are often upw ind of the Mid-Atlantic region during major pollution events. The poorer perform a nce in these regions could indicate that CMA Q is

not accu rately represen ting ozone pollutant trans port in to the Mid Atlan t ic region fro m these two regions. More conc lus i ve statem ents m u st awa it c o m p arisons w ith aircraf t

m easure m ents taken downwind of the Mid-Atlantic region.

Table 1 lists the surface m onitors us ed in the surface com p ar isons presen ted in Figures 2 and 3. Information on how CMAQ perform s at individual m o nitors is also shown in Figure 1. For exam ple, CMAQ-cal culated and m easured 8-hour ozone at Edgewood is well correlated (R=0.84). The m e a n bias (-2 ppbv) is sm all but larger on high ozone days (-9 ppbv). The best f it line to th e relation s hip betw een C M A Q

calculated (x) and m easured (y) 8-hour ozone at Edgewood is given by y = 10.0 + 0.88x. Figure 2 is a tim e series of the average, p eak 8-h our ozone v a lues, from surface

com p arisons of 66 ozone m onitors located wi thin V i rginia; M a ryland ; W a shington, D

. C

. ; and the Philadelphia non-attainm e nt area. Figure 3 shows surface ozone m onitor data categorized by EPA’s classifi cation of m onitor location (u rban, suburban and rural).

Table 2 pres ents sum m ary average statis tics asso ciated with Figure 2 and Figure 3. Considering 66 m onitors, CMAQ displays a 1.6 ppbv low bias and a 7.0 ppbv centered

root m ean square e rror (R MS

c ) for the entire ozone season and a low bias of 7.0 ppbv and

an RMS

c 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 f o r R on high ozone days is a consequence of

subsam pling the data set and does not by its elf imply poorer mode l perform a nce on high ozone days. Figure 3 shows that biases are sm all when averaged over th e ozone seaso n ranging from a 2.4 ppbv low bias at suburban site s 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 locatio ns. Differences in biases between urban

and suburban/rural locations s hould not be over interpreted as the site classification by EPA appears to be dated. Several site s (e.g., Rockville, MD or Greenbelt, MD) designated as “rural” are now suburban. The larger biases at suburban/rural locations

5exist b ecause CMAQ tends to underestim ate the sp atial ex ten t of high ozo n e events. i.e.,

it tends to u nderestim ate the reg i onal character of ozone episo d es. Both deficiencies in

the chem ical algorithm (see Chapter 4) and tr ansport (it is often underestim ated) m a y be responsible for these dif f erences in biases. Correlation coefficients are insensitive to monitor location. Overall, urban perf orm a nce is bette r than su burban, w h ich is in turn

bette r than r u ral pe rf orm a nce. The p oorer perform a nce at m o re rural locations could be caused by CMAQ underestim ating the relative importance of ozone and precursor transport in determ ining ozone am ounts.

Figure 2. Com p arison o f the averag e peak 8-ho ur ozone fro m 66 surface ozone m o nitors in Virginia, Maryland, W a shington D.C., and th e Philadelphia NAA. Out of the 123 days, 38 days are con s idered “hig h ozone day s ” when 8-h our peak ozone is greater than or equal to 85 ppbv at one or m o re m o nitors within the Balt im o r e NAA. CMAQ is generally lower than the peak observations and higher than th e nighttim e m i nim a

.

6F i g u r e 3

. S u r f a c e o z o n e c o m p a r i s o n s f o r ( a ) u r b a n , ( b ) s u b u r b a n , a n d ( c ) r u r a l s u r f a c e sites in V i rg inia, M a ryla nd, W a s h i n g t o n D

. C

. , a n d t h e P h i l a d e l p h i a n o n

- a t t a i n m e n t a r e a ( n = t h e n u m b e r o f m o n i t o r s )

.

7Table1. CMAQperformance atindividualmonitorsSite IDMonitor NameClassificationaLatitudeLongitudeRbRMScBias (alldays)Bias (highO3days)Intercept +(Slope)x100010002KillensPondR-75.5638.980.8210.2-2.1-8.7-1.5 +1.06x100031007LumsPondR-75.7339.550.8410.75.3-0.9-5.7+1.01x100031010BrandywineR-75.5639.820.8511.7-2.3-8.7-4.3+1.11x100031013BellefonteS-75.539.770.8510.63-0.20.3+0.94x100051002SeafordS-75.6138.640.8210.5-3.4-10.1-4.0+1.13x100051003LewesR-75.1638.780.859.6-0.3-6-6.3+1.11x110010025TakomaParkU-77.0238.980.899.241.53.1 +0.89x110010041RiverTerraceU-76.9538.90.899.4-2.2-6.95.4+0.94x110010043McMillanReserU-77.0138.920.99.2-8.1-13.88.5+0.99x240030014DavidsonvilleR-76.6538.90.8810.3-4.5-10.40.7+1.06x240030019FortMeadeS-76.7339.10.889.8-2.4-5.87.2 +0.93xaMonitorclassification (Urban, Suburban, orRural)fromhttp://www.epa.gov/air/data/index.htmlbCorrelation coefficient (R)cCentered rootmean square error after removingbias (apositive bias indicates that CMAQ-calculated 8-hour ozoneexceedsmeasured ozone)8Table1 (continued)CMAQperformance atindividualmonitorsSite IDMonitor NameClassificationaLatitudeLongitudeRbRMScBias (alldays)Bias (highO3days)Intercept +(Slope)x240051007PadoniaS-76.6339.460.8311.5-1.2-6.75.4+0.93x240053001EssexS-76.4739.310.8213.62.41.915.7+0.72x240130001South CarrollR-77.0439.440.829.90.3-4.62.4 +0.96x240150003Fair HillR-75.8639.70.8512-1.6-10.5-8.3+ 1.16x240170010S.Md(Hughesville)R-76.8138.50.8410.30.1-6.6-4.7+1.07x240210037FrederickAptS-77.3839.410.859.6-0.4-53.9 +0.94x240251001EdgewoodR-76.339.410.8412.4-2-910.0+0.88x240259001AldinoS-76.239.560.8711.1-3.2-113.1+1.00x240290002MillingtonR-75.839.310.8311.3-2.8-11.5-2.4+1.09x240313001RockvilleR-77.1139.110.869.95.32.51.5+0.89xaMonitorclassification (Urban, Suburban, orRural)fromhttp://www.epa.gov/air/data/index.htmlbCorrelation coefficient (R)cCentered rootmean square error after removingbias (apositive bias indicates that CMAQ-calculated 8-hour ozoneexceedsmeasured ozone)9Table1 (continued)CMAQperformance atindividualmonitorsSite IDMonitorNameClassificationaLatitudeLongitudeRbRMScBias (alldays)Bias (highO3days)Intercept +(Slope)x240330002GreenbeltR-76.8339.020.8710.31.2-2.43.4+0.93x240338003PG EquestCtrR-76.7438.810.8312.12.7-4.7-7.5+1.07x240430009HagerstownR-77.7239.570.879-2.7-6.71.1+1.03x245100053BaltimoreCountyU-76.5539.290.7814.75.95.76.8+0.79x340010005NacoteCreekR-74.4639.530.859.63-1.5-4.7+1.03x340070003Camden LabS-75.139.920.8412.2-3.3-7.56.8 +0.94x340071001AncoraHospitalR-74.8639.670.8810.7-3.5-8.7-4.2+1.12x340110007MillvilleR-75.0339.420.8710.3-1.6-8.4-8.1+1.16x340210005Rider US-74.7540.280.8611.6-2.6-8.51.5+ 1.02x340290006Colliers MillsR-74.4540.070.8513.2-4.6-12.7-7.8 +1.20xaMonitorclassification (Urban, Suburban, orRural)fromhttp://www.epa.gov/air/data/index.htmlbCorrelation coefficient (R)cCentered rootmean square error after removingbias (apositive bias indicates that CMAQ-calculated 8-hour ozoneexceedsmeasured ozone)10Table1 (continued)CMAQperformance atindividualmonitorsSite IDMonitor NameClassificationaLatitudeLongitudeRbRMScBias (alldays)Bias (highO3days)Intercept +(Slope)x420170012BristolS-74.8840.110.8711.6-0.5-4.81.3+0.99x420290050WestChesterS-75.639.940.8710.1-5.7-11.70.7 +1.09x420290100NewGarden(AiR-75.7739.830.8511.8-7.7-17.3-3.5+1.19x420450002ChesterU-75.3739.840.8611.4-4.1-10.12.5+1.03x420910013NorristownS-75.3140.110.8710.40.1-3.7-0.2+1.00x421010004Frankford (LabU-75.140.010.8411.213.613.30.8 +0.76x421010014Northwest(RoxS-75.2440.050.8411-1.9-4.38.8+ 0.88x421010024Northeast(AirS-75.0140.080.8811.2-6.6-12.93.5+ 1.05x421010136Southwest(ElmU-75.2239.930.86115.31.9-0.4+0.92x510130020ArlingtonCo.U-77.0638.860.99.7-4.5-11.32.7 +1.03xaMonitorclassification (Urban, Suburban, orRural)fromhttp://www.epa.gov/air/data/index.htmlbCorrelation coefficient (R)cCentered rootmean square error after removingbias (apositive bias indicates that CMAQ-calculated 8-hour ozoneexceedsmeasured ozone)11Table1 (continued)CMAQperformance atindividualmonitorsSite IDMonitor NameClassificationaLatitudeLongitudeRbRMScBias (alldays)Bias (highO3days)Intercept +(Slope)x510330001Caroline Co.R-77.3838.20.7610.12.11.35.3 +0.88x510360002Charles CityS-77.2637.340.8412.1-4.6-11.9-9.4+1.23x510410004ChesterfieldR-77.5937.360.7611.2-4.6-12.73.9+1.01x510590005ChantillyR-77.4738.890.869.3-5.6-10.17.4+0.97x510590018Mt. VernonS-77.0838.740.8511.72.1-4.4-1.6+0.99x510590030Lee ParkS-77.1138.770.8810.3-3.6-10.32.9 +1.01x510591005AnnandaleS-77.1638.840.8312.1-1.8-7.14.9+0.95x510595001McLeanS-77.238.930.7912.36.43.79.0+0.76x510610002FauquierCo.R-77.7738.470.779.7-0.2-7-6.8+1.12x510690010FrederickCo.R-78.0839.280.810.6-0.9-7-5.0+1.10x510850003HanoverCo.S-77.2237.610.811.6-5.3-14.3-2.8+1.13x510870014HenricoCo.S-77.437.560.859.9-5.1-9.15.8 +0.99xaMonitorclassification (Urban, Suburban, orRural)fromhttp://www.epa.gov/air/data/index.htmlbCorrelation coefficient (R)cCentered rootmean square error after removingbias (apositive bias indicates that CMAQ-calculated 8-hour ozoneexceedsmeasured ozone)1213Table1 (continued)CMAQperformance atindividualmonitorsSite IDMonitor NameClassificationaLatitudeLongitudeRbRMScBias (alldays)Bias (highO3days)Intercept +(Slope)x511071005LoudounCo.S-77.4939.020.8510.3-6.6-13.70.2 +1.11x511130003Madison Co.-R-78.4438.520.759.3-9.1-15.14.0 +1.09x511390004Page Co.R-78.538.660.6610.4-2-7.44.9 +0.95x511530009Prince WilliamS-77.6438.860.8110.3-3.4-12.1-8.3+ 1.20x511611004Roanoke Co.S-79.8837.290.89.2-4.9-9.80.9 +1.07x511630003RockbridgeCo.R-79.5137.630.6111.2-2.2-7.81.2+1.02x511790001StaffordCo.S-77.3738.480.879.2-0.3-4.63.1 +0.95x511970002Wythe Co.R-81.2536.890.729.8-6.6-12.4-4.1+1.20x515100009AlexandriaU-77.0438.810.889.74.9-0.3-2.7+0.96x516500004HamptonS-76.4370.7214.8-9.9-11.724.4+0.71x518000004Suffolk- TCCS-76.4436.90.869.40-5.1-3.1+1.05x518000005Suffolk- HollS-76.7336.670.7911.9-3.4-12.6-11.0+1.27xaMonitorclassification (Urban, Suburban, orRural)fromhttp://www.epa.gov/air/data/index.htmlbCorrelation coefficient (R)cCentered rootmean square error after removingbias (apositive bias indicates that CMAQ-calculated 8-hour ozoneexceedsmeasured ozone)Table 2. Comparison of 2002 Base B1 Sim u lation and 2002 Observations

Bias (ppbv) RMS error * R **

All 66 Site s Entire o z one Season - 1

. 6 7

. 0 0

. 9 2

High ozone Days -7.0 5.8 0.81

Urban Sites

Entire o z one Season - 1

. 5 7

. 7 0

. 9 2

High ozone Days -2.2 7.1 0.86

Suburban Sites

Entire o z one Season - 2

. 4 7

. 0 0

. 9 2

High ozone Days -7.7 6.3 0.78

Rural Sites

Entire o z one Season - 1

. 7 7

. 5 0

. 9 0

High ozone Days -7.7 6.1 0.76

* Centered root m ean square error in ppbv

** R is the correlation co efficient

Figure 4 shows the m e dian, 25 th and 75 th percen tiles for all aircraft-m easured ozone (136 profiles) and m a tc hing CMAQ ozone predictions fo r 2002. W h ile differences

between the m odel-calcu lated a nd observed profiles are substa ntia l, the m odel-c a lcu l ate d prof ile a l w a ys rem a ins betw een the 2 5 th and 75 th percentile of the observed profile. T h e large width of the observed prof ile (roughly 25 ppbv between the 25 th and 75 th percen tile) shows that lower tropospheric ozone am ounts va ry substantially even on days when

observed ozone am ounts were expected to be large (m ost flights were m a de on days when high ozone concen trations were forecast) . The large v a riab ility in o b served ozo n e concentratio ns is also a function of th e larg e spatial and tem p oral range of the data. The

aircraft m easurem ents were taken at locati ons extending from North Carolina to M a ine,

and therefore som e ti m e s sam p led very different chem ical regim e s. Another factor that

can cre a te v a riab ility is the occas ion a l natu ral v a riab ility in o z one w ith a l titud e tha t produces scattered data at the high tim e reso lution of the ozone instrum e nt onboard the aircraft. Longer averaging pe riods would produce sm oother 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 sam p ling peri od implies m o re vertical distance will be covered per sam p ling period). The aircraft ozone instrum e nt collects a running 1 m i nute average ozone value, which is obtained from 10-second sam p ling 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 CM AQ profiles aloft, from 600 – 2600 m

.

14Figure 4. M e dian CMAQ and aircraft O 3 profiles from 2002 (June–August, 136 profiles). The ends of the horizontal bars represent the 25 th and 75 th percentiles.

Grand statis tical averag ing (i .e. over an entire year or ozone season) is helpful in identifying m a jor problem s associated with perf orm a nce, (e.g. a larg e error in an

em issions in ventory or im proper lan d surf ace us e) but less h e lpful from a forensic perspective when subtle errors or co m p ensa ting errors are p r esent that m a y change from ozone episode to ozone episode. Types of error that m a y evade inspectio n by grand

statistical av eraging, include biases in th e tim i ng and spatial coverage of convection

and/or biases in the tim ing and extent of pl anetary boundary layer ventilation. To further

elucidate C M AQ perform a nce during specific oz one episodes, several case studies were perform e d. For organizationa l purpo ses, selected case stud ies include p e riods when CMAQ perform ance was characterized, based on aircraft data, as below-m e dian (t he 95 th

percen tile differences as calcu lated b y equation 1 )

, average (the 50 th percentile differences) and above-median (the 5 th percen tile differences). For each case, surface layer ozone distributions and vertical profiles are com p ar ed to m easurem ents. In addition, 24-hour back trajectories were calc ulated from 40 km North American W eather Model (NAM) data using m odeled wind ve locities fromHYSPLIT (HYbrid SingleParticle Lagrangian Integrated T r ajectory , [Draxler, 1991]). To com p le ment this analys is, ge neral wea t he r conditions will be d i scu s sed to e l ab orate on the history of a i r parcels in the context of air quality. Through presenta tion of CMAQ perform ance during individual ozone episodes (occurring over the B a ltim ore- W a shington, D.C. non15atta inm e nt areas), the ab ility of C M A Q to replic ate cond itio ns during “b ase cas e”

em issions conditions is evaluated.

Stoeckenius and Ke m b all-Cook (2005) de veloped a classification scheme and applied it to all OTR ozone ep isodes during the summ er of 2 002. They used a clustering technique to classify ozone episodes ba sed on spatial ozone patterns and weather conditions (wind direction, tem p erature, etc.). T a ble 3 below is a guide to episode types

and com posite ozone patterns determ ined in the i r w o rk. To bette r iden tif y the episodes discussed, the episode type as classified by Stoeckenius and Ke m b all-Cook are reported in this study

. Table 4 is a summary table of the ten flight s selected for clo s er inspectio n in the following section. The ten flights co ntain three instances each of occasio ns when the CMAQ perform ance (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

. Tw ice, m u ltiple f lights w e re pe rf orm e d on the sa m e day (June 25 and August 2). CM AQ perform a nce during each flight is eva l uated separ a tely. Table 5 and Figure 5 provide

sum m a ry data reg a rding airc raft spiral and airport locations.

Table 3. Composite Pattern and Episode Type determ ined by Stoeckenius and

Ke m b all-Cook (2005). Composite Pattern Episode Type

3 Type A: High ozone throughout the OTR

2 Type B: High ozone confined to extrem e 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 OT R

16Table 4. Summary of CMAQ Comp arisons of ozone vertical profiles to aircraft ozone vertical profiles CMAQ

Performance Flight

# Date/Time Spiral

Location Composite

oz on e 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 Cum b erland, 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

Bel o w Median RF42 08/02/02 19:00 UTC Fort Meade, MD 5

Bel o w Median RF14 06/25/02 14:00 UTC W i nchester, VA 4

Bel o w Median RF17 08/12/02 18:00 UTC Bennington, VT 4

Bel o w Median RF49 08/13/02 14:00 UTC Morrisville, V T 3

17Table 5. Reference Guide for Aircraft Spiral Lo cation s

ID # Spiral Airport To w n Latitude Longitude Elevation*

1 LKU Louisa Co. Freem an Louisa, VA 38.01o N 77.97o W 150

2 0W3 Harford Co. Churchville, MD 39.56o N 76.20o W 121

3 CBE Cum b erland Regl. Cum b erland, MD 39.62o N 78.76o W 237

4 W79 Tappahannock Mun. Tappahannock, VA 37.92o N 76.87o W 10

5 W81 Crewe Mun. Crewe, VA 37.18o N 78.10o W 131

6 OFP Hanover Co. Mun. Ashland, VA 37.71o N 77.44o W 62

7 FME Tipton AFB Fort Meade, MD 39.09o N 76.76o W 42

8 OKV W i nchester Regl W i nchester, VA 39.14o N 78.14o W 222

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

10 MVL Morrisville-Stowe Morrisville, VT 44.54o N 72.61o W 223

Figure 5. Airport spiral locations of oz one profiles collected by the UMD aircraft presented in this analysis. * Elevat ion in m e ters above Mean Sea Level.

18Figure 6 is a legend for HYSPLIT trajecto ries and aircraft/C MAQ ozone profiles presented in the subsequ e nt pages. For each case study, a back trajectory is calculated using the H Y SPLIT program to present a hist ory of the air en tering the area where an

airc raft spira l w a s perform e d. Back trajectories f o r all cases extend 24-h ours, and are perform e d a t 500, 1000 and 1500 meters. Thes e levels were selected to obtain

inform ation about the boundary layer ( 500, 1000 m e ters) and lower free troposphere (1500 m e ters). The 24-hour duration was selected to m i ni m i ze m odel (HYSPLIT)

uncertainties that can result from random e rrors contained in the num erical weather

model used to determ ine the back trajector ies, which m a y propagate when longer backtrajecto r ies are em ployed. Figure 6 also provides a le gend called “Ozone Data” for panels show ing a com p arison of aircraft profiles (pink stars) against CMAQ profiles (blue d i am onds). Occas ionally, CMAQ pr ofiles will show discontinu i ties of 5-10ppbv that repeat back and forth throughout the prof ile. These discontinui ties usually occur at

sam p ling locations near the edges of CMAQ grid boxes. At these locations, slight changes in horizontal locati on result in sam p ling from a different CMAQ grid box. ,

Figure 6. Guide to v i ewing HYSPLI T back tr ajectories, aircraft profiles and surface o z one difference plots.

Figure 7 is a sam p le 4-panel surface p l ot to fam iliarize the reader with sub s equent figures tha t will be p r ese n ted. The up per left pan e l is a m a p of observed 8-hour m a xi m u m ozone over the OTR, the upper right pane l shows CMAQ-calculated 8-hour m a xi m u m

19ozone interp olated to oz one m easurem ent sites w ithin the O T R

, and the lo w e r lef t pan e l is a difference plot created by subtracting the CMAQ-calculate d ozone (upper right) from

m easured ozone (upper left). The lowe r right panel shows CMAQ-calculated 8-hour ozone on the original m o del grid (m eaning that the ozone values have not been interpolated to the m easurem ent sites). Com p arison of the upper-r ight and lower-right hand plots shows that interpolation of m odel output onto the OTR dom a i n causes

m i nim a l loss of inf o rm ation. For s i m p licity, subsequent figures wi ll exclude the plot showing m o del output on the original grid.

Figure 7. Sa m p le plots f o r June 5, 2 002. Latitu de and longitude coord i n a tes a r e prov ided on the x axis and y axis o f each plot. Th e upper left plot shows the observ e d daily

8-hour ozone m a xi m a (ppbv). Ozone ( ppbv) from the 2002 base B1 simulation interpolated to the locations of ozone m onitors is show n in the upper right panel.

The difference between m easurem ents and m odel outputs is in the lower left panel in ppbv, and the 2002 base B1 sim u lation on the original m o del grid is shown in the lower right panel, also in ppbv. Since the upper right plot is alm o st entirely

the sam e as the low e r rig h t plot, the u pper right plot is not be included in subsequent s u rface ozon e com p arison plots.

20Above 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 s how back trajectories ending on June 11 over northern VA (Louisa County) and a corres ponding aircraft prof ile collected on the morning (08:00 EST) of the 11 th , res p ectiv ely. T h e back trajectory showed that winds were light and that the air m a ss was ove r southw estern VA and eastern T N 24-hours

earlier. CM AQ perfor m a nce is excellent on this occasion, with CMAQ accurately replicating the boundary layer o z one m o rphol ogy as near-surface m i ni m a 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 observ e d pollution alof t w a s likely f r om transport ra the r than f r om local em issions

. The 500- and 1000m back trajectories show a sh ift from a westerly fl ow to a flow along the I-95 corridor. Figure 8C-E com p ares su rface ozone data o n the 11 th t o a CMAQ

sim u lation. CMAQ accurately rep r es ents ozone o v er m u ch of PA and MD but generally under underpredicts ozone concentrations else where with peak underpredictions (~ 30 ppbv) occurring over central OH and the sout hern extrem e of the m odeling dom a in.

21Figure 8. Tuesday, June 11, 2002, 08:00 AM EST, Louisa, VA Case Study A. 24-hour HYSPLIT back trajectori es term inating over Louisa, VA.

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

B

. A i rcraft (pink stars ) and CMAQ (blue diam onds) ozone profiles.

C. OTR sur f ace ozone m onitor data. D. 2002 base B1 CMAQ sim u lation av eraged for OTR monitor locations. E. Difference plot. Negative valu es indicate m o del under-prediction.

22Tuesday, 06/25/2002, 19:00 UTC Case Study

June 25 was class i fied as a type 4 ep is ode (h igh ozone in the w e stern OTR). Like June 24, the highest ozone concentrations on June 25 th were confined to a box south and west of New York City and north of VA, w ith widespread ex ceedances in the Ohio River Valley and scattered Code Red concentra tions along the W a sh ington-Philadelphia Corridor. Figure 9A shows back-trajectorie s ter m inating over north eas tern MD in the early afternoon (15:00 E S T) of June 25 and Figure 9B shows a corresponding aircraft profile. The back trajectory orig inates from eastern O H at th e low e st lev e ls and ex te nds

to near D e tr oit, MI f a r

t h e r alof t. Like the vertical profile from June 11 th (Figure 8B) the boundary layer appears to be st ratified, with m i nimum ozone values at the surface of ~70 ppbv increasing to a well-m i xed layer of ~95 ppbv from 500 m to 2500 m. The stratification is surprising as this is an afternoon profile . CM AQ does not capture this stratification; however, CM AQ perform ance wa s still defined as good since as noted previously, for consistency (a nd to accommodate for change s in elevations of ozone monitors), C M AQ and aircraft data w e re co m p ared from 250 m to 2150 m, and therefore, the lowest portion of the boundary layer was not included in this com p arison. To further

com p licate this analysis, it appears, b a sed on surf ace ozone m a ps, that ozo n e was spatially highly variable in the area surroundi ng the aircraft profile (Churchville, MD ). Thus CMAQ perfor m a nce at the lo w e st leve ls, w h ich initia lly did not se em f

a vorable, is

actua lly (on a sligh tly lar g er sca l e) re f l ective of th e variable sp atia l ozone p a tte rn in the

area. This n o tion is supp orted when co m p aring m odeled an d observed s u rface ozon e over the entire OTR (Figure 9C -E) and underscores som e of the challenges encountered

when m a king aircraft co m p arisons. CMAQ continued to underpredict peak va lues, but m odel perfor m a nce improved

com p ared to earlier days in this episode wh en underpredictions were severe. Differences

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

with differe nces in VA of ±5 ppbv. There are m u ltip le a r eas of m odel overpred i ction (~15-25 ppbv) found in western MD -eastern W V -northern VA, southw estern and central OH and northern NJ-Long Island-Boston, MA.

23Figure 9. Tuesday, June 25, 2002, 14: 00 EST, Churchville, MD. Case Study A. 24-hour HYSPLIT back trajectories term inating over , Churchville, M D

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

B

. A i rcraft (pink stars ) and CMAQ (blue diam onds) ozone profiles.

C. OTR sur f ace ozone m onitor data. D. 2002 base B1 CMAQ sim u lation av eraged for OTR monitor locations. E. Difference plot. Negative valu es indicate m o del under-prediction.

24Friday, 08/02/2002, 19:00 UTC Case Study August 2 was classified as a type 4 ep isode (high ozone in the western OTR). Figure 10A indicates a 24-hour back trajectory term inati ng over western MD, passing through the clean corrid or in eastern Pennsylvania, a nd originating over western NY and southern Ontario. Figure 10B shows good ai r quality and excellent agreem ent between CMAQ and aircraft obser vations. Vertical profiles show relatively constant, low ozone with values ranging from ~40 to 60 ppbv. P eak ozone values are located over southern OH, central PA and along the I-95 C o rridor. CMAQ does not capture the very low ozone values over eastern KY, registering a hi gh 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 m a gnitude of the area of high ozone over eastern OH, we stern PA, and along the I-95 corridor (Figur e 10C-E). However, at individual sites within a re gion, CM AQ som e tim es perfor ms differently.

For exam ple, on August 2, CMAQ overpredicts ozone am ounts near Edgewood, MD (a

site within the I-95 co rridor). These sm a ll-scale differences in m odel perform a nce are not surprising given sm all-scale variations in terrain and circula tion (e.g., Edgewood is located near the Chesapeake Bay and is ofte n sub j ect to land -sea breezes ). However, these differences do indicate that no single site should be given extra focus when evaluating model perform a nce. For m o st applic ations, this is not a problem

, but it is a

concern for SIP m odeling, since m odel perform a nce at the w o rst a i r qua lity sites w ith in a

nonattainm e nt area is relevant when determ in ing if em ission reductions are sufficient to bring a non-attainm e nt area into complianc e with the 8-hour ozone standard.

25Figure 10. Friday, August 02, 2002, 10: 00 EST, Cum b erland, MD. Case Study A. 24-hour HYSPLIT back trajectories term inating over Cumberland, MD.

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

B

. A i rcraft (pink stars ) and CMAQ (blue diam onds) ozone profiles.

C. OTR sur f ace ozone m onitor data. D. 2002 base B1 CMAQ sim u lation av eraged for OTR monitor locations. E. Difference plot. Negative valu es indicate m o del under-prediction.

26Median CMAQ Performance Cases in 2002

Tuesday, 07/16/2002, 20:00 UTC Case Study July 16 was class i fied as a type 2 ep is ode (high ozone confined to the extrem e southeastern OTR). Figure 11A shows relative ly fast 24-hour back trajectories (ending over Tappahannock, VA at 15:00 ES T) 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. F r om Figure 13 it can be seen that peak ozone levels on the 16 th

occurred alo ng a line fro m the OH-KY borde r eas t-south eas t across W V , VA and NC.

Most of those values were in excess of 85 ppbv, with levels reaching 105 ppbv near Cincinnati, OH. On ave r age the m o del perf orm a nce appears to be good with differences

over the dom a in m o stly below 15 ppbv. The la rgest discrepancy is associated with the peak value n ear Cincinnati, OH; the local m a xi m u m that CMAQ places in that region is too low and displaced south of the actual peak. The result is an area of un derpred i ctio n over southw estern OH and a sm all area of ove rprediction in norther n KY . The spatial

extent of the underestimation is probably exaggerated as only a few m onitors exist in southeastern Ohio and northern Kentucky. As has been noted, in other comparisons, CMAQ tends to overpredict ( 15-25 ppbv) surface layer ozon e along the Ohio River and over portions of W V

.

27Figure 11. Tuesday, July 16, 2002, 15:00 EST, Tappahannock, VA. Case Study A. 24-hour HYSPLIT back trajectories term inating over Tappahannock, VA.

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

B

. A i rcraft (pink stars ) and CMAQ (blue diam onds) ozone profiles.

C. OTR sur f ace ozone m onitor data. D. 2002 base B1 CMAQ sim u lation av eraged for OTR monitor locations. E. Difference plot. Negative valu es indicate m o del under-prediction.

28Wednesday, 07/17/2002, 14:00 UTC Case Study July 17 was classified as a type 3 epis ode (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 na ture, 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 ju st above 50 ppbv by 600 m

. Observations in crease to ~80 ppb v by 1100 m and then tail off to a constant va lue of ~ 60 ppbv by 1500 m

. On this m o r n ing CMAQ does an excellent job of replicating rela tively low ozone levels n ear th e surface, but it struggles to sim u late the structure of ozone between 900m and 1200m. Instead, the

ozone rem a ins constant above 900 m. This is a case when it appears that CMAQ is not capturing a regional transport signal. Fi gure 12C-E shows that 8-hour ozone m i xing ratios exceeding 85 ppbv were reported from SC to as far north as CT and westward into OH. Scattered peak 8-h our ozone concen tration s exceed ing 105 ppbv occurred along and

east of the I-95 Corridor as well as n e ar Cleveland and Buffalo. Surface m odel perform a nce appears to b e sligh tly les s accu rate th an on the prior case s t ud y of July 16 th

, with the m a jority of differences in the range of 15 to 25 ppbv. Of note is the large area of underprediction covering m u ch of the northern two thirds of OH and portions of southern NY, especially near the Canadian bo rder.

29Figure 12. W e dnesday, July 17, 2002, 09:00 EST, Crewe, VA. Case Study A. 24-hour HYSPLIT back trajectori es term inating over Crewe, VA.

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

B

. A i rcraft (pink stars ) and CMAQ (blue diam onds) ozone profiles.

C. OTR sur f ace ozone m onitor data. D. 2002 base B1 CMAQ sim u lation av eraged for OTR monitor locations. E. Difference plot. Nega tive values indicate m odel under-prediction.

30Tuesday, 06/11/2002, 20:00 UTC Case Study June 11 was classified as a type 5 epis ode (high ozone along the I-95 corridor and

in north ern New England). Recall that CMAQ-calcu lated and obs erved ozone agreed well during a m o rning fl ight on June 11 th . The flight addressed he re is an afternoon flight at a different location (see Table 4). F i gure 13A shows weak westerly winds that advected air parcels originating over southw estern 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 la yer) but underpredicts ozone throughout the profile by 10-20 ppbv. Air quality is quite poor with ozone m i xing ratios of ~100 ppbv to

~ 2000m

. Given that this is an afternoon ( 15:00 EST) profile it is possible that CMA Q has vented the boundary layer too early or too vigorously which could account for the differences between CMAQ and obs ervations. However, the low- bias also illus t rates the tendency of CMAQ to underestim ate ozone am ou nts during particularly poor air quality days. . Figure 13C-E shows high ozone concentrations reported along the I-95 Corridor, with scattered location s sho w ing va lues exceeding 1 05 ppbv from central N C to
northern CT . The m o st concentrated area of hi gh ozone is in a band fr om just northeast of

B a ltim ore in to southe astern PA betw een N e w C a stle, D E and Lancaste r, PA . The back trajectories show a shift from a west-north west flow to one along the I-95 corridor. CMAQ underpredicts ozone c oncentrations on June 11 over much of the model

dom a in, with the greatest underpredictions (~ 30 ppbv) occurring over portions of

western PA and southwe s tern VA. Along the I-9 5 corrido r, just north of Baltim ore, MD extending to near Boston, MA CMAQ overpredicts oz one levels by 5-15 ppbv.

31Figure 13. Tuesday, June 11, 2002, 15:00 EST, Ashla nd, VA Case Study A. 24-hour HYSPLIT back trajectori es term inating over Ashland, VA.

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

B

. A i rcraft (pink stars ) and CMAQ (blue diam onds) ozone profiles.

C. OTR sur f ace ozone m onitor data. D. 2002 base B1 CMAQ sim u lation av eraged for OTR monitor locations. E. Difference plot. Negative valu es indicate m o del under-prediction.

32Below Median CMAQ Performance Cases in 2002

Friday, 08/02/2002, 19:00 UTC Case Study August 2 was classified as a type 4 ep isode (high ozone in the western OTR). Figure 24A shows that 24-hour back traject ories ending over Fort Meade, Maryland in the afternoon (15:00 EST) show a high de gree of variab ility. R ecall that C M A Q

calculated and observed ozone agreed well dur ing a m o rning flight on August 2. The flight addressed here is an afternoon flight at a different location (see T a ble 4). At the upper levels, air parcels originated over sout hern Ontario, Canada but back trajectories only extend to southeastern PA at the lowe st levels. Figure 14B shows CMAQ greatly overpredicted ozone, with sim u lated valu es (~115 ppbv) well m i xed throughout the boundary layer. Aircraft observa tions show slightly m o re va riability, but only range f r om ~80 ppbv at the lowes t le vels to ~60 ppbv by 1500 m

, before falling off to less th an 40 ppbv. Figure 14C-E show that CMAQ is actua lly doing a reasonable job replicating the spatial pattern of ozone, especially along the I-95 corridor. T h e high variability in the

observed su rface m a xi m a creates po ckets of relatively large disagreem e nt over sm all areas. The aircraft spiral was perform e d over one of those pockets. It is possible, given the prox im ity of the spir al to the C h e s apeak e Bay, that land-sea interactions m a y have

affected the sim u lation locally. Peak ozone va lues appear over sout hern OH, central PA, and throughout the Interstate- 95 Corridor. Isolated areas of CMAQ overprediction occur

over eas tern KY (~40-50 ppbv), the P h ilade l phia

- Baltim ore-W a shington region, and much of southern New England ( ≤ 25 ppbv).

33Figure 14. Tuesday, August 2, 2002, 14: 00 EST, Fort Meade, MD. Case Study A. 24-hour HYSPLIT back trajectories term inating over Fort Meade, MD

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

B

. A i rcraft (pink stars ) and CMAQ (blue diam onds) ozone profiles.

C. OTR sur f ace ozone m onitor data. D. 2002 base B1 CMAQ sim u lation av eraged for OTR monitor locations. E. Difference plot. Negative valu es indicate m o del under-prediction.

34Tuesday, 06/25/2002, 14:00 UTC Case Study

June 25 was class i fied as a type 4 ep is ode (high ozone in the w e stern OTR). Figure 15A is a back trajector y for the m o rning of June 25 (10:00 EST) over W i nchester VA. The trajecto r y is weak from the nor thwest reaching only s outhwestern PA and

eastern OH. Recall that an afte rnoon (15:00 EST) flight on the 25 th (see Figures 9A an d 9B) was classified as a good CMAQ perform a nce day. Analysis of the morning flight presented in this case study reveals th at CMAQ overpredicted ozone aloft (~200 m through 700 m ) by ~20 ppbv (Figure 15B). At 700 m

, aircraft observations sharply

increase to m a tch CMAQ sim u lations of ~90 pp bv in a th in layer a t 900m ; farther aloft,

the tw o m easurem ents diverge aga i n, w ith C M A Q still h i ghe r than obse r vations (by as much as 50 ppbv). Based on surface com p arisons from Figure 23 (also F i gure 7) over that region, one conclusion is tha t C M A Q is just m i ssing the location of a local o z one plum e (which the aircraft interacts with briefly at ~700 m )

. Thus, spatially, CMAQ appears to b e representing existing conditi ons w i th reason able accuracy. However, because of the sharp ozo n e gradient ove r the m easurem ent location and resolu tion

lim itations o f the m odel, C M A Q does not com p ar e favorably with aircraft observations.

35Figure 15. Tuesday, June 25, 2002, 14:00 EST, W i nchester, VA. Case Study A. 24-hour HYSPLIT back trajectories term inating over W i nchester, VA.

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

B

. A i rcraft (pink stars ) and CMAQ (blue diam onds) ozone profiles.

C. OTR sur f ace ozone m onitor data. D. 2002 base B1 CMAQ sim u lation av eraged for OTR monitor locations. E. Difference plot. Negative valu es indicate m o del under-prediction.

36Monday, 08/12/2002, 18:00 UTC Case Study August 12 was classified as a type 3 episode (high ozone throughout the O T R). A 24-hour back trajectory shows fast westerly flow ending over Bennington, VT on the 12 th
of August, originating from over MI and sout hern Ontario (F igure 16A). A vertical

com p arison from the early afternoon reveal s CMAQ is underpredicting ozone com p a r ed to aircraft observations by as m u ch as 40 ppbv in the layer between 200 m and 1000 m (Figure 16B ). CMAQ reports a near-constan t value of ~60 ppbv from the surface to

~2000 m

, while aircraft observa tions show values closer to 100 ppbv from the ground to

~1000 m

. This appears to be a case where CM AQ, rather than nearly m i ssing an ozone plum e, is not fully describing the spatial extent of the ozone episode. One possible reason why CMAQ underpredicted ozone is a failure to ac curately sim u late boundary layer m i xing. Inspection of the August 12 s ynoptic conditions reveal s strong, widespread convection developing late in the day. The underprediction of ozone and the apparent

well-m i xed boundary layer from CMAQ, suggest that convection was initiated earlier in CMAQ (in the parent M M 5 sim u lation) than ac tually occurred. Another cause of CMAQ underprediction could be a failure to capture westerly transport of ozone and ozone precursors that originated over southern Ontari o. Precursor em issions in Canada are less

certain th an they are over the U.S. Surface com p arisons in Figure 16C-E are cons istent with the aloft observations in that CMAQ unde rpredicts ozone over m u ch of the eastern half of PA, NJ and portions of the southe rn New England area. In contrast, CMAQ overpredicts ozone over much of Long Isla nd, C o astal MA and southern CT. The locations of the overpredictions (e.g., L ong Island, NY) suggest that CMAQ fails to develop the boundary layer fully over water, which can lead to relatively high ozone values just o ffshore. This is a potential prob lem over the Ches apeake Bay. CMAQ is known to have problem s with coastal lo cations, since CMAQ a pportions em issions

accord ing to m u ltiple lan d uses w ithin a grid c e ll, w h ile MM5 assum e s that the dom inant land use in the grid c e ll dicta t es boundary layer developm ent. In a co astal grid cell, MM5 often produces a very tight boundary layer, while CMAQ ofte n puts a lot of e m issions (say from a sm all coastal ci ty) into the very tight boundary layer that MM 5 produces.

Under these artificial con d itions, o z o n e concen tra tions c a n so ar unre a listically in the

m odel. It is possible that biases at co astal locatio ns are ex agg e rated. Ozone m onitors are underrepresented at coastal locations, so it is di f f icult to judge. Biases offshore m u st be

inte rpre ted c a utious ly as of f

s hore m e asurem ents w e re not ava ilab l e f o r this com p arison.

37Figure 16. Monday, August 12, 2002, 14:00 EST, Bennington, VT. Case Study A. 24-hour HYSPLIT back trajectori es term inating over Bennington, VT. Blue = 500m, Green = 1000m

, Red = 1500m

B

. A i rcraft (pink stars ) and CMAQ (blue diam onds) ozone profiles.

C. OTR sur f ace ozone m onitor data. D. 2002 base B1 CMAQ sim u lation av eraged for OTR monitor locations. E. Difference plot. Negative valu es indicate m o del under-prediction.

38Tuesday, 08/13/2002, 14:00 UTC Case Study August 13 was classified as a type 3 episode (high ozone throughout the O T R). A 24-hour back trajectory shows west-southwest flow ending over Morrisville, VT on the

13 th of August, passing over Buffalo, NY and s outhern Ontario, Canada (Figure 17A). Figure 17B is a late m o rning (10:00 AM EST) aircraf t p r of ile over Mor r is ville, V T . A s on the 12 th , CMAQ underpredicts ozone in the lower boundary layer. A i rcraft observations show a sharp ozone gradient wi th n ear-su r face values of ~ 40 ppbv rapidly increasing to over 100 ppbv by ~400 m

. The p eak ozone levels persist until ~1100 m a nd then gradually fall off to a constant va lue of 60 ppbv by 1500 m

. The CMAQ si m u lation

is m a rginally im proved from the previous day. Even though CM AQ underpredicts ozone throughout the entire profile it does m i m i c the vertical m o rphology of the observations. CMAQ perform ance on August 13 is sim ilar to th e 12 th in that it is g e ner a lly characterized by widespread underpredictions (F igure 17C-E). Underpredictions ranged

from 15-25 ppbv over much of the dom a in w ith extrem e excursions of over 50 ppbv occurring in isolated areas of central PA, eastern NY, a nd portions of the New England region.Unlike August 12 th , this p r of ile occurs at ~ 10:00 A M EST, so biases in th e tim ing

of convection on the 13 th cannot be blam ed; however, it is possible that convection on the

12 th was too strong in M M 5 leadi ng to an underestim ation of the am ount of residual layer

ozone available to m i x down in the morning. H o w e ver, inspe c tion of sate llite im agery showed widespread con v ectiv e activ ity ove r m u c h of the MM5 dom a in on both the 12 th

and 13 th of August.

39Figure 17. Tuesday, August 13, 2002, 09:00 EST, Morrisville, VT. Case Study A. 24-hour HYSPLIT back trajectories term inating over Morrisville, VT.

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

B

. A i rcraft (pink stars ) and CMAQ (blue diam onds) ozone profiles.

C. OTR sur f ace ozone m onitor data. D. 2002 base B1 CMAQ sim u lation av eraged for OTR monitor locations. E. Difference plot. Negative valu es indicate m o del under-prediction.

40Effect o f th e NO

x SIP Call on Maryland Oz one

W h en considering the question of whether NO

x r e duction s re lated to the N O

x 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 attrib uted to m e teorology. It is ge nerally accepted that m e teorological pro cesses strongly influence tropospheric ozone levels (National Rese arch Council, 1991, Biswas and Rao, 2001, EPA, 2006). Meteorology dir ectly determ ines whether ozone precursors

rem a in local or are tran s ported down w ind al ong with the res u lting o z one . Ozone lev e ls

build when tem p eratures are high, en hanci ng the rate of ozone for m ation, and air stagnates, lim iting vertical m i xing of polluta nts. These weather processes can vary spatially and tem porally from sm all-s cale feat u r es such as clo uds covering only tens of

kilom e ters to large tropical system s spanni ng several thousand kilom e ters. Of all the

m e teorological variables, tem p erature displays the strongest correlation with peak ozone concentrations (W olff and Lioy, 1978; Ryan, et al., 2000). T h is analysis attem p ts to

exam ine the effect of NO

x reductions on peak 8-h our ozone le vels over the B a ltim ore Non-Attainm e nt Area (BNAA) by analyzing oz one and weather before and after the NO

x SIP Call went into effect. In order to acc ount for year-to-year weather differences,

conditional m ean ozone values were calcu lated a s outlin ed be low.

Peak ozone data were b i nned accord ing to tem p erature for th e tim e perio d s before (1997-2002) and after (2003-2006) the NO

x SIP Call (i.e. 20 02). Fr om t h e two groups of ozone values, statistical data were calculated (i.e . conditional m ean, standard deviation, etc.) and plotted to determ ine the probability that differences betw een the tw o data se ts
were the result of random fluc tuations. In this study, oz one observations were grouped

into 5°F tem p eratu r e bin s before and after the N O

x SIP Call (e.g. 1997-2002 and 2003- 2006). Daily m a xim u m tem p er ature data from Baltim ore- W a shington International Airport (BW I) were used. W h en calculati ng statis tic s f o r the bi nned ozone data, the degree of au tocorrelation of m a xi m u m tem p erature was also determ ined. Autocorrelation

is a m a them atical too l us ed for analy z ing functions or series of values (e.g. daily

m a xi m u m tem p erature v a lues). Inform ally, it is a m easure of how w e ll a signal m a tch e s a tim e-shifted version of itself, as a func tion of the am ount of t i m e shift. The

autocor r elation is im portant in de ter m ining the num b er of degrees of freedom

, which strongly affects the sta tistical significan ce of a calculation. The degree of autocorrelation

for m a xi m u m tem p erature was determ ined to be three days using the SPS S ® statis tic al

program (see Appendix A in Chapter 1 for a description of SPSS ® ). A three-d a y autocorrelation m eans th at after three days , the tem p erature is independent of prior

tem p erature s , or, put an other way, s ynoptic con d itions at B W I change every three d a ys on average. The autocorrelation is n eeded to calculate the s t andard dev i ation of the m ean of the binned ozone data (Equation 2).

41mean 

## σ = σ

n

N Equation 2

σ mean = standard deviation of the conditional m ean

σ = standard deviation

n = total number of (daily) observations

N = degree of auto correlation (days )

The effect of autocorrelation is to reduce the num ber of inde pendent observations, in this

case m eaning the num be r of independent airm a sses. If autocorrelation were not taken into account, the num ber of independent obser vations could balloon if observations were

taken m o re frequently and cons ider ed entirely ind e pendent. T h e data in Figure 18 and Table 6 sug g est th at, ev en when taking into acco unt seasonal differences in tem p eratu r e between the two tim e periods (pre and post NO

x SIP Call), the m ean of peak 8-hour

ozone observations over the BNAA i s signi ficantly lower for the tim e period 2003-2006

than it was for 1997-2002.

Table 6. Summary Binned Ozone Statis tic s used in Figure 18

. 1997-2002

Tem p erature Bin 80-84°F 85-89°F 90-94°F >95°F Ozone (ppbv)

Minim u m 2 8 4 1 4 9 4 0

M e a n 6 6

. 8 8 0

. 7 9 3

. 7 9 9

. 3

Maxim u m 1 0 6 1 2 3 1 3 1 1 4 9 First quartile 58.6 71.0 82.7 85.3 Third quartile 76.0 91.0 104.1 116.1

Mean +1 σ mean 6 8

. 2 8 3

. 1 9 6

. 2 1 0 4

. 9

Mean -1 σ m ean 6 5

. 4 7 8

. 4 9 1

. 2 9 3

. 7

σ 1 3

. 0 1 6

. 0 1 5

. 6 2 2

. 9

σ me a n 1

. 4 2

. 3 2

. 5 5

. 6

n 2 4 8 1 4 0 1 1 9 5 1

2003-2006

Tem p erature Bin 80-84°F 85-89°F 90-94°F >95°F Ozone (ppbv)

Minim u m 2 1

. 0 3 7

. 8 5 3

. 0 5 0

. 0

Mean 60.6 70.1 83.2 84.9

Maxim u m 9 5

. 1 1 0 4

. 0 1 3 0

. 1 1 1 3

. 0 First quartile 54.0 61.9 72.7 77.0 Third quartile 69.0 77.8 92.2 95.0

Mean +1 σ mean 6 2

. 6 7 2

. 3 8 6

. 8 9 3

. 4

Mean -1 σ m ean 5 8

. 7 6 7

. 9 7 9

. 7 7 6

. 3

σ 1 3

. 8 1 3

. 2 1 6

. 8 1 7

. 1

σ me a n 1

. 9 2

. 2 3

. 5 8

. 5

42n 1 5 3 1 0 9 6 7 1 2

43Figure 18. A box and whisker plot of the c onditional m ean peak 8-hour ozone over the BNAA for the years 1997-2002. The red as terisk corresponds to the m ean peak 8-hour ozone over the BNAA for the year s 2003-2006. Inspection of the plot

shows that when tem p e r ature is accounted for, the m ean peak 8-hour ozone for the years following NO

x controls (2003-2006) is more than two standard

deviations lower than th e m ean peak for the years before the NO

x controls

occurred. The difference between th e two tim e periods increases at higher

tem p eratures, suggesting that the reducti ons are most benefi cial during weather

conditions most conducive to elevated ozone.

Maxim u m t e m p erature is the bes t single predictor of ozone (e.g. Ryan et al., 2000), and accounts for most of the m e teorologi cal variability in oz one, so a correction for peak tem p eratu r e co rrects for m o st of th e m e teorological vari ab ility, a nd allow s a trend in ozone to be seen. After correcting for the effects of peak tem p erature, ozon e af

ter the N O

x SIP Call has decreased significantly wh en com p ared with th at before th e NO

x SIP Cal l . The average ozone in each te m p erature b i n is now located where the

low e r quartile (25 th perc entile) in ea ch te m p erature bin had been pr ev io usly. These data suggest that the NO

x reductions at po wer plants im plem ented as a resu lt o f the NO

x SI P Call, and those through other coincident pr ogram s, were highly e ffective at reducing

ozone under all m e teorological c onditions, and even m o re effec tive when ozone was at its highest.

44Conclusions CMAQ model perform a nce was evaluated for the base year 2002 by comparing surface and aloft CMAQ sim u lations to corr espon ding observ a tions from the sum m er of

2002. Based on these com p arisons, it is conc luded that CM AQ replicates the spatial patterns of high ozone events. The ability of CMAQ to captu re the spatial m o rphology of ozone events suggests that from a m e teorologi cal perspective certain key processes such as synoptic-scale winds and tem p erature fields are being m o deled correctly. In m o st cases CMAQ does replicate the sp atial patterns of an ozone event, but d o es not fully capture the m a gnitude or sp atial extent of peak ozone values. A com p arison of CMAQ to

observations from selected surface m onito rs sho w s that CM AQ has a low bias of approxim a tely 7 ppbv during pollution events but only ~1 ppbv when averaged over the entire ozone season. Clearly, high-biases during low ozone periods com p ensate for lowbiases during pollution events. As discussed later, in chapter 9, the com p ensating biases are part of a general tend ency of CMAQ to capture the m ean ozone concentrations and

m i ss the extrem e ozone concentration s . Or to put it another w a y, CMAQ is adroit at capturing temporal fluctuations in 8-hour ozone (correlation coefficients of 0.8-0.9 are common); however, the am plitude of these fl uctuations is generally underestim ated. Biases also vary between urba n and suburban/rural locations. W ith the cav eat that the

class i ficatio ns appear to be dated, C M A Q has sm aller biase s at urban lo cation s than it

does at m o re rural loca tio ns. The com p arisons 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 perform e d in this report,

com p arisons between aircraft observations and model sim u lations can be problem atic because of rapidly ch an ging tem poral and sp atial scales. Co m p arisons o f individual cases high light the com p lex nature of ozone predictions and m easurem ents. More generally, the underprediction of alof t ozone by C M AQ suggests that CM AQ is not fully capturing all of the transport and/or boundary layer processes a ssociated with ground level ozone and the formation, transport and de struction of its precursors. This m a y he lp explain some of the ozo n e underprediction at so m e surface monitors. S o m e possible reasons as to why CMAQ is underpredicting ozone in the lower boundary layer or aloft

are as follo w s:

• Errors in mesoscale aero metric da ta ( MM5 wind fields) whic h could compromise the advection of ozone and precurso rs by the mean wind components

• Errors in C M

AQ’s handling of turb ulent d iffu sion or parameter ized con vection

• Incomplete chemical reaction schemes (e .g. see Chapter 4 of this Appendix)

• Improper accounting of actinic flux aloft w h er e actin ic flux is defined as the quantity of light available to molecules at a particular point in the atmosphere and which, on absorption, drives photoche mical processes in the atmosphere (See Chapters 4 and 9 of this Appendix for details)

• Unaccounted sources of ozone precursors The relatively poor perform a nce of CMAQ at rural/suburban sites during pollution events provides inform ation on the sh ortcom ings of CMAQ. In som e instances, rural/suburban sites are more power-plant dom i n ated than motor vehicle dom inated. The

underestim ation of ozone fluctuat ions at these sites m a y indicat e that variati ons in power plant em issions are not f u lly felt at these sites. Consequently, CMAQ is unlikely to

45reproduce the full benefit of power plant em issi on reductions at these sites and also sites

downwind from these sites. These biases a dd unc erta inty to f u tu re ozone predictions

from CMAQ (see also Chapter 9). Analysis of the im pacts of the NO

x SIP C a ll on M a ryland o z o n e leve ls sh ow that ozone dropped significantly af te r im plem entatio n of the N O

x SIP C a ll. T h e signif i c a n t reduction suggests that sim ilar m eas ures shoul d be expected to have sim i larly large

im pacts in the f u ture.

Future Work

Future efforts to com p are C M A Q si m u lations (o r other de term inistic photochem i cal m odels) to observa tions will rem a in vital to b e tte r understanding the

com p lex nature of ground-level ozone. Som e of the m e thods that could facilitate future

efforts inclu d e:

• Sim u lations w ith highe r vertical reso luti on m i ght do a better job of capturing the initial dispersion and transport of ozone plum es from power plants. A denser

network of observations woul d be useful for evaluating the representativeness of

individual ozone m onitors. Increased, m o re-coo rdinated m e asurem ents aloft (e.g. hourly ozon esonde laun ches during high- ozone periods cou p led with n i ghttim e aircraft flights) would help e xplain the diurnal cycle of ozone in rural, urban and

elevated surface sites.

• C M A Q

- calculated C O p r of ile conc e n tra tions a t r u ral location s w e re typic a lly 50

- 150 ppbv lower than concentrations obs erved on RAMMPP flights. The

im plications of the ef f ect of this bias on upwind ozone need to be investigated.

• Aircraft observation rem a ins one of the best tools to collect al oft data because of the ability to cover vas t d i stan ces (bo t h vertically and horizon tally ) in a relative l y short period of tim e and, perhaps m o re im por tantly, the ability to collect ancillary

m easure m ents (e.g., VOC’s, NO X , SO 2 , CO, PM2.5), especially NO

x .

• Ozonesondes provide another m easurem ent tool that over tim e, if collected regularly, can produce an ozone clim atology in the lower boundary layer, stable nocturnal boundary layer, and lower free troposphere.

• A rigorous evaluation of input parameters from MM5 would also be usef ul. For

exam ple, comparison of MM5 m e teorologi cal param e ters with balloon soundings, FAA on-board aircraft data, and m o st im portantly satellite cloud fields.

Increased use of all the tool s detailed in this analysis will help produce a better

com p arative product which will incre a se conf iden ce in th e rob u stness of future ozon e sim u lations.

46References

Biswas J. and Rao., S. T. , 2001. Uncertain ties in E p isodic Ozone Modeling Stemm i ng f r om U n certainties in the Meteoro l og ical F i elds. J. App. Met., 40 #2, pp 117–136.

Draxler, R. A., Stunder, B., Rolph, G. a nd Taylor, A., 2006. HY SPLIT4 USER's GUIDE

Version 4.8 - Last Revision: June 2006. NOAA Air Resources La boratory, 327 E W e st Hwy, Silver Spring, 20910. http://www.arl.noaa.gov/data/web/m odels/hysplit4/wi n95/user_guide.pdf

EPA, 2006. W eather Makes a Differen ce: 8-hour Ozone Trends for 1997-2005. U.S.

Environmental Protectio n Agency, Office of Air and Radiation, O ffice o f

Air Q uality Planning and Standards. (http://www.epa.gov/airtre nds/2006/weather/region03.pdf)

Hains, J.C., Taubm an, B.F., Thom ps on, A.M., Marufu, L.T., Doddridge, B.G. and Stehr,

J. W ., Dickerson, R.R., 2007. A clustering m e thodology to identify distinct chem ical events in the troposphere using aircraft trace g a s and aerosol v e rtical p r ofiles, m a nuscript.

National Research Council, 1991: Rethinking the Ozone Probl em in Urban and Regional

Air Pollu tio n . 1st. National Academy Press, pp. 93-107.

Ryan, W

. F., Piety C.A., Luebehusen E.D., “A ir quality f o reca sts in the m i d-A tlan tic region: Current practice and Benchmark skill”, Weather And Forecasting 15 (1): 46-60

Feb 2000.

Stoeckenius, T. and Ke mball-Cook, S., 2005. Determ ination of representativeness of

2002 ozone season for ozone tran sport region SIP m odeling. Environ International Corporation

.

Taubm an, B.F., Hains, J.C., Thom ps on, A.M ., Marufu, L.T., Doddridge, B.G., Stehr,

J. W ., Piety, C.A. and Di ckerson, R.R., 2006. Aircraft vertical pr ofiles of trace gas and aerosol pollution over the m i d-Atlantic Un ite d States : Sta tisti cs and meteorological cluster analy s is, J. Geo. Res. , 111, D10S07.

W o lff G.T., Lioy P.J., “Em p irical M odel Fo r Forecasting Maxim u m Daily Ozone Levels

In Northeas tern United

- States ”, Jou r nal Of The Air Pollu tion Control Association 28 (10): 1034-1038, 1978.

47Acronym List

C M A Q

- C o m m unity Multi-sca le A i r Q u ality RAMMPP- Regional Atmospheric Measurem en t Modeling and Prediction Program

AGL- Above Ground Level OTR- Ozone Transport Region

UMD- Uni v er sit y of Mar y l a nd

EPA- Environm ental Protection Agency

NAA- Non At t a i n m e nt Ar ea
R- Rur a l

S- Suburban

U- Ur ban HYSPLIT- HYbrid Single-Pa rticle Lagrangian Integrated T r ajectory

MM5- The PSU/NCAR m e soscale m odel (5 th generation)

48Analysis of the Detail s of CMAQ 4.5.1 Chemistry

March 16, 2007

R. R. Dickerson, J. C. Hains, D. J. Allen, J. W

. Stehr, C. A. Piety Departm e nt of At m o sphe ric and Oceanic Science The University of Maryland College Park, MD

Abstract

In order to a ccura tely p r edict change s in ozone resulting from changes in em issions, CMAQ must accu rately repres ent th e chem istr y of the lower atm o sphere in both urb a n and rural locations and during both daytim e and nighttim e conditions. S e veral studies suggest that CMAQ underestim a tes the benefit from reduced em issions of NO

x f

r om elevated sou r ces. Com p arison of aircraft profiles to CMAQ-generated ozone profiles

show that the m odel calculates too much ozone in the lowest few hundred m e ters and too

little b e tween 600 and 2500 m altitu de. This analysis add r ess e s the ques t ions:

• Does CMAQ capture a l l the relev a n t re actions that sequester or rem ove NO

x in urban plum es?

• Would consideration of the im pact of aerosols on the rate of photolysis of NO 2 change the colum n content and shape of the ozone profile calculated by CMAQ?

The take aw ay m e ssage from this study is that the CB4 m e chanism and photochem i cal processor used in the version of CMAQ r un for this SIP are sim p lified and m i ssing reactions that were thought to be inconsequential, but are now known or in som e instances suspected to play a m a jor role. All higher aldehyd es are treated as acetaldehyde (C 2 ), but other higher aldehydes (such as C 3 and C 4 ) are certainly form ed and they react

f a ster with NO 3 radicals to form HNO 3 at night, representing an irreversible rem oval of

NO

x . CB4 a l so neglects the sm all fraction of alkanes that react directly with NO 3 radicals

to f o rm HNO 3 at night, as well as a fr action of higher alkanes that rearrange to form alkyl

nitrates in d a ytim e reactions with O H a nd NO. Altogether, these reactio ns probably sequester at least 1.5 ppbv NO

x , and unless th ere are com p en sating errors, CMAQ m a y be

overestim ating the m i xing ratio of ozone for m ed in the Baltimore urban plum e by about 6 ppbv at the surface. Scattering of rad i ation by aerosols can accelerate o z o n e for m atio n in the lower free troposphere and inhib i t it closer to the Earth ’ s surface. Mo del sim u lations

of the im pac t of aerosols on jNO 2 indicates that CMAQ should calculate 1-18 ppbv less

ozone in the lowest few hundred m e ters and 1-3 ppbv m o re ozone above - this m oves the

model closer to aircraft obser vations, but not into agreem ent. Indirect evidence suggests

that MM5/C MAQ is underestim ating low leve l cloud cover, and this could contribute

substantia lly to the d i sag r eem ent between m easurem ent and model.

1Maryland ’s atta inm e nt dem onstratio n m odel run s m a y well o v erestim a te the ra te
of for m ation and concentration of ozone, es pecially in VOC-rich urban plum es. The

overall chem istry m a y be m o re NO

x -lim ited than CMAQ would suggest. In com p arison to aircraft observations, the base-cas e m odel run underestim ates the rate of photochem i cal sm og production above about 5 00 m and overestim ates it below this altitude. Comparison of the details of th e chem ical processes sim u lated in CMAQ to

observations shows that the m odel may still underestim ate the im portance of photochem i stry in larg e-s cale, m u lti-d a y pro cess e s involving transport and process i ng at higher altitudes, thus the sim u lations m a y underestim ate the benefit of decreasing NO

x

em issions, especially fro m elevated em i ssions sources such as power plants.

4.1 Introduction UMD has investig ated th e chem ical m ech anis m of CMAQ to see if it accurately represents observed chem istry of s m og even ts in the Northe as t, with an e m phasis on

reasons that the m odel might overestim at e ozone production and under-represent the

benefit of reduced NO

x em issions from elevated non-urba n sources such as power plants.

These stud ies also help q u antify the u n cer tainty in CMAQ calculations of ozone.

Observed co ncentration s of oz one in the eastern United States have fallen substantially in the pas t few years during a tim e when the m a jor change in em issions was the NO

xSIPCall for diminished NO

x em issions from power plants e.g., (G égo et al. 2007). This

section deals with the C B 4 chem ical m echanism of CMAQ and where it m i ght m i ss details of NO

x and volatile organic com pound (VOC) photochem i stry that enhance the efficacy of em issions reductions.

4.2 NO 3 -VOC Reactio n s Recent m easurem ents of ozone form a ti on during the 2003 electrical b l acko u t and investigations of nighttim e boundar y layer chem istry suggest that som e of the NO

x fro m urban areas m a y be lost or sequestered. This reduces the rate of ozone form ation in areas of high concentrations of VOC’s and im plie s lesser ozone form ation potential for urban plum es (Brown et al. 2006a; Brown et al. 2006b; EPA 2006; Marufu et al. 2004).

NO 2 can react with ozone to form hi ghly reactive nitrate radicals, NO 3 . During daylight hours, the nitrate radical NO 3 is eas ily returned to NO

x by photolysis or reaction with NO, but at night N O 3 concentrations can build up a nd these rad i cals attack some VOC’s, as shown schematically in Fi gure 4.1. Organo-nitrates sequester NO

x for several days, and HNO 3 is basically inactive in sm og photochem i stry. If NO 3 reacts with VOC’s

to f o rm HNO 3 quickly at night, it could m a ke NO

x in urban plum es relatively less

im portant for O 3 form ation than NO

x in power plant plum es, where VOC concentratio ns are lower.

2Figure 4.1 Schem a tic diagram of nighttim e VOC/NO 3 chem istry in the urban boundary layer. VOC’s can react with NO3 radi cals to form organo-nitrates such as alkyl nitrates that sequester NO

x , or to for m nitric acid, which is lost

perm anently from the system

.

CMAQ 4.5.1, as configured for this SIP, em ploys an abridged set of NO 3 reactions.

Different treatm ents of VOC reactions are know n to lead to substantial differences in calculated ozone (Kuhn et al. 1998). This ve rsion of CMAQ uses CB-4, which explicitly

includes form aldehyde, cresols (CRES) and reaction products of isoprene (ISPD).

H 2 CO + NO 3 → HNO 3 + HCO·

CRES + NO 3 → HNO 3 + CRO

ISPD + NO 3 → HNO 3 + PROD (7.5% yield)

CB4 also contains a variable called XO2N which is an alkene adduct species.

OLE + NO 3 →

... → RO 2 + 0.09*XO2N + products

Although this reaction does not m a ke HNO 3 , it sequesters NO

x ; CB4 uses a rate constant

of 7.7x10 -1 5 cm 3 s -1 that is appropriate for propene, but butenes react about 50 tim es faster. The Regional Acid Deposition M odel (R ADM2), no longer an option for CMAQ,

has m o re NO 3 + ORG r eactions including GLY, (glyoxal) MGLY ( m ethyl glyoxal;

H 3 CC(O)C(O)H), DCB (unsat urated dicarbonyl), CSL (cresol), and MACR (m ethacrolein).

In CB4, higher aldehydes are grouped as ALD2 and assum e d to be composed entirely of acetaldehyd e for reactio n with the n i trate rad i ca l; this pro ceed s with a ra te constan t of

2.5x10 -1 5 cm 3 s -1

. 

### Surface NO 3 NO 3 

## VOC’s HNO 3 

### Organo-nitrate

Reservoir

3ALD2 + NO 3 → HNO 3 + C 2 O 3

The rate con s tants f o r re action s of higher aldehydes with NO 3 radicals are faster; 6x1 0 -1 5

cm 3 s -1 for propanal and 11.0x10 -1 5 for butanal, so the lifetim e of these carbonyls in the polluted nig h ttim e environm ent is h ours (Bossmeyer et al. 2 006); see als o http://www.iupac-kinetic.ch.cam .ac.uk/.

CMAQ estim ated the concentration of ALD2 as about 3 ppbv at Essex during high ozone days of 2002. Because the rate constant used is appropriate f o r aceta ldeh yde, the rate of reaction of higher aldehydes with NO 3 radicals is underestim ate d. Table 4.1 shows that carbonyl concentrations can be substantial even at rem o te sites in the Northeast, but

m easure m ents of aldehydes longer than fo r m aldehyde are rare, and there are no wellaccepted detection and calib ration techniques. T h e concentration of higher aldehyd es can be estim a ted from the concentration of alkene s. For reactions with OH, sim p le alkenes (m ethy-substitu ted ethen e s, and 1-alk e nes) form aldehydes (two per alkene) with h i g h yield, essentially 100% for butenes and decreasi ng for the larger 1-alkenes to about 50- 30% for 1-hexene through 1-octene. The O 3 reactions form roughly 50% carbonyls per alkene reacted. The aldehydes ar e less reactive than the larg er alkenes and have longer

lifetim es in the atm o sphere. Table 4.2 give s the m ean concentrations of higher alkenes (up to C 5 ) measured at E ssex, MD during high ozone days in 2002. The total of

m easured C 3 to C 5 alkenes is about 1 ppbv with little diurnal variation, and the lifetim es can be short (e.g., a few hours for trans-2- butene in the presence of 20 ppbv ozone), indicating that these unsaturated hydro carbons are roughly in steady state.

For a concentration of 5x10 9 cm -3 (200 ppt) NO 3 , the lifetim e of propanal is 9 hr

and the lifetim e of butanal is 5 hr. T h e steady state total of hi gher aldehydes (greater than or equ a l to C 2 ) is thus at least 1 ppbv. Over th e course of a night, reactions of aldehydes C 3 and greater, not included in CB4, generate a m i xing ratio of HNO 3 of at least 1 ppbv.

A s m all fraction (about 2%) of al kanes reacts directly with NO 3 radicals at night,

and this lead s to alkyl radicals and HNO 3 production.

RH + NO 3 → HNO 3 + R·

Table 4.3 sh ows the nigh ttim e m ean alkane con c entrations, relative rate constants, an d

the am ount of HNO 3 for m ed. The total for all alkanes is about 0.2 ppbv NO

x converted

to HNO 3 by this process every nigh t. Together these m echan ism s account for in excess of 1.2 ppbv conversion of NO 3 to HNO 3 each night, and one can reasonably expect that CMAQ m i ss ed the irreversible loss of at least 1.2 ppbv NO

x .

4Table 4.1 Carbonyl Measurements 1

M i n i mu m M a x i mu m M e a n M e d i a n σ N

PSP 2

H C H O 4 5 6 2 1 5 2 0 3 8 2 0 2 7 1 0 7 9 1 7 9 1

CH 2 O H C H O 5 9 6 6 3 0 1 3 2 3 1 8 7 6 7 3

CH 2 OHCOCH 3 3 0 1 6 5 0 3 7 2 3 3 2 2 5 6 1 7 4 0

WFM 3

H C H O 3 0 6 1 7 0 1 3 4 0 1 2 6 0 8 2 1 3 1 7 8

All units pptv (parts pe r trillion by volum e ).

1 Personal Comm unication from Xia n liang Zhou Research Scientist, W a dsworth Center, New York State Dept. Health Phone (518) 474-6693

Associate P r ofessor, School of P ublic Health Fax (518) 473-8117 University at Albany, SUNY E-m a il: zhoux@wadsworth.org

2 Pinnacle S t ate Park in Addison, NY, from June 26 to July 14, 1998

3 Summ it of Whiteface Mountain, from June 14 to July 20, 1 999

Table 4.2 Mean alkene concentrations for h i gh oz one days in 2002 at Essex, MD.

V O C M e a n p p b C Cn * Mean (ppbv) Propene 1

. 4 4 3 3 0

. 4 8 1

1-butene 0

. 3 7 4 4 0

. 0 9 3

trans-2-butene 0

. 4 5 1 4 0

. 1 1 3

cis-2

- buten e 0

. 3 1 4 4 0

. 0 7 8 1-pentene 0

. 4 1 3 5 0

. 0 8 3 trans-2-pen t ene 0

. 6 3 7 5 0

. 1 2 7 cis-2-pentene 0

. 3 3 6 5 0

. 0 6 7

Total 1.043

*Carbon number.

Notes: VOC concentrations as m easured at Essex, MD on high ozone days in 2002. Concentration units are ppbv or parts per billi on substance derived from ppbC. The total of 1.043 ppbv represents the lo wer lim it m i xing ratio of unsaturated VOC’s that could

for m carbonyls in reactions not appropriately represented in the CB4 ozone for m ation

m echanism

.

5Table 4.3 Alkane reactions to produce HNO 3 for high oz one days, 2002 Essex, MD.

V O C M e a n ( p p b v ) (k OH /k NO3 )*10 3 HNO 3 formed (ppbv) Propane 2

. 8 2 0

. 0 1 5 0

. 0 4 3

n-butane 1

. 4 4 0

. 0 2 7 0

. 0 3 8

Isobutane 0

. 8 7 0

. 0 3 3 0

. 0 2 8

n-pentane 1

. 7 0 0

. 0 2 0 0

. 0 3 4

n-hexane 0

. 4 8 0

. 0 1 9 0

. 0 0 9
Cyclohexane 0

. 1 1 0

. 0 1 8 0

. 0 0 2
2-me-pen ta ne 0

. 6 2 0

. 0 1 8 0

. 0 1 1

3-me-hexan e 0

. 2 3 0

. 0 2 0 0

. 0 0 5

2-me-hexan e 0

. 1 8 0

. 0 2 0 0

. 0 0 4

n-heptane 0

. 2 1 0

. 0 1 9 0

. 0 0 4

n-octane 0

. 0 8 0

. 0 2 1 0

. 0 0 2
2-me-hep ta ne 0

. 0 5 0

. 0 2 3 0

. 0 0 1
3-me-hep ta ne 0

. 0 7 0

. 0 3 6 0

. 0 0 2

n-nonane 0

. 0 7 0

. 0 2 4 0

. 0 0 2

n-decane 0

. 0 8 0

. 0 2 3 0

. 0 0 2

n-undecane 0

. 0 7 0

. 0 0 2 0

. 0 0 0

Total 0.187

Notes: VOC concentrations m easured between 0:00 to 05:00 EST as m easured at Essex, MD on high ozone days in 2002 and calculated f o rm ation of nitric ac id. Concentration units are ppbv or parts per bi llion substance, not ppbC. Th e fraction reacting with the

nitr ate radic a l is com puted with the r a tio of rate constants for reaction with OH to that

with NO 3 , and assum i ng 1000 tim es more NO 3 than OH. The total of 0.187 ppbv represents the m i xing ratio of NO

x that would be irrevers ibly removed from the possibility of ozone form ation. The reactions considered here are not part of the CB4

m echanism

.

64.3 Alkyl Nitrates

Most alkanes react with OH radicals in th e daylight hours to f o rm alkyl radicals that quickly for m alkyl peroxy radicals:

RH + OH (+ O 2 ) → H 2 O + RO 2 ·

The reaction of alkyl peroxy radica ls with NO usually leads to NO 2 and rapid ozone

for m ation.

RO 2 · + NO → RO· + NO 2

Som e fraction of the alkyl peroxy radical s, however, rearranges to form not NO 2 , but alkyl nitrates.

RO 2 · + NO + M → RONO 2 + M

The reactions producing alkyl nitrates are not included in the CB-4 m echanism for the CMAQ runs perform e d. For the alkyl nitrates that have been investigated, the lifetim e with respect to photolysis or OH attack is se veral days or longer; alkyl nitrates thus sequester NO

x to slow the rate of form ation of oz one ((JPL 2006; Luke et al. 1989), R. Atkinson personal com m unication, 2007). In the m i d Atlantic region, the residence tim e of an air m a ss is typically only a few days, so form ation of alkyl nitrates m a y e ffectively delay the form ation of oz one until air parc els have been advect ed over th e Atlantic

Ocean.

Alkane concentrations m easured at E ssex, MD on high ozone days in 2002 are listed on Table 4.4; this list is not exhaustive. The m i xing ratio of alkyl nitrate formed in OH attack is the product of the m ean alkane m i xing ratio, the frac tion reacting, and the yield. The fraction reacting is com p uted with the rate constan t for reaction with OH tim es the assum e d m ean OH c oncentration of 5 x 10 6 cm -3 for an eight hour period. The yield

is the rate of for m ation of a ll alkyl nitrates divided by the to tal product yield (Arey et al. 2001) R. Atkinson personal comm unication 2007 ]. The total of 0.24 ppbv represents the

m i xing ratio of NO

x that would be sequestered against ozone form ation for a period of

severa l days . This is a f e w percent o f the tota l NO

x of 5-10 ppbv; the reactions considered here are not part of the C B 4 m echanism

, although they are included in other

m echanism s

.

7Table 4.4 Alkyl nitrate formation fo r high oz one days at Essex, MD.

VOC Mean (ppbC) Fraction Reacting Yield C

n * Alk y l Nitrate formed (pp b v)

propane 3

. 5 6 7 0

. 1 7 0

. 0 3 6 3 0

. 0 0 7

n-butane 2

. 9 1 8 0

. 3 6 0

. 0 7 7 4 0

. 0 2 0

isobutane 1

. 5 4 3 0

. 3 6 0

. 0 7 7 4 0

. 0 1 1

n-pentane 2

. 5 0 1 0

. 6 0

. 1 0 5 5 0

. 0 3 2

n-hexane 1

. 2 1 0 0

. 8 0

. 1 4 1 6 0

. 0 2 3
cyclohexan e 0

. 2 6 1 0

. 8 0

. 1 7 6 0

. 0 0 6
2-me-pen ta ne 1

. 6 4 4 1 0

. 1 4 6 0

. 0 3 8

3-me hexan e 0

. 8 7 3 1 0

. 1 7 8 7 0

. 0 2 2

2-me-hexan e 0

. 6 2 7 1 0

. 1 7 8 7 0

. 0 1 6

n-heptane 0

. 8 0 9 1 0

. 1 7 8 7 0

. 0 2 1

n-octane 0

. 3 1 8 1 0

. 2 2 6 8 0

. 0 0 9
2-me-hep ta ne 0

. 2 1 1 1 0

. 2 2 6 8 0

. 0 0 6
3-me-hep ta ne 0

. 2 2 6 1 0

. 2 2 6 8 0

. 0 0 6

n-nonane 0

. 3 2 2 1 0

. 2 5 9 0

. 0 0 9

n-decane 0

. 4 1 2 1 0

. 2 5 1 0 0

. 0 1 0

n-undecane 0

. 3 6 4 1 0

. 2 5 1 1 0

. 0 0 8

T o t a l 0

. 2 4 4

* Carbon Num b er Notes: VOC concentrations are m eans for 8: 00 to 16:00 EST as m easured at Essex, MD on high ozo n e days in 2 002. Concentration units are ppbC o r parts per b i llion of the

substance m u ltip lied by the num ber of carbon ato m s in the molecule, C

n . The fraction reacting is com puted with the rate constant f

o r r e action with OH tim e s the assum e d m ean OH concentration of 5 x 10 5 cm -3 for an eight hour period. T h e yield is the rate of

for m ation of all alkyl nitrat es divided by the total product yield (Arey et al. 2001; R. Atkinson personal com m unication 2007). The total alkyl nitrate form ed is the product of

the m ean alkane m i xing ratio (ppbC/C

n ), the fraction reacting

, and the yield. The total of 0.244 ppbv represents the m i xing ratio of NO

x that would be rem oved, sequestered against ozone for m ation for a pe riod of several days. This is a few percent of the total

NO

x of 5-10 ppbv, and the reactions considered here are not represented in the CB4

m echanism

. If each ppb v NO

x produces 4 ppbv ozone, then f o rm ation of alkyl nitrates accounts for about 1 ppbv O 3

.

84.5 NO

x /VOC Chemistr y Conclusions High concentrations of VOC’s can sequester or remove NO

x from the atmosphere, and

m a ny of these processes are m i ssing from the CB4 m echanism e m ployed in CMAQ as

used for thes e studies; C B 5 attem p ts to correct som e of these shortcom ings (Luecken and Sarwar 2006). NO

x rem o val due to re action s with hi gher aldehydes can be expected to exceed 1 pp bv; about 2 % of alkanes are rem ove d by direct attack by NO 3 radical s for about 0.2 ppbv NO

x loss; OH attack on alkanes will produce about 0.24 ppbv alkyl nitrates. The total is thus about 1.5 ppbv NO

x – a fair fraction of the m ean 5-15 ppbv NO

x present. C M AQ showed a high bias for NO

x of 1.3 ppbv (G. Sistla, personal communication 2006). CB4 m a y ho wever have com p ensating errors; early versions of CB5 calculate a few ppbv m o re ozone (Luecken and Sarwar 2006). For the typical ozone production efficiency of 4 ppbv O 3 per ppbv NO

x , this represents about 6 ppbv of ozone

loss unacco unted for in CMAQ. The 6 ppbv estim ated is a lo wer lim it for m odel uncertainty. The loss of NO

x in urban plum es m a y also explain the apparent greater efficiency of ozone production in plu m es from elevated NO

x sources and help to explain why ozone in the m i d-Atlantic has decreased substantially in response to power plant

NO

x e m issions reductions.

4.6 Photolysis Rates We have identified the impact of aeros ols on photolysis ra tes as a process occurring in the atm o sphere and not represen ted in the runs of CMAQ for this SIP. CMAQ generates ozone m i xing ratios sim ilar to or sligh tly g r eater th an observations near

the Earth’s s u rface, but u nderestim ates ozone aloft. The im pact of highly s cattering

aerosols on the rate of NO 2 photolysis is to inhibit ozone formation near the Earth’s

surface and to accelerate ozone form ation aloft with an overall im pact of more ozone (Dickerson et al. 1997). Hain s (2007) used an improved pr ofile of photolysis rates to bring CMAQ output into closer agre em ent with aircraft observations.

4.6a Recalculated NO 2 photolysis r a te coe fficie n ts
Figure 4 of Chapter 2 shows the m e dian aircraft m easured and CMAQ oz one profiles for 2002. The aircraft ozone is on averag e 10% (6 ppbv) larg er than in the CMAQ profiles between 600 and 2600 m

, while about 6 ppbv sm aller than CMAQ below

500 m in the PBL. CMAQ under-predicts oz one in the lower f r ee troposphere and overpredicts ozo n e near the s u rf ace, with the m odel integrated ozo n e colum n content 4 %

sm aller than observed.

Reasons for m easure m ent/m odel differences m a y include problem s with

em issions inventories, advection, vertical m i xing, cloud cover, and chem istry. The NO 2 photolysis rates that CMAQ uses impact how mu ch ozone is produced by the m odel. The rate coefficient for the photolysis of NO 2 (hereafter referred to as jNO 2 value) used by the default version of CMAQ assum e s 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 jNO 2 values by 30% above the boundary layer and increases

the total m a ss of ozone form ed. Par k et al. [2001] perfor m e d a sens itiv ity test of CMAQ

9using jNO 2 values associated with CMAQ aerosols. UMD (Hains 2007) perform e d a sensitivity study using jNO 2 values associated w ith typ i cal aerosols m easured in the MidAtlantic from July 15-18, 2002 using the (Par k 2001) program . The opt ical properties of the aerosols must be specifie d in the model. These include the Angstrom exponent (also called Angstrom slope) and intercept related to the size of the pa rticles (slope of 2.0, intercept of 0.1 were used); th e sing le scat tering albedo, which gives th e p r obability th at a photon will be scattered rather than absorbed (0.9 6); and the asymmetry param e ter that

indicates the fraction scatte red in the forward directi on (0.8). Based on aircraft

m easure m ents, aerosols were assumed to be confined to the lowest 1.5 km of the

atm o sphere. The observed m ean aerosol optical depth was 0.3 at 550 nm , which corresponds to 0.6 at 380 nm , the wavelength m o st relevant for NO 2 photolysis. Results (Figure 4.2) show dim i nution in jNO 2 near the Earth’s surface but substan t ial incre a se aloft and at all tim es of the day.

10Figure 4.2 S t andard (without aerosols, green ) and revised (with aerosols, red) jNO 2 values used in CMAQ for several dayli ght hours. Aerosols reduce photolysis

rate coefficients in the first few hundred m e ters of the PBL, but increase them

alof t. Altitu de is in m

- AGL. 

### j- N O 2 

### val u es w i t h an d w i t h ou t aeroso l s

0 20 00 40 00 60 00 80 00 10 00 0 12 00 0

0. 00 0 0

. 1 0 0 0

. 2 0 0 0

. 3 00 0. 40 0 0

. 5 00 0

. 6 0 0 0

. 7 0 0 0. 800

j- N O 2 

### va l u e ( m i n -1 

### )Altitude(m) 12 noon w i t hout

1pm w i t h o u t

2 p m w i t hout

3 p m w i t hout

4 p m w i t hout

12 noon w i t h

1 p m w i th

2 p m w i th

3 p m w i th

4 p m w i th

114.6b CMAQ runs w i th revised ph otolysis rates

UMD ran CMAQ from July 15-18, 2002 with the standard jNO 2 values (assum i ng no aerosol) and revised jNO 2 values, using aerosol concentr ations and properties typical for the episode. The same aerosol values were used throughout the dom ain. The aircraft

flies over rural and suburban areas, but also downwind of ur ban areas with large op tical depths, so using the average optical depth from all of the flights s hould be a reasonable approxim a tion of the Mid-Atlan tic average optical depth.

Two e m issions scenarios were used; one with 2002 em issions and one with substantially lower em issions for 2018. This re sulted in four model runs for com p arison:

• 2002 em issions with standard jNO 2 values (2002, j-values no aerosols)

• 2002 em issions with revised jNO 2 values (2002, j-values with aerosols)

• 2018 em issions with standard jNO 2 values (2018, j-values no aerosols)

• 2018 em issions with revised jNO 2 values (2018, j-values with aerosols).

Figures 4.3 – 4.7 show ozone generate d by CMAQ using the standard jNO 2 values, ozone generated using the m odified jNO 2 values, and ozone m easured from the aircraft; note

Universal T i m e (UT) is 5 hr ahead of Eastern Standard Tim e (EST), which is close to

local sola r tim e (LST). W h en the jNO 2 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 pp bv n ear th e Earth’s surface. Using improved photolysis rates did not elim inate measur em ent/model differences, but brought the CMAQ output closer to observations. Airc raft observations for the 2002 season are

com p ared to CMAQ out put for the sam e ti m e and in the grid box. The spatial resolution of the observations is greater than th at of the CM AQ (12 km ) ; som e tim es the aircraft flew out of one model box into another, and this can result in a discontinuity of 5-10 ppbv

ozone.

12

# a b

c

A i rc raft

CM A Q ( n o

aeros o l s )

CM A Q ( w i t h

aeros o l s 

# * A i rc raft

CM A Q ( n o

aeros o l s )

CM A Q ( w i t h

aeros o l s 

# *

Figure 4.3 Ozone profiles from the aircra ft (pink stars), CMAQ using standard jNO 2 values (without aerosols, show n in blue open squares), and CMAQ using revised jNO 2 v a l u (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) C h u r c h v i l l e , M D 1 5 : 0 0 E S T

. A b o v e 1 0 0 0 m the revised CMAQ profiles (with revised jNO e s

b v 2 v a l u e s , s h o w n i n g r e e n ) a r e a b o u t 1 p p larger than the standard CMAQ profiles s hown in blue. Below 1000 m the standard CMAQ profiles are up to 5 ppbv larger than the revised CMAQ profiles. Note the discontinuity in observe d ozone at 5 00 m altitu d e over Chur chville, MD – the m odel also shows a strong gradient there.

13Figure 4.4 Ozone profiles from the aircraft (p ink stars), CMAQ using standard jNO 2 values (without aerosols, and shown in blue open sq uares), and CMAQ using revised jNO 2 values ( w ith aerosols, shown in green closed squares) fo r the morning of July 16, 2002 at a) Louisa, VA 9:00 EST, b) Crewe, VA 10:00 EST, and c) Richm ond, VA 10:00 EST. Above 1000 m the revised CMAQ profiles (with revised jNO 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 1-10 ppbv larger th an the revised CMAQ profiles. 

# a b

c

Ai r c r a ft

CM AQ ( n o

aer o s ol s )

CM AQ ( w i t h

aer o s ol s 

# * Ai r c r a ft

CM AQ ( n o

aer o s ol s )

CM AQ ( w i t h

aer o s ol s 

# *

14Figure 4.5 Ozone profiles from the aircraft (p ink stars), CMAQ using standard jNO 2 values (without aerosols, and shown in blue open squares), and CMAQ us ing revised jNO 2 values (with aerosols, shown in green closed squares) for the afternoon of July 16, 2002 at a) W a kefield, VA 13:00 EST, b) Quinton, VA 14:00 EST, c) Tappahannock, VA 15:00 EST, d) Richm ond, VA 15:00 EST.

Above 1000 m

, the revised CMAQ profiles (with revised jNO 2 values, shown in green) are about 1 ppbv larger than the standard CMAQ profiles shown in

blue. Below 1000 m the standard C M AQ profiles are about 1 ppbv larger than the revised CMAQ profiles. 

## a b

d

Ai r c r a f t

CM A Q ( n o

a e r os ols )

CM A Q ( w i t h

a e r os ols 

# * Ai r c r a f t

CM A Q ( n o

a e r os ols )

CM A Q ( w i t h

a e r os ols 

# * c

15

# a b

c

Ai rc r a f t

CM AQ ( n o

aer o s o ls )

CM AQ ( w i t h

aer o s o ls 

# * Ai rc r a f t

CM AQ ( n o

aer o s o ls )

CM AQ ( w i t h

aer o s o ls 

# *

Figure 4.6 Ozone profiles from the aircra ft (pink stars), CMAQ using standard jNO 2 values (without aerosols, and shown in blue ope n squares), and CMAQ using revised jNO 2 values (with aerosols, shown in green clos ed squares) for July 17, 2002 at a) Louisa, VA 8:00 EST, b) Richmond, VA 10:00 EST, a nd c) Crewe, VA 9:00 EST. Above 1000 m the revised CMAQ pr ofiles (with revised jNO 2 values, shown in green) are about 1 ppbv larger than the standard CMAQ prof iles shown in blue. Below 1000 m the

standard C M AQ profiles are as m u ch as 18 ppbv larger than the revised CMAQ profiles.

16Figure 4.7 Ozone profiles from the aircraft (p ink stars), CMAQ using standard jNO 2 values (without aerosols, and shown in blue open sq uares), and CMAQ using revised jNO 2 values ( w ith aerosols, shown in green closed squares) for July 18 2002 at a) Richm o nd, VA 7:00 EST, b) Fort Meade, MD 9:00 EST, and c) As hland, VA 8:00 EST. Above 1000 m the revised CMAQ profiles (with revised jNO 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 larger than the revised CMAQ pr ofiles. 

# a b

c

Ai rc r a f t

CM AQ (n o

aer o s o ls )

CM AQ ( w i t h

aer o s o ls 

# * Ai rc r a f t

CM AQ (n o

aer o s o ls )

CM AQ ( w i t h

aer o s o ls 

# *

17Differences in CMAQ runs with and without re vised photolysis rate co efficients are seen in Figure 4. 8 for m odel levels 1, 8, and 16 (a ppro x im ately at the surface, 5 00 m

, and 2000

m altitud e ) a t 9:00 EST ( 14 UT) when the la rges t dif f e rences occurr ed. Values f r om the

revised run are subtracted from the standard run so that negative num bers m ean the

standard C M AQ overestim ated ozo n e (gene r ally at low altitudes) and po sitiv e num bers

m ean that the standard CMAQ underestim ated ozone (generally in the free troposphere). There are po sitiv e chang e s of 10 ppbv or m o re near the su rface and sm all negativ e changes (m ean of 1 ppbv) above 500 m.

A curtain plot (Figures 4.9 and 4.10) was us ed to exam ine the diurnal variation in

the first 2000 m of CMAQ s howing the differences in ozone generated from :

2002, j-no aerosols

− 2002, j-with aerosols

Positive n u m bers (gre e n, yellow, or ange, red ) in dicate tha t standard CM AQ

overestim ates ozone; negative num bers (gray, blue, dark blue, i ndigo) indicate that CMAQ underestim a tes ozone. Th e x-axis represents a one grid cell swath at 77o W longitude running north-south through th e W a shington/Baltimore urban area and extending from 31o to 47o N over the eastern Un ited States. The y-axis represents the first 16 layers of CMAQ reach ing from the surface to about 2 000 m altitu de. Six tim e 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 m o rni ng, there are negative changes (where the standard version of CMAQ underestim ates oz one) above 500 m that are m i xed down to the surface. At 10:00 to 17:00 EST (15 and 23 UT) there is a positiv e cha nge (where the

standard v e rsion of CMAQ overestim ates ozone) near the su rface.

The altitude dependence of the changes in ozone due to changes in photolysis

rates is sum m a rized in Figure 4.11. The m e dian change is a little m o re than 2 ppbv near the surface and about 1 p pbv aloft with the st andard version of the m odel overestim ating

ozone at lower altitudes and undere stim ating at higher altitudes.

18Figure 4.8 Differences between standard and re vised CMAQ ozone (standard – revised). The

standard C M AQ used jNO 2 values that did not account for aerosols, while the revised CMAQ used jNO 2 values that did account for aerosols m easured for a July 2002 sm og and haze episod e. These plo t s are for 9 : 0 0 EST; a) su rface, b) 500 m , c) 3400 m AGL. The differences are positiv e at th e surface (a) m eanin g that th e standard CMAQ overestim ated

ozone there. Above 500 m

, the differen ces are negative and the revised CMAQ run produces more ozone than the standard CM AQ, genera lly in bette r agr e e m ent with

observations. 

## a b

c

19Figure 4.9. Map of eastern United S t ates with swath used for curtain plot (Figure 4.10) shown as a thick, red line through th e W a shington/Baltim o re area at 77 ◦ W longitud e

.

2021 Figure 4.10. Ozone differences (sta ndard – revised) for a single swath in the CMAQ grid. The yaxis rep r es ents the 16 altitud e layers used in CM AQ. The 16th altitud e layer is aroun d 2000 m and the 8th altitude layer is around 500 m. The x-axis represents the grid cells exam ined, where 1 is

the south e rn most grid cell and 172 is the northern m ost grid cell. Here pos itiv e differences m ean that the standard CMAQ run generates m o re ozone than the revised CMAQ run, and these positive

differences are seen clos er to the surf ace.Figure 4.11 Differences in ozone m i xing ratios generated with CMAQ wi th standard photolysis rates m i nus – that calculated with revised photolysis rates) for 16 profiles generated between July 15-18, 2002. Diam onds represent m e dians;

error b a rs re present the 2 5 th and 75 th percen tiles. Near the su rface the stan dard CMAQ run generates more ozone than the revised CMAQ run, and above 500 m the standard CMAQ run generates le ss ozone than the revised CMAQ run. 0 500 1000 1500

-3 -2 -1 0 1 2 3 4 5

2002 ozon e differenc es, ppbv

(j standard - j revised) 2000 2500

Altitude (m)

224.6c Photolysis rate conclusions

Com p ared to aircraft m e a s u r e m e n t s of ozone, CMAQ under-predicts ozone by

10% between 600 and 2600 m

- AGL and over-predict s by ~15% at lower altitudes, with the CMAQ ozone column content 4% sm a l l r a f t o z o n e c o l u m n content.
UMD i m proved the sim u lation of photochem i st ry in CMAQ to account ch anges in th e UV flux due to aerosol scattering. T h e photolysis rate coefficient for NO 2 increased aloft

and decreased near the Earth’s surface. Oz one m i xing ratio s increased aloft (above 5 00

m ) by 1-2 ppbv and decreased below 500 m wi t h a m e dian change of ~2 ppbv and a range of 1-16 ppbv. Maxim u m d i f s w e r e observed in late afternoon. The im pa ct of including aerosol radiative effects was not sufficient to bring m odel output into

agreem ent, but it did im p r o v e t h e s i n a n d s h o w t h a t t h e s t a n d a r d C M AQ runs

underestim ate the role of ozone production aloft. This m a y c ontribute to the sim u lations’

underestim ating the benefit from reduced em issions of NO

x from tall stac ks.

4.7 Future w o

. To m o re rig o rously qua ntif C B 4 m echanism

UMD recomm ends com p aring ex i s t h a l t e rnative

l m e chanism s t h a t e x p l i c i t l y g l o n g e r V O C’s. It would

strative to co mpare CB4 to m odel with detailed ch em istry such as the Ma ster

o b s e r v a t i o

a t n i g h t

, a

com p a r i s o N i g h t f l i g of NO

x re m The im pact of changes in photolysis rate s was tested for only one episode, and a more thorough study is called for. T h e entire summer should be run with well characterized aerosol loading. This will en tail com p iling data from the aircraft, MODIS (Levy 2007) and GASP ( K napp et al. 2005) sate llite instrum e nts as well as AERONET sun photometer observations. CMAQ appears to underestimate subs tantially aerosol optical depth, so self-generated aerosols would not be appropriate (Levy 2007). Low level clouds, such as the fair weather cum u lus common on hot summ er da ys in the MidAtlantic region, have an effect on UV flux si m ilar to that of aerosols. They m a y also

affect m i xing. CMAQ CO fields should be im proved so that they can be used to evaluate

m i xing. Cl ouds accelerate NO 2 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 m a y und erestim a te the cloud cover. In prior investigations of sim u la ted cl ouds (Mueller et al. 2006), a se rious bias was discovered in MM5/CMAQ – the m o del consistently under-es tim ated spatially averaged clouds below

3.7 km altitu de. Over the Mid-Atlantic region, CMAQ overestim ates S O 2 concentrations (Hains 2007) and underesti m a tes aerosol loading, dom inated by sulfate (Levy 2007). Insufficient low-level cl oud cover would explain both of these observations. Clouds in MM5 and CMAQ shoul d be evaluated for the summer of 2002 agains t National W eather Service surface observa tions as well as satellite observations

. The m odels should be modified to better m a tch cloud observations, and then evaluated for agreem ent with SO 2 , sulfate, and aerosol optical depth m e asurem ents. e r t h a n t h e a i r c

f e r e n c e

t u a t i o

r k y the im pact of sim p lif ica tions in t h e

t r u n s w i i n g CMAQ r u n s t o C M A Q

i n c l u d e r e a c t i o n s i n v o l v i n chem i c a also be illu

Mechanism for a reduced dom a in such as Essex, MD, where a variety of direct

n s a r e a v a i l a b l e

. Direct measurem ents of NO

x , NO y , and nitric acid form a t i o n

s w e l l a s V O C ’ s a n d

, i f p o s s i b l e , higher aldehydes are called f o r. Further

n s t o o b s e r v a t i o n s o f s u r f a c e a n d h i gh altitude trace gases m u st be m a de.

h t s i n t h e B a l t i m o r e o r W a shington urban plum es would help determ i n e t h e r o l e

o v a l i n t h e e f f i c i e n c y o f o z o n e f o r m a tion.

23A c know ledgments: MDE’s Am bient Air Monitoring Program supplied the VOC data used for this analysis; w e t h a n k R

. A t k i n s o n o f U C R a n d S

. T

. R a o o f N O A A f o r h e l p f u l

d i s c u s s

:

.

r o c e s s i n g and its role in regional air quality. Science

, 311, 67-70.

a u t h o r s , 2 0 0 6 b : N o c t u r nal odd-oxygen budget and its im plications

n d

ent

e r

H a i n s ,

l

l

o v e r

y o f

A l k y l i o n s

.

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Boss m e yer, J., T. Brauers, C. Richter, F. Rohrer, R. W e gener, and A. W a h n e r , 2 0 0 6

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. Journal of Applied Mete orology and Climatology

, in

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Che m ical Clim atology. Ph.D., Che m i s t r y , T h e U n i v e r s i t y o f M a r y l a n d

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Evaluation No. 15. Knapp, K. R., R. Frouin, S. Kondragunta, and A

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m echanis m s on m odel predictions C M

AS Confer ence

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. T., R. R. Dickerson, and L. J. Nunnerm a c ker, 1989: Direct Measurem ents of the Photolysis Rate Coefficients and H e n r y L a w C o n s t a n t s o f S e v e r a l

Nitra t es. Journal of Geophysica l Research-Atmospheres

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. Stehr,

and R. R. Di ckerson, 2004: The 2003 No rth Am erican electrical blackout: An

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28751-28770.

25Oz one Sensitiv ity to NO

x Emissio n s

March 16, 2007

J. C. Hains Deptartm ent of Chem istry University o f Maryland College Park, MD

Abstract Upwind power plant em ission sources of NO

x and SO 2 from W e st Virginia, Ohio, and Pennylvania along the Ohio River Valley pl ay a crucial role in the amount of ozone

and aerosol m easured in the lower troposphere in the Mid

- Atlantic region. Greater NO

x

em issions along back trajectories were posi tively correlated with greater ozone m i xing

ratios m easured by 232 aircraft vertical pr ofiles perform e d in the Mid-Atlantic and

Northeast U.S. between 1997 and 2003. Ozone colum n contents during the flights are strongly influenced by point source em issions with a slope of 61.6 ppbv ozone / (g NO

x

m -2 day -1 ), an intercept of 1.3 ppbv, and a correlation (r 2 ) of 0.997 (e.g. Hains et a l

., 2007).

Introductio n In this analysis a hierarch ical clustering m e thod was us ed to separate distinct

chem ical and m e teorological events from 232 lower tropospheric aircraf t vertical profiles

m easuring ozone in the Mid-Atlantic and Northeast U.S. between 1997 and 2003. NOAA HYSPLIT 48-hour back-trajectories endi ng at 1 km (above ground level), using

EDAS 80 km m e teorology and m o deled vertical velocity, were run for each profile to

determ ine th e m o st inf l uentia l NO

x sources. W h en exam ining individual episodes, 24- hour back-trajectories are better th an 48-hour back-trajectories because th ere are fewer

fluctuations in the m e teorology. However, when exam ining more than one episode, 48- hour back-trajectories are m o re suitable. This is especially true fo r large sets of ozone episodes, because ozone has a lifetime longer than a single day. For this reason 48-hour back-trajectories were u s ed in th is analysis. The 2-day integrated NO

x em issions

encountered by each trajectory were calcu late d u s ing the EP A Clean Air Market Div i sion point source em issions database. A regressi on was m a de between the m e dian integrated

NO

x em issions and the m e dian ozone colum n contents for each cluster to d e term ine the relationship between NO

x e m issions and ozone. Only point source integrated NO

x

em issions were exam ined. There is a positive co rrelation between point and area sou r ce

em issions, and the relationship betw een point source NO

x em issions and ozone should be

extended to include area source NO

x em issions as well.

Methods A statistical cluster analysis generall y involves determ ining the differences between the objects being analyzed, and gr ouping those objects with the sm allest

1differences. Taubm an et al ., (2006) exam ined statistics for University of Maryland research aircraft profiles of ozone and showed median profil e s and the s t a tistica l spr e a d around those profiles. Ozone showed distin ctive profiles (T aubm an et al., 2006); for

exam ple, ozone was m o st concentrated a bove 500 m in the morning and was m o re uniform from the surface to 2000 m in the afternoon (the larg est values in the profiles

were found near th e 110 0 m level). The diffe rences between each pair of profiles were

calculated in a m a nner sim ilar 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. Th e slope and correlation of the points in each pair of profiles under comparison were cons idered as well as the total difference in

m i xing ratio between the two prof iles within ea ch altitude bin. The following equation

was used to calculate the differen ces am ong the aircraft profiles: 

# ( ) ( ) ∑ ∑ =

= =

= ⎪ ⎭ ⎪ ⎬ ⎫

⎪ ⎩ ⎪ ⎨ ⎧

− − − +

− +

− = 4

1

2

_ 2

1

_ 2 ] ) 1 ( exp( 1 [ ] 1 [ 1 * ) ( k

k

Part Part m a

n a ja ia ij s R c c abs D

4 4 4 4 4 4 4 4 4 4 2 1

4 4 4 3

4 4 4 2 1 3 Equation 1

Here, k denotes four different altitude bi ns for the trace gas profiles, and a denotes the

100 m layers ( n through m within each altitude b i n (Table 1 ) . The species value is represented by c for t h e i th and j th pro f ile. In each altitude b i n, k , there are at least four

100 m layers of ozone data. A reg r ession be tween the ozone m i xing ratios for each pair

of

prof iles at the f our lay e rs in e ach o f the bins was used to o b tain the slo p e, s , and the correlation coefficient, R . The first part of Equation 1 de term ines the squa re of the su m of the differences between valu es at each altitude level, k . The second part of the equation m u ltip lie s the d i f f

e rence by one plus dif f

e rence s asso ciated with the corr ela tio n and slope. When the correlation is sm all or negative, the profiles are very different and

the 1- R por tion inc r ease s , which inc r eases the to tal d i f f

e rence D ij . The exponent of the slope portion is used to account for the slope be tween the pairs of profiles. A slope near unity suggests that the profiles are sim ila r and thus should add little to the total difference. The exponent of the slope was us ed to guarantee that slopes much different

from unity will m a ke the expone ntial term sm all and thus in crease the to tal difference.

Once the dif f

erence between each p a ir of prof iles was calcu lated, th e pro f iles with th e

sm allest differences were clustered together us in g a standard hierarchical agglom erative clustering algorithm in Matlab.

Table 1. Altitude bins used in Equation 1.

k le v e l Altitud e bins for ozon e

1 151-650 m

2 651-115 0 m

3 1151 -16 50 m

4 1651 -24 50 m

Results and Discussion Figure 1 sho w s the m e dian profiles f o r each of th e six ozone clusters calculated in

the above m a nner. Figure 2 show s back trajectory density plot s for five of the clusters. These were generated from back trajectories th at term inated at the locatio n and tim e the profile was m a de. Flights were m a de from North Carolina to Maine between 1997 and 2003, and these are shown in Figure 3. Cl uster 6 has very large values of ozone

2m easured above 2000 m

. There are only two prof iles in this cluster, and they were m a de on 8 and 9 July 2002 when sm oke from Canadian forest fires im pacted the Mid-Atlantic region (Colarco et al., 2004; Ta ubm a n 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 larg e ozone values (F igure 1) and the back trajectory density plots (Figur e 2) show passage over the No rthern Ohio River Valley, where there is a high con centration of NO

x sources. Taubm an et al

. (2006) found a

sim ilar relationship betw een back trajectories con centrated ov er the North e rn Ohio River Valley and large values of ozone and suggest ed that the high concentration of power plants in th is region con t r i bute to ozo n e in th e Mid-Atlantic region. The b ack trajectory density plots for clusters 3, 4 and 5 also show large densities around the I-95 corridor, which is sug g estiv e of weak winds th at le ad to higher ozone values. Cluster 2 has the

sm allest ozo n e values an d back trajectories that cross over the Atlantic Ocean, which may explain the sm all ozone values associated wi th this cluster. Cl uster 1 has the second

sm allest ozo n e values (cluster 1 co lu mn conten t is 19% less than clusters 3), however, the back trajectories associated with clus ter 1 ar e s i m ila r to tho s e of cluster 3. To address

this discr e pa ncy, the in te grated NO

x point source em issions along the back trajectories

were exam ined.

0 500 1000 1500 2000 2500

0 5 0 1 00 150 2 00



### M e di an O 3 

### ( ppb)Altitude(mMSL) C l us t e r 1 ( 8 0 )

C l us t e r 2 ( 5 2 )

C l us t e r 3 ( 6 6 )

C l us t e r 4 ( 2 8 )

C l us t e r 5 ( 4 )

C l us t e r 6 ( 2 )

3Figure 2. Back trajectory density plots for ozone clusters 1-5. The largest 0.3% N O

x sources are shown with a + sym bol. Clus ters 3, 4

, and 5, asso ciated with larger ozone values, show larger densiti es near point sources as well as along the I-95 corridor. Clusters 1 and 2, with low ozone m i xing ratios, are

associated w ith m o re variable wind s.

4Figure 3. Locations of University of Maryla nd research ai rcraft f light s p ira l s m a de in

the eastern United States between 1997 and 2003

To understand the influence of em iss i ons upwind of the spiral locations, the integ r ated N O

x em issions along each back trajectory were calculated. Emissions from the daily EPA Clean Air Market unit level em issions database ( http://cfpub.epa.gov/gdm /index.cf m? fuseaction=em issions.wizard ) were used in th is analys is. A circle was drawn, centered at each b a ck trajectory position for each hour o f the two day back trajectory (F igure 4). The radius of the circle was 80 km to account for

uncertainties associated with the back trajectory position and th e influence of eddy

diffusion and m i xing processes. The em issions within each circle were s u mm ed. The

sum of the em issions for each circle was divi ded by the area of the ci rcle. The em issions

from the day on which th e back trajectory cro sse d a circle were used for the date of each

back tr ajec tory position. The sum m ed em issions will be ref e rred to as integra t ed

em issions. Statis tics (m edian, 5 th , 25 th , 75 th and 95 th percen tiles) f o r th e integra t ed N O

x

em issions are shown in Figure 5.

5Figure 4. Circles draw n around an exam ple back trajectory. The em issions contained in

each circle were summ ed and divided by the area of the circle. Then

em issions from each circle were sum m e d. Each asterisk represents a po int source location (all point sources in the dom ain are shown).

6Figure 5. Statistics for NO

x e m issions encountered by back trajecto r ies for each ozo n e cluster. The NO

x e m issions are sum s of all em issions (g day -1 ) encountered by a back trajectory divided by the ar ea of the circle drawn around the back

tra j ecto r y po int (m 2 ). Clusters 1 and 2, with the s m allest ozon e values are also

associated w ith th e sm allest NO

x e m issions. Clusters 3, 4 and 5, with the larges t ozon e values, are associated with the larg est NO

x e m issions. 0 1 2 3 4

C l u s te r 1 (80 ) C l u s te r 2 (5 2) C l u s te r 3 (66 ) C l u s te r 4 (28 ) Cl u s t e r 5 (4 )

O 3 Cl u s te rIntegratedNOxemissions(gm-2day-1)

Q2 5

Q5

m e di an

Q9 5

Q7 5

Ozone column contents were calculated by in tegr ating the pro f iles f r om 100 m to
2500 m ; these colum n contents were convert ed to average colum n contents in ppbv,

assum i ng standard tem p erature and pressure (273 .15 K and 1 atm ) . No te that this is not

a m easure m ent of ozone at th e surface but what the ozone m i xing ratio w ould be in a profile with a constant mixing ratio. Clusters 3, 4 and 5 have the largest ozone values and the larg est NO

x em i ssions, while clusters 1 and 2 ha ve the sm allest ozone values and

the sm allest NO

x e m issions. The m e dian ozone colum n content was regressed against the integ r ated N O

x em issions (Figure 6 )

. The correlation coefficient, r 2 , of 0.997 is very high. The slope is 61.6 ppbv ozone /(g NO

x m -2 day -1 ) with an intercept of 1.3 ppbv ozone. The slope of the line in Figure 6 does not provide a quantitati ve evaluation of the relationship of ozone m i xing ratio with NO

x e m issions, but it does i ndicate that o z one is

rela ted to po int sour ce NO

x e m issions and a reduction in NO

x corresponds to roughly the

sam e reduction in ozone. Part of the reason f o r p o sitiv e cor r e l ation betwe e n ozone an d point source NO

x em issions is tha t p o int sour ces are co rre late d with are a s ources of NO

x . Figure 7 sho w s a positiv e relation s hip between ar ea and poin t source em issions of NO

x from eight states in the Mid-Atlantic region.

7y = 61. 6x + 1. 3

R 2 = 0. 997

0 20 40 60 80 100

0 0 .5 1 1 .5

NO x e m i ssi o n s ( g m -2 da y -1 )ColumnaverageO3(ppb)

Figure 6. Median ozone colum n average and m e dian integrated NO

x em issions (from point sources) for ozone cl usters 1-5. The ozone is positive l y corre lated with integ r ated N O

x e m issions.

y = 0

. 32 x + 0

. 46 x 1 0 8

R 2 = 0

. 3 2

0. E + 0 0 1. E + 0 8 2. E + 0 8 3. E + 0 8 4. E + 0 8 5. E + 0 8 6. E + 0 8

0

. E + 00 1. E + 08 2

. E + 0 8 3. E + 08 4. E + 0 8 5. E + 08 6. E + 0 8

N O x ar ea so u r ce em i s si o n s ( k g year -1 )NOxpointsourceemissions(kg year-1)

Figure 7. A regression between the sum of NO

x area sou r ces and NO

x point sour ces f o r eight locatio ns in the M i d-Atlan tic re gion (Distric t of Colum b ia, Delaware, Maryland, New Jersey, New York, Pe nnsylvania, Virginia, and W e st Virginia). T h ere is a pos itiv e cor r ela t ion between point and a r ea sourc e s o f

NO

x .

8Profiles were also grouped by tim e of da y, where m o rning profiles are defined as

those m a de 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 clus ters 3 and 4 were m easured in the afternoon, whereas only 38% of the profiles in clusters 1 and 2 were m easured in

the afternoon. Greater ozone values in clus ters 3 and 4 m a y be partly explained by the increased number of afternoon profiles, whic h were generally m a de downwind of urban centers, and had a longer tim e period for ozone production.

Conclusions A cluster analysis of ozone profiles wa s performed to determine m e teorological and point source influences on the profiles. Pr ofiles with elev ated levels o f ozone are positiv ely c o rrelated with NO

x point source em issions en countered by a 48-hour backtrajecto r y. The linear re gression of ozone column content and integ r ated NO

x e m issions (from a 48-hour back-trajectory) gi ves a slope of 61.6 ppbv ozone / (g N O

x m -2 day -1 ), intercept of 1.3 ppbv and a correlation coefficient, r 2 , of 0.997. Point source em issions of

NO

x are positiv ely co rre lated with ar ea sourc e e m issions and both point and area sou r ce

em issions affect ozone production. The linear relationship and strong correlation suggests that reductions of NO

x e m issions (from both point and area sou r ces) shou ld reduce ozone levels. Chapter 2 show s that CMA Q generally underestim ates ozone aloft, and because ozone, as sh own in this an alys is, is positiv ely c o rrelated with NO

x point

source em issions, CMAQ m a y not adequately de monstrate o r capture the efficacy of NO

x point source em issions controls, and m a y undere stim ate the role of transported ozone in Maryland.

Future Work This study has laid the groundwork for a m o re rigorous study relating both point and area sources (on, for exam ple, the county scale) of NO

x to vertical ozone profiles. Because EPA regulates surface ozon e, an additio nal exam ination should focus on the

relationsh i p between surface oz one and the in tegrated NO

x em issions (from point and area sources) encountered over a two day peri od. This study exam ined data from 1997- 2003. In 2003, large scale reductions of point source NO

x em issions were im plem ented. It would be enligh t ening to dete rm ine how thes e reductions impacted ve rtical profiles and

surface m easurem ents of ozone in years subs equ e nt to the NO

x SIP Call.

9References

Colarco, P.R., M.R. Sc hoeberl, B.G. Doddridge, L.T. Marufu, O. Torres, and E.J. Welton, 2004. Transport of s m oke fr om Canadian forest fires to the su rface near Washington, D.C.: Injection height, entrainm ent, and optical properties, Journal of Geophysical. Research , 109, D06203.

Taubm an, B.F., Hains, J.C., Thom ps on, A.M., Marufu, L.T., Doddridge, B.G., Stehr,

J. W ., Piety, C.A., Dickerson, R.R., 2006. Aircraft vertical prof iles of trace gas and aerosol pollution over the m i d-Atlantic United S t ates: Statistics and m e teorological cluster analy s is, Journal of Geophysical Research

, 111 , D10S07.

Hains, J.C., Taubm an, B.F., Thom ps on, A.M., Marufu, L.T., Doddridge, B.G., Stehr,

J. W ., Dickerson, R.R., 2007. A clustering m e thodology to identify distinct chem ical events in the troposphere using aircraft trace g a s and aerosol v e rtical p r ofiles, m a nuscript in preparatio n.

10Regional Nature of Oz one Transport

March 7, 2007

R. Hudson and J. Hains.

Departm e nt of At m o sphe ric and Oceanic Science University o f Maryland College Park, MD

Abstract The transport of ozone into and within the State of Maryland above the nocturnal boundary layer has been exam ined using a co m b ination of aircraft and ground-based

m easurem ents. The evid ence from both the ai rcraft and the station ozone data clearly points to the im portance of transport in the overall quality of Maryland’s air. Ozone

introduced to the surface after the breakdown of the nocturnal inve rsion can either be transported or derived from locally produ ced ozone, depending on nighttim e residual layer wind speeds. The contribution of re gional transport to afternoon boundary layer

ozone has been quantified using ozon e profile data obtained on aircraft flights. W h en the source regions lay over the Ohio River Valley (~ 59% of the profiles), transport accounted for 69-82% of the afternoon boundary layer ozone. W h en winds were weak (~27% of the profiles), transport only accounted f o r 58% of the after noon boundary layer ozone

. The ground level ozone data obtained from MDE monitoring stations has also been exam ined

for evidence of downwar d m i xing. On days wh en 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 E S T, co rresponding to the brea kdown of the nocturnal boundary layer. The rate of incr ease of ozone within this peak is about four tim es greater than that due to pure photochem i stry. A st udy was m a de of t h e re lative contribution of

transpo r ted and local ph otochem i stry to the ozo n e data for six 8-hour ex ceedance d a ys in August, 2002. The results show that if local photochem i stry were the only source of ozone, none of the 6 days exam ined would ha ve exceeded th e 8-hour ozo n e standard. The effect of the transported ozone is to add oz one early in the day and hence to expand the

tim e interval over which the ozone level ex ceeds 85 ppbv. This directly affects the 8- hour ozone averages.

Introductio n Land surfaces are far m o re efficient at ra diating heat than the atm o sphere above

them , hence on a cloudless night, the Earth’s surface cools more rap i dly than the air. That

tem p erature drop is then conveyed to the lowe st few hundred feet of the atmosphere. The

air abov e this altitude co ol s m o re slowly, and a temperat ure inversion form s only a fe w hundred feet above the ground. The inversion, known as the nocturnal inversion, divides

the atm o sphere into two layers which do not m i x. Below the nocturnal inversion the

surface win d s are weak, and any pollutan t s em itted overnigh t accum u late beneath the

1inversion. Above the nocturnal inversion, wi nds continue throughout the night and can

becom e even stronger as the inversion isolat es the winds from th e friction of the rough ground surface. This situ ation is especially pr evalent when a high pressure system exists,

which is the hallm ark of high pollution events along the East Coast. It should be noted

that the tim e used throughout this section is E a stern Standard Ti m e (EST, or UTC-5).

In the m o rning, the sun warm s the Earth’ s surface, and condu ction and convection

transfer h eat upward. This warm s the air near th e surface m o re eff ectively than higher up, and by about 10:00 EST the tem p erature of the air above th e surface has risen sufficien tly

to rem ove the nocturnal inversion. As a resu lt, the ozone and its precursors that were

above the in version m i x down to the surface. Th is m i xing can either lower or increase the air po llution levels at the surface, dep e nding on whether the air above th e nocturnal

inversion is cleaner or m o re polluted than th e air at the surface. W h en wind speeds above the nocturnal boundary layer are brisk overn ight, and the area where the air m a ss originated had high ozone levels the previ ous day, the ozone and precu rs ors that are

m i xed down in the central Mid-Atlantic region ca n be referred to as transported ozone. However, when wind speeds are weak above the nocturnal boundary layer overnight, the ozone produced in the centra l Mid-Atlantic area during the previous day can becom e decoupled from the surface with th e form ation of the nigh ttim e invers ion, rem a in in place over the region, and be m i xed down the follo wing day after the breakdown of the nocturnal boundary layer. This added ozone can be referred to as yesterday’s local

recirculated ozone. Vukovich and S carborough (2005) have modeled the effect of this transported ozone and precursors using the ai rcraft data obtained by the University of Maryland (Doddridge et al, 1995). They found a m i xed response. On som e days there was a larg e increas e in th e local recirculated ozon e, indicating an increase in the ch em i c al production of ozone, but on other days, the incr ease in local ozone was due only to the transported ozone m i xed down from above the nocturnal inversion.

Results fro m airc raft measureme n ts.

From 1997 t o 2003, aircraft m easurem ents of oz one were m a de, from the surface

to 3 km , dur ing summ ertim e air pollution episode s over the Mid-Atlantic U.S. as part of the University of Maryland’s Regional Atm o spheric Measurem ent, Modeling, and

Prediction P r ogram (RAMMPP). Morning f lights (before 12 noon EST) were generally

m a de upwind of urban centers over locations such as Luray, VA, W i nche ster, VA, and

Cum b erland, MD (Figure 1). Afternoon flights (after 12:00 EST) over locations such as Hartford, MD and Easton, MD were m a de downwind of urban centers (Figure 1). Boundary layer ozone m easured in the af ternoon was generally greater than that

m easured in the m o rning, though the lower fr ee tropospheric ozone show ed little variance (Taubm an et al., 2006). The increased afternoon ozone is explained by three factors: (1) longer tim e periods of daylight that a llow for photochem i cal buildup, (2) afternoon

m e teorology that allows for m i xing of tran sported ozone, and (3) the location of the flights.

2Cumber la nd

Winche ster

Lur a y Easton Hartf o rd

Figure 1. A typical flight path m a de by Un iversity of Maryland research aircraft. Morning spirals were m a de in Cu m b e r land, W i nchester, and Luray, all upwind of the Baltim ore-Washington corri do r. Afternoon sp irals were m a de in Hartford and Easton, downwind of the Baltim ore-W a shington corrido r

.

Taubm an et al. (2006) used a cluster analys is of back trajecto ries in conjunction

with the ve rtical p r of ile d a ta to identif y source reg i ons and characteristic transport patterns during summertim e pollution episodes. Four of those cl usters representing

different m e teorological regi m e s are discussed b e low. Cluster 1, labeled as Moderate Northweste rly flow (Figure 2a), has large amounts of ozone (Figure 3), a large SO 2 /CO ratio, large, highly scatte ring particles, and a large aerosol optical depth (A OD). These values ar e indicative of aged point source pollution. The greatest trajecto r y d e nsity lies o v er the no rth e rn Ohio River Valley where there are

severa l la rge NO

x and SO 2 sources. Fresh NO

x and SO 2 e m issions from t h ese sources have had ample opportunity under a moderate flow regim e to produce ozone and secondary aerosols en rout e to the Mid-Atlantic.

Cluster 2 shows wind directions similar to cluster 1 (Figure 2b), but with faster wind speeds (Fast North w esterly flo w ). The gr e a tes t tr ajec to ry density also lies m a inly 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 sm aller. The ozone values

associa t ed w ith th is c l uster ar e sm aller than those in Cluster 1 (Figur e 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 as sociated with cluster 2 like l y result from t h e faster wind speeds as sociated wi th this cluster. Faster winds inhibit local photochem i cal ozone production and increase ventilation of urban m obile source pollution. Cluster 3 is typified by Sl ow 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 oz one values likely result from the previous day’s ozone being brought down from the nocturnal boundary layer where it com b ines with the current day’s ozone. The previous day’s ozone, which was decoupled from the

surface with the form ation of the nig h ttim e inve rsion, rem a ined in place o v er the central

3Mid-Atlantic region un til being rein trodu ced to the surface layer the n e xt day upon breakdown of the nocturnal boundary layer. Th e previous day’s ozone could have been transported ozone or locally grown ozone. Fi gure 2c shows that there are few large NO

x and SO 2 sources in the area of greate st trajectory density. The pa rticle p r o p erty va lues are

moderate and the CO values are larg e. These valu es, t ogether with the weak winds

associated w ith cluster 3, indicate local, ur ban pollution dom inated by m o bile sources. The transpo r t pattern ide n tif ied by c l us ter 4 is ch arac terized as Moderate Southwesterly flow and the grea test traje c tory d e nsity lies over the southern Ohio River Valley. For the m o st part, this cluster is as sociated with little pollution loading, which suggests that there are fewer point sources lo cated farther south along the Ohio River. Figure 2d shows no large NO

x or SO 2 sources in the area of greatest trajectory density, although m a ny do encircle this area. Also, th e afternoon ozone values are particularly

sm all (Figure 3), and not m u ch larger than the m o rning values, indicating there was little photochem i cal production during tran sport of the air parcels. The contribution of regional transp ort to afternoon boundary layer ozone is quantified as follows. The percent of the afternoon ozone boundary layer colum n content for each cluster that can be accounted for by regional transpo r t was estim ated with th e f

o llowing eq uation :

% 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 m o rning profiles

MBL: The m o rning boundary layer colum n cont ent, defined as the layer between

100 m and 2000 m in the m o rning profiles

ABL The afternoon boundary layer column content, defined as the layer

between 100 m and 2000 m in the afternoon profiles

The equatio n sim p lif ies to the ratio o f RL / ABL aft e r the MBL s cancel ou t.
Assum i ng the RL ozone was transpo r ted to the morning spiral location from an upwind regional source and subsequently transported to the afternoon spiral location where it

m i xed with the locally produced ozone to give the ABL ozone colum n content, the equation should provide the relative contribu tion of regionally transported ozone to the afternoon boundary layer colum n content. The accuracy of this m e thod depends upon the Lagrangian nature of the m o rning and afte rnoon profiles from each cluster. Because flight plans were designed in a Lagrangian m a nner, wher e morning flights were upwind of afternoon flights (F ig ure 1), th e estim ate s hou ld be accu rate. W h en the greatest clu s ter trajectory density lay over the Ohio R i ver Valley (~59% of the profiles), transport

accounted f o r 69-82% of the afternoon boundary layer ozone. W h en winds were weak (~27% of the profiles), transport only account ed for 58% of the afternoon boundary layer

ozone.

4Figure 2. Trajectory density m a ps for back tr ajectory 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 w e re generated using a linear interpolation m e thod between the trajectory end points. They indicate the

rela tive d e ns ity ( %) of air parc els ove r the to tal area described by th e spaghetti plots. Also pictu r ed are th e locations of the top 0.3% em itters annu ally of NO

x (diam onds)

and SO 2 (cr o sses) in the easte rn United State s

.

50 0. 5 1 1. 5

2 2. 5

3 3. 5

0 2 0 4 0 6 0 8 0

Figure 3. The m e dian morning (a) and afternoo n (b) profiles for back trajectory clusters with Moderate Northwesterly flow (C luster 1), Fast Northwesterly flow (Cluster 2), Slow Southerly flow (Clu ster 3), and Moderate Southwesterly flow (Cluster 4). 1 0 0

Me d i a n O 3 ( ppb)Altitude(km) Mo d e r a t e
N o r t hw es t e r l y (4 5) Fa s t

N o r t hw es t e r l y (3 4) Sl o w So u t h e r l y (5 6)

Mo d e r a t e S o u t hw es t e rl y (2 0)

0 0. 5 1 1. 5

2 2. 5

3 3. 5

0 2 0 4 0 6 0 8 0

Me d i a n O 3 ( ppb)Altitude(km) Mo d e r a t e
N o r t hw es t e r l y (6 2) Fa s t

No rh w e s t e r l y (4 3) Sl o w So u t h e r l y (5 2)

Mo d e r a t e S o u t hw es t e rl y (1 8)

1 0 0

6Results from Ground level Oz one Measurements Figure 4 shows the hourly ozone m e a s urem ents made at the F o rt Meade air monitoring site for the first 9 days of A ugust 2002, a m onth when there were m a ny ozone episodes that led to 8-hour ozone exceed ances. T h e hourly ozone data for August 2 nd and August 8 th show different slopes between 10:00 ES T and 15:00 EST. This is shown more clearly in F i gure 5 where the two days are plotted on top of one another.

Figure 4. Ground based ozone m easurem ents made at the F o rt Meade station for August 1 to August 9, 2002.

Figure 5. Ozone data for August 2 nd (blue asterisks) and August 8 th (green crosses) as a function of tim e (EST, UTC- 5). The red diam onds are the difference

between the two ozone curves.

7The data for August 8 are typical for local photochem ical production of ozone. Em issions occur early in the m o rning, and as the sola r radiation and ground tem p erature increase,

the ozone climbs to a m a xim u m at about 15: 00 E S T. The surface tem p erature m a ximum

occurs sim u ltaneously. The red curve on Figur e 5 is the difference between the data for August 2 nd and August 8 th . The m o st striking feature of the difference plot is the peak of ozone centered at about 11:00 EST. Figure 6 is the sam e as Figure 5 except that the ozone data for August 8 th (O 3 (8)) have b een m u ltiplied b y a norm a lization facto r of 1.3 (the

ratio of the m easured ozone at 15:00 EST on August 2 nd (O 3 (2,1500))and August 8 th

(O 3 (8,1500), that is:

O 3r ev (8) = O 3 (8) * O 3 (2,1500)//O 3 (8,1500) This factor accounts for the fact that the ground temperature on August 8 th is lower than the ground temperature on August 2 nd . The difference curve (red) now shows

a m o re pronounced peak. The m odi fied August 8 data in F i gure 6 can be interpreted as the local photochem i cal curve for August 2 nd . 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 photochem i st ry. This rem a rkable in crease is the product of ozone and its precursors being brought dow n from aloft as the nocturnal inversion slowly erodes and eventually break s down at about 10:00 ES T (Vukovich and Scarborough, 2005). The tim ing of the 11:00 EST peak in Figure 6 and the 10:00 EST peak found by Vukovich is rem a rkably sim ilar given the lim ited data em ployed for the single case study presented here.

Figure 6. Ozone data for August 2 nd (blue asterisks) and August 8 th (gree n crosses )

norm a lized to the August 2 nd data at 17:00 EST (UTC-5). The red diam onds

are th e difference between these two curves. The solid black curve is a

repres entative Gaussian curve

.

8Another inference of the difference between the ozone data on August 2 nd and August 8 th
is that there is no transported ozone evident on A ugust 8 th . The reason for this can be seen

if one com p ares th e back trajecto r ies of ai r parcels for these two days that reached

Baltim ore at 10:00 EST. These are sh own in Figure 7. On August 2 nd the upper level parcels of air com e from eastern Pennsylvan ia. The air that reaches Baltimore at the 500

m e ter level has been lifted from an altit ude of 200m 15 hours earlier , and one can expect

that lower altitud e s will have com e from clos e to the ground. This transpo r ted air will be polluted. However, on August 8 th , the air parcels arrived from Canada accom panied by strong subs idence. Thes e air p a rcels w ill have p i cked up little pollu tion.

Figure 7. 24 hour back trajectories for August 2 nd and August 8 th ending at 10:00 EST. Red asterisks are for back-trajectories that begin at Baltim ore at an altitude of 500 m AGL. The green asterisks are for 1000 m AG L, and the blue asterisks are for 1500 m

AGL.

9Figure 8. Calculated results for the trans ported and local photoch em istry for August 2 nd,

2002 with tim e in EST ( U TC-5). The bl ue asterisks are the m easured ozone data. The orange crosses are the best f it to the local photochem i stry. The green

diam onds are the transported ozone, and th e red triangles are the best fit to the

overall data.

If one assumes that the ef fect of the tr ansported ozone shown in Figure 6 can be approxim a ted by a Gaussian curve, then one can fit the overall ozone data from 6:00 EST

to 18:00 ES T by a com b ination of a Gaussian curve and the ozone data f o r August 8 th
multiplied b y a constan t

. The Gaussian curve is a f

unction of three consta nts, the

am plitude, the width, and the tim e of the p eak. T h e overa ll d a ta a r e f it to these f our constants using a non-linear l east squares procedure (Press et al., 1986). T h e result of such a fit for August 2 nd is shown in Figure 8. The green cu rve repres ents th e effect of the transported ozone, the blue curve is the loca l photochem i cal curve, and the red curve is

the sum of t h ese two. The best fits for six days in August 2002 (2, 4, 11, 12, 13, and 19) when Maryland exceeded the 8-hour ozone stand a rd 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.

10Figure 9. Best fit for the transported and lo cal photochem i cal ozone for selected days (days that ex ceeded th e 8 hour ozone standard ) in August 2002. The color

schem e is the sam e as for Figure 8, and tim e is in EST (UTC- 5 ).

11Figure 10. 24-hour back-trajectories for the days shown in Figure 9. Red asterisks are for back-trajectories that e nd at 10:00 EST at Baltim ore at an altitude of 500 m AGL. The green asterisks are for 1000 m AGL, and the blue asterisks for 1500

m AG L.

12Conclusions

The evidence from both the Univers i ty of Maryland aircraft and the Fort Meade ozone data clearly highlight th e im portance of transport to the overall air quality in the State of Maryland. However, one point that com e s out of the study is that the transport need not be fast, and that ozone in tro duced to th e surface after the breakd o wn of the

nocturnal inversion can either be transported or de rived from the previous day’s locally produced ozone, dependent on nighttim e residua l layer wind speeds. The contribution of regional transport to af ternoon boundary layer ozone has been quantified using ozone profile data obtained on aircraft flights. W h en the source regions lay over the Ohio River Valley (~59% of the profiles) , transport accounted for 69- 82% of the afternoon boundary layer ozone. W h en winds were weak (~27% of the profiles), transport only accounted for 58% of the afternoon boundary layer ozone

. The study of the rela tive contribution of

transpo r ted and local ph otochem i stry to the su rface ozone d a ta for six exceedence d a ys in August 2002 suggests that if local photochem i st ry were the only source of ozone, none of the 6 days exam ined would have exceeded th e 8-hour ozone standard. The effect of the transported ozone is to add ozone early in the day and hence to expand the tim e interval

over which the ozone lev e ls m a y exceed 85 ppbv. Clearly, if the transported portion o f the daily ozone content were rem oved from Ma ryland, all monitors within the state w ould be well below the 8-hour ozone NAAQS.

Future Work The analys is of the statio n ozone data would have benef ited from ozone data taken at

shorter intervals than one hour . It is recomm ended that da ta be archived at every 5

m i nutes for this purpose. In this study only a few days were analyze d. It is clear that a

more extens ive ana l ysis needs to be implem e nted, and that other species be considered for detailed monitoring.

13References

Dodderidge, B.G., W.F. Ryan, R.R. Dickerson, R.M. Morales, R.M. Kelley, P.T. Roberts,

D.L. Blum e n thal, J.A. Anderson, K.L. Cive rolo, 1993, ‘Observations of summ ertim e air quality over the U.S. m i d-Atlantic region’, Transactions of the AGU , #76

Press, W

. H., B.R.Flannery, S.A.Teukol sky, and W.T.Vetterling, 1986, ‘Num erical Recipes’, Cambridge University Press.

Taubm an, B.F., J.C. Hains, A.M. Thom pson, L. T. Marufu, B.G. Doddridge, J.W

. Stehr, C.A. Piety, and R.R. Di ckerson (20 06), 2006, ‘A ircraft vertical p r of iles of trace gas and aerosol pollution over the m i d-Atlantic United S t ates: Statistics and m e teorological cluster analy s is’, J. Geophys. Res. , 111(D10), D10S07 MAR 29 2006.

Vukovich, F.M. and J. Scarborough. 2005, ‘Asp ects of ozone transport, m i xing, and

chem istry in the gre a te r Maryland ar ea’ , Atmos. Env ., 39, 7008-7019, 2005

.

141516Air Quality Benefits of an Aggressive Telecommute Strategy

March 16, 2007

Jef f r ey Stehr, Charles P i ety, Dale Allen, Patric ia Castellanos*

Departm e nt of At m o sphe ric and Oceanic Science *Departm ent of Che m ical and Biomolecular Engineering University o f Maryland College Park, MD

Abstract

To sim u late the effects o f an aggressive telecommute program, the University of Maryland m odeled the air quality change th at would result if 40% of all light duty

vehicles were taken off the road in th e non- attain m e nt areas of Baltim ore, Philadelphia,

and W a shington, D.C. on 38 high ozone days in the summ er of 2002. Changes in

em issions were im plem e n ted as a flat 40% reduction in vehicle m iles traveled in each county of the three non-attainm e nt areas. The ef fects of im pl em enting such a program

were m odeled using version 4.4 of the CMAQ model. The model results showed that

across the three non-attainm e nt areas tested, an aggressive telecommute program has the potential for considerable benefit to air quali ty, with f a irly u n if orm benef its acros s all three areas. The highest monitoring locatio ns in the Philadelphia and W a shington, D.C.

non-attainm e nt areas would see the largest be nefits from this program , suggesting that

reductions in m obile emissions would have th e g r eatest im pact at the m o st troub lesom e monitoring locations on the worst ozone days. Benefits in all three non-attainm e nt areas averaged a reduction in ozone of over 2 ppbv.

1Introductio n Ozone levels are episodic, and high ozone concentrations are largely influenced by m e teorology, so a forecast-driven program of em issions reductions m a kes a lo t of sense. To this end, it has been suggested th at telecomm uting be strongly encouraged on high ozone days in the summ er to take vehicles off of the roads and vehicle em issions out

of the air. T o sim u late the effects of a very aggressive telecommute program , the University of Maryland perform e d a sensitivit y test, assum i ng that 40 % of all light duty

vehicles were taken off the road in th e non- attain m e nt areas of Baltim ore, Philadelphia,

and W a shington, D.C. on the 38 days in th e sum m e r of 2002 when 8-hour ozone was ≥ 85 ppbv (AQI of code orange or higher). Ch anges in em issions were implem ented as a

flat 40% reduction in vehicle m iles travel ed (V MT) in each c ounty of the three nonattainm e nt areas. No attem p t was made to determ ine areas where workers were m o re or less like ly to tele commute. Air quality was m odeled for the su mmer of 2002, using the 2009 base A1

em issions inventory as a star ting point, and reducing the li ght duty vehicle VMT in that inventory only on high ozone days and onl y within the three non-attainm e nt areas

m e ntioned. The m odel s t arted on May 22, a nd ran through the end of Septem ber, though benefits were only calculated from June 1 to Septem ber 30 to allow concentrations in the

model tim e to reach realistic levels, also known as “spin up” tim e . Since the m odel starts initially with clean conditions, it m u st be run for a few days to allow pollution levels to build up to representative levels. In keeping with other base A1 m odeling, CMAQ

version 4.4 was used so that the sam e 2002 a nd 2009 base case values determ ined from

other m odeling runs could be used for com p arisons. Relative reduction factors were calculated as per EPA guidance, usi ng 2002 design values as a base.

Results

As m i ght be expected for an aggressive loca l pro g ram targete d to the wor s t air pollution days in a region, the results indi cated significant benef its at all monitoring locations throughout the region. As pointed ou t in Chapter 2, CMAQ appears to be most

sensitive to local em issions changes, and m ode l perform a nce is at its best in urban and

suburban areas. These are the sam e areas c overed by this scenario, so the strong m o del response of lower ozone concentrations is not surprising. In all th ree areas, the average

benefit acro ss all m onito ring lo cation s exceeds 2 ppbv. Throughout the Baltim ore nonattainm e nt area, the im provem e nts are substan tia l (Table 1 ) , with an average benefit of 2.4 ppbv. The sm allest benefit is a 1.3 ppbv drop at the Essex m onitoring location, while the largest is a 3.3 ppbv benef it at the South Carroll m onitoring location. In the Washington, D.C. non-attainm e nt 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 nor th, in the Philadelphi a non-attainm e nt area (Table 3), the average benefit is 2.0 ppbv, ranging from 1.1 to 3 ppbv.

2Table 1. CMAQ Results for Monitoring Locations in the B a ltim ore, Maryland

Non-Attainm e nt Area

Monitoring

Location EPA AQS

Site Code 2002

Base Design

Value (ppbv) 2009

OTB/OT W (ppbv) Telecomm ut e Scenario

(ppbv) Change due to

Telecomm ut ing (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 Res u lts for Monitoring Lo cations in the Washington, D.C. NonAttainm e nt Area

Monitoring

Location EPA AQS

Site Code 2002

Base Design

Value (ppbv) 2009-A1

OTB/OT W (ppbv) Telecomm ut e Scenario

(ppbv) Change due to

Telecomm ut ing (ppbv)

Takom a Par k 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 C o

. 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

W illiam 511530009 85.0 73.6 71.7 -1.9

Alexandria 515100009 90.0 78.9 76.2 -2.7

3Table 3. CMAQ Res u lts for Monitoring Locati ons in the Philadelphia Non-Attainm e nt
Area

Monitoring

Location EPA AQS

Site Code 2002

Base Design

Value (ppbv) 2009-A1

OTB/OT W (ppbv) Telecomm ut e Scenario

(ppbv) Change due to

Telecomm ut ing (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 Pon d 100010002 88.3 77.8 76.4 -1.4

Lewes 100051003 87.0 77.5 76.4 -1.1

Lum s Pond 100031007 94.5 79.3 76.9 -2.4

Seaford 100051002 90.0 75.1 73.7 -1.3

Collie rs 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

Ca m d en Lab 340070003 98.3 89.0 87.0 -2.0

Clarksboro 340155001 98.3 87.8 86.2 -1.6

Millv ille 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 Chest e r 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 som e areas, the biggest benefits occurre d at the m onitoring lo cations that have the leading design values for the non -attai nm ent area (e.g. th e Colliers M ills m onitoring location in the Philadelphia non- attainm e nt area). This wa s not uniform ly the case, but

all m onitoring locations did see benefit. For example, the Edgewood m o nitoring location would be brought well into attainm e nt by im plem enting this telecommute scenario, dropping 1.8 ppbv from a projected 2009 desi gn value of 85.1 ppbv to 83.3 ppbv. For the

Baltim ore area, this is es pecially im portan t , since Edgewood is the lon e rem a ining Maryland m onitoring location with a desi gn value above 85 ppbv. Benefits in the Washington, D.C. area are likewise fortuit ous, with the high m onitoring location in Arlington C ounty, Virginia dropping from a projected 2009 design value of 85.6 to 82.6.

4Conclusions

Across the three non-attainm e nt areas that are tested, an aggressive telecommute

program has the potential for considerable be nefit to air quality, with fairly uniform

benefits across all three areas. The highest m onitoring locatio ns in the Philadelphia and Washington, D.C. non-attainm e nt areas see the largest benefits from this program

, suggesting that it is targeting the m o st troublesom e monitoring locations on the worst ozone days.

Future Work

The sim u lation m i ght be updated in the future when newer versions of the

em issions inventories are availa ble. The sim u lation m i ght al so benefit from a different

chem ical m e chanism such as SAPRC99. As outlined in Chapter 4, there are solid scientific reasons why CMAQ’s CB4 chem istry does not entirely represent the chem i s try
of the atm o sphere. As o u tlin ed in C h apter 9, C M AQ underpredicts th e changes in o z one due to changes in NO

x em issions, so a future m o del run m i ght be expected to m o re accurately p r oject the b e nefits of a telecomm uting program

.

Acronyms

AQI Air Quality Index

CB4 Carbon Bond IV chem ical m echanism CMAQ Community Multisc ale Air Quality model

EPA United States Environm ental Protection Agency

NO

x Reactive oxides of nitrog en, the sum of only NO and NO 2

. ppbv Parts of ozone (or any other substanc e) per billion parts of air, by volum e SAPRC99 Statewide A i r Pollution Research Center (1999) chem ical m e chanism

VMT Vehicle M i les Traveled

OTB All regulations on the books

OTW All regu latio n s on the wa y

5Uncertainty in CMAQ and Over-p redictions o f Future Year Oz one Design Values

March 15, 2007

Jeffrey Stehr, Charles P i ety, Dale Allen

Departm e nt of At m o sphe ric and Oceanic Science University o f Maryland College Park, MD

Abstract

Several different m e thods have been used to compare the measured effects from

historical ch anges in em issions to tho s e fo recas t b y the CMAQ m odel, and all affirm the idea that the response of CMAQ is less than th at seen in nature. A study of the August

2003 Northeast Blackout shows that the electric al blackout caused a ch ange of at least 7 ppbv ozone, and likely more, while a CMAQ simu lation of the sam e event predicted only a 2.2 ppbv change. Other studies suggest sim ilar CMAQ underpredictions of ozone changes in response to em issions changes. An ongoing study by EPA reveals that the

NO

x SIP call likely produced double the bene fit predicted by CMAQ. Me anwhile, the State of New Jersey rep o rts th at its ozone m onitors appear to have reached their projected 2009 ozone concentrations already, three years ahead of schedule. Even when com p ared

to results from within the 2002 ozone season, CMAQ underpredicts daily ozone variability, and shows important perf orm a n ce shortcom ings in areas just upwind of Maryland on high ozone days, namely in the Ohio River V a lley and central Virginia. CMAQ is therefore likely underpredicting ch anges in ozone due to em issions changes and underpredicting ozone transport, so its results should be viewed with such an understanding.

In an ef f

o rt to inte llig ently repres ent CMAQ results, unc ertainties h a ve b e en estim ated for two types of errors in CMAQ modeling. One source of uncertainty is the

like l y m e teorological va r i ability of f

u ture ye ars. This is no t to say tha t 20 02 was not representative, but instead that m e teorological variability from year to year is well known, and any future p r ojections m u st accoun t for this to achieve a reasonable m a rgin of safety so a particularly bad future year wi ll not result in num e rous exceedances of the standard. A second type of uncertainty com e s from the m odel and its em issions. This uncertainty was partially estim ated by exam ining several dif f

erent 2009 scenarios and

determ ining the range of possi ble 2009 ozone values from thos e scenarios. These two sources of uncertainty do not cover all th e possible sources of uncertainty in CMAQ projections, since errors in the inventory, m e teorology, and model form ul ation all play a role, bu t are signif i can tly m o re dif f i cult to es tim a te. Most of the rem a ining errors are

system atic, not random , in natu re, so they should not be acco unted for by expanding the uncertainty in the future year projection, but by altering CMAQ’s prediction of future year ozone accordingly. As de m onstrated earlier, CMAQ is known to underpredict

1change, likely underpred i cting ch ange by 100%. To account for CMAQ’s underprediction, CMAQ changes were increas ed by a conservative 50%, and probable future ozone levels were calculated, al ong with probable ranges to account for uncertainty in repres entativeness and som e m odel errors. The picture for the future is

that the best estim ates of 2009 ozone c oncentrations are ve ry much like the 2012

concentrations m odeled directly by CMAQ. Th is is in line with current observations

from New J e rsey (hom e to the highest ozone in the Northeast) that show modeled 2009 ozone concentrations turning up in 2006.

Introductio n Air quality models have ge nera lly b een evalu a te d by their ab ility to repro duce average concentrations. Sta tistical m easures such as m ean error and bias show good perform a nce when the av erage o r m e dian is well captured. Som e perform a nce m e as ures that em phasize peak pollution perform a nce have also been used, but seldom if ever has

the m odel’s ability to rep r oduce a ch a nge in pollution in response to a change in

em issions been evaluated. Rapid changes in em issions are rare, so relatively few opportunites for such comparisons have existed. Most of the changes in ozone from day to day are caused by m e teorology, with em issions rem a ining relatively constant. Because static perform a nce m eas ures have been used in the past, CMAQ’s developm ent has been geared toward im proving these perform a n ce m e asures instead of toward im proving CMAQ’s ability to respo nd to change s in em issi ons. Therefore, it is entirely possible, indeed likely, that CMAQ would pass tr ad itional perform a nce criteria while underpredicting changes in air po llution in response to a chan ge in em issions. In this section, we use several independent evaluati ons to dem onstrate, and to som e extent quantify, CMAQ’s response to changes in em issions. Since CMAQ has been evaluated against, and therefore is developed w ith an ey e to ward, perform a nce m easures that

em phasize the m ean (e.g. m ean bias, error, m e dian, etc.) its dynam i c response and its ability to respond to a change in em issions ha ve not been evaluated, nor have they been incorporated into the developm ent of the m odel. CMAQ, therefore, can do a good job of

m eeting trad ition a l pe rf orm a nce crite ria that em phasize avera g e perf orm a nce while s t ill failing to capture ozon e extrem es and ozone in certain areas.

Several studies com b ine to suggest that CMAQ, and likely photochem i cal m odels in general, underpredict the change in ozone concentrations that re sult from a change in

NO

x e m issions, particularly those from power pl ants. In th is chapte r, the reasons behind the idea that CMAQ underpredicts change ar e explored, and then a plausible range of

f u ture desig n values is d e term ined, e x am ining several sources of variability and uncertainty.

The 2003 Northeast Blackout is exam ined from three observati onal perspectives

and com p ared with the lone air quality m ode ling study to date to show that CMAQ falls short of simulating the changes expected on this day. Next, an upcom ing paper (in preparation) is explored that m odels the effects o f the NOx SIP call on air quality, an d

finds that C M AQ captures roughly half the bene f its that were actua lly observed between 2002 and 2004. CMAQ predictions f r om the atta inm e nt m ode ling in this S I P are then

exam ined against current trends at ozone m onitors in New Jersey (hom e to the highest design values in the Northeast), showing that the projected 2009 future year design values have already been achieved. CMAQ’s perform a n ce is then ex am ined in more detail, and

2its in ability to f o llow the daily range of oz one values is exp l or ed. CMAQ also does no t perform as well in rural and upwind areas as in cities and suburban areas, especially in central Virginia and the Oh io River Valley, two known sour ce regions for ozone on high ozone days in Maryland. This rural underp erform ance is likely a reflection of CMAQ’s difficulty in handling ozone transport, as discussed in chapter 2. Finally, CMAQ’s perform a nce is analyzed as a function of NOx e m i ssions from n earby sources, revealing that CMAQ does best w h en nearby sources are abundant, and worst when few sources are nearby. This again points to CMAQ’s tendency to underpredict the m a gnitude of transported ozone. Having laid the bas i s for why it appear s that CM AQ will under rep r esen t changes in ozone, a second section explor es sources of uncertainty in future year design values, and lays out ranges of probable design values. In light of these i ssues, a single future year des i gn value canno t be accurately predicte d. Instead, a range of values is given.

1. Dyna m i c Evaluation of CMAQ Lead s to the Conclusion that CMAQ Ozone Predictions Lack the Dynam i c Range Found in Nature

Air Quality Changes Due to the August 14, 2003 Northeast B l ackout

On August 14, 2003, a chain reaction triggered the shutdown of m u ch of the generating capacity in the northeastern U.S. and south eastern Canada. On August 14, 2003, just after 4:00 PM Eastern Daylight Tim e , the power grid of the United States and Canada experienced the larges t b l ac kout ever. The black out aff ected an estim a ted 50 m illion people, and more than 61,800 m e gawatts (MW ) of electrical load wa s lost in parts of Ohio, Michigan, New York, Pennsylvania, New Jersey, Connecticut, Massachusetts,

Verm ont and the province of Ontario. The No rth Am erican Electric Re liability Coun cil (NERC) reported that m a ny units shut down comp letely a t the star t of the blackout, and

that a cascad e of events continued for se veral m i nutes until m a xim u m i m pact. This resulted in over 263 power plants with 531 un its in the U.S. and Canada (NERC reports

that a ll of th e f o ssil-f u el units in On ta rio were blacked out) being shut down. Investigation of hourly CEM (Continuous Em issions Monito ring) data from the power plant sm okestacks indicate that m a ny of the units affected by the blackout reported no heat inpu t at all du ring this 24-hour period un til attem p ting to start up.

3Figure 1. Visible satellite photos twenty hours before the blackout (l eft) and seven hours after the blackout (right), indicating near total loss of power around Lake Erie and Lake Ontario, and substantial power lo ss in Detroit and Long Island, NY, am ong others.

The obvious problem with studying air quality du e to a blacko u t is th at m a ny groundbased m onitoring stations were without power as well. Fortu n ately, the UMD aircraft was flying on this day (h aving been f o recas t as a high ozone day—a forecast that d i d not pan out), and was rerouted to Selinsgrove, PA , downwind fr om the heart of the blackout.

Airborne m easurem ents were taken over Ma ryland and Virginia (outside the blackout area) and Pennsylvania (in the center of th e area affected by the blackout) on August 15, 2003, 24 hours into the blackout [Marufu et al., 2004].

This flight, in conjunction with others, pr ovides an excellent opportunity to test the response of air quality models to a large, sudden drop in em issions. Several different

com p arisons based on observations suggest that the 2003 Northeast Blackout produced a large change in ozone, while the lone m odeli ng study performed to date [Hu et al, 2006]

shows m u ch sm aller changes in ozone.

Airborne m easurem ents on August 15, 2003, th e day after the 2003 Northeast Blackout began, show that ozone was 30 ppbv lower throughout the lowe st 1.5 km of the

atm o sphere on that day, and 38 ppbv lower at ground level, than on a m e teorologically

sim ilar day, August 4, 2002 (Figure 2).

4a 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) Augus t 15, 2003, one day into the blackout, b) August 4, 2002, the first reference day, and c) A ugust 3, 2005, the second reference day. The newer reference day is m e teorologically mo re like the day of the blackout, but it occurs after controls m a ndated by the NO

x SIP Call were

insta lled.

Reference Days for Comparison with the Day of the 2003 Northeast Blackout.

A subsequent com p arison to August 3, 2005, which was, from a transport standpoint (Figure 3), more like the blac kout day than the day in 2002 wa s, found sm aller changes in ozone. Unfortunately, the August 3, 2005 fli ght took place after the large reductions

fro m t h e NO

x SIP Call had been put in place, so th e com p arison is not as direct as one

would like. Com p arisons with the A ugust 3, 2005 reference day therefore represent changes th at are sm aller because the NO

x SIP Cal l and other em issions reduction

program s reduced ozone on that day, so cha nges are less dram atic. Regardless, on the 2005 reference day, ozone concentrations we re 7 ppbv higher than on the blackout day. Since this flight took place after NO

x SIP Call co ntrols we re installed, th e actua l chang e in ozone due to the blackout m u st have been substantially larger. Therefore, the actual

baseline m u st be higher than the ozone profile m easured on August 3, 2005.

5Figure 3. Ozone profiles taken from the UMD aircraft on the day of the blackout (leftm ost, in black), the 2005 control day (m iddle, in blue), and the 2002 control day (rightmost, in red). As shown in Figure 2, the m e teorology on the ref e rence day in 2005 was m o re like that on the da y of the blackout, but 2005 was also after

the installation of controls from the NO

x SIP Call, so the sm alle r ef f

ect is in par t
due to controls installed between 2002 and 2005.

Com p arisons with Air Q u ality in Areas not Affected by the B l ackout.

Parts of the Northeast w e re 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 affect ed by the blackout. W h en compared with

observations taken outside the area of the blackout, ozone was m i nim a lly 10 ppbv lower,

and m o re likely 20-25 ppbv lower in the area around Selinsgrove. The areas around Selinsgrove are indicated in Figure 4 by dashed lines lead ing from “SEG”, the sym bol for Selinsgrov e ’ s airpo r t.

6Figure 4. Ozone (diam o nds, in re d) and aircraft altitude (bla ck line) over the course of the flight from Cumberland, MD to the ai rport in Selinsgrove, PA (SEG), and on to Ft. Meade, MD one day into the A ugust, 2003 blackout. T h e area indicated by

the lower en ds of the das h ed line s is w ithin th e area affected b y the blacko ut, where ozone was approxim a tely 50 ppbv. Outside the area af fected by the blackout, th ough not at the sam e altit ude, ozone is roughly 60 ppbv, and well outside, ozone is roughly 75 ppbv. The e ffect of the blackout therefore ranges

from a conservative estim a te of 10 ppbv to 20 or 25 ppbv.

Com p arisons Between the Blackout and Clim atological Ozone from Past Flights.

To exam ine the possibility that the day of the blackout was a low- ozone day, but one well

within norm a l ranges, the data from the bl ackout flights have been com p a r ed to the

clim atological data set of all UMD aircraft flights. The University of Maryland has flown hundreds of flights throughout the North east on high ozone days. Back-trajectories (48 hour) w e re calculated for all the spiral s flown by the UMD aircraft and grouped into clusters, with each clu s ter therefore repres enting sim ilar m e teorolog ical conditions.

W h en com p ared with f lights f a lling within th e sam e cluster, the oz one observations taken

below ~2000 m over Selinsgrove during the blackout are the lowest of all past observations. Air quality observations on the bl ackout flight therefor e represent a highly unusual day. The clim atology of past ozone f lights is indicated below in Figure 5, with the ozone profile also superim posed.

70 500 1000 1500 2000 2500 3000 3500

0 2 0 4 0 6 0 8 0 100 120

Ozone (ppb)

Figure 5. Clim atology of ozone flights. B ack trajectories were clustered, and all 62 flights in this cluster are com p ared agai nst the blackout profil es (solid lines in color) of ozone taken over Selinsgrove on August 15, 2003. Data from al l 62 profiles were grouped in to 100 m altitude bi ns, an d analyzed s t atistically. The

m e dian is shown by the solid ve rtical black line, and the 10 th and 90 th perc entiles

f

o r all f ligh t s in this clu s ter a r e show n as horizontal error bars. The blackout flight (one spiral upward a nd one downward) stands out as distinct, and those data points below 2000 m represent the lowest ozone of all flights in this cluster.

Com p arison with the C M AQ Model.

These three com p arisons (ozone on the blackou t day vs. two reference days, ozone inside the area affected by the blackout vs. outside , and blackout profiles over Selinsgrove vs.

clim atology) suggest that the blackout produ ced a substantial change in ozone over central Pennsylvania. Unfortunately, these changes are not reflected in CMAQ m odel i ng. Hu et al. [2006] used the Direct Decoupled Method (DDM), which their group em bedded in CMAQ, to calculate changes in ozone on th e blackout due to a variety of sources from

severa l reg i ons. DDM calcu late s se nsitiv ities to em issions changes as w e ll a s the us ual pollutant concentrations that are calculated in CMAQ. Using this m e thod, Hu et al.

calcu lated th e sensitiv ity of ozone to cha nges in power plant em issions and another sensitivity to changes in m obile source em i ssions, calculating an oz one reduction of only 4.2% (2.2 ppbv) to power plant NO

x , and very little change due to changes in m obile

source NO

x , which is far less than the res ponse calculated by any of the above

observation-based m e thods. The m o bile source NO

x e m issions changes used in this modeling exercise were not cal culated by any direct m eans, but determ ined by assum i ng sensitivity to changes in m obile source em i ssions, calculating an oz one reduction of only 4.2% (2.2 ppbv) to power plant NO

x , and very little change due to changes in m obile

source NO

x , which is far less than the res ponse calculated by any of the above

observation-based m e thods. The m o bile source NO

x e m issions changes used in this modeling exercise were not cal culated by any direct m eans, but determ ined by assum i ng

8that weeken d travel patterns existed on the day of the blackout. This simulation was perform e d using an updated version of CMAQ 4.3 with the S A PRC99 chem ical

m echanis m
, which is thought to be m o re re sponsive to changes in em issions than the

CB4 che m ical m echanism that has been us ed for OTC/MANE-VU m ode ling, owing to its

more detailed chem istry. Since even the sm allest calcu lated c h ange in ozo n e (f rom observational m e thods) was seven ppbv (14%), a nd the changes were likely considerably higher (e.g. exceeding 3 0 %), it appears that th e model is un derrep r esen ting the ch an ge in ozone due to changes in em issions. The com p arison of changes sim u lated in the m odel to changes deduced from a num b er of observati on-based analyses suggests that the m odel is not appropriately capturing the response in ozone due to changes in power plant

em issions.

EPA Modeling Study of the Effects of the NO

x SI P call.

EPA, in association with several unive rsity colleagues, is perfor m ing a CMAQ

sim u lation of 2002 and 2004 summertim e air quality to determine the benefits of the NO

x SIP call [Gilliland et al., m a nuscript in prepar ation, 2007]. Since this represents a large change in em issions over a relatively short tim e , the benefits should be noticeable.

Em issions and m e teorology were simulated for both 2002 and 2004. The final results are not out yet, and the paper is currently in preparation, but pr elim inary results suggest that though observed m e dian 8-hour ozone levels changed by about 18 ppbv, the CMAQ model only sim u lates a change of 8 ppbv. It is possible that the m odel’s lack of responsiveness m i ght be due to som e quirk of the m e teorological sim u lation, whereby the

m e teorological sim u lation was in error in su ch a way as to render the pho tochem ical

sim u lation unresponsive to em issions changes or that the m e teorol ogical simulation could have produced an artificial environment that would have yielded higher ozone in reality, but these results suggest that the CMAQ m odel underpredicts changes in ozone, especially w h ere power plant em issions ar e concerned. The only m obile em issions changes that would have occurred from 2002 to 2004 would have been from the m o dest

vehicle fleet turnover in those years.

Critical m onitors in New Jersey have alrea dy reached their 2009 design values in 2006.

Analysis of the latest 8-hour ozone desi gn values (based on observations from the summers of 2004, 2005, and 2006) for the Collie rs Mill ozone m onitoring location in central New Jersey show that this m onito r has already reached its projected 2009 design value in 2006 [R. Papalski, NJDEP, persona l co mmunicatio n]. These calculations u s e a base year of 2002, so 2006 is only four years into the seven-year period, and New Jersey

DEP calculates th at au to mobile fleet turnover alo n e will s t ill generate so m e substantial

reductions in NO

x e m iss i ons over the rest of the tim e 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. Furt herm ore, this is not a fluke result at an

isolated m o nitor. All other New Jersey ozone monitoring lo cation s are n o w close to their projected 2009 design values (Tab le 1), and the average value at all of the ozone monitoring locations is the sam e as the 2009 av erage projected value. L a stly, none have 2004-2006 design values that are higher than Col liers Mills, so that is still the high ozone

9monitoring location for the state of New Jerse y, and indeed the enti re M i d

- Atlan tic an d

Northeast.

Table 1. Predicted and Measured D e sign Values in New Jersey

CCOUNTY OZ ONE M O NI T O R L O C A T I ON 2004-200 6
DESIG N

VA L U E Modele d
Predicte d O 3 Conce n tra t io n (BO T W

- v

. 3 )

(pp b v ) (pp b v )

Atlantic Nacot e Creek 78 75

Passaic Rama po 79 79

Monmouth W est Lo ng Bea c h (M on mou t h Uni v ) 81 83

Morri s Chest er 82 85

Camden Camd en Lab 83 89

Bergen Teanec k 85 87

Cumberland Mill v ill e 85 82

Gloucester Clarksb oro 85 88

Hudson Ba y o n n e 85 80

Mercer Rider U 87 87

Middlesex Rut g ers U 88 85

Hunterdo n Flemi n g t on 88 84

Camden An c o r a 88 90

Ocean Colliers Mills 92 93

A v e r age (p pb v ) 85 85

CMAQ Does Not Capture the Diurn a l Cycle, and Therefore the Dyna m i c Range of

Ozone Values.

CMAQ’s response lacks the diurnal and longe r-tim e scale cycles that are in the observations. CMAQ ge nerally fa lls short of peak ozone a nd overpredicts low ozone at night. These shortcom ings likel y have specific causes (e.g. NO

x titratio n has been

blam ed in the past for failures in predic ting ozone at night) bu t those sp ecific causes generally add up to a lack of response to ch ange in the m odel. Figure 6 below com p ares CMAQ predictions with surface ozon e m easure m ents taken across Maryland, Washington, D.C., Virginia, and the Philadelphia nonattainment area. CMAQ consistently falls short of peak ozone valu es, especially on the highest ozone days. CMAQ’s perf orm a nce is theref ore at its wors t when it is need ed the m o st. As will be shown in the following section, CMAQ’s perform a nce is also worse in the geographic

locations wh ere it is m o st needed.

10a)

b)

11c)

Figure 6. Ozone CMAQ sim u lations (dashed li ne) and m easurem ents (solid line) across Washington, D.C., Maryland, Virginia and the Philadelphia nonattainm e nt area. Observations were averaged across all m onitor locations in Washington, D

. C., Maryland, V i rginia, and the Philadelphia non-attainm e nt area. Model outputs

were sam p led f r om the grid ce ll con t ai ning each monitored location and also averaged. O z one is consistently overpre dicted 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.

Perform a nce in Upwind and Rural Areas is W o rs e than in Urban and Suburban Areas.

As m e ntioned above, CMAQ’s low bias on high ozone days is larger (m ore negative) at

rural sites than in suburban or urban areas. Th is topic has already been covered in chapter 2, so only a brief rev i ew will be g i ven here. Sub u rban perform a nce is som e what poorer than that in urban areas, though not as bad as in rural areas. S i nce these rural areas are generally upwind of sites with high design va lues, CMAQ is underpredicting transport in

crucial areas at crucial tim es. Furtherm or e, CM AQ’s perform a nce 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 th is point, the correlation coefficient, R , was calcu lated by perform i ng a least squares fit between oz one observations and CM AQ predictions for

each m onitoring lo cation

. These correlation coefficien ts were then com p ared with

em issions data for poin t sources within an 80 k m radius of each observing site. In this

12part of the country, and on this scale, poi nt source em issions are correlated with

em issions from autom o biles and other sour ces, so areas with fewer point source

em issions also tend to have fewer emissions of other types. T h e results are presented in Figure 7, and show that CMAQ perform s poorly in areas with few local em issions. As has been found earlier, C M AQ has som e trouble in properly capturing transported ozone, and this is likely another re flection of that problem.

Figure 7. Correlation co efficient between surface ozone obs ervations an d CMAQ ozone predictions vs. ozone season point source em issions of NO

x within an 80 km

radius of the ozone m oni tor. Point sour ce em issions in this region tend to be colocated with other em issions, and serv e as a proxy for total em issions.

Perform a nce at sites with lower nearby NO

x e m iss i ons is notably poorer than in

areas where NO

x is relatively abundant. Since areas with lower local nearby NO

x tend to be upwind rural areas, CMA Q is underperform ing in upwind areas.

2. Uncerta in ties and Pro b ability in F u ture Yea r Design Valu es.

Two sources of uncertainty in projecting future year de sign values are relatively straightforward to quantify: representative ness of 2002 design values as indicators of

curren t air q u ality, and u n certainty in the rang e of future year design values as calculated

from several different simulations of base ye ar and future year design values (e.g. similar

runs with dif f

erent versions of the m o del and different versions of the inventory as well

as a run with sm all changes in the future year em issions would all produce som e what different results, but would not represent th e changes expected from the im ple m entation

of a m a jor control program ). Other sources of uncertainty include m odel for m ulation and

13the degree to which the m e teorological fields represent actual conditions in 2002. These additional sources of uncertainty are extrem el y difficult to quantify, since they represent

unknowns in the form ul ation of the model or sensitiv ity to d i fferences b e tween m o deled and actual meteorology that cannot easily be de term ined. Therefore, the approach taken here is to generate reasonable estim ates of uncertainty (note that th is is different from

model error ) due to the things tha t ar e read ily quantified and to m odify them only slightly based on the preceding discussion.

Range of Ba se Year Design Values.

EPA’s recommended procedure for calculating design values calls for creating 3-year averages of the fourth highest 8-hour averag e ozone reading for the individual years.

Since the r e is som e variability in thes e 3-year averages, EPA further recommends

averag ing th ree such v a lues from successi ve years to obtain a design value that is centered on the base year (e.g., for 2002, one w ould 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 averag e). Since variations in

m e teorology 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 uncertain ty in determ ining whether or not an area

will com e into attainm e nt in th e f u tur e

.

Currently, most ozone m onitoring locations throughout the Mid-Atlantic and Northeast

show im proving trends in ozone concentrations over the y e a r s tha t we nt into the 5-year weighted average, though the design values at som e have risen m odestly. The average difference between the highest and lowest 3- year design values is 6 ppbv. Sim ilarly, the

average stan dard deviation for each site ov er th is tim e period is +/- 3 ppbv (using standard deviation only as an estim a te of vari ability and not suggesti ng that a 3-data point standard deviation is adequate for any indivi dual station). Both these m e a s ures suggest
that variations in m e teorology can reasona bly be expected to produce substantial variability in the design values themselves.

It appears reasonable that the representativeness of the current year can th erefore prod uce a variation of 3 ppbv about som e central value. F u rther analysis of these design values

shows that the vast m a jority of sites s how substantial im provem e nts in ozone design

values over this tim e period, likel y due to the combination of NO

x reductions from the

NO

x SIP call and m e teorological conditions in 2003 and 2004 that were less conducive to ozone production.

The largest improvem e nt was a 14 ppbv reduction at Colliers Mills, the ozone m onitor location with the highest current and future y ear d e sign value in the entire Northeast, and the worst was a 5 ppbv increase in the Bronx, NY. No Maryland m onitors show any increases in design values, w ith all showing decreases, as shown in Table 2. Beyond the scope of the analysis time period, the d ecreases continued in Maryland through 2005, and experienced a slight increase in 2006. Th e summer of 2006 was a very warm summer, with conditions highly conduciv e to m a king ozone, so the slight increase from 2005

14likely represents progress, since ozone levels would have risen som e what higher if not for the control p r ogram s that were going into place.

Table 2. Trends in Design Values at Maryland Monitors
Design Values by Year

Site Nam e 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 perm ission to use location. **Monitoring began in 2002 at this location.

Ranges of Future Year Predictions.

Exam ining the results from si m ilar 2009 scen arios gives insight into how the m odel responds to changes in em issions . Different future year scenarios have been m odeled to support the Maryland S I P m odeling effort. Ta ken together, the variations am ong these

attem p ts at representing future year em issions give som e id ea as to the precision, though

not the tota l error in th e mode l, since the la rges t e rrors in the modeling ar e system atic

, and will no t show up in tests of m ode l and em issions invento r y precis i on. As discussed above, the C M AQ m odel likely does not respond as m u ch to changes in em issions as the

atm o sphere does. Therefore, it is not surpri sing that its future year predictions do not

vary m u ch between different em issio n s scenar ios. The scenarios used to exam ine this source of uncertainty were: OTC base A a nd base B m odeling, VIST AS model outputs at overlapping monitors, and OTC’s “beyond on the books on the way” m odeling run. The VISTAS modeling represents a different, partially indepe ndent attem p t at m odeling future year design values using som e what different em issions, di fferent meteorology, and a slightly different m odeling 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 th e way” run was also examined because

its r e sults a r e sim ilar to the other 200 9 f u ture bas e scena r ios, except f o r a f e w addition a l

em issions control strategies, so in this contex t it represents perturba tions to the em issions

15projected for 2009. Exam ining this run allo ws one to gain som e insight into the sensitivity of the m odel to errors and uncerta inties in the em iss i ons inven t o r ies. The r e are not enough scenarios to gene rate a proper standard deviati on at each site, or to give

much m eaning to a range generated from t

hose individual predicti ons, bu t the av erage range and th e averag e standard dev i ation g i ve a n estim ate o f variability between run s at
sites across the m odeling dom a in. The av erage range was +/- 0.83 ppbv, and the average standard deviation was +/- 0.75 ppbv. Splitting the difference gives roughly +/-0.8 ppbv.

This repr ese n ts the v a ria b ility to be e xpected from different attem p ts at modeling future year air quality, and som e of the variability expected from s m all errors in the em issions inventory. The range of 2009 proj ections does not represent the full uncertainty in future year results, but the sensitivity of the model to small variations in em issions. Therefore, it represents only part of the uncertainty in th e modeled result. Em issions are likely more uncertain than these simple estim ates woul d suggest, with errors in som e em issions inventory categories as high as 50% [ e .g. Choi et al., 2006].

The two m e asures abov e can be co mbined to give a conservative estim a te of the uncertainty in future year projections. Sin ce the represen tativ e ness of the base year and variations in future year projections are uncorrelated, standard error propagation techniques can be used, nam e ly by squaring and adding the uncertainties, and taking the square root of the sum t o get the combined uncertainty. The un certainties do not add because they are not correlated, so o n e is as likely to be positive as the other is to be negative. The com b ination gives an uncertainty in future year design values of 3.1 ppbv.

The sizes of other sources of uncertainty are considerably m o re difficult, if not
im possible (since they represent unknown asp ects of the science of air pollution), to

estim ate reasonably. Many, such as CMAQ’s lack of responsiveness to changes in

em issions, are system atic, and therefore canno t reasonably be represen te d with erro r bars, but should instead be represented with a shift of the projected value. The discussion in the previous part of this chapter centered on sev e ral com p arisons between m odeled

values and m easure m ents around tim e s of ch ange. As discussed, CMAQ underrepresents change, and should be corrected for that tenden cy. 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 m a rgin for error, the likely

100% increase in benefit is cut in ha lf, and changes predicted by CMAQ will be increased by only 50% to account for its l ack of sensitivity. Doubling the effects predicted by CMAQ more clos ely represents the previous discussion, but the m o re 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% underr esponse estim a te, and the upper and low e r bounds of that 2009 design value, combining th e estim ates of uncertainty established

before with the estim a te of CMAQ ’s lack of responsiveness.

16Table 3. Current and Projected Design Valu es and Their Uncertai nties f o r Maryland

Monitors

Observed M o d e l e d P r o b a b l e Monitor

Name AIRS-ID 2002 Design

Value 2009 Design

Value 2012 Design

Value 2009 Design

Value 2009

Low er 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. Count y 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 illus t ra tes th e be st estim a tes f o r f u ture design values and their un certainties. Even with a broad range of un certain ty due to rep r esenta tiven e ss issue s and som e model uncertainty, given CMAQ’s under-response to cha nges in em issions, it is highly probable

that M a ryland will attain the 8-hour ozone stand a rd in 2009, even allowing for adverse weather conditions. The upper bounds are a ll below 81 ppbv, with m o st well below. Probable 2009 design values are also very sim ilar to calcu lated design values for 2012. Therefore, much like th e current (20 06) New Jersey design values presented in Table 1,

which showed that 2009 values had arrived three years early, thes e projections suggest that 2012 design valu es will arriv e early for Maryland m onitors as well. Regardless, by 2012, CMAQ predicts that Mary lan d will be well in attainm e nt at all of its m onitors, with only Edgewood rem a ining as high as 80 ppbv.

1784 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

D a v i d s onv

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a

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r ian

H a ger

st

ow

n

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 da ta from Table 3, depicting 2002 base year design values (blue columns), m odeled 2009 design values (black diam onds), and

the m o st probable future year design va lues along with the upper and low e r bounds for those future year values (r ound circles and associated error bars, respectively). All Maryland m on itoring locations are shown.

1860 65 70 75 80 85 90

D a vi dsonvi l l e

Ft

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e

P a don

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S out

h Ca r ro l l

F ai

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P. G.

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o u nt

y E q uest

r i an

H ager

st

own

Monitoring Location A ll sites attain the 85ppb NAAQS for 8-Hour Ozone

Range of 2009 8-Hour Ozone Design Values from Weight of Evidence

Figure 9. T h e ranges of probable future ye ar design values from Table 3, given in a graphical form at. The lower end of each bar rep r esents 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 m e thods have been used to compare the measured effects from changes in em issions to those predicted by th e C M AQ m odel

, and all affirm the idea that the changes in ozone will be larger (e.g. ozone will be be tter) than pred icted by CMAQ. A study of the 2003 Northeast Blackout [Maruf u et al., 2004] shows that the blackout caused a drop of at least 7 ppbv ozone, and likely considerably m o re, while a m odeling study of the sam e event [Hu et al., 2006] us ed CMAQ to predict only a 2.2 ppbv change. An ongoing study by EPA reveals that the NO

x SIP call likely produced double the

benef it tha t CMAQ predicted. Mean while, the S t ate of New Jersey reports that its o z o n e monitor locations appear to have reached their 2009 design values in 2006, three years

ahead of time. W h en compared to ob servations from the 2002 ozone season, CMAQ underpred i cts diurnal variabilit y, and shows im portan t perform a nce shortcom ings in areas just upwind of Maryland on high ozone days, na m e ly the Ohio River Valley and th e s t ate of Virginia. Furtherm ore, perform a nce on hi gh ozone days tends to be best in urban areas, next best in suburban areas, and wo rst in rural areas, so CMAQ is underpredicting ozone in upwind areas f r om which it w ould enter the largely urban and suburban

nonattainm e nt areas.
Uncertainties have been estim ated for two types of errors in CMAQ modeling.

One source of uncertain ty is the rang e of po ssible m e teorolog ical cond itio ns that m i ght be encountered in future years. This is not to say that 2002 w a s not representative, but

19instead that m e teorological va riability from year to year is well known, and any future projections must account for this to achieve a reasonable m a rgin of safety, so particularly bad future y ear m e teorology will not result in nu m e rous exceedances of the standard.

Som e of the uncertainty arising from th e m odel and its em issions was estim a ted by

exam ining several different 2009 scenarios a nd determ ining 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,

m e teorology, and m odel for m ulation all play a ro le, but are significantly more difficult to

estim ate. The erro r es tim a te and the future year m e teorological variab ility estim ate were

com b ined to generate an estim ate of future year uncertainty in ozone design values. To account for CMAQ’s resistance to ch ange, CMAQ changes were increased by 50%, and probable future ozone design valu es w e re calculated, along with probable ranges of ozone concentrations to account fo r m e teorological variab ility and som e model errors. The resulting picture of future oz one is that likely 2009 ozone design values correspond to 2012 design values calculated di rectly by CMAQ. This is in line with current observations from New Jersey (hom e to the ozone m onitoring 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 underw ay throughout the num e rical m o deling community to determ ine the proper way to ca lculate the uncertainty from a com puter models. Currently, proper calc ulations of m odel uncertainties are difficult to determ ine, especially w h en the m o del is us ed in a relativ e sense by calcu lating chang e s from a baseline instead of absolute predictions of pollutant concentrations . For photochem i cal modeling, the largest errors ar e thought to be system atic, and should be correctable. Meteorological m odeling, in contrast, often suffers from random errors and nonlinear behavior, so sm all changes are am plified. Since the two system s are differ e nt (though coupled), m e thods different from those that have worked so well in m e teorological modeling (e. g. ensem b le modeling te chniques) w ill h a ve to b e developed to asses s photochem i cal m odeling uncertainties. In Chapter 4 of this Appendix, som e of the shortcom ings of the CMAQ chem ical

m echanis m were laid out, along with som e possible suggestions for rem e dies. One possible cou r se of action would be to perf or m som e sensitivity tests with another m odel

or another chem ical m echanism such as SAP RC99, which is more detailed and therefore should be more representative of atmospheric chem istry. Some of the lack of response in CMAQ is likely due to problem s with em i ssions inventories and their processing.

Em issions from point sources are likely underestim ated on high energy dem a nd days, since peaking units com e on line for only a short tim e in the heat of the day in response to peak dem a nd. Scattering of light by aeros ols is known to increase photolysis rates aloft

and decrease them near the surface, there by altering photoch e m i str y. Likewise, clou ds alter photochem i stry, and rem a in a very di fficult forecasting problem , so they too are likely poorly represented in both MM5 and CMAQ. If clouds and the ef fects of aerosols

are m i srepresented, then CMAQ’s photoc hem i stry will not p e rform properly, overpred i cting ozone at the surface and underpre dicting ozon e, and therefore transport,

20aloft. Finally, the m e teorology by itself is another source of uncertainty, and alm o st certainly plays a role in CMAQ’s lack of response. CMAQ is a state-of-the-art air quality m odeling tool, and has proven its utility

with im pressive capabilities. The purpose of this chapte r is n o t to disp ara g e CMAQ, but to advocate using a tool responsibly, with a proper acknowledgem ent of its abilities and

shortcom ings. By assessing the m odel’s perf ormance and exam ining the im plications of

the m odel’s shortcom ings one can im prove upon its predictions. A com p arable approach is used in generating the MOS (Model Output Statistics) by comparing observations to outputs from National Weather Se rvice forecast m odels. The skill of weather predictions

increases su bstantially in going from raw m odel output to the MOS because MOS takes into account (through a more com p l e x sta tistical procedure) model tendencies and corrects them by com p a r ing the m o del to obs ervations. Furtherm ore, the com p arisons

m a de in this and other chapters likely were not possible in the past, since large, sudden

reductions in em issions like those from the NO

x SIP Call and the 2003 blackout are relatively rare. The prospect of dynam i c m odel e v aluation ho lds great prom ise for rapid model developm ent in the com i ng years.

21References

Choi, Y.-J., S. H. Ehr m a n, R. V. Cal a brese, J. W

. Stehr, R. R. Dickerson, A Com b ine d Approach for the Evaluation of a VOC Em issions Inventory, J. Air Waste Manage.

Assoc ., 56:169–178, February, 2006.

Gilliland, A. , Personal comm unication, January 12, 2007; manuscrip t in preparation.

Hu, Y. M. T. Odm a n, and A. G. Rus s ell, Re-exam i nation of the 2003 North Am erican

electrical blackout im pacts on regional air quality, Geophysical Research Letters , V o l. 33, L22810, doi:10.1029/2006GL027252, 2006.

Marufu, L.T., B. F. Taubm an, B. Bl oom e r, C. A. Piety, B. G. Doddridge, J. W

. Stehr,

and R. R. Di ckerson, The 2003 North Am eri can electrical blackout: An accidental experim e nt in atm o spheric chem istry, Geophysical Research Letters , Vol. 31, L13106, doi:10.1029/2004GL019771, 2004.

22

# Attachment 16_OTC_2007_SpecialMtg_

HEDDMOU_Final_070302 .pdfMemorandum of Understanding Among the States of the Ozone Transport Commission Concer ning the Incorporation of High Electrical Demand Day Emissi on Reduct i on Strate gi es 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 implem entation of regional strat egies to reduce ground-level
ozone to healthful lev e ls; and

Whereas the adverse health effects of ground-level ozone are well documented, and in spite of significant reductions of ozone precursor

emissions achieved t o date as a resu lt of our NO x MOU of 1994, the US

Environme n tal Protec tion Age n c y (EPA) NOx SIP ca ll effective in 2003, an d expected r eductions t o be further achiev ed by federal and st ate programs over the next decade, a signific ant portion of the ozone pr oblem cont inues to be caused by nitrogen oxides (NO X ) transported into and generated within our region by electrical ge nerating units (EGUs); and

Whereas , high electrical demand day (HE DD) operat ion of EG Us generally

have not been addres sed under existing ai r 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 s i gnificant contri butor to NOx emiss i ons on high oz one days; and

Whereas , the NOx cap and trade program, al though effective generally has, by its very nature, had limited suc c ess in reducing emis sions from HEDD unit s on HEDDs; and

Whereas , OTC staff, state environmental and utility regulator s, EPA staff, EGU owners and operator s and the independent regi onal systems operators have been meeting to assess emissions associated with HEDD during the

ozone seas on and to address exc e ss NOx emissions on HEDDs, and

Whereas , OTC is guided by its precepts to seek reductions in the most comprehensive, cost effective manner pos sible in order to maximiz e public health, environmental and economic benefits while ens uring an adequate

electrical c apacity and reliability for the region; and ___________

Connecticut

Delaware

District of Colu mbia

Maine

Mar y land

Massachusetts

New Hampshire

New Jersey

New York

Penns y l van i a

Rhode Island

Vermont

Virginia

___________

Christopher R e cchia

Execu tive Dire ct or

___________

444 N. Capitol St. NW

Suite 638

W a shington, DC 20001 ( 202) 508- 384 0 FAX ( 202) 508- 3841

Em ail: ozone@ o tcair

. or g

___________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 a pproximately 25% in the short term through the application of known cont rol technologies to HEDD combustion turbine, coal and residual oil burning units; and

Whereas , installing typically used NOx cont rol technologies may not be available to, or be the most cost effective met hod 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 standar d 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 estima te that using a cap and trade mechanism alone to provide sufficient finan cial incentives to cause the clean up
of HEDD units would need an 18:1 retirem ent 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 commi t 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: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 wi ll 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 cert ainty and appropriate flexibility for that state and its electric gener ators. 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 par tnership agreements;

• energy efficiency programs;

• demand response programs, provided that such programs reduce and/or preclude the installation or us e 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 st ate-specific target emission reduction is specified above sign this MOU in support and appreciation of the listed states making this commitment, will continue to evaluat e the HEDD issue in their state and, as necessary and appropr iate, 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 progr ams 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 operat ors regarding energy efficiency, dynamic pricing and other market oriented in centives toward significant demand reduction and clean new or repowered supply• develo p ment of long-term perfo rmance standards that will ens ure reliable, clean future generation.

• development of emissi ons 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 Stat es this 2 nd day of March , 2007:

__________________ _____________ Connectic u t __________________ _____________ Dela ware

__________________ _____________ District of Columbia __________________ _____________ Maine

______//s//_________

________________ Maryland __________________ _____________ Massachusetts

__________________ ____________ New Hamp shire __________________ _____________ New Jerse y

__________________ _____________ New York __________________ _____________ Pennsylv a nia

__________________ _____________ Rhode Island __________________ _____________ Vermont

__________________ _____________ Virgin ia