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Modeling an air pollution episode in northwesternUnited States: Identifying the effect of nitrogen oxideand volatile organic compound emission changes on airpollutants formation using direct sensitivity analysisAlexandra P. Tsimpidi a , Marcus Trail a , Yongtao Hu a , Athanasios Nenes b c & Armistead G.Russell aa School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta,GA, USAb School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA,USAc School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta,GA, USA

Accepted author version posted online: 18 Jun 2012.

To cite this article: Alexandra P. Tsimpidi, Marcus Trail, Yongtao Hu, Athanasios Nenes & Armistead G. Russell (2012):Modeling an air pollution episode in northwestern United States: Identifying the effect of nitrogen oxide and volatileorganic compound emission changes on air pollutants formation using direct sensitivity analysis, Journal of the Air & WasteManagement Association, 62:10, 1150-1165

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TECHNICAL PAPER

Modeling an air pollution episode in northwestern United States:Identifying the effect of nitrogen oxide and volatile organiccompound emission changes on air pollutants formation usingdirect sensitivity analysisAlexandra P. Tsimpidi,1 Marcus Trail,1 Yongtao Hu,1 Athanasios Nenes,2,3

and Armistead G. Russell1,⁄1School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA2School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA3School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA⁄Please address correspondence to: Armistead G. Russell, School of Civil and Environmental Engineering, Georgia Institute of Technology,Atlanta, GA, 30332; e-mail: ted.russell@ce.gatech.edu

Air quality impacts of volatile organic compound (VOC) and nitrogen oxide (NOx) emissions from major sources over thenorthwestern United States are simulated. The comprehensive nested modeling system comprises three models: CommunityMultiscale Air Quality (CMAQ), Weather Research and Forecasting (WRF), and Sparse Matrix Operator Kernel Emissions(SMOKE). In addition, the decoupled direct method in three dimensions (DDM-3D) is used to determine the sensitivities ofpollutant concentrations to changes in precursor emissions during a severe smog episode in July of 2006. The average simulated8-hr daily maximum O3 concentration is 48.9 ppb, with 1-hr O3 maxima up to 106 ppb (40 km southeast of Seattle). The averagesimulated PM2.5 (particulate matter with an aerodynamic diameter <2.5mm) concentration at the measurement sites is 9.06mg m

�3,which is in good agreement with the observed concentration (8.06 mg m�3). In urban areas (i.e., Seattle, Vancouver, etc.), the modelpredicts that, on average, a reduction of NOx emissions is simulated to lead to an increase in average 8-hr daily maximum O3

concentrations, and will be most prominent in Seattle (where the greatest sensitivity is�0.2 ppb per% change of mobile sources). Onthe other hand, decreasing NOx emissions is simulated to decrease the 8-hr maximum O3 concentrations in remote and forestedareas. Decreased NOx emissions are simulated to slightly increase PM2.5 in major urban areas. In urban areas, a decrease in VOCemissions will result in a decrease of 8-hr maximum O3 concentrations. The impact of decreased VOC emissions from biogenic,mobile, nonroad, and area sources on average 8-hr daily maximum O3 concentrations is up to 0.05 ppb decrease per % of emissionchange, each. Decreased emissions of VOCs decrease average PM2.5 concentrations in the entire modeling domain. In major cities,PM2.5 concentrations are more sensitive to emissions of VOCs from biogenic sources than other sources of VOCs. These results canbe used to interpret the effectiveness of VOC or NOx controls over pollutant concentrations, especially for localities that may exceedNational Ambient Air Quality Standards (NAAQS).

Implications: The effect of NOx and VOC controls on ozone and PM2.5 concentrations in the northwestern United States isexamined using the decoupled direct method in three dimensions (DDM-3D) in a state-of-the-art three-dimensional chemicaltransport model (CMAQ). NOx controls are predicted to increase PM2.5 and ozone in major urban areas and decrease ozone in moreremote and forested areas. VOC reductions are helpful in reducing ozone and PM2.5 concentrations in urban areas. Biogenic VOCsources have the largest impact on O3 and PM2.5 concentrations.

Introduction

Ground-level ozone (O3) and fine particulate matter (aero-dynamic diameter <2.5 mm; PM2.5) are major constituents ofurban and regional smog and are suspected of affecting human(Bell et al., 2005; Ito et al., 2005; Schwartz et al., 1996; Zhouet al., 2011) and ecosystem health (Likens et al., 1996;Mauzerall

and Wang, 2001). Emissions of oxides of nitrogen (NOx) andvolatile organic compounds (VOCs) drive a complex series ofchemical and physical transformations that result in the forma-tion of ozone and secondary particulate matter, and both ambientO3 and PM2.5 concentrations can be reduced by controls onemissions of NOx or VOCs, depending on which is the limitingprecursor (Dodge, 1987; Seinfeld and Pandis, 2006). Emissions

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from industrial facilities and electric utilities, motor vehicleexhaust, gasoline vapors, and chemical solvents are some ofthe major sources of NOx and/or VOCs.

The National Ambient Air Quality Standards (NAAQS) for 8-hr O3 and 24-hr PM2.5, set by the U.S. Environmental ProtectionAgency (EPA), are 75 ppb and 35 mg m�3, respectively (http://www.epa.gov/air/criteria.html), whereas the annual PM2.5 stan-dard is currently 15mg m�3. Moreover, the Canadian standard fordaily maximum 8-hr average O3 concentration is set at 65 ppb andthe 24-hr average PM2.5 standard is set at 30 mg m�3. Eulerian-grid-based air quality chemical transport models (CTMs) aretypically used for understanding the factors leading to ozone andPM2.5 exceedances and to test emission control strategies to lowerozone and PM2.5 to meet standards. The Community MultiscaleAir Quality model (CMAQ) (Byun and Schere, 2006) and theComprehensive Air Quality model with extensions (CAMx)(Environ, 2003) have been applied and evaluated over differentregions (Eder and Yu, 2006; Karydis et al., 2007; Tesche et al.,2006; Tong andMauzerall, 2006; Zhang et al., 2004, 2005). A fewmodeling studies (Barna and Lamb, 2000; Barna et al., 2000,2001; Elleman and Covert, 2009; Jiang et al., 2003; O’Neill andLamb, 2005) have investigated ozone and aerosol production inthe northwestern United States. Although the region is not gen-erally known for having a photochemical smog problem, it has ahistory of episodes of elevated ozone (Barna and Lamb, 2000).Barna et al. (2000) investigated the air pollution in the Cascadiaregion of the Pacific Northwest, highlighting several features ofthis area that are different than those in other areas typicallyevaluated in ozone modeling studies, including (1) complex topo-graphic and land–sea features; (2) an anthropogenic emissioninventory that consists primarily of mobile sources, with relativelyfew large industrial point sources; (3) biogenic emissions domi-nated by coniferous forests; and (4) a predominant inflow consist-ing of relatively cleanmaritime air. Jiang et al. (2003) used processanalysis to determine the relative importance of chemical produc-tion, advection, diffusion, and deposition to ozone concentrationdownwind from urban Seattle, Washington. This analysis showedthat ozone concentrations increase during the day as chemicalproduction exceeds the net effects of deposition and verticaldiffusion but decrease after mid-afternoonwhen horizontal advec-tion begins to dominate the other processes. Within the samestudy, the importance of VOC/NOx effects versus differences inmeteorology was also considered. Barna et al. (2001) investigatedthe sensitivity of ozone formation to VOC and NOx reductionswithin the Cascadia airshed of the Pacific Northwest. For thisparticular case, reductions in VOC emissions alone, or a combina-tion of reduced VOC and NOx emissions, were generally found tobe most effective, whereas reducing NOx emissions alone resultedin increased ozone in the Seattle area.

CTMs are widely used to calculate the response of ambientconcentrations of various gaseous and aerosol pollutants in theatmosphere to changes in emissions (Blanchard andStoeckenius, 2001; Chock et al., 1999; Mendoza-Dominguezet al., 2000; Pun et al., 2008; Tsimpidi et al., 2007, 2008). Thenonlinear relationship between ozone and secondary particulatematter components with their precursors suggests potential stra-tegies for obtaining lower ambient concentrations, and the levelsof control required are not directly known. The decoupled direct

method in three dimensions (DDM-3D) has proven to be apowerful and efficient approach to identifying how sourcesimpact ozone and particulate matter air quality for use in policydevelopment (Dunker, 1984; Dunker et al., 2002; Mendoza-Dominguez et al., 2000; Odman et al., 2002). DDM is found tobe in good agreement with brute force emission reduction simu-lations when calculating the sensitivity of the 3D model toperturbations up to 30% from the base case (Dunker et al.,2002; Yang et al., 1997). DDM has been also used in CMAQto estimate PM2.5 sensitivities and source impact analysis(Napelenok et al., 2006), and for PM2.5 source apportionment(Boylan and Russell, 2006; Koo et al., 2009).

In this work, we investigate photochemical ozone and PM2.5

production in the Puget Sound region of the northwestern UnitedStates during a period with exceedances of the current 8-hrNAAQS for ozone. The Pacific Northwest is home to severallarge urban areas surrounded by forests, mountains, and agricul-ture areas. Recent high levels of O3 suggest that the area may beat risk of falling in nonattainment, particularly if the standard istightened or if transported O3 increases background levels. HereCMAQ with DDM-3D is used to identify the impact of NOx andVOC sources on O3 and PM2.5 formation in the area. Theanalysis focuses on the parallel sensitivities of ozone andPM2.5 concentrations to mobile, nonroad, point, area, and bio-genic sources of VOCs and NOx.

Modeling Approach

Air quality model

The Community Multiscale Air Quality model (CMAQ ver-sion 4.7) (Foley et al., 2010) is run on various grid resolutions tosimulate the transformation and fate of air pollutants. Gas-phasechemistry is modeled using the SAPRC-99 chemical mechanism(Carter, 2000). CMAQ version 4.7 includes the following: (i)updates to the heterogeneous N2O5 hydrolysis parameterization(Davis et al., 2008); (ii) improved treatment of secondary organicaerosol (SOA) formation (Carlton et al., 2008, 2009, 2010); and(iii) a new treatment of gas-to-particle mass transfer for coarseaerosol (Kelly et al., 2010).

CMAQ is applied here using a nested-grids approach. Themodeling domain uses a Lambert Conformal Projection centeredat 40�N, 97�Wwith true latitudes of 33�N and 45�N. The inner-most domain covers a 192 � 312-km region focusing on theSeattle area, with 4-km horizontal grid-spacing nests inside a432 � 576-km region, with a 12-km horizontal grid-spacingdomain that covers the state of Washington and portions ofOregon and British Columbia (Figure 1). The outer domainuses a 36-km horizontal grid-spacing that covers the entire con-tinental United States as well as portions of Canada and Mexico(5328 � 4032 km). All three grids have 13 vertical layersextending �15.9 km above ground, with seven layers below 1km and the first layer is 18 m thick. Key features of the innerdomains include rugged terrain and a complex coastline. Theperiod modeled is 12–24 July 2006, which captures a pollutionevent when high O3 concentrations were observed across theregion. The highest O3 level observed was 134 ppb at MudMountain, near Enumclaw (outside of Seattle, Washington), on

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21 July. This period is characterized by high temperatures andrelatively little cloud cover. The default profiles of CMAQ ver-sion 4.7 with slight revisions are used to prepare initial condi-tions (ICs) and boundary conditions (BCs) for the 36-kmdomain. Because the 36-km-resolution domain extends beyond

most populated and industrial areas (except for some parts ofMexico), concentrations typical of a “clean” background atmo-sphere are used at the boundaries in the base simulation. Theinitial condition for O3 is 35 ppb and the value of the O3

concentrations at the boundaries of the 36-km domain is 35ppb at the north and west boundaries, and 30 ppb at the southand east boundaries. The simulated coarse-grid species concen-trations are used as initial and boundary conditions for the finergrids. DDM-3D is used to identify the impact of boundaryconditions on the predicted O3 concentrations and found thatboundary conditions contribute approximately 17 ppb to thepredicted average O3 concentration in the Puget Sound region.The impact of initial conditions on the predicted ozone concen-tration in the Puget Sound areawas found to be close to zero aftera spin-up period of 2 days.

Sensitivity analysis method

The decoupled direct method in three dimensions (DDM-3D;Dunker, 1980) is used to calculate the seminormalized sensitiv-ity, Sð1Þij , of the ambient concentrations of a pollutant, to pertur-bations in an input parameter (e.g., emission rate, initialcondition, or boundary condition):

Sð1Þij ¼ Pj@Ci

@Pj(1)

where Ci is the ambient concentration of species i, and Pj is theunperturbed “base case” emissions rate of source j. The sensi-tivity coefficients are computed for all modeled species and varyspatially and temporally. DDM-3D uses the same numericalalgorithms and operator splitting processes as for calculatingconcentrations (Dunker, 1981, 1984; Hakami et al., 2003; Yanget al., 1997).

DDM-3D has been successfully integrated into CMAQ byCohan et al. (2005) and updated to include algorithms for com-puting particulate matter (PM) sensitivity coefficients(Napelenok et al., 2006). Second-order sensitivities have alsobeen successfully calculated by differentiating the governingsensitivity equations with respect to the parameter of interest(Hakami et al., 2003). Higher-order sensitivity coefficients areuseful when considering large perturbations away from the basecase, up to 50% (Hakami et al., 2004), whereas first-ordersensitivities are typically accurate for up to 30% perturbationsin input parameters (Dunker et al., 2002). Our focus is oncalculating ambient O3 and PM2.5 S

ð1Þij to emissions of NOx and

VOCs from area (small businesses, offices and residences, wildfires, dirt roads), biogenic (vegetation, soils), mobile (roadwayvehicles), nonroad (construction, industrial and agriculturalequipment, trains, airplanes), and point (factories, power plants,refineries, and other large facilities) source emission categoriesin CMAQ, as well as the impact of Canadian anthropogenicemissions on potential nonattainment areas.

Meteorological fields

The Weather Research and Forecasting model (WRF version3.1.1) was used to generate meteorological fields for the 12–24

Figure 1. Top: Modeling domains with horizontal grid-spacing resolutions of 36and 12 km, respectively. Bottom:Modeling domains with horizontal grid-spacingresolutions of 12 and 4 km, respectively. Black dots represent the location of the4-km-domain ozone monitoring sites: (a) Issaquah, (b) North Bend, (c)Enumclaw, (d) Jackson Visitor Center, (e) Pack Forest, (f) Tahoma WoodsAdmin, (g) Casino Drive/North End, and (h) Custer/Loomis. UrbanizedU.S. areas are shown in gray.

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July 2006 episode. The configuration of physics schemes is asfollows: long-wave Rapid Radiative Transfer Model (RRTM)(Mlawer et al., 1997) and Dudhia scheme (Dudhia, 1989) areused for long-wave and short-wave radiation, respectively;Yonsei University (YSU) scheme (Hong et al., 2006) is usedfor the planetary boundary layer; Noah scheme is used for landsurface model (LSM); a revised version of Kain-Fritsch scheme(Kain and Fritsch, 1990) is used to represent the effects of bothdeep and shallow cumulus clouds; and the scheme proposed byLin et al. (1983) is chosen as the microphysics option. A sum-mary of the various schemes and their uses can be foundSkamarock et al. (2005). The initial and boundary conditionswere taken from North American Mesoscale (NAM) modelanalysis products at a 12-km spatial resolution at 6-hr intervals.

WRF simulations use three nested grids with one-way nest-ing. The 36-, 12-, and 4-km domains extend over 165 � 129, 43� 55, and 55 � 85 grids, respectively, with 40 layers verticallywith the top pressure of 5000 Pa, and are chosen to overlap withthe corresponding CMAQ model domains. Four-dimensionaldata assimilation grid nudging is employed for the 36- and the12-km domain solutions. For the inner domain (4-km resolu-tion), observational nudging is used. The 4-km model domain isdominated by several complex features and the meteorologywithin this region is strongly influenced by orographic effectsand land water interactions (Mass, 1982). Barna and Lamb(2000) recommended using observational nudging rather thanother meteorological model configurations for predicting ozonespatial patterns over this region.

Simulated surface meteorological fields were examinedagainst surface hourly observations from the United States andCanada (Table 1), with performancewell within the typical rangefor air quality modeling (Emery et al., 2001; Hanna and Yang,2001). The WRF hourly output data were processed with theMeteorology-Chemistry Interface Processor (MCIP) version3.4.1 for CMAQ. The highest predicted temperature during thesimulation period occurred on 21 July 2006 (303 K). This is inagreement with measurements that also report the highest tem-perature on that day (not shown).

Emission inventory

The Sparse Matrix Operator Kernel Emissions (SMOKEversion 2.6) model (Carolina Environmental Program [CEP],

2003) was used to generate hourly, gridded and speciated emis-sions for input to CMAQ. The emission inventory containscounty-level emissions from different emission source cate-gories for the contiguous United States, southern Canada, andnorthern Mexico. SMOKE simulations are based on the 2005-based modeling platform version 4.0 using the 2005 version 2National Emissions Inventory (NEI) data (http://www.epa.gov/ttn/chief/emch/index.html#2005) and ancillary data for tem-poral, spatial, and chemical allocation of emissions. Within thisversion of the modeling platform, nonroad sources were simu-lated using the NONROAD 2004 model via National MobileInventory Model (NMIM). Mobile sources were simulated usingMOBILE6 model via NMIM (not including refueling emis-sions). The Continuous Emissions Monitor (CEM) data foryear 2006 were obtained from the EPA’s Clean Air MarketsDivision and supplied to provide hourly emissions for electricgenerating utilities (EGUs) for 2006. The database contains theNOx and SO2 emissions that replaced the 2005 NEI NOx andSO2 emissions for EGU sources. Biogenic emissions were esti-mated by using the Biogenic Emissions Inventory System 3.14(BEIS3.14) with the Biogenic Emissions Landcover Database3.0 (BELD3), which includes 232 vegetation classes (exceptCanada and Mexico where only the standard 19U.S. Geological Survey [USGS] categories are available).Canadian and Mexican anthropogenic emissions used in theSMOKE modeling are the 2006 National Pollutant ReleaseInventory (NPRI) and the 1999 Mexico National EmissionsInventory (MNEI), respectively, coming from the modeling plat-form too. A uniform monthly temporal profile is used for allo-cating emissions from wood-burning sources such as fireplaceand wood fuel facilities in Canada. This means that these wood-smoke emissions are uniformly distributed throughout the yearbut, in reality, at least fireplaces are operated in winter, less likelyin summer. SMOKE uses spatial surrogates (e.g., population andland use distributions), allocating the NEI’s county-level emis-sions to the modeling grid cells. The resulting inventory consistsof pollutants emitted from area, mobile, point, fire, ocean, bio-genic, and agricultural sources (Table 2).

CMAQ Predictions and Evaluation

The average domain-wide, simulated 8-hr daily maximum O3

concentration for the 14–24 July 2006 episode is 48.9 ppb

Table 1. Bias and error in WRF-generated meteorological parameter fields during 14–24 July 2006 with respect to the Techniques Development Laboratory (TDL)surface observations

Surface Wind Speed Surface Wind DirectionSurface AirTemperature Surface Humidity

Model DomainResolution

Bias(m sec�1)

RMSE(m sec�1)

Bias(degree)

Gross Error(degree)

Bias(K)

RMSE(K)

Bias(g kg�1)

Gross Error(g kg�1)

36 km �0.15 1.7 5.19 36.5 0.13 2.8 �0.22 1.412 km �0.40 1.9 6.18 46.4 �0.85 3.0 0.22 1.04 km �0.25 1.3 2.22 45.6 0.12 2.4 0.51 1.0

Note: RMSE ¼ root mean square error.

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(Figure 2a). High O3 concentrations during this episode aresimulated south of the Seattle urban center over Pierce Countywith 1-hr O3 peak value of 106 ppb simulated at 4 p.m. local timeon 21 July (Figure 2b). Time series of the observed and simu-lated surface O3 mixing ratios at eight monitoring sites in theregion suggest that the calculation tracks the observations well,although peak levels are not fully captured (Figure 3). The meanbias (MB), mean absolute gross error (MAGE), normalizedmean bias (NMB), normalized mean error (NME), and the rootmean square error (RMSE) were also calculated (Table 3) toquantify the model skill:

MAGE ¼ 1

N

XNi¼1

Pi � Oij j (2)

MB ¼ 1

N

XNi¼1

Pi � Oið Þ (3)

NME ¼PN

i¼1 Pi � OiPNi¼1 Oi

(4)

NMB ¼PN

i¼1 Pi � Oið ÞPNi¼1 Oi

(5)

RMSE ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1

N

XN

i�1Pi � Oið Þ2

r(6)

where Pi is the predicted value of the pollutant concentration, Oi

is the observed value of the pollutant at the same time, and N isthe total number of the predictions used for the comparison.NME (in %) and MAGE (in ppb) describe the overall discre-pancy between predictions and observations, whereas NMB (in%) and MB (in ppb) are sensitive to systematic errors. RMSE (in

Table 2. Criteria air pollutants emissions summary in tons day�1 for the 4-km domain

Source Category NOx VOC SO2 NH3 CO PM2.5 PM2.5–10

U.S. area 22 173 12 59 164 12 24U.S. mobile 311 214 6 12 2161 5 0.1U.S. nonroad 91 117 9 0.1 1331 5 0.6U.S. point 133 17 78 45 2800 65 44U.S. biogenic 7 621 0 0 295 0 0Canadian emissions 133 176 10 2.6 1093 30 25

Figure 2. (a) Average simulated surface concentration of 8-hr daily maximum O3 during 14–24 July 2006. (b) Surface concentration of O3 at 11:00 p.m. (23:00)coordinated universal time (UTC) or 4:00 p.m. (16:00) local time (LT) on 21 July, when the overall maximum peak occurred.

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ppb) is the root of the mean square error, which incorporatesboth the variance of the prediction and its bias. Additional detailsfor the above evaluation metrics can be found in Yu et al. (2006).The predicted and the measured values were compared for everyhour during the simulation period, resulting in sometimes rela-tively high MAGE and NME values (Table 3).

The average simulated O3 concentration over the observa-tional stations in the 4-km resolution domain is 43.1 ppb,whereas the average measured value is 30.2 ppb. The NMB,excluding the observations that are below 40 ppb, is �7.9%compared with 42.7% when all the data points are taken intoaccount. This difference indicates that the model has a low bias

compared with high ozone levels but has a high bias at the lowconcentrations of ozone, which occur mainly during nighttime(Figure 3). This overprediction during nighttime has to do withthe difficulty in capturing the vertical diffusivity coefficient inthe CMAQ model for application in this area (Zhang et al.,2006). EPA has established model performance goals for 1-hrozone NMB and NME, for which the observed ozone is 60 ppbor greater, of ��15% and �35%, respectively (EPA, 1991).These performance goals have been used for over two decadesto assist in evaluating ozone models. Model performance for 1-hr ozone concentration over the 4-km domain is �7.9% (NMB)and 25.1% (NME), achieving the suggested performance goals.

Figure 3. Time series of hourly observed (dotted line) and predicted (full line) ozone concentrations at the Puget Sound monitoring sites: (a) Issaquah, (b) North Bend,(c) Enumclaw, (d) JacksonVisitor Center, (e) Pack Forest, (f) TahomaWoods Admin, (g) Casino Drive/North End, and (h) Custer/Loomis in coordinated universal time(UTC) during 14–24 July. The correspondence with the local time (LT) is LT ¼ UTC � 7 hr.

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NMB and NME for the 12- and 36-km domains are also wellwithin these limits (Table 3). It is worth mentioning that thecutoff point used in this study is 40 ppb, instead of 60 ppbproposed by EPA, because the O3 concentration in the PugetSound region rarely exceeds the 60 ppb during the modelingepisode. Similar performance has been reported by a large vari-ety of CMAQ applications in different locations within the con-tinental United States (Tong and Mauzerall, 2006; Zhang et al.,2006). Across these studies the tendency of CMAQ to under-predict the peak ozone values and overestimate the nighttimeozone concentration has been also highlighted. In individualsites, the best agreement is found in “Jackson visitor center”mountainous forest area (RMSE ¼ 8.6 ppb). The model, how-ever, tends to be biased high in the northern part of the domain.The MB and the RMSE in the “Casino Drive/North End” site are9.9 and 14.7 ppb, respectively. The highest mean ozone concen-tration is observed in the Enumclaw area (38.5 ppb), whereCMAQ tends to be biased slightly high until 20 July, whereasduring the last 4 days the observed concentrations increase (up to120 ppb), resulting in a low bias in ozone from the model. Theoverall MB in that area is 7.9 ppb.

The average simulated domain-wide PM2.5 surface concen-tration is 5.6 mg m�3, with the highest concentrations occurringaround Vancouver (Figure 4a). Nevertheless, PM2.5 concentra-tions over Vancouver are overestimated due to the uniformmonthly temporal profile used for Canadian wood-burningemission sources and uncertainties in Canadian biogenic emis-sions. The most abundant component of PM in the domain isorganic matter (OM), accounting for 56% (3.14 mg m�3) of thePM mass during the simulated period (Figure 4b). SimulatedOM concentrations tend to be higher in Canada, especiallyVancouver and Victoria, than in the United States, possibly dueto the difference in biogenic isoprene and monoterpene emis-sions estimated in the United States and Canada. This discre-pancy is originated from the bulk land use description ofCanadian forests, compared with the United States, used toprepare the biogenic emissions. Sulfate, soil, elemental carbon,ammonium, and nitrate account for 16% (0.89 mg m�3), 12%(0.65 mg m�3), 6% (0.36 mg m�3), 6% (0.35 mg m�3), and 3%(0.16 mg m�3) of total PM2.5 mass, respectively (Figure 4c–f),with the rest 1% (0.015 mg m�3) being sodium chloride trans-ported from the ocean. Simulated and measured values of dailyaverage surface PM2.5 concentrations are compared for everythird day of the simulation period, beginning on 14 July(Table 4). The average simulated PM2.5 concentration duringthe days when measurements are available is 9.06 mg m�3,whereas the average measured concentration is 8.26 mg m�3.The NMB is 9.6%, indicating that the results are biased slightlyhigh for PM2.5 concentrations. Speciated PM2.5 measurementswere only available from two sites during 2 days within thesimulated period (resulting only in four comparisons).According to these observations, the OM is indeed the mostabundant component of PM in the domain, as predicted by themodel, with sulfate being the second most important. The aver-age measured concentrations of organic mass, sulfate, elementalcarbon, ammonium, and nitrate were 7.4, 2.5, 0.9, 0.6, and 0.5mg m�3, respectively, and the corresponding predicted MB were�0.8, �0.8, 0.15, �0.05, and �0.4 mg m�3.T

able3.

CMAQozon

esurfacemixingratio

sperformance

usingsurfaceob

servations

from

thecontinentalU

nitedStatesfortheepisod

eof

14–24

July

2006

AirsMonito

rNetwork

4-km

Dom

ain

12-km

Dom

ain

36-km

Dom

ain

Variables

1-hr

O3b

1-hr

O3

(40ppb)a

Max.1

-hr

O3c

Max.8

-hr

O3d

1-hr

O3b

1-hr

O3

(40ppb)a

Max.1

-hr

O3c

Max.8

-hr

O3d

1-hr

O3b

1-hr

O3(40

ppb)a

Max.1

-hr

O3c

Max.8

-hr

O3d

Meanobserved

(ppb)

30.2

57.0

56.0

49.7

30.3

57.0

56.2

49.6

38.3

57.7

65.1

58.0

Meansimulated

(ppb)

43.1

52.5

58.7

58.9

39.1

51.8

58.1

58.2

48.6

59.2

68.4

68.9

Totaln

o.1610

460

7168

3283

910

142

138

226597

104496

9624

9375

MB(ppb)

12.9

�4.5

2.7

9.2

8.8

�6.2

1.9

8.6

10.3

1.5

3.8

10.9

MAGE(ppb)

18.5

14.3

17.5

17.0

15.8

14.3

15.3

14.8

16.1

12.4

14.3

15.1

RMSE(ppb)

22.2

19.4

21.0

19.3

19.2

19.6

18.8

17.7

20.6

16.5

19.8

20.9

NMB(%

)42.7

�7.9

4.8

18.6

28.9

�11.0

3.35

17.4

26.8

2.6

5.9

18.8

NME(%

)61.3

25.1

31.4

34.3

52.2

25.1

27.1

29.9

42.0

21.5

22.0

26.0

Notes:aA40

-ppbvcutoffwas

used

forO

3ob

servations.bThe

average1-hr

O3representstheaverageconcentrationforallob

servationstations

ineach

ofthedo

mains.cThe

averagemaxim

um1-hr

O3representsthe

averageof

alld

aily

maxim

um1-hr

values

from

each

station.

dThe

averagemaxim

um8-hr

O3representstheaverageof

alld

aily

maxim

um8-hr

values

from

each

station.

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DDM Results

The first-order, seminormalized sensitivities calculated byDDM in this study represent the sensitivity of O3 and PM2.5

concentrations to small perturbations in NOx and VOC emis-sions. As such, first-order sensitivities describe the linearresponse of the model and are expressed in ppb per % for O3

andmgm�3 per % for PM2.5. Positive values represent a decrease

in concentration after a 1% decrease of emissions and, corre-spondingly, negative values represent an increase. The responseof O3 and PM2.5 concentrations to NOx and VOC emissionchanges can be calculated by multiplying the DDM coefficientby the magnitude of emission perturbation and is typically accu-rate for up to 30% perturbations (Dunker et al., 2002). The DDMresults presented in this study express the impact of NOx andVOC emissions on the average 8-hr daily maximum ozone and

Figure 4. Simulated average surface concentrations of (a) total PM2.5, (b) PM2.5 organic mass, (c) PM2.5 sulfate, (d) PM2.5 ammonium, (e) PM2.5 nitrate, and (f) PM2.5

elemental carbon during 14–24 July 2006.

Table 4. CMAQ PM2.5 surface concentrations (mg m�3) performance using surface observations for the episode of 14–24 July 2006

InnerDomains

MeanObservedPM2.5

(mg m�3)

MeanSimimulted

PM2.5

(mg m�3)TotalNo.

MB(mg m�3)

MAGE(mg m�3)

RMSE(mg m�3)

NMB(%)

NME(%)

4 km 8.26 9.06 11 0.8 2.3 2.9 9.6 27.512 km 8.82 9.47 16 0.65 3.5 4.2 7.3 39.9

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24-hr PM2.5 concentrations. This representation will be moreuseful to policymakers given that control strategies shouldfocus on reducing the 8-hr daily maximum ozone and longeraverages PM2.5 concentrations that contribute to NAAQSexceedances.

O3 sensitivity to NOx emissions

The average simulated sensitivity of 8-hr daily maximum O3

concentrations to total, domain-wide, NOx emission changesduring 14–24 July 2006 in the Seattle urban area is about�0.27 ppb per % (Figure 5a), although with significant spatialvariability throughout the modeling domain. This behavior istypical for urban areas, such as Seattle, which are characterizedby high NOx-to-VOC ratios (NOx-saturated areas) (Duncanet al., 2010), particularly in cooler areas with lower ultraviolet(UV) radiation fluxes. Similarly, reducing NOx emissions wouldincrease O3 concentrations in Vancouver. On the peak ozone day(21 July), the highest 1-hr O3 sensitivity is observed in Tacoma(�1 ppb per %), which is located 40 km southwest of Seattle(Figure 5b). In Seattle, during the same day, the impact of NOx

emissions on 1-hr O3 concentrations is predicted to be up to�0.3ppb per %. Decreased NOx emissions would decrease 1-hr O3

concentrations in more remote and forested areas (i.e., 0.2 ppbper % close to Enumclaw where the maximum O3 concentrationis observed). In such areas, biogenic VOC emissions during 21July were high due to higher temperatures and solar insolationpredicted byWRF, resulting in a low NOx-to-VOC ratio. In theseNOx-limited areas, a control of NOx emissions would result indecreased O3 concentrations. Average sensitivities of 8-hr daily

maximum O3 concentrations to changes of mobile, point, bio-genic, nonroad, and area NOx emissions were calculated for thesimulation period. 8-Hour daily maximum O3 concentrations inthe Seattle urban area are found to be most negatively sensitive tomobile (up to�0.2 ppb per %) NOx emissions followed by point(up to �0.1 ppb per %) and nonroad (up to �0.05 ppb per %)sources (Figure 6). Biogenic and area NOx emissions are pre-dicted to have almost no impact on O3 concentrations (less than�0.01 ppb per %; not shown) because they correspond only to5% of total NOx emissions (Table 2). Mobile NOx emissionsimpact the 8-hr daily maximum O3 concentrations over a largeportion of the modeling domain (Figure 6a). On the other hand,point NOx emissions can affect 8-hr daily maximum O3 concen-trations only in areas close to sources at Seattle and Tacoma aswell as close to the refineries located on the west coast ofWashington near the “Custer/Loomis” and the “Casino Drive/North End” monitoring sites (Figure 6b). 8-Hour daily maxi-mum ozone sensitivities to Canadian anthropogenic emissions ofNOx were also simulated (Figure 7). On average, Canadianemissions of NOx have a negative impact on 8-hr daily maximumO3 concentrations around Vancouver (up to�0.2 ppb per %) butdo not have a significant impact on 8-hr daily maximum O3

concentrations in Seattle during most of the simulated period(Figure 7a). However, during the peak ozone event on 21 July,Canadian NOx emissions is simulated to lead to a change of�0.05 ppb per % in 1-hr O3 concentration over Enumclaw,where the peak O3 concentrations were observed (Figure 7b).Back trajectories, using the Hybrid Single-Particle LagrangianIntegrated Trajectory (HYSPLIT) model of the National Oceanicand Atmospheric Administration (NOAA), also support thisconclusion, because the air mass in Puget Sound region does

Figure 5. Spatial distributions of the predicted sensitivity of O3 to total NOx emissions. (a) Average 8-hr daily maximum O3 sensitivity during 14–24 July 2006. (b)Sensitivity during the maximum O3 concentration, 11:00 p.m. (23:00) UTC or 4:00 p.m. (16:00) LT on 21 July.

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not originate from Canada during the simulation period (exceptthe period of 20–22 July 2006), particularly since ozone is veryeffectively transported over cooler water.

O3 sensitivity to VOC emissions

Spatial distributions of the impact of total VOC emissions on8-hr daily maximum ozone concentrations during the entiresimulation period and on 1-hr maximum ozone concentrationduring the most polluted day (21 July) were simulated (Figure 8aand b, respectively). In a NOx-saturated environment (i.e.,

Seattle, Vancouver), a decrease in VOC emissions will result indecreases of O3 concentrations (Seinfeld and Pandis, 2006;Tsimpidi et al., 2008; White et al., 1976). The average simulatedimpact of VOC emissions on 8-hr daily maximum O3 concentra-tions in Seattle is 0.18 ppb per %. The peak simulated impact ofVOC emissions on 1-hr O3 concentrations during 21 July is pre-dicted in Tacoma (0.75 ppb per %). In Seattle, during the same day,the maximum simulated positive impact is 0.4 ppb per %. Averagesensitivities of 8-hr daily maximumO3 concentrations to mobile,point, biogenic, nonroad, and area VOC emissions were calcu-lated for the simulation period (Figure 9). Biogenic sources

Figure 6. Spatial distributions of the predicted sensitivity of averaged 8-hr daily maximum O3 concentrations to (a) mobile, (b) point, and (c) nonroad NOx emissionsduring 14–24 July 2006.

Figure 7. Spatial distributions of the predicted sensitivity of O3 to Canadian NOx emissions. (a) Average 8-hr daily maximum O3 sensitivity during 14–24 July 2006.(b) Sensitivity during the maximum O3 concentration, 11:00 p.m. (23:00) UTC or 4:00 p.m. (16:00) LT on 21 July.

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impact O3 concentrations (up to 0.05 ppb per %) over awide areathat covers most of the fine-scale model domain (Figure 9b).Mobile, nonroad, and area VOC emissions impact O3 concentra-tions (up to 0.05 ppb per %, each) in the urban areas of thewestern part of the King and Pierce counties. Finally, ozone istypically insensitive to point VOC emissions (less than 0.002ppb per %; not shown), primarily due to the low VOC emissionsfrom point sources (Table 2). This suggests that a potentialcontrol of the VOC emissions coming from the refineries,which are located on the northwest coast of Washington state,will have a negligible impact on ozone concentration over thePuget Sound region. On average, Canadian anthropogenic VOCemissions is simulated to impact O3 concentrations in Vancouverand the northwest corner of Whatcom County by up to 0.05 ppbper % (Figure 10a). However, ozone levels in the remaining areasof the United States within the domain are not very sensitive toCanadian anthropogenic VOC emissions. During the peak O3

event, Canadian anthropogenic VOC emissions are simulated toimpact O3 concentrations in Vancouver by 0.06 ppb per %(Figure 10b). Simulated O3 sensitivities to Canadian VOC emis-sions are less than 0.01 ppb per % in Seattle and the Enumclawmonitoring station, where the peak O3 concentrations wereobserved.

O3 sensitivity to VOC and NOx emissions at themaximum monitored ozone location

At the location with the highest observed ozone concentration(MudMountain, near Enumclaw), the sensitivity of 1-hr ozone isalways positive to total VOC emission changes (up to 0.03 ppbper %), whereas the impact of total NOx emission changes can beeither positive (up to 0.02 ppb per %) or negative (up to �0.02ppb per %), depending on the relative availability of NOx and

VOCs and the time of day (Figure 11). During the day with thehighest ozone concentration in Enumclaw (21 July), reduction ofeither NOx or VOC emissions results in reductions of ozoneconcentration (sensitivities are both positive: 0.17 and 0.28ppb, respectively). In particular, O3 concentrations during thatday are most sensitive to mobile NOx and biogenic VOC emis-sion sources. The sensitivities of the average 1-hr maximum O3

concentrations to VOC emissions from biogenic, mobile, area,and nonroad sources are 0.15, 0.06, 0.04, and 0.03 ppb per %,respectively. The corresponding sensitivities to NOx emissionsfrom mobile, nonroad, and area sources are 0.11, 0.05, and 0.01ppb per %, respectively.

PM2.5 sensitivity to VOC and NOx emissions

The sensitivity of PM2.5 to emissions of VOCs and NOx wasinvestigated for Seattle, Portland, Vancouver (Canada), andVictoria (Canada) (Table 5). NOx has the greatest impact onPM2.5 in Vancouver, where simulation results suggest that low-ering NOx emissions would increase the average PM2.5 concen-trations. PM2.5 concentrations in Victoria, Portland, and Seattleare also negatively sensitive to NOx emissions. The negativesensitivity of PM2.5 to NOx occurs in urban areas with highNOx-to-VOC ratios (NOx-saturated environments), where thedecrease of NOx leads to an increase of O3 and OH radicalconcentrations, which play a role in the oxidation of SO2 tosulfate aerosol and VOCs to secondary organic aerosols (SOA).Therefore, whereas nitrate concentrations decrease with decreas-ing NOx emissions, sulfate and SOA increase. For instance, inthis application, nitrate decreases over Seattle by 0.0008 mg m�3

per % on average in response to NOx emission decreases,whereas sulfate and SOA increase by 0.0002 and 0.0022mg m�3 per %, respectively. This results in an overall increase

Figure 8. Spatial distributions of the predicted sensitivity of O3 to total VOC emissions. (a) Average 8-hr daily maximum O3 sensitivity during 14–24 July 2006. (b)Sensitivity during the maximum O3 concentration, 11:00 p.m. (23:00) UTC or 4:00 p.m. (16:00) LT on 21 July.

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of PM2.5 by 0.0016mgm�3 per % on average. Increases of PM2.5

from decreasing NOx emissions are driven by mobile and non-road sources in Seattle and Portland. Decreased emissions ofVOCs always decrease PM2.5 concentrations. The greatestimpacts are observed in Victoria and Vancouver where PM2.5

sensitivities are 0.037 and 0.034 mg m�3 per %, respectively,followed by Seattle and Portland (0.012 mg m�3 per %, each).The decrease of VOC emissions in NOx-saturated environmentsdecreases oxidant levels, which in turn decrease sulfate and

organic aerosols, and can also decrease nitrate. In Portland,Seattle, and Victoria, PM2.5 concentrations are more sensitiveto emissions of VOCs from biogenic sources than other sourcesof VOCs. In Vancouver, PM2.5 concentrations are sensitive tobiogenic emissions of VOCs and Canadian anthropogenic emis-sions of VOCs. The differences in biogenic emissions of iso-prene and monoterpene in Canada and the United States, due toinconsistencies on the vegetation classes used to prepare thebiogenic emissions, also play a role in the simulated elevated

Figure 9. Spatial distributions of the predicted sensitivity of average 8-hr daily maximum O3 concentration to (a) mobile, (b) biogenic, (c) nonroad, and (d) area VOCemissions during 14–24 July 2006.

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impacts of VOC emissions in the Canadian cities compared withthe U.S. cities.

Conclusions

This paper describes the simulated sensitivity of air pollutantsto their sources by using a nested regional modeling system thatsimulates the air quality over the northwestern United Statesduring a summer smog episode of 2006. The system consistsof the WRFmesoscale meteorological model, SMOKE emission

model, and CMAQ air quality model. Model evaluation is con-ducted to assess its skill for meteorology, O3, and PM2.5 over thenorthwestern United States. The meteorological model perfor-mance is within the typical range for air quality modeling. Forthe 14–24 July 2006 period, the average simulated 8-hr dailymaximum O3 concentration is 48.9 ppb. Comparing model per-formance with observations reveals that O3 is underpredicted forobserved concentrations above 40 ppb (NMB ¼ �7.9%) but isbiased high at low O3 levels that occurred during nighttime. Thepositive bias during nighttime, though, cannot affect the DDMcalculations, as the sensitivity analysis is focused only during thehours that contribute to the 8-hr daily maximum. Overall, themodel does well in representing the daily ozone trend and meetsthe recommended criteria by EPA for a good performance of 1-hrozone simulation (NMB ¼ �7.9%, NME ¼ 25.1). The averagesimulated domain-wide fine particulate matter (PM2.5) surfaceconcentration is 5.6 mg m�3, with the highest concentrationsoccurring around Vancouver. Organic mass, sulfate, soil, ele-mental carbon, ammonium, and nitrate account for 56%, 16%,12%, 6%, 6%, and 3% of PM2.5 mass, respectively. Comparedwith measurements, the NMB is 9.6%, indicating that the modelresults for PM2.5 are biased slightly high.

DDM-3D is applied to compute sensitivities of 8-hr dailymaximum O3 and PM2.5 ambient concentrations to VOC andNOx emissions. The impact of each source category (area, point,mobile, nonroad, and biogenic) as well as the Canadian anthro-pogenic VOC and NOx emissions on 8-hr daily maximum O3

and PM2.5 concentrations over the northwestern United States isalso investigated. In urban areas, which are characterized by highNOx-to-VOC ratios (NOx saturated), a reduction of NOx emis-sions will lead to an increase of O3 concentration (negativeimpact). For instance, in Seattle, the average simulated

Figure 10. Spatial distributions of the predicted sensitivity of O3 to Canadian VOC emissions. (a) Average 8-hr daily maximumO3 sensitivity during 14–24 July 2006.(b) Sensitivity during the maximum O3 concentration, 11:00 p.m. (23:00) UTC or 4:00 p.m. (16:00) LT on 21 July.

Figure 11. Time series of the calculated DDM-3D 8-hr O3 sensitivities to the totalNOx (light gray line) and VOC (black line) emissions of the 4-km domain at themonitoring site Mud Mountain, near Enumclaw, Washington.

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sensitivity of 8-hr daily maximum O3 concentration to anthro-pogenic NOx emissions is�0.27 ppb per %. In this case, mobileNOx emissions have the largest impact on O3 concentration (upto �0.2 ppb per %), followed by point and nonroad sources (upto�0.1 and�0.05 ppb per %, respectively). In more remote andforest areas, the NOx-to-VOC ratio is low (NOx limited) due tothe high biogenic VOC concentrations. In such areas, a decreaseof NOx emissions will result in a decrease of O3 concentrations(positive impact). In NOx-saturated environments (urban areas),a decrease in VOC emissions will lead to increased O3 concen-trations. The average simulated sensitivity of 8-hr daily max-imum O3 concentrations in Seattle to VOC emissions is 0.18 ppbper %, with maxima up to 1.8 ppb per %. The impact of mobile,biogenic, nonroad, and area emission sources on average 8-hrdaily maximum O3 concentrations is up to 0.05 ppb per %, each.Canadian anthropogenic VOC emissions also have a positiveimpact on O3 concentrations in the northwest corner ofWhatcom County, by up to 0.03 ppb per %.

The sensitivity of PM2.5 to emissions of VOCs and NOx isinvestigated at four major cities (Seattle, Portland, Vancouver,and Victoria). PM2.5 concentration is negatively sensitive to NOx

emissions. In urban areas (NOx-saturated environments), areduction of NOx concentrations results in an increase of OHand O3 concentrations and, as a result, aerosol concentration(mainly sulfate and SOA) in such areas can increase. On theother hand, the decrease of VOC emissions results in lower OHradical and ozone concentrations and consequently a decrease ofsulfate and SOA concentration levels. Sensitivity analysis findsthat decreased biogenic VOC emissions lead to a decrease ofPM2.5, up to 0.028mgm

�3 per %. As a result, a positive impact isobserved in all four cities, with the largest PM2.5 sensitivitiespredicted in Victoria and Vancouver (0.037 and 0.034 mg m�3

per %, respectively), followed by Seattle and Portland (0.012 mgm�3 per %, each). PM2.5 concentrations are more sensitive toemissions of VOCs from biogenic sources than other sources ofVOCs.

Acknowledgments

This research was supported by ConocoPhillips.

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PM2.5 Sensitivity to NOx (mg m�3) PM2.5 Sensitivity to VOCs (mg m�3)

Source Seattle Portland Vancouver Victoria Seattle Portland Vancouver Victoria

Total �0.0016 �0.0002 �0.0101 �0.0031 0.0122 0.0119 0.0340 0.0370Area �0.0001 0 0 0 0.0004 0.0005 0 �0.0001Point �0.0001 0.0001 0.0003 0.0002 0 0.0003 0.0001 0Biogenic — — — — 0.0106 0.0106 0.0168 0.0275Mobile �0.0008 �0.0001 0.0003 0.0001 0.0003 0.0003 0 �0.0001Nonroad �0.0003 �0.0002 0.0001 0.0002 0.0004 0.0002 0 0Canadian 0 0 �0.0106 �0.0035 �0.0004 0 0.0170 0.0090

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About the AuthorsAlexandra P. Tsimpidi is postdoctoral research fellow in the School of Civil andEnvironmental Engineering at the Georgia Institute of Technology in Atlanta, GA.

Marcus Trail is Ph.D. candidate in the School of Civil and EnvironmentalEngineering at the Georgia Institute of Technology in Atlanta, GA.

Yongtao Hu is a researcher in the School of Civil and EnvironmentalEngineering at the Georgia Institute of Technology in Atlanta, GA.

Athanasios Nenes is Professor and Georgia Power Faculty Scholar in the Schoolof Earth and Atmospheric Sciences and in the School of Chemical andBiomolecular Engineering at the Georgia Institute of Technology in Atlanta, GA.

Armistead G. Russell is the Georgia Power Distinguished Professor ofEnvironmental Engineering in the School of Civil and EnvironmentalEngineering at the Georgia Institute of Technology in Atlanta, GA.

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