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Atmospheric Environment 44 (2010) 2799e2812

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Atmospheric Environment

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WRF/Chem simulated springtime impact of rising Asian emissionson air quality over the U.S.

Yongxin Zhang a,*, Seth C. Olsen b, Manvendra K. Dubey c

aResearch Applications Laboratory, National Center for Atmospheric Research, 3450 Mitchell Lane, Boulder, CO 80301, USAbDepartment of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Illinois, USAc Earth and Environmental Science Division, Los Alamos National Laboratory, New Mexico, USA

a r t i c l e i n f o

Article history:Received 18 October 2009Received in revised form30 April 2010Accepted 3 May 2010

Keywords:Pollution from China and IndiaRegional impactWRF/Chem

* Corresponding author. Tel.: þ1 303 497 2829; faxE-mail address: zhangyx@ucar.edu (Y. Zhang).

1352-2310/$ e see front matter � 2010 Elsevier Ltd.doi:10.1016/j.atmosenv.2010.05.003

a b s t r a c t

This paper examines the impact of tripled anthropogenic emissions from China and India over the baselevel (gaseous species and carbonaceous aerosols for 2000) on air quality over the U.S. using the WRF/Chem (Weather Research and Forecasting e Chemistry) model at 1� resolution. WRF/Chem is a state-of-the-science, fully coupled chemistry and meteorology system suitable for simulating the transport anddispersion of pollutants and their impacts. The analyses in this work were focused on MAM (March, Apriland May). The simulations indicate an extensive area of elevated pollutant concentrations spanning fromthe Arabian Sea to the Northern Pacific and to the Northern Atlantic. MAM mean contributions from thetripled Asian emissions over the U.S. are found to be: 6e12 ppbv for CO, 1.0e2.5 ppbv for O3, and0.6e1.6 mg m�3 for PM2.5 on a daily basis.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

As the economies of Asia (primarily China and India) continue togrow more and more greenhouse gases, ozone precursor gases,acidifying gases, and particular matter (PM) will be emitted into theatmosphere. Fig. 1 shows the time series of the March-April-May(MAM) mean MOPITT (Measurements of Pollution in the Tropo-sphere; Edwards et al., 2004) CO total column averaged separatelyoverChina and India for theperiodofMarch2000 through June 2009.Both countries showpositive trends that are statistically significant atthe 5% significance level. When China and India are combined (notshown), the linear regression line becomes Y ¼ 0.0040X þ 1.99 andthe positive trend is also statistically significant. Assuming business-as-usual scenario (i.e., continuing current trends in emissions rates)and assuming that the CO total column over China and India isprimarily related to the emitted CO by these two countries (i.e.,neglecting regional contribution), then fromthe linear regression linewefindout that the emittedCObyChina and Indiawill be tripledoverthe 2000 level (w2.0 � 1018 mol cm�2) around 2080.

According to the IPCC-defined development pathway (IPCC,2001), total emissions by Asia (mainly by China and India) wereprojected to triple over the 1990 level around the middle of thiscentury under the business-as-usual scenario (A2 scenario). Using

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a bottom-up methodology with comparisons to satellite measure-ments, Zhang et al. (2007) estimated a 70% increase in NOx(NOx ¼ NO þ NO2) emissions for China between 1995 and 2004. Ifthis 70% trend continues under a business-as-usual scenario, thenthe NOx emissions by China will be tripled over the 2000 level(w14.7 Tg N based on the same work) around 2040. Recently,Carmichael et al. (2009) specifically examined the Asian aerosolsfor year 2030 using simulations that consider future changes inemissions. It appears that our estimate that CO emissions by Chinaand India will be tripled over the 2000 level by 2080 may bea rather conservative one since our estimate is based on a lineartrend without considering accelerated growth rate.

The rising anthropogenic emissions of the pollutants by Chinaand India have potential impacts on the entire Pacific region due todispersion and transport (Duce et al., 1980; Levy and Moxim, 1989;Merrill, 1989; Kritz et al., 1990; Balkanski et al., 1992; Kato andAkimoto, 1992; Elliott et al., 1997; Talbot et al., 1997; Jaffe et al.,1999). Characterization of these impacts involves detailed simula-tions using state-of-the-art dynamic models coupled with chem-istry, in addition to extensive analyses of field observations andsurface, aircraft and satellite measurements.

Several modeling studies have investigated the impacts of Asianemissions on regional climate and air quality over the Pacific andNorthern America. Jacob et al. (1999) examined the effects ofincreasing fossil fuel combustion in eastern Asia on surface O3 airpollution in the United States with a global three-dimensionaltropospheric chemistry model. They found that tripling of Asian

a

b

Fig. 1. MarcheAprileMay (MAM) mean MOPITT CO total column (1018 mol cm�2) forthe period of March 2000 through June 2009 averaged over (a) China and (b) India.Bottom panel shows the geographic bounds of China (dotted area) and India (hatchedarea) where CO total column is averaged. The straight lines in (a) and (b) are the linearregression lines with the linear equation shown in the upper-left corner of each plot.Both linear trends are significant at 5% level based on t-statistics.

Y. Zhang et al. / Atmospheric Environment 44 (2010) 2799e28122800

anthropogenic emissions from 1985 to 2010 was expected toincrease the monthly mean O3 concentrations by 2e6 ppbv in thewestern United States and by 1e3 ppbv in the eastern United States,the maximum effect being in AprileJune. Berntsen et al. (1999)used a global 3-D chemical transport model (CTM) to study theimpact of current and future emissions from Asia on CO, PAN(peroxyacetyl nitrate) and O3 across the North Pacific. Their resultssuggested a significant contribution from Asian sources in the airarriving to the North Western United States, especially duringSpring, with the mean contributions being 37 ppbv, 23 pptv and3 ppbv for CO, PAN and O3, respectively. Yienger et al. (2000)employed a global CTM to examine the episodic nature of trans-Pacific pollution. They noted large Asian contributions reaching40 ppbv on background O3 over North America in a future emis-sions scenario in which Asian NOx emissions increase by a factor of4 from those of 1990.

Hudman et al. (2004) examined ozone production in trans-pacific Asian pollution plumes in AprileMay 2002 by using aircraftand surface observations and a global CTM. Their analyses suggestthat Asian pollution influence could make a significant contributionto exceedances of the air quality standard at California mountainsites. Using an ensemble of aircraft, satellite, sonde, surface obser-vations and a global CTM, Zhang et al. (2008) investigated themechanisms for transpacific ozone pollution and showed thatAsian pollution enhanced surface ozone concentrations by5e7 ppbv over western North America in spring 2006. Morerecently, the influence of foreign vs. North American emissions onsurface ozone in the U.S. was studied by Reidmiller et al. (2009)using results from 15 global and 1 hemispheric CTMs. Theyshowed that the greatest response of daily 8-h average ozone tochanges in the foreign emissions occur during spring in thewesternU.S. with East Asia being the largest contributor followed byEurope. A comprehensive assessment of long-range transport ofkey air pollutants to and from the U.S. is provided by the NationalResearch Council (2009).

The above-mentioned model simulations provide useful infor-mation on mean contributions from Asian emissions; however,global CTM tend to suffer from using coarse horizontal resolutionsand simplified treatment of meteorology. Furthermore, impactassessment of future climate change on air quality requires high-resolution, detailed dynamic information which is currently avail-able only at regional scales. The focus of this work is to apply thestate-of-the-science, next-generation, fully coupled chemistrywithinWRF (Weather Research and Forecasting) model (Grell et al.,2005), often referred to as WRF/Chem, to examine the regionaltransport and potential effects of increasing Asian emissions on airquality over the North America in general and over the U.S. inparticular. This is a very important step in using WRF/Chem forimpact studies under global climate change asWRF has been one ofthe primary tools for downscaling global climatemodel simulationsto regional and local scales.

This work is presented in the following way. Section 2 containsbrief descriptions of the WRF/Chem model, the emissions inven-tory, and the experimental design. Analyses of the model simula-tions are given in Section 3. Conclusions and discussion arepresented in Section 4.

2. Methods

2.1. WRF/Chem model

WRF is a mesoscale numerical weather system designed forboth short-term weather forecasts and long-term climate simula-tions with horizontal resolutions ranging frommeters to thousandsof kilometers (http://www.wrf-model.org). It is a non-hydrostaticmodel, with several dynamic cores including a fully mass- andscalar-conserving coordinate version that is widely used in airquality prediction systems. Model physics include microphysics,cumulus parameterization, planetary boundary layer (PBL), landsurface model (LSM), longwave and shortwave radiation(Skamarock et al., 2006).

WRF/Chem was developed at NOAA (National Oceanic andAtmospheric Administration). Detailed descriptions of the modelwere given in Grell et al. (2005). Fast et al. (2006) updated WRF/Chem by incorporating complex gas-phase chemistry, aerosoltreatments and photolysis scheme. The air quality component ofWRF/Chem is fully consistent with the meteorological component;both components use the same transport scheme (mass and scalarpreserving), the same horizontal and vertical grids, the samephysical schemes for subgrid-scale transport, and the same timestep for transport and vertical mixing. There are several different

Y. Zhang et al. / Atmospheric Environment 44 (2010) 2799e2812 2801

chemistry, aerosol and photolysis schemes to choose from in WRF/Chem (Grell et al., 2005; Fast et al., 2006).

In this work, the Lin et al. microphysics scheme (Lin et al., 1983),the KaineFritsch cumulus parameterization (Kain and Fritsch,1993), the YSU (Yonsei University) PBL scheme (Hong et al.,2006), and the NOAH (National Centers for Environmental Predic-tion e NCEP, Oregon State University, Air Force, and HydrologicResearch Lab) LSM (Chen and Dudhia, 2001) were used. Atmo-spheric shortwave and longwave radiations were computed by theDudhia scheme (Dudhia, 1989) and by the RRTM (Rapid RadiativeTransfer Model) scheme (Mlawer et al., 1997), respectively. Chem-istries used were the RADM2 (Regional Acid Deposition Modelversion 2) chemical mechanism (Chang et al., 1989; Stockwell et al.,1990), the MADE (Modal Aerosol Dynamics Model for Europe)aerosol mechanism (Ackermann et al., 1998) coupled with theSORGAM (Secondary Organic Aerosol Model) parameterization(Schell et al., 2001), and the Fast-J photolysis scheme (Wild et al.,2000).

2.2. Emissions

We used the global anthropogenic emissions data for gaseousspecies (CO2, CO, NOx ¼ NO þ NO2, SO2, NH3, and total VolatileOrganic Compounds e VOCs) compiled and distributed by theEmission Database for Global Atmospheric Research (EDGAR)

a

b

c

Fig. 2. MAMmean CO total column for 2001 from (a) MOPITT measurements (1018 mol cm�2

differences between the simulated and the measured divided by the measured. White smeasurements are missing except over the North Pacific and the northern part of the Sout

system (http://www.mnp.nl/edgar) (Olivier et al., 2005). Theseemissions data were based on the EDGAR estimates for 1995 andwere updated by applying trend analyses at country level for eachstandard source category to get the annual emissions rates for2000. The speciated anthropogenic VOC emissions rates wereobtained from the GEIA (Global Emissions Inventory Activity)database (http://www.geiacenter.org) and were adjusted based onthe total VOC estimates from EDGAR to get the emissions data for2000. These emissions data were gridded with a horizontal reso-lution of 1� by 1�.

The global emissions inventory of black and organic carbon ona 1� � 1� horizontal grid was obtained from the Bond Group ofUniversity of Illinois at Urbana-Champaign (Bond et al., 2004)(http://cee.uiuc.edu/research/bondresearch/BC_inventory.html).The black and organic carbon annual estimates were based on 1996fuel-use data, including emissions from fossil fuels, biofuels, openbiomass burning, and burning of urban waste. In this work, weupgraded the 1996 emissions estimates of black carbon and organiccarbon to those of 2000 using a simple population growth-scalingfactor obtained as the ratio between the 2000 population and the1996 population for a given country. The annual emissions esti-mates for 2000 were then uniformly distributed in temporal scaleto get the hourly emissions rates for use in the model. Biogenicemissions as in the work of Guenther et al. (1994) and Simpsonet al. (1995) are used.

), (b) WRF/Chem simulations (1018 mol cm�2), and (c) percent change (%) defined as thepots in (a) are where measurements are missing. White spots in (c) are also whereh America where the white spots represent changes less than �20%.

a

b

c

Fig. 3. MAM mean CO concentrations (ppmv) at the measurement sites over theContinental U.S. based on (a) the Environmental Protection Agency (EPA) Air QualitySystem database and (b) WRF/Chem simulations. Ratio of the simulated to theobserved MAM mean CO concentrations is shown in (c). Marker sizes correspond tothe range of values that are indicated at the bottom.

Y. Zhang et al. / Atmospheric Environment 44 (2010) 2799e28122802

2.3. Experimental design

The WRF/Chem model was set up at 1� resolution for a largedomain that includes all of Asia except for the Central Asia, thePacific Ocean, North and South Americas, and the Atlantic Ocean(see Fig. 2b). The model was run for 2001 for two emissionsscenarios: base case scenario and pollution scenario. In the basecase scenario, the emissions rates for 2000 were used over theentire model domain. In the pollution scenario, the anthropogenicemissions rates over China and India were tripled over the 2000level simultaneously while the rest of the model domain still usedthe emissions rates for 2000. The model runs were initialized at0000 UTC December 1, 2000 and ended at 0000 UTC January 1,2002 for a 13-month continuous simulation. Themodel simulationsfor the first month were discarded as model spin-up. Current workwill focus on springtime (MAM: March, April and May), whichcorresponds to slightly larger impacts of the Asian emissions on theair quality over the U.S. when compared to the other seasons (notshown). Several works (e.g., Liang et al., 2004; Reidmiller et al.,2009) have shown that Asian influence on pollutants levels in theNorth America maximizes during spring and minimizes duringsummer. Our one-year simulation will be further used to investi-gate seasonal variations in the transport and dispersion of thepollutants over the Asia-Pacific region and the results will bereported in a future work.

The initial and lateral boundary conditions for meteorology wereinterpolated fromtheNCEPFinalAnalysisdataat1� resolution (http://www.nomad3.ncep.noaa.gov/ncep_data). The boundary conditionswere updated every 6 h. Model provided values for chemical andaerosol species based on the globally averaged climatology con-structed from long-term observations and CTM simulations wereused as the initial pollutant concentrations at the start of the modelruns. The initial background conditions for some of the trace gasesare: CO, 80 ppbv; O3, 50 ppbv; NO, 0.02 ppbv; NO2, 0.05 ppbv; SO2,0.1 ppbv; PM2.5, 0.19 mgm�3; and PM10, 0.26 mgm�3. This is one of theoptions that WRF/Chem provides for initial pollutants conditions.Sensitivity tests using the other options in WRF/Chem did not showappreciable changes formodel integration time longer than10days. Itis also expected that the effect of initial conditions on model simu-lations is small after severalmonthsofmodel integrationas in thecaseof thiswork. The aerosol feedbacks on radiation and convectionwerenot turned on in the model runs. 31 vertical levels were used in themodel with the highest resolution (w20e100 m) in the boundarylayer. Themodel topwas fixed at 50mb. Themodel datawere outputevery 6 h, and included daily averages.

2.4. MOPITT CO measurements

CO measurements obtained from MOPITT are used in thesubsequent section to compare with the WRF/Chem simulations.MOPITT is an instrument flying on NASA’s EOS Terra spacecraft,measuring the global distributions of carbon monoxide in theatmosphere since 2000 (Edwards et al., 2004). MOPITT views theEarth over all latitudes with a pixel size of 22 km by 22 km anda cross-track swath that measures a near-global distribution of COevery 3 days. The MOPITT CO measurements have been validatedextensively over the Pacific and other parts of the world (e.g.,Emmons et al., 2004, 2009).

3. Results

3.1. Model validation

Fig. 2 shows the MAM mean CO total column for 2001 obtainedfrom the MOPITT measurements and from the WRF/Chem

simulations for the base case scenario. Percent change defined asthe differences between the simulated and the measured CO totalcolumn divided by the measured is also shown in Fig. 2. Both theMOPITT observations and the WRF/Chem simulations showsignificant CO total column (�2.25 � 1018 mol cm�2) over India,China and the Northeastern U.S., as well as the downstreamdispersion and transport of the pollutants from these sourceregions (Fig. 2a, b). The simulations are much smoother spatiallywith less horizontal gradient as compared to the measurements,due mainly to the relatively coarse resolution of the model.

The model is noted to underestimate the observed CO totalcolumn over the Northern Pacific and the Northern Atlantic byw20% (Fig. 2c). One likely reason for this discrepancy is related touncertainties in the global emissions rates. In the pollution scenariowith the tripled Asian emissions, the simulated CO total column

a b c

fed

g h i

lkj

Fig. 4. Scatter plots of the observed and simulated MAM mean CO concentrations at the EPA measurement sites over (a) Pacific Northwest, (b) Pacific Southwest, (c) MountainNorth, (d) Mountain South, (e) West North Central, (f) West South Central, (g) East North Central, (h) East South Central, (i) Middle Atlantic, (j) South Atlantic, (k) New England, and(l) Florida. Unit is ppmv. Region categories are based mainly on Census Regions of the Continental U.S.

Y. Zhang et al. / Atmospheric Environment 44 (2010) 2799e2812 2803

a

b

Y. Zhang et al. / Atmospheric Environment 44 (2010) 2799e28122804

over the Northern Pacific and the Northern Atlantic are significantlyelevated (not shown). Another likely reason for this discrepancy isrelated to the fact that the model runs were for a relatively shortterm. In reality, the Northern Pacific and the Northern Atlantic arepersistently impacted by the pollution from the Asia and the NorthAmericawhich gives rise to high background concentrations. Lastly,CO has a relatively long lifetime (w1.5 months) that enables the COpollutants to circle around the globe and contribute to the sameregions to some extent. This wrap-around contribution was notaccounted for in the WRF/Chem simulations on regional scales. Themodel overestimates the observed CO total column south of theEquator with the percent change ranging from 20% in the lowlatitudes to asmuch as 120% near themiddle latitudes (Fig. 2c). Twolikely reasons for the model overestimation are: (a) CO emissionsfor the South Hemisphere may be overestimated in the emissionsinventory, and (b) CO background concentrations over the SouthernHemisphere may be overestimated by the globally averagedclimatology.

To further validate the model simulations over the U.S., we usemeasurements from theU.S. EPA (Environmental ProtectionAgency)Air Quality System database (available at: http://www.epa.gov/ttn/airs/airsaqs). Fig. 3 presents the observed and simulated MAMmean CO concentrations and the ratio (defined as the simulateddivided by the observed) at the measurement sites across theContinental U.S. The model did not resolve the observed COconcentrations greater than 2 ppmv (Fig. 3a, b), likely due to uncer-tainties in the emissions inventory and the coarse horizontal reso-lution (1� or about 100 km) that we used for the model runs. At thishorizontal resolution, the model is unable to resolve fine-scalecirculations that are essential in distributing the local pollutants.Model underestimation (i.e., the ratio is between0and0.8) is evidentover the Pacific Northwest while overestimation (i.e., the ratio isgreater than 1.2) is noted mainly over the Northeast and Florida(Fig. 3c). For California, model performance is mixedwith some sitesshowingunderestimationwhile others showingoverestimation. Themeasurement sites where the ratio lies between 0.8 and 1.2 areconsidered to bewhere themodel performs relativelywell, and suchsites are found all over the domainwithout showing area preference.

Scatter plots of the observed and simulated MAM mean COconcentrations at the EPA measurement sites over regional subdi-visions of the Continental U.S. (Fig. 4) show notable variations inmodel performance from region to region. These subdivisions arebased primarily on the U.S. Census Regions and include Pacific

Table 1Correlation coefficient and bias of the simulated MAM mean CO, O3 and PM2.5

concentrations at the Environmental Protection Agency (EPA) measurement sitesover various regions of the U.S.

U.S. regionsa CO O3 PM2.5

CCb BAc CC BA CC BA

Pacific Northwest 0.27 �0.57 0.54 �9.4 �0.26 �3.8Pacific Southwest 0.09 �0.01 0.48 �9.7 0.50 �5.7Mountain North 0.48 �0.55 0.35 �19.7 0.42 �3.0Mountain South 0.21 �0.10 0.14 �9.5 0.73 �3.8West North Central 0.16 �0.01 �0.04 �11.4 0.75 �6.8West South Central 0.05 0.01 0.01 �5.0 0.39 �5.3East North Central 0.02 0.12 �0.03 �12.7 0.43 �8.1East Couth Central �0.13 �0.05 �0.11 �10.2 0.26 �6.7Middle Atlantic 0.54 0.48 0.11 �13.1 �0.07 �4.2South Atlantic 0.03 0.01 0.40 �12.8 0.30 �7.2New England 0.14 0.07 0.02 �16.3 0.49 �2.7Florida �0.52 0.08 �0.26 �2.3 0.27 �4.2

a Region categories are based mainly on Census Regions of the Continental U.S.b CC refers to correlation coefficient.c BA refers to bias averaged over the EPA measurement sites for a specific region.

Unit is ppmv for CO, ppbv for O3, and mg m�3 for PM2.5.

Northwest, Pacific Southwest, Mountain North, Mountain South,West North Central, West South Central, East North Central, EastSouth Central, Middle Atlantic, South Atlantic, New England andFlorida. Correlation coefficient (Table 1) ranges from 0.54 forMiddle Atlantic and 0.48 for Mountain North to near zero for PacificSouthwest, West South Central, East North Central and SouthAtlantic. Correlation coefficient is �0.52 for Florida and �0.13 forEast South Central. Large negative biases are noted for PacificNorthwest (�0.57 ppmv) and Mountain North (�0.55 ppmv) whilelarge positive biases are noted for Middle Atlantic (0.48 ppmv).Other regions show rather small biases (Table 1).

For O3 (Fig. 5), the model simulations compare favorably withthe observations in distributions. The model fails to simulate O3concentrations smaller than 15 ppbv and greater than 60 ppbv

c

Fig. 5. MAM mean O3 concentrations (ppbv) at the measurement sites over theContinental U.S. based on (a) the Environmental Protection Agency (EPA) Air QualitySystem database and (b) WRF/Chem simulations. Ratio of the simulated to theobserved MAMmean O3 concentrations is shown in (c). Marker sizes correspond to therange of values that are indicated at the bottom.

a b c

fed

g h i

lkj

Fig. 6. Scatter plots of the observed and simulated MAM mean O3 concentrations at the EPA measurement sites over (a) Pacific Northwest, (b) Pacific Southwest, (c) MountainNorth, (d) Mountain South, (e) West North Central, (f) West South Central, (g) East North Central, (h) East South Central, (i) Middle Atlantic, (j) South Atlantic, (k) New England, and(l) Florida. Unit is ppbv. Region categories are based mainly on Census Regions of the Continental U.S.

Y. Zhang et al. / Atmospheric Environment 44 (2010) 2799e2812 2805

Y. Zhang et al. / Atmospheric Environment 44 (2010) 2799e28122806

(Fig. 5a, b), probably due to the reasonsmentioned before. The ratioof themodeled to the observed O3 concentrations shows values lessthan 0.8 (i.e., model underestimation) over a majority of themeasurement sites (Fig. 5c). Themeasurement sites where the ratiois close to 1 are located mainly over the southern part of the U.S.

Model underestimation of the observed O3 concentrations arealso clearly indicated in the scatter plots as most points are locatedunder the 1:1 line for each region (Fig. 6). Model biases range from�19.7 ppbv for Mountain North to �2.3 ppbv for Florida with theother regions showing biases in between (Table 1). Correlationcoefficient is 0.54 for Pacific Northwest, 0.48 for Pacific Southwest,0.40 for South Atlantic, 0.35 for Mountain North, and �0.26 forFlorida with near zero correlation coefficient for the other regions.

The model underestimates the observed PM2.5 mass at most ofthe measurement sites (Fig. 7) and for each region (Fig. 8) with

a

b

c

Fig. 7. MAM mean PM2.5 concentrations (mg m�3) at the measurement sites over theContinental U.S. based on (a) the Environmental Protection Agency (EPA) Air QualitySystem database and (b) WRF/Chem simulations. Ratio of the simulated to theobserved MAM mean PM2.5 concentrations is shown in (c). Marker sizes correspond tothe range of values that are indicated at the bottom.

negative model biases in the range of�8.0 to�3.0 mgm�3 (Table 1).The model fails to simulate the observed PM2.5 mass that is greaterthan 30 mg m�3. Correlation coefficient is 0.75 for West NorthCentral, 0.73 for Mountain South, 0.50 for Pacific Southwest, 0.49for New England, 0.43 for East North Central, 0.42 for MountainNorth, 0.39 for West South Central, and around 0.30 for SouthAtlantic, Florida and East South Central. Negative correlation coef-ficient of�0.26 is noted for Pacific Northwest while Middle Atlanticshows almost no correlation (Table 1). For PM10 (not shown), modelunderestimation is indicated over all measurement sites.

The simulated MAM mean surface air temperatures match theobservations reasonably well (Fig. 9). High temperatures are notedover the southern states with lower temperatures over the PacificNorthwest both in the observations and simulations. Over thenortheastern part of the U.S., the model simulated lower temper-atures extend more southward than what was observed. This isrelated to the fact that the lower temperatures extend moresouthward in the driving data (i.e., the NCEP Final Analyses data)than in the observations (not shown). Model warm biases smallerthan 6 �F are noted mainly over the Central Plains and the SouthernStates while the rest of the domain shows primarily cold biases inthe range of �10 to �4 �F (Fig. 9c).

The simulated precipitation for MAM is generally consistentwith the observations in distributions (Fig. 10a, b). Model wetbiases in the range of 1e5 mm day�1 are noted over the PacificNorthwest and the eastern part of the U.S. whilemodel dry biases inthe similar range are evident over the Central Plains and theSouthern States (Fig. 10c). Southern California and Arizona corre-spond to relatively small dry biases.

The above analyses suggest that the model simulations areconsistent with the observed distributions in chemical species andmeteorological variables that are examined. Model underestima-tion of the observed CO, O3 and PM2.5 concentrations are evidentover the U.S. Model performance also varies from region to regionover the U.S. depending on the examined variables. Further anal-yses suggest that the biases in O3 could be related to the temper-ature biases to some extent. For example, over the northeastern andsouthwestern parts of the U.S. where the simulated temperatureshows significant cold biases (Fig. 9c), large underestimation in O3concentrations are noted (Figs. 5c and 6). While over the SouthernStates where the simulated temperature shows large warm biases(Fig. 9c), model underestimation in O3 concentrations tends to bereduced (Figs. 5c and 6). Model underestimation in PM2.5 concen-trations over the Pacific Northwest and the eastern part of the U.S.(Fig. 7c) may be related to largewet biases there (Fig.10c); however,such a correspondence does not appear to be consistent across thedomain.

3.2. Simulated impacts

MAMmean differences in chemical concentrations between thepollution scenario and the base case scenario are calculated. Thedifferences are averaged daily and then averaged over the MAMmonths. Thus, such differences represent the MAM mean contri-butions of the tripled Asian emissions on regional chemicalconcentrations on a daily basis. By examining the differences wecan largely eliminate the model deficiency in background pollutantconcentrations although it is also noted that chemical reactions arehighly nonlinear. Fig. 11a shows the MAM mean CO concentrationdifferences at the lowest model level (w10 m above ground level).A noticeable feature is that elevated CO (>4 ppbv) is transportedover a broad region (i.e., the influence area) that extends from theArabian Sea across the North Pacific to the North Atlantic Ocean.The southern part of Canada and all of the U.S. and Mexico arewithin this influence area. The MAM mean contribution from the

a

d e f

g

j k l

h i

b c

Fig. 8. Scatter plots of the observed and simulated MAM mean PM2.5 concentrations at the EPA measurement sites over (a) Pacific Northwest, (b) Pacific Southwest, (c) MountainNorth, (d) Mountain South, (e) West North Central, (f) West South Central, (g) East North Central, (h) East South Central, (i) Middle Atlantic, (j) South Atlantic, (k) New England, and(l) Florida. Unit is mg m�3. Region categories are based mainly on Census Regions of the Continental U.S.

Y. Zhang et al. / Atmospheric Environment 44 (2010) 2799e2812 2807

a

b

c

Fig. 9. MAM mean surface air temperature (�F) over the Continental U.S. based on (a)observations and (b) WRF/Chem simulations. The differences (simulations e obser-vations) are shown in (c).

a

b

c

Fig. 10. MAM mean precipitation rate (mm day�1) over the Continental U.S. based on(a) observations and (b) WRF/Chem simulations. The differences (simulations e

observations) are shown in (c).

Y. Zhang et al. / Atmospheric Environment 44 (2010) 2799e28122808

tripled Asian emissions over the U.S. ranges from 8 to 12 ppbv overthe western part to 6e10 ppbv over the eastern part. Over theequatorial Atlantic Ocean, the tripled Asian emissions alsocontribute about 6e8 ppbv of CO in MAM (Fig. 11a). Appreciableamounts of CO pollutants are also transported to the North Atlanticand Greenland. Over China and India, CO concentration differencesare noted to be larger than 100 ppbv.

Fig. 11b shows the simulated MAM mean horizontal wind flowat the lowest model level for the base case scenario. An identicalwind pattern is also noted for the pollution scenario (not shown).Prominent atmospheric circulations such as the intertropicalconvergence zone (ITCZ), the equatorial high-pressure systemsover the Pacific and the Atlantic Oceans, the Aleutian low and theIcelandic low, are well resolved by the model. This flow pattern andthe sea level pressure distributions (not shown) are very similar tothose shown in Liang et al. (2004; their Fig. 6). 2001 also corre-sponds to relatively strong transpacific transport based on

meteorological indices described in Liang et al. (2005). Conditionsare favorable for the pollutants emitted over Asia to be transportedto the North America through the westerlies located between thesemipermanent Aleutian low and the equatorial high-pressuresystem (see also Liang et al., 2004, 2005). Once over the NorthAmerica, the pollutants are carried further downstream by themid-latitudewesterlies and onto Greenland and the high latitudes of theNorth Atlantic through the southerly flow associated with theequatorial high-pressure system.

Longitude-height cross section along 40�N featuring COconcentration differences is presented in Fig. 12a. This cross sectionspans roughly through middle China and middle U.S. (the solid linein Fig. 11a). It is noted that the elevated CO pollutants due to thetripled emissions in China and India are first lifted up in and aroundthe source regions while being transported northeastward and arethen carried over further eastward in the middle troposphere bythe prevailing mid-latitude westerlies (Fig. 12b). CO concentration

a

b

Fig. 11. WRF/Chem simulated (a) MAM mean CO concentration differences (ppbv) atthe lowest model level between the pollution scenario (emissions over China and Indiatripled) and the base case scenario (emissions over China and India not changed), (b)MAM mean horizontal wind vectors (m s�1) at the lowest model level for 2001. Grayshading in (b) denotes the land area.

a

b

Fig. 12. Longitude-height cross section of WRF/Chem simulated (a) MAM mean COconcentration differences (ppbv) and (b) MAM mean wind vectors (m s�1) along 40�N(the straight line in Fig. 11a). Vertical velocity in (b) was increased by a factor of 500.

Y. Zhang et al. / Atmospheric Environment 44 (2010) 2799e2812 2809

differences in excess of 6 ppbv are evident over the North America.The CO pollutants are confined primarily within the troposphere(surface to 12 km) as the tropopause located around 12 km and theassociated strong westerlies act as a lid and prevent the upwardtransport of the pollutants. There is possibility that the pollutantscan be transported into the stratosphere by intense convectionassociated with strong storm systems along the mid-latitude stormtrack and can potentially impact even larger areas.

Fig. 13a presents the MAMmean O3 concentration differences atthe lowest model level. A rather extensive influence area situatedbetween the Equator and 45�N is noted. Compared to CO, large O3concentrations are located relatively southward due to the fact thatthe production of O3 requires both sunlight and NOx and VOCemissions. MAM mean contributions of 1.5e2.5 ppbv are evidentfor the western part of the U.S. and Mexico. The eastern part of theU.S. receives relatively low contributions (<2.0 ppbv) from thetripled Asian emissions for this particular case. The equatorialAtlantic Ocean receives about 0.5e1.0 ppbv of O3 from the tripledAsian emissions. Over the East Asia and the Southeast Asia, severeO3 pollution problem from the tripled emissions is noted with theO3 concentration differences ranging from 10 to 50 ppbv in Chinaand India to 5e10 ppbv in the surrounding areas.

The longitude-height cross section along 40�N (Fig. 13b) revealselevated O3 concentrations that extends eastward to the NorthAmerica and the Atlantic Ocean. The pollutant is mainly confinedwithin the tropospherewithpossible indications of deep convectionover India. Large (>2 ppbv) O3 concentrations are located mainlyover the PacificOcean. Cross sections constructed further south (e.g.,along 30�N and 25�N, not shown) display maximum O3 concen-trations located over the East Asia and its downstream oceans.

PM2.5 resulting from the increased Asian emissions also impactsa large area (Fig. 14a). PM10 (not shown) is identical to PM2.5 interms of both magnitude and distributions so our discussions willbe focused on PM2.5. Notice that the model simulated particulatematter (PM2.5 and PM10) mass is comprised of both primaryemissions (black and organic carbon) and secondary aerosols. Overthe western part of Canada and the U.S., the MAM mean contri-bution for PM2.5 from the tripled Asian emissions is approximately1.0e1.6 mg m�3. The eastern part of the U.S. receives about0.6e1.2 mg m�3 of PM2.5 from the elevated Asian emissions. PM2.5mass over the equatorial Atlantic Ocean is also increased by1.0e1.6 mg m�3 due to the tripled Asian emissions. Lower values ofPM2.5 mass than the surrounding areas are evident immediately offthe western coast of the Northern America. This aerosol deficit areacoincides with the persistent marine stratocumulus over the region(Kloesel, 1992).

The longitude-height cross section along 40�N (Fig. 14b) showsthat the PM2.5 pollutants associated with the increased Asianemissions are lifted up in the source regions and are then trans-ported eastward towards the North America and the Atlantic Oceanand beyond. Large aerosol mass (>1.6 mg m�3) due to the elevatedemissions is located mainly in the lower troposphere. Export of theAsian aerosols into the regional environments would have largeimpacts on regional and global radiative balance through the directand indirect aerosol effects (Twomey, 1974, 1977; Albrecht, 1989;Hansen et al., 1997), which was not included in the current work.

a

b

Fig. 13. WRF/Chem simulated (a) MAM mean O3 concentration differences (ppbv)between the pollution scenario and the base case scenario, and (b) longitude-heightcross section of O3 concentration differences (ppbv) along 40�N (the straight line in a).

a

b

Fig. 14. WRF/Chem simulated (a) MAMmean PM2.5 concentration differences (mg m�3)between the pollution scenario and the base case scenario, and (b) longitude-heightcross section of PM2.5 concentration differences (mg m�3) along 40�N (the straight linein a).

Table 2MAM mean contributions of the tripled China and India emissions to surface CO, O3

and PM2.5 concentrations in various regions of the U.S.

U.S. regionsa CO O3 PM2.5

ppbvb %c ppbv % mg m�3 %

Pacific Northwest 10.2 11.1 1.0 4.7 1.6 58.3Pacific Southwest 11.0 10.3 1.3 5.1 1.9 49.7Mountain North 11.1 12.0 1.2 5.1 1.8 59.4Mountain South 13.0 12.7 1.7 5.9 2.5 58.6West North Central 8.9 9.0 0.9 3.9 1.5 46.2West South Central 11.0 10.0 1.3 4.6 2.0 36.4East North Central 7.5 5.7 0.6 3.0 1.2 22.8East Couth Central 9.7 7.8 1.0 3.8 1.5 22.8Middle Atlantic 6.8 4.3 0.5 2.7 0.9 13.7South Atlantic 9.3 7.2 1.0 3.6 1.5 27.1New England 6.4 4.1 0.5 2.5 0.8 12.1Florida 10.7 7.3 1.3 4.1 2.0 31.8

a Region categories are based mainly on Census Regions of the Continental U.S.b Contributions are computed as the differences in concentrations between the

tripled scenario and the base case scenario.c Percentage is computed as the ratio between the contributions and the

concentrations for the base case scenario.

Y. Zhang et al. / Atmospheric Environment 44 (2010) 2799e28122810

MAM mean contributions to CO, O3 and PM2.5 concentrationsfrom the tripled China and India emissions for regional subdivi-sions of the Continental U.S. are listed in Table 2. Both theconcentration differences and the contribution percentage areshown. Regions over the western part of the U.S. generallyreceive higher contribution from the increased Asian emissionsthan those over the eastern part. In addition, aerosol contribu-tions from the increased Asian emissions are much larger inpercentage unit than CO and O3. The top six regions that are mostaffected in terms of the CO, O3 and PM2.5 concentration differ-ences are Mountain South, West South Central, Pacific Southwest,Florida, Mountain North and Pacific Northwest (Table 2). Thecontribution percentage for these top regions is large: 7e13% forCO, 4e6% for O3, and 32e60% for PM2.5.

A note on policy relevance is in place here. The model simula-tions are compared against both the 1-h and the 8-h NationalAmbient Air Quality Standards (NAAQS) for CO and O3 (i.e., 35 ppmvand 9 ppmv for CO, 120 ppbv and 75 ppbv for O3, respectively). Forboth pollutants (not shown), the model simulations with thetripled emissions do not show changes in the frequency of sites/regions exceeding the NAAQS when compared to the base casescenario. There are probably two reasons for this. One is that ona daily basis, the MAM mean contributions from the tripled Asianemissions over the U.S. are relatively small (i.e., 6e12 ppbv for CO,1.0e2.5 ppbv for O3, and 0.6e1.6 mg m�3 for PM2.5). Another one isthat the model tends to underestimate the observed CO and O3 (as

well as PM2.5) concentrations at most of the measurement siteseven under the base case scenario.

For PM2.5 (not shown), therewere only two sites over the Bostonand New York areas, respectively, that exceeded the NAAQS

Y. Zhang et al. / Atmospheric Environment 44 (2010) 2799e2812 2811

standards of 24-h averaged concentration of 35 mg m�3 after theemissions in China and India were tripled. The Boston site alreadyshowed violation even before the tripled emissions, while the NewYear site became non-compliant after the emissions were tripled.

4. Conclusions and discussion

The fully coupledWRF/Chemmodel is employed to simulate theimpact of tripled China and India emissions over the base (gaseousspecies and carbonaceous aerosols for 2000) level on air qualityover the U.S. in MAM. The results show a broad influence areaspanning from the Arabian Sea to the Northern Pacific to theAtlantic Ocean. MAM mean contributions from the tripled Asianemissions over the U.S. are noted: 6e12 ppbv for CO, 1.0e2.5 ppbvfor O3, and 0.6e1.6 mg m�3 for PM2.5 on a daily basis. The northernAtlantic Ocean and Greenland also receive appreciable contributionof gaseous and aerosol pollutants from the increased Asian emis-sions. Increasing pollution from China and India will significantlyimpact the air quality over all of the Eastern Asia and SoutheasternAsia.

A relatively wide range of Asian contributions to the future U.S.air quality are reported in the literature. Jacob et al. (1999) showedthat tripling of Asian anthropogenic emissions from 1995 to 2010would increase the monthly mean O3 concentrations by 2e6 ppbvand 1e3 ppbv in the western and eastern U.S., respectively.Berntsen et al. (1999) showed springtime mean contributions of37 ppbv, 3 ppbv and 23 pptv for CO, O3 and PAN, respectively, fromdoubling Asian emissions. Yienger et al. (2000) noted large episodicAsian contributions reaching 40 ppbv on background O3 over theNorth America after the Asian NOx emissions were increased bya factor of 4 over the 1990 level. In comparison, this work foundMAMmean contributions of 6e12 ppbv for CO, 1.0e2.5 ppbv for O3and 0.6e1.6 mg m�3 for PM2.5 after the Asian anthropogenic emis-sions were tripled. Discrepancies among these works arise due todifferences in the emissions rates, reference meteorologicalconditions, models, and valid time. For this work, notable modelunderestimation in the observed CO, O3 and PM2.5 concentrationssuggests that the estimates of the contributions may be on theconservative side.

As China and India are fast developing in their economy, theemitted pollutants are affecting not only these two countries andtheir neighbors but also a much broad region related to theatmospheric circulations. The export of these pollutants to regionaland eventually to global environments is a major concern becauseof their potential consequences for human health, weather andclimate, ecosystems, visibility degradation, changes in radiativeforcing, and tropospheric (self-cleaning) oxidation. Large contri-butions from the increased Asian emissions over the North Americaalso bring challenges to Canada, the U.S., and Mexico as thesecountries strive to develop emissions control strategies to improvelocal air quality especially in urban areas. Asian pollution isbecoming a regional and global issue.

Acknowledgements

This work (LA-UR-06-4878) was supported by the Los AlamosNational Laboratory through the Laboratory Directed ResearchDevelopment (LDRD) Program (project numberLDRD200500014DR, PI: Dr. Manvendra K. Dubey). Four anonymousreviewers are acknowledged for their constructive comments andsuggestions for improving the original version of the manuscript.Drs. Manvendra Dubey and Seth Olsen are thanked for their inputsand suggestions. The National Center for Atmospheric Research(NCAR) MOPITT team is acknowledged for providing the satellitemeasured CO data. Themodel simulations were performed at NCAR

Computational and Information System Laboratory (CISL). NCAR issponsored by the National Science Foundation. The Los AlamosNational Laboratory is operated by Los Alamos National Security,LLC, for the Department of Energy.

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