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Atmospheric Environment 42 (2008) 7304–7309

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Atmospheric Environment

journal homepage: www.elsevier .com/locate/atmosenv

Application of aerosol speciation data as an in situ dust proxy forvalidation of the Dust Regional Atmospheric Model (DREAM)

Patrick Shaw*

Department of Atmospheric Sciences, University of Arizona, 1118 E 4th Street, PO Box 210081, Tucson, AZ 85721-0081, USA

a r t i c l e i n f o

Article history:Received 4 February 2008Received in revised form 26 March 2008Accepted 13 June 2008

Keywords:Dust model validationAerosol speciationParticulate matter pollutionPM2.5 measurements

* Tel.: þ1 520 626 1534; fax: þ1 520 621 6833.E-mail address: shaw@atmo.arizona.edu

1352-2310/$ – see front matter � 2008 Elsevier Ltddoi:10.1016/j.atmosenv.2008.06.018

a b s t r a c t

The Dust REgional Atmospheric Model (DREAM) predicts concentrations of mineral dustaerosols in time and space, but validation is challenging with current in situ particulatematter (PM) concentration measurements. Measured levels of ambient PM often containanthropogenic components as well as windblown mineral dust. In this study, twoapproaches to model validation were performed with data from preexisting air qualitymonitoring networks: using hourly concentrations of total PM with aerodynamic diameterless than 2.5 mm (PM2.5); and using a daily averaged speciation-derived soil component.Validation analyses were performed for point locations within the cities of El Paso (TX),Austin (TX), Phoenix (AZ), Salt Lake City (UT) and Bakersfield (CA) for most of 2006. Hourlymodeled PM2.5 did not validate at all with hourly observations among the sites (combinedR < 0.00, N ¼ 24,302 hourly values). Aerosol chemical speciation data distinguishedbetween mineral (soil) dust from anthropogenic ambient PM. As expected, statisticallysignificant improvements in correlation among all stations (combined R ¼ 0.16, N ¼ 343daily values) were found when the soil component alone was used to validate DREAM. Thevalidation biases that result from anthropogenic aerosols were also reduced using the soilcomponent. This is seen in the reduction of the root mean square error between hourly insitu versus hourly modeled (RMSEhourly ¼ 18.6 mg m�3) and 24-h in situ speciation valuesversus daily averaged observed (RMSEsoil ¼ 12.0 mg m�3). However, the lack of a totalreduction in RMSE indicates there is still room for improvement in the model. While thesoil component is the theoretical proxy of choice for a dust transport model, the currentsparse and infrequent sampling is not ideal for routine hourly air quality forecastvalidation.

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1. Introduction

Predicting concentrations of particulate matter (PM) intime and space is important for air quality concerns,weather prediction and climate studies. Some climatechange scenarios show regional transitions to arid climates(Seager et al., 2007), and the resulting desertification mightlead to higher frequency and/or intensity of dust storms(Kuehn, 2006). Continued human land use may alsocontribute to more atmospheric dust (Mahowald et al.,

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2004; Tegen et al., 2004). Soil dust, also called mineral dust,is suspected to play a large role in the earth’s radiationbudget (IPCC, 2007), interacts with local modeled meteo-rology (Perez et al., 2006b) and impacts health (Dockeryand Stone, 2007) and human welfare (Kleeman et al., 2001;Pauley et al., 1996). PM models are often validated using insitu surface aerosol measurements and/or remote sensingobservations. Here a validation method is described thatuses the soil contribution in PM2.5 from in-place moni-toring networks as a more accurate comparison betweenmodel and observation.

The forecast model used in this validation study is theDust REgional Atmospheric Model (DREAM) applied to the

P. Shaw / Atmospheric Environment 42 (2008) 7304–7309 7305

southwest United States. Because this study focuses moreon validation methods in general, only the most importantaspects of DREAM are presented. A more detailed modeldescription can be found in Nickovic et al. (2001). DREAMhas two components: the widely used Eta regional atmo-spheric model (Janjic, 1990) and a dust generating module.An initialization scheme was designed to ingest andincorporate land cover data sets as dust source regions intothe particle routine and ultimately into the regionalatmospheric model. Given the proper land cover inputs,DREAM can be operated for any region in the world, but isespecially relevant to dry, arid areas. Based on the landcover data sets, DREAM uses preset particle characteristicsthat describe how particles will be emitted with modeledwinds. DREAM can be configured for multiple particle sizebins. For this study, four bins are defined and the concen-tration of each is described by its own independent Euler-ian transport equation. The calculation of modeled PM2.5

uses the first size bin (aerodynamic diameter range 0–1.46 mm) and only the portion of the second bin thatincludes up to diameters of 2.5 mm. This bin configurationsometimes results in biases in the PM2.5 calculation, sofuture versions will be improved to correct for the particlesize resolution. Along with dust production, advection andturbulent diffusion are included in the dust cycle module.Atmospheric sinks (dry deposition, rain out, etc.) of theparticles are parameterized but are assumed to affect onlythe concentrations and not the composition. It is importantto note that neither PM transported from outside the modeldomain nor localized anthropogenic PM were included inthis version of DREAM, which accounts only for windblowndust.

DREAM is primarily intended to provide hourly dustpredictions for air quality forecasts. The fine fraction of PMcan penetrate deeper into the respiratory system than thecoarse aerosol mode, so more focus is given in DREAM toPM2.5 rather than PM10. Although PM10 may have a largercontribution to mechanically produced mineral dustcomponents, this study focuses entirely on PM2.5 due todata availability and relevance to human health. Surfaceconcentration validation was previously performed usingobserved hourly in situ total PM2.5 data for a December 15–17, 2003, west Texas test case (Yin et al., 2005) and withdifferent land cover data sets (Yin et al., 2007). Modeledmeteorological fields such as wind speed (agreementindex ¼ 0.73), wind direction (agreement index ¼ 0.74)and temperature (agreement index ¼ 0.73) matched rela-tively closely with observed winds and temperatures,indicating that the weather portion of the model performsas it should. Modeled spatial distributions of dust plumesoften appeared qualitatively to match MODIS satelliteobservations. The timing of maximum particle concentra-tions was modeled well (R ¼ 0.87), but the magnitude ofpeak and background concentrations generally verifiedpoorly with in situ sampling networks. Non-peak hoursgenerally underestimated concentration. Presumably, thisis because DREAM did not account for anthropogenicaerosols as only windblown dust was predicted by themodel. Here it is investigated whether other sources ofparticulates affect the comparison of predicted aerosolconcentrations in model performance.

Remote sensing has also been used to validate DREAM.Several satellite-derived data sets were tested by Mahleret al. (2006). It was found that the retrieval of dust opticalproperties with passive systems is problematic over highlyreflective surfaces (for most DREAM scenarios, is the desertfloor). Also, the sun photometers used in AERONET do notwork well during windy conditions and this makes vali-dating windblown dust a problem. Ground-based activeLIDAR has shown qualitative similarities in aerosol verticalprofiles in the Mediterranean, but biases may result fromanthropogenic boundary layer aerosols (Perez et al.,2006a). Clouds and smoke also provide challenges inidentifying dust plumes from satellite imagery (RiveraRivera et al., 2006). Use of many sensors, active and passive,space-borne and ground-based, would provide a bettervalidation approach but the expense begs for a sounderversion of the conveniently located in situ PMmeasurements.

2. Site descriptions

In this study, only the five cities of El Paso (31.756 N,106.455 W), Austin (30.483 N, 97.872 W), Phoenix (33.504N, 112.095 W), Salt Lake City (40.736 N, 111.872 W) andBakersfield (35.356 N, 119.040 W) were selected as vali-dation sites because each has in situ records of the twotypes of PM data pertinent to this study: hourly PM2.5

observations and chemical speciation of the same sizefraction. Each area is known to experience particulatematter episodes (both natural and anthropogenic) and poorvisibility. El Paso and Phoenix also experience highfrequencies of natural windblown dust events. Agriculture,industry and off-road automobile traffic provide sporadic,isolated hot spots that may become especially importantduring wintertime stagnant air and inversion layers. Thesouthwest region is considered an important source fordesert dust in North America due to the dry playas anddisturbed/fallow agricultural fields (Prospero et al., 2002).High PM episodes are associated with both high and lowwinds, indicating that windblown dust as well as anthro-pogenic aerosols trapped by inversions in stagnant airaffect observed concentrations (Currey et al., 2005). Winterinversions are characterized by low surface temperaturesand higher concentrations of trapped anthropogenicparticles including wood smoke from domestic heating.Eventually a validation scheme will be needed to differ-entiate between the diverse meteorological scenarios thatresult in aerosol pollution, but no such method exists as ofthe date of publication.

A recent dust storm record exists for El Paso, as reportedby the Texas Commission on Environmental Quality (TCEQ,2007). The month of January 2006 was modeled for El Paso(Fig. 1) to illustrate how aerosol composition and concen-tration depends on meteorology. The TCEQ reportedblowing dust on January 9, 16,17 and 19 of 2006 and theseevents appear to be hindcasted by DREAM. Unstable lapserates and high southwesterly surface winds are evident inskew-T soundings (UWyoming, 2007) and are consistentwith the climatology (Garcia et al., 2004). DREAM did notpredict PM peaks on January 3, 7 or 11, although they areclearly evident in the observations. Skew-T soundings for

Fig. 1. Hourly comparison of PM2.5 in situ observations and hourly DREAMoutput for El Paso, Texas for January 2006.

P. Shaw / Atmospheric Environment 42 (2008) 7304–73097306

these days show boundary layer inversion layers thatpersist throughout the day as well as weak surface windspeeds, both of which are conducive to the buildup ofanthropogenic particles. With wind speeds too low for dustemission, DREAM fails to predict elevated concentrationsduring these inversions. Here it is hypothesized thatchemical speciation should be able to show specificallyhow much soil contributes to observed PM2.5 during theseinversions and if DREAM was able to model the low windmineral dust concentrations.

3. Data

3.1. Hourly data

PM2.5 is defined as the mass concentration (mg m�3) ofall particles with aerodynamic diameter 2.5 mm or less.Hourly ambient PM2.5 is measured by the EnvironmentalProtection Agency in each of the cities of interest and isreported to the AirNow network. Average hourlymeasurements are taken with a tapered element oscillatingmicro-balance (TEOM). These measurements are used forNational Ambient Air Quality Standards compliance and arenot intended for model validation, but the large networkrange and public availability to the data make routinevalidation convenient for urban locations.

Table 1Summary of PM2.5 species (El Paso, TX 2004–2006)

Component Range (mg m�3) Mean (mg m�3) Max uncertainty(mg m�3)

Soil (mineral dust)a 0.0311–13.716 1.403 0.637b

Trace metals 0.0236–3.583 0.398 0.473b

Others 0.267–11.18 2.116 0.507b

Elemental carbon 0–4.13 0.778 0.370Organic carbona 0.721–16.52 3.986 0.900Total observed 1.2–35.1 8.407 1.800Total reconstructedc 1.9–32.27 8.824 1.186b

a Weighting applied.b Using error prorogation.c Computed as sum of soil, trace, others, EC and OC.

3.2. Chemical composition data

Speciation data used in this study were obtained fromthe EPA Speciation Trends Network (STN) (EPA, 2007).PM2.5 is chemically analyzed every third or sixth day,depending on the location. Concentrations of 49 species arereported as a 24-h composite. The speciation monitorswere collocated with the hourly monitors and DREAM wasprogrammed to give PM output at the same coordinates.Unfortunately, hourly chemical speciation data were notavailable.

Various laboratory methods are used to determinecomposition (Malm et al., 1994). Elemental carbon andorganic carbon are determined by total optical trans-mittance (TOT). Sulfates, nitrates, ammonium, potassium

and sodium ions are determined by ion chromatography(IC). The remaining elements are determined by energydispersive X-ray fluorescence (XRF). Independent totalPM2.5 mass (PMtotal) is determined gravimetrically usinga TEOM.

Elements generally assumed to be associated with soilare aluminum, silicon, calcium, iron and titanium (Malmet al., 1994). This study also arbitrarily includes cobalt,copper, lead, nickel, magnesium, manganese, phospho-rous, potassium and sodium, but the trace amounts ofthese elements contribute very little to the mass concen-tration. The soil component (PMsoil) is the weighted sumof each species (Eq. 2). The weights are meant to accountfor the oxygen contribution in soil oxides. For example,aluminum commonly exists as aluminum oxide (Al2O3), sothe measured elemental aluminum concentration (deno-ted by brackets) must be multiplied by the molecular massratio, i.e. Al2O3/(Al � 2) ¼ 1.89. Several standard categoriesare used to define the composition of PM: elementalcarbon (EC), organic carbon (OC), nitrates, sulfates, ions,soil, and trace metals. For simplicity, we have arbitrarilycombined the remainder of the speciated components asPManthropogenic (Eq. 2), although not all of these speciesmay result solely from human activities. A summary of thecomponents measured by the STN methods is given inTable 1.

½SOIL� ¼ 1:89½Al� þ 2:14½Si� þ 1:4½Ca� þ 1:66½Mg�þ 1:43½Fe� þ 1:67½Ti� þ 1:35½Na� þ 1:2½K�þ 1:29½Mn� þ 1:27½Co� þ 1:25½Cu�þ1:08½Pb� þ 3:07½P� þ 1:27½Ni� (1)

PMtotal ¼ PMsoil þ PManthropogenic (2)

4. Analysis

4.1. AirNow validation

In this study, the model was programmed to simulatethe year of 2006 for every grid box in the entire DREAMmodel domain. To avoid repeated model spin up timeproblems, the model was operated in a hindcast mode toproduce continuous hourly data for the same coordinatesas the predetermined validation sites. At various times,model data and/or observations were unavailable. Of

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a possible 8760 hourly data pairs in a year, roughly 65% ofmodel data had corresponding observations to compare.When hourly model data and hourly in situ data weredirectly compared, the validation showed there was almostno correlation at El Paso (R ¼ 0.01; Table 2) and Austin(R < 0.00), and was slightly anti-correlated at Bakersfield(�0.09) and Salt Lake City (�0.08). Only Phoenix showeda positive correlation, albeit a small value (R ¼ 0.10).

For the entire 2006 model simulation, 24,302 hourlyobservations were compared by combining output from allfive sites. The model under-predicted PM2.5 values over 82%of the time. This bias could be due to the presence of otherspecies in the observed PMtotal. During extreme duststorms, much of the PMtotal would be soil; however, duringnon-windy conditions when PMsoil is relatively small,background anthropogenic aerosols would dominate thePMtotal levels. Anthropogenic emissions and atmosphericinversion layers produce high concentrations of PMtotal thatare dominated not by PMsoil but rather by PManthropogenic.This suggests calculating PMsoil from chemically speciateddata rather than PMtotal to compare to model predictedPM2.5.

4.2. Wind speed

DREAM dust production is forced by the winds pre-dicted by the Eta model; therefore, a strong correlationbetween observed wind speeds and modeled hourlysurface dust concentrations should exist. El Paso windspeed relationships were analyzed for January 2006.Hourly mean wind data are recorded by the TCEQ as the 1-haverage of 5-min wind measurements. Correlations ofmodeled dust concentrations with hourly mean windspeed were unexpectedly small (R ¼ 0.35, N ¼ 744) whichcould be due to the parameterized saltation process andinaccurate description of source regions. DREAM hasa threshold surface friction velocity as a requirement for

Table 2Summary of validation correlations

Location Relationship tested R (at 99%) N pairs

Austin, TX DREAMa vs. AirNowa 0.00 4320AirNowb vs. PMtotal

b 0.91 33DREAMb vs. PMsoil

b 0.24 33PMsoil

b vs. PMtotalb 0.54 33

Bakersfield, CA DREAMa vs. AirNowa �0.09 4961AirNowb vs. PMtotal

b 0.95 72DREAMb vs. PMsoil

b �0.13 72PMsoil

b vs. PMtotalb 0.19 72

El Paso, TX DREAMa vs. AirNowa 0.01 5160AirNowb vs. PMtotal

b 0.61 72DREAMb vs. PMsoil

b 0.31 72PMsoil

b vs. PMtotalb 0.52 72

Phoenix, AZ DREAMa vs. AirNowa 0.10 5758AirNowb vs. PMtotal

b 0.82 83DREAMb vs. PMsoil

b 0.15 83PMsoil

b vs. PMtotalb 0.41 83

Salt LakeCity, UT

DREAMa vs. AirNowa �0.08 5731AirNowb vs. PMtotal

b 0.84 83DREAMb vs. PMsoil

b 0.10 83PMsoil

b vs. PMtotalb 0.20 83

a Hourly values.b Daily averages.

dust uplift that is lower for dry (Bagnold, 1941) than for wetsoil (Fecan et al., 1999); this may be another source of errordue to coarse resolution of soil moisture land coverinitialization. No significant statistical correlation existsbetween observed hourly PM2.5 and hourly wind speed(R ¼ �0.20, N ¼ 744). While some levels of wind speed andhigh PM2.5 concentration occur together, many PM2.5

values occur with low wind speeds and/or inversions. Thisagrees with the previous climatology findings (Currey et al.,2005). Low winds are indicative of stagnant air and thebuildup of anthropogenic aerosols. DREAM models onlywindblown soil, so low wind anthropogenic PM2.5 spikes(such as January 3, 7 and 11) will not show up in the modeldata. It is suspected that speciation of these peaks wouldshow the large contributions from non-soil components;furthermore, speciation would show the large soilcomponent contribution during high wind dust events.Spikes caused by even shorter wind gusts, on the order ofminutes, cannot be completely resolved by either model orobservation. Such microscale events can be natural (suddenthunderstorm downbursts) or anthropogenic (off roadvehicles, industrial grinding) and pose a problem tovalidation.

4.3. Speciation validation

The purpose of this study is not to validate variousregulatory agency data sets with each other. But in order touse speciation and hourly non-speciated data inter-changeably, it is still necessary to determine if STN dataproduce consistent observations as daily averaged AirNowhourly data. To compare the two in situ data sets at thesame time scales, the AirNow data were averaged fora midnight to midnight local time. Austin had data availableevery sixth day (N ¼ 33 daily values) while the other fourstations sampled every third day (N ¼ 72 for Bakersfieldand El Paso; N ¼ 83 for Phoenix and Salt Lake City). Overall,correlation analysis between the daily averaged AirNowand the STN at all five sites (R ¼ 0.89; N ¼ 343) implies thetwo data sets are measuring much but not the same totalPM2.5. El Paso presents a noticeable difference (R ¼ 0.61)between speciated PMtotal and averaged AirNow PM2.5,which puts some of the analysis at this site in question. Itcan be said that the speciation at the other four sites doesa reasonable job at capturing the daily average of hourlymeasurements of the same size fraction. With this result,PMsoil correlations are assumed to be accurate. Severalreasons exist why the correlations will not be perfect:difference in sampling methods, losses due to delayedlaboratory analysis, and discrepancies due to missinghourly data that skew the 24-h average. There may beenough of a difference in data sets to make a difference inmodel validation. The statistical similarity, however,suggests the use of daily averaged– albeit less frequent–speciation data as a possible model validation data set.

While the heterogeneity of the surrounding soils maycomplicate the generic soil equation, it is assumed for thisstudy that the derivation of the soil component is valid.Speciation into PMsoil is justifiable because the recon-struction of PMtotal using the other major componentsworked well over a 2-year period of STN data (Table 1). It is

Table 3Root mean square errors (mg m�3) between modeled and observed proxies

Location RMSE hourly PM2.5 RMSE 24-h soil

Austin, TX 9.4 3.3Bakersfield, CA 21.4 2.1El Paso, TX 15.1 9.3Phoenix, AZ 29.9 18.9Salt Lake

City, UT11.3 1.7

Combined totals 18.6 12.0

P. Shaw / Atmospheric Environment 42 (2008) 7304–73097308

expected that PMsoil will usually comprise a small constantproportion of the daily averaged AirNow PM2.5 exceptduring dust storm events or local anthropogenic mechan-ical events. The relationships between the daily averagedAirNow PM2.5 and the PMsoil at each site are positive incorrelation, but cannot explain all of the variance. PMsoil

appears to be best correlated with daily averaged AirNowPM2.5 in the desert cities of Austin (R ¼ 0.54), El Paso(R ¼ 0.52) and Phoenix (R ¼ 0.41). This makes intuitivesense as soil dust in the desert is known to comprise moreof the regional aerosol. The cities of Salt Lake City (R ¼ 0.20)and Bakersfield (R ¼ 0.19) have smaller correlationspresumably due to different regional aerosol production.

While hourly extremes in PM2.5 may be observed inexcess of 100 mg m�3, daily means are much smaller inmagnitude. Many dust storms exhibit short spikes in hourlyPMtotal concentration due to rapidly passing frontal gusts(Yin et al., 2007). The daily averaging smoothes out the shortduration peaks observed for dust storms. Without pro-longed winds, larger particles will settle out quickly. Underconditions approximating stirred settling (Hinds, 1999), theconcentration of 2.5 mm aerodynamic diameter particleswould drop by approximately 80% in the surface layer afteronly 10 h. Dusts transported from other continents (Asiaand Africa) have longer atmospheric residence timesbecause they are lofted into higher regions of the tropo-sphere that allow for longer suspension. In the absence ofthese non-local events, background conditions of PMsoil areexpected after the characteristic stirred settling time. Itwould take a sustained windy event to keep particles sus-pended and to produce a large daily average. Furthermore,several high dust days may have been missed in theobservations due to the infrequent sampling frequency.

To validate DREAM with PMsoil, hourly model outputmust also be averaged midnight to midnight to match thefrequency of the STN data. When DREAM is compared toPMsoil at each site, the correlation improved for four of thefive sites (Table 2). El Paso (Rhourly ¼ 0.01 to Rsoil ¼ 0.31) andAustin (Rhourly < 0.00 to Rsoil ¼ 0.24) showed the greatestimprovement. Phoenix and Salt Lake City improved, but notas dramatically. Bakersfield, however, actually exhibiteda worse correlation with speciated soil (Rsoil ¼ �0.13) thanwith hourly PM2.5 (Rhourly ¼ �0.08). It is suspected thatmore intensive agricultural activity and larger influence oftranscontinental soil contributions from Asian dust stormsplay a larger role in ambient soil in Bakersfield, both ofwhich are presently not modeled by DREAM. Butcombining each speciation and daily averaged model valuepair into one correlation calculation, the overall improve-ment from hourly statistics with PM2.5 is apparent(Rhourly < 0.00 improved to Rsoil ¼ 0.16). The hypothesisthat the use of soil component will improve validationstatistics is justified.

The primary motivation for use of the natural soilcomponent was to reduce the bias in observations caused byanthropogenic particles. This offset is apparent when theroot mean square error (RMSE) is calculated for hourly pairsat each site (Table 3). When the RMSEsoil is computed fordaily values, the error is reduced across the board, mostevident at Austin (9.4–3.3 mg m�3), Bakersfield (21.4–2.1 mgm�3) and Salt Lake City (11.3–1.7 mg m�3). RMSE is reduced at

Phoenix and El Paso as well, but the errors are still large atthese two sites, resulting in an overall RMSEsoil of 12.0 mg m�3

in a combined analysis. This inability to completely reducethe bias between modeled and observed, even using thesoil component, warrants further model improvement.However, the promising improvement in statistics encour-ages the continued use of the soil component.

5. Discussion and conclusions

An in situ mineral dust proxy, derived from speciation ofPMtotal into PMsoil, was used to validate DREAM for 1 yearof modeled hourly data. This is considered to be the mostsuitable proxy for validation of such a model. Overallcorrelations improved and biases were reduced. Correla-tions, however, were far from ideal and a large bias stillexisted. There are several suspected reasons for thesediscrepancies: coarse sampling frequency, sampling biases,generic nature of the soil component and model resolutiondeficiencies.

The amount of data points and thus the number ofdegrees of freedom used in correlation is significantlyreduced when speciation data are used. When hourlyobservations were used, the data set contained 24,302concentration values. When the every-third-day or sixth-day 24-h averages were applied, only 343 values wereavailable for correlation and the test for statisticalsignificance became more stringent. Over the course ofa year-long simulation, enough speciation data points areavailable for statistical robustness. The same cannot be saidfor isolated week-long to month-long test cases. Hourly ElPaso PMtotal data, when averaged over the same timewindow as speciation data and compared every third orsixth day, are highly correlated with the STN PMtotal. PMsoil

is strongly correlated with the PMtotal. This would suggestthat PMsoil is suitable as a proxy for validation. However,the hourly peaks in both observed data and model data canbe extreme, and sudden hourly maxima may be smoothedout by daily averaging. If this information is lost, and theless frequent sampling frequency fails to land on days withextreme dust events, the validation will inevitably missimportant events and the model will fair poorly ina statistical sense. Speciation on a higher samplingfrequency (preferably hourly, but even if only daily) couldresolve more variation and provide better validation.Although hourly PMsoil is the ideal proxy, it is probably toodifficult to obtain in practice. Daily averaged observationswill validate concentration trends but not the precisetiming of extreme events, which is the primary focus of thispredictive air quality model.

P. Shaw / Atmospheric Environment 42 (2008) 7304–7309 7309

Calculation of model surface concentration remains inquestion. The bin resolution of the run used in this studymay differ slightly from the size that the stations actuallymonitor. This version of DREAM did not predict at the 2–10�m vertical level where a monitor would typically befound, but instead gave an average value of its bottommostdynamic layer. Again, this can lead to a discrepancy whencomparing modeled with observed.

While the model may miscalculate surface concentra-tion, the most obvious weakness is in the initializing of dustsource regions. Small-scale spatial hotspots that change onseasonal and smaller timescales are overlooked orcompletely missed by using a coarse, static land cover dataset. A study by Yin et al. (2007) showed that updated andhigher resolution land cover greatly increase the validationstatistics for PM2.5. Work is underway to make DREAMcompatible with the National Center for EnvironmentalPrediction (NCEP) Non-hydrostatic Mesoscale Model(NMM), which would increase spatial resolution andimprove wind forcing in the model. Sensitivity tests arealso required for improved validation. A difference inmodel initiation of soil moisture, for example, may yielda spectrum of different results. Such an experiment isbeyond the scope of this study. While more monitoringstations are needed to validate DREAM at multiple loca-tions (urban and rural) it is impractical to increase the insitu network. Hourly PM2.5 validation is still the mostpractical approach, considering the data available, soa method must be used to flag and scale anthropogenicinfluences from natural events in future validationattempts.

Acknowledgments

This work is funded by the National Aeronautics andSpace Administration (NASA) under the Public HealthApplications in Remote Sensing (PHAiRS) project(NN504AA19A). Thanks to the United States EnvironmentalProtection Agency and the Texas Commission on Environ-mental Quality for providing public meteorological and airquality observational data, and Dr Karl Benedict and DrWilliam Hudspeth from the University of New Mexico andDr Dazhong Yin for organizing the model data, theUniversity of Arizona, including the efforts of Dr Eric Bet-terton and Dr Christopher Castro. Special thanks should goto Dr William Sprigg and Brian Barbaris for their guidanceon this study.

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