ARTICLE IN PRESS

1352-2310/$ - se

doi:10.1016/j.at

�Correspondfax: +1703 527

E-mail addr

Atmospheric Environment 40 (2006) 8056–8067

www.elsevier.com/locate/atmosenv

Integrating lidar and satellite optical depth with ambientmonitoring for 3-dimensional particulate characterization

Jill A. Engel-Coxa,�, Raymond M. Hoff b, Raymond Rogersb, Fred Dimmickc, AlanC. Rushc, James J. Szykmand, Jassim Al-Saadie, D. Allen Chub, Erica R. Zella

aBattelle, 2101 Wilson Boulevard, Suite 800, Arlington, VA 22201, USAbJoint Center for Earth Systems Technology, UMBC CREST and the Physics Department, University of Maryland, Baltimore County, 1000

Hilltop Circle, Baltimore, MD 21250, USAcUS Environmental Protection Agency, Office of Air Quality Planning and Standards, 1200 Pennsylvania Avenue NW,

Washington, DC 20460, USAdUS Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory,

Research Triangle Park, NC 27711, USAeUS National Aeronautics and Space Administration, Langley Research Center, Hampton, VA 23681, USA

Received 26 July 2005; accepted 28 February 2006

Abstract

A combination of in-situ PM2.5, sunphotometers, upward pointing lidar and satellite aerosol optical depth (AOD)

instruments have been employed to better understand variability in the correlation between AOD and PM2.5 at the surface.

Previous studies have shown good correlation between these measures, especially in the US east, and encouraged the use of

satellite data for spatially interpolating between ground sensors. This work shows that cases of weak correlation can be

better understood with knowledge of whether the aerosol is confined to the surface planetary boundary layer (PBL) or

aloft. Lidar apportionment of the fraction of aerosol optical depth that is within the PBL can be scaled to give better

agreement with surface PM2.5 than does the total column amount. The study has shown that lidar combined with surface

and remotely sensed data might be strategically used to improve our understanding of long-range or regionally transported

pollutants in multiple dimensions.

r 2006 Elsevier Ltd. All rights reserved.

Keywords: Air quality; Satellite; MODIS; Lidar; Particulate matter; Policy

1. Introduction and background

Air quality has traditionally been monitored atthe surface through ground-based monitors. Thesemonitors are used to characterize air quality and to

e front matter r 2006 Elsevier Ltd. All rights reserved

mosenv.2006.02.039

ing author. Tel.: +1 703 875 2144;

5640.

ess: engelcoxj@battelle.org (J.A. Engel-Cox).

determine compliance with ambient air qualitystandards. However, recent research and policyemphasis on regional and intercontinental transportof air pollutants such as fine particulate mattersmaller than 2.5 mm in diameter (PM2.5) has high-lighted the need for additional data sources tomonitor air pollution as it moves in multipledimensions, both spatially and temporally. Satellitedata can add synoptic information and visualization

.

ARTICLE IN PRESSJ.A. Engel-Cox et al. / Atmospheric Environment 40 (2006) 8056–8067 8057

to ground-based air quality data modeling. Theutility of satellite remote sensing has been docu-mented, although not yet fully implemented (Engel-Cox et al., 2004a). The National Aeronautics andSpace Administration (NASA) and the US Envir-onmental Protection Agency (EPA) have coopera-ted in the integration of Moderate ResolutionImaging Spectrometer (MODIS) aerosol opticaldepth (AOD) data and EPA in situ PM2.5 monitor-ing data, showing good correlation and applicationfor forecasting in the eastern US (Engel-Cox et al.,2004b; Al-Saadi et al., 2005). We will show here thatthe addition of light detection and ranging (lidar)systems, consisting of a laser to measure aerosolscattering in the atmosphere as a function ofaltitude, provides a vertical dimension of airpollutant monitoring when combined with satelliteand ground-based concentration measurements. Weshow results from the integration of lidar verticalprofiles from the University of Maryland, BaltimoreCounty (UMBC), AOD measurements by theNASA MODIS sensor, and PM2.5 concentrationsfrom EPA ground-based mass and speciation trendsnetworks.

2. Data collection and processing methodology

2.1. Ground-based monitoring network data

EPA and its state and local government partnersmaintain several air quality-monitoring networks inthe United States, including State and Local AirMonitoring Stations (SLAMS), National AmbientMonitoring Stations (NAMS), and SpeciationTrends Network (STN). The networks monitor theconcentration (and, at some sites, speciation) ofparticulate and gaseous air pollutants at the groundlevel. Most relevant to this project is the monitoringof PM2.5, both mass and speciation data. Inaddition to the filter-based 24-h average FederalReference Monitors (FRM), Tapered ElementOscillating Microbalance (TEOM) and other in-struments provide continuous PM2.5 concentrationdata in real-time. State and local agency partnersprovide hourly monitoring data to EPA’s AIRNowprogram.

For this study, PM2.5 data were obtained for theOld Town Baltimore site that includes both a FRMmonitor and a TEOM continuous mass monitor.The Old Town Baltimore site is located in down-town Baltimore, approximately 10miles northeastof the UMBC lidar (see Section 2.3). In addition,

PM2.5 speciation data were obtained from the FortMeade STN site, located approximately 15milessouth of the UMBC lidar. Daily (24-h) averageconcentration and speciation PM2.5 data wereobtained from the EPA Air Quality System (AQS)(US EPA, 2005). Additionally, hourly averagePM2.5 data were obtained from the AIRNow DataManagement Center data feed to the IDEA site.

2.2. Satellite data

NASA designs, launches, and operates a networkof Earth Observing System (EOS) satellites, eachwith several sensors. Data for this pilot project camefrom the MODIS sensor, located on the Terra andAqua satellite platforms with daytime overpasstimes of approximately 10:30 a.m. and 1:30 p.m.local standard time. The product most relevant toair quality is AOD (also denoted as ta), adimensionless measure of the scattering and absorp-tion of light by aerosols over the total verticalcolumn from ground to satellite.

The Infusing satellite Data into EnvironmentalApplications (IDEA) product (http://idea.ssec.wisc.edu/) uses both hourly data from AIRNow andAOD from the NASA MODIS sensor on the Terraplatform (Al-Saadi et al., 2005). As noted on theirsite, IDEA is a partnership between NASA, EPA,and the National Oceanic and Atmospheric Admin-istration (NOAA) to improve air quality assess-ment, management, and prediction. IDEA processesMODIS radiance data to provide an AOD productin near-real-time for use by the federal, state andlocal aerosol forecasting community.

The specific data products used in this study wereMOD04 and MYD04 Level 2 Aerosol Product forthe Terra and Aqua platforms, respectively. TheAOD is taken from the MOD04/MYD04 variable‘‘Optical_Depth_Land_And_Ocean,’’ representingthe AOD from both the land and ocean models at0.55mm and averaged over 10� 10km horizontally(for derivation of AOD, see Remer et al., 2005).Optical depth of aerosols typically ranges from zeroto about five, with values over unity generally beingclassified as heavy haze. The staff at the IDEA siteprovided the MOD04 AOD values nearest Baltimore(IDEA, 2004) and one of the authors processed theMYD04 raw data from the Goddard DistributedActive Archive Center (DAAC) and matched withlocation of the Old Town Baltimore site. SomeMOD021KM datasets were used to produce red–green–blue (RGB) ‘‘true color’’ imagery, either

ARTICLE IN PRESSJ.A. Engel-Cox et al. / Atmospheric Environment 40 (2006) 8056–80678058

processed by the authors or by the University ofWisconsin–Madison’s Space Science and Engineer-ing Center (UW SSEC, 2004).

Quantitative analysis has indicated limitations tothe satellite data, specifically poor correlations ofAOD to PM2.5 in the western United States andgood correlations of about 0.6–0.9 east of 1001W(Engel-Cox et al., 2004b; Rush et al., 2004). In thatearlier work, we identified aerosol source type, highsurface albedoes, and aerosol transport at elevatedlevels to be probable causes of the poor correlationin the western US. In the eastern US, a moreuniform, sulfate dominated aerosol type, moreuniform topography and planetary boundary layerdepth, and more dominant and widely distributedanthropogenic emissions likely led to a highercorrelation between surface aerosol concentrationand aerosol optical depth. Remaining variability inthe eastern United States is partially due to loftedaerosols.

2.3. Lidar system data

A lidar system uses a laser to measure aerosolscattering in the atmosphere as a function of heightin the atmosphere. Fig. 1 shows a diagram of theUMBC Polarization-diverse Elastic Lidar Facility(POLAR ELF). The system uses a ContinuumSurelite II Nd:YAG, Q–switched laser operating at10Hz and frequency doubled to 0.532 mm (in the

Fig. 1. University of Maryland, Baltimore County, Elastic Lidar Facili

lidar images (in Figs. 1–6), time listed is UTC and color gradations ra

green region of the spectrum). The light is directedinto the atmosphere with a steerable launch mirrorwhere the light scatters due to constituents in theatmosphere. A 14-inch telescope collects the lightscattered in the backwards direction. The angles aregreatly exaggerated in Fig. 1; the light collected bythe telescope is within a small fraction of 1801. Thecollected light passes through a polarizing beamsplitter as well as interference filters centered at0.532 mm and is detected using two photomultipliertubes (PMT). The signals generated by the PMTsare digitized at 10MHz, averaged over 1min, andrecorded to a computer for further analysis. Comer(2003) gives a detailed description of the instrumentsetup.

The power received by the PMT can be written as

PðzÞ ¼OðzÞK

z2bAðzÞ þ bRðzÞ� �

� exp �2

Z z

0

aAðz0Þ þ aRðz0Þ½ �dz0� �

, ð1Þ

where K represents all range independent para-meters, bA(z) and bR(z) are the aerosol and Rayleighbackscatter coefficients, respectively, aA(z0) andaR(z0), respectively, represent the aerosol andRayleigh extinction coefficients, and O(z) is theoverlap correction that accounts for the geometricoverlap of the field of view of the telescope and thelaser beam at low altitudes. The signal is then usedto determine the backscatter coefficient bA(z) from

ty (ELF) (Comer 2003) and lidar image with low aerosols. For all

nging from 0–0.5 indicate light extinction in km�1.

ARTICLE IN PRESSJ.A. Engel-Cox et al. / Atmospheric Environment 40 (2006) 8056–8067 8059

Eq. (1), as follows:

bAðzÞ ¼ bRðzÞz2PðzÞ

KOðzÞbRðzÞexp �2R z

0 aRðz0Þ þ aAðz0Þ½ �dz0� �� 1

!.

(2)

The parameter relevant to the PM2.5 concentra-tion is the aerosol extinction coefficient, which isdetermined from backscatter by assuming a con-stitutive relationship between the extinction coeffi-cient and the backscatter coefficient.

aA ¼ SAbA. (3)

The aerosol lidar ratio, SA, depends on thecomposition, relative humidity, and the size andshape distributions of the aerosols; SA is computedfrom the column measurements made at the Mary-land Science Center AERONET station. Thisimplicitly assumes a constant lidar ratio with respectto height and the implications of that assumptionare discussed below.

This extinction algorithm allows derivation ofextinction as a function of height and the data aregenerally presented in a time–height diagram withcolor modulation such that the colors are propor-tional to the extinction coefficient of the aerosol. Asa reference point for future figures, Fig. 1 includes atime series of extinction calculated from Eqs. (2)and (3) on an exceptionally clear day in September2004. The optical depth is less than 0.1 on this day.With this scheme, warmer colors represent higherextinction values and blue colors are close toRayleigh scattering. The dark horizontal line at1.8 km in the lidar timeseries is an electronic artifactof the acquisition computer.

Optical depth t is then obtained from theextinction measurement by vertically integrating

t ¼Z z2

z1

aAðzÞdzþ

Z z1

0

aAðz1Þdz (4)

over the range of interest, z1–z2. The second term inEq. (4) assumes a well-mixed, constant extinctionlayer from the ground to z1. This compensates forthe near-field region in which the lidar cannot makeaccurate extinction measurements, described byEq. (1) by O(z). The well mixed assumption is asource of error in conditions where the lowest partof the boundary layer is not well mixed, such asbeneath low, strong inversions or local sources ofaerosol that do not mix upwards. For this dataset,z1 was 150m and z2 was determined by the signal tonoise in the extinction, typically around 6 km.

Range to the target is computed from the 10MHzdigitization rate of two GAGE 1012 PC cards(giving 12 bits of resolution at 10MHz or 15m ofvertical resolution per point). One thousand pointsare digitized giving the lidar a 15 km range. Ingeneral for this study, we were interested in aerosolsin the lower troposphere up to 4–6 km. Since wewish to compare these results with the columnmeasurement of MODIS, elimination of high cirruswas required.

The lidar method of determining optical depthhas an advantage over satellite instruments in thatthe lidar method can calculate optical depth forselect ranges in the atmosphere. This allows theoptical depth, and hence the scaling to aerosolconcentration, as is done in the IDEA product, tobe quantified below the boundary layer as well asabove the boundary layer. The optical depth belowthe boundary layer best represents the PM2.5

measurements in a well-mixed boundary layer whilethe optical depth above the boundary layer repre-sents elevated particulate matter. For the workshown here, we wished to examine cases wheretransport aloft may have affected the columnoptical depth and, thus, reduced the correlationbetween optical depth and PM2.5 at the surface.With the ability of the lidar to determine theextinction profile, the total optical depth was splitinto two values, that above and below the boundarylayer. We did this in order to begin to quantify theaerosol concentrations below the boundary layer,presumably where it is well-mixed and would bestrepresent the ground-based PM2.5 measurements.Breaking the AOD into two values also helps usunderstand how lofted aerosols may alter therelationship between satellite optical depth andPM2.5 concentrations.

2.4. Real-time integration of multiple datasets

In order to coordinate the use of these multipledatasets using all three dimensions, this analysisused the UMBC US Air Quality Weblog (the‘‘Smog Blog,’’ http://alg.umbc.edu/usaq). Everyday during the study period from 1 July to 31August 2004, the authors obtained, posted, andbriefly summarized data from multiple data sources,including PM2.5 concentrations from EPA’s real-time ground-based monitoring network, 2-D truecolor and AOD images from the MODIS sensor,and the changing vertical profile of PM from lidar.We used this daily weblog approach to allow

ARTICLE IN PRESSJ.A. Engel-Cox et al. / Atmospheric Environment 40 (2006) 8056–80678060

collaboration between investigators, integration ofthe datasets in real-time, and simplified eventanalysis at the end of the study period. Whenreviewed together for a single event and as anoverview of the summer, the synthesis of thesesources of data provided a useful set of tools for anintegrated characterization of air quality, illustratedin the following case studies.

3. Case studies demonstrating different aerosol

sources

Fig. 2 summarizes the hourly and daily averagePM2.5 concentrations, selected speciation data, andthe MODIS AOD data for Baltimore, Maryland,for Summer 2004 (1 July–31 August). Four specificevents within this time range were selected forvisualization and quantitative analysis: a commonhazy day (Tuesday, 10 August 2004), a day withtransported haze (Tuesday, 24 August 2004), a daywith high-altitude smoke (Friday, 9 July 2004), andan event of smoke mixing down to ground-level(Tuesday–Thursday, 20–22 July 2004). These eventswere chosen to demonstrate how a 3-D monitoringsystem can enhance understanding of transport over

Jul 032004

Jul 132004

Jul 232004

A0

20

40

60

80

100

Jul 09high altitude

smoke

Jul 21mixed down

smoke

Old Town (Baltimo

Jul 032004

Jul 132004

Jul 232004

A

20

40

60

80

100

Sur

face

PM

2.5

(µg/

m3 )

Hourly PM2.5Daily Average PM

Air

Qua

lity

Inde

x C

ateg

orie

s fo

r P

M2.

5

Goo

dM

oder

ate

Sen

sitiv

eU

nhea

lthy

<150.5

Fig. 2. Ground-level PM2.5 concentrations and satellite AOD readings

the Old Town Baltimore TEOM monitor are shown as black fine lines a

FRM monitor are shown as black squares, both corresponding to the s

shown as red dots, corresponding to the scale on the right axis. Event

a variety of conditions. The haze events arediscussed first, followed by the smoke events.

3.1. Common summer haze

The first case is a common hazy summer period ofdays in Baltimore with elevated PM2.5 concentra-tions (�35 mgm�3) on 10 and 11 August, until afrontal system clears the air late on the evening of 11August. The entire region was experiencing a hazeevent during this period. Fig. 3–A shows that thePM2.5 concentrations, the AERONET AOD fromBaltimore, the satellite AOD readings, and the lidaroptical depth below the boundary layer track eachone another over the period. Fig. 3–B shows thelidar data for 10 August 2004; the morning startedout very hazy below a height of 0.5 km (note thatthe lidar data is not plotted below 200m), with theaerosol concentration near the surface slightlydecreasing after 1500Z as convection in the bound-ary layer occurred. The boundary layer continued torise, becoming cloud-topped by 1730Z. Addition-ally, there was a sharp increase in aerosol concen-tration below a height of 0.5 km at 2030Z. Weinterpret the surface concentrations before 1500Z

ug 022004

Aug 122004

Aug 222004

Sep 012004

Aug 10normal haze

Aug 24transported

haze

re) Summer 2004

ug 022004

Aug 122004

Aug 222004

Sep 012004

2.5Speciation

Total Fine MassTotal CarbonTotal SulfateK_ion * 100

0.0

0.5

1.0

1.5

MO

DIS

AO

D

MODIS AODTerraAqua

for Baltimore, Maryland, Summer 2004. PM2.5 concentrations at

nd daily average PM2.5 concentrations at the Old Town Baltimore

cale on the left axis. The MODIS satellite sensor AOD values are

days discussed in the text are shaded in gray.

ARTICLE IN PRESS

Fig. 3. (A) PM2.5 concentrations, satellite AOD readings, and

lidar readings for 9–11 August 2004. (B) Lidar data for 10 August

2004. Time is UTC. (Data and images processed by UMBC and

Battelle). (C) MODIS optical depth data for 10 August 2004.

(MODIS AOD data provided by IDEA site).

J.A. Engel-Cox et al. / Atmospheric Environment 40 (2006) 8056–8067 8061

(11:00 AM local) and after 20:30 (4:30 PM local) tobe due to rush-hour traffic aerosols. UMBC isrelatively close to the I-95 corridor while the Old

Town, Baltimore, surface PM2.5 site is within thecity of Baltimore itself and represents more theregional behavior of the aerosol. In making anestimate of the lowest 150m of the atmosphere notseen by the lidar, we assume that the aerosol is wellmixed from the surface to 150m. Clearly, in themorning and evening rush hour this assumptionmay not be entirely true. Fig. 3–C shows theMODIS optical depth data, documenting elevatedaerosol levels in multiple locations in the mid-Atlantic and Northeast United States. In this case ofa well-mixed haze layer in the region, the satellite,ground-based, and lidar data generally agree, andconfirm a hazy period in the region, includingBaltimore, a common occurrence in the summer.

3.2. Transported haze

The second case is much different, starting withlower concentrations of PM2.5 (less than 10 mgm�3)that increased over several days (23–25 August2004) to 40–50 mgm�3. Fig. 4–A shows that thePM2.5 concentrations, the satellite AOD readings,and the lidar readings once again generally correlatewith one another. Fig. 4–B is the lidar image, whichshows at least two elevated plumes at approximateheights of 0.5 km (starting at approximately 1300UTC) and 1.5 km (starting at 1900 UTC), indicatingtransported aerosol aloft, mixing down into theboundary layer. As the day progressed, there was anincrease in aerosol concentration near the surfaceobserved by ambient surface monitors. Extinctionwas moderate at a height of about 1.5 km and after1800Z the plume of aerosols aloft at 1.5 km inten-sified further.

Another indication that this may be transportedpollutants is the streaming haze off the East Coastacross the Atlantic Ocean shown in the MODIS truecolor image (Fig. 4–C). An additional source of datafor this example comes from the STN, as shown bythe green and orange datapoints in Fig. 2, repre-senting the sulfate and carbon components of themeasured PM2.5, respectively. One major source ofsulfate particles is coal-fired power plants that tendto be concentrated upwind (west) of Baltimore inthe midwestern US. Because sulfate particles are asecondary pollutant requiring time for formation,they are typically found at a distance from theoriginal source, often on the scale of hundreds ofmiles (e.g., Park et al., 2004; Bergin et al., 2005).Carbon particles, on the other hand, may beprimary pollutants (directly emitted) and, thus,

ARTICLE IN PRESSJ.A. Engel-Cox et al. / Atmospheric Environment 40 (2006) 8056–80678062

may be concentrated near their point of emission.Because Fig. 2 shows a relatively higher percentageof sulfate particles during the period studied, thissuggests transport of pollution into the Baltimorearea. Accordingly, in this case study, we are able to

Fig. 4. (A) PM2.5 concentrations, satellite AOD readings, and

lidar readings for 22–24 August 2004. (B) Lidar data for 24

August 2004. Time is UTC. Note the plumes mixed down at

0.5 km/1400 UTC and 1.5 km/1900 UTC, indicating long-

distance transport. (C) MODIS true color image showing

streaming haze off the East Coast (Image from UW SSEC).

identify transported haze based on the combinationof several data sources.

3.3. High-altitude smoke

The third case study is an event of high-altitudesmoke. The primary source of the smoke was majorwildfires in Alaska occurring around the time of thisstudy. Massive smoke plumes were transportedacross Canada and, in several cases, to the UnitedStates. Fig. 5–A shows the PM2.5 concentrations,satellite AOD readings, and lidar readings duringthis time period. The high smoke produced elevatedAOD readings on 9 July, but ground-level PM2.5

concentrations were low to moderate (10–20mgm�3)since the smoke plume did not mix down to ground.Fig. 5–B of the lidar image confirms that the smokewas very high in the troposphere (4 km) on 9 July,and subsequent lidar readings confirm that itsubsided over the succeeding days. The lidar optical

Fig. 5. (A) PM2.5 concentrations, satellite AOD readings, and

lidar readings for 8–10 July 2004. (B) Lidar image showing smoke

very high in the atmosphere on 9 July 2004. Time is UTC.

ARTICLE IN PRESSJ.A. Engel-Cox et al. / Atmospheric Environment 40 (2006) 8056–8067 8063

depth below the boundary layer corresponds well tothe PM2.5 concentration. Earlier we discusseda potential error from using a constant Sa ratioto compute the extinction. This introduces asource of error particularly in the cases of upperlevel smoke aerosol transport because smokehas a higher Sa value than typical continentalair masses. In this case, the lidar ratio used wouldoverestimate the optical depth in the boundarylayer and underestimate the optical depth ofthe aloft aerosol. For the 9 July 2004 aloft smokecase increasing the assumed lidar ratio in thesmoke layer by 10% causes a 12% increase in theoptical depth of the layer. It should be notedthat the error introduced into the optical depthfrom Sa will be larger for vertically thickerlayers; the layer in the 9 July case was only0.5 km thick.

Fig. 6. (A) PM2.5 concentrations, satellite AOD readings, and lidar r

showing lofted smoke at 2 and 4 km, and 21–22 July 2004, showing lo

UTC.

3.4. Smoke mixed to ground

The fourth case study (20–22 July) is an event inwhich the smoke from Alaskan wildfires mixeddown to the ground in Baltimore, putting Baltimorefirmly into Code Orange (under EPA’s Air QualityIndex, unhealthy for sensitive groups) with a 24-haverage PM2.5 concentration of 48 mgm�3 andhourly concentrations peaking in Baltimore at62 mgm�3 on 22 July. During this event, the smokeimpacted much of the Eastern and mid-WesternUnited States. The daily MODIS images showed thesmoke traveling through the Midwest, as far southas Louisiana, then moving northward all the way upthe East Coast. Fig. 6–A shows the PM2.5 concen-trations, the satellite AOD readings, and lidarreadings during this time period (note that on 20July, PM2.5 concentrations were already over

eadings for 19–22 July 2004. (B) Lidar images on 20 July 2004,

fted smoke mixed mixing down into the boundary layer. Time is

ARTICLE IN PRESSJ.A. Engel-Cox et al. / Atmospheric Environment 40 (2006) 8056–80678064

30 mgm�3). The lidar image on 20 July (Fig. 6–B)shows that lofted smoke can be seen at 2 km andabove 4 km. On 21 July, the lidar image shows thatthe aerosol scattering at the ground has lessened,but the lofted smoke plume merges into the growingboundary layer about mid-day and remains thereinto 22 July. Fig. 6–A shows good agreementbetween lidar optical depth below the boundarylayer with the PM2.5 measured at the surface and thetotal lidar optical depth with the total AODmeasured by MODIS. These results indicate thattransport above the boundary layer (presumablywith significant components of the smoke aerosol)consisted of nearly half the aerosol optical depthseen. Speciation data near this location are onlytaken one in every 6 days and no relevant data wereavailable to confirm high carbon levels. This case isalso being studied in detail as part of the Inter-continental Transport Experiment (INTEX-NE)and has implications for the forecasting of aerosolPM2.5 because the smoke aerosol source was notincluded in model inventories.

If Baltimore (or another Eastern or mid-Westernurban area) had exceeded the daily PM2.5 standardof 65 mgm�3 during this smoke event, the ability tocombine the horizontal satellite image (for regionalscale) with lidar (for vertical scale) would haveprovided information on whether transport washaving an effect at ground-level in real-time. Thecombination of these data would also be helpful inanalyzing any future exceedances throughout theUnited States. The lidar data can provide additionalinsight on the amount of pollution above and belowthe boundary layer.

Fig. 7. (A) Linear regression Hourly and Daily Average PM2.5

versus MODIS AOD. (B) Linear regression Hourly PM2.5 versus

Lidar Total and Below Boundary Layer Optical Depth. Lidar

data is average of 60 one-minute profiles. (C) Linear regression

Daily Average PM2.5 versus Lidar Total and Below Boundary

Layer Optical Depth.

4. Quantitative analysis

4.1. Relationship between AOD and measured

surface PM2.5

Building on the qualitative and semi-quantitativeevent analysis presented in Section 3, simpleregression analysis can be conducted on thecomplete dataset for Summer 2004. Fig. 7–A showsthe linear regression of the hourly and daily averagePM2.5 versus MODIS AOD. The relationships aresimilar to those found in Engel-Cox et al. (2004b)—slopes around 25–30 (Dmgm�3)/(DAOD), positiveintercepts, and correlation coefficients around 0.6.For the comparison to the daily PM2.5 concentra-tion, an average MODIS AOD value from the Terra

and Aqua platforms was used for days where bothAOD values were available.

The impact of elevated aerosols can be evaluatedby examining the linear regression of PM2.5 with thelidar optical depth total and below the boundary

ARTICLE IN PRESSJ.A. Engel-Cox et al. / Atmospheric Environment 40 (2006) 8056–8067 8065

layer. Fig. 7–B shows hourly PM2.5 concentrationsversus 1-h lidar total optical depth and lidar opticaldepth below the boundary layer; Fig. 7–C is similarusing daily PM2.5 concentrations. The hourly PM2.5

versus lidar total optical depth has a similarrelationship as the PM2.5 and MODIS AOD: slopearound 25 mgm�3 and correlation around 0.56.However, when only the lidar optical depth belowthe boundary layer is used, essentially eliminatingelevated aerosols, the slope increases to about48 mgm�3 and the correlation improves slightlyto 0.65. For daily PM2.5 and lidar optical depth(Fig. 7–C), the correlation coefficient is higher(around 0.75) than for lidar optical depth versushourly PM2.5 values. Also, the slope for daily PM2.5

is much higher and increases when using only lidaroptical depth below the boundary layer, from 40 to65 mgm�3.

Previous work (Engel-Cox et al., 2004b) positedthat a portion of the variability in the PM2.5–AODrelationship was due to elevated aerosols. Theseresults using lidar indicate that correction of opticaldepth for elevated aerosols could improve under-standing of the relationship between optical depthand ground-based PM2.5 concentrations.

4.2. Characteristics of particulate matter

Monitoring data from the Fort Meade PM2.5

chemical speciation site were used to examineaerosol composition during the PM2.5 event days.The Fort Meade site uses STN sampling andanalytical methods to quantify mass concentrationsand significant PM2.5 constituents, including numer-ous trace elements, organic and elemental carbon,anions (sulfate and nitrate), and cations (ammo-nium, sodium, and potassium (K+)) (US EPA,1997).

Table 1

Summary of Fort Meade speciation data by datea (mgm�3)

Date Organic carbon (OC)

08/7/2004 3.668

20/7/2004 6.552

01/8/2004 1.484

07/8/2004 1.876

19/8/2004 4.298

25/8/2004 3.5

31/8/2004 1.652

03 June–August 2004 mean7std. dev. 4.53572.590

aNo measurements were reported for sampling days 14, 26, July and

Speciation data from the Fort Meade site werecompared with the 2003–2004 summer seasonalaverage and standard deviation (June–August) forthe site to provide further characterization of thePM2.5 events. Table 1 shows the sampling days withvalid data. The Fort Meade site-sampling scheduledid not provide for any coincident observationswith the classified event days. Therefore, speciationdata were reviewed for 1 day prior or subsequent tothe event days listed.

Sulfate, organic carbon (OC), K+ concentrationswere examined to identify difference between PM2.5

events. These components were chosen because OCand K+ are indicator species of particles originatingfrom biomass burning (Kreidenweis et al., 2001)and sulfate is an indicator species of coal combus-tion and transport. Therefore, one would expect asignificant increase in both OC and K+ on daysimpacted by smoke. In addition, the ratio of sulfate/OC was calculated to look at potential differences inthis ratio during the classified events.

No speciation data were collected close enough tothe 10 August event classified as ‘‘common summerhaze’’ to provide any meaningful analysis. For the24 August event classified as ‘‘transported haze,’’the speciation data from the next day, 25 August,show the sulfate concentrations at Fort Meade wereat their highest concentration for the summer of2004. Also, the ratio of sulfate to OC concentrationon 25 August shows a significant deviation from themean sulfate to OC ratio; a possible indication of amore regional scale sulfate transport event.

On the 9 July ‘‘high altitude smoke’’ and 21 July‘‘mix down smoke’’ days, the high level of K+

on the prior days, along with average to highlevel of OC, may indicate the PM2.5 concentrationswhere already influenced by smoke from biomassburning.

Sulfate Potassium+ Sulfate/OC

6.15 0.059 1.67

10.6 0.057 1.62

2.46 0.0065 1.66

1.63 0.0065 0.87

7.93 0.0065 1.85

19.8 0.0065 5.66

4.74 0.0065 2.87

8.2876.56 0.015270.0190 1.9071.25

13 August.

ARTICLE IN PRESSJ.A. Engel-Cox et al. / Atmospheric Environment 40 (2006) 8056–80678066

5. Conclusions and further research

The case studies demonstrate that the combina-tion of lidar, satellite, and ground-based PM2.5

monitoring data can form the basis of an integratedcharacterization of air quality in 3-D. Together, theintegrated data increase the usefulness of any singledataset. There are a number of steps that need to betaken to further the understanding and applicabilityof these datasets. Further work needs to beconducted to better understand the quantitativerelationships between lidar, sunphotometer, satel-lite, and ground-level monitored PM2.5 data. Dataanalyses with lidars in other regions and otherdatasets over a longer time period are needed todefine how other variables may affect the variabilityof the PM2.5–AOD relationship, such as relativehumidity/temperature, assumptions of the opticaldepth models, particulate species, and geospatialbiases. These could lead to correcting for aerosolheight and for meteorological effects and mayimprove the PM2.5–AOD correlation sufficiently tomake it useful for quantitative policy application.

In order the conduct this research, consistent andregionally distributed lidar data needs to be avail-able. Over the next few years, existing lidar systemswill be used to support validation for the Cloudsand Aerosol Lidar for Pathfinder SpaceborneObservations (CALIPSO) lidar that will belaunched in 2006. Extinction data from CALIPSOand the ground-based systems should be evaluatedto determine the impact of multiple profiling lidarsources on transport related hazes. Recent work(Hoff et al., 2005) has shown, however, that a polarorbiting lidar and ground-based lidars are still quitelimited in determining the location of sporadic hazeevents without the use of passive sensors such asMODIS and without the use of interpretativemodels.

Acknowledgements

This work was supported by the US Environ-mental Protection Agency, Office of Air QualityPlanning and Standards, with special acknowledge-ment to Mr. Bryan Bloomer of EPA for hiscomments and interest. Thanks to Dr. KevinMcCann, Ms. Nikisa Jordan, and Ms. KamonayiMubenga of University of Maryland, BaltimoreCounty, who helped run the Smog Blog during thesummer. Special thanks to Matthew Cupp ofBattelle for assistance with data processing. RMH

is supported by a NOAA CREST Grant CCNY-49866-00-01C and a CALIPSO grant from NASALangley Research Center (NAS1-99107).

Disclaimer: The views, opinions, and findingscontained in this report are those of the author(s)and should not be construed as official USEnvironmental Protection Agency, National Aero-nautics and Space Administration, National Ocea-nographic and Atmospheric Administration, or USGovernment position, policy, or decision.

References

Al-Saadi, J., Szykman, J., Pierce, B., Kittaka, C., Neil, D., Chu,

A., Remer, L., Gumley, L., Prins, E., Weinstock, L.,

MacDonald, C., Wayland, R., Dimmick, F., Fishman, J.,

2005. Improving national air quality forecasts with satellite

aerosol observations. Bulletin of the American Meteorologi-

cal Society 86, 1249–1261.

Bergin, M., West, J., Keating, T., Russell, A., Comer, J., 2005.

Regional atmospheric pollution and transboundary air

quality management. Annual Review of Environment and

Resources 30, 1–37.

Comer, J., 2003. UMBC Elastic Backscatter Lidar Facility

(ELF): subvisible cirrus cloud and aerosol measurements

during ABOVE 2002. M.S. Thesis, University of Maryland,

Baltimore County.

Engel-Cox, J., Hoff, R., Haymet, A., 2004a. Recommendations

on the use of satellite remote-sensing data for urban air

quality. Journal of the Air and Waste Management Associa-

tion 54, 1360–1371.

Engel-Cox, J., Holloman, C., Coutant, B., Hoff, R., 2004b.

Qualitative and quantitative evaluation of MODIS satellite

sensor data for regional and urban scale air quality. Atmo-

spheric Environment 38, 2495–2509.

Hoff, R., Palm, S., Engel-Cox, J., Spinhirne, J., 2005. GLAS

Long-range transport observation of the 2003 California

forest fire plumes to the northeastern US. Geophysical

Research Letters 32, L22SO8, doi:10.1029/2005GL023723.

Infusing satellite Data into Environmental Applications (IDEA),

2004. http://idea.ssec.wisc.edu/index.php. Accessed Septem-

ber 2004.

Kreidenweis, S., Remer, L., Bruintjes, R., Dubovik, O., 2001.

Smoke aerosols from biomass burning in Mexico: hygro-

scopic smoke optical model. Journal of Geophysical Research

106, 4831–4844.

Park, R., Jacob, D., Field, B., Yantosca, R., Chin, M., 2004.

Natural and transboundary pollution influences on sulfate–

nitrate–ammonium aerosols in the United States: implications

for policy. Journal of Geophysical Research 109, D15204.

Remer, L., Kaufman, Y., Tanre, D., Mattoo, S., Chu, D.,

Martins, J., Li, R., Ichoku, C., Levy, R., Kleidman, R., Eck,

T., Vermote, E., Holben, B., 2005. The MODIS aerosol

algorithm, products and validation. Journal of the Atmo-

spheric Sciences, Special Section 62, 947–973.

Rush, A., Dougherty, J., Engel-Cox, J., 2004. Correlating

seasonal averaged in situ monitoring of fine PM with satellite

ARTICLE IN PRESSJ.A. Engel-Cox et al. / Atmospheric Environment 40 (2006) 8056–8067 8067

remote sensing data using geographic information system

(GIS). In: Chu, A., Szykman, J. (Eds.), Remote Sensing in

Atmospheric Pollution Monitoring and Control. Proceedings

of SPIE, vol. 5547. SPIE, Bellingham, WA, pp. 91–102.

University of Wisconsin-Madison’s Space Science and Engineer-

ing Center. 2004. http://www.ssec.wisc.edu/overview/.

Accessed September 2004.

US Environmental Protection Agency, 1997. Revised require-

ments for designation of reference and equivalent methods for

PM2.5 and ambient air quality surveillance for particulate

matter; final rule. Federal Register 62, (38763).

US Environmental Protection Agency. 2005. Air Quality

System, http://www.epa.gov/ttn/airs/airsaqs/. Accessed

February 2005.