Remote Sensing of Environment 114 (2010) 50–63

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Remote Sensing of Environment

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Satellite-based estimation of regional particulate matter (PM) in Beijing usingvertical-and-RH correcting method

Zifeng Wang a,b, Liangfu Chen a,⁎, Jinhua Tao c,a, Ying Zhang a,b, Lin Su a

a State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University,Beijing 100101, Chinab Graduation University of Chinese Academy of Sciences, Beijing 100049, Chinac State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

⁎ Corresponding author. State Key Laboratory of ReSponsored by Institute of Remote Sensing Applications oand Beijing Normal University, P. O. Box 9718, Beijing 16483 6589.

E-mail address: lfchen@irsa.ac.cn (L. Chen).

0034-4257/$ – see front matter © 2009 Elsevier Inc. Aldoi:10.1016/j.rse.2009.08.009

a b s t r a c t

a r t i c l e i n f o

Article history:Received 12 March 2009Received in revised form 8 August 2009Accepted 10 August 2009

Keywords:Particulate matterSatellite remote sensingMODIS AOTAir pollution monitoring

Theoretical analysis based on the atmospheric radiative transfer indicated a positive correlation between theaerosol optical thickness (AOT) and the surface-level particulate matter (PM) concentrations, and thiscorrelation is improved significantly using vertical-and-RH correcting method. The correlative analysis of theground-based measurement indicates that, (a) the correlation between AOT and the aerosol extinctioncoefficient at surface level (ka,0) is improved as a result of the vertical correction, with the coefficient ofdetermination R2 increasing from 0.35 to 0.56; (b) the correlation between ka,0 and PM concentrations canbe significantly improved by the RH correction with the R2 increasing from 0.43 to 0.77 for PM10, and from0.35 to 0.66 for PM2.5. Based on the in-situ measurements in Beijing, two linear correlative models betweenthe ground-based AOT and PMs (e.g. PM10 and PM2.5) concentrations were developed. These models areused to estimate the regional distribution of PM10 and PM2.5 using the satellite-retrieved AOT in Beijingarea. Validation against the in-situ measurements in Beijing shows that both of the correlations of thesatellite-estimated PM10 and PM2.5 with the measurements are R2=0.47, and the biases are 26.33% and6.49% respectively. When averaged in the urban area of Beijing, the R2 between the estimated PM10 and themeasurements increased to 0.66. These results suggest that by using the vertical-and-RH correcting methodwe can use the MODIS data to monitor the regional air pollution.

© 2009 Elsevier Inc. All rights reserved.

1. Introduction

Aerosols have extensive impacts on our climate and our environ-ment (Kaufman et al., 2002), particularly, the tropospheric aerosols(also known as particulate matters, PM) can bring adverse effects onpublic health (WHO, 2000). Epidemiologic studies indicate stronglinks between the concentration of PM10 or PM2.5 (particulatematters with aerodynamic diameter less than 10 µm or 2.5 µmrespectively) with public morbidity, mortality of respiratory andcardiovascular diseases (e.g., Pope, 2000; Wallace, 2000; Kan & Chen,2002; Li et al., 2006). The PM concentration has become an importantindex of air pollution, and gained more and more attention from theadministrations and organizations of environmental protection,public health and science all over the world. Both of the EuropeanUnion (1999) and the United States have set the air quality standards

mote Sensing Science, Jointlyf Chinese Academy of Sciences00101, China. Tel./fax: +86 10

l rights reserved.

that prescribe the strict limits to PM concentrations in the ambient air.In recent years, with the rapid development of industrialization andurbanization, PM has become the primary air pollutant in mostmajor cities in China (Li et al., 2003), which not only threatenspeople's health, but also causes the decrease of atmosphericvisibility and the degradation of the city scenery (Song et al.,2003). Therefore, the Chinese government has enacted the ambientair quality standards (MEPC & AQSC, 1996) in which the limit valuesof PM10 concentration and classification rules of air pollution havebeen set.

From the in-situ measurements of PM at the ground sites we canobtain the concentration of PM and its composition. However, inmost urban and rural regions in China, the environmental monitor-ing sites are located sparsely and asymmetrically, so measurementsat these sites are insufficient to depict the spatial and temporalvariation of PM, as well as their sources and transport (Li et al.,2005a). The satellite remote sensing has been used to observe thecharacteristics of aerosols, such as AOT, for nearly 30 years, and theaccuracy and applicability of sensing techniques are increasinglyimproved (King et al., 1999). Benefiting from the large spatial andtemporal coverage, the satellite remote sensing of aerosols would

51Z. Wang et al. / Remote Sensing of Environment 114 (2010) 50–63

provide some valuable information for epidemiological studies, thedesign of air quality control strategies, and air quality forecasting,and thus is a beneficial supplement to the conventional measure-ments on routine basis (Al-Saadi et al., 2005).

The relationship between the optical properties of aerosols and PMconcentrations has been investigated in several studies. For example,Chu et al. (2003) and Slater et al. (2004) found the AOT obtained atground is correlated with PM10 and PM2.5 concentrations, and thecoefficients of determination R2 are ~0.67 and ~0.76, respectively;Wang & Christopher (2003) and Engle-Cox et al. (2004) compared thesatellite-retrieved AOT with hourly mean PM2.5 concentrations, andfound their R2 are ~0.49 and ~0.40, respectively; Li et al. (2005a,b)compared the satellite-retrieved AOTwith the PM10 derived from theair pollution index (API)1 in Beijing, and the PM10 directly measuredin Hong Kong, and found agreements between AOT and PM10concentrations in both of these comparisons. The direct correlativemodels between AOT and PM have been applied to estimate thePM2.5 concentrations in several major cities worldwide by using thesatellite-derived AOT (Gupta et al. 2006).

However, as AOT reflects the aerosol optical properties of the totalcolumn while the PM concentration is usually measured at surfacelevel, the correlation between them is severely influenced by thevertical distribution of aerosols and the relative humidity (RH) thatimpacts aerosol extinction coefficient. These two factors are related toatmospheric profiles, ambient conditions, as well as the sizedistributions and chemical compositions of aerosols, all of whichmay have large spatial and temporal variations. Thus, to estimate thePM concentrations simply from AOT would have large uncertainties.To reduce these uncertainties the atmospheric boundary layer (ABL)height and ambient RH have been introduced into the correlativemodel (Koelemeijer et al., 2006; Li et al. 2005a). Furthermore, severalmeteorological factors are assimilated to exactly depict ambientimpacts, and the linear regressive models are updated with non-linear, poly-parameter models to better characterize the correlationbetween AOT and PM concentrations (Liu et al., 2005; Pelletier et al.,2007). These enhanced models were applied in estimating PM withthe satellite-retrieved AOT, and performed better than those directmodels (Liu et al., 2005; Koelemeijer et al., 2006; Vidot et al., 2007). Asummary of these correlativemodelsmentioned above is presented inTable 1.

Most of these works mentioned above were carried out in Europeand the United States, where abundant ground-based measurementsof PM are available from operational or experimental compliancenetworks for air quality monitoring (Koelemeijer et al., 2006; Vidotet al., 2007; Engle-Cox et al., 2004; Liu et al., 2005). However, muchfewer studies have been made in developing regions and countries.With the growing of population and economics, Beijing has sufferedmore and more from the air pollution caused by emissions fromautomobiles and nearby power plants and factories. Dust storms fromwestern China and Mongolia can also deteriorate the air quality inBeijing. Although the potential to use the satellite-retrieved AOT toestimate the surface-level PM in Beijing area has been discussed byseveral authors (for example, Chu et al., 2003; Li et al. 2005a), todevelop a correlative model between AOT and PM concentrations hasbeen hampered by the lack of measurements of PM concentrationsand aerosol vertical profiles (Chu et al., 2003).

The purpose of this study is to develop the correlative linearmodels between AOT and PM10 as well as PM2.5 in Beijing, basedupon the ground-based measurements of ambient air conditions in

1 In China, API is computed as a piecewise-linear function of the daily average of theprincipal pollutant in the ambient air, which is defined as the one most exceeding itsstandard; in 85% to 90% of cases of a year, API is correlated with PM10 concentrationsin most of the cities in China.

Beijing from 2007 to 2008. For this study, the impacts of the ABLheight and RH on the correlation between AOT and PM concentrationsare analyzed, and for simplification, this method is referred as thevertical-RH correcting method in this paper. These models areimplemented to derive the regional distribution of PM10 and PM2.5in Beijing area using satellite-retrieved AOT.

Descriptions of the ground-based measurements and satelliteobservations are presented in Section 2. Section 3 gives the theoreticalbasis and the analysis of the vertical-RH correcting method, and thendevelops the correlative models between the corrected AOT and thePM concentrations. Section 4 shows the distribution of PM concen-trations in Beijing area derived from satellite-retrieved AOT, and itsvalidation against the ground-based measurements. The summariesand conclusions are given in Section 5.

2. Data

2.1. Ground-based measurements

To support the protection of ambient air quality in Beijing duringthe 2008 Olympic Games and Paralympic Games, the ChineseAcademy of Sciences implemented the Project of Ambient AirMonitoring in Beijing Area from July 2007 to October 2008, duringwhichmore than 20 observing stations have been set up in Beijing andits surrounding regions. One of the best stations operated is the SuperSite (39.9°N, 116.1°E), which is located in the northern part of anurban area in Beijing, and is right beside the Olympic Park and only2 km away from the new National Stadium (also known as Bird Nest).The Super Site is also close to the Olympic Village, a forest park andseveral main roads. Nearly 20 equipments were installed to compose acomplete air monitoring system, and the measurements at the SuperSite include AOT, the surface-level aerosol extinction coefficient ka,0,the concentrations of PM10, PM2.5, black carbon (BC), the verticaldistribution of aerosols, as well as the major gaseous pollutants, suchas the surface-level O3, SO2, NOx and total column amount of SO2 andNO2. These ground-based measurements acquired at the Super Sitewill be used for the correlative analysis in Section 3. All these datadescribed below are preprocessed to derive their hourly mean valuesfor later use, which are expected to reduce uncertainties to somedegree.

2.1.1. Ground-based aerosol optical thickness (AOT)At the Super Site a CE318 sun-photometer was used to measure

the direct solar radiation, from which the AOT at 0.55 µm could bederived. The calibration coefficients of CE318 have been developedusing Langley method (Xin et al., 2007) based upon the measure-ments obtained near the top of Lingshan Hill (122 km away from theurban area of Beijing and with a altitude of 2303 m) under clear sky,and thus the calibration accuracy could be less than 10% (Zhang et al,2000). The original measurements have been processed with qualitycontrol using the algorithms of Smirnov et al. (2000).

2.1.2. Surface-level aerosol extinction coefficient (ka,0)A visibility-meter was used at the Super Site to measure the

horizontal visibility Rvis. Based on the empirical correlation betweenthe total atmospheric extinction coefficient at 0.55 µm (katm,0) andRvis, namely katm,0≈3.912/Rvis (Koschmieder, 1925), we can derivekatm,0 using the measured Rvis. After removing the molecularextinction coefficient (km,0) from katm,0, we can get the aerosolextinction coefficient, ka,0, at 0.55 µm as below (Xu, 2005):

ka;0 = 3:912= Rvis−km;0 ð1Þ

Here, km,0 only depends on the wavelength under a givenatmospheric condition (Liou, 2004).

Table 1Summary of correlative models associating the satellite-derived AOT with PM concentrations developed in some of the previous studies.

Author (year) Study area Model description Coefficient of determination

Wang and Christopher (2003) Alabama Linear model between MODIS 10 km AOT and PM2.5 0.49Engle-Cox et al. (2004) USA Linear model between MODIS 10 km AOT and PM2.5 0.40Li et al. (2005b) Hong Kong Linear model between MODIS 1 km AOT and PM10 0.50Li et al. (2005a) Beijing Linear model between corrected MODIS 10 km AOT and PM10(API) 0.29Koelemeijer et al. (2006) Europe Linear models between corrected MODIS 10 km AOT and PMs 0.59 for PM2.5; 0.46 for PM10Liu et al. (2005) Eastern USA Non-linear and poly-parameter model between MISR AOT and PM2.5 0.48Pelletier et al. (2007) France Non-linear and poly-parameter model between ground-based AOT and PM10 0.76Vidot et al. (2007) Western Europe Non-linear and poly-parameter models between SeaWiFS AOT and PMs 0.43 for PM10; 0.61 for PM2.5

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2.1.3. Atmospheric boundary layer (ABL) layer heightThe Super Site is equipped with a scanning duo-channel

polarization LIDAR (Chi et al., 2008), with a detecting range of 3 kmand a resolution of 3 m when measuring vertically. Using the methodproposed by Fernald (1984), the vertical distribution of aerosolextinction coefficient (ka) could be obtained from the back scatteredsignals received by LIDAR, thenwe can derive the ABL height using thealgorithm of He & Mao (2005).

2.1.4. PM concentrationTwo tapered-element oscillating microbalances (TEOM) (Lu et al.,

2007) were used at the Super Site to measure the PM10 and PM2.5concentrations, respectively. The accuracy is ±1.50 µg/m3 (for hourlymean). Both the TEOM continuously pump in the ambient air and heatit up (to remove the water vapor's influence), and the particles of theright size (smaller than 10 µm or 2.5 µm) are selected by passingthrough a special head and then weighed. Thus, the dry mass of PM10or PM2.5 within a unit volume of ambient air is acquired and recordedevery 15 minutes. It should be noted that by heating the air sample,the measured PM mass (dry) might be less than the real PM level inthe ambient air due to the volatilization of semi-volatile aerosolcomponents (Koelemeijer et al., 2006).

2.1.5. Meteorological dataAn automatic meteorological station was equipped at the Super

Site to measure the temperature, relative humidity, surface pressure,as well as the wind speed and wind direction.

2.2. Satellite observations

The moderate-resolution imaging spectroradiometer (MODIS)provides the best monitoring aerosol properties from the space sofar (Remer et al., 2006). Data acquired from two MODIS instrumentsduring the daytime passes are used, and is onboard EOS-Terra withthe overpasses at 10:30 am (LST), and the other is onboard EOS-Aquawith the overpasses at 1:30 pm.

The retrieving process of MODIS AOT over land is only performedduring daytime and for the cloud-free and dark pixels. Hence the AOTretrievals are restricted to the situations over moderately brightsurfaces where the measured reflectance at 2.13 µm falls between0.15 and 0.25 (Remer et al., 2005). The global MODIS AOT productshave been provided on NASA's website.2 An extensive effort ofvalidation using over 8000MODIS retrievals collocatedwith AERONETAOT measurement indicates that globally, the MODIS products havean accuracy of ±0.05±0.15 τ over land and ±0.03±0.05 τ overocean (Remer et al., 2005).

The AOT at a resolution of 10⁎10 km at nadir is derived at 0.47 and0.66 µm through a procedure that includes the discrimination of cloud,selection of dark pixels, and determination of aerosol model (Remer

2 http://modis-atmos.gsfc.nasa.gov/MOD04_L2/index.html.

et al., 2005). TheAOTat0.55 µm is interpolated from theAOTat0.47 andthat at 0.66 µmbased on theÅngströmLaw, lnτa(λ)=lnβ+αlnλ. As thestandard AOT products of MODIS (10⁎10 km at nadir) are not fineenough to resolve the large spatial variation of PM concentrations inmega cities like Beijing, the 1-km AOT has been derived using themodified algorithm (Li et al., 2005b; Wang et al., 2008) on the basis ofthe MODIS operational algorithms (Kaufman et al., 1997; Remer et al.,2005). The new algorithm updates the aerosol properties and thesurface reflectance models according to the features of the localatmosphere and surface, so that the radiative transfer calculationscould better characterize the real conditions. For validation, the new1-km MODIS AOT is compared with the ground-based AOT from theSuper Site from August to September 2007 (Fig. 1). The MODIS AOT ismatched up with the ground-based measurements within ±30 min ofthe overpass time of the satellite. This comparison indicated a goodagreement between the 1-km MODIS AOT and the ground-based AOTwith theR2=0.77. The regressive line (the solid line) between themhasa slope of 0.94 and a relatively small intercept of−0.102, illustrating thevalidity of the new algorithm. According to Remer et al. (2005), theaccuracy of MODIS standard AOT over land, namely Δτ=±0.05±0.15 τ, is also given as references in Fig. 1 (the dashed lines).

3. Theory and correlative analysis

3.1. Vertical correction on AOT

3.1.1. Theoretical basisThe direct solar irradiance E attenuated through the atmosphere

and reached the ground can be expressed as

E = Es expð−mτÞ ð2Þ

where Es is the solar irradiance at the top of atmosphere, and m is theair mass defined as m=1/cosθs, with θs denoting the solar zenithangle. τ is the total atmospheric optical thickness, and when thegaseous absorption could be neglected and under clear sky, it is equalto the sum of molecular optical thickness, τm, and AOT, τa .τm onlydepends on the wavelength and the pressure, and it is easy tocompute using Rayleigh scattering theory. Removing τm from τ, wecan obtain AOT. Under the assumption of plane parallel atmosphere,AOT is the integral of the ka at all altitude along the verticalorientation (Liou, 2004), that is

τaðλÞ = ∫∞0kaðλ; zÞdz ð3Þ

where ka(λ,z) stands for the ka at the altitude of z and the wavelengthof λ. Assuming the vertical distribution of ka (λ,z) as the negativeexponent form:

kaðλ; zÞ≈ka;0ðλÞexpð−z =HAÞ ð4Þ

Fig. 1. Comparison between 1-km MODIS AOT and the ground-based AOT from the Super Site from August to September 2007.

53Z. Wang et al. / Remote Sensing of Environment 114 (2010) 50–63

where HA is the scale height of aerosol. Substituting Eq. (4) to Eq. (3),we have

τaðλÞ≈ka;0ðλÞ∫∞0expð−z =HAÞdz = ka;0ðλÞ⋅HA ð5Þ

Eq. (5) shows that ka,0 could be calculated from AOT and HA

approximately. Here HA can be approximately represented by the ABLheight (Liu et al., 2005; Koelemeijer et al., 2006).

3.1.2. Correlative analysisFrom the measurements at the Super Site, the ground-based AOT

retrieved by CE318 is correlated with the ka,0 obtained by visibility-meter (denoted by kVIS) in September 2007 and August 2008 (N=131)(Fig. 2a), with the R2=0.35. However, as AOT equals to theaccumulation of ka in the whole atmosphere column, while kVIS onlyrepresents the aerosol optical property near the surface, theircorrelation strongly depends on the vertical distribution of aerosols.

If using the ABL height obtained from LIDAR data (He & Mao, 2005)to representHA, from Eq. (5) we can compute the ka,0, which is denotedby kAOT. Comparison of the kAOT with kVIS (Fig. 2b) shows a much betteragreement (R2=0.56). Although the remaining deviations betweenthem indicate the necessity of further study and improvement of thevertical correction, this comparison demonstrated that the optical depthof the entire atmospheric column could be represented as an equivalentextinction coefficient at surface, as long as the vertical distribution ofaerosols is well known.

3.2. RH Correction on aerosol extinction coefficient (ka)

3.2.1. Theoretical basisAt a certain altitude there exists a positive correlation between ka and

PMconcentrations (Sun&Zhang, 1982). However, since thehygroscopicgrowth of particles significantly affects the complex refraction index, the

extinction cross-sectional area and some other optical properties ofaerosols (Malm et al., 2000; Zhang & Shi, 2002), the correlationbetween ka and PM concentration varies with the chemicalcomponents of particles and RH of the ambient air. The impactsof the RH on aerosol radiative properties, which are characterizedas the humidification factors, have been investigated (Kotchen-ruther & Hobbs, 1998; Kotchenruther et al., 1999; Sheridan et al.,2002). However, each of the different models that depict thevariation of aerosol optical characteristics with the RH of theambient air is suitable for a certain chemical composition ofaerosols, but may not be for others (Kotchenruther et al., 1999).

As the air sample is heated up to 50 °C (Lu et al., 2007), the PMconcentrations measured by TEOM are almost the “dry mass” of theparticles in the air sample, so a proper RH correction on ka should beintroduced to reduce the impact of the variation of RHon the correlationbetween ka and PM concentrations. Suppose we can define ahygroscopic growing factor, f(RH), as the ratio of the “wet” ka obtainedat the ambient RH to the “dry” ka obtained under a relatively drycircumstance, e.g. at the RH below 40%, and based on the previousstudies (Kotchenruther et al., 1999; Im et al., 2001; Li et al., 2005a), the f(RH) used in this paper can be represented as:

f ðRHÞ = ð1−RH=100Þ−g ð6Þ

where g is an empirical fit coefficient. Hence the “dry” ka,0 is obtainedthrough the RH correction:

ka;DryðλÞ = ka;0ðλÞ= f ðRHÞ ð7Þ

3.2.2. Correlative analysisData of the concentrations of PM10 and PM2.5, kVIS and RH were

obtained in August 2008, and the number of samples selected foranalysis isN=633.Adirect comparisonofkVISwithPM10 concentration(Fig. 3a) shows their R2 is only 0.43.

Fig. 2. a. Correlation between the surface-level aerosol extinction coefficient, kVIS, and the ground-based AOT at 0.55 µm (both acquired at the Super Site in September 2007 andAugust 2008, N=131). b. Same as a but between kVIS and the AOT with vertical correction, kAOT, at 0.55 µm.

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Fig. 3. a. Correlation between kVIS at 0.55 µm and the surface-level PM10 concentration (both acquired at Super Site in August 2008, N=633). b. Same as a but between the RH-corrected kVIS,Dry at 0.55 µm and PM10 concentration.

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According to Eqs. (6) and (7), kVIS,Dry could be computed from kVIS. Thecomparison of kVIS,Dry with PM10 concentration (Fig. 3b) shows that theircorrelation (R2=0.77) is much higher than that between kVIS and PM10

concentration. The correlationofkVIS,DrywithPM2.5 concentrationwasalsoimproved, and the R2 increases from 0.35 to 0.66 after the RH correction.These results confirm the validity and necessity of the RH correction.

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3.3. Correlation between the corrected AOT and the PM concentrations

3.3.1. Theoretical basisBased on Eqs. (5) and (7), the “dry” aerosol extinction coefficient

at surface level could be approximately retrieved from AOT throughthe vertical correction and RH correction:

kAOT;DryðλÞ = τaðλÞ= ½HA⋅f ðRHÞ� ð8Þ

According to the Mie theory, ka is a function of the particle sizedistribution and the extinction characteristics of particles in theambient air, and could be expressed as (Liou, 2004):

ka = π∫X = 2

0Qextðm̃; r;λÞnðrÞr2dr ð9Þ

where Q ext ( m̃, r, λ) is the extinction efficiency, m̃ stands for thecomplex refraction index that depends on the chemical compositionof aerosols, and n(r) describes the particle size distribution. X/2denotes the upper limit of the particle's radius in the integral. Giventhe aerosol size distribution, the mass concentration of PM isexpressed as:

PMX =43πρ∫X = 2

0r3nðrÞdr ð10Þ

where ρ is the averaged mass density of the particles. According toHansen and Travis (1974), the size-distribution integrated extinctionefficiency ⟨Qext⟩is defined as

⟨Qext⟩ =∫X = 2

0Qextðm̃; r;λÞnðrÞr2dr∫X = 20 nðrÞr2dr

ð11Þ

and the effective radius reff is defined as

reff =∫X = 2

0r3nðrÞdr

∫X = 2

0r2nðrÞdr

ð12Þ

Combining Eqs. (9) to (12), we have:

ka =3⟨Qext⟩

4reffρPMX ð13Þ

If we can assume the chemical composition and the sizedistribution of aerosols have little change under some circumstances,ρ,⟨Qext⟩and reff of particles are approximately constant, then ka will beproportional to the PM concentrations. It is expected that, when thePM concentrations are the drymass of particles asmeasured by TEOM,there is a better correlation between kAOT,Dry and PM concentration.

3.3.2. Correlative analysisData of the ground-based AOT, the ABL height, RH, as well as PM10

and PM2.5 concentrations were selected for the correlative analysis.Due to the lack of the ABL height, both of the measurements obtainedin September 2007 and August 2008were used. Direct comparisons ofAOT with PM10 and with PM2.5 show the coefficients of determina-tion R2 are 0.52 and 0.48, respectively (Fig. 4a and b).

After applying the vertical correction using the ABL height and theRH correction, we can derive kAOT,Dry from AOT according to Eq. (8).Comparisons of kAOT,Dry with PM10 and with PM2.5 concentrationsindicated that both of the correlations were greatly improved with R2

increasing from 0.52 to 0.65 and from 0.48 to 0.62 respectively,demonstrating the validity of the vertical-RH correcting method onAOT (Fig. 5a and b).

Based upon the above analysis, we found it is possible to developthe linear correlative models between kAOT,Dry and PM (PM10 andPM2.5 respectively) concentrations, in which the ABL height and RHwill be used as auxiliary data. The equations are

PM10 = a1kAOT;Dry + b1PM2:5 = a2kAOT;Dry + b2

ð14Þ

where a1, b1, a2 and b2 are fit coefficients. These coefficients shouldhave been obtained through the linear regression based upon thevertical-RH-corrected AOT from MODIS and the corresponding PMconcentrations. However, all the necessary ground-based measure-ments are from only the Super Site for less than three months. Sincethere is only one or two MODIS AOT available during a clear day, thesamples of matched data for the linear regression are very limited. Thevalidation of the 1-km MODIS AOT in Section 2.2 indicated that it iswell correlated with the ground-based AOT, and data of the ground-based AOT from continuous observation of CE318 are much moreabundant than the MODIS AOT. Therefore, instead of the MODIS AOT,the data of ground-based AOT are for the regression analysis, and thefit coefficients are used in Eq. (14).

4. Result and validation

4.1. PM estimation using satellite remote sensed data

The Level 1B image acquired by Terra-MODIS on 15 August 2007 isselected to estimate the distribution of PM in Beijing area. The 1-kmAOT is first retrieved from the MODIS data using the algorithmproposed by Li et al. (2005b) andWang et al. (2008). The hourlymeanof the ABL height, derived from LIDAR measurements, and RH (bothobtained at the Super site within ±30 min of the overpass of EOS-Terra) are used to correct the MODIS AOT using Eq. (8). According tothe correlative models (Eq. (14)) developed in Section 3.3, thedistributions of PM10 and PM2.5 are estimated at the spatialresolution of 1 km⁎1 km. As examples, the distributions of thePM10 (Fig. 6a) and PM2.5 (Fig. 6b) in the urban area of Beijing(39.40°N–40.40°N, 115.80°E–117.00°E) on August 15 2007 are shown.In addition, a true-color image of the same region is downloaded fromGoogle Earth to illustrate the terrain and land-use of Beijing urbanarea (Fig. 6c).

As we can see the estimated PM concentrations are distributedcontinuously and smoothly in Fig. 6a and b. The highest valuesmostly occur around the center of Beijing (marked by a yellowpentacle in Fig. 6c), and correspond to the more urbanized districts(surrounded by cyan lines in Fig. 6a, b and c) and several largeeconomic-technological or industrial development zones of Beij-ing. This area is occupied by 61.98% of the population and includesmost of the commercial and industrial activities, but its area is only8.31% of the city. On the other hand, the PM concentrationsdecrease progressively with distance away from the central part. Inthe mountain and rural areas (along the left and the upper bordersand by the right border of the figures, respectively) with lessresidential or industrial activities, the PM concentrations becomemuch lower. The minimum occurs in the western mountains ofwhich the average altitude is over 600 m.

Owing to the 1-km resolution of the MODIS data, a more detaileddistribution of PM concentrations could be retrieved in the urban area.Compared with the ground-based monitoring that only has one ortwo sites in each district of Beijing, the MODIS estimation couldprovide contiguous spatial distribution of PM concentrations, andthus help to study the transport paths and to identify the local sourcesof PM. Comparing the Fig. 6a and b with c, it is found that in the lessurbanized districts and counties of Beijing (namely Changping,Huairow, Miyun, Mentougou and Fangshan), the centers of relatively

Fig. 4. a. Correlation between ground-based AOT at 0.55 µm and PM10 concentration (both acquired at Super Site in September 2007 and August 2008, N=96). b. Same as a butbetween AOT at 0.55 µm and PM2.5 concentration, and the number of samples is N=77.

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high PM concentrations match the downtown areas very well. Inparticular, the location with distinctly higher PM10 and PM2.5concentrations in the southwest of Shunyi in Fig. 6a and b exactly

corresponds to the Beijing International Airport, as denoted by a blueplane in Fig. 6c, suggesting that high amount of PM is produced at thisairport.

Fig. 5. a. Same as Fig. 4a but between kAOT,Dry at 0.55 µm and PM10 concentration. b. Same as Fig. 4b but between kAOT,Dry at 0.55 µm and PM2.5 concentration.

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4.2. Validation

To validate the MODIS estimation of PM10, we chose the Super Site(marked by the red triangle in Fig. 6c) and three other temporaryobserving stations in Beijing: Shougang site, Yungangzhen site and

Fig. 6. a and b are the distribution of PM10 and PM2.5, derived from the 1-km MODIS AOT p17.0°E, c is the true-color remote sensing image downloaded from Google Earth.

Yongledian site (marked by the green triangles in Fig. 6c), which havein-situ measurements of PM10. These temporary stations had fewerinstruments than the Super Site, and only worked for three monthsaround the Beijing Olympics. The Super Site and Shougang site arelocated in the urban area while the other two are in the suburb of

roducts on August 15 2007 respectively. The covering area is 39.4°N–40.4°N, 115.8°E–

Fig. 6 (continued ).

60 Z. Wang et al. / Remote Sensing of Environment 114 (2010) 50–63

Beijing. The PM10value of a singleMODIS pixel is comparedwith the in-situ hourlymean of themeasured PM10 from the observing station thatlies inside this pixel. Comparisons using data from July to August 2008(Fig. 7a) indicated that the MODIS-estimated PM10 is correlated withthe ground-basedmeasurements, withR2=0.47 and the bias of 26.33%.

Similarly the MODIS estimation of PM2.5 is also validated againstthe measurements from the Super Site and three other observingstations in Beijing in August 2007 (Fig. 7b), namely, Beijing Tower site,Longtan Lake site and Yangfang site (marked by the yellow trianglesin Fig. 6c). The last two were also temporary stations and onlyoperated for no more than five months. Beijing Tower site andLongtan Lake site are located in the urban area and the Yangfang site isin the suburb. The correlation between the MODIS-estimated PM2.5and the ground-based measurements is R2=0.47 and the bias is only6.49%. This is close to some other studies that use satellite-derivedAOT to estimate PM concentrations in the eastern USA (Liu et al.,2005) and in Europe (Vidot et al., 2007), in both of which R2 is ~0.48.Considering that the correlative models used in this paper weredeveloped from the match-up data of the ground-based AOT andother parameters, better performance could be expected if there weresufficient samples of the satellite-derived AOT and other parametersto train the models.

A detailed histogram of the MODIS-estimated PM10 and theground-based measurements at the Super Site from July to August2008 (N=20) is presented in Fig. 8. The smallest difference of theMODIS-estimated PM from the corresponding measurement is 0.08%(on July 25), while the largest one is 126.75% (on August 19). The biasbetween the MODIS-estimated PM10 and the measurements duringthis period is −2.10% at the Super Site. The biases in the other threestations are 21.26%,−40.75% and 52.10%, respectively, indicating thatthe correlative model performs much better near the Super Site. Thisis understandable since this model is trained using the observations atthe Super Site and the ABL height and RH used as the key auxiliarydata are also from the Super Site.

To further investigate the reliability of MODIS estimation on alarger scale, we compared the averaged value of the PM10 measuredsimultaneously at all the four stations with the mean of the PM10derived from the corresponding MODIS pixels. 18 cases are available,and their coefficient of determination R2 increases to 0.66 and the biasis 21.40%, which are better than the correlation using the data inindividual sites (Fig. 9). These comparison results indicate that, on alarger spatial scale and a longer temporal scale, the PM10 derivedfrom MODIS is more consistent with the ground-based measure-ments. Similar results were obtained for the comparison of PM2.5 andare not shown here.

5. Conclusion

According to the theories of atmospheric radiative transfer and theoptical properties of aerosols, it is demonstrated that the verticaldistribution of aerosols and RH have significant impacts on therelationship between AOT and PM concentrations. A better correlationof AOT with PM concentration can be obtained when applying thevertical-and-RH correcting method on the measurements from theSuper Site in Beijing. With these corrections the coefficients ofdetermination R2 between AOT and PM10 and that between AOT andPM2.5 have increased from 0.52 to 0.65 and from 0.48 to 0.62respectively. The correlative models between the corrected AOT andPMs(PM10andPM2.5)weredeveloped and implemented inestimatingthe PM concentrations in Beijing using the MODIS 1-km AOT products.

The ground-basedmeasurements from several stations in Beijing areselected for validation. The comparison results show that the estimatedPMs are correlated with the ground-based measurements withR2=0.47, which is comparable with the results of some other studies(Liu et al., 2005; Vidot et al., 2007). When averaged on a larger spatialscale (among several stations) or on a longer temporal scale (duringnearly a month), the estimated PMs from MODIS are in a much betteragreement with ground-based measurements with R2=0.66.

Fig. 7. a. Comparison between MODIS-estimated PM10 and the measurements from the Super Site, Shougang site, Yungangzhen site and Yongledian site in Beijing from July toAugust 2008 (N=84). The 1:1 line is also shown for reference. b. Comparison between MODIS-estimated PM2.5 and the measurements from the Super Site, Beijing Tower site,Longtan Lake site and Yangfang site in Beijing in August 2007 (N=74). The 1:1 line is also shown for reference.

61Z. Wang et al. / Remote Sensing of Environment 114 (2010) 50–63

Fig. 8. Daily comparisons of PM10 concentration between the MODIS estimation and ground-based measurements at the Super Site from July 21 to August 27, 2008 (N=20).

62 Z. Wang et al. / Remote Sensing of Environment 114 (2010) 50–63

The differences between the MODIS-estimated PM concentrationsand the ground-based measurements are caused by several factors:(a) the retrieval of AOT tends to be influenced by ambient conditions,e.g. cloud or fog, which may cause deviations on the estimation ofPMs; (b) the ground-based measurements only reflect the PMconcentrations at one point, which mainly depend on the localpollutant source and meteorological features, while the satelliteestimation represents the average situation within a pixel, and thusthe information they obtained may be very different; (c) as the key

Fig. 9. Same as Fig. 7a but the spatial average of the PM10 measured from

auxiliary data, the ABL height and RH used to correct AOT are bothacquired from one single site, which cannot describe the regionalatmospheric conditions accurately.

The MODIS data could be used to provide the spatial and temporalvariation of the PM concentrations on local and regional scales, and itis competent to serve as an effective supplement to the ground-basedmonitoring of air quality. The vertical-and-RH correctingmethod usedto derive PM10 and PM2.5 concentrations from the satellite AOT isdeveloped based on the measurements from the Super Site only.

the four stations and the corresponding PM10 estimated by MODIS.

63Z. Wang et al. / Remote Sensing of Environment 114 (2010) 50–63

Therefore if implemented for regional scale, its performance indifferent ambient conditions might be a little different, so someoptimization may be required. An alternative way is to obtain theregional ABL height and RH from meteorological models, which willbe investigated in the next step.

Acknowledgement

This work was supported by the program “Algorithm study ofretrieving surface-level PM concentrations using satellite remotesensed data” sponsored by the State Key Laboratory of RemoteSensing Science, and by the Knowledge Innovation Program ofChinese Academy of Sciences (Grant No. kzcx1-yw-06-01). We reallyappreciate the valuable directions and help from Dr. Xiaozhen Xiongin NOAA. We also thank the instructive suggestions from Dr. ChengcaiLi of Peking University. Special thanks to Prof. Jianguo Liu of AnhuiInstitute of Optics and Fine Mechanism, CAS and Prof. Yuesi Wang ofInstitute of Atmospheric Physics, CAS, who offered some of the PMmeasurements in this paper. Thanks to Dr. Zhongting Wang, Dr. ChaoYu, Dr. Dong Han, Dr. Shenshen Li and Dr. Zhe Jiang for their help.

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