A satellite-based geographically weighted regression ...

Remote Sensing of Environment 154 (2014) 1–7

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Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse

A satellite-based geographically weighted regression model for regional PM2.5 estimation over the Pearl River Delta region in China Weize Song a, Haifeng Jia a,⁎, Jingfeng Huang b, Yiyue Zhang a a b

School of Environment, Tsinghua University, Beijing 100084, China Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA

a r t i c l e

i n f o

Article history: Received 16 May 2014 Received in revised form 7 August 2014 Accepted 7 August 2014 Available online xxxx Keywords: PM2.5 Satellite remote sensing Aerosol optical depth Geographically weighted regression Pearl River Delta region

a b s t r a c t To estimate the daily concentration of ground-level PM2.5 coincident to satellite overpass at regional scale, a satellite-based geographically weighted regression (GWR) model was developed. The model enhances PM2.5 estimation accuracy by considering spatial variation and nonstationarity that might introduce significant biases into PM2.5 estimation. The model was evaluated and validated against the PM2.5 data collected over the Pearl River Delta (PRD) region, China for the period of May 2012 to September 2013. The evaluation evidenced that, with meteorological parameters assimilated, the GWR model is able to explain 73.8% of the variability in ground-level PM2.5 concentration, a better performance than the two conventional statistical models (a general linear regression model Model-I, 56.4% and a semi-empirical model Model-II, 52.6%, respectively). The vertical correction on satellite-derived AOD and relative humidity significantly improve the AOD–PM2.5 correlative relationship. The findings from the study demonstrated the great potential and value of the GWR model for regional PM2.5 estimation. © 2014 Elsevier Inc. All rights reserved.

1. Introduction Many epidemiological studies have shown that fine particulate matters with aerodynamic diameters less than 2.5 μm (PM2.5) are associated with adverse human health effects, such as respiratory problems and cardiovascular diseases (See & Balasubramanian, 2008; Donkelaar et al., 2010; Lim et al., 2011). The ground-level PM2.5 concentration measurement is therefore very important to addressing public health concerns. Although measurements from stationary ambient monitoring sites are generally considered to be accurate, their data frequency and quality consistency often restrict continuous spatial monitoring (Hu et al., 2013). On the other hand, complex processbased air pollution models are often hampered by incomplete information of natural sources and anthropogenic emission inventories (Koelemeijer, Homan, & Matthijsen, 2006; Dawson et al., 2007; Pelletier et al., 2007; Tian & Chen, 2010). Over recent years, remote sensing technology has been widely employed to monitor PM2.5 from space (Wang and Christopher, 2003; Liu et al., 2005; Donkelaar et al., 2006; Jia & Liu, 2006). By using satellite-derived aerosol optical depth (AOD), PM2.5 estimation over large regions can be achieved. Satellite-derived AOD measures columnar light extinction by aerosol particles during satellite overpass (Tian & Chen, 2010). Among many satellite sensors that can retrieve AOD,

⁎ Corresponding author. E-mail address: [email protected] (H. Jia).

http://dx.doi.org/10.1016/j.rse.2014.08.008 0034-4257/© 2014 Elsevier Inc. All rights reserved.

the Moderate resolution Imaging Spectroradiometer (MODIS) provides one of the most quality assured long term aerosol data records (Chu et al., 2002; Engel-Cox, Holloman, Coutant, & Hoff, 2004; Kaufman, Tanre, & Boucher, 2002; Lee, Liu, Coull, Schwartz, & Koutrakis, 2011; Levy, Remer, Mattoo, Vermote, & Kaufman, 2007; Remer et al., 2005; Yap & Hashim, 2013). To explore the quantitative relationship between satellite-derived AOD and ground-measured PM2.5, various models were developed, such as the semi-empirical model (Koelemeijer et al., 2006), mixed effects model (Lee et al., 2011; Yap & Hashim, 2013), generalized additive model (Paciorek, Liu, Moreno-Macias, & Kondragunta, 2008), Alternating Conditional Expectation (ACE) model (Benas, Beloconi, & Chrysoulakis, 2013), Artificial Neural Network (ANN) model (Gupta & Christopher, 2009; Wu et al., 2012), and general linear or nonlinear regression model (Donkelaar et al., 2010; Liu, Franklin, Kahn, & Koutrakis, 2007). Furthermore, meteorological and geographical factors were recommended to be integrated to the AOD–PM2.5 relationship to improve models' performance (Guo et al., 2009; Liu, Paciorek, & Koutrakis, 2009; Tian & Chen, 2010). However, the estimation accuracy of the above models still has space to improve (De Leeuw et al., 2006). The spatial variability of the AOD–PM2.5 relationship is not fully taken into account, or in other words, the strength of the AOD–PM2.5 correlation should not be constant across space and it should change with spatial context (Hu et al., 2013). The spatial variability and nonstationarity can be examined by GWR model, which is based on local regression technique (Fotheringham et al., 1996; Zhao, Yang, & Zhou, 2010; Hu et al., 2013).

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W. Song et al. / Remote Sensing of Environment 154 (2014) 1–7

In this paper, first we establish two satellite AOD based conventional models with meteorological parameters (e.g., relative humidity, temperature, and wind speed) to estimate PM2.5 concentrations at regional scale. Secondly, the new GWR model is introduced and developed to derive the AOD–PM2.5 relationship. In order to quantitatively evaluate the performance of the models, the PRD region is used as the case study to compare the modeled PM2.5 with ground measurements. Lastly, the estimation accuracy of the above three models were compared. And the spatial distribution of the satellite-retrieved PM2.5 using the GWR model is demonstrated. 2. Data and methods 2.1. Data 2.1.1. Satellite-retrieved AOD data MODIS is onboard NASA Terra and Aqua satellites, and the MODIS aerosol product provides columnar optical property observations of atmospheric aerosol particles at 10 km spatial resolution. The best quality assured MODIS AOD products (Quality flag = 3) are believed meeting the lowest tolerance for uncertainty at regional scale over eastern China (Levy et al., 2010; Xiong et al., 2005). Furthermore, the average accuracy of the MODIS aerosol data (Collection 5.1, level 2 product) was larger than 80% by comparison to ground-based CE318 sun photometer measurements in the PRD region (Li, Mao, & Liu, 2005). It was also found that the original AOD can be corrected to better correlate with ground-measured PM2.5 by using the vertical correction equation (Koelemeijer et al., 2006; Tian & Chen, 2010): Corrected AOD ¼

AOD : BLH

ð1Þ

The AOD at 550 nm in the Collection 5.1 MODIS Dark Target level 2 aerosol retrievals over land product were acquired from the NASA LAADS Web (http://ladsweb.nascom.nasa.gov/). The data field of Optical_Depth_Land_And_ Ocean with best quality assurance (AOD Quality flag = 3) was used in this study. For the PRD region (latitude range [21°17. 6′–23°55. 9′], longitude range [111°59. 7′–115°25. 3′]), there were 357 and 319 MODIS AOD images collected from Terra and Aqua, respectively, over the time period of May 2012 to September 2013. To enhance the contrast of the regional variability of PM2.5, the 10 km MODIS AOD data were resampled to 5 km spatial resolution grid using Kriging method. 2.1.2. Ground-measured PM2.5 data The ground-measured PM2.5 data over the PRD region from May 2012 to September 2013 was acquired from the Chinese Guangdong Environment Information Issuing Platform (http://www.gdep.gov.cn/). There are 37 monitoring stations within the study area, and the hourly ground level PM2.5 concentration values were obtained by the Tapered Element Oscillating Microbalance (TEOM) method (Wang, Chen, Tao, Zhang, & Su, 2010). Thus, the “dry” PM2.5 measurements after particles are being heated up to 50 ° C may undervalue the aerosol particle mass due to evaporation (Tian & Chen, 2010). The corrected PM2.5 (ambient

Fig. 1. Scatter plot of original AOD vs. original PM2.5.

PM2.5) can be obtained by using the following relative humidity (RH) correction equation (Koelemeijer et al., 2006; Tian & Chen, 2010): Corrected PM2:5 ¼ PM2:5

1 : 1−RH=100

2.1.3. Auxiliary data Besides relative humidity, the AOD–PM2.5 relationship can also be affected by some other external factors, such as boundary layer height, relative humidity, temperature, wind and land use (Gupta et al., 2006; Koelemeijer et al., 2006; Liu et al., 2007, 2009; Vidot, Santer, & Ramon, 2007; Barman et al., 2008; Schaap et al., 2009; Pateraki et al., 2012). The meteorological data were downloaded from the China Meteorological Data Sharing Service System (CMDSSS on http://cdc.cma.gov.cn/). Within the study domain, there are 26 monitoring sites having these meteorological data available. The acquired ground-based meteorological measurements were spatially interpolated to the fine grid of 5 km, the same regridded resolution as the MODIS AOD datasets. The seasonal boundary layer height (BLH) measurements, including spring (1076 m), summer (1880 m), autumn (1358 m) and winter (1061 m) (Zheng, Chen, Zheng, Zhong, & Liu, 2011) were utilized for the satellite-derived AOD vertical adjustment described in the Section 2.1.

Table 1 Definitions of predictor variables used in Eqs. (3), (4) and (5). Name

Unit

Description

AOD TEMP WS RH α, β1–β5 ε1–ε5

Unitless °C m/s %

Terra/Aqua MODIS AOD Temperature Wind speed Relative humidity Parameters' fixed term Parameters' random term

ð2Þ

Fig. 2. Scatter plot of corrected AOD vs. corrected PM2.5.


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Table 2 Parameterization of both Model-I and Model-II for the estimation of daily PM2.5 concentrations. Statistical models

Modeling dataset (N = 680)

Model-I (linear regression model)

Model-II (semi-empirical model)

Model predictors

Estimate

Std. error

p value

Model predictors

Estimate

Std. error

p value

CI factor

Corrected AOD TEMP WS RH Constant

151.482 −0.827 −11.027 −0.481 103.835

6.314 0.107 1.263 0.093 8.463

b0.01 b0.01 b0.01 b0.01 b0.01

Ln (corrected AOD) TEMP Ln (WS) RH Constant

0.359 −0.018 −0.329 −0.014 6.189

0.018 0.002 0.054 0.002 0.162

b0.01 b0.01 b0.01 b0.01 b0.01

(Corrected AOD) 0.359 e−0.018 × TEMP WS-0.329 e−0.014 × RH 487.3585039

R2 Adjusted R2 RMSE EM (error mean) ME (median error)

Model evaluation Validation dataset (N = 358)

0.566 0.564 19.76 0.310 0.287

0.529 0.526 19.61 0.279 0.258

with AOD, TEMP and RH are exponentially related to PM2.5 (Liu et al., 2007).

2.2. Methods 2.2.1. Conventional statistical models As the indicators of the changes in particle composition and vertical profile, the sensitive impact factors (e.g. relative humidity and temperature) can influence the association between satellite-retrieved AOD and ground-measured PM2.5 significantly (Liu et al., 2007). To describe the numerical or quantitative relationship between these predictors and PM2.5 effectively at the regional scale, two conventional statistical models were developed: a general linear regression model (termed as Model-I) and a semi-empirical model (Model-II). They were also compared to similar models established in previous research and with the proposed GRW model in the latter Section 3.2.2. The Model-I can be expressed as Eq. (3): PM2:5 ¼ ðα þ ε1 Þ þ ðβ1 þ ε2 Þ AOD þ ðβ2 þ ε3 Þ TEMP þ ðβ3 þ ε4 Þ RH þ ðβ4 þ ε5 Þ WS

ð3Þ

where PM2.5 is the dependent variable, and the predictor variables include AOD, TEMP, RH and WS. The relationship is defined in a general linear regression form same as in some previous papers mentioned in the Introduction. Moreover, fit coefficients (e.g. α, β1, β2, β3, and β4) and random errors (e.g. σ1, σ2, σ3, σ4, and σ5) are listed and explained in Table 1. The Model-II, a semi-empirical model, can be expressed as Eq. (4). It is data driven and shaped by related theories (Liu et al., 2007; Tian & Chen, 2010). As a result, PM2.5 is assumed to have nonlinear relationships with AOD, wind speed (WS), temperature (TEMP) and relative humidity (RH). While AOD and WS have power law functional relations

ðαþε 1 Þþðβ2 þε 3 ÞTEMPþðβ 3 þε 4 ÞRH

PM2:5 ¼ e

ðβ4 þε 5 Þ

WS

ð4Þ

where PM2.5, the dependent variable on the left-hand side, is the daily concentration measured at the ground-based monitors. The predictor variables on the right-hand side include AOD, TEMP, RH and WS. The parameters (α and β1 − 4) denote regression coefficients, and random errors are σ1, σ2, σ3, σ4, and σ5, respectively. To facilitate parameterization (Tian & Chen, 2010) and reduce the skewness of data distribution (Liu et al., 2007), Eq. (4) was logtransformed into a linear regression form, as shown in Eq. (5). ln ðPM2:5 Þ ¼ ðα þ ε1 Þ þ ðβ2 þ ε3 Þ TEMP þ ðβ3 þ ε4 Þ RH þ ðβ1 þ ε2 Þ ln ðAODÞ þ ðβ4 þ ε5 Þ ln ðWSÞ

ð5Þ

All the above predictor variables are summarized and described in Table 1. 2.2.2. Geographically weighted regression (GWR) model Some previous studies showed that the AOD–PM2.5 correlation varies significantly over space and changes with spatial context. Such spatial variability of the relationship resulted in poor accuracy of those models that use globally constant parameters (Engel-Cox et al., 2004; Hu et al., 2013). GWR is a practical technique to examine the spatial variation and nonstationarity for continuous surface of parameter values at regional scale (Brunsdon, Fotheringham, & Charlton, 1996; Fotheringham, Charlton, & Brunsdon, 1996). In this study, a GWR model was utilized to generate a local R2 for each PM2.5 monitoring site on daily basis. The adaptive bandwidths were used to count for the uneven distribution of the monitoring sites, and then the spatial autocorrelation analysis for the model's residual was conducted. The GWR model can be expressed as the Eq. (6): PM2:5 AOD þ RH þ WS þ TEMP þ BLH:

Fig. 3. Scatter plot of observed vs. fitted PM2.5 for Model-I.

ðβ1 þε 2 Þ

AOD

ð6Þ

The predictor variables in the GWR model were spatially interpolated to the fine grid of 5 km, the same spatial resolution as the Terra/Aqua MODIS AOD data were regridded to. The specific GWR was calculated using the function of ‘Geographically Weighted Regression’ of the ‘Spatial Statistics Tool’ in the ArcGIS software. Then, to visualize the spatial distribution of annual PM2.5 concentration in the study area, the superimposed calculation based on daily satellite-retrieved PM2.5 surface dataset was conducted using the ‘band math’ tool in the ENVI software.

4


Fig. 4. Scatter plot of observed vs. fitted PM2.5 for Model-II.

3. Results and discussion 3.1. Exploratory analysis General linear regression between the original AOD and PM2.5 was shown in Fig. 1, in comparison to a similar result with corrected AOD and PM2.5 in Fig. 2. Both figures showed that the satellite-retrieved AOD is potentially useful for estimating PM2.5 with R2 of 0.34 or 0.47 if a simple linear regression method is used. A significant increase of R2 value from 0.34 to 0.47 indicated that the vertical correction on AOD (for details in Section 2.1.1) and the relative humidity correction on PM2.5 (for details in Section 2.1.2) can significantly improve the model performance. In comparison to Figs. 1, 2 showed higher correlation slope (156.3 ± 6.25 vs. 39.78 ± 2.09) and more concentrated data points closer to the regression line. The two regression intercepts were close (31.97 ± 1.46 and 30.92 ± 1.2, respectively), indicating that 31 μg/m3 is the overall mean minimum background ground-level PM2.5 concentration threshold for satellite AOD observation over the study domain during the study period. However, the coefficients of determination (R2) for both regression models with AOD as the sole predictor variable are still relatively low. This means that the AOD–PM2.5 relationship can also be affected by other factors, such as meteorological condition and spatial context (Kumar, Chu, & Foster, 2007).

3.2. Model results and validation 3.2.1. Model-I and Model-II The Model-I described in Eq. (3) was developed using the model training dataset (N = 680) and was evaluated using the validation dataset (N = 358). Overall, this model with meteorological predictors assimilated showed significant correlation (p b 0.01) and was able to explain 56.4% of the variability in the corresponding daily PM2.5 concentrations (Table 2). The model surpasses the general linear regression model with corrected satellite-retrieved AOD as the only predictor by

an R2 increase of 9%. In other words, other predictors (e.g. temperature and relative humidity) can be attributable for improving model performance as indicated from the current analysis. The Model-II described in Section 2.2.2 was also developed using the datasets same as the Model-I. Although the correlation in the Model-II appears to be significant (p b 0.01), the semi-empirical model explained slightly lower variability in the daily PM2.5 concentrations (adjusted R2 = 0.526) than the general linear regression model (Model-I, adjusted R2 = 0.564) (Table 2). This result might be attributed to the following reasons: 1) the deterministic mechanism process between PM2.5 and all related predictors is still unknown; 2) the log-transformation of Model-II can constrain the association between PM2.5 and each of these predictors to an exponential form (Liu et al., 2007), resulting in underestimation of high PM2.5 concentrations. However, the predictive power of Model-II is significantly better than Model-I. More detailed information of the evaluation indicators (e.g. RMSE, EM and ME) was summarized in Table 2, Figs. 3 and 4. Furthermore, the concentration impact (CI) factors based on the Model-II were calculated (Table 2) to interpret the role of predictors on the AOD–PM2.5 relationship, especially physical significance of these predictors. Using the minimum and maximum values of the meteorological predictors, we calculated the CI factors to be between 0.58 and 0.84 for temperature, between 0.32 and 0.70 for relative humidity, and between 0.63 and 1.08 for wind speed. Essentially, higher temperature accelerates the transformation of nitrate versus sulfate particles and thus the generation of secondary particles, which may be able to explain the response of PM2.5 to temperature near surface. The increase of humidity can shift the equilibrium of the ammonia–nitric acid system toward the aerosol phase (Tian & Chen, 2010), and therefore results to the elevation of PM2.5 concentrations. In additional, wind speed can be linked to the long range transport of aerosols from ‘polluted’ region to the ‘local or neighboring’ areas and also blows off more local dust particles off ground, thus contributes to high PM2.5 as well. 3.2.2. GWR model As showed in Table 3, daily PM2.5 and matched predictors were fitted at 37 monitoring sites (N = 712) using the GWR model described in Section 2.2.2. The adjusted overall mean local R2 was 0.74, meaning 74% of the variability in the PM2.5 concentrations can be explained in the modeling dataset. Furthermore, the spatial autocorrelation and two-tailed significance test (α = 0.05) were conducted, respectively, and the results showed no significant spatial autocorrelation for the residual terms. More information about model fitting such as corrected Akaike Information Criterion (AICc) as one of the bandwidth selection criteria, was summarized in Table 3. The GWR model was also evaluated against validation dataset from another 15 monitoring sites (N = 315) within the study domain. In particular, Fig. 5 illustrated quantitatively the relative difference between the satellite-retrieved and ground-measured PM2.5 at each site, and the mean relative accuracy was 88.6% in the validation dataset. 3.3. Discussion Three models that describe the relationship between satellite AOD and ground ambient PM2.5 were developed for the PRD region during

Table 3 Description of the results and evaluation for the satellite-based GWR model. GWR model

Variable name

Value

Variable name

Value

Modeling fitting

Site number N AICc Neighbors Effective number Site number N

37 712 243.99 11 16.405 15 401

Overall mean local R2 Overall mean local R2 adjusted Residual squares Sigma

0.8501 0.7380 406.002 4.4400

Model evaluation

Relative accuracy (%)

88.61


5

Fig. 5. The observed and fitted PM2.5 at each site in the cross validation dataset.

the period of May 2012 to September 2013 in this study. The results from the three models indicate that satellite remote sensing technology can provide a simple but effective way to estimating regional PM2.5 concentrations, especially for some rural areas without ground-based monitoring stations. Inclusion of meteorological predictors such as relative humidity, temperature, and wind speed in the models can reduce the satellite-based PM2.5 estimation bias significantly. The results also showed that the GWR model outperforms the Model-I and Model-II, implying that the spatial variation of model parameters and the spatial nonstationarity within the data should be considered when the AOD– PM2.5 association is explored. In addition, the spatial distribution of annual PM2.5 concentrations based on the GWR model was visualized. As shown in the Fig. 6, several cities (e.g. Guangzhou, Foshan, and Dongguan) with higher economic and urbanization levels can be easily identified from the regional PM2.5 map, and the results represented the actual situation of PM2.5 regional variability in the study area. The annual PM2.5 concentration in Guangzhou was observed to be relatively lower than neighboring cities, which might be attributable to the following reasons: (1) Over recent years, industrial reconstruction has made Guangzhou less polluted than neighboring cities in controlling

the PM2.5 pollution, especially during the period of the Sixteenth Asian Games. Many heavy-polluting enterprises had to shut down or relocate to somewhere else; (2) There are more small and medium-sized industrial companies in Foshan and Dongguang than in Guangzhou, for instance, the ceramic industry in Foshan causes serious PM2.5 pollution; and (3) The ground-based monitoring also showed that the PM2.5 pollution concentration in Guangzhou was lower than the neighboring Foshan and Dongguan cities. 4. Conclusions A novel GWR technology was proposed and model developed to derive the AOD–PM2.5 relationship and estimate PM2.5 from satellite AOD observations. For the study area of the PRD region, a specific satellite-based GWR model was developed, validated, and compared to two conventional models (Model-I and Model-II). The evaluation results of above three models showed that estimation accuracy can be significantly improved if spatial variation of model parameters and spatial nonstationarity within the data are considered in the models. Moreover, the vertical correction on satellite-retrieved AOD and relative

PM2.5 observation sites PM2.5 (µg/m3)

Fig. 6. The spatial distribution of annual PM2.5 concentrations for the GWR model.

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humidity correction on ground-measured PM2.5 can also improve the model performance. Inclusion of meteorological predictors in the models was found useful to enhance the correlative sensitivity in the correlation between AOD and PM2.5. The satellite-retrieved PM2.5 from the GWR model showed a reasonable and representative spatial pattern of the regional variability of PM2.5, similar to that from ground measurements, indicating the great value of the GWR model for providing more accurate large scale PM2.5 monitoring over urban areas such as the PRD region in China. However, further research on satellite-based PM2.5 estimation models is still warranted. Sensitivity of the models to the integration of more model predictors (e.g. land use, wind direction, season) and better characterized information (e.g. higher satellite spatial resolution, finer vertical structure of aerosol particles size and composition) should be explored. Research on model uncertainty and even deterministic mechanism are currently on the way. Nonetheless, these satellite-based models discussed in this study provide us the new capability to estimate ground ambient PM2.5 at regional scale over large areas from space. Such capability is valuable and essential for regional air quality management and decision-making. Acknowledgments We thank Ms. Jiewen Wang of the Foshan Municipal Environmental Protection Bureau for her assistance in the data collection. Funding for the study was provided by the Chinese Foshan Environmental Protection Bureau (Grant No. 20122001735). Special thanks to Prof. Armistead G. Russell from Georgia Institute of Technology for his constructive suggestions on this research paper. The authors also would like to thank the NASA MODIS aerosol team for making their valuable dataset publicly accessible. References Barman, S.C., Singh, R., Negi, M. P.S., & Bhargava, S. K. (2008). Fine particles (PM2.5) in residential areas of Lucknow city and factors influencing the concentration. Clean-Soil Air Water, 36, 111–117. http://dx.doi.org/10.1002/clen.200700047. Benas, N., Beloconi, A., & Chrysoulakis, N. (2013). Estimation of urban PM10 concentration, based on MODIS and MERIS/AATSR synergistic observations. Atmospheric Environment, 79, 448–454. http://dx.doi.org/10.1016/j.atmosenv.2013.07.012. Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis, 28, 281–298. http://dx.doi.org/10.1111/j.1538-4632.1996.tb00936.x. Chu, D. A., Kaufman, Y. J., Ichoku, C., Remer, L. A., Tanré, D., & Holben, B. N. (2002). Validation of MODIS aerosol optical depth retrieval over land. Geophysical Research Letters, 29, 1617–1620. http://dx.doi.org/10.1029/2001GL013205. Dawson, J. P., Adams, P. J., & Pandis, S. N. (2007). Sensitivity of PM2.5 to climate in the Eastern US: A modeling case study. Atmospheric Chemistry and Physics, 7, 4295–4309. http://dx.doi.org/10.5194/acpd-7-6487-2007. De Leeuw, J., Jia, H., Yang, L., Liu, X., Schmidt, K., & Skidmore, A. K. (2006). Comparing accuracy assessments to infer superiority of image classification methods. International Journal of Remote Sensing, 27(1), 223–232. http://dx.doi.org/10.1080/01431160500275762. Donkelaar, A. V., Martin, R. V., Brauer, M., Kahn, R., Levy, R., Verduzco, C., & Villeneuve, P. J. (2010). Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: Development and application. Environmental Health Perspectives, 118, 847–855. http://dx.doi.org/10.1289/ehp.0901623. Donkelaar, A. V., Martin, R. V., & Park, R. J. (2006). Estimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensing. Journal of Geophysical Research, D21. http://dx.doi.org/10.1029/2005JD006996. Engel-Cox, J. A., Holloman, C. H., Coutant, B. W., & Hoff, R. M. (2004). Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality. Atmospheric Environment, 38, 2495–2509. http://dx.doi.org/10.1016/j. atmosenv.2004.01.039. Fotheringham, A. S., Charlton, M., & Brunsdon, C. (1996). The geography of parameter space: An investigation of spatial non-stationarity. International Journal of Geographical Information Systems, 10, 605–627. http://dx.doi.org/10.1080/ 02693799608902100. Guo, J. P., Zhang, X. Y., Che, H. Z., Gong, S. L., An, X. Q., Cao, C. X., Guang, J., Zhang, H., Wang, Y. Q., Zhang, X. C., Xue, M., & Li, X. W. (2009). Correlation between PM concentrations and aerosol optical depth in eastern China. Atmospheric Environvionment, 43, 5876–5886. http://dx.doi.org/10.1016/j.atmosenv.2009.08.026. Gupta, P., & Christopher, S. A. (2009). Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: 2. A neural network approach. Journal of Geophysical Research, D20205. http://dx.doi.org/10.1029/ 2008JD011497.

Gupta, P., Christopher, S. A., Wang, J., Gehrig, R., Lee, Y., & Kumar, N. (2006). Satellite remote sensing of particulate matter and air quality assessment over global cities. Atmospheric Environvionment, 40, 5880–5892. http://dx.doi.org/10.1016/j.atmosenv. 2006.03.016. Hu, X. F., Waller, L. A., Al-Hamdan, M. Z., Crosson, W. L., Estes, M. G., Jr., Estes, S. M., Quattrochi, D. A., Sarnat, J. A., & Liu, Y. (2013). Estimating ground-level PM2.5 concentrations in the southeastern U.S. using geographically weighted regression. Environmental Research, 121, 1–10. Jia, H. F., & Liu, X. H. (2006). Environmental Remote Sensing Principles and Applications. Beijing: Tsinghua University Press (in Chinese). Kaufman, Y. J., Tanre, D., & Boucher, O. (2002). A satellite view of aerosols in the climate system. Nature, 419, 215–223. http://dx.doi.org/10.1038/nature01091. Koelemeijer, R. B.A., Homan, C. D., & Matthijsen, J. (2006). Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe. Atmospheric Environment, 40, 5304–5315. http://dx.doi.org/10.1016/j.atmosenv. 2006.04.044. Kumar, N., Chu, A., & Foster, A. (2007). An empirical relationship between PM2.5 and aerosol optical depth in Delhi Metropolitan. Atmospheric Environment, 41, 4492–4503. http://dx.doi.org/10.1016/j.atmosenv.2007.01.046. Lee, H. J., Liu, Y., Coull, B.A., Schwartz, J., & Koutrakis, P. (2011). A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations. Atmospheric Chemistry and Physics, 11, 7991–8002. Levy, R. C., Remer, L. A., Kleldman, R. G., Mattoo, S., Ichoku, C., Kahn, R., & Eck, T. F. (2010). Global evaluation of the collection 5 MODIS dark-target aerosol products over land. Atmospheric Chemistry and Physics, 10, 14815–14873. http://dx.doi.org/10.5194/acp10-10399-2010. Levy, R. C., Remer, L. A., Mattoo, S., Vermote, E. F., & Kaufman, Y. J. (2007). Secondgeneration operational algorithm: Retrieval of aerosol properties over land from inversion of moderate resolution imaging spectroradiometer spectral reflectance. Journal of Geophysical Research, 112, D13. http://dx.doi.org/10.1029/ 2006JD007811. Li, C. C., Mao, J. T., & Liu, Q. H. (2005). Remote sensing of high spatial resolution aerosol optical depth with MODIS data over Hong Kong. Chinese Journal of Atmospheric Sciences, 29, 335–342 (in Chinese). Lim, J. M., Jeong, J. H., Lee, J. H., Moon, J. H., Chung, Y. S., & Kim, K. H. (2011). The analysis of PM2.5 and associated elements and their indoor/outdoor pollution status in an urban area. Indoor Air, 21, 145–155. http://dx.doi.org/10.1111/j.1600-0668.2010. 00691.x. Liu, Y., Franklin, M., Kahn, R., & Koutrakis, P. (2007). Using aerosol optical thickness to predict ground-level PM2.5 concentrations in the St. Louis area: A comparison between MISR and MODIS. Remote Sensing of Environment, 107, 33–44. Liu, Y., Paciorek, C., & Koutrakis, P. (2009). Estimating regional spatial and temporal variability of PM2.5 concentrations using satellite data, meteorology, and land use information. Environmental Health Perspectives, 117, 886–892. http://dx.doi.org/10. 1289/ehp.0800123. Liu, Y., Sarnat, J. A., Kilaru, V., Jacob, D. J., & Koutrakis, P. (2005). Estimating groundlevel PM2.5 in the eastern United States using satellite remote sensing. Environmental Science & Technology, 39, 3269–3278. http://dx.doi.org/10.1021/ es049352m. Paciorek, C. J., Liu, Y., Moreno-Macias, H., & Kondragunta, S. (2008). Spatiotemporal associations between GOES aerosol optical depth retrievals and ground-level PM2.5. Environmental Science & Technology, 42, 5800–5806. http://dx.doi.org/10.1021/ es703181j. Pateraki, S., Asimakopoulos, D. N., Flocas, H. A., Maggos, T., & Vasilakos, C. (2012). The role of meteorology on different sized aerosol fractions (PM10, PM2.5, PM2.5-10). Science of the Total Environment, 419, 124–135. Pelletier, B., Santer, R., & Vidot, J. (2007). Retrieving of particulate matter from optical measurements: A semiparametric approach. Journal of Geophysical Research, 112, D6. http://dx.doi.org/10.1029/2005JD006737. Remer, L. A., Kaufman, Y. J., Tanre, D., Mattoo, S., Chu, D. A., Martins, J. V., Li, R. R., Ichoku, C., Levy, R. C., Kleidman, R. G., Eck, T. F., Vermote, E., & Holben, B. N. (2005). The MODIS aerosol algorithm, products, and validation. Journal of the Atmospheric Sciences, 62, 947–973. http://dx.doi.org/10.1175/JAS3385.1. Schaap, M., Apituley, A., Timmermans, R. M.A., Koelemeijer, R. B.A., & Leeuw, G. D. (2009). Exploring the relation between aerosol optical depth and PM2.5 at Cabauw, the Netherlands. Atmospheric Chemistry and Physics, 9, 909–925. http://dx.doi.org/10. 5194/acpd-8-17939-2008. See, S. W., & Balasubramanian, R. (2008). Chemical characteristics of fine particles emitted from different gas cooking methods. Atmospheric Environment, 42, 8852–8862. http:// dx.doi.org/10.1016/j.atmosenv.2008.09.011. Tian, J., & Chen, D.M. (2010). A semi-empirical model for predicting hourly ground-level fine particulate matter (PM2.5) concentration in southern Ontario from satellite remote sensing and ground-based meteorological measurements. Remote Sensing of Environment, 114, 221–229. Vidot, J., Santer, R., & Ramon, D. (2007). Atmospheric particulate matter (PM) estimation from SeaWiFS imagery. Remote Sensing of Environment, 111, 1–10. http://dx.doi.org/ 10.1016/j.rse.2007.03.009. Wang, Z. F., Chen, L. F., Tao, J. H., Zhang, Y., & Su, L. (2010). Satellite-based estimation of regional particulate matter (PM) in Beijing using vertical-and-RH correcting method. Remote Sensing of Environment, 114, 50–63. http://dx.doi.org/10.1016/j.rse.2009.08. 009. Wang, J., & Christopher, S. A. (2003). Intercomparison between satellite-derived aerosol optical thickness and PM2.5 mass: Implications for air quality studies. Geophysical Research Letters, 30, 2095. Wu, Y., Guo, J., Zhang, X., Tian, X., Zhang, J., Wang, Y., Duan, J., & Li, X. (2012). Synergy of satellite and ground based observations in estimation of particulate matter in eastern

W. Song et al. / Remote Sensing of Environment 154 (2014) 1–7 China. Science of the Total Environment, 433, 20–30. http://dx.doi.org/10.1016/j. scitotenv.2012.06.033. Xiong, X. X., Sun, J. Q., Wu, A. S., Chiang, K. F., Esposito, J., & Barnes, W. (2005). Terra and Aqua MODIS calibration algorithms and uncertainty analysis. Sensors, systems, and next-generation satellites IX. Proceedings of SPIE, 5978. http://dx.doi.org/10.1117/12. 627631. Yap, X. Q., & Hashim, M. (2013). A robust calibration approach for PM10 prediction from MODIS aerosol optical depth. Atmospheric Chemistry and Physics, 13, 3517–3526. http://dx.doi.org/10.5194/acp-13-3517-2013.

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Zhao, N., Yang, Y. H., & Zhou, X. Y. (2010). Application of geographically weighted regression in estimating the effect of climate and site conditions on vegetation distribution in Haihe Catchment, China. Plant Ecology, 209, 349–359. http://dx.doi.org/10.1007/ s11258-010-9769-y. Zheng, Z., Chen, L., Zheng, J., Zhong, L., & Liu, Q. (2011). Application of retrieved highresolution AOD in regional PM monitoring in the Pearl River Delta and Hong Kong region. Acta Scientiae Circumstantiae, 31, 1154–1161 (in Chinese).