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Estimating national-scale ground-level PM25 concentration in China using geographically weighted regression based on MODIS and MISR AOD.
Environ Sci Pollut Res Int. 2016 May; 23(9):8327-38.ES

Abstract

Taking advantage of the continuous spatial coverage, satellite-derived aerosol optical depth (AOD) products have been widely used to assess the spatial and temporal characteristics of fine particulate matter (PM2.5) on the ground and their effects on human health. However, the national-scale ground-level PM2.5 estimation is still very limited because the lack of ground PM2.5 measurements to calibrate the model in China. In this study, a national-scale geographically weighted regression (GWR) model was developed to estimate ground-level PM2.5 concentration based on satellite AODs, newly released national-wide hourly PM2.5 concentrations, and meteorological parameters. The results showed good agreements between satellite-retrieved and ground-observed PM2.5 concentration at 943 stations in China. The overall cross-validation (CV) R (2) is 0.76 and root mean squared prediction error (RMSE) is 22.26 μg/m(3) for MODIS-derived AOD. The MISR-derived AOD also exhibits comparable performance with a CV R (2) and RMSE are 0.81 and 27.46 μg/m(3), respectively. Annual PM2.5 concentrations retrieved either by MODIS or MISR AOD indicated that most of the residential community areas exceeded the new annual Chinese PM2.5 National Standard level 2. These results suggest that this approach is useful for estimating large-scale ground-level PM2.5 distributions especially for the regions without PMs monitoring sites.

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Publisher Full Text (DOI)

Authors+Show Affiliations

You W
Institute of Meteorology and Oceanography, PLA University of Science and Technology, No.60, Shuanglong Street, Nanjing, 211101, China.
Zang Z
Institute of Meteorology and Oceanography, PLA University of Science and Technology, No.60, Shuanglong Street, Nanjing, 211101, China. zzlqxxy@163.com.
Zhang L
Institute of Meteorology and Oceanography, PLA University of Science and Technology, No.60, Shuanglong Street, Nanjing, 211101, China.
Li Y
Institute of Meteorology and Oceanography, PLA University of Science and Technology, No.60, Shuanglong Street, Nanjing, 211101, China.
Wang W
Institute of Meteorology and Oceanography, PLA University of Science and Technology, No.60, Shuanglong Street, Nanjing, 211101, China.

MeSH

AerosolsAir PollutantsAir PollutionCalibrationChinaEnvironmental MonitoringHumansModels, TheoreticalParticulate MatterSatellite ImagerySpatial Regression

Pub Type(s)

Journal Article

Language

eng

PubMed ID

26780051

Citation

You, Wei, et al. "Estimating National-scale Ground-level PM25 Concentration in China Using Geographically Weighted Regression Based On MODIS and MISR AOD." Environmental Science and Pollution Research International, vol. 23, no. 9, 2016, pp. 8327-38.
You W, Zang Z, Zhang L, et al. Estimating national-scale ground-level PM25 concentration in China using geographically weighted regression based on MODIS and MISR AOD. Environ Sci Pollut Res Int. 2016;23(9):8327-38.
You, W., Zang, Z., Zhang, L., Li, Y., & Wang, W. (2016). Estimating national-scale ground-level PM25 concentration in China using geographically weighted regression based on MODIS and MISR AOD. Environmental Science and Pollution Research International, 23(9), 8327-38. https://doi.org/10.1007/s11356-015-6027-9
You W, et al. Estimating National-scale Ground-level PM25 Concentration in China Using Geographically Weighted Regression Based On MODIS and MISR AOD. Environ Sci Pollut Res Int. 2016;23(9):8327-38. PubMed PMID: 26780051.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Estimating national-scale ground-level PM25 concentration in China using geographically weighted regression based on MODIS and MISR AOD. AU - You,Wei, AU - Zang,Zengliang, AU - Zhang,Lifeng, AU - Li,Yi, AU - Wang,Weiqi, Y1 - 2016/01/16/ PY - 2015/09/08/received PY - 2015/12/28/accepted PY - 2016/1/19/entrez PY - 2016/1/19/pubmed PY - 2017/4/5/medline KW - Aerosol optical depth KW - Geographically weighted regression KW - MISR KW - MODIS KW - PM2.5 SP - 8327 EP - 38 JF - Environmental science and pollution research international JO - Environ Sci Pollut Res Int VL - 23 IS - 9 N2 - Taking advantage of the continuous spatial coverage, satellite-derived aerosol optical depth (AOD) products have been widely used to assess the spatial and temporal characteristics of fine particulate matter (PM2.5) on the ground and their effects on human health. However, the national-scale ground-level PM2.5 estimation is still very limited because the lack of ground PM2.5 measurements to calibrate the model in China. In this study, a national-scale geographically weighted regression (GWR) model was developed to estimate ground-level PM2.5 concentration based on satellite AODs, newly released national-wide hourly PM2.5 concentrations, and meteorological parameters. The results showed good agreements between satellite-retrieved and ground-observed PM2.5 concentration at 943 stations in China. The overall cross-validation (CV) R (2) is 0.76 and root mean squared prediction error (RMSE) is 22.26 μg/m(3) for MODIS-derived AOD. The MISR-derived AOD also exhibits comparable performance with a CV R (2) and RMSE are 0.81 and 27.46 μg/m(3), respectively. Annual PM2.5 concentrations retrieved either by MODIS or MISR AOD indicated that most of the residential community areas exceeded the new annual Chinese PM2.5 National Standard level 2. These results suggest that this approach is useful for estimating large-scale ground-level PM2.5 distributions especially for the regions without PMs monitoring sites. SN - 1614-7499 UR - https://www.unboundmedicine.com/medline/citation/26780051/Estimating_national_scale_ground_level_PM25_concentration_in_China_using_geographically_weighted_regression_based_on_MODIS_and_MISR_AOD_ DB - PRIME DP - Unbound Medicine ER -