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Estimating PM2.5 in Xi'an, China using aerosol optical depth: a comparison between the MODIS and MISR retrieval models.
Sci Total Environ. 2015 Feb 01; 505:1156-65.ST

Abstract

Satellite measurements have been widely used to estimate particulate matter (PM) on the ground, which can affect human health adversely. However, such estimation from space is susceptible to meteorological conditions and may result in large errors. In this study, we compared the aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging SpectroRadiometer (MISR) to predict ground-level PM2.5 concentration in Xi'an, Shaanxi province of northwestern China, using an empirical nonlinear model. Meteorological parameters from ground-based measurements and NCEP/NCAR reanalysis data were used as covariates in the model. Both MODIS and MISR AOD values were highly significant predictors of ground-level PM2.5 concentration. The MODIS and MISR models had overall comparable predictability of ground-level PM2.5 concentration and explained 67% and 72% of the daily PM2.5 concentration variation, respectively. Seasonal analysis showed that the MODIS and MISR models had overall comparable predictability of ground-level PM2.5 concentration, with the MISR model having a higher correlation coefficient (R) and thus giving a better fit in all seasons. The MISR model had high prediction accuracy in all seasons, with average R(2) and absolute percentage error (APE) of 0.84 and 15.3% in all four seasons, respectively. The prediction of the MODIS model was best during winter (R(2)=0.83) with an APE of 19%, whereas it was relatively poor in spring (R(2)=0.56) with an APE of 21%. Further analysis showed that there was a significant improvement in correlation coefficient when using the nonlinear multiple regression model compared to using a simple linear regression model of AOD and PM2.5. These results are useful for assessing surface PM2.5 concentration and monitoring regional air quality.

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Publisher Full Text (DOI)

Authors+Show Affiliations

You W
Institute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101, China.
Zang Z
Institute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101, China. Electronic address: zzlqxxy@163.com.
Pan X
Institute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101, China.
Zhang L
Institute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101, China.
Chen D
National Center for Atmospheric Research, Boulder, CO 80305, USA.

MeSH

AerosolsAir PollutantsAir PollutionChinaEnvironmental MonitoringParticulate MatterSatellite Imagery

Pub Type(s)

Comparative Study
Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

25466686

Citation

You, Wei, et al. "Estimating PM2.5 in Xi'an, China Using Aerosol Optical Depth: a Comparison Between the MODIS and MISR Retrieval Models." The Science of the Total Environment, vol. 505, 2015, pp. 1156-65.
You W, Zang Z, Pan X, et al. Estimating PM2.5 in Xi'an, China using aerosol optical depth: a comparison between the MODIS and MISR retrieval models. Sci Total Environ. 2015;505:1156-65.
You, W., Zang, Z., Pan, X., Zhang, L., & Chen, D. (2015). Estimating PM2.5 in Xi'an, China using aerosol optical depth: a comparison between the MODIS and MISR retrieval models. The Science of the Total Environment, 505, 1156-65. https://doi.org/10.1016/j.scitotenv.2014.11.024
You W, et al. Estimating PM2.5 in Xi'an, China Using Aerosol Optical Depth: a Comparison Between the MODIS and MISR Retrieval Models. Sci Total Environ. 2015 Feb 1;505:1156-65. PubMed PMID: 25466686.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Estimating PM2.5 in Xi'an, China using aerosol optical depth: a comparison between the MODIS and MISR retrieval models. AU - You,Wei, AU - Zang,Zengliang, AU - Pan,Xiaobin, AU - Zhang,Lifeng, AU - Chen,Dan, Y1 - 2014/11/20/ PY - 2014/04/24/received PY - 2014/11/05/revised PY - 2014/11/05/accepted PY - 2014/12/4/entrez PY - 2014/12/4/pubmed PY - 2015/7/24/medline KW - Aerosol optical depth KW - MISR KW - MODIS KW - Nonlinear model KW - PM2.5 SP - 1156 EP - 65 JF - The Science of the total environment JO - Sci Total Environ VL - 505 N2 - Satellite measurements have been widely used to estimate particulate matter (PM) on the ground, which can affect human health adversely. However, such estimation from space is susceptible to meteorological conditions and may result in large errors. In this study, we compared the aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging SpectroRadiometer (MISR) to predict ground-level PM2.5 concentration in Xi'an, Shaanxi province of northwestern China, using an empirical nonlinear model. Meteorological parameters from ground-based measurements and NCEP/NCAR reanalysis data were used as covariates in the model. Both MODIS and MISR AOD values were highly significant predictors of ground-level PM2.5 concentration. The MODIS and MISR models had overall comparable predictability of ground-level PM2.5 concentration and explained 67% and 72% of the daily PM2.5 concentration variation, respectively. Seasonal analysis showed that the MODIS and MISR models had overall comparable predictability of ground-level PM2.5 concentration, with the MISR model having a higher correlation coefficient (R) and thus giving a better fit in all seasons. The MISR model had high prediction accuracy in all seasons, with average R(2) and absolute percentage error (APE) of 0.84 and 15.3% in all four seasons, respectively. The prediction of the MODIS model was best during winter (R(2)=0.83) with an APE of 19%, whereas it was relatively poor in spring (R(2)=0.56) with an APE of 21%. Further analysis showed that there was a significant improvement in correlation coefficient when using the nonlinear multiple regression model compared to using a simple linear regression model of AOD and PM2.5. These results are useful for assessing surface PM2.5 concentration and monitoring regional air quality. SN - 1879-1026 UR - https://www.unboundmedicine.com/medline/citation/25466686/Estimating_PM2_5_in_Xi'an_China_using_aerosol_optical_depth:_a_comparison_between_the_MODIS_and_MISR_retrieval_models_ DB - PRIME DP - Unbound Medicine ER -