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An Ensemble Machine-Learning Model To Predict Historical PM2.5 Concentrations in China from Satellite Data

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    An Ensemble Machine-Learning Model To Predict Historical PM2.5 Concentrations in China from Satellite Data
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    • Qingyang Xiao
      Qingyang Xiao
      Department of Environmental Health, Emory University, Rollins School of Public Health, Atlanta, Georgia 30322, United States
      More by Qingyang Xiao
      Orcidhttp://orcid.org/0000-0001-5910-884X
    • Howard H. Chang
      Howard H. Chang
      Department of Biostatistics and Bioinformatics, Emory University, Rollins School of Public Health, Atlanta, Georgia 30322, United States
      More by Howard H. Chang
    • Guannan Geng
      Guannan Geng
      Department of Environmental Health, Emory University, Rollins School of Public Health, Atlanta, Georgia 30322, United States
      More by Guannan Geng
      Orcidhttp://orcid.org/0000-0002-1605-8448
    • Yang Liu*
      Yang Liu
      Department of Environmental Health, Emory University, Rollins School of Public Health, Atlanta, Georgia 30322, United States
      *Mailing address: Emory University, Rollins School of Public Health, 1518 Clifton Road NE, Atlanta, GA 30322, USA. Phone: (404) 727-2131. E-mail: [email protected]
      More by Yang Liu
      Orcidhttp://orcid.org/0000-0001-5477-2186
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    Environmental Science & Technology

    Cite this: Environ. Sci. Technol. 2018, 52, 22, 13260–13269
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    https://doi.org/10.1021/acs.est.8b02917
    Published October 24, 2018
    Copyright © 2018 American Chemical Society
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    Abstract

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    The long satellite aerosol data record enables assessments of historical PM2.5 level in regions where routine PM2.5 monitoring began only recently. However, most previous models reported decreased prediction accuracy when predicting PM2.5 levels outside the model-training period. In this study, we proposed an ensemble machine learning approach that provided reliable PM2.5 hindcast capabilities. The missing satellite data were first filled by multiple imputation. Then the modeling domain, China, was divided into seven regions using a spatial clustering method to control for unobserved spatial heterogeneity. A set of machine learning models including random forest, generalized additive model, and extreme gradient boosting were trained in each region separately. Finally, a generalized additive ensemble model was developed to combine predictions from different algorithms. The ensemble prediction characterized the spatiotemporal distribution of daily PM2.5 well with the cross-validation (CV) R2 (RMSE) of 0.79 (21 μg/m3). The cluster-based subregion models outperformed national models and improved the CV R2 by ∼0.05. Compared with previous studies, our model provided more accurate out-of-range predictions at the daily level (R2 = 0.58, RMSE = 29 μg/m3) and monthly level (R2 = 0.76, RMSE = 16 μg/m3). Our hindcast modeling system allows for the construction of unbiased historical PM2.5 levels.

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.8b02917.

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    Cite this: Environ. Sci. Technol. 2018, 52, 22, 13260–13269
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