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Bayesian Aerosol Retrieval-Based PM 2.5 Estimation through Hierarchical Gaussian Process Models

Author

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  • Junbo Zhang

    (School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China)

  • Daoji Li

    (Department of Information Systems and Decision Sciences, California State University, Fullerton, CA 92831, USA)

  • Yingzhi Xia

    (School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China)

  • Qifeng Liao

    (School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China)

Abstract

Satellite-based aerosol optical depth (AOD) data are widely used to estimate land surface PM 2.5 concentrations in areas not covered by ground PM 2.5 monitoring stations. However, AOD data obtained from satellites are typically at coarse spatial resolutions, limiting their applications on small or medium scales. In this paper, we propose a new two-step approach to estimate 1-km-resolution PM 2.5 concentrations in Shanghai using high spatial resolution AOD retrievals from MODIS. In the first step, AOD data are refined to a 1 × 1 km 2 resolution via a Bayesian AOD retrieval method. In the second step, a hierarchical Gaussian process model is used to estimate PM 2.5 concentrations. We evaluate our approach by model fitting and out-of-sample cross-validation. Our results show that the proposed approach enjoys accurate predictive performance in estimating PM 2.5 concentrations.

Suggested Citation

  • Junbo Zhang & Daoji Li & Yingzhi Xia & Qifeng Liao, 2022. "Bayesian Aerosol Retrieval-Based PM 2.5 Estimation through Hierarchical Gaussian Process Models," Mathematics, MDPI, vol. 10(16), pages 1-13, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2878-:d:886040
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    References listed on IDEAS

    as
    1. Finley, Andrew O. & Banerjee, Sudipto & Carlin, Bradley P., 2007. "spBayes: An R Package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 19(i04).
    2. Yueqing Wang & Xin Jiang & Bin Yu & Ming Jiang, 2013. "A Hierarchical Bayesian Approach for Aerosol Retrieval Using MISR Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 483-493, June.
    3. Finley, Andrew O. & Banerjee, Sudipto & Gelfand, Alan E., 2015. "spBayes for Large Univariate and Multivariate Point-Referenced Spatio-Temporal Data Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i13).
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