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

Junbo Zhang, Daoji Li, Yingzhi Xia and Qifeng Liao
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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

Mathematics, 2022, vol. 10, issue 16, 1-13

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.

Keywords: Bayesian retrieval algorithm; PM 2.5; hierarchical Gaussian process model; MAIAC (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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