Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting

• Type of Material: Article
• Authors: Wenxi Yu, Nanjing University; Yang Liu, Emory University; Zongwei Ma, Nanjing University; Jun Bi, Nanjing University
• Language: English
• Date: 2017-08-01
• Publisher: Nature Publishing Group: Open Access Journals - Option C
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2017
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s41598-017-07478-0
• Title of Journal or Parent Work: Scientific Reports
• ISSN: 2045-2322
• Volume: 7
• Start Page: 7048
• End Page: 7048
• Grant/Funding Information: This work was supported by the National Natural Science Foundation of China (71433007, 91644220, and 41601546) and Jiangsu Natural Science Foundation of China (BK20160624).
• Keywords: MODIS; GROUND-LEVEL PM2.5; Science & Technology - Other Topics; Science & Technology; Atmospheric science; AEROSOL OPTICAL DEPTH; Environment sciences; Multidisciplinary Sciences; AOD; PARTICULATE AIR-POLLUTION; LAND; STATES; GEOGRAPHICALLY WEIGHTED REGRESSION
• Research Categories: Health Sciences, Public Health; Geography; Environmental Sciences

Abstract

Using satellite-based aerosol optical depth (AOD) measurements and statistical models to estimate ground-level PM 2.5 is a promising way to fill the areas that are not covered by ground PM 2.5 monitors. The statistical models used in previous studies are primarily Linear Mixed Effects (LME) and Geographically Weighted Regression (GWR) models. In this study, we developed a new regression model between PM 2.5 and AOD using Gaussian processes in a Bayesian hierarchical setting. Gaussian processes model the stochastic nature of the spatial random effects, where the mean surface and the covariance function is specified. The spatial stochastic process is incorporated under the Bayesian hierarchical framework to explain the variation of PM 2.5 concentrations together with other factors, such as AOD, spatial and non-spatial random effects. We evaluate the results of our model and compare them with those of other, conventional statistical models (GWR and LME) by within-sample model fitting and out-of-sample validation (cross validation, CV). The results show that our model possesses a CV result (R 2 = 0.81) that reflects higher accuracy than that of GWR and LME (0.74 and 0.48, respectively). Our results indicate that Gaussian process models have the potential to improve the accuracy of satellite-based PM 2.5 estimates.