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

Abstract Using satellite-based aerosol optical depth (AOD) measurements and statistical models to estimate ground-level PM2.5 is a promising way to fill the areas that are not covered by ground PM2.5 monitors. The statistical models used in previous studies are primarily Linear Mixed Effects (LME) a...

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Autores principales:	Wenxi Yu, Yang Liu, Zongwei Ma, Jun Bi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/38379f76c318465b87bbed02f065fc88
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spelling	oai:doaj.org-article:38379f76c318465b87bbed02f065fc882021-12-02T11:40:21ZImproving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting10.1038/s41598-017-07478-02045-2322https://doaj.org/article/38379f76c318465b87bbed02f065fc882017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-07478-0https://doaj.org/toc/2045-2322Abstract Using satellite-based aerosol optical depth (AOD) measurements and statistical models to estimate ground-level PM2.5 is a promising way to fill the areas that are not covered by ground PM2.5 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 PM2.5 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 PM2.5 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 (R2 = 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 PM2.5 estimates.Wenxi YuYang LiuZongwei MaJun BiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-9 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Wenxi Yu Yang Liu Zongwei Ma Jun Bi Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting
description	Abstract Using satellite-based aerosol optical depth (AOD) measurements and statistical models to estimate ground-level PM2.5 is a promising way to fill the areas that are not covered by ground PM2.5 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 PM2.5 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 PM2.5 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 (R2 = 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 PM2.5 estimates.
format	article
author	Wenxi Yu Yang Liu Zongwei Ma Jun Bi
author_facet	Wenxi Yu Yang Liu Zongwei Ma Jun Bi
author_sort	Wenxi Yu
title	Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting
title_short	Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting
title_full	Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting
title_fullStr	Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting
title_full_unstemmed	Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting
title_sort	improving satellite-based pm2.5 estimates in china using gaussian processes modeling in a bayesian hierarchical setting
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/38379f76c318465b87bbed02f065fc88
work_keys_str_mv	AT wenxiyu improvingsatellitebasedpm25estimatesinchinausinggaussianprocessesmodelinginabayesianhierarchicalsetting AT yangliu improvingsatellitebasedpm25estimatesinchinausinggaussianprocessesmodelinginabayesianhierarchicalsetting AT zongweima improvingsatellitebasedpm25estimatesinchinausinggaussianprocessesmodelinginabayesianhierarchicalsetting AT junbi improvingsatellitebasedpm25estimatesinchinausinggaussianprocessesmodelinginabayesianhierarchicalsetting
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Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting

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