{"id":332,"date":"2019-08-29T18:45:25","date_gmt":"2019-08-29T23:45:25","guid":{"rendered":"https:\/\/scholarblogs.emory.edu\/remote-sensing-group\/?p=332"},"modified":"2022-01-13T08:55:51","modified_gmt":"2022-01-13T13:55:51","slug":"332","status":"publish","type":"post","link":"https:\/\/scholarblogs.emory.edu\/remote-sensing-group\/2019\/08\/29\/332\/","title":{"rendered":"Murray et al.: new method on PM2.5 Bayesian Ensemble Models"},"content":{"rendered":"\n

We develop a method to combine PM2.5 estimated from satellite-retrieved aerosol optical depth (AOD) and chemical transport model (CTM) simulations using statistical models. While most previous methods utilize AOD or CTM separately, we aim to leverage advantages offered by both data sources in terms of resolution and coverage using Bayesian ensemble averaging. Our approach differs from previous ensemble approaches in its ability to not only incorporate uncertainties in PM2.5 estimates from individual models but also to provide uncertainties for the resulting ensemble estimates. In an application of estimating daily PM2.5 in the Southeastern US, the ensemble approach outperforms previously developed spatial-temporal statistical models that use either AOD or bias-corrected CTM simulations in cross-validation (CV) analyses.<\/span><\/p>\n

Publication Link<\/a><\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

We develop a method to combine PM2.5 estimated from satellite-retrieved aerosol optical depth (AOD) and chemical transport model (CTM) simulations using statistical models. While most previous methods utilize AOD or CTM separately, we aim to leverage advantages offered by both data sources in terms of resolution and coverage using Bayesian ensemble averaging. Our approach differs […]<\/p>\n","protected":false},"author":5080,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"ngg_post_thumbnail":0,"footnotes":""},"categories":[7,5],"tags":[],"class_list":["post-332","post","type-post","status-publish","format-standard","hentry","category-air-pollution-exposure","category-papers"],"yoast_head":"\nMurray et al.: new method on PM2.5 Bayesian Ensemble Models - Emory Environmental Remote Sensing Group<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/scholarblogs.emory.edu\/remote-sensing-group\/2019\/08\/29\/332\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Murray et al.: new method on PM2.5 Bayesian Ensemble Models - Emory Environmental Remote Sensing Group\" \/>\n<meta property=\"og:description\" content=\"We develop a method to combine PM2.5 estimated from satellite-retrieved aerosol optical depth (AOD) and chemical transport model (CTM) simulations using statistical models. While most previous methods utilize AOD or CTM separately, we aim to leverage advantages offered by both data sources in terms of resolution and coverage using Bayesian ensemble averaging. 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He is a member of the NASA MAIA and Terra MISR science team, the NASA Aura science team, a PI member of the NASA Air Quality Applied Science Team (AQAST) and the following Health and Air Quality Applied Science Team (HAQAST). 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