A Bayesian Downscaler Model to Estimate Daily PM2.5 Levels in the Conterminous US
There has been growing interest in extending the coverage of ground particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5) monitoring networks based on satellite remote sensing data. With broad spatial and temporal coverage, a satellite-based monitoring network has a strong pote...
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doaj-28ac320e823949c4b6f859506b22764f2020-11-25T00:15:24ZengMDPI AGInternational Journal of Environmental Research and Public Health1660-46012018-09-01159199910.3390/ijerph15091999ijerph15091999A Bayesian Downscaler Model to Estimate Daily PM2.5 Levels in the Conterminous USYikai Wang0Xuefei Hu1Howard H. Chang2Lance A. Waller3Jessica H. Belle4Yang Liu5Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USADepartment of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USADepartment of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USADepartment of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USADepartment of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USADepartment of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USAThere has been growing interest in extending the coverage of ground particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5) monitoring networks based on satellite remote sensing data. With broad spatial and temporal coverage, a satellite-based monitoring network has a strong potential to complement the ground monitor system in terms of the spatiotemporal availability of the air quality data. However, most existing calibration models focus on a relatively small spatial domain and cannot be generalized to a national study. In this paper, we proposed a statistically reliable and interpretable national modeling framework based on Bayesian downscaling methods to be applied to the calibration of the daily ground PM2.5 concentrations across the conterminous United States using satellite-retrieved aerosol optical depth (AOD) and other ancillary predictors in 2011. Our approach flexibly models the PM2.5 versus AOD and the potential related geographical factors varying across the climate regions and yields spatial- and temporal-specific parameters to enhance model interpretability. Moreover, our model accurately predicted the national PM2.5 with an R2 at 70% and generated reliable annual and seasonal PM2.5 concentration maps with its SD. Overall, this modeling framework can be applied to national-scale PM2.5 exposure assessments and can also quantify the prediction errors.http://www.mdpi.com/1660-4601/15/9/1999PM2.5Bayesian downscalerexposure modelingaerosol optical depthMODIS |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yikai Wang Xuefei Hu Howard H. Chang Lance A. Waller Jessica H. Belle Yang Liu |
spellingShingle |
Yikai Wang Xuefei Hu Howard H. Chang Lance A. Waller Jessica H. Belle Yang Liu A Bayesian Downscaler Model to Estimate Daily PM2.5 Levels in the Conterminous US International Journal of Environmental Research and Public Health PM2.5 Bayesian downscaler exposure modeling aerosol optical depth MODIS |
author_facet |
Yikai Wang Xuefei Hu Howard H. Chang Lance A. Waller Jessica H. Belle Yang Liu |
author_sort |
Yikai Wang |
title |
A Bayesian Downscaler Model to Estimate Daily PM2.5 Levels in the Conterminous US |
title_short |
A Bayesian Downscaler Model to Estimate Daily PM2.5 Levels in the Conterminous US |
title_full |
A Bayesian Downscaler Model to Estimate Daily PM2.5 Levels in the Conterminous US |
title_fullStr |
A Bayesian Downscaler Model to Estimate Daily PM2.5 Levels in the Conterminous US |
title_full_unstemmed |
A Bayesian Downscaler Model to Estimate Daily PM2.5 Levels in the Conterminous US |
title_sort |
bayesian downscaler model to estimate daily pm2.5 levels in the conterminous us |
publisher |
MDPI AG |
series |
International Journal of Environmental Research and Public Health |
issn |
1660-4601 |
publishDate |
2018-09-01 |
description |
There has been growing interest in extending the coverage of ground particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5) monitoring networks based on satellite remote sensing data. With broad spatial and temporal coverage, a satellite-based monitoring network has a strong potential to complement the ground monitor system in terms of the spatiotemporal availability of the air quality data. However, most existing calibration models focus on a relatively small spatial domain and cannot be generalized to a national study. In this paper, we proposed a statistically reliable and interpretable national modeling framework based on Bayesian downscaling methods to be applied to the calibration of the daily ground PM2.5 concentrations across the conterminous United States using satellite-retrieved aerosol optical depth (AOD) and other ancillary predictors in 2011. Our approach flexibly models the PM2.5 versus AOD and the potential related geographical factors varying across the climate regions and yields spatial- and temporal-specific parameters to enhance model interpretability. Moreover, our model accurately predicted the national PM2.5 with an R2 at 70% and generated reliable annual and seasonal PM2.5 concentration maps with its SD. Overall, this modeling framework can be applied to national-scale PM2.5 exposure assessments and can also quantify the prediction errors. |
topic |
PM2.5 Bayesian downscaler exposure modeling aerosol optical depth MODIS |
url |
http://www.mdpi.com/1660-4601/15/9/1999 |
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