A Bayesian Downscaler Model to Estimate Daily PM2.5 Levels in the Conterminous US

• Type of Material: Article
• Authors: Yikai Wang, Emory University; Xuefei Hu, Emory University; Howard Chang, Emory University; Lance Waller, Emory University; Jessica H. Belle, Emory University; Yang Liu, Emory University
• Language: English
• Date: 2018-09-01
• Publisher: MDPI
• Publication Version: Final Published Version
• Copyright Statement: © 2018, MDPI AG. All rights reserved.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3390/ijerph15091999
• Title of Journal or Parent Work: International Journal of Environmental Research and Public Health
• ISSN: 1661-7827
• Volume: 15
• Issue: 9
• Grant/Funding Information: This publication was developed under Assistance Agreement No. 83586901 awarded by the US Environmental Protection Agency to Emory University (PI: Liu). It has not been formally reviewed by the EPA.; This work was partially supported by the NASA Applied Sciences Program (grant no. NNX16AQ28G; PI: Liu).
• Keywords: REGRESSION; Public, Environmental & Occupational Health; Environmental Sciences & Ecology; Science & Technology; 5; AEROSOL OPTICAL DEPTH; Environmental Sciences; exposure modeling; PM2; Bayesian downscaler; aerosol optical depth; Life Sciences & Biomedicine; MODIS
• Research Categories: Biology, Biostatistics; Health Sciences, Public Health; Environmental Sciences

Abstract

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 PM2.5concentrations across the conterminous United States using satellite-retrieved aerosol optical depth (AOD) and other ancillary predictors in 2011. Our approach flexibly models the PM2.5versus 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 PM2.5with an R2at 70% and generated reliable annual and seasonal PM2.5concentration maps with its SD. Overall, this modeling framework can be applied to national-scale PM2.5exposure assessments and can also quantify the prediction errors.