Next Article in Journal
Wearable Monitoring Devices for Biomechanical Risk Assessment at Work: Current Status and Future Challenges—A Systematic Review
Next Article in Special Issue
Elevated Black Carbon Concentrations and Atmospheric Pollution around Singrauli Coal-Fired Thermal Power Plants (India) Using Ground and Satellite Data
Previous Article in Journal
Determinants of Health Care-Seeking Delay among Tuberculosis Patients in Rural Area of Central China
Previous Article in Special Issue
The Spatial-Temporal Characteristics and Influential Factors of NOx Emissions in China: A Spatial Econometric Analysis
Article

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

1
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
2
Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2018, 15(9), 1999; https://doi.org/10.3390/ijerph15091999
Received: 3 August 2018 / Revised: 8 September 2018 / Accepted: 10 September 2018 / Published: 13 September 2018
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. View Full-Text
Keywords: PM2.5; Bayesian downscaler; exposure modeling; aerosol optical depth; MODIS PM2.5; Bayesian downscaler; exposure modeling; aerosol optical depth; MODIS
Show Figures

Figure 1

MDPI and ACS Style

Wang, Y.; Hu, X.; Chang, H.H.; Waller, L.A.; Belle, J.H.; Liu, Y. A Bayesian Downscaler Model to Estimate Daily PM2.5 Levels in the Conterminous US. Int. J. Environ. Res. Public Health 2018, 15, 1999. https://doi.org/10.3390/ijerph15091999

AMA Style

Wang Y, Hu X, Chang HH, Waller LA, Belle JH, Liu Y. A Bayesian Downscaler Model to Estimate Daily PM2.5 Levels in the Conterminous US. International Journal of Environmental Research and Public Health. 2018; 15(9):1999. https://doi.org/10.3390/ijerph15091999

Chicago/Turabian Style

Wang, Yikai, Xuefei Hu, Howard H. Chang, Lance A. Waller, Jessica H. Belle, and Yang Liu. 2018. "A Bayesian Downscaler Model to Estimate Daily PM2.5 Levels in the Conterminous US" International Journal of Environmental Research and Public Health 15, no. 9: 1999. https://doi.org/10.3390/ijerph15091999

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop