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...
Main Authors: | Yikai Wang, Xuefei Hu, Howard H. Chang, Lance A. Waller, Jessica H. Belle, Yang Liu |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2018-09-01
|
Series: | International Journal of Environmental Research and Public Health |
Subjects: | |
Online Access: | http://www.mdpi.com/1660-4601/15/9/1999 |
Similar Items
-
Using linear mixed effect model to estimate ground-level PM2.5: case study for Tehran
by: S Sotoudeheian, et al.
Published: (2017-09-01) -
Spatial Correlation of Satellite-Derived PM2.5 with Hospital Admissions for Respiratory Diseases
by: Ching-Ju Liu, et al.
Published: (2016-11-01) -
Incorporation of Remote PM<sub>2.5</sub> Concentrations into the Downscaler Model for Spatially Fused Air Quality Surfaces
by: Brett Gantt, et al.
Published: (2020-01-01) -
AEROSOL OBSERVATIONS FROM SPACE, AIRCRAFT AND SURFACE ANALYZED WITH A GLOBAL MODEL
by: van Donkelaar, Aaron
Published: (2011) -
Comparison of Different Missing-Imputation Methods for MAIAC (Multiangle Implementation of Atmospheric Correction) AOD in Estimating Daily PM<sub>2.5 </sub>Levels
by: Zhao-Yue Chen, et al.
Published: (2020-09-01)