Estimating Ground-Level PM2.5 in China Using Satellite Remote Sensing

Estimating ground-level PM2.5 from satellite-derived aerosol optical depth (AOD) using a spatial statistical model is a promising new method to evaluate the spatial and temporal characteristics of PM2.5 exposure in a large geographic region. However, studies outside North America have been limited d...

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Bibliographic Details
Published in:Environmental science & technology 2014-07, Vol.48 (13), p.7436-7444
Main Authors: Ma, Zongwei, Hu, Xuefei, Huang, Lei, Bi, Jun, Liu, Yang
Format: Article
Language:English
Subjects:
Aerosols - analysis
Air. Soil. Water. Waste. Feeding
Applied sciences
Atmospheric pollution
Biological and medical sciences
Calibration
China
Environment. Living conditions
Environmental Monitoring - methods
Exact sciences and technology
Geography
Medical sciences
Models, Statistical
Models, Theoretical
Optical Phenomena
Particulate Matter - analysis
Pollutants physicochemistry study: properties, effects, reactions, transport and distribution
Pollution
Public health. Hygiene
Public health. Hygiene-occupational medicine
Remote Sensing Technology - methods
Reproducibility of Results
Satellite Communications
Seasons
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Summary:Estimating ground-level PM2.5 from satellite-derived aerosol optical depth (AOD) using a spatial statistical model is a promising new method to evaluate the spatial and temporal characteristics of PM2.5 exposure in a large geographic region. However, studies outside North America have been limited due to the lack of ground PM2.5 measurements to calibrate the model. Taking advantage of the newly established national monitoring network, we developed a national-scale geographically weighted regression (GWR) model to estimate daily PM2.5 concentrations in China with fused satellite AOD as the primary predictor. The results showed that the meteorological and land use information can greatly improve model performance. The overall cross-validation (CV) R 2 is 0.64 and root mean squared prediction error (RMSE) is 32.98 μg/m3. The mean prediction error (MPE) of the predicted annual PM2.5 is 8.28 μg/m3. Our predicted annual PM2.5 concentrations indicated that over 96% of the Chinese population lives in areas that exceed the Chinese National Ambient Air Quality Standard (CNAAQS) Level 2 standard. Our results also confirmed satellite-derived AOD in conjunction with meteorological fields and land use information can be successfully applied to extend the ground PM2.5 monitoring network in China.
ISSN:0013-936X
1520-5851
DOI:10.1021/es5009399