Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China, 2004-2013

• Published in: Environmental health perspectives Vol. 124; no. 2; p. 184
• Main Authors: Ma, Zongwei, Hu, Xuefei, Sayer, Andrew M, Levy, Robert, Zhang, Qiang, Xue, Yingang, Tong, Shilu, Bi, Jun, Huang, Lei, Liu, Yang
• Format: Journal Article
• Language: English
• Published: United States National Institute of Environmental Health Sciences 01-02-2016
• Subjects: Aerosols; Air Pollutants - analysis; Algorithms; China; Economic development; Environmental monitoring; Environmental Monitoring - instrumentation; Estimates; Land use; Models, Statistical; Optical analysis; Particle Size; Particulate matter; Particulate Matter - analysis; Quality; Satellite Imagery; Satellites; Seasonal variations; Seasons; Spacecraft; Statistical models; Studies; Time series; Trends; United States > US; North America; China; China, People's Rep
• ISSN: 0091-6765; 1552-9924
• DOI: 10.1289/ehp.1409481

Three decades of rapid economic development is causing severe and widespread PM2.5 (particulate matter ≤ 2.5 μm) pollution in China. However, research on the health impacts of PM2.5 exposure has been hindered by limited historical PM2.5 concentration data.
We estimated ambient PM2.5 concentrations from 2004 to 2013 in China at 0.1° resolution using the most recent satellite data and evaluated model performance with available ground observations.
We developed a two-stage spatial statistical model using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) and assimilated meteorology, land use data, and PM2.5 concentrations from China's recently established ground monitoring network. An inverse variance weighting (IVW) approach was developed to combine MODIS Dark Target and Deep Blue AOD to optimize data coverage. We evaluated model-predicted PM2.5 concentrations from 2004 to early 2014 using ground observations.
The overall model cross-validation R(2) and relative prediction error were 0.79 and 35.6%, respectively. Validation beyond the model year (2013) indicated that it accurately predicted PM2.5 concentrations with little bias at the monthly (R(2) = 0.73, regression slope = 0.91) and seasonal (R(2) = 0.79, regression slope = 0.92) levels. Seasonal variations revealed that winter was the most polluted season and that summer was the cleanest season. Analysis of predicted PM2.5 levels showed a mean annual increase of 1.97 μg/m(3) between 2004 and 2007 and a decrease of 0.46 μg/m(3) between 2008 and 2013.
Our satellite-driven model can provide reliable historical PM2.5 estimates in China at a resolution comparable to those used in epidemiologic studies on the health effects of long-term PM2.5 exposure in North America. This data source can potentially advance research on PM2.5 health effects in China.