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

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https://ehp.niehs.nih.gov/doi/10.1289/ehp.1409481

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https://doi.org/10.1289/ehp.1409481
 http://hdl.handle.net/11603/33396

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UMBC GESTAR II

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https://orcid.org/0000-0001-9149-1789

Date

2015-07-24

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journal articles
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Citation of Original Publication

Ma, Zongwei, Xuefei Hu, Andrew M. Sayer, Robert Levy, Qiang Zhang, Yingang Xue, Shilu Tong, Jun Bi, Lei Huang, and Yang Liu. “Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China, 2004–2013.” Environmental Health Perspectives 124, no. 2 (February 2016): 184–92. https://doi.org/10.1289/ehp.1409481.

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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Abstract

Background 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. Objectives 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. Methods 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. Results The overall model cross-validation R² 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 (R² = 0.73, regression slope = 0.91) and seasonal (R² = 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/m³ between 2004 and 2007 and a decrease of 0.46 μg/m³ between 2008 and 2013. Conclusions 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.