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Satellite-Based Spatiotemporal Trends in PM(2.5) Concentrations: China, 2004–2013

BACKGROUND: Three decades of rapid economic development is causing severe and widespread PM(2.5) (particulate matter ≤ 2.5 μm) pollution in China. However, research on the health impacts of PM(2.5) exposure has been hindered by limited historical PM(2.5) concentration data. OBJECTIVES: We estimated...

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Detalles Bibliográficos
Autores principales: Ma, Zongwei, Hu, Xuefei, Sayer, Andrew M., Levy, Robert, Zhang, Qiang, Xue, Yingang, Tong, Shilu, Bi, Jun, Huang, Lei, Liu, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Institute of Environmental Health Sciences 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4749081/
https://www.ncbi.nlm.nih.gov/pubmed/26220256
http://dx.doi.org/10.1289/ehp.1409481
Descripción
Sumario:BACKGROUND: Three decades of rapid economic development is causing severe and widespread PM(2.5) (particulate matter ≤ 2.5 μm) pollution in China. However, research on the health impacts of PM(2.5) exposure has been hindered by limited historical PM(2.5) concentration data. OBJECTIVES: We estimated ambient PM(2.5) 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 PM(2.5) 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 PM(2.5) concentrations from 2004 to early 2014 using ground observations. RESULTS: 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 PM(2.5) 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 PM(2.5) 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. CONCLUSIONS: Our satellite-driven model can provide reliable historical PM(2.5) estimates in China at a resolution comparable to those used in epidemiologic studies on the health effects of long-term PM(2.5) exposure in North America. This data source can potentially advance research on PM(2.5) health effects in China. CITATION: Ma Z, Hu X, Sayer AM, Levy R, Zhang Q, Xue Y, Tong S, Bi J, Huang L, Liu Y. 2016. Satellite-based spatiotemporal trends in PM(2.5) concentrations: China, 2004–2013. Environ Health Perspect 124:184–192; http://dx.doi.org/10.1289/ehp.1409481