Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China, 2004–2013
By Zongwei Ma, Xuefei Hu, A. Sayer, Robert C. Levy, Qiang Zhang, Yingang Xue, S. Tong, J. Bi, Lei Huang, Yang Liu. Environmental Health Perspectives, 2015
674 citations recorded by the source corpus.
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. 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 PM2.5 concentrations: China, 2004–2013. Environ Health Perspect 124:184–192; http://dx.doi.org/10.1289/ehp.1409481
Subfields: Environmental Engineering, Global and Planetary Change, Health, Toxicology and Mutagenesis
View Paper: https://www.semanticscholar.org/paper/d71b1209e1a907de6956ac216d28a2e2310a7546
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Citations in the corpus
- Severe haze in northern China: A synergy of anthropogenic emissions and atmospheric processes (2019, result): “1 in the main text) is consistent with a previous study retrieved by satellite aerosol optical depth (AOD) data, which could be attributed to the implement of the “Energy Conservation and Emissions Reduction” policy from 2006 (1).”
- Air quality improvements and health benefits from China’s clean air action since 2013 (2017, background): “Becauseof the ‘smoothingeffects’ of regression models [17, 29, 42], our optimal PM2.”
- Estimating Ground‐Level PM2.5 by Fusing Satellite and Station Observations: A Geo‐Intelligent Deep Learning Approach (2017, background): “However, it should be noted that the national-scale estimates of PM 2.5 in China (Fang et al., 2016; Lin et al., 2015; Ma et al., 2016; You et al., 2016) are mostly underestimated (slopes of 0.79 – 0.83) when ground-level PM 2.5 is greater than 60 μ g/m 3 .”
- Point-surface fusion of station measurements and satellite observations for mapping PM 2.5 distribution in China: Methods and assessment (2016, background): “To date, many researchers have made attempts to study the AOD-PM relationship in China (Guo et al., 2009; Li et al., 2011; Li et al., 2005; Lin et al., 2015; Ma et al., 2014; Ma et al., 2016; Song et al., 2014; Wang et al., 2010; Wu et al., 2012).”