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. 2016 Feb;124(2):184-92.
doi: 10.1289/ehp.1409481. Epub 2015 Jul 24.

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

Zongwei Ma  1 , Xuefei Hu, Andrew M Sayer, Robert Levy, Qiang Zhang, Yingang Xue, Shilu Tong, Jun Bi, Lei Huang, Yang Liu
Affiliations

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

Zongwei Ma et al. Environ Health Perspect. 2016 Feb.

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 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.

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.

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Conflict of interest statement

The contents of this publication are solely the responsibility of the grantee and do not necessarily represent the official views of the U.S. EPA. Further, the U.S. EPA does not endorse the purchase of any commercial products or services mentioned in the publication.

The authors declare they have no actual or potential competing financial interests.

Figures

Figure 1
Figure 1
Spatial distribution of ground PM2.5 monitoring sites. Open circles denote the sites with data available only from January to June 2014. Solid circles denote the sites with data available for not only 2014 but also for 2013 or earlier years. Note that many clustered sites are overlapped because of their proximity. The spatial resolution of the background gridded population is 0.1° × 0.1°.
Figure 2
Figure 2
Spatial distribution of annual mean available days for MODIS’s operational combined AOD (A), our IVW–combined AOD (B), and percentage improvement of data coverage (C).
Figure 3
Figure 3
Workflow for estimating spatiotemporal PM2.5 concentrations.
Figure 4
Figure 4
Density scatterplots of model fitting and cross-validation (CV) at the daily level (n = 63,031). (A) and (B) are model-fitting results for the first-stage linear mixed effects (LME) model and the full LME + generalized additive model (GAM) model, respectively. (C) and (D) are model CV results for the first-stage LME model and the full LME + GAM model, respectively. Abbreviations: MPE, mean prediction error (μg/m3); RMSE, root mean squared prediction error (μg/m3); RPE, relative prediction error (%). The dashed line is the 1:1 line.
Figure 5
Figure 5
Evaluation of historical PM2.5 estimations (2004–2012 and January–June 2014) at daily (A), monthly (B), and seasonal (C) levels. Because there were few ground PM2.5 data for mainland China before 2013, we also estimated PM2.5 for the first half of 2014 using the 2013 model and compared the results with the ground measurements to validate the accuracy of the historical estimations.
Figure 6
Figure 6
Spatial distributions of 10-year (2004–2013) mean PM2.5 estimations for all of China (A), the Beijing-Tianjin metropolitan region (B), the Yangtze River delta (C), the Pearl River delta (D), and the Sichuan Basin (E).
Figure 7
Figure 7
Time series of monthly, satellite-derived PM2.5 anomaly (μg/m3) for all of China (A), the Beijing-Tianjin metropolitan region (B), the Yangtze River delta (C), and the Pearl River delta (D); and spatial distribution of PM2.5 trends for 2004–2007 (E) and 2008–2013 (F). The white areas in (E) and (F) indicate missing data. The black lines in A–D denote the PM2.5 trends for 2004–2013, the red lines represent the trends for 2004–2007, and the blue lines represent the trends for 2008–2013. The PM2.5 trends (μg/m3 per year), 95% confidence intervals (CIs) in parentheses (μg/m3 per year), and significance levels (*p < 0.05; **p < 0.01; ***p < 0.005) are also shown in A–D.
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