Satellite-Based Spatiotemporal Trends in PM _2.5 Concentrations: China, 2004–2013

Abstract

BackgroundThree 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.ObjectivesWe 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.MethodsWe 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.ResultsThe 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.ConclusionsOur 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.CitationMa 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

Estimation of Satellite-Based SO42− and NH4+ Composition of Ambient Fine Particulate Matter over China Using Chemical Transport Model

“…Regarding the satellite-derived sulfate results (Figure 4a), the highest values over China were located in the SCB, followed by those in the NCP and the PRD, which were similar not only to the overlaid ground measurements but also to regional variations in satellite-observed tropospheric SO 2 (SO 2 vertical column density, VCD_SO 2 ) obtained using OMI from 2005 to 2014 [43] and using the 2004-2009 SCIAMACHY [44]. As a major contributor to these results, the geographical patterns of sulfate concentrations were in agreement with the total PM 2.5 estimations conducted by Ma et al [27]. Compared with the spatial distribution at the coarse resolution, Figure 4c,e,g show finer regional patterns and similar variations in the corresponding region in Figure 4a.…”

“…Given that the existing advanced statistical methods are mature, the method put forward by Ma et al [27] was employed in our work. Ma [33].…”

Two decades of satellite observations of AOD over mainland China using ATSR-2, AATSR and MODIS/Terra: data set evaluation and large-scale patterns

“…Many studies have been published on aerosols in relation to air quality in the eastern part of China, including satellite remote sensing, ground-based measurements, modeling and combinations thereof, which often focus on local or regional aspects (e.g., Song et al, 2009;Ma et al, 2016;Zou et al, 2017;Xue et al, 2017;Miao et al, 2017;Guo et al, 2017). Satellites offer the opportunity to obtain information, using the same instruments and methods, over a large area during a longer period of time.…”

“…Xu et al, 2015;Ma et al, 2016; but also using multiple satellite data (Lin et al, 2010;Guo et al, 2011Guo et al, , 2016bDong et al, 2017;Zhao et al, 2017;Zhang et al, 2017). However, these studies used MODIS C5.1 AOD data and substantial differences exist between C6 and C5.1 (e.g., Levy et al, 2013;Sayer et al, 2014;Tao et al, 2015;Xiao et al, 2016).…”

Improving Spatial Coverage for Aqua MODIS AOD using NDVI-Based Multi-Temporal Regression Analysis

“…Satellite-derived AOD fills this vacancy with spatially continuous observations, providing better spatial and temporal coverage for regional assessments on a large scale [9]. AOD products from the Moderate Resolution Imaging Spectroradiometer (MODIS) have been widely used in aerosol-related studies, such as regional aerosol optical properties [10] and public environmental health [11], because of their assured quality [12][13][14]. Along with the MODIS Collection 6 (C6) satellite product released in 2014, the second generation of the Deep Blue AOD (AOD DB) retrieved algorithm, which is referred to as enhanced DB, was expanded to cover vegetated land surfaces, urban areas, and brighter deserts by dynamic surface reflectance determination [15,16].…”