title: Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China, 2004-2013.
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.
  
  
  OBJECTIVE
  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.
authors.affiliation: State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu, China.
authors.forename: Zongwei
authors.initials: Z
authors.lastname: Ma
authors.forename: Xuefei
authors.initials: X
authors.lastname: Hu
authors.forename: Andrew M
authors.initials: AM
authors.lastname: Sayer
authors.forename: Robert
authors.initials: R
authors.lastname: Levy
authors.forename: Qiang
authors.initials: Q
authors.lastname: Zhang
authors.forename: Yingang
authors.initials: Y
authors.lastname: Xue
authors.forename: Shilu
authors.initials: S
authors.lastname: Tong
authors.forename: Jun
authors.initials: J
authors.lastname: Bi
authors.forename: Lei
authors.initials: L
authors.lastname: Huang
authors.forename: Yang
authors.initials: Y
authors.lastname: Liu
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D002681:China; D004784:Environmental Monitoring / Q000295:instrumentation*; D015233:Models, Statistical; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis*; D063809:Satellite Imagery; D012621:Seasons; D018531:Spacecraft
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter