Satellite-based estimation of hourly PM2.5 levels during heavy winter pollution episodes in the Yangtze River Delta, China. (January 2020)

• Record Type: Journal Article
• Authors: She, Qiannan; Choi, Myungje; Belle, Jessica H.; Xiao, Qingyang; Bi, Jianzhao; Huang, Keyong; Meng, Xia; Geng, Guannan; Kim, Jhoon; He, Kebin; Liu, Min; Liu, Yang
• Is Part Of: Chemosphere. Volume 239(2020)
• Publication Date: 2020-01
• Subjects: Geostationary satellite -- GOCI -- PM2.5 -- Spatial and temporal statistical model -- Heavy air pollution episode -- Yangtze River Delta; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2019.124678
• Languages: English
• ISSNs: 0045-6535

Abstract

Abstract: In the developing countries such as China, most well-developed areas have suffered severe haze pollution, which was associated with increased premature morbidity and mortality and attracted widespread public concerns. Since ground-based PM2.5 monitoring has limited temporal and spatial coverage, satellite aerosol remote sensing data has been increasingly applied to map large-scale PM2.5 characteristics through advanced spatial statistical models. Although most existing research has taken advantage of the polar orbiting satellite instruments, a major limitation of the polar orbiting platform is its limited sampling frequency (e.g., 1–2 times/day), which is insufficient for capturing the PM2.5 variability during short but intense heavy haze episodes. As the first attempt, we quantitatively investigated the feasibility of using the aerosol optical depth (AOD) data retrieved by the Geostationary Ocean Color Imager (GOCI) to estimate hourly PM2.5 concentrations during winter haze episodes in the Yangtze River Delta (YRD). We developed a three-stage spatial statistical model, using GOCI AOD and fine mode fraction, as well as corresponding monitoring PM2.5 concentrations, meteorological and land use data on a 6-km modeling grid with complete coverage in time and space. The 10-fold cross-validation R 2 was 0.72 with a regression slope of 1.01 between observed and predicted hourly PM2.5 concentrations. After gap filling, the R 2 value for the three-stage model was 0.68. We further analyzed two representative large regional episodes, i.e., a "multi-process diffusion episode" during December 21–26, 2015 and a "Chinese New Year episode" during February 7–8, 2016. We concluded that AOD retrieved by geostationary satellites could serve as a new valuable data source for analyzing the heavy air pollution episodes. Graphical abstract: Image 1 Highlights: This is the first study using GOCI satellite to estimate heavy winter PM2.5 episode. The predicted hourly PM2.5 concentrations matched the trends seen in observations. Predicted PM2.5 trends reveals different mechanisms of heavy haze episodes. Geostationary satellite could serve as a new data source for heavy air pollution.