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 R2 was 0.72 with a regression slope of 1.01 between observed and predicted hourly PM2.5 concentrations. After gap filling, the R2 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.
Bibliographical noteFunding Information:
The work of Y. Liu and Q. She was partially supported by the NASA Applied Sciences Program (grant number NNX16AQ28G , PI: Liu). The work of Q. She at Emory University as a vising scholar was also supported by the China Scholarship Council (CSC) under the State Scholarship Fund. The work of M. Liu was supported by the National Key Research and Development Program of China ( 2016YFC0208700 , 2016YFC0500204 ) and the Natural Science Foundation of Shanghai (grant number 17ZR1408700 ). The work of M. Choi and J. Kim was supported by the National Strategic Project-Fine particle of the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT , the Ministry of Environment , and the Ministry of Health and Welfare (grant number NRF-2017M3D8A1092021 ) in Korea. The authors thank Changjiang Gou of ENS de Lyon in France for technical support on ECMWF data. We are also very grateful for the excellent reviewers in providing invaluable suggestions and comments, which helped us to improve this manuscript.
© 2019 Elsevier Ltd
All Science Journal Classification (ASJC) codes
- Environmental Engineering
- Environmental Chemistry
- Health, Toxicology and Mutagenesis