气候变化领域集成服务门户(CCPortal): Estimating PM2.5 concentrations in Northeastern China with full spatiotemporal coverage, 2005

CCPortal

DOI	10.1016/j.rse.2020.112203
	Estimating PM2.5 concentrations in Northeastern China with full spatiotemporal coverage, 2005–2016
	Meng X.; Liu C.; Zhang L.; Wang W.; Stowell J.; Kan H.; Liu Y.
发表日期	2021
ISSN	00344257
卷号	253
英文摘要	Predicting long-term spatiotemporal characteristics of fine particulate matter (PM2.5) is important in China to understand historical levels of PM2.5, to support health effects research of both long-term and short-term exposures to PM2.5, and to evaluate the efficacy of air pollution control policies. Satellite-retrieved aerosol optical depth (AOD) provides a unique opportunity to characterize the long-term trends of ground-level PM2.5 at high spatial resolution. However, the missing rate of AOD in Northeastern China (NEC) is very high, especially in winter, and challenges the accuracy of long-term predictions of PM2.5 if left unresolved. Using random forest algorithms, this study developed a gap-filling approach combing satellite AOD, meteorological data, land use parameters, population and visibility in the NEC during 2005–2016. The model, including all predictors, combined with a model without AOD was able to fill the gap of PM2.5 predictions caused by missing AOD at 1-km resolution. The R2 (RMSE) of the full-coverage predictions was 0.81 (18.5 μg/m3) at the daily level. Gap-filled PM2.5 predictions on days with missing AOD reduced the relative prediction error from 28% to 2.5% in winter. The leave-one-year-out-cross-validation R2 (RMSE) of the full-coverage predictions was 0.65 (16.3 μg/m3) at the monthly level, indicating relatively high accuracy of predicted historical PM2.5 concentrations. Our results suggested that AOD helped increase the reliability of historical PM2.5 prediction when ground PM2.5 measurements were unavailable, even though predictions from the AOD model only accounted for approximate 37% of the whole dataset. Predicted PM2.5 level in NEC have increased since 2005, reached its peak during 2013–2015, then saw a major decline in 2016. Our high-resolution predictions also showed a south to north gradient and many pollution hot spots in the city clusters surrounding provincial capitals, as well as within large cities. Overall, by combining predictions from the AOD model with higher accuracy and predictions from the non-AOD model to achieve full coverage, our modeling approach could produce long-term, full-coverage historical PM2.5 levels in high-latitude areas in China, despite the widespread and persistent AOD missingness. © 2020 Elsevier Inc.
英文关键词	AOD; Gap-filling; Northeastern China; PM2.5; Random forest
语种	英语
scopus关键词	Air pollution control; Decision trees; Land use; Meteorology; Population statistics; Aerosol optical depths; Fine particulate matter (PM2.5); High spatial resolution; High-resolution prediction; Long-term prediction; Random forest algorithm; Short-term exposure; Spatiotemporal characteristics; Forecasting; accuracy assessment; aerosol; atmospheric pollution; land use change; optical depth; particulate matter; pollution control; prediction; satellite altimetry; China
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179022
作者单位	School of Public Health, Fudan University, Shanghai, China; Children's Hospital of Fudan University, National Center for Children's Health, Shanghai, 201102, China; Boston University, School of Public Health, Boston, MA, United States; Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States
推荐引用方式 GB/T 7714	Meng X.,Liu C.,Zhang L.,等. Estimating PM2.5 concentrations in Northeastern China with full spatiotemporal coverage, 2005–2016[J],2021,253.
APA	Meng X..,Liu C..,Zhang L..,Wang W..,Stowell J..,...&Liu Y..(2021).Estimating PM2.5 concentrations in Northeastern China with full spatiotemporal coverage, 2005–2016.Remote Sensing of Environment,253.
MLA	Meng X.,et al."Estimating PM2.5 concentrations in Northeastern China with full spatiotemporal coverage, 2005–2016".Remote Sensing of Environment 253(2021).