气候变化领域集成服务门户(CCPortal): Satellite-based spatiotemporal trends of ambient PM2.5 concentrations and influential factors in Hubei; Central China

CCPortal

DOI	10.1016/j.atmosres.2020.104929
	Satellite-based spatiotemporal trends of ambient PM2.5 concentrations and influential factors in Hubei; Central China
	Huang Y.; Ji Y.; Zhu Z.; Zhang T.; Gong W.; Xia X.; Sun H.; Zhong X.; Zhou X.; Chen D.
发表日期	2020
ISSN	0169-8095
卷号	241
英文摘要	Accurate estimations of the concentration of ambient fine-particle matter with aerodynamic diameters of less than 2.5 μm (PM2.5) are necessary for human health studies. In this study, individual city-scale linear mixed effect models (LME) were employed to accurately estimate ground PM2.5 concentrations considering the spatiotemporal variability of the relationship between PM2.5 and atmospheric, meteorological, and land observations. The contributions of diverse influential factors including aerosol optical depth, planetary boundary layer height, relative humidity, vegetation index, and wind on local PM2.5 pollution were also determined. High correlation coefficient (R2 = 0.89) and low root mean square error (RMSE = 13.1 μg/m3) ensured satisfactory LME model performances in estimating ground-level PM2.5 concentrations. Spatiotemporal analyses of satellite-based PM2.5 showed high concentrations in eastern, southern, and northern Hubei, and low concentrations in the northwest and southeast because of unbalanced development. These analyses also displayed a mitigation trend of PM2.5 concentrations with a mean annual decline rate of 3–12% from 2016 to 2018. Moreover, from the statistical results of the model, the influential factor of aerosol optical depth was positively correlated with PM2.5 concentration, while planetary boundary layer height, relative humidity, and the normalized difference vegetation index were negatively correlated to local PM2.5 pollution. However, the winds had contradictory contributions on PM2.5 pollution; the northerly wind in western Hubei and the southerly and northeasterly winds in eastern Hubei alleviated local PM2.5 pollution, while the westerly wind in eastern Hubei facilitated PM2.5 diffusion between cities and aggravated PM2.5 pollution. The analysis of the spatiotemporal trend of local PM2.5 pollution at a city scale and the identification of the influence of wind on PM2.5 pollution provide a theoretical reference for regional pollution warnings and controls. © 2020
英文关键词	Central China; Haze pollution; Influential factors; Linear mixed effect model; Wind
语种	英语
scopus关键词	Aerosols; Atmospheric humidity; Atmospheric thermodynamics; Boundary layer flow; Boundary layers; Mean square error; Optical properties; Vegetation; Wind; Central chinas; Correlation coefficient; Haze pollutions; Influential factors; Mixed-effect models; Normalized difference vegetation index; Planetary boundary layers; Spatiotemporal variability; Air pollution; ambient air; atmospheric modeling; atmospheric pollution; concentration (composition); haze; particulate matter; satellite data; spatiotemporal analysis; trend analysis; variance analysis; wind; China; Hubei
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/141918
作者单位	State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan, 430079, China; Wuchang Shouyi University, Wuhan, 430064, China; Collaborative Innovation Center for Geospatial Technology, Wuhan, 430079, China; Institute for Environmental and Climate Research, Jinan University, Guangzhou, China
推荐引用方式 GB/T 7714	Huang Y.,Ji Y.,Zhu Z.,et al. Satellite-based spatiotemporal trends of ambient PM2.5 concentrations and influential factors in Hubei; Central China[J],2020,241.
APA	Huang Y..,Ji Y..,Zhu Z..,Zhang T..,Gong W..,...&Chen D..(2020).Satellite-based spatiotemporal trends of ambient PM2.5 concentrations and influential factors in Hubei; Central China.Atmospheric Research,241.
MLA	Huang Y.,et al."Satellite-based spatiotemporal trends of ambient PM2.5 concentrations and influential factors in Hubei; Central China".Atmospheric Research 241(2020).