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DOI10.1016/j.atmosres.2019.04.023
The characteristics of hourly wind field and its impacts on air quality in the Pearl River Delta region during 2013–2017
Xie J.; Liao Z.; Fang X.; Xu X.; Wang Y.; Zhang Y.; Liu J.; Fan S.; Wang B.
发表日期2019
ISSN0169-8095
起始页码112
结束页码124
卷号227
英文摘要The behavior of wind with respect to local features, especially over complex heterogeneous underlying surfaces, is of particular interest. Studies on objective wind field classification on the local scale are limited. To better understand wind behavior and its impact on air quality in the Pearl River Delta (PRD)region, an objective clustering technique is applied to five-year (2013–2017)23-station observational hourly wind data, and the effect of modulation of the clustered local wind fields on regional air quality is explored using simultaneous 55-site pollutant (PM2.5, PM10, NO2, and O3)concentrations. The clustering results capture the features of five local wind field types (Type_N, Type_NE, Type_S, Type_SE, and Type_Calm)driven by synoptic/local circulation in the PRD region and their spatiotemporal evolution. Excellent synoptic interpretations of each wind field type are given. The results confirm the applicability of the clustering technique in objective classification of wind fields in the PRD region. The PM2.5, PM10, and NO2 concentrations show characteristics of wind-dependent spatial distributions in which pollutant transport within the PRD city cluster is significant. The quantified concentration difference (ΔC)between cities with higher and lower pollutant concentrations within the PRD region is also presented. The spatial distribution of the O3 concentration is quite different from those of PM2.5, PM10, and NO2, because central cities in the PRD region have obviously lower O3 concentrations than the surrounding cities except under Type_S wind fields. The emission source intensity is reflected in the spatial distribution of air pollutant concentrations under Type_Calm wind fields. This work enables further analysis of the formation process and mechanism of air pollution, and also provides important background information for air pollution prediction. © 2019 Elsevier B.V.
英文关键词Air quality; Cluster analysis; Objective surface wind classification; Pearl River Delta region; Spatial distribution of air pollutant concentration; Synoptic interpretation
语种英语
scopus关键词Air quality; Cluster analysis; Clustering algorithms; Gems; Nitrogen oxides; Quality control; Rivers; Spatial distribution; Air pollutant concentrations; Air pollution predictions; Background information; Pearl River Delta region; Pollutant concentration; Spatiotemporal evolution; Surface winds; Synoptic interpretation; River pollution; air quality; atmospheric pollution; classification; cluster analysis; concentration (composition); spatial distribution; wind field; China; Guangdong; Zhujiang Delta
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/162294
作者单位School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, Guangdong, China; Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Guangdong, Hong Kong and Macao Joint Laboratory for Tropical Oceanic-Atmospheric System Science, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, Guangdong, China; State Environmental Key Laboratory of Regional Air Quality Monitoring, Guangdong Environmental Monitoring Center, Guangzhou, Guangdong, China; Guangdong Climate Center, Guangdong Meteorological Service, Guangzhou, Guangdong, China
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Xie J.,Liao Z.,Fang X.,等. The characteristics of hourly wind field and its impacts on air quality in the Pearl River Delta region during 2013–2017[J],2019,227.
APA Xie J..,Liao Z..,Fang X..,Xu X..,Wang Y..,...&Wang B..(2019).The characteristics of hourly wind field and its impacts on air quality in the Pearl River Delta region during 2013–2017.Atmospheric Research,227.
MLA Xie J.,et al."The characteristics of hourly wind field and its impacts on air quality in the Pearl River Delta region during 2013–2017".Atmospheric Research 227(2019).
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