Local and long-range transport influences on PM2.5 at a cities-cluster in northern China, during summer 2008--颗粒学报PARTICUOLOGY

Gao, L., Tian, Y., Zhang, C., Shi, G., Hao, H., Zeng, F., Shi, C., Zhang, M., Feng, Y., & Li, X. (2014). Local and long-range transport influences on PM2.5 at a cities-cluster in northern China, during summer 2008. Particuology, 13, 66–72. https://doi.org/10.1016/j.partic.2013.06.006

Lijie Gao ^a, Yingze Tian ^a, Caiyan Zhang ^a, Guoliang Shi ^a *, Huize Hao ^a, Fang Zeng ^a, Chunli Shi ^a, Meigen Zhang ^b, Yinchang Feng ^a, Xiang Li ^a c

a State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
b Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
c Department of Computer Science, University of Georgia, Athens, GA, USA

10.1016/j.partic.2013.06.006

Volume 13, April 2014, Pages 66-72

Received 3 April 2013, Revised 20 June 2013, Accepted 21 June 2013, Available online 4 September 2013.

E-mail: nksgl@hotmail.com; tianyingze@hotmail.com

Highlights

• PM_2.5 concentrations of 10 cities in the cluster were measured simultaneously.

• Local and regional influences were studied.

• Spatial characteristics of PM_2.5 concentrations were determined by hierarchical cluster analysis.

• Cities controlled by similar meteorological factors have similar PM concentration variability.

Abstract

Hourly PM_2.5 concentrations were observed simultaneously at a cities-cluster comprising 10 cities/towns in Hebei province in China from July 1 to 31, 2008. Among the 10 cities/towns, Baoding showed the highest average concentration level (161.57 μg/m³) and Yanjiao exhibited the lowest (99.35 μg/m³). These observed data were also studied using the joint potential source contribution function with 24-h and 72-h backward trajectories, to identify more clearly the local and countrywide-scale long-range transport sources. For the local sources, three important influential areas were found, whereas five important influential areas were defined for long-range transport sources. Spatial characteristics of PM_2.5 were determined by multivariate statistical analyses. Soil dust, coal combustion, and vehicle emissions might be the potential contributors in these areas. The results of a hierarchical cluster analysis for back trajectory endpoints and PM_2.5 concentrations datasets show that the spatial characteristics of PM_2.5 in the cities-cluster were influenced not only by local sources, but also by long-range transport sources. Different cities in the cities-cluster obtained different weighted contributions from local or long-range transport sources. Cangzhou, Shijiazhuang, and Baoding are near the source areas in the south of Hebei province, whereas Zhuozhou, Yangfang, Yanjiao, Xianghe, and Langfang are close to the sources areas near Beijing and Tianjin.

Graphical abstract

Keywords

Local influence; Regional influence; Joint potential source contribution function; Hierarchical cluster analysis

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