1. College of Geoscience and Surveying Engineering, China University of Mining and Technology(Beijing), Beijing 100083, China;
2. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221116, China
Both HYSPLIT backward trajectory mode and Global Data Assimilation System (GDAS) meteorological data from the National Centers for Environmental Prediction (NCEP) were used to analyze the three-day backward trajectories of hourly airflow in Beijing urban from May 1st, 2014 to April 30st, 2015. Clustering analysis was used to classify the airflow backward trajectories of Beijing urban in different seasons. The hourly ground PM2.5 observations were also used to analyze the spatial characteristics of different transport pathways and its contribution to the PM2.5 concentration in Beijing urban. Potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) were applied to identify the potential source-zones (PSZs) and its contribution to Beijing urban PM2.5 in different seasons during the study period. This study revealed that the Beijing airflows were significantly characterized by monsoons, and the PTZs of Beijing urban PM2.5 varied a lot in different seasons during the study period: a) in the spring, it mainly located in northwest China, North China Plain and YellowRiver-HuaiRiver plain; b) in the summer, it mainly located in Shandong, north Jiangsu and Yellow Sea; c) in the fall, it mainly located in south Hebei, west Shandong, central Shandong and the adjoining areas of Jiangsu, Shandong, Henan and Anhui Provinces; d) in the winter, it mainly located in south Hebei, northwest Shandong, north Shanxi, Shaanxi, central Inner Mongolia and south Mongolia. The airflows from Shandong and south Hebei carried high concentrations of PM2.5 in all seasons, while the airflows from northwest China carried middle concentrations of PM2.5 in winter and spring.
Wang J L, Zhang Y H, Shao M, et al. Quantitative relationship between visibility and mass concentration of PM2.5 in Beijing[J]. Journal of Environmental Sciences, 2006,18(3):475-481.
[3]
Dan M, Zhuang G, Li X, et al. The characteristics of carbonaceous species and their sources in PM2.5 in Beijing[J]. Atmospheric Environment, 2004,38(21):3443-3452.
Rozwadowska A, Zieliński T, Petelski T, et al. Cluster analysis of the impact of air back-trajectories on aerosol optical properties at Hornsund, Spitsbergen[J]. Atmospheric Chemistry and Physics, 2010,10:877-893.
Han Y J, Holsen T M, Hopke P K. Estimation of source locations of total gaseous mercury measured in New York State using trajectory based models[J]. Atmospheric Environment, 2007, 41(28):6033-6047.
[18]
Zhao M, Huang Z, Qiao T, et al. Chemical characterization, the transport pathways and potential sources of PM2.5 in Shanghai: Seasonal variations[J]. Atmospheric Research, 2015,158:66-78.
[19]
Hsu Y K, Holsen T M, Hopke P K. Comparison of hybrid receptor models to locate PCB sources in Chicago[J]. Atmospheric Environment, 2003,37(4):545-562.
Yan R, Yu S, Zhang Q, et al. A heavy haze episode in Beijing in February of 2014: Characteristics, origins and implications[J]. Atmospheric Pollution Research, 2015,6(5):867-876.
[22]
Zhang Z Y, Wong M S, Lee K H. Estimation of potential source regions of PM2.5 in Beijing using backward trajectories[J]. Atmospheric Pollution Research, 2015,6(1):173-177.
[23]
Zhang L, Wang S X, Wang L, et al. Atmospheric mercury concentration and chemical speciation at a rural site in Beijing, China: implications of mercury emission sources[J]. Atmospheric Chemistry and Physics, 2013,13(20):10505-10516.
Sirois A, Bottenhein J W. Use of backward trajectories to interpret the 5-year record of PAN and O3ambient air concentrations at Kejimkujik National Park, Nova Scotia[J]. Journal of Geophysical Research, 1995,100(D2):2867-2881.
[26]
Draxler R R, Hess G D. An overview of the HYSPLIT_ 4modeling system for trajectories[J]. Australian Meteorological Magazine, 1998,47(4):295-308.
Wang Y Q, Zhang X Y, Draxler R R. TrajStat: GIS-based software that uses various trajectory statistical analysis methods to identify potential sources from long-term air pollution measurement data[J]. Environmental Modeling & Software, 2009,24(8):938-939.
[29]
Draxler R R, Stunder B, Rolph G D, et al. HYSPLIT4User's Guide[EB/OL]. http://www.arl.noaa.gov/documents/reports/hysplit_user_guide.pdf.
[30]
Polissar A V, Hopke P K, Harris J M. Source regions for atmospheric aerosol measured at Barrow, Alaska[J]. Environmental Science & Technology, 2001,35(21):4214-4226.
McDonald A G, Bealey W J, Fowler D, et al. Quantifying the effect of urban tree planting on concentrations and depositions of PM10 in two UK conurbations[J]. Atmospheric Environment, 2007,41(38):8455-8467.
[35]
Mircea M, Stefan S, Fuzzi S. Precipitation scavenging coefficient: influence of measured aerosol and raindrop size distributions[J]. Atmospheric Environment, 2000,34(29):5169-5174.
[36]
Gu J, Du S, Han D, et al. Major chemical compositions, possible sources, and mass closure analysis of PM2.5 in Jinan, China[J]. Air Quality, Atmosphere & Health, 2014,7(3):251-262.
