Using an improved Source Directional Apportionment method to quantify the PM2.5 source contributions from various directions in a megacity in China

被引:32
|
作者
Tian, Ying-Ze [1 ]
Shi, Guo-Liang [1 ]
Han, Bo [2 ]
Wu, Jian-Hui [1 ]
Zhou, Xiao-Yu [1 ]
Zhou, Lai-Dong [3 ]
Zhang, Pu [3 ]
Feng, Yin-Chang [1 ]
机构
[1] Nankai Univ, Coll Environm Sci & Engn, State Environm Protect Key Lab Urban Ambient Air, Tianjin 300071, Peoples R China
[2] Nankai Univ, Coll Software, Tianjin 300071, Peoples R China
[3] Chengdu Res Acad Environm Sci, Chengdu 610042, Peoples R China
基金
中国国家自然科学基金;
关键词
SDA; Source apportionment; PMF; Trajectories cluster analysis; PM; Chemical species; POLYCYCLIC AROMATIC-HYDROCARBONS; PARTICULATE MATTER; CLUSTER-ANALYSIS; POLLUTION; AMBIENT; AEROSOL; TIANJIN; TRENDS; ATHENS; RIVER;
D O I
10.1016/j.chemosphere.2014.08.015
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The transport of particulate matter (PM) and chemical species is an essential mechanism for determining the fate of PM pollutants and their effects. To determine source transport quantitatively, an ambient PM2.5 dataset from a megacity in China was analysed using a novel method called "Source Directional Apportionment" (SDA). The SDA method is developed in this work to quantify contributions of each source category from various directions. The three steps of SDA are (1) to estimate source categories and time series of source contributions to PM with a factor analysis model, (2) to identify directions by trajectory cluster analysis and (3) to quantify source directional contributions for each source category by combining the time series of source contributions to the back trajectories in each direction. For PM2.5 in Chengdu, crustal dust, vehicular exhaust, coal combustion and secondary sulphate are all important contributors to PM; secondary nitrate and cement dust are relatively less influential. Four potential source directions were identified in Chengdu during the sampling period from 2009 to 2011. The percentages of source directional contributions from Directions 1-4 (northeast, southwest to south, southwest and west) were estimated as follows; crustal dust (7.9%, 9.1%, 6.4% and 6.2%, respectively), cement dust (1.0%, 1.2%, 1.3% and 1.1%, respectively), vehicular exhaust (6.4%, 6.0%, 5.6% and 7.0%, respectively), secondary sulphate (5.1%, 5.2%, 5.6% and 8.6%, respectively) and secondary nitrate (2.0%, 2.4%, 2.5% and 2.3%, respectively). Finally, the source directional contributions to important chemical species were quantified to determine their transport from sources to receptor. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:750 / 756
页数:7
相关论文
共 50 条
  • [41] Seasonal variations and source apportionment of water-soluble inorganic ions in PM2.5 in Nanjing, a megacity in southeastern China
    Zhang, Xiaoyu
    Zhao, Xin
    Ji, Guixiang
    Ying, Rongrong
    Shan, Yanhong
    Lin, Yusuo
    JOURNAL OF ATMOSPHERIC CHEMISTRY, 2019, 76 (01) : 73 - 88
  • [42] Source apportionment of PM2.5 nitrate and sulfate in China using a source-oriented chemical transport model
    Zhang, Hongliang
    Li, Jingyi
    Ying, Qi
    Yu, Jian Zhen
    Wu, Dui
    Cheng, Yuan
    He, Kebin
    Jiang, Jingkun
    ATMOSPHERIC ENVIRONMENT, 2012, 62 : 228 - 242
  • [43] Source apportionment of PM2.5 at the coastal area in Korea
    Choi, Jong-kyu
    Heo, Jong-Bae
    Ban, Soo-Jin
    Yi, Seung-Muk
    Zoh, Kyung-Duk
    SCIENCE OF THE TOTAL ENVIRONMENT, 2013, 447 : 370 - 380
  • [44] Source apportionment of PM2.5 in Beijing using positive matrix factorization
    Xiangchun Jin
    Caijin Xiao
    Jue Li
    Donghui Huang
    Guojun Yuan
    Yonggang Yao
    Xinghua Wang
    Long Hua
    Guiying Zhang
    Lei Cao
    Pingsheng Wang
    Bangfa Ni
    Journal of Radioanalytical and Nuclear Chemistry, 2016, 307 : 2147 - 2154
  • [45] Source apportionment of PM2.5 in Xinzhen, Beijing using PIXE and XRF
    Jin, X.-C., 1600, Atomic Energy Press (48):
  • [46] Source Apportionment of PM2.5 in Delhi, India Using PMF Model
    S. K. Sharma
    T. K. Mandal
    Srishti Jain
    A. Saraswati
    Mohit Sharma
    Bulletin of Environmental Contamination and Toxicology, 2016, 97 : 286 - 293
  • [47] Source apportionment of PM2.5 in Tangshan, China—Hybrid approaches for primary and secondary species apportionment
    Wei Wen
    Shuiyuan Cheng
    Lei Liu
    Gang Wang
    Xiaoqi Wang
    Frontiers of Environmental Science & Engineering, 2016, 10
  • [48] PM2.5 Source Apportionment Using a Hybrid Environmental Receptor Model
    Chen, L-W Antony
    Cao, Junji
    ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2018, 52 (11) : 6357 - 6369
  • [49] Source apportionment of PM2.5 in Beijing using positive matrix factorization
    Jin, Xiangchun
    Xiao, Caijin
    Li, Jue
    Huang, Donghui
    Yuan, Guojun
    Yao, Yonggang
    Wang, Xinghua
    Hua, Long
    Zhang, Guiying
    Cao, Lei
    Wang, Pingsheng
    Ni, Bangfa
    JOURNAL OF RADIOANALYTICAL AND NUCLEAR CHEMISTRY, 2016, 307 (03) : 2147 - 2154
  • [50] Source Apportionment of PM2.5 in Delhi, India Using PMF Model
    Sharma, S. K.
    Mandal, T. K.
    Jain, Srishti
    Saraswati
    Sharma, A.
    Saxena, Mohit
    BULLETIN OF ENVIRONMENTAL CONTAMINATION AND TOXICOLOGY, 2016, 97 (02) : 286 - 293