Source Apportionment of Volatile Organic Compounds (VOCs) by Positive Matrix Factorization (PMF) supported by Model Simulation and Source Markers - Using Petrochemical Emissions as a Showcase

被引:30
作者
Su, Yuan-Chang [1 ]
Chen, Wei-Hao [1 ]
Fan, Chen-Lun [1 ]
Tong, Yu-Huei [1 ]
Weng, Tzu-Hsiang [1 ]
Chen, Sheng-Po [2 ]
Kuo, Cheng-Pin [1 ]
Wang, Jia-Lin [3 ]
Chang, Julius S. [2 ]
机构
[1] Environm Simulat CO LTD, Taipei, Taiwan
[2] SUNY Albany, Atmospher Sci Res Ctr, Albany, NY 12222 USA
[3] Natl Cent Univ, Dept Chem, Chungli 320, Taiwan
关键词
Source-receptor; Petrochemical complex; Photochemical assessment measurement stations (PAMS); AMBIENT PARTICULATE MATTER; AIR-QUALITY; RIVER DELTA; VEHICULAR EMISSION; OZONE FORMATION; COMPLEX; URBAN; HOUSTON; IDENTIFICATION; PRECURSORS;
D O I
10.1016/j.envpol.2019.07.016
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study demonstrates the use of positive matrix factorization (PMF) in a region with a major Petrochemical Complex, a prominent source of volatile organic compounds (VOCs), as a showcase of PMF applications. The PMF analysis fully exploited the quality and quantity of the observation data, sufficed by a cluster of 9 monitoring sites within a 20 km radius of the petro-complex. Each site provided continuous data of 54 speciated VOCs and meteorological variables. Wind characteristics were highly seasonal and played a decisive role in the source-receptor relationship, hence the dataset was divided into three subsets in accordance with the prevailing wind flows. A full year of real-time data were analyzed by PMF to resolve into various distinct source types including petrochemical, urban, evaporative, long-range air parcels, etc., with some sites receiving more petro-influence than others. To minimize subjectivity in the assignment of the PMF source factors, as commonly seen in some PMF works, this study attempted to solidify PMF results by supporting with two tools of spatially/temporally resolved air-quality model simulations and observation data. By exploiting the two supporting tools, the dynamic process of individual sources to a receptor were rationalized. Percent contributions from these sources to the receptor sites were calculated by summing over the occurrence of different source types. Interestingly, although the Petro-complex is the single largest local VOC source in the 20 km radius study domain, all monitoring sites in the region received far less influence from the Petro-complex than from other emission types within or outside the region, which together add up to more than 70% of the total VOC abundance. (C) 2019 Elsevier Ltd. All rights reserved.
引用
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页数:11
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共 43 条
  • [21] Tracer-based source apportionment of polycyclic aromatic hydrocarbons in PM2.5 in Guangzhou, southern China, using positive matrix factorization (PMF)
    Bo Gao
    Hai Guo
    Xin-Ming Wang
    Xiu-Ying Zhao
    Zhen-Hao Ling
    Zhou Zhang
    Teng-Yu Liu
    [J]. Environmental Science and Pollution Research, 2013, 20 : 2398 - 2409
  • [22] Source apportionment of Volatile Organic Compounds (VOCs) in the South Coast Air Basin (SoCAB) During RECAP-CA
    Wu, Shenglun
    Alaimo, Christopher P.
    Green, Peter G.
    Young, Thomas M.
    Zhao, Yusheng
    Liu, Shang
    Kuwayama, Toshihiro
    Kleeman, Michael J.
    [J]. ATMOSPHERIC ENVIRONMENT, 2024, 338
  • [23] Source Apportionment of Total Suspended Particulates in an Arid Area in Southwestern Iran Using Positive Matrix Factorization
    Sowlat, Mohammad Hossein
    Naddafi, Kazem
    Yunesian, Masud
    Jackson, Peter L.
    Shahsavani, Abbas
    [J]. BULLETIN OF ENVIRONMENTAL CONTAMINATION AND TOXICOLOGY, 2012, 88 (05) : 735 - 740
  • [24] Source apportionment of particle-bound polycyclic aromatic hydrocarbons in Lumbini, Nepal by using the positive matrix factorization receptor model
    Chen, Pengfei
    Li, Chaoliu
    Kang, Shichang
    Yan, Fangping
    Zhang, Qianggong
    Ji, Zhengming
    Tripathee, Lekhendra
    Rupakheti, Dipesh
    Rupakheti, Maheswar
    Qu, Bin
    Sillanpaa, Mika
    [J]. ATMOSPHERIC RESEARCH, 2016, 182 : 46 - 53
  • [25] Ambient volatile organic compounds (VOCs) in two coastal cities in western Canada: Spatiotemporal variation, source apportionment, and health risk assessment
    Xiong, Ying
    Bari, Md Aynul
    Xing, Zhenyu
    Du, Ke
    [J]. SCIENCE OF THE TOTAL ENVIRONMENT, 2020, 706 (706)
  • [26] Source Apportionment and Risk Assessment of Soil Heavy Metals due to Railroad Activity Using a Positive Matrix Factorization Approach
    Wang, Zhen
    Zhang, Jianqiang
    Watanabe, Izumi
    [J]. SUSTAINABILITY, 2023, 15 (01)
  • [27] Source apportionment of polycyclic aromatic hydrocarbons in PM2.5 using positive matrix factorization modeling in Shanghai, China
    Wang, Fengwen
    Lin, Tian
    Feng, Jialiang
    Fu, Huaiyu
    Guo, Zhigang
    [J]. ENVIRONMENTAL SCIENCE-PROCESSES & IMPACTS, 2015, 17 (01) : 197 - 205
  • [28] Three-year Long Source Apportionment Study of Airborne Particles in Ulaanbaatar Using X-ray Fluorescence and Positive Matrix Factorization
    Gunchin, Gerelmaa
    Manousakas, Manousos
    Osan, Janos
    Karydas, Andreas Germanos
    Eleftheriadis, Konstantinos
    Lodoysamba, Sereeter
    Shagjjamba, Dagva
    Migliori, Alessandro
    Padilla-Alvarez, Roman
    Streli, Christina
    Darby, Iain
    [J]. AEROSOL AND AIR QUALITY RESEARCH, 2019, 19 (05) : 1056 - 1067
  • [29] Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia
    Vlachou, Athanasia
    Tobler, Anna
    Lamkaddam, Houssni
    Canonaco, Francesco
    Daellenbach, Kaspar R.
    Jaffrezo, Jean-Luc
    Cruz Minguillon, Maria
    Maasikmets, Marek
    Teinemaa, Erik
    Baltensperger, Urs
    El Haddad, Imad
    Prevot, Andre S. H.
    [J]. ATMOSPHERIC CHEMISTRY AND PHYSICS, 2019, 19 (11) : 7279 - 7295
  • [30] Source Characterization and Apportionment of PM10, PM2.5 and PM0.1 by Using Positive Matrix Factorization
    Gugamsetty, Balakrishnaiah
    Wei, Han
    Liu, Chun-Nan
    Awasthi, Amit
    Hsu, Shih-Chieh
    Tsai, Chuen-Jinn
    Roam, Gwo-Dong
    Wu, Yue-Chuen
    Chen, Chung-Fang
    [J]. AEROSOL AND AIR QUALITY RESEARCH, 2012, 12 (04) : 476 - 491