Source Apportionment of PM2.5 in Gyeongsan Using the PMF Model

被引:11
|
作者
Jeong, YeongJin [1 ]
Hwang, InJo [1 ]
机构
[1] Daegu Univ, Dept Environm Engn, Gyongsan, South Korea
关键词
PM2.5; PMF; CPF; Mass contribution; Receptor model;
D O I
10.5572/KOSAE.2015.31.6.508
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
The objective of this study was to quantitatively estimate PM2.5 source contribution in Gyeongsan. Ambient PM2.5 samples have been collected on zefluor, quartz and nylasorb filter by PM2.5 samplers of cyclone method from September 2010 to December 2012. Collected samples were analyzed for determining 17 inorganic elements, 8 ions, and 8 carbon components after pretreatment. Based on these chemical information, the PMF model was applied to estimate the quantitative contribution of air pollution sources. The results of the PMF modeling showed that the sources were apportioned by biomass burning source (15.5%), secondary sulfate source (16.0%), industry source (10.4%), soil source (7.0%), gasoline source (9.1%), incinerator source (10.4%), diesel emission source (11.0%), and secondary nitrate source (20.6%), respectively. To analyze local source impacts from various wind directions, the CPF analysis were performed using source contribution results with the wind direction values measured at the site.
引用
收藏
页码:508 / 519
页数:12
相关论文
共 50 条
  • [31] Aerosols in Northern Morocco-2: Chemical Characterization and PMF Source Apportionment of Ambient PM2.5
    Benchrif, Abdelfettah
    Tahri, Mounia
    Guinot, Benjamin
    Chakir, El Mahjoub
    Zahry, Fatiha
    Bagdhad, Bouamar
    Bounakhla, Moussa
    Cachier, Helene
    Costabile, Francesca
    ATMOSPHERE, 2022, 13 (10)
  • [32] Source apportionment of PM10 and PM2.5 at multiple sites in the strait of Gibraltar by PMF: impact of shipping emissions
    Marco Pandolfi
    Yolanda Gonzalez-Castanedo
    Andrés Alastuey
    Jesus D. de la Rosa
    Enrique Mantilla
    A. Sanchez de la Campa
    Xavier Querol
    Jorge Pey
    Fulvio Amato
    Teresa Moreno
    Environmental Science and Pollution Research, 2011, 18 : 260 - 269
  • [33] Source Apportionment of PM2.5 Using a CMB Model for a Centrally Located Indian City
    Pipalatkar, Pradeep
    Khaparde, Vaishali V.
    Gajghate, Daulat G.
    Bawase, Mouktik A.
    AEROSOL AND AIR QUALITY RESEARCH, 2014, 14 (03) : 1089 - U1105
  • [34] Regional source apportionment of PM2.5 in Seoul using Bayesian multivariate receptor model
    Oh, Man-Suk
    Park, Chee Kyung
    JOURNAL OF APPLIED STATISTICS, 2022, 49 (03) : 738 - 751
  • [35] Estimation of Source Apportionment for PM2.5 Data of Saemangeum Area Using the CMB Model
    Hwang, InJo
    Song, Jihan
    JOURNAL OF KOREAN SOCIETY FOR ATMOSPHERIC ENVIRONMENT, 2023, 39 (05) : 661 - 674
  • [36] Source apportionment of phoenix PM2.5 aerosol with the Unmix receptor model
    Lewis, CW
    Norris, GA
    Conner, TL
    Henry, RC
    JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION, 2003, 53 (03): : 325 - 338
  • [37] Estimation of Source Apportionment for Filter-based PM2.5 Data using the EPA-PMF Model at Air Pollution Monitoring Supersites
    Hwang, InJo
    Yi, Seung-Muk
    Park, Jinsoo
    JOURNAL OF KOREAN SOCIETY FOR ATMOSPHERIC ENVIRONMENT, 2020, 36 (05) : 620 - 632
  • [38] Improving apportionment of PM2.5 using multisite PMF by constraining G -values with a priori information
    Dai, Qili
    Hopke, Philip K.
    Bi, Xiaohui
    Feng, Yinchang
    SCIENCE OF THE TOTAL ENVIRONMENT, 2020, 736
  • [39] Characterization and source apportionment of PM2.5 based on error estimation from EPA PMF 5.0 model at a medium city in China
    Liu, Baoshuang
    Wu, Jianhui
    Zhang, Jiaying
    Wang, Lu
    Yang, Jiamei
    Liang, Danni
    Dai, Qili
    Bi, Xiaohui
    Feng, Yinchang
    Zhang, Yufen
    Zhang, Qinxun
    ENVIRONMENTAL POLLUTION, 2017, 222 : 10 - 22
  • [40] Combination of Multiple Isotopes and PMF Model Provide Insights Into the Method Optimization of PM2.5 Source Apportionment During Haze Episodes
    Feng, Xinxin
    Chen, Yingjun
    Jiang, Hongxing
    Cai, Junjie
    Liu, Zeyu
    Feng, Yanli
    Li, Menglong
    Mu, Yujing
    Chen, Jianmin
    Chen, Tian
    JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2024, 129 (23)