Source apportionment of fine particulate matter in Phoenix, AZ, using positive matrix factorization

被引:32
|
作者
Brown, Steven G.
Frankel, Anna
Raffuse, Sean M.
Roberts, Paul T.
Hafner, Hilary R.
Anderson, Darcy J.
机构
[1] Sonoma Technol Inc, Environm Data Anal Grp, Petaluma, CA 94954 USA
[2] Sonoma Technol Inc, Air Qual Data Anal Div, Petaluma, CA 94954 USA
关键词
D O I
10.3155/1047-3289.57.6.741
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Speciated particulate matter (PM),., data collected as part of the Interagency Monitoring of Protected Visual Environments (IMPROVE) program in Phoenix, AZ, from April 2001 through October 2003 were analyzed using the multivariate receptor model, positive matrix factorization (PMF). Over 250 samples and 24 species were used, including the organic carbon and elemental carbon analytical temperature fractions from the thermal optical reflectance method. A two-step approach was used. First, the species excluding the carbon fractions were used, and initially eight factors were identified; non-soil potassium was calculated and included to better refine the burning factor. Next, the mass associated with the burning factor was removed, and the data set rerun with the carbon fractions. Results were very similar (i.e., within a few percent), but this step enabled a separation of the mobile factor into gasoline and diesel vehicle emissions. The identified factors were burning (on average 2% of the mass), secondary transport (7%), regional power generation (13%), dust (25%), nitrate (9%), industrial As/Pb/Se (2%), Cu/Ni/V (7%), diesel (9%), and general mobile (26%). The overall contribution from mobile sources also increased, as some mass (OC and nitrate) from the nitrate and regional power generation factors were apportioned with the mobile factors. This approach allowed better apportionment of carbon as well as total mass. Additionally, the use of multiple supporting analyses, including air mass trajectories, activity trends, and emission inventory information, helped increase confidence in factor identification.
引用
收藏
页码:741 / 752
页数:12
相关论文
共 50 条
  • [1] Source identification and apportionment of fine particulate matter in Houston, TX, using positive matrix factorization
    Buzcu, B
    Fraser, MP
    Kulkarni, P
    Chellam, S
    [J]. ENVIRONMENTAL ENGINEERING SCIENCE, 2003, 20 (06) : 533 - 545
  • [2] Source apportionment with uncertainty estimates of fine particulate matter in Ostrava, Czech Republic using Positive Matrix Factorization
    Vossler, Teri
    Cernikovsky, Libor
    Novak, Jiri
    Williams, Ronald
    [J]. ATMOSPHERIC POLLUTION RESEARCH, 2016, 7 (03) : 503 - 512
  • [3] Source Apportionment of Personal Exposure to Fine Particulate Matter and Volatile Organic Compounds using Positive Matrix Factorization
    Hakan Pekey
    Beyhan Pekey
    Demet Arslanbaş
    Zehra Bulut Bozkurt
    Güray Doğan
    Gürdal Tuncel
    [J]. Water, Air, & Soil Pollution, 2013, 224
  • [4] Source Apportionment of Personal Exposure to Fine Particulate Matter and Volatile Organic Compounds using Positive Matrix Factorization
    Pekey, Hakan
    Pekey, Beyhan
    Arslanbas, Demet
    Bozkurt, Zehra Bulut
    Dogan, Guray
    Tuncel, Gurdal
    [J]. WATER AIR AND SOIL POLLUTION, 2013, 224 (01):
  • [5] Source apportionment of fine particulate matter by positive matrix factorization in the metropolitan area of Sao Paulo, Brazil
    de Miranda, Regina Maura
    Andrade, Maria de Fatima
    Dutra Ribeiro, Flavia Noronha
    Mendonca Francisco, Kelliton Jose
    Perez-Martinez, Pedro Jose
    [J]. JOURNAL OF CLEANER PRODUCTION, 2018, 202 : 253 - 263
  • [6] Source apportionment of fine particulate matter in Macao, China with and without organic tracers: A comparative study using positive matrix factorization
    Wang, Qiongqiong
    Huang, X. H. Hilda
    Tam, Frankie C. V.
    Zhang, Xiaxia
    Liu, Kin Man
    Yeung, Claisen
    Feng, Yongming
    Cheng, Yuk Ying
    Wong, Yee Ka
    Ng, Wai Man
    Wu, Cheng
    Zhang, Qingyan
    Zhang, Ting
    Lau, Ngai Ting
    Yuan, Zibing
    Lau, Alexis K. H.
    Yu, Jian Zhen
    [J]. ATMOSPHERIC ENVIRONMENT, 2019, 198 : 183 - 193
  • [7] Fine particulate matter source apportionment for the Chemical Speciation Trends Network site at Birmingham, Alabama, using Positive Matrix Factorization
    Baumann, Karsten
    Jayanty, R. K. M.
    Flanagan, James B.
    [J]. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION, 2008, 58 (01) : 27 - 44
  • [8] Chemical characterization of submicron particulate matter (PM1) and its source apportionment using positive matrix factorization
    Jhamaria, Charu
    Sharma, Shivani
    Yadav, Manish
    Tiwari, Suresh
    Singh, Namrata
    [J]. CLEAN-SOIL AIR WATER, 2024, 52 (07)
  • [9] Apportionment of ambient primary and secondary fine particulate matter at the Pittsburgh National Energy Laboratory particulate matter characterization site using positive matrix factorization and a potential source contributions function analysis
    Martello, Donald V.
    Pekney, Natalie J.
    Anderson, Richard R.
    Davidson, Cliff I.
    Hopke, Philip K.
    Kim, Eugene
    Christensen, William F.
    Mangelson, Nolan F.
    Eatough, Delbert J.
    [J]. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION, 2008, 58 (03) : 357 - 368
  • [10] Application of positive matrix factorization in source apportionment of particulate pollutants in Hong Kong
    Lee, E
    Chan, CK
    Paatero, P
    [J]. ATMOSPHERIC ENVIRONMENT, 1999, 33 (19) : 3201 - 3212