Impact of Gas/Particle Partitioning of Semivolatile Organic Compounds on Source Apportionment with Positive Matrix Factorization

被引:33
|
作者
Xie, Mingjie [1 ]
Hannigan, Michael P. [1 ]
Barsanti, Kelley C. [2 ]
机构
[1] Univ Colorado, Coll Engn & Appl Sci, Dept Mech Engn, Boulder, CO 80309 USA
[2] Portland State Univ, Dept Civil & Environm Engn, Portland, OR 97207 USA
关键词
REGIONAL AIR-POLLUTION; FINE-PARTICLE MASS; SPATIAL VARIABILITY; PARTICULATE MATTER; ABSORPTION-MODEL; SPECIATION DATA; PM2.5; UNCERTAINTY; AEROSOL; PHOTOOXIDATION;
D O I
10.1021/es5022262
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
To quantify and minimize the influence of gas/particle (G/P) partitioning on receptor-based source apportionment using particle-phase semivolatile organic compound (SVOC) data, positive matrix factorization (PMF) coupled with a bootstrap technique was applied to three data sets mainly composed of "measured-total" (measured particle- + gas-phase), "particle-only" (measured particle-phase) and "predicted-total" (measured particle-phase + predicted gas-phase) SVOCs to apportion carbonaceous aerosols. Particle- (PM2.5) and gas-phase SVOCs were collected using quartz fiber filters followed by PUF/XAD-4/PUF adsorbents and measured using gas chromatography mass spectrometry (GC-MS). Concentrations of gas-phase SVOCs were also predicted from their particle-phase concentrations using absorptive partitioning theory. Five factors were resolved for each data set, and the factor profiles were generally consistent across the three PMF solutions. Using a previous source apportionment study at the same receptor site, those five factors were linked to summertime biogenic emissions (odd n-alkane factor), unburned fossil fuels (light SVOC factor), road dust and/or cooking (n-alkane factor), motor vehicle emissions (PAH factor), and lubricating oil combustion (sterane factor). The "measured-total" solution was least influenced by G/P partitioning and used as reference. Two out of the five factors (odd n-alkane and PAH factors) exhibited consistent contributions for "particle-only" vs "measured-total" and "predicted-total" vs "measured-total" solutions. Factor contributions of light SVOC and n-alkane factors were more consistent for "predicted-total" vs "measured-total" than "particle-only" vs "measured-total" solutions. The remaining factor (sterane factor) underestimated the contribution by around 50% from both "particle-only" and "predicted-total" solutions. The results of this study confirm that when measured gas-phase SVOCs are not available, "predicted-total" SVOCs should be used to decrease the influence of G/P partitioning on receptor-based source apportionment.
引用
收藏
页码:9053 / 9060
页数:8
相关论文
共 50 条
  • [31] Source apportionment of ambient particle number concentrations in central Los Angeles using positive matrix factorization (PMF)
    Sowlat, Mohammad Hossein
    Hasheminassab, Sina
    Sioutas, Constantinos
    ATMOSPHERIC CHEMISTRY AND PHYSICS, 2016, 16 (08) : 4849 - 4866
  • [32] Gas-Particle Partitioning of Semivolatile Organic Compounds in a Residence: Influence of Particles from Candles, Cooking, and Outdoors
    Kristensen, Kasper
    Lunderberg, David M.
    Liu, Yingjun
    Misztal, Pawel K.
    Tian, Yilin
    Arata, Caleb
    Nazaroff, William W.
    Goldstein, Allen H.
    ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2023, 57 (08) : 3260 - 3269
  • [33] 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
  • [34] Application of positive matrix factorization in source apportionment of particulate pollutants in Hong Kong
    Lee, E
    Chan, CK
    Paatero, P
    ATMOSPHERIC ENVIRONMENT, 1999, 33 (19) : 3201 - 3212
  • [35] Comparison of Positive Matrix Factorization and Multilinear Engine for the source apportionment of particulate pollutants
    Ramadan, Z
    Eickhout, B
    Song, XH
    Buydens, LMC
    Hopke, PK
    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2003, 66 (01) : 15 - 28
  • [36] 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
  • [37] Source apportionment of VOCs in the Los Angeles area using positive matrix factorization
    Brown, Steven G.
    Frankel, Anna
    Hafner, Hilary R.
    ATMOSPHERIC ENVIRONMENT, 2007, 41 (02) : 227 - 237
  • [38] Source apportionment of urban PM2.5 using positive matrix factorization with vertically distributed measurements of trace elements and nonpolar organic compounds
    Liao, Ho -Tang
    Lee, Chien-Lin
    Tsai, Wei-Cheng
    Yu, Jian Zhen
    Tsai, Shih-Wei
    Chou, Charles C. K.
    Wu, Chang-Fu
    ATMOSPHERIC POLLUTION RESEARCH, 2021, 12 (04) : 200 - 207
  • [39] PCDD/F Source Apportionment in the Baltic Sea Using Positive Matrix Factorization
    Sundqvist, K. L.
    Tysklind, M.
    Geladi, P.
    Hopke, P. K.
    Wiberg, K.
    ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2010, 44 (05) : 1690 - 1697
  • [40] Source apportionment using positive matrix factorization on daily measurements of inorganic and organic speciated PM2.5
    Dutton, Steven J.
    Vedal, Sverre
    Piedrahita, Ricardo
    Milford, Jana B.
    Miller, Shelly L.
    Hannigan, Michael P.
    ATMOSPHERIC ENVIRONMENT, 2010, 44 (23) : 2731 - 2741