Environmental Source Tracking of Per- and Polyfluoroalkyl Substances within a Forensic Context: Current and Future Techniques

被引:55
作者
Charbonnet, Joseph A. [1 ]
Rodowa, Alix E. [2 ]
Joseph, Nayantara T. [3 ]
Guelfo, Jennifer L. [4 ]
Field, Jennifer A. [5 ]
Jones, Gerrad D. [6 ]
Higgins, Christopher P. [1 ]
Helbling, Damian E. [3 ]
Houtz, Erika F. [7 ]
机构
[1] Colorado Sch Mines, Dept Civil & Environm Engn, Golden, CO 80401 USA
[2] NIST, Biochem & Exposure Sci Grp, Charleston, SC 29412 USA
[3] Cornell Univ, Sch Civil & Environm Engn, New York, NY 14853 USA
[4] Texas Tech Univ, Dept Civil Environm & Construct Engn, Lubbock, TX 79409 USA
[5] Oregon State Univ, Dept Environm & Mol Toxicol, Corvallis, OR 97331 USA
[6] Oregon State Univ, Dept Biol & Ecol Engn, Corvallis, OR 97331 USA
[7] Arcadis, San Francisco, CA 94104 USA
关键词
per- and polyfluoroalkyl substances; environmental forensics; conceptual site models; multivariate statistics; source tracking; high-resolution mass spectrometry; FILM-FORMING FOAM; AFFF-IMPACTED GROUNDWATER; FIRE-TRAINING AREAS; PERFLUOROALKYL SUBSTANCES; AEROBIC BIOTRANSFORMATION; SUBSURFACE TRANSPORT; SPATIAL-DISTRIBUTION; ALKYL SUBSTANCES; PFASS; WATER;
D O I
10.1021/acs.est.0c08506
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The source tracking of per- and polyfluoroalkyl substances (PFASs) is a new and increasingly necessary subfield within environmental forensics. We define PFAS source tracking as the accurate characterization and differentiation of multiple sources contributing to PFAS contamination in the environment. PFAS source tracking should employ analytical measurements, multivariate analyses, and an understanding of PFAS fate and transport within the framework of a conceptual site model. Converging lines of evidence used to differentiate PFAS sources include: identification of PFASs strongly associated with unique sources; the ratios of PFAS homologues, classes, and isomers at a contaminated site; and a site's hydrogeochemical conditions. As the field of PFAS source tracking progresses, the development of new PFAS analytical standards and the wider availability of high-resolution mass spectral data will enhance currently available analytical capabilities. In addition, multivariate computational tools, including unsupervised (i.e., exploratory) and supervised (i.e., predictive) machine learning techniques, may lead to novel insights that define a targeted list of PFASs that will be useful for environmental PFAS source tracking. In this Perspective, we identify the current tools available and principal developments necessary to enable greater confidence in environmental source tracking to identify and apportion PFAS sources.
引用
收藏
页码:7237 / 7245
页数:9
相关论文
共 87 条
  • [71] United Nations, 2013, STOCKH CONV PERS ORG, P11
  • [72] US EPA, 2006, EPAHQOPPT20060621
  • [73] Contaminant source identification using semi-supervised machine learning
    Vesselinov, Velimir V.
    Alexandrov, Boian S.
    O'Malley, Daniel
    [J]. JOURNAL OF CONTAMINANT HYDROLOGY, 2018, 212 : 134 - 142
  • [74] Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking
    Wang, Mingxun
    Carver, Jeremy J.
    Phelan, Vanessa V.
    Sanchez, Laura M.
    Garg, Neha
    Peng, Yao
    Don Duy Nguyen
    Watrous, Jeramie
    Kapono, Clifford A.
    Luzzatto-Knaan, Tal
    Porto, Carla
    Bouslimani, Amina
    Melnik, Alexey V.
    Meehan, Michael J.
    Liu, Wei -Ting
    Criisemann, Max
    Boudreau, Paul D.
    Esquenazi, Eduardo
    Sandoval-Calderon, Mario
    Kersten, Roland D.
    Pace, Laura A.
    Quinn, Robert A.
    Duncan, Katherine R.
    Hsu, Cheng-Chih
    Floros, Dimitrios J.
    Gavilan, Ronnie G.
    Kleigrewe, Karin
    Northen, Trent
    Dutton, Rachel J.
    Parrot, Delphine
    Carlson, Erin E.
    Aigle, Bertrand
    Michelsen, Charlotte F.
    Jelsbak, Lars
    Sohlenkamp, Christian
    Pevzner, Pavel
    Edlund, Anna
    McLean, Jeffrey
    Piel, Jorn
    Murphy, Brian T.
    Gerwick, Lena
    Liaw, Chih-Chuang
    Yang, Yu-Liang
    Humpf, Hans-Ulrich
    Maansson, Maria
    Keyzers, Robert A.
    Sims, Amy C.
    Johnson, Andrew R.
    Sidebottom, Ashley M.
    Sedio, Brian E.
    [J]. NATURE BIOTECHNOLOGY, 2016, 34 (08) : 828 - 837
  • [75] Nontargeted massspectral detection of chloroperfluoropolyether carboxylates in New Jersey soils
    Washington, John W.
    Rosal, Charlita G.
    McCord, James P.
    Strynar, Mark J.
    Lindstrom, Andrew B.
    Bergman, Erica L.
    Goodrow, Sandra M.
    Tadesse, Haile K.
    Pilant, Andrew N.
    Washington, Benjamin J.
    Davis, Mary J.
    Stuart, Brittany G.
    Jenkins, Thomas M.
    [J]. SCIENCE, 2020, 368 (6495) : 1103 - +
  • [76] Concentrations, Distribution, and Persistence of Perfluoroalkylates in Sludge-Applied Soils near Decatur, Alabama, USA
    Washington, John W.
    Yoo, Hoon
    Ellington, J. Jackson
    Jenkins, Thomas M.
    Libelo, E. Laurence
    [J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2010, 44 (22) : 8390 - 8396
  • [77] Geochemical and Hydrologic Factors Controlling Subsurface Transport of Poly- and Perfluoroalkyl Substances, Cape Cod, Massachusetts
    Weber, Andrea K.
    Barber, Larry B.
    LeBlanc, Denis R.
    Sunderland, Elsie M.
    Vecitis, Chad D.
    [J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2017, 51 (08) : 4269 - 4279
  • [78] Wei ZS, 2019, ENVIRON SCI-WAT RES, V5, P1814, DOI [10.1039/c9ew00645a, 10.1039/C9EW00645A]
  • [79] Wellington Laboratories, WELL LAB CAT 2016 20
  • [80] Wellington Laboratories, 537 US EPA WELL LAB