Source attribution of poly- and perfluoroalkyl substances (PFASs) in surface waters from Rhode Island and the New York Metropolitan Area

Abstract

Exposure to poly and perfluoroalkyl substances (PFASs) has been associated with adverse health effects in humans and wildlife. Understanding pollution sources is essential for environmental regulation but source attribution for PFASs has been confounded by limited information on industrial releases and rapid changes in chemical production. Here we use principal component analysis (PCA), hierarchical clustering, and geospatial analysis to understand source contributions to 14 PFASs measured across 37 sites in the Northeastern United States in 2014. PFASs are significantly elevated in urban areas compared to rural sites except for perfluorobutane sulfonate (PFBS), N-methyl perfluorooctanesulfonamidoacetic acid (N-MeFOSAA), perfluoroundecanate (PFUnDA) and perfluorododecanate (PFDoDA). The highest PFAS concentrations across sites were for perfluorooctanate (PFOA, 56 ng L^-1) and perfluorohexane sulfonate (PFOS, 43 ng L^-1) and PFOS levels are lower than earlier measurements of U.S. surface waters. PCA and cluster analysis indicates three main statistical groupings of PFASs. Geospatial analysis of watersheds reveals the first component/cluster originates from a mixture of contemporary point sources such as airports and textile mills. Atmospheric sources from the waste sector are consistent with the second component, and the metal smelting industry plausibly explains the third component. We find this source-attribution technique is effective for better understanding PFAS sources in urban areas.

Figure 1

Concentrations of PFASs measured in surface waters from Rhode Island and the New York Metropolitan Area. Full names of individual compounds are listed in Table S2. N-MeFOSAA and N-EtFOSAA are not shown but were detected in ~70% of the samples at concentrations <1 ng/L.

Figure 2

Multivariate statistical analysis of surface water data. Panel (A) shows loadings of principal components analysis (PCA) and Panels (C) and (D) show score plots for three components across sampling sites. Panel (B) compares PCA results to hierarchical clustering of compounds and sites. Sites with statistically distinct PFAS profiles are indicated on plots (C) and (D) and highlighted on the hierarchical clustering diagram. The three principal components together explain 80% of the variance in PFAS composition.