The spatial distribution and partitioning of 22 poly- and perfluoroalkyl substances (PFASs) in 133 selected rivers and lakes were investigated at a nationwide scale in mainland France. ΣPFASs was in the range<LOD-725 ng L(-1) in the dissolved phase (median: 7.9 ng L(-1)) and <LOD-25 ng g(-1) dry weight (dw) in the sediment (median: 0.48 ng g(-1) dw); dissolved PFAS levels were significantly lower at "reference" sites than at urban, rural or industrial sites. Although perfluorooctane sulfonate (PFOS) was found to be the prevalent compound on average, a multivariate analysis based on neural networks revealed noteworthy trends for other compounds at specific locations and, in some cases, at watershed scale. For instance, several sites along the Rhône River displayed a peculiar PFAS signature, perfluoroalkyl carboxylates (PFCAs) often dominating the PFAS profile (e.g., PFCAs>99% of ΣPFASs in the sediment, likely as a consequence of industrial point source discharge). Several treatments for data below detection limits (non-detects) were used to compute descriptive statistics, differences among groups, and correlations between congeners, as well as log Kd and log Koc partition coefficients; in that respect, the Regression on Order Statistics (robust ROS) method was preferred for descriptive statistics computation while the Akritas-Theil-Sen estimator was used for regression and correlation analyses. Multiple regression results suggest that PFAS levels in the dissolved phase and sediment characteristics (organic carbon fraction and grain size) may be significant controlling factors of PFAS levels in the sediment.
Keywords: Artificial neural networks; Non-detects; Partitioning; Perfluoroalkyl substances; Sediment; Water.
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