Spatial distribution and partitioning behavior of selected poly- and perfluoroalkyl substances in freshwater ecosystems: A French nationwide survey Gabriel Munoz a , Jean-Luc Giraudel a , Fabrizio Botta c , François Lestremau c , Marie-Hélène Dévier a , Hélène Budzinski b , Pierre Labadie b, ⁎ a University of Bordeaux, EPOC, UMR 5805, LPTC, 351 Cours de la Libération, F-33400 Talence, France. b CNRS, EPOC, UMR 5805, LPTC, 351 Cours de la Libération, F-33400 Talence, France. c INERIS, Parc Technologique Alata, BP2, 60550 Verneuil-en-Halatte, France. HIGHLIGHTS • A large-scale survey of PFASs in 133 French rivers and lakes is reported. • Descriptive statistics, correlations and partitioning coefficients were deter- mined. • Non-detects were taken into account using functions from the NADA R- package. • Hot spots of PFAS contamination were found near large urban and industrial areas. • Sediment levels were partly controlled by grain size and organic carbon con- tent. GRAPHICAL ABSTRACT abstract article info Article history: Received 7 December 2014 Received in revised form 12 February 2015 Accepted 12 February 2015 Available online xxxx Editor: D. Barcelo Keywords: Perfluoroalkyl substances Water Sediment Partitioning Artificial neural networks Non-detects 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 b LOD–725 ng L -1 in the dissolved phase (median: 7.9 ng L -1 ) and b 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 indus- trial 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 lo- cations and, in some cases, at watershed scale. For instance, several sites along the Rhône River displayed a pecu- liar PFAS signature, perfluoroalkyl carboxylates (PFCAs) often dominating the PFAS profile (e.g., PFCAs N 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 K d and log K oc partition coefficients; in that respect, the Re- gression 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 Science of the Total Environment 517 (2015) 48–56 ⁎ Corresponding author. E-mail address: pierre.labadie@u-bordeaux.fr (P. Labadie). http://dx.doi.org/10.1016/j.scitotenv.2015.02.043 0048-9697/© 2015 Elsevier B.V. All rights reserved. Contents lists available at ScienceDirect Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv