Journal
SCIENCE OF THE TOTAL ENVIRONMENT
Volume 517, Issue -, Pages 48-56Publisher
ELSEVIER
DOI: 10.1016/j.scitotenv.2015.02.043
Keywords
Perfluoroalkyl substances; Water; Sediment; Partitioning; Artificial neural networks; Non-detects
Categories
Funding
- French Ministry of Ecology
- French National Agency for Water and Aquatic Environments (ONEMA)
- River Basin agencies
- French National Research Agency (ANR) within Cluster of Excellence COTE [ANR-10-LABX-45]
- IdEx Bordeaux [ANR-10-IDEX-03-02]
- INTERREG ORQUE SUDOE project [SOE3/P2/F591]
- Aquitaine Regional Council
- European Union (CPER A2E)
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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. Sigma PFASs was in the range 99% of Sigma PFASs 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 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. (C) 2015 Elsevier B.V. All rights reserved.
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