Contamination of the terrestrial food chain by per- and polyfluoroalkyl substances (PFAS) and related human health risks: A systematic review

Hair analysis as a non-invasive method for assessing the exposure of wildlife to per- and poly-fluoroalkyl substances (PFAS)

In mammals, exposure to PFAS is usually assessed by measuring burdens in internal organs (i.e., liver and brain) or plasma, while less emphasis is devoted to non-invasive and non-destructive methods. We assess the suitability of hair as a non-invasive matrix for monitoring the exposure of mammals to 33 PFAS, including perfluoroalkyl carboxylic acids (PFCAs), perfluoroalkane sulfonic acids (PFSAs), perfluoroalkylether carboxylic acids (PFECAs), perfluoroalkylether sulfonic acids (PFESAs), perfluoroalkane sulfonyl fluoride-based substances (PASFs), and fluorotelomers (FTs). The Red fox is chosen as the target species due to its apical position in the terrestrial food web of the study area, the Cavallino-Treporti peninsula in North-East Italy. All analysed samples (n = 24) are positive for PFAS, with eight compounds quantified in all samples, including PFHxA, PFOA, PFDA, PFUnDA, PFDoDA, PFTriDA, PFTeDA, and PFOS. The highest mean concentration in hair samples is measured for PFOS (1.40 ± 0.48 ng g-1 dw) followed by PFDoDA (0.31 ± 0.05 ng g-1 dw), and PFOA (0.31 ± 0.19 ng g-1 dw), while the mean ∑33PFAS was 3.41 ± 0.93 ng g-1 dw. The dominance of PFOS and long-chain PFAS in the PFAS profile and the occurrence of compounds with even-numbered carbon chains at higher concentrations than the odd-numbered compounds with a one-carbon longer chain (i.e., PFOA > PFNA, PFDA > PFUnDA, PFDoDA > PFTriDA) suggest the trophic transfer along the terrestrial food web as the primary exposure pathway in the study area. The data suggest hair analysis as a reliable, non-invasive method for assessing the possible exposure of mammals to PFAS and suggested that the Red fox can be used as a sentinel of the environment, embracing the One Health perspective.

A machine learning multimodal profiling of Per- and Polyfluoroalkyls (PFAS) distribution across animal species organs via clustering and dimensionality reduction techniques

Per- and polyfluoroalkyl substances (PFAS) contamination in aquatic and terrestrial organisms poses significant environmental and health risks. This study quantified 15 PFAS compounds across various tissues (liver, kidney, gill, muscle, skin, lung, blood, breast, feather) from fish (Clarias gariepinus, Oreochromis niloticus, Lates niloticus, Tilapia zilli), livestock (camel, cow, sheep, ram, goat), and birds (pigeon, chicken, turkey). Among the fishes, C. gariepinus exhibited the highest PFAS accumulation, with PFOA (46.5 ng/g) and PFTrDA (50.1 ng/g) dominant in liver and kidney, while O. niloticus showed elevated PFTrDA (56.87 ng/g) and PFUnDA (29.43 ng/g). In livestock, camel liver contained high PFNA (9.22 ng/g), and cow liver had the highest PFOS (8.1 ng/g). Among the birds, pigeon liver showed the highest PFNA (7.83 ng/g). To analyze PFAS distribution patterns, dimensionality reduction and clustering techniques were employed. Principal Component Analysis (PCA) captured 68.28 % of total variance, revealing two distinct clusters whereby fish species are strongly related with higher PFAS concentration, while poultry showed unique PFAS profiles when compared to other types of meat. Clustering of PFOS, PFOA, and other PFAS compounds near the center explained their influence across the general meat types particularly the fish species, while t-Distributed Stochastic Neighbor Embedding (t-SNE) confirmed clear separations in high-dimensional space. Clustering analyses, including K-means, hierarchical clustering, DBSCAN, and Gaussian Mixture Models (GMM), identified well-defined patterns, with DBSCAN and GMM detecting overlapping categories and outliers. Feature importance analysis using a Random Forest model highlighted total PFAS as the most significant predictor, with PFHxA and PFDODA also contributing strongly, while organ type and species played a lesser role. These findings demonstrate the effectiveness of unsupervised learning techniques in characterizing PFAS bioaccumulation patterns across species and tissues, providing valuable information for ecological and toxicological risk assessments.

Unveiling PFAS hazard in European surface waters using an interpretable machine-learning model

Per- and polyfluoroalkyl substances (PFAS), commonly known as "forever chemicals", are ubiquitous in surface waters and potentially threaten human health and ecosystems. Despite extensive monitoring efforts, PFAS risk in European surface waters remain poorly understood, as performing PFAS analyses in all surface waters is remarkably challenging. This study developed two machine-learning models to generate the first maps depicting the concentration levels and ecological risks of PFAS in continuous surface waters across 44 European countries, at a 2-km spatial resolution. We estimated that nearly eight thousand individuals were affected by surface waters with PFAS concentrations exceeding the European Drinking Water guideline of 100 ng/L. The prediction maps identified surface waters with high ecological risk and PFAS concentration (>100 ng/L), primarily in Germany, the Netherlands, Portugal, Spain, and Finland. Furthermore, we quantified the distance to the nearest PFAS point sources as the most critical factor (14%-19%) influencing the concentrations and ecological risks of PFAS. Importantly, we determined a threshold distance (4.1-4.9 km) from PFAS point sources, below which PFAS hazards in surface waters could be elevated. Our findings advance the understanding of spatial PFAS pollution in European surface waters and provide a guideline threshold to inform targeted regulatory measures aimed at mitigating PFAS hazards.