Chemosphere最新文献

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/policies/article-withdrawal)..

This study investigates the presence of perfluoroalkyl substances (PFAS) in the drinking water supplies in the Czech Republic using a risk-based monitoring approach. Tap water samples (n = 27) from sources close to areas potentially contaminated with PFAS were analysed. A total of 28 PFAS were measured using ultra-performance liquid chromatography with tandem mass spectrometry after solid phase extraction. Total PFAS concentrations (∑PFAS) varied from undetectable to 90.8 ng/L, with perfluoropentanoic acid (PFPeA), perfluorohexanoic acid (PFHxA), perfluoroheptanoic acid (PFHpA) and perfluorobutane sulfonic acid (PFBS) being the most abundant, detected in over 70% of samples. Risk-based monitoring in drinking water showed that commercial wells had higher PFAS levels compared to tap water, particularly C4-C9 perfluorocarboxylic acids (PFCAs), possibly due to proximity to industrial areas. However, the hypothesis that risk-based monitoring is more effective than random monitoring was not confirmed, possibly because specific sources did not produce the target PFAS or because of the wide range and less obvious sources of potential contamination. The study also assessed exposure risks and compliance with regulatory thresholds. Weekly intake estimates for adults and children indicated that regular consumption of most contaminated water sample would exceed the tolerable weekly intake. Compared to EU regulations, none of the tap water samples exceeded the 'Sum of PFAS' parametric value of 100 ng/L, though one sample approached this limit. In addition, surface water samples from the Jizera River (n = 21) showed a wider range of PFAS, with C7-C10 PFCAs, PFBS, and perfluorooctane sulfonic acid (PFOS) in every sample, with higher PFOS concentrations at a median of 2.56 ng/L. ∑PFAS concentrations increased downstream, rising from 1.08 ng/L near the spring to 26 ng/L downstream. This comprehensive analysis highlights the need for detailed/areal monitoring to also address hidden or non-obvious sources of PFAS contamination.

This study focused on analyzing the spatial and vertical distributions of 28 per- and polyfluoroalkyl substances (PFASs), which comprised five precursors and three alternatives, in the water columns of the regional seas surrounding South Korea, such as the Yellow Sea (YS, Y1-Y10), East China Sea (ECS, EC1-EC6), South Sea (SS, S1-S5), and East Sea (ES, E1-E7). The concentrations of these PFASs detected in 204 seawater samples varied from below the limit of detection (-1 in the YS, 0.26-17 ng L^-1 in the ECS, 0.08-3.4 ng L^-1 in the SS, and -1 in the ES, with perfluorooctanoic acid being identified as the most abundant compound. Principal component analysis grouped water masses and regions based on PFASs concentrations and compositions, enabling the identification of PFASs sources and their fate. PFASs are mainly derived from land and are transported via ocean currents, where their compositions tend to remain conservative. PFASs entering the YS are likely conveyed to the ES through ECS and SS, following the northward movement of the Taiwan Warm Current and Kuroshio Current. The ECS serves as a mixing zone for PFASs from various sources. This study provides valuable baseline data for understanding PFASs transport and the characteristics of water masses in the regional seas around South Korea.

Even at trace concentrations, micropollutants, including pesticides and pharmaceuticals, pose considerable ecological risks, and the increasing presence of synthetic chemical substances in aquatic systems has emerged as a growing concern. Moreover, limited machine-learning (ML) approaches exist for analyzing environmental data, and the increasing complexity of ML models has made it challenging to understand predictor-outcome relationships. In particular, understanding complex interactions among multiple variables remains challenging. This study applies and integrates explainable ML techniques and network analysis to identify the sources of micropollutants in a large watershed and determine the factors affecting micropollutant levels. We assessed the performance of four ML algorithms-support vector machine, random forest, extreme gradient boosting (XGB), and autoencoder-XGB-in predicting micropollutant levels based on the spatial characteristics of the watershed. We applied the synthetic minority oversampling technique to address the data imbalance. The XGB model demonstrated superior predictive performance, particularly for high concentration levels, achieving an accuracy of 87%-99%. Shapley additive explanations (SHAP) analysis identified temperature and rainfall as significant factors. Moreover, agricultural activities contributed to pesticide pollution, whereas urban activities contributed to pharmaceutical contamination. The network analysis corroborated the SHAP findings and revealed event-specific contamination characteristics. This included distinct discharge pathways during a dry summer event and shared pathways during a wet winter event. This approach enhances an understanding of contamination sources and pathways and subsequently aids in developing control measures and making informed policy decisions to preserve water quality in mixed land-use areas.

Polycyclic aromatic hydrocarbons (PAHs) are a diverse class of chemicals that occur in complex mixtures including parent and substituted PAHs. To understand the hazard posed by complex environmental PAH mixtures, we must first understand the structural drivers of activity and mode of action of individual PAHs. Understanding the toxicity of alkylated PAHs is important as they often occur in higher abundance in environmental matrices and can be more biologically active than their parent compounds. 104 alkylated PAHs were screened from 11 different parent compounds with emphasis on substituted phenanthrenes and their structurally dependent toxicity differences. Using a high-throughput early life stage zebrafish assay, embryos were exposed to concentrations between 0.1 and 100 μM and assessed for morphological and behavioral outcomes. The aryl hydrocarbon receptor (AHR) is often implicated in the toxicity of PAHs and the induction of cytochrome P4501A (cyp1a) is an excellent biomarker of Ahr activation. Embryos were evaluated for cyp1a induction using a fluorescence reporter line. Alkyl and polar phenanthrene derivatives were further assessed for spatial cyp1a expression and Ahr dependence of morphological effects. In the alkyl PAH screen 35 (33.7%) elicited a morphological or behavioral response and of those 23 (65%) also induced cyp1a. 31 (29.8%) of the chemicals only induced cyp1a. Toxicity varied substantially in response to substitution location, the amount of ring substitutions and alkyl chain length. Cyp1a induction varied by parent compound group and was a poor indicator of morphological or behavioral outcomes. Polar phenanthrenes were more biologically active than alkylated phenanthrene derivatives and their toxicity was not dependent upon the Ahr2, Ahr1a or Ahr1b when tested individually, despite cyp1a induction by 50% of polar phenanthrenes. Our results demonstrated that induction of cyp1a did not always correlate with PAH toxicity or Ahr dependence and that the type and location of phenanthrene substitution determined potency.

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/policies/article-withdrawal). .