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<p>Lake inlets serve as critical pathways for land-based pollutants, significantly affecting the ecological safety of aquatic environments. Perfluoroalkyl acids (PFAAs), including perfluoroalkyl carboxylic acids (PFCAs) and perfluoroalkane sulfonic acids (PFSAs), are persistent pollutants of global concern; however, their ecological impacts in inlet areas remain largely unknown. This study focuses on ten major inflow rivers of Chaohu Lake. The risk quotient (RQ) method, species sensitivity distribution (SSD), absolute principal component score-multiple linear regression (APCS-MLR), and partial least squares structural equation modeling (PLS-SEM) were comprehensively applied to systematically analyze the occurrence characteristics, ecological risks, and driving mechanisms of PFAAs. The results showed that a total of 20 PFAAs were detected, with total concentrations ranging from 22.50 to 41.87&#xa0;ng/L. Short-chain PFCAs (PFBA and PFPeA) and short-chain PFSAs (PFBS) were the dominant pollutants. The SSD results indicated that the ecological risk of PFAAs was relatively low, predominantly classified as no risk or low risk, with no medium or high risk observed. PFODA exhibited low risk and was identified as a relatively high-risk monomer requiring priority attention. Source apportionment revealed that municipal sewage (34.5%), fluoropolymer-related pollution sources (25.8%), surface runoff-driven non-point sources (17.6%), and fluorochemical industrial wastewater (22.1%) were the main contributors. PLS-SEM analysis showed that the occurrence of PFAAs was significantly positively driven by environmental factors such as nitrogen, phosphorus, and COD, with nitrogen and phosphorus enrichment being key factors influencing the distribution of short-chain PFAAs. This study reveals the multi-factor driving mechanisms of PFAAs and provides scientific assessment and precise management of emerging pollutants.</p>

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Distribution characteristics and multi-factor driving mechanisms of perfluoroalkyl acids at lake inlets

  • 期刊论文

  • 发表日期：

    2026年4月29日

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  • Chenyu Tao,
  • Jiasheng Li,
  • Longyu Cheng,
  • Jian Huang,
  • Hua Zhang,
  • Zihao Fan,
  • Tao Luo

摘要

Lake inlets serve as critical pathways for land-based pollutants, significantly affecting the ecological safety of aquatic environments. Perfluoroalkyl acids (PFAAs), including perfluoroalkyl carboxylic acids (PFCAs) and perfluoroalkane sulfonic acids (PFSAs), are persistent pollutants of global concern; however, their ecological impacts in inlet areas remain largely unknown. This study focuses on ten major inflow rivers of Chaohu Lake. The risk quotient (RQ) method, species sensitivity distribution (SSD), absolute principal component score-multiple linear regression (APCS-MLR), and partial least squares structural equation modeling (PLS-SEM) were comprehensively applied to systematically analyze the occurrence characteristics, ecological risks, and driving mechanisms of PFAAs. The results showed that a total of 20 PFAAs were detected, with total concentrations ranging from 22.50 to 41.87 ng/L. Short-chain PFCAs (PFBA and PFPeA) and short-chain PFSAs (PFBS) were the dominant pollutants. The SSD results indicated that the ecological risk of PFAAs was relatively low, predominantly classified as no risk or low risk, with no medium or high risk observed. PFODA exhibited low risk and was identified as a relatively high-risk monomer requiring priority attention. Source apportionment revealed that municipal sewage (34.5%), fluoropolymer-related pollution sources (25.8%), surface runoff-driven non-point sources (17.6%), and fluorochemical industrial wastewater (22.1%) were the main contributors. PLS-SEM analysis showed that the occurrence of PFAAs was significantly positively driven by environmental factors such as nitrogen, phosphorus, and COD, with nitrogen and phosphorus enrichment being key factors influencing the distribution of short-chain PFAAs. This study reveals the multi-factor driving mechanisms of PFAAs and provides scientific assessment and precise management of emerging pollutants.