A clustering approach based on high-resolution ecological vulnerability index reveals spatial patterns of per- and polyfluoroalkyl substances pollution in lakes on the Tibetan Plateau

IF 12.4 1区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2025-03-08 DOI:10.1016/j.watres.2025.123461

Xu Han , Baozhu Pan , Zhile Pan , Nan Xu , Jiang Wu , Weiling Sun , Bowen Hou , Yanran Dong

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Abstract

Per- and polyfluoroalkyl substances (PFAS) are persistent organic pollutants (POPs) with toxicity, chemical stability, and long-range transport potential. The transport and accumulation mechanisms of PFAS in specific or typical lakes have been reported. In the wake of global PFAS pollution, it is more important to unravel the distribution patterns of PFAS across larger-scale, multi-lake systems. However, traditional lake classification methods are often overly simplistic and inflexible to adapt to large lake systems with complex ecological characteristics. Here, an improved ecological vulnerability index (EVI) was introduced and applied for the first time to classify lakes in a regional, multi-lake study of PFAS pollution. We evaluated the effectiveness of EVI that integrated multi-dimensional environmental factors in revealing PFAS distribution in 12 lakes on the Tibetan Plateau. The results showed that the composition, concentration, and diversity of PFAS in water and sediment samples significantly differed between high-vulnerability lakes (HVL) and low-vulnerability lakes (LVL) clustered by EVI. The linear regression of PFAS concentration and diversity on EVI was most pronounced at the 1-km buffer zone scale compared to larger scales. EVI was strongly associated with PFAS concentration and diversity in HVL dominated by natural factors, and these associations were weakened in LVL with prevalent human interference. Our findings indicate the greater potential of EVI to predict the spatial patterns of PFAS in lakes at smaller scales and across regions with comparable dominance of natural factors. The proposed clustering approach is adaptable, as the indicators and weights in the EVI system can be adjusted based on regional ecological characteristics. This study provides a tool for unveiling the distribution patterns of PFAS and their driving mechanisms in complex lake environments.

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基于高分辨率生态脆弱性指数的聚类方法揭示了青藏高原湖泊全氟和多氟烷基物质污染的空间模式

全氟烷基和多氟烷基物质（PFAS）是持久性有机污染物（POPs），具有毒性、化学稳定性和远距离传输潜力。已有研究报道了PFAS在特定或典型湖泊中的运移和积累机制。在全球PFAS污染之后，揭示PFAS在更大尺度、多湖泊系统中的分布格局更为重要。然而，传统的湖泊分类方法往往过于简单，缺乏灵活性，无法适应具有复杂生态特征的大型湖泊系统。在区域多湖PFAS污染研究中，首次引入改进的生态脆弱性指数（EVI）对湖泊进行分类。基于多维环境因子的EVI对青藏高原12个湖泊PFAS的分布特征进行了评价。结果表明，EVI聚类的高脆弱性湖泊（HVL）与低脆弱性湖泊（LVL）水体和沉积物样品中PFAS的组成、浓度和多样性存在显著差异。PFAS浓度和多样性对EVI的线性回归在1 km缓冲带尺度上最为显著。EVI与自然因素主导的HVL中PFAS浓度和多样性密切相关，而在人为干扰普遍的LVL中，这些关联减弱。研究结果表明，EVI在预测湖泊PFAS的空间格局方面具有更大的潜力，而且在自然因子具有相当优势的小尺度和跨区域。本文提出的聚类方法适应性强，可以根据区域生态特征调整EVI系统的指标和权重。该研究为揭示复杂湖泊环境中PFAS的分布格局及其驱动机制提供了工具。

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来源期刊

Water Research 环境科学-工程：环境

CiteScore

20.80

自引率

9.40%

发文量

1307

审稿时长

38 days

期刊介绍： Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.