Clostridium perfringens as an alternative fecal indicator bacteria in surface water quality: a national investigation in Chinese rivers

IF 11.4 1区环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL

Water Research Pub Date : 2025-07-18 DOI:10.1016/j.watres.2025.124223

Wen Li, Qingbin Yuan, Xin Li, Miaomiao Teng, Zixuan Zhang, Yi Luo, Fengchang Wu

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Abstract

Fecal indicator bacteria (FIB) are widely used to assess microbial contamination in surface water. However, traditional FIB such as fecal coliforms and enterococcus often fail to reflect pathogen-related health risks due to differences in environmental persistence and behavior. This study evaluated Clostridium perfringens (C. perfringens) as an alternative FIB through a national-scale survey of 116 sites across seven major Chinese river basins impacted by anthropogenic contamination. C. perfringens was detected at all sites, with a median concentration of 700 CFU/100 mL, substantially higher than fecal coliforms (26 CFU/100 mL) and enterococcus (4 CFU/100 mL). It showed stronger correlations with priority bacterial pathogens (Shigella, Salmonella, Vibrio cholerae) and with microbial source tracking markers of human and livestock origin. A significant wastewater treatment plant downstream decay trend (k = -0.21, p < 0.05), aligned with key bacterial pathogens, further supports its diagnostic potential. Using reverse quantitative microbial risk assessment, generalized additive models, and species sensitivity distribution analysis, we derived a health-based guideline of 174 CFU/100 mL for C. perfringens, corresponding to the WHO benchmark of bacterial pathogens (10^-6 DALYs per person per year for drinking, swimming, and vegetable ingestion exposure). Based on this threshold, 55-100% of sites in Chinese rivers exceeded acceptable risk levels, far surpassing exceedance rates under current fecal coliform criteria. This represents the first large-scale application of C. perfringens as a risk-based indicator and highlights its potential to strengthen microbial water quality monitoring and public health protection.

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产气荚膜梭菌在我国河流地表水水质中的指示菌研究

粪便指示菌（FIB）被广泛用于地表水微生物污染评价。然而，由于环境持久性和行为的差异，粪便大肠菌群和肠球菌等传统FIB往往不能反映病原体相关的健康风险。本研究通过对中国7个受人为污染影响的主要河流流域116个站点的调查，评价了产气荚膜梭菌（C. perfringens）作为一种可替代的FIB。所有部位均检出产气荚膜梭菌，中位浓度为700 CFU/100 mL，显著高于粪便大肠菌群（26 CFU/100 mL）和肠球菌（4 CFU/100 mL）。它显示出与优先细菌性病原体（志贺氏菌、沙门氏菌、霍乱弧菌）以及与人类和牲畜来源的微生物来源跟踪标记有较强的相关性。显著的污水处理厂下游衰减趋势(k = -0.21,p <；0.05)，与关键细菌病原体一致，进一步支持其诊断潜力。通过反向定量微生物风险评估、广义加性模型和物种敏感性分布分析，我们得出了产气荚膜梭菌174 CFU/100 mL的健康指南，与世卫组织细菌性病原体基准（每人每年饮酒、游泳和蔬菜摄入暴露10-6 DALYs）相对应。基于这一阈值，中国河流中55% -100%的站点超过了可接受的风险水平，远远超过了现行粪便大肠菌群标准下的超标率。这是首次大规模应用产气荚膜荚膜杆菌作为一种基于风险的指标，并突出了其在加强微生物水质监测和公共卫生保护方面的潜力。

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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.