饮用水库中季节性总大肠菌群动态

IF 11.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL
Carlos Eduardo Veras, John Tobiason, Amanda Carneiro Marques, Yuehlin Lee, Emily Kumpel
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引用次数: 0

摘要

维持高质量的饮用水供应水库对保护公众健康很重要。尽管采取了广泛的流域保护措施,但水库仍然可能出现季节性的总大肠菌群浓度升高,这是通常用于监管的指示细菌。本研究旨在了解10年来总大肠菌群浓度与一系列水质、土壤和气象参数之间的关系,以确定受保护流域和清澈、少营养水域中总大肠菌群浓度升高的潜在原因和相关性。利用长期数据,我们进行了广泛的数据分析和数据驱动模型,以调查Quabbin油藏(美国马萨诸塞州)的这些关系。数据分析和数据驱动的建模结果表明,藻类、有机质、干燥条件、水温和溶解氧等指标与水库中总大肠菌群的增加最为相关。尽管指示细菌(如总大肠菌群)经常用于常规监测,但我们的研究结果强调,它们的增殖不太可能表明水库中可能存在风险升高。研究水库水质纯净,变异性低,粪便细菌指标水平低,无外界污染迹象;因此,夏季总大肠菌群的高浓度可能是一种自然过程。此外,利用机器学习方法来利用监测机构收集的长期常规数据,可以更好地了解如何在快速变化的气候中保持饮用水供应水库的高质量地表水。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Seasonal Total Coliform Dynamics in a Drinking Water Reservoir
Maintaining high-quality drinking water supply reservoirs is important for protecting public health. Despite extensive watershed protection efforts, reservoirs can still experience seasonal, elevated total coliform bacteria concentrations, indicator bacteria commonly used for regulations. This study aimed to understand associations between concentrations of total coliform and an array of water quality, soil, and meteorological parameters over 10 years to identify potential causes and correlations of elevated total coliform bacteria concentrations in a protected watershed and clear, oligotrophic waters. Leveraging long-term data, we performed extensive data analysis and a data-driven model to investigate these relationships in the Quabbin Reservoir (Massachusetts, USA). Data analysis and data-driven modeling results indicated that proxies of algae, organic matter, and dry conditions, as well as water temperature and dissolved oxygen, were most associated with increased total coliforms in the reservoir. Although indicator bacteria such as total coliform are frequently used for routine monitoring, our findings highlight that it was unlikely that their proliferation is indicating a likely elevated risk in the reservoir. The studied reservoir has pristine water quality with low variability and low fecal bacteria indicator levels with no sign of external contamination; therefore, the high concentrations of total coliform bacteria in the summer is likely an autochthonous process. Additionally, applying machine learning methods to leverage long-term routine data collected by monitoring agencies highlights opportunities to better understand how to maintain high-quality surface water in drinking water supply reservoirs through a rapidly changing climate.
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来源期刊
Water Research
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.
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