A modified robustness index for assessing operational performance of drinking water treatment plants: a comparative study within a new regulatory framework

IF 11.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2024-10-22 DOI:10.1016/j.watres.2024.122668
Federica De Marines, Santo Fabio Corsino, Alida Cosenza, Marco Capodici, Michele Torregrossa, Gaspare Viviani
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Abstract

Drinking water treatment plants (DWTPs) are facing emerging challenges affecting raw water quality. In addition, the new regulatory framework (EU 2184/2020) sets stricter limits for turbidity and percentile statistics for continuous compliance, demanding greater robustness of the treatment processes. To achieve this aim, this study proposes a turbidity robustness index (TRI), named TRI95B, to be used as a warning tool for detecting deviations from water quality standards. TRI95B has been compared with the TRIs existing in the literature. Furthermore, the TRI95B validation has been performed by a three-year monitoring dataset of a full-scale DWTP. The proposed TRI95B index has two key novelties compared to the existing indices required for adapting to the new drinking water regulation: i. introduces the 95th percentile as a statistical indicator; ii. considers an additional term that sets an alert when a threshold value is exceeded.The comparison results suggest a better correspondence to the real plant performances of TRI95B than the other TRIs. Indeed, both the sensitivity and specificity of TRI95B were significantly higher than the other TRIs, indicating a better capacity to correctly classify both positive and negative cases. Moreover, while the previous TRIs identify a critical operating condition when the turbidity goal was significantly exceeded, TRI95B highlights a failure condition at a lower discrepancy. Therefore, TRI95B is also able to identify short-duration and low magnitude failures, thus coping with the purpose of the new regulation for drinking water.

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用于评估饮用水处理厂运行性能的修正稳健性指数:新监管框架内的比较研究
饮用水处理厂(DWTP)正面临着影响原水水质的新挑战。此外,新的监管框架(EU 2184/2020)规定了更严格的浊度限制和百分位数统计,以确保持续达标,这就要求处理工艺具有更强的鲁棒性。为实现这一目标,本研究提出了一种名为 TRI95B 的浊度稳健性指数 (TRI),作为检测水质标准偏差的预警工具。TRI95B 与现有文献中的 TRI 进行了比较。此外,TRI95B 还通过一个全规模污水处理厂的三年监测数据集进行了验证。与适应新饮用水法规所需的现有指数相比,拟议的 TRI95B 指数有两个关键的创新点:i. 引入了第 95 百分位数作为统计指标;ii. 考虑了当超过阈值时设置警报的附加项。事实上,TRI95B 的灵敏度和特异性都明显高于其他 TRIs,这表明 TRI95B 能够更好地正确分类阳性和阴性案例。此外,以往的 TRIs 在浊度目标明显超标时会识别出临界运行状态,而 TRI95B 则会在较低的偏差时突出显示故障状态。因此,TRI95B 还能识别持续时间短、程度低的故障,从而满足饮用水新法规的要求。
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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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