Predictive capability of THM models for drinking water treatment and distribution
Predictive capability of THM models for drinking water treatment and distribution
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饮用水处理和分配的 THM 模型的预测能力
DOI：
10.1039/d3ew00308f
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Boyer, Treavor H.
中科院分区：
文献类型：
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作者：
Hogue, Derek;Mirchandani, Pitu B.;Boyer, Treavor H.
研究和实践表明，饮用水质量的标志物，如三卤甲烷（THMs），在处理和分配过程中可能会发生变化，可能会增加最终用户的健康风险。已经开发了模型来预测饮用水处理厂（DWTP），饮用水分配系统（DWDS）中的THM形成，并在较小程度上，建筑物前提管道（PP）。本研究的目标是评估已发布的THM模型及其开发方法的性能，以改进未来的THM模型开发。从文献中收集水质变量数据，并将其用作收集模型的输入。模型预测值的平均值和方差被用来衡量THM模型的性能相比，THM数据的趋势，从文献。研究发现，尽管评估的模型本质上是统计的，但在模型制定、水质变量选择和模型开发实践方面存在差异。这些差异导致模型输出行为的实质性不一致。用于模型开发的数据的多样性被认为是可推广的模型预测能力的最重要因素。根据这些发现，提出了一个新的框架，以鼓励新的策略，数据共享，以及研究人员和从业人员之间的合作，以提高THM模型的开发，应用和性能。还根据调查结果讨论了机器学习技术在未来模型开发中的潜在用途。
Research and practice suggest markers of drinking water quality such as trihalomethanes (THMs), can change during treatment and distribution, potentially elevating health risk of end users. Models have been developed to predict THM formation at drinking water treatment plants (DWTP), in drinking water distribution systems (DWDS), and to a lesser extent, building premise plumbing (PP). The goal of this research was to evaluate the performance of published THM models and their development methodology, with the purpose of improving future THM model development. Water quality variable data were collected from literature and used as inputs for collected models. Mean and variance of model prediction values were used to measure THM model performance compared to THM data trends from literature. The research found differences in model formulation, water quality variable selection, and model development practices, despite evaluated models being statistical in nature. These differences lead to substantial inconsistencies in model output behavior. Diversity of data used for model development was found to be the most important factor for generalizable model prediction capabilities. Following these findings, a new framework was proposed to encourage novel strategies, data sharing, and collaboration among researchers and practitioners to improve THM model development, application, and performance. Potential use of machine learning techniques for future model development was also discussed based on findings.