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模型预测饮用水处理厂中碳质消毒副产物的形成:以韩国为例。

Models for predicting carbonaceous disinfection by-products formation in drinking water treatment plants: a case study of South Korea.

机构信息

Department of Civil and Environmental Engineering, Dankook University, Yongin-si, Gyeonggi-do, 448-701, Republic of Korea.

出版信息

Environ Sci Pollut Res Int. 2020 Jul;27(20):24594-24603. doi: 10.1007/s11356-019-05490-7. Epub 2019 Jun 26.

Abstract

Chlorination in a drinking water treatment plant is the critical process for controlling harmful pathogens. However, the reaction of chlorine with organic matter forms undesirable, harmful, and halogenated disinfection by-products. Carbonaceous disinfection by-products, such as trihalomethanes (THMs) and haloacetic acids (HAAs), are genotoxic or carcinogenic and are reported at high concentration in drinking water. This study is aimed at developing a mathematical model for predicting concentration levels of THMs and HAAs in drinking water treatment plants in South Korea because no previous attempts to do so have been reported for the country. The THMs concentration levels ranged from 29 to 39 μg/L, and those for the HAAs from 6 to 7 μg/L. Multiple regression models, i.e., both linear and nonlinear, for THMs and HAAs were developed to predict their concentration levels in water treatment plants using datasets (January 2015 to December 2016) from three treatment plants located in Seoul, South Korea. The constructed models incorporated principal factors and interactive and higher-order variables. The principal factor variables used were dissolved organic carbon, ultraviolet absorbance, residual chlorine, bromide, contact time, chlorine dose and temperature for treated water, and pH for both raw and treated water at the plant. The linear models for both THMs and HAAs were found to give acceptable fits with measured values from the water treatment plants and predictability values were found to be 0.915 and 0.772, respectively. The models developed were validated with a later dataset (January 2017 to July 2017) from the same water treatment plants. In addition, the models were applied to two different water treatment plants. Application and validation results of the constructed model showed no significant differences between predicted and observed values.

摘要

在饮用水处理厂进行氯化是控制有害病原体的关键过程。然而,氯与有机物的反应会形成不良的、有害的、含卤素的消毒副产物。含碳消毒副产物,如三卤甲烷(THMs)和卤乙酸(HAAs),具有遗传毒性或致癌性,并且在饮用水中报告的浓度很高。本研究旨在开发一种用于预测韩国饮用水处理厂中 THMs 和 HAAs 浓度水平的数学模型,因为此前该国没有尝试过这样做。THMs 浓度范围为 29 至 39μg/L,HAAs 浓度范围为 6 至 7μg/L。使用来自韩国首尔的三个处理厂的数据集(2015 年 1 月至 2016 年 12 月),针对 THMs 和 HAAs 开发了线性和非线性的多元回归模型,以预测其在水处理厂中的浓度水平。所构建的模型纳入了主要因素以及交互和更高阶变量。使用的主要因素变量是溶解有机碳、紫外吸光度、余氯、溴化物、接触时间、处理水的氯剂量和温度以及处理厂中原水和处理水的 pH 值。发现 THMs 和 HAAs 的线性模型与水处理厂的实测值拟合良好,可预测性值分别为 0.915 和 0.772。使用来自同一水处理厂的后续数据集(2017 年 1 月至 2017 年 7 月)对所开发的模型进行了验证。此外,还将模型应用于两个不同的水处理厂。构建模型的应用和验证结果表明,预测值与观测值之间没有显著差异。

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