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

机译:预测饮用水处理植物中碳质消毒副产品的模型:韩国案例研究

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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 mu g/L, and those for the HAAs from 6 to 7 mu g/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.
机译:饮用水处理厂中的氯化是控制有害病原体的关键过程。然而,氯与有机物的反应形成不希望的,有害和卤化消毒副产物。碳质消毒副产物,例如三卤代甲烷(THM)和卤乙酸(HAAs),是遗传毒性或致癌性,并以高浓度报告饮用水中。本研究旨在开发用于预测韩国饮水水处理厂中浓度水平的数学模型,因为未来为该国报告了这样做的尝试。该浓度水平范围为29至39μg/ L,以及6至7μg/ L的HAAs的浓度。对于ZHM和HAAs的多元回归模型,即线性和非线性,用于预测使用位于韩国首尔的三个治疗厂的数据集(2015年1月至2016年12月)中的水处理厂中的浓度水平。构建的模型纳入了主要因素和交互式和更高级变量。使用的主要因子变量是溶解有机碳,紫外线吸收,残余氯,溴化物,接触时间,氯剂量和用于处理水的温度,以及在植物中的原料和处理水的pH值。发现ZM和HAAs的线性模型是可以接受的,从水处理厂的测量值,发现可预测性值分别为0.915和0.772。从同一水处理厂验证了所开发的模型,以后数据集(2017年1月至2017年7月)。此外,该模型应用于两种不同的水处理厂。构造模型的应用和验证结果显示预测和观察值之间没有显着差异。

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