Islam Nilufar, Sadiq Rehan, Rodriguez Manuel J, Legay Christelle
School of Engineering, University of British Columbia, Okanagan Campus, Kelowna, BC, V1V 1V7, Canada.
École supérieure d'aménagement du territoire et développement régional, Université Laval, Quebec City, QC, Canada.
Environ Monit Assess. 2016 May;188(5):304. doi: 10.1007/s10661-016-5306-3. Epub 2016 Apr 21.
Inactivating pathogens is essential to eradicate waterborne diseases. However, disinfection forms undesirable disinfection by-products (DBPs) in the presence of natural organic matter. Many regulations and guidelines exist to limit DBP exposure for eliminating possible health impacts such as bladder cancer, reproductive effects, and child development effects. In this paper, an index named non-compliance potential (NCP) index is proposed to evaluate regulatory violations by DBPs. The index can serve to evaluate water quality in distribution networks using the Bayesian Belief Network (BBN). BBN is a graphical model to represent contributing variables and their probabilistic relationships. Total trihalomethanes (TTHM), haloacetic acids (HAA5), and free residual chlorine (FRC) are selected as the variables to predict the NCP index. A methodology has been proposed to implement the index using either monitored data, empirical model results (e.g., multiple linear regression), and disinfectant kinetics through EPANET simulations. The index's usefulness is demonstrated through two case studies on municipal distribution systems using both full-scale monitoring and modeled data. The proposed approach can be implemented for data-sparse conditions, making it especially useful for smaller municipal drinking water systems.
灭活病原体对于根除水传播疾病至关重要。然而,在天然有机物存在的情况下,消毒会形成不良的消毒副产物(DBP)。存在许多法规和指南来限制DBP暴露,以消除可能的健康影响,如膀胱癌、生殖影响和儿童发育影响。本文提出了一个名为违规可能性(NCP)指数的指标,用于评估DBP的违规情况。该指数可用于使用贝叶斯信念网络(BBN)评估配水管网中的水质。BBN是一种图形模型,用于表示相关变量及其概率关系。选择总三卤甲烷(TTHM)、卤乙酸(HAA5)和自由余氯(FRC)作为预测NCP指数的变量。已经提出了一种方法,通过监测数据、经验模型结果(如多元线性回归)以及通过EPANET模拟的消毒剂动力学来实施该指数。通过对市政配水系统的两个案例研究,使用实际监测数据和模拟数据证明了该指数的实用性。所提出的方法可用于数据稀疏的情况,这使其对较小的市政饮用水系统特别有用。