Modeling Trihalomethane Formation in Drinking Water With Application to Risk-Based Decision-Making
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A model-based methodology for risk-based decision-making of water treatment and disinfection strategies that deal with the management of trihalomethanes (THMs) has been developed. Trihalomethanes (THMs) are by-products of chlorination for drinking water and have been characterized as possible and probable human carcinogens. Among THMs, bromodichloromethane (BDCM) has a much stronger association with stillbirths and neural tube defects than the other THMs species. The parameters affecting formation of THMs and BDCM were identified through multivariate statistical analysis of the Ontario Drinking Water Surveillance Program database and by reviewing publications. Formation of THMs is affected by chlorine dose, dissolved organic carbon, pH, temperature and reaction time; these parameters along with bromide ions have effects on brominated THMs formation in drinking water. Two models for predicting formation of THMs and BDCM in drinking water have been developed. Controlled experimental investigations were performed in laboratory following statistical design of the experiments using synthetic water samples. Using statistically significant parameters, models have been developed. The adequacies of the models were tested using appropriate statistical diagnostics and validation experiments. The models have been integrated into a risk-based decision making framework. Formation of THMs and BDCM were estimated by importing water quality and operational parameters into these models. The costs, technical feasibility and performances of different treatment and disinfection options were determined. Through the incorporation of multiple criteria (human health risk, cost, technical feasibility and disinfection performance), different treatment and disinfection strategies were evaluated and compared. A fuzzy logic stochastic approach has been utilized for the evaluation and tradeoff studies among different criteria. Overall, this thesis has continued to improve the environmental decision making process. The results of this thesis have provided a clearer understanding of parameters influencing THMs and BDCM formation in a complex environmental scenario. This thesis has presented a comprehensive approach to better control water quality and associated operational parameters through the use of predictive models and to perform decision making following a holistic approach. The results of this thesis will also assist in critical decision making for possible upgrading of water supply systems, analyzing uncertainty and complying with the regulatory limitations.