Predictive models for the formation of emerging disinfection by-products

Disinfection is a necessary process during drinking water treatment; however, there is potential risks due to the formation of disinfection by-products (DBPs). Besides the regulated DBPs (THMs and HAAs), there is a growing concern about emerging DBPs (eDBPs), which appear to be much more toxic than the regulated ones. Climate-driven conditions can affect the concentrations and profiles of DBPs and enhance the formation of eDBPs. Since eDBPs are not included in the current monitoring programs, there is a need to predict their concentrations in drinking water using modeling techniques. The aim of this study is the development and application of a predictive modeling framework for the formation of selected eDBPs (haloacetonitriles, haloacetamides, halonitromethanes and haloketones) under chlorination or chloramination conditions in drinking water treatment systems. Through multiple regression analysis, linear and logarithmic models are used based on a dataset that describes the formation of eDBPs under various climatic conditions (seawater intrusion, flooding events, algal blooming and enrichment by humic acid) that could impact water sources. The dataset includes information on the concentrations of eDBPs, water quality parameters and disinfection conditions. Besides DOC and UV₂₅₄, different forms of nitrogen appear to be a significant predicting parameters on the formation of eDBPs, as well as the dose and contact time of disinfection. The proposed relationships can be useful tools for the identification and control of eDBPs in drinking water.