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Disinfection is an essential process for both potable water and wastewater treatment plants. However, disinfection byproducts (DBPs) like trihalomethanes (THMs), haloacetonitriles (HANs), and nitrosamines (NOAs) are formed when organic matter precursors react with disinfectants such as chlorine, chloramine, and ozone. Formation of DBPs is strongly associated with the type of water source, type of disinfectant, and organic matter concentration, which can have seasonal variation. In this study, water samples were collected from 20 different intra-watershed locations, which included urban runoff (with and without the influence of unsheltered homeless populations), wastewater effluent discharges, and a large, terminal reservoir that serves as the local drinking water source. Samples were collected on dry and rainy days, which represent seasonal samples. DBP formation potential (FP) tests were conducted at consistent pH, contact time, and temperature. THMs, NOAs, and HANs were analyzed by gas chromatography-mass spectrometry (GC-MS). The FP tests performed on these water samples revealed that chlorine formed the highest THM concentrations, while THM concentrations were low for the ozone FP test as expected. Chloramine produced the greatest HAN concentrations, with dichloroacetonitrile representing the highest concentration. With respect to sample type, more DBPs were formed at the non-wastewater-impacted runoff sites as compared to the wastewater effluent discharge sites. With respect to TOC levels, rain event samples for all locations had higher TOC concentrations compared to dry sampling days. Similarly, rain event samples showed increased DBP formation; a significant amount of precursors for THMs was found in runoff waters that were influenced by wastewater effluent discharges and unsheltered homeless locations (concentration of total THMs for chlorine FP test was >200 μg/L). Therefore, urban runoff waters should be considered as potential sources of DBP precursors to drinking water source waters, and runoff water is prone to seasonal variation.
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Predictive capability of THM models for drinking water treatment and distribution
Research and practice suggest markers of drinking water quality such as trihalomethanes (THMs), can change during treatment and distribution, potentially elevating health risk of end users. Models have been developed to predict THM formation at drinking water treatment plants (DWTP), in drinking water distribution systems (DWDS), and to a lesser extent, building premise plumbing (PP). The goal of this research was to evaluate the performance of published THM models and their development methodology, with the purpose of improving future THM model development. Water quality variable data were collected from literature and used as inputs for collected models. Mean and variance of model prediction values were used to measure THM model performance compared to THM data trends from literature. The research found differences in model formulation, water quality variable selection, and model development practices, despite evaluated models being statistical in nature. These differences lead to substantial inconsistencies in model output behavior. Diversity of data used for model development was found to be the most important factor for generalizable model prediction capabilities. Following these findings, a new framework was proposed to encourage novel strategies, data sharing, and collaboration among researchers and practitioners to improve THM model development, application, and performance. Potential use of machine learning techniques for future model development was also discussed based on findings.
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- Award ID(s):
- 2027444
- PAR ID:
- 10470500
- Publisher / Repository:
- RSC
- Date Published:
- Journal Name:
- Environmental Science: Water Research & Technology
- ISSN:
- 2053-1400
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/d3ew00308f
