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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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Machine Learning Applications for Chemical Fingerprinting and Environmental Source Tracking Using Non-target Chemical Data
A frequent goal of chemical forensic analyses is to select a panel of diagnostic chemical featurescolloquially termed a chemical fingerprintthat can predict the presence of a source in a novel sample. However, most of the developed chemical fingerprinting workflows are qualitative in nature. Herein, we report on a quantitative machine learning workflow. Grab samples (n = 51) were collected from five chemical sources, including agricultural runoff, headwaters, livestock manure, (sub)urban runoff, and municipal wastewater. Support vector classification was used to select the top 10, 25, 50, and 100 chemical features that best discriminate each source from all others. The cross-validation balanced accuracy was 92− 100% for all sources (n = 1,000 iterations). When screening for diagnostic features from each source in samples collected from four local creeks, presence probabilities were low for all sources, except for wastewater at two downstream locations in a single creek. Upon closer investigation, a wastewater treatment facility was located ∼3 km upstream of the nearest sample location. In addition, using simulated in silico mixtures, the workflow can distinguish presence and absence of some sources at 10,000-fold dilutions. These results strongly suggest that this workflow can select diagnostic subsets of chemical features that can be used to quantitatively predict the presence/absence of various sources at trace levels in the environment.
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- PAR ID:
- 10320407
- Date Published:
- Journal Name:
- Environmental Science & Technology
- ISSN:
- 0013-936X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1021/acs.est.1c06655
