Predicting the concentration range of trace organic contaminants in recycled water using supervised classification

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dc.contributor.authorFarzanehsa, M.
dc.contributor.authorCarvajal, G.
dc.contributor.authorMcDonald, J.
dc.contributor.authorKhan, S.
dc.date.accessioned2024-09-09T16:21:30Z
dc.date.available2024-09-09T16:21:30Z
dc.date.issued2024-02
dc.descriptionTEXTO COMPLETO EN INGLÉS
dc.description.abstractTrace Organic Contaminants (TrOCs) have evidence for many health and environmental issues. Frequent monitoring of TrOC concentration is a time-consuming and costly process, which cannot be achieved easily. Identifying surrogate markers for these contaminants is a practical solution to monitor and ensure water quality. However, this topic is seldom explored in previous literature. This study aimed to find surrogate markers to predict concentration class (i.e., three classes: low concentration, medium concentration, high concentration) for a set of widely used pharmaceutical and personal care product TrOCs (e.g., Fluoxetine, Primidone, Saccharin, Sucralose to name a few) in recycled water from Melbourne Eastern Treatment Plant (ETP), Melbourne, Australia. For this purpose, three popular supervised learning classification algorithms namely Naïve Bayes, Random Forest and Support Vector Machines were utilized. Physicochemical parameters colour, Chemical Oxygen Demand (COD) and Total Organic Carbon (TOC) were found to be the top three predictive features for the majority of the investigated TrOCs. UV Transmittance (UVT) and the total amount of suspended solids (TSS) were the next frequent features. The Random Forest model resulted in the highest classification accuracy (≥73 %) for the majority of compounds. This paper presents evidence that with the acquired intelligence of supervised machine learning, the concentration range of hard to measure TrOCs in water can be predicted from a handful of low-cost and easy-to-measure physicochemical parameters.
dc.description.urihttps://www-sciencedirect-com.recursosbiblioteca.unab.cl/science/article/pii/S2214714423012291
dc.identifier.citationJournal of Water Process Engineering, Volume 58 , February 2024, 104709
dc.identifier.doihttps://doi.org/10.1016/j.jwpe.2023.104709
dc.identifier.issn2214-7144
dc.identifier.urihttps://repositorio.unab.cl/handle/ria/59977
dc.language.isoen
dc.publisherElsevier
dc.rights.licenseAttribution 4.0 International
dc.subjectTrace Organic Contaminants (TrOCs)
dc.subjectSurrogate markers
dc.subjectSupervised learning classification
dc.subjectPhysicochemical parameters
dc.subjectRandom Forest model
dc.subjectWater quality monitoring
dc.titlePredicting the concentration range of trace organic contaminants in recycled water using supervised classification
dc.typeArtículo
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