Development of statistical models for trihalomethane (THM) occurrence in a water distribution network in Central Thailand

Feungpean, Manunart; Panyapinyopol, Bunyarit; Elefsiniotis, P.; Fongsatitkul, Prayoon

doi:10.1080/1573062x.2013.871042

Cited by 13 publications

(13 citation statements)

References 21 publications

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“…Moreover, the results demonstrate that under these experimental conditions, indicating the variations of chlorine dosage, chlorine contact time, TOC concentration and SWCNT and MWCNT dosage, TTHM removal can be well described by multilinear regression technique. These findings are consistent with the results obtained by similar previous studies (Babei et al, 2015;Feungpean et al, 2015).…”

Section: Modelling the Tthm Removal By Cntssupporting

confidence: 94%

Development of statistical models for trihalomethane (THM) removal in drinking water sources using carbon nanotubes (CNTs)

Özdemır

Güngör

2018

WSA

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This research developed models using the multiple linear regression technique for prediction of trihalomethane (THM) removal from chlorinated drinking water sources through a combination of a coagulation process with carbon nanotubes (CNTs). Terkos Lake water (TLW), Buyukçekmece Lake water (BLW) and Ulutan Lake water (ULW) samples were coagulated by a conventional coagulant (alum) and increasing doses of single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) with the addition of alum. Also, chlorination experiments were conducted with water reservoirs from TLW, BLW and ULW, with different water quality regarding bromide concentration and organic matter content. The factors studied affecting THM removal were contact time, chlorine dose, coagulation process, total organic carbon (TOC), and specific ultraviolet absorbance (SUVA). Statistical analysis of the results focused on the development of multiple regression models, as Models 1 and 2, for predicting total trihalomethane (TTHM) based on the use of contact time, SWCNTs and MWCNTs doses, chlorine dose and TOC. When the two models were compared, Model 1 proved best suited to describe THM removal for the three water sources. The developed models provided satisfactory estimations of THM removal; the model regression coefficients for Models 1 and 2 were 0.88 and 0.77, respectively. Furthermore, the root-mean-square error (RMSE) values of 0.083 and 0.126 confirm the reliability of the two models. The results show that THM removal can be simply predicted by using the multiple linear regression technique in chlorinated drinking water sources.

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Section: Modelling the Tthm Removal By Cntssupporting

confidence: 94%

Development of statistical models for trihalomethane (THM) removal in drinking water sources using carbon nanotubes (CNTs)

Özdemır

Güngör

2018

WSA

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“…Concentrations of THMs are known to vary with season [ 12 , 13 , 14 ], consistent with our study findings. Reasons for seasonal variation in THM concentrations may include temperature differences [ 11 , 14 ], which are known to influence THM concentrations, as well as seasonal changes in water usage which may impact upon water residence times that also affect THM concentrations [ 14 ]. Because of issues with access to water sources, not all of the same sites in the same WDZs were available at all sampling events.…”

Section: Discussionmentioning

confidence: 99%

“…While within-WDZ variability of THM concentrations was not the focus of the present study, data from multiple sites within particular WDZs suggest that a relatively small degree of variability of THM concentrations may occur. The variability detected may relate to water residence time in WDZ [ 14 ], and pipe distance (i.e., from treatment plant to sampling point) [ 11 , 12 ]. For the most part, the range of values (within-season) did not exceed 30 µg/L.…”

Section: Discussionmentioning

confidence: 99%

“…Spatial and temporal variability in THM concentrations may occur both between and within WDZs. Variability may result from differences in the characteristics of the source water (namely soil variability, geology, whether it is surface or groundwater, and concentrations of natural organic matter, dissolved organic matter, and halide ions [ 10 ]), concentration of total and residual chlorine [ 11 ], season [ 12 , 13 , 14 ], temperature [ 11 , 14 ], as well as water residence time in WDZ [ 14 ], and pipe distance (i.e., from the treatment plant to sampling point) [ 11 , 12 ]. A local appreciation of the temporal and spatial variation in THM concentrations through WDZs also has implications for monitoring THM concentration in public water supplies.…”

Section: Introductionmentioning

confidence: 99%

“…Although the spatial and temporal variability of THM concentrations in public water supplies has been investigated previously [ 10 , 11 , 12 , 13 , 14 ], these studies have not been done in locations where brominated THMs (BrTHMs) account for >90% of TTHMs, such as in Perth, Australia [ 18 ]. The Swan Coastal Plain of Western Australia, including the metropolitan area of Perth, provides an ideal site for such an investigation due to high concentrations of BrTHMs in treated surface water, groundwater, and mixed water [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

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Spatial and Temporal Variability in Trihalomethane Concentrations in the Bromine-Rich Public Waters of Perth, Australia

Stanhope

Davidson

McAuley

et al. 2020

IJERPH

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High concentrations of trihalomethanes (THMs) in public water supplies potentially pose a health hazard, but exposure assessment remains a complex task. To interpret research findings and monitoring data for THMs, it is important to evaluate spatial and temporal variations in both total THM and the individual constituent compounds (including brominated species). We therefore aimed to determine the concentrations, and spatial and temporal variability of concentrations, of THMs public water supplies in Perth, Western Australia, which is known historically to have high brominated THM concentrations. We analysed water samples from 21 water distribution zones around Perth (including Busselton and Bunbury) across different seasons over a period of two years. A total of 250 samples provided a median total THM of 72 µg/L (range of 0–157 µg/L), which falls well within Australia’s National Health and Medical Research Council guidelines. The concentration of all species, including brominated forms, also fell the World Health Organization’s guidelines. Total THM concentrations were typically higher in spring and summer. A high degree of spatial variability was detected and appears to relate to the source water. Both the temporal and spatial variability in THM concentrations have implications for epidemiological studies, and monitoring.

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Modeling the formation of trihalomethanes in rural and semi-urban drinking water distribution networks of Costa Rica

Kelly-Coto

Jiménez

Mora-Campos

et al. 2022

Environ Sci Pollut Res

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Chlorination is one of the most important stages in the treatment of drinking water due to its effectiveness in the inactivation of pathogenic organisms. However, the reaction between chlorine and natural organic matter (NOM) generates harmful disinfection by-products (DBPs), such as trihalomethanes (THMs). In this research, drinking water quality data was collected from the distribution networks of 19 rural and semi urban systems that use water sources as: springs, surfaces, and a mixture of both, in three provinces of the Paci c slope of Costa Rica during April 2018 to September 2019. Twelve models were developed from four data sets: all water sources, spring, surface, and mixture of spring and surface waters. Linear, logarithmic, and exponential multivariate regression models were developed for each data set to predict the concentration of total trihalomethanes (TTHMs) in the distribution networks. Concentrations of TTHMs were found between < 0.20 to 91.31 µg/L, with chloroform being the dominant species accounting for 62% of TTHMs on average. Turbidity, free residual chlorine, total organic carbon (TOC), dissolved organic carbon (DOC) and ultraviolet absorbance at 254 nm (UV 254 ) showed a signi cant correlation with TTHMs. In all the data sets the linear models presented the best goodness-of-t and were moderately robust. Four models, the best of each data set, were validated with data from the same systems, and, according to the criteria of R 2 , SE, MSE and MAE, spring water and mixed spring/surface water models showed a satisfactory level of explanation of the variability of the data. Moreover, the models seem to better predict TTHMs concentrations below 30 µg/L. These models were satisfactory and could be useful for decision-making in drinking water supply systems and be considered in possible modi cations in current legislation. prediction in Costa Rica, and to the best of the authors knowledge in the Central American and Caribbean region. Furthermore, this study was focused in rural and semi urban areas, where no studies was found in the literature.In Costa Rica, 93% of the population received drinking water in 2019 (PEN and CONARE 2020). Moreover, in the same year, 19.4% of homes in rural and semi-urban areas were supplied with water by local Associations Administrators of Aqueduct and Sewerage Systems, (ASADAs in Spanish) (Sánchez-Hernández 2019). In addition, in 2016, 14.3% of the population was supplied by 24 municipalities and the rest by duly organized public companies (AyA 2016). The main water sources used are groundwater, springs, surface water and the mixture of the two latter ones, moreover, in all cases chlorine disinfection is the method used (Arellano-Hartig et al. 2020). In general, due to economic and analytical capacity limitations, monitoring of THMs is scarce, mainly at the ASADAs and municipal level. Thus, the objective of this study was to develop a series of prediction models of TTHMs in the distribution systems of rural and semi-urban areas supplied by springs, surface water and t...

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Development of statistical models for trihalomethane (THM) occurrence in a water distribution network in Central Thailand

Cited by 13 publications

References 21 publications

Development of statistical models for trihalomethane (THM) removal in drinking water sources using carbon nanotubes (CNTs)

Development of statistical models for trihalomethane (THM) removal in drinking water sources using carbon nanotubes (CNTs)

Spatial and Temporal Variability in Trihalomethane Concentrations in the Bromine-Rich Public Waters of Perth, Australia

Modeling the formation of trihalomethanes in rural and semi-urban drinking water distribution networks of Costa Rica

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