Monitoring and modeling of trihalomethanes (THMs) for a water treatment plant in Istanbul

Uyak, Vedat; Toröz, İsmail; Meriç, Süreyya

doi:10.1016/j.desal.2004.10.023

Cited by 59 publications

(22 citation statements)

References 20 publications

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“…(Morris and Baum, 1978;Stevens et al, 1976;Fleischacker and Randtke, 1983;Reckhow and Singer, 1985) observed that the chlorination of most of organic compounds led to large quantities of chloroform at higher pH values. Similar correlations between pH and THMs were also showed by other researchers (Peters et al, 1980, Kavanaugh et al, 1980, Uyak et al, 2005and Elsheikh and Basiouny, 2011.…”

Section: Thm Formation Kinetics At Different Phsupporting

confidence: 74%

Modeling and characterization of natural organic matter and its relationship with the THMs formation

2016

Global NEST Journal

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Natural organic matter (NOM) has been identified as the prominent precursor for disinfection by-products (DBPs) formation during chlorination. Various studies have suggests that the characteristics of NOM influence the Trihalomethanes (THMs) formation potential to the large extent. The present study represents the NOM characterisation in terms of total organic carbon (TOC), dissolved organic carbon (DOC), UV absorbance at 254 nm wavelengths (UV254) and specific ultraviolet absorbance (SUVA) to investigate the effect of NOM on THMs formation mechanism. The high rate of dependency was observed for each representative of NOM with respect to water quality characteristics and operational condition of disinfection process. In this study, values of SUVA and UV254 have been drastically reduced with respect to variable chlorine dose which represent the significance of chlorine contact is more predominant with hydrophobic fractions of NOM. The value of SUVA is decreasing with respect to temperature and reaction time, which reveled higher rate of utilization for hydrophobic fractions of NOM. Predictive modeling approach was carried out using multiple regression analysis with the combination of two surrogates at each stage of modeling with help of operational condition of disinfection process and water quality characteristics. The R 2 value of the model was found in the range of 0.927 to 0.937 from the developed model and thus present model could be recommended for prediction of THMs in drinking water.

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Section: Thm Formation Kinetics At Different Phsupporting

confidence: 74%

Modeling and characterization of natural organic matter and its relationship with the THMs formation

2016

Global NEST Journal

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“…In the case of pH and THMs formation, only CHBr 3 shows relatively significant correlation among the other THMs. In contrast to positive correlation among TOC and THMs in published literature (Uyak et al 2005) there was negative or weak correlation (r = -0.278) was observed in TOC and total THM formation in the present study. Several factors can account for such effects of TOC and total THMs.…”

Section: Methodscontrasting

confidence: 55%

Speciation and geospatial analysis of disinfection byproducts in urban drinking water

Siddique

Saied

Zahir

et al. 2013

Int. J. Environ. Sci. Technol.

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Trihalomethanes (THMs) are the most abundant disinfection byproducts (DBPs) of the chlorination disinfection. THMs speciation and their geospatial distribution were examined in 58 locations throughout the water distribution network of Karachi city. THMs (CHCl 3 , CHCl 2 Br, CHClBr 2 and CHBr 3 ) and physico-chemical parameters (pH, TDS, DO, Residual chlorine, temperature and TOC) were determined. CHCl 3 was the major THM found in all water samples of 58 locations, which accounted for 91.69 % of the total THMS followed by CHCl 2 Br (5.69 %), CHClBr 2 (1.78 %) and CHBr 3 (0.85 %). Total THMs level exceed the maximum contamination level of WHO and USEPA at some locations. Varying nature of correlation from high to low was found within THMs and among the physico-chemical parameters. GIS linked geospatial analysis revealed the association of THMs level with demographical and geological based variations from east to west of Karachi city. Continuous monitoring program and legislation for the contaminant levels were suggested to avoid adverse public health impact of THMs in drinking water supplies.

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“…However, a negative relationship between FRC and categories of THM4 presence was obtained. This result could be explained by the consumption of FRC to form THMs (Courtis et al 2009b;Rodriguez and Sérodes 2005;Uyak et al 2005). For the temporal fixed effect, spring, summer and fall were significant with winter as a reference.…”

Section: Single-level Modelmentioning

confidence: 75%

“…In fact, water quality or characteristics of location for which CBP levels are modelled are not integrated. Other authors use in their modelling the same type of water quality parameters as in previous studies and additional parameters such as residual and consumed chlorine, but in this case relative to each specific sampling site within DS (Chaib and Moschandreas 2006;Courtis et al 2009a;Li and Zhao 2006;Lou and Chiang 1994;Rodriguez and Sérodes 2005;Rodriguez et al 2003;Uyak et al 2005). However, these models do not consider the presence of spatial associations between CBP measurements in drinking water and integrate all the explanatory variables at the same level (i.e.…”

Section: Previous Modelling Of Cbp Within Dsmentioning

confidence: 94%

“…According to the literature, previous modelling efforts that focused on CBP evolution and occurrence in DS were based on multivariate statistical regression analysis and, in some cases, on the kinetics of CBP formation (Abdullah et al 2003;Chaib and Moschandreas 2006;Courtis et al 2009a;GarciaVillanova et al 1997;Li and Zhao 2006;Lou and Chiang 1994;Rodriguez and Sérodes 2005;Rodriguez et al 2004Rodriguez et al , 2003Uyak et al 2005). Based on field or laboratory-scale databases (generally of small size), these models usually estimate the variability of CBPs in DS in function of operational and water quality parameters associated with water treatment plants.…”

Section: Previous Modelling Of Cbp Within Dsmentioning

confidence: 99%

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Multi-level modelling of chlorination by-product presence in drinking water distribution systems for human exposure assessment purposes

Legay

Rodriguez

Miranda-Moreno

et al. 2010

Environ Monit Assess

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During drinking water treatment and distribution, chlorine reacts with organic matter occurring in water to form various chlorination by-products (CBPs) such as trihalomethanes (THMs) and haloacetic acids (HAAs). This paper presents the occurrence of THMs and HAAs in different water distribution systems (DS) of the same region and their modelling for exposure assessment purposes. This study was conducted in eight DS supplying chlorinated water to the population of Québec City, Canada. These systems differ in type of water source (i.e. surface, ground or mixed water), in treatment applied at the plant, and in size and structure of the DS. Two spatio-temporal databases for THMs and HAAs were implemented, one for model development and the other for model validation. The analysis of the data demonstrates significant seasonal and spatial variations of these compounds. A multi-level statistical modelling approach was applied to estimate the ranges for occurrence of THMs and HAAs in the eight DS (i.e. a single model for the study region for each CBP species). The modelling approach integrates available or easily measurable parameters. For both THMs and HAAs, a two-level model considering a sampling-site random effect was selected among various models initially developed. The model capacity for estimating the presence of THMs and HAAs in drinking water and its usefulness for exposure assessment purposes in the studied region was demonstrated.

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Monitoring and modeling of trihalomethanes (THMs) for a water treatment plant in Istanbul

Cited by 59 publications

References 20 publications

Modeling and characterization of natural organic matter and its relationship with the THMs formation

Modeling and characterization of natural organic matter and its relationship with the THMs formation

Speciation and geospatial analysis of disinfection byproducts in urban drinking water

Multi-level modelling of chlorination by-product presence in drinking water distribution systems for human exposure assessment purposes

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