Peer Reviewed: Disinfection Byproducts: The Next Generation

Richardson, Susan D.; Simmons, Jane Ellen; Rice, Glenn

doi:10.1021/es022308r

Cited by 121 publications

(60 citation statements)

References 32 publications

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“…Disinfection by-products result from the reactions of chlorine with natural organic compounds in water (Rodriguez et al 2004;Ghebremichael et al 2008) and are associated with adverse health effects (Nieuwenhuijsen et al 2000;Richardson et al 2002;Nieuwenhuijsen 2005;Hebert et al 2010). …”

Section: Constitutive Equationsmentioning

confidence: 99%

Integration of Hydraulic and Water Quality Modelling in Distribution Networks: EPANET-PMX

Seyoum

Tanyimboh

2017

Water Resour Manage

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Simulation models for water distribution networks are used routinely for many purposes. Some examples are planning, design, monitoring and control. However, under conditions of low pressure, the conventional models that employ demand-driven analysis often provide misleading results. On the other hand, almost all the models that employ pressure-driven analysis do not perform dynamic and/or water quality simulations seamlessly. Typically, they exclude key elements such as pumps, control devices and tanks. EPANET-PDX is a pressure-driven extension of the EPANET 2 simulation model that preserved the capabilities of EPANET 2 including water quality modelling. However, it cannot simulate multiple chemical substances at once. The single-species approach to water quality modelling is inefficient and somewhat unrealistic. The reason is that different chemical substances may co-exist in water distribution networks. This article proposes a fully integrated network analysis model (EPANET-PMX) (pressure-dependent multi-species extension) that addresses these weaknesses. The model performs both steady state and dynamic simulations. It is applicable to any network with various combinations of chemical reactions and reaction kinetics. Examples that demonstrate its effectiveness are included.

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Section: Constitutive Equationsmentioning

confidence: 99%

Integration of Hydraulic and Water Quality Modelling in Distribution Networks: EPANET-PMX

Seyoum

Tanyimboh

2017

Water Resour Manage

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“…Therefore, 51 brands of bottled water imported from Gulf, Arab and European countries, in addition to three local brands, were analyzed for HAAs and dalapon. HAAs were detected in 31 brands from SA (13), UAE (7), Kuwait (3), four European countries (5) and two Arab countries (3). The lowest and highest THAA levels were 0.73 and 10.01 g/L, respectively.…”

Section: Bottled Watermentioning

confidence: 99%

“…Since the 1970s, research in the drinking water field has focused on documenting and understanding the occurrence of DBPs in drinking water. DBPs are formed when disinfectants react with natural organic matter (NOM) and/or inorganic substances (precursors) present in water [1][2][3][4][5][6]. More than 250 DBPs have been identified, but the behavioral profiles of only about 20 of these DBPs are adequately understood [7].…”

Section: Introductionmentioning

confidence: 99%

Survey of Haloacetic Acids in Household Drinking Water Produced from Thermal Desalination in Kuwait

Al–Mudhaf¹,

Abu-Shady²,

Selim³

et al. 2009

TOENVIRSJ

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Abstract:The main objectives of this study were to survey and document the existence of haloacetic acids (HAAs) in Kuwait drinking water. Levels of HAAs were determined in 516 samples collected from residential and government buildings and 51 brands of bottled water from December 2003 to May 2005. In household water, the levels of HAA5 were found to be between 85% and 99% (average of 95%) of the levels of total HAAs. HAA5 levels exceeded the MCL of US-EPA in 8% of the samples. The average percent increase of these levels was 106% of the MCL value. The use of charcoal filters showed significant efficiency in decreasing HAAs levels. The percent dominance frequencies of HAA5 components were in the order TCAA, MCAA, MBAA, DBAA, and DCAA with values 43%, 23%, 22%, 9% and 4%, respectively. Strong correlations between residual chlorine and HAA5 levels were observed. Seasonal variations indicated that HAA5 levels were much higher in the summer than in the winter.

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“…The reactions between NOM and chlorine form different types of DBPs such as trihalomethanes (THMs); haloacetic acids (HAAs); haloacetonitriles (HANs), haloketones (HKs), aldehydes, carboxylic acids, nitrosamines and cyanogen halides. The epidemiological studies have indicated that exposure to these by-products increases the risk of bladder cancer, colon-rectum cancer, leukemia, stomach and rectal cancers as well as miscarriage, low birth weight, and birth defects (Mills et al, 1998;IARC, 1991;Calderon, 2000;Gallard and Gunten, 2002;Richardson et al, 2002;Villanueva et al, 2004). In 1986, as part of the Safe Drinking Water Amendments, the US Environmental Protection Agency (USEPA) proposed the Disinfectants/DBPs Rule Stage I & II.…”

Section: Introductionmentioning

confidence: 99%

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

2016

Issue 4

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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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Peer Reviewed: Disinfection Byproducts: The Next Generation

Cited by 121 publications

References 32 publications

Integration of Hydraulic and Water Quality Modelling in Distribution Networks: EPANET-PMX

Integration of Hydraulic and Water Quality Modelling in Distribution Networks: EPANET-PMX

Survey of Haloacetic Acids in Household Drinking Water Produced from Thermal Desalination in Kuwait

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

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