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dc.contributor.authorErsan, Gamze
dc.contributor.authorErsan, Mahmut S.
dc.contributor.authorKanan, Amer
dc.contributor.authorKaranfil, Tanju
dc.date.accessioned2021-06-08T11:50:25Z
dc.date.available2021-06-08T11:50:25Z
dc.date.issued2021-05-31
dc.identifier.citationGamze Ersan , Mahmut S. Ersan , Amer Kanan , Tanju Karanfil , Predictive modeling of haloacetonitriles under uniform formation conditions, Water Research (2021), doi: https://doi.org/10.1016/j.watres.2021.117322en_US
dc.identifier.otherhttps://doi.org/10.1016/j.watres.2021.117322
dc.identifier.urihttps://dspace.alquds.edu/handle/20.500.12213/6398
dc.descriptionHighlights • HAN model linearity is highly affected by water type • pH is the most influential parameter for HAN modeling for chlorination and chloramination • A positive correlation between DON/DOC term and HANs models was observed in EfOM impacted waters • The r2 of DCAN model in chlorination (r2=0.88) was higher than chloramination (r2=0.49) • Higher correlation between HAN4 and THM4 was found at pH range of 7 to 8en_US
dc.description.abstractThe objective of this study was to develop models to predict the formation of HANs under uniform formation conditions (UFC) in chlorinated, choraminated, and perchlorinated/chloraminated waters of different origins. Model equations were developed using multiple linear regression analysis to predict the formation of dichloroacetonitrile (DCAN), HAN4 (trichloroacetonitrile [TCAN], DCAN, bromochloroacetonitrile [BCAN], and dibromoacetonitrile [DBAN]) and HAN6 (HAN4 plus monochloroacetonitrile, monobromoacetonitrile). The independent variables covered a wide range of values, and included ultraviolet absorbance, dissolved organic carbon, dissolved organic nitrogen, SUVA254, bromide, pH, oxidant dose, contact time, and temperature. The r2 values of HAN4 and HAN6 models of NOM, AOM, and EfOM impacted waters were within the range of 60-88%, while the r2 values of HAN4 and DCAN models for both groundwater and distribution systems were lower, in the range of 41-66%. The r2 values for the DCAN model were mostly higher in the individual types as compared to the cumulative analysis of all source water data together. This was attributed to differences in HAN precursor characteristics. For chlorination, among all variables, pH was found to be the most significant descriptor in the model equations describing the formation of DCAN, HAN4, and HAN6, and it was negatively correlated with HAN formation in the distribution system, groundwater, AOM, and NOM samples, while it showed an inverse relationship with HAN6 formation in effluent organic matter (EfOM) impacted waters. During chloramination, pH was the most influential model descriptor for DCAN formation in the NOM. Prechlorination dose was the most predominant parameter for prechlorination/chloramination, and it was positively correlated with HAN4 formation in AOM impacted waters.en_US
dc.language.isoen_USen_US
dc.publisherWater Researchen_US
dc.subjectHaloacetonitrilesPredictive ModelingUniform Formation ConditionsChlorinationChloraminationPrechlorinationen_US
dc.titlePredictive modeling of haloacetonitriles under uniform formation conditionsen_US
dc.typeArticleen_US


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