English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Natural organic matter (NOM) has been identified as the prominent precursor for disinfection by-products (DBPs) formation during chlorination. Various studies have shown that the characteristics of NOM influence the Trihalomethanes (THMs) formation mechanism. The present study represents NOM categorization 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 effects of NOM on THMs formation mechanism. The high rate of dependency was found for each representative of NOM with respect to water quality characteristics and operational condition of disinfection process. Values of SUVA and UV254 is drastically reduced with respect from higher to a moderate chlorine dose which represent the 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 value of the model was found in the range of 0.927 to 0.937 from the developed model and a model could be recommended for prediction of THMs in drinking water.

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