4.6 Article

Modeling Bacterial Regrowth and Trihalomethane Formation in Water Distribution Systems

期刊

WATER
卷 13, 期 4, 页码 -

出版社

MDPI
DOI: 10.3390/w13040463

关键词

EPANET-MSX; chlorine; bacteria; drinking water; water distribution; multispecies; reactive-transport; trihalomethanes; water quality

资金

  1. Ministry of Science & Technology of the State of Israel
  2. Federal Ministry of Education and Research (BMBF), Germany

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A mechanistic model describing the interrelationship between chlorine, total organic carbon (TOC), and bacteria in water distribution systems was developed to analyze water quality variations. Results showed that increasing free chlorine concentration and reducing TOC at the source can decrease bacterial activity but increase Trihalomethane (THM) formation. Model outputs are sensitive to operating conditions and network characteristics.
The formation of bacterial regrowth and disinfection by-products is ubiquitous in chlorinated water distribution systems (WDSs) operated with organic loads. A generic, easy-to-use mechanistic model describing the fundamental processes governing the interrelationship between chlorine, total organic carbon (TOC), and bacteria to analyze the spatiotemporal water quality variations in WDSs was developed using EPANET-MSX. The representation of multispecies reactions was simplified to minimize the interdependent model parameters. The physicochemical/biological processes that cannot be experimentally determined were neglected. The effects of source water characteristics and water residence time on controlling bacterial regrowth and Trihalomethane (THM) formation in two well-tested systems under chlorinated and non-chlorinated conditions were analyzed by applying the model. The results established that a 100% increase in the free chlorine concentration and a 50% reduction in the TOC at the source effectuated a 5.87 log scale decrement in the bacteriological activity at the expense of a 60% increase in THM formation. The sensitivity study showed the impact of the operating conditions and the network characteristics in determining parameter sensitivities to model outputs. The maximum specific growth rate constant for bulk phase bacteria was found to be the most sensitive parameter to the predicted bacterial regrowth.

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