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Chloramine demand estimation using surrogate chemical and microbiological parameters.

Sina Moradi1, Sanly Liu1, Christopher W K Chow2

  • 1School of Chemical Engineering, The University of New South Wales, Sydney, NSW 2052, Australia.

Journal of Environmental Sciences (China)
|June 26, 2017
PubMed
Summary

A new model estimates chloramine demand in drinking water by analyzing water quality and microbial factors. This tool helps water treatment operators manage chloramine disinfection effectively.

Keywords:
Chloramine demandDrinking water treatment plantsModelling

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Area of Science:

  • Water treatment technologies
  • Environmental chemistry
  • Microbiology

Background:

  • Chloramine is a key disinfectant in drinking water, but its decay rate is influenced by various water quality parameters.
  • Accurate estimation of chloramine demand is crucial for maintaining effective disinfection and preventing byproduct formation.
  • Existing methods for predicting chloramine decay may not fully account for complex interactions between chemical and microbiological factors.

Purpose of the Study:

  • To develop and validate a predictive model for chloramine demand in full-scale drinking water supplies.
  • To identify key chemical (specific ultraviolet absorbance - SUVA) and microbiological (Fm) factors influencing chloramine decay.
  • To assess the model's applicability across diverse Australian water treatment systems.

Main Methods:

  • Development of a nonlinear regression model incorporating SUVA and Fm.
  • Laboratory analysis of water samples to determine decay rates.
  • Statistical analysis comparing model predictions with experimental data from multiple water treatment plants.
  • Incorporation of both fast and slow chloramine decay pathways into the model.

Main Results:

  • The developed model accurately simulates and estimates chloramine demand in real-world drinking water systems.
  • Statistical analysis confirmed the model's applicability across different Australian water sources.
  • Kinetic parameters (fast and slow decay rate constants) were found to be consistent for a given water source.
  • The model successfully elucidated chloramine loss across a wide range of water quality variations.

Conclusions:

  • The model provides a reliable method for estimating chloramine demand based on water quality and microbial activity.
  • This approach can serve as a valuable decision support tool for water treatment operators.
  • Effective management of chloramine disinfection is achievable with this predictive modeling technique.