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Modeling Source Water TOC Using Hydroclimate Variables and Local Polynomial Regression.

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Predicting total organic carbon (TOC) in drinking water sources is crucial for controlling disinfection byproduct (DBP) formation. A new method uses climate and land surface data, avoiding streamflow, to model TOC and aid water treatment decisions.

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Area of Science:

  • Environmental Science
  • Water Quality Management
  • Climate Change Impact

Background:

  • Disinfection byproduct (DBP) formation in drinking water is linked to source water total organic carbon (TOC).
  • Existing TOC modeling methods often rely on streamflow data, which can be unavailable or difficult to ascertain, especially under climate change scenarios.
  • TOC concentration variability poses challenges for water treatment plants in meeting regulatory compliance.

Purpose of the Study:

  • To develop a novel modeling approach for predicting source water TOC concentrations.
  • To overcome limitations of streamflow-dependent models by incorporating climate and land surface variables.
  • To provide a tool for water utilities to manage DBP formation under changing environmental conditions.

Main Methods:

  • Proposed a local polynomial regression modeling approach.
  • Utilized climate variables (e.g., temperature) and land surface variables (e.g., soil moisture) as predictors for TOC concentration.
  • Validated the methodology using source water quality and climate data from three diverse surface water locations (river and reservoir).

Main Results:

  • The local polynomial regression models demonstrated good predictive skill for source water TOC concentrations across the studied locations.
  • The models effectively captured non-Gaussian and nonlinear relationships between predictors and TOC.
  • Predictive skill was observed to be lower in streams with significant anthropogenic influences.

Conclusions:

  • The developed modeling approach successfully predicts source water TOC without requiring streamflow data.
  • This method offers a valuable tool for water treatment utilities to anticipate TOC variability and manage DBP formation.
  • The findings support informed decision-making for regulatory compliance, particularly in the context of climate change impacts on water resources.