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Related Experiment Video

Updated: Feb 7, 2026

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources
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Capturing Microbial Sources Distributed in a Mixed-use Watershed within an Integrated Environmental Modeling

Gene Whelan1, Keewook Kim2, Rajbir Parmar1

  • 1U.S. Environmental Protection Agency, Office of Research and Development, Athens, GA USA.

Environmental Modelling & Software : with Environment Data News
|August 7, 2018
PubMed
Summary

This study introduces a microbial source module for watershed models, calculating loading rates from land and point sources. It automates data integration for microbial source-to-receptor modeling in impacted catchments.

Keywords:
Integrated environmental modelingQMRAmanurepathogensrisk assessmentwatershed modeling

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Area of Science:

  • Environmental science
  • Hydrology
  • Microbial ecology

Background:

  • Watershed models often lack detailed microbial loading rates from various land and point sources.
  • Accurate microbial source tracking is crucial for understanding water quality in impacted catchments.

Purpose of the Study:

  • To develop and describe equations for microbial loading rates from diverse sources.
  • To integrate these equations into a modeling infrastructure for watershed-scale microbial source-to-receptor analysis.

Main Methods:

  • Formulated microbial loading rates for land-applied manure, direct animal/wildlife shedding, urban areas, and point sources.
  • Developed a microbial source module within a flexible modeling infrastructure.
  • Demonstrated a hypothetical application using real-world data for automated watershed assessment.

Main Results:

  • The microbial source module provides essential loading rate calculations.
  • The modeling infrastructure automates manual data processing steps.
  • Calibrated flow and microbial densities were achieved at the watershed's pour point.

Conclusions:

  • The developed module and infrastructure enhance watershed-scale microbial modeling capabilities.
  • Automated data handling improves the efficiency of watershed assessments.
  • The approach is effective for analyzing animal- and human-impacted catchments.