Embracing epistemic uncertainty: a risk evaluation method for pollutants in stormwater

Area of Science:

• Environmental Science
• Environmental Modeling
• Risk Assessment

Background:

• Pollutants of emerging concern (PECs) are inherently prone to epistemic uncertainty.
• Current pollutant modeling primarily uses parameter uncertainty, insufficient for structural model uncertainty.

Purpose of the Study:

• To propose a paradigm shift in pollutant modeling by incorporating epistemic uncertainty.
• To investigate the impact of epistemic uncertainty on pollutant distribution during wet-weather discharge.

Main Methods:

• Developed a novel modeling approach with an explicit term for epistemic uncertainties.
• Applied the approach to analyze pollutant mass distribution based on wet-weather discharge fluctuations.

Main Results:

• Epistemic uncertainty significantly impacts the tail of output pollutant distributions.
• Fluctuation time, linked to discharge-concentration covariance, is a key driver of output distributions.
• The approach identifies critical unknown information regarding pollutant concentration variability.

Conclusions:

• The proposed method enhances understanding of epistemic uncertainty in pollutant modeling.
• This approach aids in risk management and the design of targeted monitoring campaigns for PECs.