Assessing uncertainty in pollutant build-up and wash-off processes

Area of Science:

• Environmental Science
• Hydrology
• Water Quality Modeling

Background:

• Stormwater runoff from urban areas carries pollutants, impacting water quality.
• Accurate modeling of pollutant build-up and wash-off is essential for effective mitigation strategies.
• Current models often fail to capture the inherent variability of these processes, leading to inaccurate predictions.

Purpose of the Study:

• To assess the importance of uncertainty in build-up and wash-off processes for stormwater quality models.
• To investigate methods for incorporating process variability into mathematical models.
• To understand the propagation of uncertainty from build-up to wash-off.

Main Methods:

• Modifying the mathematical form of existing stormwater quality models to include process variability.
• Analyzing the impact of incorporating variability on model prediction performance.
• Investigating uncertainty propagation from the build-up to the wash-off phase.

Main Results:

• Incorporating process variability into model structures improves the representation of variations in pollutant loads.
• Changes to model mathematical forms can account for uncertainty without significantly degrading prediction performance.
• Uncertainty in the build-up process substantially influences uncertainty in the wash-off process.

Conclusions:

• Accurate assessment of build-up and wash-off uncertainty is vital for informed decision-making in stormwater management.
• Mathematical models can be adapted to better represent process variability, enhancing stormwater pollution assessment.
• The behavior of fine particles (<150 μm) during build-up is a key factor driving uncertainty propagation in wash-off events.