Modeling stochastic load variations in sewer systems

Area of Science:

• Environmental science and engineering
• Wastewater treatment technologies
• Chemical risk assessment

Background:

• Micropollutants in wastewater pose challenges for detection and quantification due to dynamic load fluctuations.
• Accurate sampling is difficult when substance variation is unknown, complicating environmental risk assessments.
• Understanding micropollutant fate is crucial for effective wastewater treatment and environmental protection.

Purpose of the Study:

• To develop a predictive model for stochastic load variations of micropollutants in sewer systems.
• To validate the model's predictions using real-world data for a specific chemical compound.
• To provide a framework for forecasting chemical loads for various substances and catchments.

Main Methods:

• Gathering population and consumption data from existing databases.
• Integrating household activity and appliance usage characteristics into the model.
• Developing a concept to model stochastic load variations in sewer systems.
• Validating model predictions with a high-frequency measuring campaign for benzotriazole.

Main Results:

• Successfully predicted realistic short-term variations in benzotriazole loads.
• Validated the model's accuracy through a high-frequency measurement campaign.
• Demonstrated the applicability of the method for other household chemicals.

Conclusions:

• The developed model effectively forecasts stochastic load variations for micropollutants in sewer systems.
• This approach aids in planning measurement campaigns and estimating loads from combined sewer overflows.
• The method provides valuable input for environmental modeling and risk assessment of chemical compounds.