Modeling urban pollutant wash-off processes with ecological memory

Area of Science:

• Environmental Science
• Hydrology
• Water Quality

Background:

• Urbanization leads to increased impervious surfaces, causing degraded water quality through runoff.
• Existing urban wash-off models fail to account for the time delay between rainfall and peak pollutant concentration.

Purpose of the Study:

• To introduce and quantify ecological memory in urban wash-off processes.
• To improve the accuracy of urban surface water pollutant models.

Main Methods:

• Ecological memory was calculated to represent the lagging effect of rainfall on pollutant wash-off.
• An existing urban wash-off model was modified to include ecological memory.
• Statistical tests (Kolmogorov-Smirnov) were used to analyze the impact of rainfall intensity.

Main Results:

• Incorporating ecological memory significantly improved model performance for all pollutants and rainfall conditions (R-squared increased).
• Rainfall intensity was a significant factor influencing ecological memory distributions (p < 0.05).
• The first flush phenomenon did not significantly alter ecological memory distributions.

Conclusions:

• The enhanced urban wash-off model provides a more accurate simulation of pollutant dynamics.
• The model can support real-time monitoring and the development of effective nonpoint source pollution control strategies.