Modeling urban pollutant wash-off processes with ecological memory

Urbanization increases the extent of impervious surfaces, runoff, sediment, and nutrient loadings downstream, leading to the deterioration of urban surface waters. During pollutant wash-off from urban surfaces, the peak concentration of pollutants typically occurs after the rainfall peak. However, current urban wash-off models do not consider this time delay, assuming that the effect of rainfall on the wash-off process is immediate. Ecological memory (EM) is defined as the capacity of past states or experiences to influence the present or future ecological responses of a community ecosystem. In this study, ecological memory was calculated to reflect the lagging impact of rainfall on the wash-off process. Incorporating ecological memory into the original wash-off model improved its performance across all pollutant types and rainfall conditions, with the adjusted R-squared value increasing from -0.01-0.61 to -0.08-0.83. Among the factors impacting ecological memory, rainfall intensity significantly affected the distributions of ecological memory (p < 0.05 according to the Kolmogorov-Smirnov test). The first flush phenomenon, which involves a larger concentration or mass of pollutants in the initial portion of a storm event compared to the rest, showed no significant difference in the distributions of ecological memory between rain events with and without this phenomenon. The improved urban wash-off model can be applied for real-time simulation of urban pollutants and help develop site-specific measures for reducing urban nonpoint source pollution.