Strategies for reduction of micropollutant (MP) discharges from stormwater drainage systems require accurate estimation of the potential MP removal in stormwater treatment systems. However, the high uncertainty commonly affecting stormwater runoff quality modelling also influences stormwater treatment models. This study identified the major sources of uncertainty when estimating the removal of copper and zinc in a retention pond and a biofilter by using a conceptual dynamic model which estimates MP partitioning between the dissolved and particulate phases as well as environmental fate based on substance-inherent properties. The two systems differ in their main removal processes (settling and filtration/sorption, respectively) and in the time resolution of the available measurements (composite samples and pollutographs). The most sensitive model factors, identified by using Global Sensitivity Analysis (GSA), were related to the physical characteristics of the simulated systems (flow and water losses) and to the fate processes related to Total Suspended Solids (TSS). The model prediction bounds were estimated by using the Generalized Likelihood Uncertainty Estimation (GLUE) technique. Composite samples and pollutographs produced similar prediction bounds for the pond and the biofilter, suggesting a limited influence of the temporal resolution of samples on the model prediction bounds. GLUE highlighted model structural uncertainty when modelling the biofilter, due to disregard of plant-driven evapotranspiration, underestimation of sorption and neglect of oversaturation with respect to minerals/salts. The results of this study however illustrate the potential for the application of conceptual dynamic fate models base on substanceinherent properties, in combination with available datasets and statistical methods, to estimate the MP removal in different stormwater treatment systems and compare with environmental quality standards targeting the dissolved MP fraction.
Water Research, 2012, Vol 46, p. 6891-6903
Stormwater pollution control; Dynamic models; Heavy metals; Stormwater treatment model; Micropollutants; Global sensitivity analysis; Uncertainty analysis