Efficiency of a dry detention basin with a biofilter as an outlet (DDBBO) in treating stormwater pollutants

Efficiency of a dry detention basin with a biofilter as an
outlet (DDBBO) in treating stormwater pollutants

Dry Detention Basin with a Biofilter as an Outlet (DDBBO) system is currently playing an essential role as Water Sensitive Urban Design (WSUD) measure due to its pleasing appearance and benefits in the management of peak flows, runoff volume, pollutant removal and groundwater recharge. Unfortunately, the effectiveness of this device and the environmental benefits when implemented in urban settings have not been appropriately evaluated and validated due to the lack of fieldwork data. Therefore, this study provided an exciting research opportunity to increase our knowledge in the performance treatment efficiency of this system under real storm events and specific site conditions as those presented in Toowoomba.

This study focused on assessing the effectiveness of a DDBBO system located in the suburb of Glenvale in Toowoomba for the removal of Total Nitrogen (TN), Total Phosphorous (TP) and Total Suspended Solids (TSS). The study obtained data for real storm events and developed models using eWater’s MUSIC modelling software. The results have been compared with the objectives set out by the Australian legislation and verified with the findings from the literature. This study considered six storm events from which 36 samples were taken at the inlet and outlet of the DBBOO system via automatic sampling devices to subsequently being tested in the Laboratory. The results were then used to calculate the pollutant loads by applying three different mathematical techniques (regression, average and ratio estimator). The removal efficiency of the DDBBO was also analysed by implementing three different approaches (efficiency ratio, the summation of loads and regression). The results produced an average per cent removal efficiency of 58% for TSS, 17% for TP and 42% for TN that demonstrated that the DDBBO could facilitate the removal of pollutants. However, some negative values were reported for TN and TP for some of the individual sampled events. This may be explained due to denitrification processes generated by the organic decomposition of grass clipping as a result of maintenance activities and resettling and resuspension of sediment particles at the bottom of the DDBBO, which could not be picked up in the observed data for the selected events. A longer-term monitoring program is recommended to be implemented to validate the performance of the DDBBO system.

The fieldwork results were compared against the results obtained from the MUSIC model developed for this study. The results showed that observed TSS and TN inflow concentrations were considerably lower than the lower deviation level set by the model. While, for TP, it was found that 50% of the samples were within the upper and lower levels and the remaining 50% fell below the lower deviation level. The model showed that the predicted removal efficiency for TSS was considerably higher than those figures reported in the field-observed study. While for TN and TP the model reported a better prediction. This study concluded that the TN, TSS and TP observed data were below the removal targets established by the legislation.

This study did demonstrate that the DDBBO at Glenvale could be effective at removing pollutant loads. However, the results from this study need to be used with caution as the number of samples fell below the minimum protocol (SQIDEP) requirements for stormwater quality treatment devices. Nevertheless, valuable information was gained in this study that could be used in future research projects that investigate DDBBO systems or similar structures in urban settings.