Identification of sewer pipes to be cleaned for reduction of CSO pollutant load

Abstract

To reduce the CSO (Combined Sewer Overflow) pollutant discharge, one of the effective options is cleaning of sewer pipes before rainfall events. To maximize the efficiency, identification of pipes to be cleaned is necessary. In this study, we discussed the location of pipe deposit in dry weather in a combined sewer system using a distributed model and investigated the effect of pipe cleaning to reduce the pollutant load from the CSO. First we simulated the dry weather flow in a combined sewer system. The pipe deposit distribution in the network was estimated after 3 days of dry weather period. Several specific pipes with structural defect and upper end pipes tend to have an accumulation of deposit. Wet weather simulations were conducted with and without pipe cleaning in rainfall events with different patterns. The SS loads in CSO with and without the pipe cleaning were compared. The difference in the estimated loads was interpreted as the contribution of wash-off in the cleaned pipe. The effect of pipe cleaning on reduction of the CSO pollutant load was quantitatively evaluated (e.g. the cleaning of one specific pipe could reduce 22% of total CSO load). The CSO simulations containing pipe cleaning options revealed that identification of pipes with accumulated deposit using the distributed model is very useful and informative to evaluate the applicability of pipe cleaning option for CSO pollutant reduction.