Minimizing the Ecological Risk of Combined-Sewer Overflows in an Urban River System by a System-Based Approach

Abstract

As urban and suburban areas expand, the problem of sewage disposal spreads as well. Inappropriate planning of a sewage management system could impair water quality, destroy habitat, and threaten public health. Simply building a sewage interceptor system along the urban river corridor to handle the wastewater effluents without regard to the impacts from combined-sewer overflows (CSOs) in the storm events cannot fulfill the ultimate goal of environmental restoration in the receiving water body. This study therefore carries out a system-based assessment to search for the optimal operating strategy of the interceptor facilities with respect to biocomplexity or biodiversity in an urban river system. In particular, it focuses on the richness of the fish community in the biological systems, the effect of stress on the fish community by storm events, and their capacity for adaptive behavior in response to the CSOs’ impact in the Love River estuarine system, South Taiwan. By integrating the biological indicators in an environmental context, two simulation models describing the quality and quantity of storm water and their impact on the river water quality are calibrated and verified. The interactions of natural systems and engineered systems covering both spatial and temporal aspects can then be explored in terms of the predicted levels of dissoved oxygen (DO) along the river reaches so as to strengthen an ultimate optimal search for the best operational alternative for the interceptor system. In view of the inherent complexity of integrating simulation outputs at various scales to aid in building the optimization step, three regression submodels were derived beforehand. These submodels present a high potential for exhibiting, eliciting, and summarizing the nonlinear behavior between the CSO impacts and the DO levels in the river reaches. With the aid of such findings, this study finally applies a linear programming model to determine the optimal size of a constructed storage pond (i.e., a detention pond), based on several types of storm events in the study area. This is proved essential for minimizing the ecological risk in such a way so as to indirectly improve the biodiversity in the estuarine river system.