Methodology for Identifying Optimal Locations of Water Quality Sensors in River Systems

Abstract

A method to optimally site river water quality sensors is expanded and applied to a case study river to explore the application of mathematical siting methods to the design of river sampling networks. Fecal coliform contamination due to flooded swine waste lagoons was modeled as it moves downstream, and optimal sensor locations are located by minimizing the objective function of the optimization problem. The results of the simulations are analyzed by varying the number of allowed sampling locations and the simulated contamination event. For the case study application, the model suggests three sampling locations along the modeled river section. These three suggested sensing points did not greatly vary in location for different river flows and contamination events, indicating the robustness of the model results for this specific case study. Generally, the application of mathematical contaminant modeling is a useful and systematic approach to aid the design of river water quality monitoring networks.

Optimizing the Design of an Estuarine Water Quality Monitoring Network by Optimal Control Techniques

“…The aim of our research is to present a novel and effective approach to the problem of the optimal sampling points allocation within a simulation-based optimization framework, in the spirit of previous works of the authors for the case of a river water quality monitoring system [12][13][14], although this previous one-dimensional issue was a much simpler problem, both from the simulation viewpoint and from the optimization one. Here, we formulate the problem as a two-level optimization problem, where the upper level problem is the optimal fixing of the sampling points locations -given by their ability to capture the correct information on intensity and location of possible pollution releases -and the lower level problems are related to the optimal determination of these pollution sources.…”