Estimation of a contaminant source in an estuary with an inverse problem approach

A great challenge today is conciliation of water resources utilization with the expansion of cities and human activities. Considering that the water quality of a given water body is necessarily evaluated through the analysis of some biological, physical and chemical parameters, mathematical and computational models able to describe the behavior of such parameters can be a useful tool, given their ability to generate scenarios and, as a consequence, the possibility to support decisions regarding water resources management. In this work Inverse Problems techniques are applied to estimate the source parameters (location and intensity) of a hypothetical conservative pollutant released in estuarine waters. The case study considered here is the Macaé River estuary, located in the Brazilian southeast coast. The pollutant transport was modeled by the advection–diffusion equation. For the source location estimation were used the Luus–Jaakola (LJ), the particle collision algorithm (PCA) and the ant colony optimization (ACO) methods, and to estimate the source intensity was used the golden section (GS) method. In this study, synthetic sampling data of concentrations with and without noise were used. For the noiseless data, all methods have successfully achieved the objective function target low value in more than 95% of executions. On the other hand, for the data with ±5% of noise level, that happened only in 80% of the runs. Considering the number of different estimated points on the location and also the computational cost, the method LJ-GS showed the best performance. The results of this study demonstrated the feasibility of the inverse problem approach to estimate with satisfactory accuracy the location and intensity of a given pollutant source released in estuarine environments, which can also contribute to possible environmental liabilities identification.