Optimization of well field management to mitigate groundwater contamination using a simulation model and evolutionary...

Groundwater represents the most important available freshwater reserves and is of critical importance to global water and food security. Old environmental burdens that have led to the spread of contaminants in groundwater limit its use, thus interventions to mitigate contamination must often be carried out to ensure a safe drinking water supply. This study presents the optimization of well field management designs to reduce the desethylatrazine (DEA) concentration in the deep wells of the Brest Water Works (Central Slovenia). It investigates artificial recharge by injection wells using water from the nearby river and elaborates five well field management scenarios prioritizing different objectives. A multi-objective simulation-optimization framework was developed. A transient groundwater flow and solute transport model was applied to simulate the effects of the proposed recharge and pumping regimes. The shuffled complex evolution method was used to identify optimal values of well field management variables (location of injection well(s), minimum required injection rate, maximum pumping rate from production well) in the proposed scenarios. Model simulations showed that optimized well field management designs can significantly reduce DEA concentration in production wells (below 0.05 μg/L), assure compliance with water quality standards with (26%) reduced injection rate, and, with the implementation of two injection wells, achieve lower DEA concentration and higher pumping rate (up to 27 L/s). The optimization solutions depend on the defined well field management priorities and reveal a trade-off between the objectives (reduction of DEA concentration, increase of pumping rate, and reduction of injection rate). The impact of management variables on mitigation efficiency is not uniform and largely depends on the location of the injection well(s), which increases the complexity of mitigation design. The study has shown that the presented approach can be efficiently used for finding optimal mitigation designs and supporting water managers with information for planning mitigation measures.