CHANCE-CONSTRAINED OPTIMAL MONITORING NETWORK DESIGN FOR POLLUTANTS IN-GROUND WATER

A mathematical model for designing a ground-water-quality monitoring n etwork is developed that links a ground-water pollution-transport simu lation model and an optimization model. Tritium is considered as the ( radioactive) pollutant. The model is formulated using chance constrain ts and solved by using a mixed-integer programming algorithm. It incor porates uncertainties in the prediction of pollutant movement in the s aturated zone. Nonlinearities due to the inclusion of cumulative distr ibution functions (CDFs) of actual spatial concentrations are accommod ated in the optimization model through a piecewise linearization schem e. The design of the optimal monitoring network is based on the soluti on of two mathematical models: a simulation model for the prediction o f radioactive pollutant transport in the saturated zone, and an optimi zation model. Constraints of the optimization model are formulated by incorporating results from the prediction-simulation model. The simula tion model provides information about pollution transport with respect to time and space. The chance-constrained optimization model solution specifies the optimal location of the monitoring wells subject to the maximum limit on the number of such wells. Performance evaluation of the developed model demonstrates potential applicability of this model for designing ground-water-quality monitoring networks.