Water Waste Management and Mercury Trading: An Optimization Approach

Effluent trading to manage water pollution holds considerable potential for industries and policy makers alike. This paper proposes an optimization based ap-proach to assist decision making in pollutant trading which is beyond heuristics. The optimization model, formulated as an Mixed Integer Linear Programming (MILP) problem, allows decision makers to incorporate watershed and technology specific information in regulation development. The problem solution, as a consequence, suggests optimal approach to the industries to achieve the assigned load reduction targets. The basic model is extended to include health care costs to compare various Preprint submitted to Elsevier Science 20 October 2005 decisions and also contribute towards decision making. The effect of uncertainty on the problem solutions is also analyzed by formulating a chance constrained pro-gramming problem as the extension of the original MILP problem. This ensures that no hotspots are created due to discharge uncertainty. The optimization model is implemented on a watershed level mercury pollution reduction case study. The results, indicating significant cost reductions due to trading, also emphasize the im-portance of considering watershed specific data in decision making. Health care cost is shown to be an important parameter for comparison and effects of uncertainty are observed to be more pronounced at tighter regulations