Developing real time operating rules for trading discharge permits in rivers: Application of Bayesian Networks

Transferable discharge permit (TDP) programs show potential cost-effective methods of pollution control in river systems. Nevertheless, there remain uncertainties that, if not adequately addressed, might impair their success. Trading Ratio System (TRS) suggested by Hung and Shaw [2005. A trading-ratio system for trading water pollution discharge permits. Journal of Environmental Economics and Management 49, 83–102] is a cost-effective tool for water quality management in river systems, which provides the optimum trading pattern among dischargers. TRS has been designed for a single conservative water quality variable and the existing uncertainties are not incorporated. In this study, TRS is extended to be applicable to Biochemical Oxygen Demand (BOD) and Dissolved Oxygen (DO) management in river systems and uncertainties in input variables of river water quality simulation model are also considered. In the proposed methodology, low water quality is also quantified as a fuzzy event and fuzzy risk of violating the water quality standards is estimated at each checkpoint along the river. The Extended Trading Ratio System (ETRS) is used in a Monte Carlo Analysis to provide the required data for training and validating a Bayesian Network (BN). The trained BN can be used for real time river water quality management and provides the probability density functions of treatment levels and trading discharge permit policies. The methodology is successfully applied to the Zarjub River in the northern part of Iran to show its usefulness as a cost-effective and risk-informed decision-making tool in real time river water quality management.