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In recent decades, river quality management has received enormous attention by researchers as an important water resources management issue. The main reason for this is saving in wastewater treatment costs by optimal allocation of the assimilative capacity of the river system to dischargers. Regarding the unidirectionality of the river flow toward the lowest level, the Trading Ratio System (TRS) and Bayesian Networks are utilized in this paper to develop new, real-time operating policies for discharge permit trading in rivers. TRS is used in a Monte Carlo Analysis to provide the required data for training and validating a Bayesian Network (BN). The trained BN are then used for real time river water quality management to provide probability distribution functions of treatment levels and trading discharge permit policies. The methodology is successfully applied to a case study and its results are compared with those of the TRS. The comparisons show the usefulness of the methodology as a cost-effective and probabilistic decision-making tool in real-time river water quality management.