Multiple interactive pollutants in water quality trading

Developing a methodology for real-time trading of water withdrawal and waste load discharge permits in rivers

In this paper, a new methodology is proposed for the real-time trading of water withdrawal and waste load discharge permits in agricultural areas along the rivers. Total Dissolved Solids (TDS) is chosen as an indicator of river water quality and the TDS load that agricultural water users discharge to the river are controlled by storing a part of return flows in some evaporation ponds. Available surface water withdrawal and waste load discharge permits are determined using a non-linear multi-objective optimization model. Total available permits are then fairly reallocated among agricultural water users, proportional to their arable lands. Water users can trade their water withdrawal and waste load discharge permits simultaneously, in a bilateral, step by step framework, which takes advantage of differences in their water use efficiencies and agricultural return flow rates. A trade that would take place at each time step results in either more benefit or less diverted return flow. The Nucleolus cooperative game is used to redistribute the benefits generated through trades in different time steps. The proposed methodology is applied to PayePol region in the Karkheh River catchment, southwest Iran. Predicting that 1922.7 Million Cubic Meters (MCM) of annual flow is available to agricultural lands at the beginning of the cultivation year, the real-time optimization model estimates the total annual benefit to reach 46.07 million US Dollars (USD), which requires 6.31 MCM of return flow to be diverted to the evaporation ponds. Fair reallocation of the permits, changes these values to 35.38 million USD and 13.69 MCM, respectively. Results illustrate the effectiveness of the proposed methodology in the real-time water and waste load allocation and simultaneous trading of permits.

Effluent trading in river systems through stochastic decision-making process: a case study

The objective of this paper is to provide an efficient framework for effluent trading in river systems. The proposed framework consists of two pessimistic and optimistic decision-making models to increase the executability of river water quality trading programs. The models used for this purpose are (1) stochastic fallback bargaining (SFB) to reach an agreement among wastewater dischargers and (2) stochastic multi-criteria decision-making (SMCDM) to determine the optimal treatment strategy. The Monte-Carlo simulation method is used to incorporate the uncertainty into analysis. This uncertainty arises from stochastic nature and the errors in the calculation of wastewater treatment costs. The results of river water quality simulation model are used as the inputs of models. The proposed models are used in a case study on the Zarjoub River in northern Iran to determine the best solution for the pollution load allocation. The best treatment alternatives selected by each model are imported, as the initial pollution discharge permits, into an optimization model developed for trading of pollution discharge permits among pollutant sources. The results show that the SFB-based water pollution trading approach reduces the costs by US$ 14,834 while providing a relative consensus among pollutant sources. Meanwhile, the SMCDM-based water pollution trading approach reduces the costs by US$ 218,852, but it is less acceptable by pollutant sources. Therefore, it appears that giving due attention to stability, or in other words acceptability of pollution trading programs for all pollutant sources, is an essential element of their success.

A decision-making framework for river water quality management under uncertainty: Application of social choice rules

An important issue in river water quality management is taking into account the role played by wastewater dischargers in the decision-making process and in the implementation of any proposed waste load allocation program in a given region. In this study, a new decision-making methodology, called 'stochastic social choice rules' (SSCR), was developed for modeling the bargaining process among different wastewater dischargers into shared environments. For this purpose, the costs associated with each treatment strategy were initially calculated as the sum of treatment cost and the fines incurred due to violation of water quality standards. The qualitative simulation model (QUAL2Kw) was then used to determine the penalty function. The uncertainty associated with the implementation of strategies under the economic costs (i.e., the sum of treatment and penalty costs) was dealt with by a Monte-Carlo selection method. This method was coupled with different social choice methods to identify the best solution for the waste load allocation problem. Finally, using the extended trading-ratio system (ETRS), the most preferred treatment strategy was exchanged among dischargers as the initial set of discharge permits aimed at reducing the costs and encouraging dischargers to participate in the river water quality protection scheme. The proposed model was finally applied to the Zarjoub River in Gilan Province, northern Iran, as a case study. Results showed the efficiency of the proposed model in developing waste load allocation strategies for rivers.