Optimization of waste load allocation in Kor River using trading pollutant discharge permits approach and uncertainty consideration

Document Type : Research Paper


1 Department of Civil Engineering, Water Resources, Yasouj University, Yasouj, Iran.

2 Department of Environmental Engineering, Department of Water and Wastewater, Shahid Beheshti University, Tehran, Iran.



In this thesis, in order to manage the quality of the river, a structure with the objectives of minimizing the environmental protection costs and providing the river water quality standard according to the implementation aspects of the policies is presented. After identifying the polluting sources in the studied area and grouping them, a number of sewage treatment scenarios were considered for each discharge group, and the cost related to each scenario was calculated.Considering the inherent errors and uncertainties in the estimation of treatment costs and penalty for violating water quality standards, a random method was created by combining decision-making approaches and the Monte Carlo simulation method to identify the best treatment scenarios.The decision-making approaches used to increase the implementation capability of pollution load trading programs and examine the desirability of dischargers include stochastic social choice rules (SSCR), stochastic fallback bargaining (SFB) and stochastic multi-criteria decision-making (SMCDM), each approach includes different methods. Finally, in order to reduce the costs and motivate the dischargers to voluntarily participate in the quality protection of the river, the best option by any approach based on the cost criteria, as the initial permission to discharge the pollution load into the optimization model, the extended trading-ratio system (ETRS).The results of the implementation of the proposed model show that according to the SSCR approach, the costs are reduced by 4/53 Percent and by the SFB approach, the costs are reduced by 1/73 Percent while providing a relative agreement between polluting sources. Also, based on the SMCDM approach, the costs are reduced by 24/5 Percent, but it is less acceptable in terms of polluting sources, because in addition to the optimal distribution of dissatisfaction among the beneficiaries, efforts are made to group the evacuees into one group. Become, that the dynamic state of power existing among groups in real conditions is not considered.


Main Subjects

  1. Dash, S., Borah, S., Singh, K.R., & Kalamdhad, A.S. (2020). Seasonal and Spatial Variation of DO and BOD for Assessment of the Water Quality of Brahmaputra River. Recent Developments in Waste Management. Springer, 473-483.
  2. Duke, J.M., Liu, H., Monteith, T., McGrath, J., & Fiorellino, N.M. (2020). A method for predicting participation in a performance-based water quality trading program. Ecological Economics, 177, 106762.
  3. Galderisi, S., Mucci, A., Dollfus, S., Nordentoft, M., Falkai, P., Kaiser, S., Giordano, G.M., Vandevelde, A., Nielsen, M.Ø., & Glenthøj, L.B. (2021). EPA guidance on assessment of negative symptoms in schizophrenia. European Psychiatry, 64, e23.
  4. Ghorbani M, M., Nikoo, M.R., & Sadegh, M. (2019). A fuzzy multi-stakeholder socio-optimal model for water and waste load allocation. Environmental monitoring and assessment, 191, 1-16.
  5. Haghighat Esfahani, E., Niko, M.R., Karimi Jashni, A., & Taleb, N. (2020). Developing a multi-objective optimization model for water and waste load allocation in rivers based on Borda bargaining model, A case study: part of Kor river system. Water resources engineering scientific-research quarterly, 12, 1-13.
  6. Hung, M.-F., & Shaw, D. (2005). A trading-ratio system for trading water pollution discharge permits. Journal of Environmental Economics and Management, 49, 83-102.
  7. Jamshidi, S., & Niksokhan, M.H. (2015). Waste load allocation in Sefidrud using water quality trading. Water and Irrigation Management. 5, 243-259.(in PPersian)
  8. Liu, Y., Liang, Y., Ouyang, K., Liu, S., Rosenblum, D., & Zheng, Y. (2020). Predicting Urban Water Quality with Ubiquitous Data-A Data-driven Approach. IEEE Transactions on Big Data, 8(2), 564-578.
  9. Mardani, R., Montaseri, H., Fazeli, M., Khalili, R., & Esmaeili, H. (2022). Spatio-temporal variation of meteorological drought and its relation with temperature and vegetation condition indices using remote sensing and satellite imagery in Marvdasht city. Water and Soil Management and Modelling, 3(3), 72-89. (in Persian)
  10. Mazlomi Mochani, M., Hatami, A., Moridi, A., & Khalili, R. (2023). Sensitivity assessment of the National Sanitation Foundation Water Quality Index (NSFWQI) and IRan Water Quality Index for Surface Water Resources (IRWQIsc) on the water quality of the Neka River. Water and Irrigation Management, 13(3), 581-592.(in Persian)
  11. Mesbah, S.M., Kerachian, R., & Nikoo, M.R. (2009). Developing real time operating rules for trading discharge permits in rivers: Application of Bayesian Networks. Environmental modelling & software, 24, 238-246.
  12. Motallebi, M., Hoag, D.L., Tasdighi, A., Arabi, M., & Osmond, D.L. (2017). An economic inquisition of water quality trading programs, with a case study of Jordan Lake, NC. Journal of environmental management 193, 483–490.
  13. Sadak, D., Ayvaz, M.T., Elçi, A., Dilaver, M., & Ayaz, S. (2023). A Novel Wastewater Load Allocation Approach for River Basins Using Simulation-Optimization Models. Scientific Research Communications, 3(1).
  14. Şener, Ş., Şener, E., & Davraz, A. (2017). Evaluation of water quality using water quality index (WQI) method and GIS in Aksu River (SW-Turkey). Science of the Total Environment 584, 131-144.
  15. Tian, Y., Jiang, Y., Liu, Q., Dong, M., Xu, D., Liu, Y., & Xu, X. (2019). Using a water quality index to assess the water quality of the upper and middle streams of the Luanhe River, northern China. Science of the Total Environment, 667, 142-151.
  16. Ustaoğlu, F., Tepe, Y., & Taş, B. (2020). Assessment of stream quality and health risk in a subtropical Turkey river system: A combined approach using statistical analysis and water quality index. Ecological Indicators, 113, 105815.
  17. Xu, H., Brown, D.G., Moore, M.R., & Currie, W.S. (2018). Optimizing spatial land management to balance water quality and economic returns in a Lake Erie watershed. Ecological Economics, 145, 104-114.
  18. Zhang, J.L., Li, Y.P., Zeng, X.T., Huang, G.H., Li, Y., Zhu, Y., Kong, F.L., Xi, M., & Liu, J. (2019). Effluent trading planning and its application in water quality management: a factor-interaction perspective. Environmental research, 168, 286-305.
  19. Zolfagharipoor, M.A., & Ahmadi, A. (2017). Effluent trading in river systems through stochastic decision-making process: a case study. Environmental Science and Pollution Research, 24, 20655-20672.