1. United Nations, The sustainable development goals report 2018, 2018. [Online]. Available: https://unstats.un.org/ sdgs/report/2018/goal-06/.
2. Sun, Y., Xu, Z., Zhang, N., Hu, D., Liang, Y., Liu, C., Zhu, Y. and Chen, X., 2025. Unveiling the intrinsic relationship between ammonia and hydrogen sulfide generation during composting. Environmental Technology & Innovation, 39, 104298. https://doi.org/10.1016/j.eti.2025.104298
3. Lu, M., Lamichhane, P. and Liang, F., 2008. Identification of odor causing compounds in a commercial dairy farm. Water Air & Soil Pollution: Focus, 8, pp.359–367. https://doi.org/10.1007/s11267-007-9150-x
4. Han, B., Liu, Y.T. and Wu, J.H., 2018. Characterization of industrial odor sources in Binhai New Area of Tianjin, China. Environmental Science And Pollution Research, 25, pp.14006–14017. https://doi.org/10.1007/s11356-018-1596-z
5. Riaz, S., Mehmood, R., Kareem, H.A. and Sadia, H., 2023. Livestock waste, types, sources, pollution potential, and country‑wise comparisons. In: Waste Problems And Management In Developing Countries. 1st ed. Apple Academic Press, pp.1–27.https://doi.org/10.1201/9781003283621-9
6. Valipoori Goodarzi, H., Fadaei Tehrani, M.R. and Mohammad Zadeh, S., 2024. Investigation regarding assessment of potentially toxic elements (PTEs) contamination risk in Rasht city with emphasis on the two rivers, Goharroud and Zarjoub, Gilan province, Iran. Sustainable Water Resources Management, 10, 50. https://doi.org/10.1007/s40899-023-01025-8.
7. Khalili, R., Parvinnia, M. and Motaghi, H., 2020. Evaluation of Bashar River water quality using CCME water quality index. Journal Of Environmental Science Studies, 5(3), pp.2807–2814.
8. Shakeri, A. and Moore, F., 2010. The impact of an industrial complex on freshly deposited sediments, Chener Rahdar river case study, Shiraz, Iran. Environmental Monitoring And Assessment, 169, pp.321–334. https://doi.org/10.1007/s10661-009-1173-5.
9. Seyednezhad, N., Salimi Gol Sheikhi, M. and Barati, R., 2022. Analytical note: Discussion to determine the network of problems of Kashaf Rood river. Journal Of Water And Sustainable Development, 9(2), pp.135–137.
10. Cao, J., Sun, Q., Zhao, D., Xu, M., Shen, Q., Wang, D., Wang, Y. and Ding, S., 2020. A critical review of the appearance of black-odorous waterbodies in China and treatment methods. Journal Of Hazardous Materials, 385, 121511. https://doi.org/10.1016/j.jhazmat.2019.121511
11. Choe, L.J., Jung, S.W., Kim, D.G., Baek, M.J., Kang, H.J., Lee, C.Y. and Bae, Y.J., 2014. Temporal changes in benthic macroinvertebrates and their interactions with fish predators after restoration in the Cheonggyecheon, a downtown stream in Seoul, Korea. Entomological Research, 44(6), pp.338–348.https://doi.org/10.1111/1748-5967.12080
12. Gerner, N.V., Nafo, I., Winking, C., Wencki, K., Strehl, C., Wortberg, T., Niemann, A., Anzaldua, G., Lago, M. and Birk, S., 2018. Large-scale river restoration pays off: A case study of ecosystem service valuation for the Emscher restoration generation project. Ecosystem Services, 30, pp.327–338. https://doi.org/10.1016/j.ecoser.2018.03.020
13. Romero, E., Le Gendre, R., Garnier, J., Billen, G., Fisson, C., Silvestre, M. and Riou, P., 2016. Long-term water quality in the lower Seine: Lessons learned over 4 decades of monitoring. Environmental Science & Policy, 58, pp.141–154. https://doi.org/10.1016/j.envsci.2016.01.016
14. Chiţescu, C.L., Ene, A., Geana, E.I., Vasile, A.M. and Ciucure, C.T., 2021. Emerging and persistent pollutants in the aquatic ecosystems of the Lower Danube Basin and North West Black Sea Region—A review. Applied Sciences, 11(20), 9721. https://doi.org/10.3390/app11209721
15. Krishna, C.R., Klein, C.R., Jones, K.W., Clesceri, N.L. and Stern, E.A., 1995. Human exposure to toxic materials. The Mount Sinai Journal Of Medicine, 62, p.5.
16. Liang, K., Feng, X., Yang, B., Dou, Z. and Chen, W., 2021. Research on treatment measures and treatment effects of black and smelly water body in Shenyang area. IOP Conference Series: Earth And Environmental Science, 651(4), 042043. https://doi.org/10.1088/1755-1315/651/4/042043
17. Cruz, L.P., Alves, R.S., da Rocha, F.O., Moreira, M.S. and dos Santos Júnior, A., 2022. Atmospheric levels, multivariate statistical study, and health risk assessment of odorous compounds (H₂S and NH₃) in areas near polluted urban rivers in the city of Salvador, in Northeastern Brazil. Air Quality, Atmosphere & Health, 15(1), pp.159–176. https://doi.org/10.1007/s11869-021-01095-7
18. Hawko, C., Verriele, M., Hucher, N., Crunaire, S., Leger, C., Locoge, N. and Savary, G., 2021. A review of environmental odor quantification and qualification methods: The question of objectivity in sensory analysis. Science Of The Total Environment, 795, 148862. https://doi.org/10.1016/j.scitotenv.2021.148862
19. Bax, C., Sironi, S. and Capelli, L., 2020. How can odors be measured? An overview of methods and their applications. Atmosphere, 11(1), 92. https://doi.org/10.3390/atmos11010092
20. Sado‑Inamura, Y. and Fukushi, K., 2018. Considering water quality of urban rivers from the perspectives of unpleasant odor. Sustainability, 10(3), 650. https://doi.org/10.3390/su10030650
21. Wang, Y., Shao, L., Kang, X., Zhang, H., Lü, F. and He, P., 2023. A critical review on odor measurement and prediction. Journal Of Environmental Management, 336, 117651. https://doi.org/10.1016/j.jenvman.2023.117651
22. Lebrero, R., Bouchy, L., Stuetz, R. and Muñoz, R., 2011. Odor assessment and management in wastewater treatment plants: a review. Critical Reviews In Environmental Science And Technology, 41(10), pp.915–950. https://doi.org/10.1080/10643380903300000
23. Wei, L., Huang, C., Wang, Z., Wang, Z., Zhou, X. and Cao, L., 2019. Monitoring of urban black-odor water based on Nemerow index and gradient boosting decision tree regression using UAV-borne hyperspectral imagery. Remote Sensing, 11(20), 2402. https://doi.org/10.3390/rs11202402
24. Yang, F., Pluth, T.B., Fang, X., Francq, K.B., Jurjovec, M. and Tang, Y., 2021. Advanced machine learning application for odor and corrosion control at a water resource recovery facility. Water Environment Research, 93(11), pp.2346–2359. https://doi.org/10.1002/wer.1618
25. Mulrow, J., Kshetry, N., Brose, D.A., Kumar, K., Jain, D., Shah, M., Kunetz, T.E. and Varshney, L.R., 2020. Prediction of odor complaints at a large composite reservoir in a highly urbanized area: A machine learning approach. Water Environment Research, 92(3), pp.418–429. https://doi.org/10.1002/wer.1191
26. Kang, J.H., Song, J., Yoo, S.S., Lee, B.J. and Ji, H.W., 2020. Prediction of odor concentration emitted from wastewater treatment plant using an artificial neural network (ANN). Atmosphere, 11(8), 784. https://doi.org/10.3390/atmos11080784
27. Qiu, S., Hou, P., Huang, J., Han, W. and Kang, Z., 2021. The monitoring of Black‑Odor River by electronic nose with chemometrics for pH, COD, TN, and TP. Chemosensors, 9(7), 168. https://doi.org/10.3390/chemosensors9070168
28. Iran, Tehran Municipality, 2015. Operational studies for implementation of the comprehensive surface water management plan and preparation of improvement plans for streams and canals in the 22 districts of Tehran City – Conceptual design studies of District 3 – Second Edition, Volume 1. Tehran: Yekom Consulting Engineers. [In Persian]
29. Iran, Tehran Municipality, 2015. Operational studies for implementation of the comprehensive surface water management plan and preparation of improvement plans for streams and canals in the 22 districts of Tehran City – Conceptual design studies of District 3 – Second Edition, Volume 2. Tehran: Yekom Consulting Engineers. [In Persian]
30. Breiman, L., 2001. Random forests. Machine Learning, 45, pp.5–32. https://doi.org/10.1023/A:1010933404324
31. Wang, F., Wang, Y., Zhang, K., Hu, M., Weng, Q. and Zhang, H., 2021. Spatial heterogeneity modeling of water quality based on random forest regression and model interpretation. Environmental Research, 202, 111660. https://doi.org/10.1016/j.envres.2021.111660
32. Alnahit, A.O., Mishra, A.K. and Khan, A.A., 2022. Stream water quality prediction using boosted regression tree and random forest models. Stochastic Environmental Research And Risk Assessment, 36(9), pp.2661–2680. https://doi.org/10.1007/s00477-021-02152-4
33. Sakaa, B., Elbeltagi, A., Boudibi, S., Chaffaï, H., Islam, A.R.M.T., Kulimushi, L.C., Choudhari, P., Hani, A., Brouziyne, Y. and Wong, Y.J., 2022. Water quality index modeling using random forest and improved SMO algorithm for support vector machine in Saf‑Saf river basin. Environmental Science And Pollution Research, 29(32), pp.48491–48508. https://doi.org/10.1007/s11356-022-18644-x
34. Fang, X., Li, X., Zhang, Y., Zhao, Y., Qian, J., Hao, C., Zhou, J. and Wu, Y., 2021. Random forest‑based understanding and predicting of the impacts of anthropogenic nutrient inputs on the water quality of a tropical lagoon. Environmental Research Letters, 16(5), 055003. https://doi.org/10.1088/1748-9326/abf395
35. Xu, J., Xu, Z., Kuang, J., Lin, C., Xiao, L., Huang, X. and Zhang, Y., 2021. An alternative to laboratory testing: random forest‑based water quality prediction framework for inland and nearshore water bodies. Water, 13(22), 3262. https://doi.org/10.3390/w13223262
36. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V. and Vanderplas, J., 2011. Scikit‑learn: Machine learning in Python. Journal Of Machine Learning Research, 12, pp.2825–2830.
37. Piccardo, M.T., Geretto, M., Pulliero, A. and Izzotti, A., 2022. Odor emissions: A public health concern for health risk perception. Environmental Research, 204, 112121. https://doi.org/10.1016/j.envres.2021.112121
