Impacts of the river water pollution control on the health of aquatic animals in downstream

Document Type : Research Paper


1 Department of Dentistry, Al-Noor University College, Bartella, Iraq

2 Medical Laboratory Techniques Department, Al-Mustaqbal University College, 51001 Hillah, Babylon, Iraq

3 college of pharmacy, Al-Farahidi University/ Iraq

4 Advanced Biomedical Science/ Al-Nisour University College/ Baghdad/ Iraq

5 Department of Prosthetic Dental Technology/ Hilla University college, Babylon, Iraq

6 Mazaya University College/ Iraq



Water pollution is one of the most significant environmental issues and problems. Surface water, running water, and rivers are always the most polluted, due to passing through numerous areas. The objective of this study is to investigate the water quality of Euphrates River in central Iraq in terms of aquaculture, as well as how to control the concentrations of pollutants. About 60-km length of Euphrates River was modeled using artificial neural networks (ANN) using qualitative data. The standard range of polluting substances for aquaculture was evaluated, and the effect of implementing the scenario of controlling point sources of pollution and preventing the flow from coming into contact with waste piles and animal excrement were studied. Statistical criteria, including NSE, RMSE, and MAE, were used to evaluate the model performance in the training and testing phases. According to the results, implementing the desired scenario has reduced the concentrations of all pollutants to an acceptable level for aquaculture. The most significant decrease occurred in the regions closest to the industries and factories (0-10 km), while the slightest change occurred in the farthest reaches of the study area (50-60 km). The findings of this study can be used to implement water quality controls at the optimal time and location to influence the Euphrates River general state.


Abdouni, A, El-Bouhout, S, Merimi, I, Hammouti, B, Haboubi, K 2021, Physicochemical characterization of wastewater from the Al-Hoceima slaughterhouse in Morocco. Caspian Journal of Environmental Sciences, 19: 423-429
Ahmed, AN, Othman, FB, Afan, HA, Ibrahim, RK, Fai, CM, Hossain, MS & Elshafie, A 2019, Machine learning methods for better water quality prediction. Journal of Hydrology, 578: 124084.
Alewi, HK, Abood, EA & Ali, G 2022, An inquiry into the relationships between BOD5, COD, and TOC in Tigris River, Maysan Province, Iraq. Caspian Journal of Environmental Sciences, 20: 37-43.
Alias, R, Noor, NAM, Sidek, LM & Kasa, A 2021, Prediction of Water Quality for Free Water Surface Constructed Wetland Using ANN and MLRA.
Brontowiyono, W, Hammid, AT, Jebur, YM, Al Sudani, AQ, Mutlak, DA & Parvan, M 2022, Reduction of Seepage Risks by Investigation into Different Lengths and Positions for Cutoff Wall and Horizontal Drainage (Case Study: Sattarkhan Dam). Advances in Civil Engineering, 2022.
Chen, Y, Song, L, Liu, Y, Yang, L, & Li, D 2020, A review of the artificial neural network models for water quality prediction. Applied Sciences, 10: 5776.
Chowdhary, P, Bharagava, RN, Mishra, S & Khan, N 2020, Role of industries in water scarcity and its adverse effects on environment and human health Environmental concerns and sustainable development, pp. 235-256: Springer.
Dutta, V, Dubey, D & Kumar, S 2020, Cleaning the River Ganga: Impact of lockdown on water quality and future implications on river rejuvenation strategies. Science of The Total Environment, 743: 140756.
Elkiran, G, Nourani, V & Abba, S 2019, Multi-step ahead modelling of river water quality parameters using ensemble artificial intelligence-based approach. Journal of Hydrology, 577: 123962.
Grbčić, L, Družeta, S, Mauša, G, Lipić, T, Lušić, DV, Alvir, M & Travaš, V 2022, Coastal water quality prediction based on machine learning with feature interpretation and spatio-temporal analysis. Environmental Modelling & Software, 155: 105458.
Israa Ibrahim, L, Neran Adnan, A 2021, Measuring pollution based on total petroleum hydrocarbons and total organic carbon in Tigris River, Maysan Province, Southern Iraq. Caspian Journal of Environmental Sciences, 19: 535-545
Kadam, A, Wagh, V, Muley, A, Umrikar, B & Sankhua, R 2019, Prediction of water quality index using artificial neural network and multiple linear regression modelling approach in Shivganga River basin, India. Modeling Earth Systems and Environment, 5: 951-962.
Kulisz, M & Kujawska, J 2021, Application of artificial neural network (ANN) for water quality index (WQI) prediction for the river Warta, Poland. Paper presented at the Journal of Physics: Conference Series.
Li, L, Jiang, P, Xu, H, Lin, G, Guo, D & Wu, H 2019, Water quality prediction based on recurrent neural network and improved evidence theory: a case study of Qiantang River, China. Environmental Science and Pollution Research, 26: 19879-19896.
Maurya, PK, Malik, D, Yadav, KK, Kumar, A, Kumar, S & Kamyab, H 2019, Bioaccumulation and potential sources of heavy metal contamination in fish species in River Ganga basin: Possible human health risks evaluation. Toxicology Reports, 6: 472-481.
Melnik, IV, Vasileva, EG, Obukhova, OV 2021, Pollution of the Volga River basin with petroleum products in the Astrakhan region, Russia. Caspian Journal of Environmental Sciences, 19: 963-972.
Mishra, S, charan Rath, C & Das, AP 2019, Marine microfiber pollution: a review on present status and future challenges. Marine Pollution Bulletin, 140: 188-197.
Molajou, A, Afshar, A, Khosravi, M, Soleimanian, E, Vahabzadeh, M & Variani, HA 2021, A new paradigm of water, food, and energy nexus. Environmental Science and Pollution Research, 1-11.
Molajou, A, Nourani, V, Afshar, A, Khosravi, M & Brysiewicz, A 2021, Optimal design and feature selection by genetic algorithm for emotional artificial neural network (EANN) in rainfall-runoff modeling. Water Resources Management, 35: 2369-2384.
Molajou, A, Pouladi, P & Afshar, A 2021, Incorporating social system into water-food-energy nexus. Water Resources Management, 35: 4561-4580.
Nasir, N, Kansal, A, Alshaltone, O, Barneih, F, Sameer, M, Shanableh, A & Al Shamma'a, A 2022, Water quality classification using machine learning algorithms. Journal of Water Process Engineering, 48: 102920.
Okereafor, U, Makhatha, M, Mekuto, L, Uche Okereafor, N, Sebola, T & Mavumengwana, V 2020, Toxic metal implications on agricultural soils, plants, animals, aquatic life and human health. International Journal of Environmental Research and Public Health, 17: 2204.
Parvan, M, Ghiasi, AR, Rezaii, TY & Farzamnia, A 2019, Transfer learning based motor imagery classification using convolutional neural networks. Paper presented at the 2019 27th Iranian Conference on Electrical Engineering (ICEE).
Prata, JC, Reis, V, da Costa, JP, Mouneyrac, C, Duarte, AC & Rocha Santos, T 2021, Contamination issues as a challenge in quality control and quality assurance in microplastics analytics. Journal of Hazardous Materials, 403: 123660.
Rajaee, T, Khani, S & Ravansalar, M 2020, Artificial intelligence-based single and hybrid models for prediction of water quality in rivers: A review. Chemometrics and Intelligent Laboratory Systems, 200: 103978.
Rajmohan, KVS, Ramya, C, Viswanathan, MR & Varjani, S 2019, Plastic pollutants: effective waste management for pollution control and abatement. Current Opinion in Environmental Science & Health, 12: 72-84.
Saravanan, A, Kumar, PS, Jeevanantham, S, Karishma, S, Tajsabreen, B, Yaashikaa, P & Reshma, B 2021, Effective water/wastewater treatment methodologies for toxic pollutants removal: Processes and applications towards sustainable development. Chemosphere, 280: 130595.
Shi, B, Wang, P, Jiang, J & Liu, R 2018, Applying high-frequency surrogate measurements and a wavelet-ANN model to provide early warnings of rapid surface water quality anomalies. Science of The Total Environment, 610: 1390-1399.
Singh, AK & Chandra, R 2019, Pollutants released from the pulp paper industry: Aquatic toxicity and their health hazards. Aquatic Toxicology, 211: 202-216.
Talib Jawad, SF, Shihab, A & Al-Taher, GM 2022, Heavy metal concentrations in water, sediments, Cladophora and two fish species from Al-Masab Alamm River, Al- Nassiriya, Iraq. Caspian Journal of Environmental Sciences, 20: 805-812.
Ustaoğlu, F & Tepe, Y 2019, Water quality and sediment contamination assessment of Pazarsuyu Stream, Turkey using multivariate statistical methods and pollution indicators. International Soil and Water Conservation Research, 7: 47-56.
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.
Yu, S, Li, X, Wen, B, Chen, G, Hartleyc, A, Jiang, M & Li, X 2021, Characterization of water quality in Xiao Xingkai Lake: Implications for trophic status and management. Chinese Geographical Science, 31: 558-570.