پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 158 |
| تعداد شمارهها | 6,223 |
| تعداد مقالات | 67,680 |
| تعداد مشاهده مقاله | 114,872,634 |
| تعداد دریافت فایل اصل مقاله | 89,545,777 |
Modeling DO and BOD5 Changes in the Dez River by Using QUAL2Kw | ||
| Pollution | ||
| دوره 8، شماره 1، فروردین 2022، صفحه 15-35 اصل مقاله (558.36 K) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/poll.2021.322725.1070 | ||
| نویسندگان | ||
| Seyyad Fazlodin Jamalianzadeh1؛ Hamidreza Rabieifar* 1؛ Ali Afrous2؛ Azim Hosseini1؛ Hossein Ebrahimi3 | ||
| 1Department of Civil Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran | ||
| 2Department of Water Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran | ||
| 3Department of Civil Engineering, Shahr-e-Gods Branch, Islamic Azad University, Tehran, Iran | ||
| چکیده | ||
| The present study evaluates the water quality of Dez River, a river 23 km long, via QUAL2Kw model, based on simulation of DO and BOD5 p98arameters, through considering water quality standards during six months in three stations of Kashefieh, Pole-Panjom, and Hamidabad. To determine the model’s validity and compare the observational data, the paper uses the square mean square error (RMES) and the squared mean square error coefficient (CV). The achieved results of the model largely indicate the actual conditions of the river, which represent the ability of QUAL2Kw model to simulate qualitative parameters. The main contamination of Dez River comes from municipal wastewater, either directly imported by river residents or collected by urban canals. It, then, enters the river at a certain point. Based on the simulation and observational results of DO at two stations of 5th and Hamidabad Bridge in all months of sampling, it is below 5 mg/L, regarded a threat to aquatic life. In addition, BOD5 parameter goes beyond 6 mg/L in Hamidabad station, being a threatening factor for aquatic life in this station. Critical conditions of Dez River, low discharge, and high loading of pollutants have increased the concentration of water quality parameters. Given the results of RMSE and CV parameters, the model has had the best conformity for DO parameter, followed by BOD5. | ||
| کلیدواژهها | ||
| Dissolved Oxygen؛ Self-purification؛ Dez River؛ BOD5؛ QUAL2Kw | ||
| مراجع | ||
|
Alizadeh, M. J., Shahheydari, H., Kavianpour, M. R., Shamloo, H. and Barati, R. (2017). Prediction of longitudinal dispersion coefficient in natural rivers using a cluster-based Bayesian network. Environmental Earth Sciences, 76; 86. https://doi.org/10.1007/s12665-016-6379-6.
Ansaripour, H., Ebrahimi, K. and Omid, M. H. (2013). A Mathematical Model for River Flow AssimilationA Case Study of Pasikhan River, Iran. Journal of Agricultural Engineering Research, 14(2); 31-42. (In Persian)
Allam, A., Tawfik, A., Yoshimura, C. and Fleifle, A. (2016). A simulation-based suitability index of the quality and quantity of agricultural drainage water for reuse in irrigation. Science of the Total Environment, 172; 82-96.
Aliffia, A. and Karnaningroem, N. (2019). Simulation of pollution load capacity using QUAL2Kw model in Kali Surabaya River (Cangkir-Sepanjang segment) IOP Conf. Ser. Environmental Earth Sciences, 259.
AsheghMalla, M., MalehMohammadi, B. and Torabian, A. (2016). Investigating the Importance of Rivers Self-Efficacy Capacity in Developing a Standard for Discharge of Wastewater. Environmental Research, 7(13); 103-116. (In Persian)
BagherianMarzouni, M., Akhoundalib, A. M., Moazed, H., Jaafarzadeh, N., Ahadian, J. and Hasoonizadeh, H. (2014). Evaluation of Karun river water quality scenarios using simulation model results. International journal of Advanced Biological and Biomedical Research, 2(2); 339-358.
Barati, R. (2011). Parameter estimation of nonlinear Muskingum models using the Nelder–Mead simplex algorithm. Journal of Hydrologic Engineering, 16(11); 946–954.
Barati, R. (2013). Application of excel solver for parameter estimation of the nonlinear Muskingum models. KSCE Journal of Civil Engineering, 17(5); 1139–1148.
Barati, R., Neyshabouri, S. A. A. S. and Ahmadi, G. (2014). Development of empirical models with high accuracy for estimation of drag coefficient of flow around a smooth sphere: an evolutionary approach. Powder Technology, 257; 11–19.
Biglari, M. R., Sima, S. and Saadatpour, M. (2019). Modeling and Management of the River Water Quality for Aquatic Habitat Health Using a Source Control Approach (Case Study: Zarrineh-rud River). Iran- Water Resources Research., 14(5); 54-70. (In Persian)
Chapra, S. C. and Canale, R. P. (2006). Numerical Methods for Engineers, 5th Ed. McGraw-Hill, New York.
Chapra, S. C. and Pelletier, G. J. (2003). QUAL2K, A modeling framework for simulating river and stream water quality (beta version): documentation and user’s manual, Civil and Environmental Engineering Department, Medford, Tufts University.
Chi, H. F., Chun, H. K. and Wei, S. W. (2009). An innovative modeling approach using Qual2K and HEC-RAS integration to assess the impact of tidal effect on river water quality simulation. Journal of Environmental Management, 90; 1824-32.
Curtis, J. and Morgenroth, E. (2013). Estimating the effects of land-use and catchment characteristics on lake water quality: Irish lakes 2004-2009. Journal of the Statistical and Social Inquiry Society of Ireland, 42; 64-80.
Chounlamany, V., Tanchuling, M. A. and Inoue, T. (2017). Spatial and temporal variation of water quality of a segment of Marikina River using multivariate statistical methods. Water Science and Technology, 76(6); 1510–1522.
Farjoudi, S. Z., Moridi, A., Sarang, A. and Lence, B. J. (2021). Application of probabilistic bankruptcy method in river water quality management. Int. International Journal of Environmental Science and Technology, https://doi.org/10.1007/s13762-020-03046-8
Gupta, R. C., Gupta, A. K. and Shrivastava, R. K. (2013). Water quality modeling of a stretch of river Kshipra (India). Nature Environment and Pollution Technology, 12(3); 511-516.
Ghosh, S. and Mujumdar, P. (2010). Fuzzy waste load allocation model: a multi-objective approach. Journal of Hydroinformatics, 12 (1); 83-96.
Ghorbani, z., Amanipoor, H. and Battaleb-Looie, S. (2020). Water quality simulation of Dez River in Iran using QUAL2Kw model, Geocarto International, DOI: 10.1080/10106049.2020.1762763.
HadipourNiktarash, P., Ghodousi, H. and Ebrahimi, K. (2019). Simulation and Evaluation of Seasonal Variations of Water Quality in Taleghan River Using a Mathematical Model. Journal of Water and Soil Science (Science and Technology of Agriculture and Natural Resources), 22(4); 399-410. (In Persian)
Haider, H. and Ali, W. (2010). Development of Dissolved Oxygen Model for Highly Variable Flow River: A Case Study of Ravi River in Pakistan. Environmental Model Assessment, 15; 583-599. https://doi.org/10.1007/s10666-010-9240-4.
Hanfeng, Y. E., Shuhai, G. U. O., Fengmei, L. I. and Gang, L. I. (2013). Water Quality Evaluation in Tidal River Reaches of Liaohe River Estuary, China Using a Revised QUAL2K Model, Chinese Geographical Science, 23(3); 301–311.
Hardyanti, N, Winardi, D. and Nugraha, S. B. (2020). Analysis of The Impact of Industrial Activities Towards River Vacancy Viewed from BODS, CODS, and TSS Using The QUAL2Kw Software Method Approach (Case Study: Klampok River, Semarang District). E3S Web of Conferences 202, 04008. ICENIS 2020. https://doi.org/10.1051/e3sconf/202020204008.
Hendriarianti, E. and Karnaningroem, N. (2015) Deoxygenation Rate of Carbon in Upstream Brantas River in the City of Malang. Journal of Applied Environmental and Biological Sciences, 5(36); 36-41.
Huang, J., Huang, Y. and Zhang, Z. (2014). Coupled effects of natural and anthropogenic controls on seasonal and spatial variations of river water quality during baseflow in a coastal watershed of Southeast China. PloS one, 9(3), e91528.
Huashan, M. A., Sujal, I. M. and Nasly, M. A. (2013). Application of QUAL2Kw for water quality modeling in Tunggak River, Kuantan, Pahang, Malaysia. Research journal of recent sciences, 3(6); 6–14.
Hosseini, K., Nodoushan, E.J., Barati, R. and Shahheydari, H. (2016). Optimal design of labyrinth spillways using meta-heuristic algorithms. KSCE Journal of Civil Engineering, 20(1); 468–477.
Hoseini, P. and Hoseini, Y. (2017). Changes in Self-Purification Capacity of the Ahvaz Karun River in 2008 and 2014 using QUAL2Kw Model. Amirkabir Journal of Civil Engineering, 49(1); 15-16. (In Persian)
Ismail, H. and Robescu, D. (2015). Rivers and streams water quality models: a brief review, RomAqua, An XXI., 106(8); 46 – 56.
Jafarzadeh, N., Lahijanzadeh, A., Ka'bi, H. and Rostami, S. (2001). Determination of self-propagation capability of Karun River 55 km long in Ahvaz city; 4th National Conference on Environmental Health, Yazd, Shahid Sadoughi University of Medical Sciences, Yazd. (In Persian)
Jha, R. and Singh, V.P. (2008). Analytical Water Quality Model for Biochemical Oxygen Demand Simulation in River Gomti of Ganga Basin, India. KSCE Journal of Civil Engineering, 12(2) ; 141-147.
Jie, G., Cheng, Fei H., Cui-ping, K. and Olaf, K. (2016). A water quality model applied for the rivers into the Qinhuangdao coastal water in the Bohai Sea, Chinese Journal of Hydrodynamics, 28(5); 905–913.
Joonwoo, N., Hyungu, C. and Sangjin, L. (2015). Water quality projection in the Geum River basin in Korea to support integrated basin-wide water resources management. Environ. Earth Sci., 73; 1745–1756.
Kannel, P. R., Lee, S., Kanel, S. R., Lee, Y.S. and Ahn, K.H. (2007). Application of QUAL2Kw for water quality modeling and dissolved oxygen control in the river Bagmati. Environmental monitoring and assessment, 125(1-3); 201-217.
Kumarasamy, M. V. (2015). Deoxygenation and Reaeration Coupled hybrid mixing cells Based Pollutant Transport Model to Assess Water Quality Status of a River”, International Journal of Environmental Research, 9(1); 341-350.
Kunwar, P., Singh, A., Amrita, M. and Gunja, J. (2009). Artificial neural network modeling of the river water quality-A case study. Ecological Modelling, 220(8); 88-95.
Liu, L., Deng, L., Yong, D. and Dong, S. (2011). Native biofilm cultured under controllable condition and used in mediated method for BOD measurement. Talanta, 84; 895-9.
Liu, D., Guo, S., Shao, Q., Jiang, Y. and Chen, X. (2014). Optimal allocation of water quality and waste load in the northwest Pearl river delta, China. Stochastic Environmental Research and Risk Assessment, 28(6); 1525–1542.
Mehrasbi, M. R. and FarahmandKia, Z. (2015). Water quality modeling and evaluation of nutrient control strategies using QUAL2K in the small sivers, Journal of Human Environment and Health Promotion, 1(1); 1-11.
Melo, R. H., Benetti, M. and Melo, R. R. (2020). Surface Water Quality Modeling of a watershednin the north of Rio Grande do Sul. International Journal of Advanced Engineering Research and Science, 7(9); 306-310. https://dx.doi.org/10.22161/ijaers.79.36
Moridi, A. (2019). A bankruptcy method for pollution load reallocation in river systems. Journal of Hydroinformatics, 21(1); 45–55. https://doi.org/10.2166/hydro.2018.156.
Nakhaei, N. and Shahidi, A. E. (2010). Water quality discharge impact modeling with QUAL2K, Case Study: the Zayandeh-rood River. International Environmental Modelling and Software Society, 2(2); 339-358.
Nikakhtar, M., Rahmati, S. H. and Massah, A. R. (2020). Simulating of Surface Water Quality Using QUAL2Kw (Ardak River, Khorasan Razavi Province). International Journal of Environmental Science and Technology, 22(11); 94-105.
Nikoo, M. R., Kerachian, R. and Niksokhan, M. H. (2012). Equitable waste load allocation in rivers using fuzzy Bi-matrix games. Water Resources Management, 26(15); 4539–4552.
Nikoo, R., Kerachian, R., Karimi, A., Azadnia, A. and Jafarzadegan, K. (2013). Optimal water and waste load allocation in reservoir-river systems: a case study. Environmental Earth Sciences, 71; 4127–4142.
Niksokhan, M. H., Kerachian, R. and Karamouz, M. (2009). A game theoretic approach for trading discharge permits in rivers. Water Science and Technology, 60(3); 793–804.
Peavy, H. S., Rowe, D. R. and Tchobanoglous, G. (1985). Environmental Engineering. McGraw-Hill Book Company, New York, 696.
Pelletier, G. and Chapra, S. (2008a). A modeling framework for simulating river and stream water quality. Environ-mental Assessment Program, Olympia, Washington, pp. 98504-7710.
Pelletier, G. and Chapra, S. (2008b). QUAL2Kw user manual (version 5.1) A modeling framework for simulating river and stream water quality. Washington.
Pelletier, G., Chapra, S. and Tao, H. (2006). QUAL2Kw A framework for modeling water quality in streams and rivers using a genetic algorithm for calibration. Researchgate http://www.researchgate.net/publication/222978409.
Prakash, R., Kannel, S., Lee, Y., Lee, S., Kanel, S. R. and Pelletier, G. J. (2007). Application of automated QUAL2Kw for water quality modeling and management in the Bagmati River, Nepal. Ecological modeling, 202(3-4); 503-517.
Rachmansyah, A., Sulianto, A. A., Lusiana, N. and Devianto, L. A. (2021). Assessment of Water Quality Index and Pollution Load Capacity in the Sukowidi River and Bendo River, Banyuwangi Region. Indonesian Journal of Environment and Sustainable Development, 12 (1); 1-5.
Ranjith, S., Shivapur, A. V., Keshava, Kumar, P. S., Hiremath, C. G. and Dhungana, S. (2020). Water Quality Modelling Qual 2KW for River Tungabhadra (India). International Journal of Recent Technology and Engineering, 9(1); 461-464.
Richarta, V., Nur, A. A., Riyanto, H. and Tri, B. P. (2021). Analisa Kualitas Air Sungai Tukad Badung, Denpasar, Bali Menggunakan Program QUAL2Kw. Jurnal Teknologi dan Rekayasa Sumber Daya Air, 1(1); 40-51.
Saberi, L. and Niksokhan, M. H. (2017). Optimal waste load allocation using graph model for conflict resolution. Water Science and Technology, 75(6); 1512–1522.
Saily, R. and Setiawan, B. (2021). Determination of carrying and load capacity using QUAL2Kw modeling simulation. The 5th International Seminar on Sustainable Urban Development. Earth and Environmental Science 737. 012022. doi:10.1088/1755-1315/737/1/012022.
Shakibaeinia, A., Kashyap, S. H., Dibike, Y. B. and Prowse, T. D. (2016). An integrated numerical framework for water quality modeling in cold-region rivers: a case of the lower Athabasca River. Science of the Total Environment, 569–570; 634–646.
Stackelberg, N. O. and Neilson, B.T. (2014). Collaborative approach to calibration of a riverine water quality model. Journal of Water Resources Planning and Management, 3(140); 393-405.
Streeter, H. W. and Phelps, E. B. (1958). A study of the pollution and natural purification of the Ohio River, III.Factors concerned in the phenomen a of oxidation andreaeration. Bulletin 146 Public Health Service, Washington, DC, USA.
Siwiec, T., Kiedrynska, L., Abramowicz, K., Rewicka,. A and Nowak, P. (2011). BOD measuring and modeling methods – review, Land Reclamation., 43(2); 143-153.
Tavakoli, A., Nikoo, M. R., Kerachian, R. and Soltani, M. (2015). River water quality management considering agricultural return flows application of a non-linear two-stage stochastic fuzzy programming. Environmental Monitoring and Assessment, 187(4); 158–171.
Vanaei, A., Marofi, S. and Azari, A. (2018). Self-study of mountain range of Abbas Abad river in Hamadan. Journal of Ecology, 43(4); 742-727. (In Persian)
Vasudevan, M., Nambi, I.M. and SureshKumar, G. (2011). Application of Qual2k for assessing waste loading scenario in rive Yamuna. International journal of advanced technology and Engineering, 2(2); 336-344.
Wang, G., Wang, S., Kang, Q., Duan, H. and Wang, X. (2016). An integrated model for simulating and diagnosing the water quality based on the system dynamics and Bayesian network. Water Science and Technology, 74(11); 2639–2655.
Wu, H. J., Lin, Z. Y. and Guo, S. L. (2000). The application of artificial neural networks in the resources and environment. Resources and Environment in the Yangtze Basin, 9; 237-41.
Xiang, S. L., Liu, Z. M. and Ma, L. P. (2006). Study of multivariate linear regression analysis model for ground water quality prediction. Guizhou Science, 24; 60-2.
Yustiani Y. M. (2021). Deoxygenation rate coefficient in modeling the quality of urban rivers in Indonesia. IOP Conference Series: Earth and Environmental Science, 802 (012022).
Zallaghi, M. and Afrous, A. (2019). Qualitative Simulation of Nitrate and Phosphate along the Dez River using QUAL2Kw Model. Journal of Soil and Water Research, 50(90); 2099-2111. (In Persian)
Zeferino, J. A., Cunha, M. C. and Antunes, A. P. (2017). Adapted optimization model for planning regional wastewater systems: a case study. Water Science and Technology, 76(5); 1196–1205.
Zhen, J. G., Fu, L. and You, A. J. (2017). Evaluation and assessment of dissolved oxygen based on the water diversion experiment in River Netwo. Environmental Science and Technology, 40(S1); 170-175. | ||
|
آمار تعداد مشاهده مقاله: 674 تعداد دریافت فایل اصل مقاله: 836 |
||