نوع مقاله : پژوهشی

نویسندگان

1 استادیار گروه مهندسی آب، دانشگاه لرستان

2 دانشجوی دکتری سازه های آبی

3 مدیر دفتر مهندسی رودخانه شرکت آب منطقه ای لرستان

چکیده

در این پژوهش به شبیه‌سازی کیفیت آب رودخانه خرم‌آباد به کمک مدل یک بعدی QUAL2Kw در طول یک بازه 35 کیلومتری از رودخانه پرداخته شد. به همین منظور از پارامترهای مهم کیفی آب از جمله اکسیژن محلول (DO)، اکسیژن‌خواهی زیست‌شیمیایی کربنی (CBODf)، اکسیژن‌خواهی شیمیایی (COD)، نیترات (NO3)، هدایت الکتریکی (EC) و اسیدیته (pH) و پارامتر دما در دو ماه تیر و شهریور سال 1397 به ترتیب برای واسنجی و صحت‌سنجی مدل استفاده گردید. نتایج نشان داد که با پیوستن رودخانه فرعی به رودخانه اصلی و تخلیه پساب منابع آلاینده صنعتی، شهری و کشاورزی به رودخانه، پارامترهای کیفی COD، NO3 و CBODf روند صعودی پیدا می‌کنند. نتایج حاصل از شاخص های ارزیابی نشان داد شاخص NRMSE در مرحله واسنجی و صحت‌سنجی مدل برای pH کمترین مقدار و برابر 83/8 و 22/9 درصد بدست آمد و برای EC به ترتیب 05/11 و 86/13 درصد محاسبه شد. شبیه‌سازی پارامتر DO نیز در طول رودخانه دارای نوساناتی بود این در حالی است که شاخص‌های آماری NRMSE، RMSE و MAE برای این پارامتر در هر دو مرحله واسنجی و صحت‌سنجی در حد قابل قبولی بدست آمدند، به طوری ‌که شاخص‎های فوق در مرحله واسنجی مدل به ترتیب 49/12، 917/0 و 72/0 و در مرحله صحت‌سنجی مدل به ترتیب 65/24، 78/1، 55/1 محاسبه شد. همچنین مدل توانست با دقت خوبی پارامتر دما را در تیرماه (92/1 RMSE=و 57/1=MAE) و شهریورماه (77/2 RMSE=و 5709/2=MAE) شبیه‌سازی کند. در نهایت نتایج حاصل از این پژوهش بیانگر دقت مناسب مدل QUAL2Kw در شبیه‌سازی پارامترهای فوق در رودخانه خرم‌آباد بود.

کلیدواژه‌ها

عنوان مقاله [English]

Evaluation of Qual2kw Model in Qualitative Simulation of Khorramabad River

نویسندگان [English]

  • babak shahinejad 1
  • zohreh izadi 2
  • behzad javadi 3

1 Assistant Professor Department of Water Engineering

2 phd student

3 Director of River Engineering Office of Lorestan Regional Water Company

چکیده [English]

1-Introduction
Rivers are the most important sources of drinking, agricultural, and industrial water supply. In recent decades, however, these resources have become the main receivers of sewer pipelines due to rapid population growth. To evaluate the effects of pollutant discharge on the self-purification of rivers, it is necessary to use numerical simulations of water quality. Today, various softwares have been designed for this purpose. One of the most important of these softwares used in this research is the one-dimensional QUAL2Kw model that simulates water quality variables in a steady and non-uniform flow mode. In the present study, the water quality of the Khorramabad River was simulated with the help of this model over a distance of 35 km from the river.
2-Methodology
The range studied in this research is about 35 km along the Khorramabad River from the source of Khorramrud upstream of Robat Namaki village to Chamjangir hydrometric station, which in the geographical coordinates of 33°36'54" to 33°26'37" north latitude; it is located at 48°17'39" to 48°14'38" longitude. Khorramabad River pollution sources are divided into three main parts: urban, industrial, and agricultural. Due to the location of pollutant sources in the river, 5 points along the river were considered as sampling sites, two stations including the beginning and end of the study area, one station in the center of Khorramabad, and the other two stations were selected before the river entered the city and after leaving the city, respectively.
In this research, the QUAL2Kw model version 5.1 was used. The required data of the model is divided into three parts: geometric-hydraulic data, qualitative data, and meteorological data. The river was divided into 11 sections and simulated using the hydraulic Manning equation.  In this study, important water quality parameters such as DO, CBODf, COD, NO3, EC, and pH and temperature parameters in July and September of 2019 for calibration and validation, respectively, the model was used.

 

Finally, RMSE, NRMSE, and MAE indices were used to evaluate the model in the simulation.
3-Results and Discussion
The results showed that the number of parameters including COD, CBODf, and NO3 increased after the Karganeh tributary joined the river and also the inflow of pollutant sources such as slaughterhouses, municipal treatment plant, milk factory, and alcohol production unit into the river. However, the pH (in both months) and EC (in July) parameters did not change much along the river; in other words, the river can self-purifying these parameters. In the research of Hashemi et al. (2019), for the simulation of the Talar River, the same result was obtained for these two parameters. Babakhani et al. (2019) in a study conducted on the Diwandara River reported a strong correlation between the measured and simulated values of the pH parameter because in surface water the pH value along the path with carbonate and bicarbonate in the path there reaches the equilibrium concentration. According to the results of the research and the fact that the Khorramabad River is used for agricultural and industrial purposes and is not a source of drinking water, at present, there is no limiting factor to achieve this purpose in the study route. Then, the calculation of statistical indices showed that the value of the NRMSE index in the calibration and validation stage of the model is the lowest for pH and equal to 8.83 and 9.22 percent and for EC is 11.05 and 13.86 percent, respectively. The simulation of DO parameter also had fluctuations along the river, while the statistical indices of NRMSE, RMSE, and MAE for this parameter in both calibration and validation stages were obtained at an acceptable level; thus, the above indices in the calibration stage of the model 12.49, 0.917 and 0.72, respectively, and in the validation stage of the model were calculated 24.65, 1.78, 1.55, respectively. In addition, the model was able to simulate the temperature parameter with high accuracy in July (RMSE = 1.92 and MAE = 1.57) and September (RMSE = 2.77 and MAE = 2.5709). Finally, the results of this study indicate the considerable accuracy of the QUAL2Kw model in simulating the above parameters in the Khorramabad River.
4-Conclusions
The results showed that the amount of chemical oxygen demand, biochemical oxygen demand, and nitrate parameters increased due to the entry of effluents from industrial pollutants. Besides, the evaluation index indicates that the QUAL2Kw model has shown good performance in estimating the acidity parameter compared to other parameters. It is suggested that in addition to the low water season, modeling be done in high water seasons and use two-dimensional quality models to simulate rivers.

 

Keywords: Qualitative Parameters, Simulation, QUAL2Kw Model, Khorramabad River, Lorestan Province
5-References
Babakhani, Z., Saraee Tabrizi, M., & Babazadeh, H. (2019). Determining the Self-Purification capacity of Diwandara River using model qual2kw. Journal of Echo Hydrology, 6(3), 673-684.
Hashemi, Z., Gholami Sefidkouhi, M. A., & Ahmadi, K. (2019). Evaluation and Simulation of Talar River Quality by using QUAL2KW Model. Iranian Journal of Irrigation & Drainage12(6), 1500-1510.
 
 

کلیدواژه‌ها [English]

  • Qualitative Parameters
  • Khorramrud
  • Simulation
  • QUAL2Kw Model
  • Lorestan Province
Afrous, A., & Zallaghi, M. (2020). Qualitative Simulation of Nitrate and Phosphate along the Dez River using QUAL2KW Model. Iranian Journal of Soil and Water Research50(9), 2099-2111.
Allam, A., Fleifle, A., Tawfik, A., Yoshimura, C., & El-Saadi, A. (2015). A simulation-based suitability index of the quality and quantity of agricultural drainage water for reuse in irrigation. Science of the Total Environment536, 79-90.
Areeyaenezhad, R., Sarai Tabrizi, M., & Babazadeh, H. (2019). Modeling Water Quality of Rivers Using QUAL2Kw Model (Case Study: Shahroud River). Journal of Environmental Science and Technology21(7), 1-13.
Azimi, M.M. (2008). Investigation of uncertainty of river quality parameters, Case study: Jajroud River. Master Thesis, University of Water and Power Industry (Shahid Abbaspour), Faculty of Water, 105.
Babakhani, Z., Saraee Tabrizi, M., & Babazadeh, H. (2019). Determining the Self-Purification capacity of Diwandara River using model qual2kw. Journal of Echo Hydrology, 6(3), 673-684.
Banejad, H., Kamali, M., Amirmoradi, K., & Olyaie, E. (2013). Forecasting some of the qualitative parameters of rivers using wavelet artificial neural network hybrid (W-ANN) model (case of study: Jajroud river of Tehran and Gharaso river of Kermanshah). Iranian Journal of Health and Environment6(3), 277-294.
Bayati khatibi, M., Shahbazi, M., & Heidari, M. A. (2015). Speculations and Analysis on the Changes in Water Quality of Ahar River and its Impacts on Human Health. Hydrogeomorphology, 1(1), 93-109.
Chang, H. (2008). Spatial analysis of water quality trends in the Han River basin, South Korea. Water Research42(13), 3285-3304.
Cox, B. A. (2003). A review of dissolved oxygen modelling techniques for lowland rivers. Science of the Total Environment314, 303-334.
Emamgholi, Z., & Yasi, M. (2020). Impacts of variation of river geometry on flowing water quality (Case Study: Ghezel Ozan River). Journal of Hydraulics14(4), 1-17.
Ghafari, P., Tavakolizadeh, A.A., & Zarshenas, M. (2006). Investigation of mathematical models for networking in rivers and wetlands. Seventh International Seminar on River Engineering, Shahid Chamran University.
Ghorbani, Z., Amanipoor, H., & Battaleb-Looie, S. (2020). Water quality simulation of Dez River in Iran using QUAL2KW model. Geocarto International, 1-13.
Hashemi, Z., Gholami Sefidkouhi, M. A., & Ahmadi, K. (2019). Evaluation and Simulation of Talar River Quality by using QUAL2KW Model. Iranian Journal of Irrigation & Drainage12(6), 1500-1510.
Hemond, H. F., & Fechner, E. J. (2014). Chemical fate and transport in the environment. Elsevier, 486.
Hoseini, P., & Hoseini, Y. (2017). Changes in Self-Purification Capacity of the Ahvaz Karun River in 2008 and 2014 using QUAL2K. Amirkabir Journal. Civil Eng. 49(1), 35-45.
Hossain, M. A., Sujaul, I. M., & Nasly, M. A. (2014). Application of QUAL2Kw for water quality modeling in the Tunggak River, Kuantan, Pahang, Malaysia. Research Journal of Recent Sciences, 3(6),6-14.
Kannel, P. R., Lee, S., Lee, Y. S., Kanel, S. R., & Pelletier, G. J. (2007). Application of automated QUAL2Kw for water quality modeling and management in the Bagmati River, Nepal. Ecological modelling202(3-4), 503-517.
Oliveira, B., Bola, J., Quinteiro, P., Nadais, H., & Arroja, L. (2012). Application of Qual2Kw model as a tool for water quality management: Cértima River as a case study. Environmental Monitoring and Assessment184(10), 6197-6210.
Pelletier, G.J., & Chapra, S.C. (2005). QUAL2Kw theory and documentation (version 5.1), a modeling framework for simulating river and stream water quality. Washington: Department of Ecology.
Sarda, P., & Sadgir, D. P. (2015). Water Quality Modeling and Management of Surface Water using Soft Tool. International Journal of Science, Engineering and Technology Research (IJSETR)4(9), 2988-2992.
Shahidi, A., & Khadempour, F. (2020). Investigating the Qualitative Satus of Groundwater in the Plain of Khorasan Razavi Province Using GWQI and AWQI Indexes and Its Zoning with Geographic Information System (GIS). Hydrogeomorphology, 6(22), 1-20.
Sharma, D., Kansal, A., & Pelletier, G. (2017). Water quality modeling for urban reach of Yamuna River, India (1999–2009), using QUAL2Kw. Applied Water Science7(3), 1535-1559.
Shokri, S., Hooshmand, A. R., & Moazed, H. (2016). Qualitative Simulation of Ammonium and Nitrate along the Gargar River Using the Qual2Kw Model. Journal of Wetland Ecology6(23), 57-68.
Torabiyan, A., & Hashemi, H. (2002). Surface water quality modeling, Tehran: University of Tehran Press, 528.