پایش میدانی کیفیت آب و شبیه‌سازی تغذیه‌گرایی رودخانه دز

نوع مقاله : پژوهشی اصیل (کامل)

نویسندگان
1 دانش آموخته کارشناسی ارشد مهندسی عمران- رودخانه، دانشگاه صنعتی جندی شاپور دزفول
2 دانشگاه صنعتی جندی شاپور دزفول
3 اداره کل حفاظت محیط زیست استان خوزستان
چکیده
آنچه امروز در مهندسی منابع آب به‌عنوان تغذیه‌گرایی موسوم است پاسخ زیستی رودخانه به بالابودن غلظت مواد مغذی از جمله فسفر و نیتروژن است. وضعیت کیفی رودخانه دز در وضع موجود به دلیل برداشت‌های روز اﻓﺰون و سال‌های کم آبی متوالی و در عین حال ﺗﺨﻠﯿﻪ پساب‌های ﺷﻬﺮی، ﺻﻨﻌﺘﯽ و ﮐﺸﺎورزی ﺑﻪ رودﺧﺎﻧﻪ در حال تهدید است. در این پژوهش به منظور ﭘﺎیﺶ میدانی و سپس شبیه­سازی تغذیه‌گرایی رودخانه دز هشت ایستگاه پایش با در نظر گرفتن نحوه ورود آلاینده­ها به رودخانه انتخاب شد. از میان متغیرهای کیفی مورد نظر، اکسیژن­ محلول و دمای آب به صورت مستقیم توسط دستگاه قابل ­حمل اندازه­گیری و متغیرهای فسفات و نیترات نیز از طریق نمونه‌برداری و ارسال به آزمایشگاه شیمی آب اندازه‌گیری شدند. سپس مسیر رودخانه ﺑﻪ ﻃﻮل 18 ﮐﯿﻠﻮﻣﺘﺮ توسط مدلHEC-RAS  شبیه‌سازی کیفی شد. ﻧﺘﺎیﺞ ﺷﺒﯿﻪ‌ﺳﺎزی ﺑﺎ داده‌ﻫﺎی مشاهداتی ﻣﻮرد ﻣﻘﺎیﺴﻪ قرار گرفت ﮐﻪ در ایﻦ ﻣﻘﺎیﺴﻪ ﭘﺎراﻣﺘﺮﻫﺎی اﮐﺴﯿﮋن ﻣﺤﻠﻮل، دﻣﺎ، نیترات و فسفات اﻧﻄﺒـﺎق ﻗﺎﺑﻞ ﻗﺒﻮﻟﯽ ﺑﺎ داده‌های مشاهداتی داﺷﺘه و مقدار آماره RMSE برای پارامترهای نیترات، دمای آب، اکسیژن محلول و فسفات به ترتیب برابر 0.25، 0.29، 0.67 .و 0.67 میلی‌گرم بر لیتر می‌باشد. ﭘﺲ از  تحلیل حساسیت، ﻛﺎﻟﻴﺒﺮاﺳﻴﻮن و تأیید ﻣﺪل از آن ﺑﺮای ﺷﺒﻴﻪﺳﺎزی ﭘﺎﺳﺦ رودخانه ﺑﻪ ﺳﻨﺎرﻳﻮ‌ی ﻛﺎﻫﺶ ﻣﻮاد ﻣﻐﺬی اﺳـﺘﻔﺎده شد که در این سناریو، تأثیر افزایش دبی رودخانه در میزان کاهش غلظت مواد مغذی بررسی شد. نتایج شبیه‌سازی نشان داد که در سناریو افزایش دبی رودخانه، به دلیل افزایش انحلال اکسیژن محلول و نیز فرایند رقیق سازی، توان خودپالایی رودخانه افزایش و غلظت مواد مغذی به میزان قابل توجهی کاهش پیدا می‌کند و شاهد افزایش DO به میزان 5/0-7/0 میلی‌گرم بر لیتر، کاهش میزان نیترات تا 6 میلی‌گرم بر لیتر و کاهش فسفات تا 3/0 میلی‌گرم بر لیتر پس از شبیه­سازی خواهیم بود.

کلیدواژه‌ها

موضوعات


عنوان مقاله English

Water Quality Monitoring and Eutrophication Simulation of Dez River

نویسندگان English

Sina Jahanimehr 1
M. Zakermoshfegh 2
B. Lashkar-Ara 2
F. Gholinezhad 3
1 Jundi-Shapur university of technology, Dezful, Iran.
2 Jundi-Shapur university of technology, Dezful, Iran.
3 General Department of environmental protection of Khuzestan province.
چکیده English

The biological response to high concentrations of nutrients such as phosphorus and nitrogen in a river is called eutrophication in water-resources engineering. Due to increasing withdrawals of water, successive dry years, and the discharge of urban, industrial, and agricultural wastewater into the Dez River, the quality of the river is being threatened. In this research, for the purpose of field monitoring and then simulating the eutrophication of Dez river, eight monitoring stations were selected considering the way pollutants enter the river. A number of water quality variables, including dissolved oxygen and water temperature, were measured directly by the portable device, while phosphate and nitrate variables were measured through sampling and laboratory tests. Then, the water quality simulation of the river with 18 Km of length was conducted by the HEC-RAS model. A comparison was made between the simulation results and the observed data, in which parameters including dissolved oxygen, temperature, nitrate, and phosphate were in good agreement with the observed data. To measure the accuracy of the model, the root mean square error (RMSE) statistical function was used. The results of the sensitivity analysis showed that the amount of dissolved oxygen is more sensitive to the parameters of oxygen demand and wind speed function coefficients, and also the model is less sensitive to the parameters of diffusion coefficient and dust coefficient, and the change of these two parameters has no effect on the dissolved oxygen graph. This was used in the model calibration process so that only parameters affecting the model were used in the calibration process. The results showed that the variables of nitrate, water temperature, dissolved oxygen and phosphate were modeled with appropriate accuracy. In model calibration, RMSE values for nitrate, water temperature, dissolved oxygen and phosphate parameters were calculated as 0.25, 0.29, 0.67 and 0.67 mg/L, respectively. Also, the results of the model confirmation show the acceptable compatibility of the simulated and observed values. After the model Sensitivity analysis, calibration and validation, it was used to simulate the river's response to the nutrition reduction scenario, which involved the impact of the discharge increasing on the concentration reduction of nutrition in the river.

The results of nitrate and phosphate concentration changes during the simulation period in the river course showed that the amount of phosphate and nitrate concentration increases from the upstream to the downstream of the river. Also, the simulation results for the dissolved oxygen variable show that in most of the sampling months, the concentration of dissolved oxygen is higher than 4 mg/l.

 The simulation results showed that in the scenario of discharge increasing, the nutrition concentration reduced significantly. On average, in all the stations, with the increase of the flow by 70 cubic meters per second, there is an increase in DO by 0.5-0.9 mg/l, a decrease in nitrate between 0.4 and 6 mg/l, and a decrease in phosphate by 3 0.0 mg/l and a decrease of 0.5 to 1 degree in water temperature. This shows the impact of this scenario on the amount of nitrate and phosphate variables, which are the main factors in creating the phenomenon of eutrophication and algal growth.

 

کلیدواژه‌ها English

Water Quality Monitoring
Eutrophication Simulation
HEC-RAS
Dez River
[1] USEPA. 2007 Hypoxia in the Northern Gulf of Mexico: anupdate by the EPA Science Advisory Board. EPA-SAB-08-003. 333 pages
[2] Bouraoui, F, Grizzetti B. 2008 An integrated modelling framework to estimate the fate of Application to the Loire (France). Journal of ecological modelling 212: 450–459
[3] Lean D. 1973 Movement of Phosphorus between Its Biologically Important Forms Lake water,J.Fish. Res. Bd. Can. 30:1525-1536
[4] Quiblier C. 2008 Christophe Leboulangerb, Seyni Sane´d, Philippe Dufourc, Phytoplankton growth control and risk of cyanobacterial blooms in the lower Senegal River delta region. Water Research, 42:1023 – 1034
[5] Milan O. 2007 Correlations between several environmental factors affecting the bloom events of cyanobacteria in Liptovska Mara reservoir (Slovakia)-A simple regression model. Ecological Modelling 209: 412–416
[6] Sickman J, Zanoli M, Mann H. 2007 Effects of Urbanization on Organic Carbon Loads in the Sacramento River,California. Water Resource. Res.43,W11422,1-15
[7] Ford A. 1999 Modeling the environment (An Introduction to System Dynamics Models of Environmental Systems). ISLAND PRESS. 380 pages
[8] Kayode O, Muthukrishna, V. 2018 Assessment of some existing water quality models. Nature Environme.
[9] Vinçon-Leite B, Casenave C. 2019 Modelling eutrophication in lake ecosystems: A review. Sci. Total Environ 651, 2985–3001
[10] Palival R, Sharma P, Kansal A. 2007 Water quality modelling of the river Yamuna (india) Using qual2E. Journal Of Enviromental Management, Vol. 83, pp.131-144
[11] Fan C, Wang W, Schanz R. 2009 An innovative modeling approach using qual2K and HEC-RAS integration to assess the impact of tidal effect on River Water quality simulation. Journals Environ Manage. vol.64
[12] Lai Y, Yang C, Surampalli R, Kao C. 2013, Development of a water quality modeling system for river pollution index and suspended solid loading evaluation. Journal of Hydrology 478: 89–101
[13] Mehrasbi M, Farahmand Kia Z. 2015 Water Quality Modeling and Evaluation of Nutrient Control Strategies Using QUAL2K in the Small Rivers. Journal of Huoman, Environment and Health Promotion. 1(1):1-10
[14] Rallapalli S, Singh A. 2018 An integrated fuzzy-based advanced eutrophication simulation model to develop the best management scenarios for a river basin, Environmental Science and Pollution Research volume 25, pages9012–9039
[15] Saha M, Madani M, Tirupati B. 2020 "Microbial Water Quality Modelling of the Detroit River. Civil and Environmental Engineering. Electronic Theses and Dissertations. 8394.1214
[16] Aleksandra Z, Magdalena K. 2021 Modeling and Monitoring of Hydrodynamics and Surface Water Quality in the Sulejów Dam Reservoir, Poland.
[17] Izni Z, Geoffrey W, Katherine B, Felix K. 2020 Water Quality Modelling for River Activities Management. Journal of Health Pollut. 2020 Dec; 10(28): 201207
[18] Saghafi B, Hassaniz A, Noori R, and Bustos M.G. 2009 Artificial neural networks and regression analysis for predicting faulting in jointed concrete pavements considering base condition. International Journal of Pavement Research and Technology, 2(1), pp.20-25
[19] Yazdizadeh R. 2012 Zoning of Physicochemical and Bacterial Properties in Dez River Using Geographic Information System (GIS), (Case study: Dezful), Iran. MSc Thesis, Islamic Azad University Science and Research Branch of Khouzestan, 222 pages (in Persian)
[20] Us Army Corps of Engineers, User Manual HEC-RAS (2010) Version 4.1.0.790 pages
[21] Noori R, Karbassi A, Ashrafi K, Ardestani M, Mehrdadi N. and Nabi Bidhendi G R, 2012. Active and online prediction of BOD 5 in river systems using reduced-order support vector machine. Environmental Earth Sciences, 67, pp.141-149