ارزیابی تغییرات کیفی آب رودخانه مرزی ارس در ورودی و خروجی سد خداآفرین با استفاده از شاخص‌های کیفی آب

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران

2 دانشیار، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران

3 مدیر پایش کیفیت آب و فاضلاب استان آذربایجان شرقی، تبریز، ایران

4 کارشناس دفتر رودخانه‌های مرزی، شرکت مدیرت منابع آب ایران، تهران، ایران

5 گروه مهندسی عمران دانشکده فنی دانشگاه محقق اردبیلی، اردبیل، ایران

6 استاد، مرکز تحقیقات سلامت و محیط زیست، دانشگاه علوم پزشکی تبریز، تبریز، ایران

چکیده

برآورد قابل اطمینان از تغییرات پارامترهای کیفی آب در مخازن کشور به منظور برنامه‌ریزی‌های کارآمد و بهره‌برداری از منابع آبی در مقیاس زمانی و مکانی حائز اهمیت است. در مطالعه حاضر ابتدا ارزیابی تغییرات پارامترهای کیفی آب سد خداآفرین در دو ایستگاه ورودی و خروجی به مخزن سد طی دوره مطالعاتی سال آبی 1400 و 1401 مدنظر قرار گرفت. سپس جهت شناسایی و طبقه‌بندی کیفیت آب برای مصارف مختلف از شاخص‌های IRWQI، NSFWQI و CWQI استفاده گردید. مطابق نتایج، حداکثر مقدار پارامترهای کیفی آب از جمله کدورت، مجموع کلیفرم، نیترات، TDS، TSS، EC، DO، BOD5 و COD به ترتیب معادل 5900 (NTU)، 35000 (MPN/100 ml)، 3/11، 7/1001، 5580، 1590 (us/cm)، 3/12، 5/5 و 32 (mg/L) برآورد گردید. مطابق نتایج به دست آمده BOD در خروجی مخزن نسبت به ورودی به میزان 3/10 درصد کاهش می‌یابد. دلیل کاهش آن نسبت به ایستگاه ورودی، کاهش تجزیه مواد آلی و تبدیل آن به ترکیبات معدنی بوده که علت آن را می‌توان فعالیت باکتری‌ها قلمداد کرد. متوسط مقدار COD برای ورودی و خروجی سد خداآفرین به ترتیب معادل 4/13 و 8/13 میلی‌گرم بر لیتر محاسبه شد که افزایش 9/2 درصدی را نشان می‌دهد. با ارزیابی مقادیر شاخص‌های کیفیت آب مشخص گردید که بازه تغییرات شاخص‌های IRWQI و NSFWQI در خروجی سد خداآفرین به ترتیب بین 39 تا 4/72 و 54 تا 78 به دست آمد که براساس شاخص CWQI نیز این مقدار معادل 40 محاسبه شد که نشان‌دهنده بهود کیفیت آب در خروجی مخزن آب است. کاهش سرعت آب و ته‌نشین شدن آب در پشت مخزن سد و رقیق‌سازی آب مخزن بواسطه ورود رودخانه هاکاری باعث گردیده تا کیفیت آب در خروجی در وضعیت قابل قبولی قرار داشته باشد. اما در ورودی سد خداآفرین به علت ورود آلاینده‌‌های مختلف کیفیت آب براساس مقادیر هر دو شاخص در طبقه نسبتا بد و بد قرار گرفت. 

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Evaluation of water quality changes in Aras border river downstream and upstream of Khodaafrin Dam using water quality indicators

نویسندگان [English]

  • mohammad babaei 1
  • Mohammad Taghi Sattari 2
  • Sara Nikmaram 3
  • Houshang Gholami 4
  • fariborz masoumi 5
  • Mohammad Pirhayati 4
  • Mohammad Mosaferi 6
1 Ph.D. Student, Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
2 Associate Professor, Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
3 Water and Wastewater Company, East Azerbaijan Province, Tabriz, Iran
4 Expert, Border Rivers Office, Iran Water Resources Management Company, Tehran, Iran
5 Civil Engineering Dept., Faculty of Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
6 Professor, Health and Environment Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
چکیده [English]

Reliable estimation of changes in water quality parameters in the country's reservoirs is important for efficient planning and exploitation of water resources on a temporal and spatial scale.  In this study, firstly, the evaluation of the changes in the water quality parameters of Khoda Afarin Dam at the two inlet and outlet stations to the dam during the study period of the water year 1400 and 1401 was considered. Then IRWQI, NSFWQI and CWQI indexes were used to identify and classify water quality for different uses. According to the obtained results, the maximum values of water quality parameters, including turbidity, total coliform, nitrate, TDS, TSS, EC, DO, BOD5 and COD are respectively equivalent to 5900 (NTU), 35000 (MPN/100 ml), 11.3, 1001.7, 5580, 1590 (us/cm), 12.3, 5.5 and 32 (mg/L) were estimated. According to the obtained results, the BOD at the outlet of the dam is reduced by 10.3% compared to the inlet. The reason for its decrease compared to the inlet station is the decrease in the decomposition of organic matter and its conversion into inorganic compounds, which can be attributed to the activity of bacteria. The average value of COD for the inlet and outlet of Khodaafrin Dam was calculated as 13.4 and 13.8 mg/lit, respectively, which shows an increase of 2.9%. By evaluating the values of water quality indicators, it was found that the range of changes of IRWQI and NSFWQI indicators at the outlet of Khodaafrin dam was between 39 to 72.4 and 54 to 78, respectively. Also, based on the CWQI index, this value was calculated as 40, which indicates the improvement of water quality at the outlet of the dam. 

کلیدواژه‌ها [English]

  • Water quality parameters
  • Aras River
  • IRWQI
  • NSFWQI and CCME

EXTENDED ABSTRACT

 

Background and purpose

Water reservoirs have become very vulnerable due to the entry of pollutants, nutrients, organic substances and suspended solids from the basin. This is caused by human activities such as mining, animal husbandry, waste production and disposal (industrial, urban and agricultural), increased runoff, sedimentation or soil erosion due to land use change and heavy metal pollution. In such a situation, paying attention to the quality monitoring and protection of surface water resources has become one of the main criteria for the sustainable development of water resources in any country. Therefore, water quality management requires the collection and analysis of data sets of water quality parameters, which can be evaluated using water quality indicators. Water quality indicators are presented according to the importance of quality parameters for each country, which will be investigated in this study.

Materials and methods

In this study, 16 water samples were taken during eight stages in the study period of the water year 1400 and 1401 in order to compare and evaluate the quality of water at the two inlet and outlet stations of the Khoda Afarin dam reservoir, and a wide range of water quality parameters were analyzed. Also, in order to obtain a better and more comprehensive understanding of the quality conditions of the Aras River in the upstream and downstream of the Khodaafrin Dam, the quality indicators of surface water resources of Iran (IRWQI), the water quality index of the National Health Foundation (NSFWQI) and the water quality index of Canada (CCME) was used to analyze the quality parameters of water and the factors influencing the reduction of water quality were identified.

Findings

According to the results, one of the largest percentage changes between the upstream and downstream of the tank is related to the water turbidity parameter. Because the average water turbidity for the inlet of the tank was recorded as 1155.6 (NTU), and the average of this parameter was measured at the outlet of the dam tank as 18.2 (NTU), which represents a change of 98.4%. Also, TSS, EC and TDS parameters have decreased by 97.5%, 17.2% and 17.1% respectively in the outlet of the tank compared to the inlet of the dam. The water quality status in terms of IRWQI_SC index is relatively normal in autumn and winter, but in spring and summer both at the entrance and exit of the dam, the quality of water is relatively bad. NSFWQI index also works seasonally in line with IRWQI_SC index, with the difference that relatively this index shows more optimistic results compared to IRWQI_SCindex, but the difference in water quality is evident in different seasons. According to the CWQI index, the general condition of water quality is in the poor category, but it has the most favorable conditions for cattle consumption and is in the excellent category. The quality of water for agriculture was also estimated to be relatively good.

Conclusion

Considering the existing conditions, it is necessary to take serious measures to prevent or reduce the entry of all kinds of pollutants into the river and the reservoir of the dam (especially the mineral effluents of Armenia).

In addition to increasing the quality of water, this will also reduce the environmental and health consequences of these pollutions in the region.

Author Contributions

For research articles with several authors, a short paragraph specifying their individual contributions must be provided. The following statements should be used “Conceptualization, M.B., M.T.S., S.N., H.G., F.M., M.P. and M.M.; methodology, M.B., M.T.S. and M.M; software, M.M.; validation, M.B., M.T.S. and M.M.; formal analysis, M.B.; investigation, M.B.; resources, S.N.; data curation, M.B.; writing—original draft preparation, M.B.; writing—review and editing, M.T.S.; visualization, M.B.; supervision, M.M.; project administration, M.M.; funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript.” Please turn to the CRediT taxonomy for the term explanation. Authorship must be limited to those who have contributed substantially to the work re-ported.

All authors contributed equally to the conceptualization of the article and writing of the original and subsequent drafts.

Data Availability Statement

Data available on request from the authors.

 

Acknowledgements

The authors would like to thank all participants of the present study.

Ethical considerations

 The authors avoided data fabrication, falsification, plagiarism, and misconduct.

Conflict of interest

 The author declares no conflict of interest.

Abdullah, H. S., Mahdi, M. S., & Ibrahim, H. M. (2017). Water quality assessment models for Dokan Lake using Landsat 8 OLI satellite images. Journal of Zankoy Sulaimani, Pure and Applied Sciences, 19(4), 25-44.
Boyacioglu, H. (2010). Utilization of the water quality index method as a classification tool. Environmental monitoring and assessment, 167, 115-124.
Brown, R. M., McClelland, N. I., Deininger, R. A., & Tozer, R. G. (1970). A water quality index-do we dare. Water and sewage works, 117(10).
Chen, P., Wang, B., Wu, Y., Wang, Q., Huang, Z., & Wang, C. (2023). Urban river water quality monitoring based on self-optimizing machine learning method using multi-source remote sensing data. Ecological Indicators, 146, 109750.
Ebraheim, G., Zonoozi, M. H., & Saeedi, M. (2020). A comparative study on the performance of NSFWQI m and IRWQI sc in water quality assessment of Sefidroud River in northern Iran. Environmental Monitoring and Assessment, 192, 1-13.
Gani, M. A., Sajib, A. M., Siddik, M. A., & Moniruzzaman, M. (2023). Assessing the impact of land use and land cover on river water quality using water quality index and remote sensing techniques. Environmental Monitoring and Assessment, 195(4), 449.
Ghosh, H., Tusher, M. A., Rahat, I. S., Khasim, S., & Mohanty, S. N. (2023, February). Water Quality Assessment Through Predictive Machine Learning. In International Conference on Intelligent Computing and Networking (pp. 77-88). Singapore: Springer Nature Singapore.
Gupta, S., & Maiti, S. (2023). Comparison between self‐organizing map and principal component analysis for water quality assessment and hydro‐geochemical characterization in dyke intruded complex geological settings. Water and Environment Journal.
Hashemi, S. H., Pourasghar, F., Nasrabadi, T., Ramezani, S., & Khoshrou, G. (2011). Guide to Iran Water Quality Index for Surface Water Resources-Conventional Parameters. Environmental Protection Organization of Iran. (In Persian)
Hurley, T., Sadiq, R., & Mazumder, A. (2012). Adaptation and evaluation of the Canadian Council of Ministers of the Environment Water Quality Index (CWQI WQI) for use as an effective tool to characterize drinking source water quality. Water research, 46(11), 3544-3552.
Jang, E., Im, J., Ha, S., Lee, S., & Park, Y. G. (2016). Estimation of water quality index for coastal areas in Korea using GOCI satellite data based on machine learning approaches. Korean Journal of Remote Sensing, 32(3), 221-234.
Kamali Maskooni, E., Naseri-Rad, M., Berndtsson, R., & Nakagawa, K. (2020). Use of heavy metal content and modified water quality index to assess groundwater quality in a semiarid area. Water, 12(4), 1115.
Khalili, R., Parvinnia, M., & Motaghi, H. (2020). Evaluation of Bashar River water quality using CWQI water quality index. Journal of Environmental Science Studies, 5(3), 2807-2814 (In Persian)
Khlaif, B. M., & Al-Hassany, J. S. (2023, December). Assessment of the Euphrates River’s Water Quality at a Some Sites in the Iraqi Governorates of Babylon and Karbala. In IOP Conference Series: Earth and Environmental Science (Vol. 1262, No. 2, p. 022021). IOP Publishing.
Kujiek, D. C., & Sahile, Z. A. (2024). Water quality assessment of Elgo river in Ethiopia using CCME, WQI and IWQI for domestic and agricultural usage. Heliyon, 10(1).
Oiry, S., & Barillé, L. (2021). Using sentinel-2 satellite imagery to develop microphytobenthos-based water quality indices in estuaries. Ecological Indicators, 121, 107184.
Saffran, K., Cash, K., Hallard, K., & Wright, R. (2001). Canadian water quality guidelines for the protection of aquatic life, CWQI water quality Index 1, 0, Users manual. Excerpt from Publication, 1299.
Sattari, M. T., Babaei, M., Masoumi, F., & Mosaferi, M. (2023). Sampling and evaluation of water quality variables of Aras River downstream and upstream of Aras Dam. Iran-Water Resources Research. 19(5), 99-113. (In Persian)
Silva, T. F. D. G., Beltrán, D., de Oliveira Nascimento, N., Rodríguez, J. P., & Mancipe-Muñoz, N. (2023). Assessing major drivers of runoff water quality using principal component analysis: a case study from a Colombian and a Brazilian catchments. Urban Water Journal, 20(10), 1555-1567.
Sutadian, A. D., Muttil, N., Yilmaz, A. G., & Perera, B. J. C. (2016). Development of river water quality indices—a review. Environmental monitoring and assessment, 188, 1-29.
Uddin, M. G., Nash, S., Rahman, A., & Olbert, A. I. (2023). Performance analysis of the water quality index model for predicting water state using machine learning techniques. Process Safety and Environmental Protection, 169, 808-828.
Varol, M., & Tokatlı, C. (2023). Evaluation of the water quality of a highly polluted stream with water quality indices and health risk assessment methods. Chemosphere, 311, 137096.
Wan Abdul Ghani, W. M. H., Abas Kutty, A., Mahazar, M. A., Al-Shami, S. A., & Ab Hamid, S. (2018). Performance of biotic indices in comparison to chemical-based Water Quality Index (WQI) in evaluating the water quality of urban river. Environmental monitoring and assessment, 190, 1-14.
Zaghloul, G. Y., Zaghloul, A. Y., Hamed, M. A., El-Moselhy, K. M., & El-Din, H. M. E. (2023). Water quality assessment for Northern Egyptian lakes (Bardawil, Manzala, and Burullus) using NSF-WQI Index. Regional Studies in Marine Science, 103010.