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:: Volume 10, Issue 4 (9-2023) ::
jehe 2023, 10(4): 433-452 Back to browse issues page
Identification of phytoplankton communities, water quality and eutrophication status in the reservoir of Karun 4
Nader Cheraghpour Ahmadmahmoudi , Mohsen Saadat * , Rasool Zamani-ahmadmahmoodi , Avid Avokh
Assistant Professor, Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Abstract:   (758 Views)
Background: One of the major problems of aquatic ecosystems is eutriphication or enrichment of water with nutrients. In this study, the phytoplankton communities in the Karun 4 reservoir were investigated in spring and summer in 2018.
Methods: Chlorophyll A and 19 water physicochemical parameters were measured and analyzed at the reservoir in 26 stations. Statistical analysis of data was done in SPSS software. In order to investigate the biological indicators, Simpson and Shannon-Wiener indices were used, and Trophic State Index (TSI) was used to determine the eutriphication status of the lake.
Results: Among the 35 identified genera, the phytoplankton populations mostly belonged to the Nephrocitium, Oonephris, Peridinium, Pyrophacus, Dinobryon, Cyclotella, Diatoma, and Chlorella genera. Also, the population of phytoplankton was more in summer than in spring. The Simpson and Shannon-Wiener biodiversity indices were calculated as 0.835±0.031 and 2.01±0.174 in spring and 0.819±0.041 and 2.02±0.21 in summer, respectively. According to the TSI index, the trophic status of the studied reservoir was in the eutrophic range (50-75). The analysis of the main components of principal component analysis showed that in spring, six components explained 80.847% of the total variance, while in summer, eight components explained 83.287% of the total variance.
Conclusion: The results showed that the Karun 4 reservoir, which is an important water source in Iran, is in a eutrophic state. This may be due to the increase in temperature in spring and summer, the growth of phytoplankton populations, and the increase in the amount of released ions of industrial and agricultural wastewater, which leads to the eutrophic condition in the reservoir.
Keywords: Karun 4 reservoir, Physicochemical parameters, Phytoplankton, Trophic Status Index (TSI), Principal Component Analysis (PCA)
Full-Text [PDF 876 kb]   (225 Downloads)    
Type of Study: Research | Subject: Special
Received: 2023/09/20 | Accepted: 2023/10/17 | Published: 2023/12/24
References
1. McIntosh A, Pontius J. Global water resources. Case Studies for Integrating Science and the Global Environment (Amsterdam: Elsevier). Elsevier, Amsterdam, Netherlands 2017:113-254. [DOI:10.1016/B978-0-12-801712-8.00002-0] [PMID] []
2. Yilmaz N. Assesment of seasonal variation of phytoplankton and related water quality parameters of Sazlidere Dam Lake (Istanbul, Turkey). Desalination and Water Treatment 2018;131:107-113. [DOI:10.5004/dwt.2018.23011]
3. Wilkinson GM. Eutrophication of freshwater and coastal ecosystems. Encyclopedia of Sustainable Technologies 2017;4:145-152. [DOI:10.1016/B978-0-12-409548-9.10160-5]
4. Reynolds C. What factors influence the species composition of phytoplankton in lakes of different trophic status? Hydrobiologia 1998;369:11-26. [DOI:10.1007/978-94-017-2668-9_2]
5. Kane DD, Conroy JD, Richards RP, Baker DB, Culver DA. Re-eutrophication of Lake Erie: Correlations between tributary nutrient loads and phytoplankton biomass. Journal of Great Lakes Research 2014;40(3):496-501. [DOI:10.1016/j.jglr.2014.04.004]
6. Bellinger EG, Sigee DC. Freshwater algae: identification, enumeration and use as bioindicators: John Wiley & Sons, 2015. [DOI:10.1002/9781118917152]
7. Yilmaz N, Ozyigit II, Demir G, Yalcin IE. Determination of phytoplankton density, and study of the variation of nutrients and heavy metals in the surface water of Riva Stream; one of the water sources of Istanbul, Turkey. Desalination and Water Treatment 2015;55(3):810-20. [DOI:10.1080/19443994.2014.952674]
8. Katsiapi M, Moustaka-Gouni M, Sommer U. Assessing ecological water quality of freshwaters: PhyCoI-a new phytoplankton community Index. Ecological Informatics 2016;31:22-9. [DOI:10.1016/j.ecoinf.2015.11.004]
9. Arab S, Hamil S, Rezzaz MA, Chaffai A, Arab A. Seasonal variation of water quality and phytoplankton dynamics and diversity in the surface water of Boukourdane Lake, Algeria. Arabian Journal of Geosciences 2019;12(2):1-11. [DOI:10.1007/s12517-018-4164-4]
10. Vollenweider R, Kerekes J. Eutrophication of waters. Monitoring, assessment and control. Organization for Economic Co-Operation and Development (OECD), Paris. 1982;156.
11. McGarrigle M, Lucey J, Cinnéide MÓ, Castle J. Water quality in Ireland 2007-2009. Environmental Protection Agency, Wexford, Ireland, 2009.
12. Axler R, Hagley C, Host G, Schomberg J. LakeSuperiorStreams. org: Making stormwater and stream data come alive for citizens, students, teachers, contractors, resource agencies, decision-makers and scientists. InProceedings US Department of the Interior, US Geological Survey 5th National Water Quality Monitoring Conference, San Jose, CA May. 2006; 5p.
13. Hu Z, Guo L, Liu T, Chuai X, Chen Q, Shi F, et al. Uniformisation of phytoplankton chlorophyll a and macrophyte biomass to characterise the potential trophic state of shallow lakes. Ecological indicators 2014;37:1-9. [DOI:10.1016/j.ecolind.2013.10.007]
14. Wang L, Liu L, Zheng B. Eutrophication development and its key regulating factors in a water-supply reservoir in North China. Journal of Environmental Sciences 2013;25(5):962-70. [DOI:10.1016/S1001-0742(12)60120-X] [PMID]
15. Christia C, Giordani G, Papastergiadou E. Assessment of ecological quality of coastal lagoons with a combination of phytobenthic and water quality indices. Marine pollution bulletin 2014;86(1-2):411-23. [DOI:10.1016/j.marpolbul.2014.06.038] [PMID]
16. Cabecinha E, Cortes R, Pardal MÂ, Cabral JA. A Stochastic Dynamic Methodology (StDM) for reservoir's water quality management: Validation of a multi-scale approach in a south European basin (Douro, Portugal). ecological indicators 2009;9(2):329-45. [DOI:10.1016/j.ecolind.2008.05.010]
17. Uttormark PD, Wall JP. Lake classification, a trophic characterization of Wisconsin lakes. National Environmental Research Center, Wisconsin, 1975.
18. Samaei M, Afshar A, Ahmadi Bargani A, Asadi R, editors. Eutrophication modeling in reservoirs with System Dynamic s approach. 12th National Conference on Environmenta l Health, Shahid Beheshti University of Medical Science, Faculty of Health, 2009.
19. Ahmadov E. Water resources management to achieve sustainable development in Azerbaijan. Sustainable Futures 2020;2:100030. [DOI:10.1016/j.sftr.2020.100030]
20. Ashjari J, Soltani F, Rezai M. Prediction of groundwater seepage caused by unclogging of fractures and grout curtain dimensions changes via numerical double-porosity model in the Karun IV River Basin (Iran). Environmental earth sciences 2019;78:1-17. [DOI:10.1007/s12665-019-8054-1]
21. Rice EW, Baird RB, Eaton AD, Clesceri LS. Standard methods for the examination of water and wastewater: American public health association Washington, DC; 2012.
22. Ghadiri A, Hashemi SH, Nasrabadi T. The efficiency of Iran's water resources quality index in comparison with three indices for assessment of Heavy Metal pollution in surface water (Case study: north and east of Tehran's runoff). Publications of Tarbiat Modares University 2021;21(2):177-188.
23. Jeffrey St, Humphrey G. New spectrophotometric equations for determining chlorophylls a, b, c1 and c2 in higher plants, algae and natural phytoplankton. Biochemie und physiologie der pflanzen 1975;167(2):191-194. [DOI:10.1016/S0015-3796(17)30778-3]
24. Astel A, Głosińska G, Sobczyński T, Boszke L, Simeonov V, Siepak J. Chemometrics in the assessment of the sustainable development rule implementation. Open Chemistry 2006;4(3):543-564. [DOI:10.2478/s11532-006-0021-5]
25. Raftery AE. Bayesian model selection in structural equation models. Sage Focus Editions 1993;154:1-20.
26. Nist/Sematceh e-Handbook of statistical methods. 2013. http://www.itl.nist.gov/div898/handbook/.
27. Abolghasem MH, Rajab-Zadeh E, Khasheei Varnamkhasti M. Studying the annual changes in water quality of the Karun River based on the IRWQI index. The second national and specialized conference on environmental research in Iran. 2014
28. Rolim HDO, Nunes ABDA, Nascimento FJDSC, Chaves JR. Proposal of a trophic state index in semiarid reservoirs using data of the Banabuiú Basin, state of Ceará, Brazil. Acta Limnologica Brasiliensia 2019;31:1-10. [DOI:10.1590/s2179-975x12517]
29. UNEP A. A snapshot of the world's water quality: towards a global assessment. Nairobi, United Nations Environment Programme. 2016.
30. Yang J, Lv H, Liu L, Yu X, Chen H. Decline in water level boosts cyanobacteria dominance in subtropical reservoirs. Science of the Total Environment 2016;557:445-52. [DOI:10.1016/j.scitotenv.2016.03.094] [PMID]
31. Leira M, Cantonati M. Effects of water-level fluctuations on lakes: an annotated bibliography. Ecological effects of water-level fluctuations in lakes: Springer 2008;613:171-184. [DOI:10.1007/978-1-4020-9192-6_16]
32. Reynolds CS, Huszar V, Kruk C, Naselli-Flores L, Melo S. Towards a functional classification of the freshwater phytoplankton. Journal of plankton research 2002;24(5):417-428. [DOI:10.1093/plankt/24.5.417]
33. Bennett CH, Brassard G. An update on quantum cryptography. InWorkshop on the theory and application of cryptographic techniques 1984 Aug 19 (pp. 475-480). Berlin, Heidelberg: Springer Berlin Heidelberg. [DOI:10.1007/3-540-39568-7_39]
34. Noori R, Karbassi A, Farokhnia A, Dehghani M. Predicting the longitudinal dispersion coefficient using support vector machine and adaptive neuro-fuzzy inference system techniques. Environmental Engineering Science 2009;26(10):1503-10. [DOI:10.1089/ees.2008.0360]
35. Brooks LJ. The effects of prey size selection by lake planktivores. Systematic Biology 1968;17(3):273-291. [DOI:10.1093/sysbio/17.3.273]
36. Rybak J, SADŁEK W. Ecological impact of a dam on benthic macroinvertebrates in montane rivers of Lower Silesia. Environment Protection Engineering 2010;36(2):143-151.
37. Pauer JJ, Taunt KW, Melendez W, Kreis Jr RG, Anstead AM. Resurrection of the Lake Michigan eutrophication model, MICH1. Journal of Great Lakes Research 2007;33(3):554-565. [DOI:10.3394/0380-1330(2007)33[554:ROTLME]2.0.CO;2]
38. Shamlou A, Naseri S, Nadafi K. Water quality monitoring of the Gilarlo reservoir. Journal of Applied Research in Water and Wastewater 2004;15(3):51-58.
39. Zhang W, Rao YR. Application of a eutrophication model for assessing water quality in Lake Winnipeg. Journal of Great Lakes Research 2012;38:158-173. [DOI:10.1016/j.jglr.2011.01.003]
40. Carlson RE. A trophic state index for lakes 1. Limnology and oceanography 1977;22(2):361-369. [DOI:10.4319/lo.1977.22.2.0361]
41. Ownegh M, Barani H, Sargazi H. Forecasting the availability of water to meet environmental needs in Sistan region. The 4th international conference on environmental planning and management. 2017.
42. Liu J, Wei C-F, Xie Q, Zhang W-H. Capacities of soil water reservoirs and their better regression models by combining "merged groups PCA" in Chongqing, China. Acta Ecologica Sinica 2014;34(1):53-65. [DOI:10.1016/j.chnaes.2013.11.007]
43. Becker V, Huszar VLM, Crossetti LO. Responses of phytoplankton functional groups to the mixing regime in a deep subtropical reservoir. Hydrobiologia 2009;628(1):137-51. [DOI:10.1007/s10750-009-9751-7]
44. Pirali Zefrehei AR, Kolahi M, Fisher J. Ecological-environmental challenges and restoration of aquatic ecosystems of the Middle-Eastern. Scientific Reports 2022;12(1):17229. [DOI:10.1038/s41598-022-21465-0] [PMID] []
45. Lewis Jr WM. A revised classification of lakes based on mixing. Canadian Journal of Fisheries and Aquatic Sciences 1983;40(10):1779-1787. [DOI:10.1139/f83-207]
46. Cai Q, Gao X, Chen Y, Ma S, Martin D. Dynamic variations of water quality in Taihu Lake and multivariate analysis of its influential factors. Chinese Geographical Science 1996;6(4):364-74. [DOI:10.1007/s11769-996-0058-6]
47. Mustaffa AR, Ku Hamid KH, Musa M, Idris J, Ramli R. High nitrate and phosphate ions reduction in modified low salinity fresh water through microalgae cultivation. Processes 2019;7(3):129. [DOI:10.3390/pr7030129]
48. Jahangiry Fard M, Amanipoor H, Battaleb-Looie S, Ghanemi K. Evaluation of effect factors on water quality of Karun River in downstream and lake of the Gotvand-e-Olya Dam (SW Iran). Applied Water Science 2019;9:1-14. [DOI:10.1007/s13201-019-1040-7]
49. Madadi-Nia M, Monavari SM, Karbasi A, Bagher Nabavi SM, Rajab-Zadeh E. Water quality survey of Karun river in Ahvaz basin using water quality index. Environmental Science and Technology Quarterly. 2014;16(1):49-60.
50. Company KRRWJS. Reducing the electrical conductivity of water with the help of ultrasound waves. 2007
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Cheraghpour Ahmadmahmoudi N, Saadat M, Zamani-ahmadmahmoodi R, Avokh A. Identification of phytoplankton communities, water quality and eutrophication status in the reservoir of Karun 4. jehe 2023; 10 (4) :433-452
URL: http://jehe.abzums.ac.ir/article-1-1005-en.html


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Volume 10, Issue 4 (9-2023) Back to browse issues page
مجله مهندسی بهداشت محیط Journal of Environmental Health Enginering
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