Volume 9, Issue 18 (1-2019)                   jwmr 2019, 9(18): 250-259 | Back to browse issues page


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Babolhakami A, Gholami Sefidkouhi M A. (2019). Analyze of Talar River Water Quality using Multivariate Techniques . jwmr. 9(18), 250-259. doi:10.29252/jwmr.9.18.250
URL: http://jwmr.sanru.ac.ir/article-1-749-en.html
Abstract:   (3375 Views)

Classification of water quality is the most important step for controlling the pollution of water. The aim of this study was classifying the water quality of the Talar River basin by analyzing the existing data of six monitoring stations. Multivariate statistical techniques such as cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) were used to assessment the spatial variations of water quality in the Talar River. The quality data which were gathered from the 2011 to 2014 were included 14 different chemical parameters. . Quality testing stations were included the Pol Sefid, Pol Shahpor, Kiakola, Kari Kola, Savadkuh, Shirgah and Paland Rodbar. The results of cluster analysis of water quality stations were divided into three groups. The first group includes stations Paland Rodbar, Kari Kola, Savadkuh and Pol Shapur, the second group includes stations Shirgah and Kiakola, and the third group was Pol Sefid station. The results of PC and FA showed that the 80 percent of the variations of water quality were done by three parameters; the first parameters are TDS, EC, Cl, So4, Ca, Mg, Na, K, SAR and TH. The second factors are the Po4 and No3, and the third factor is HCo3-.The results showed that the major factors which are polluted the water quality of the Tlar River are related to inflow of industrial, domestic, hospital and agricultural effluents into the Talar River.

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Type of Study: Research | Subject: ساير موضوعات وابسته به مديريت حوزه آبخيز
Received: 2017/01/18 | Revised: 2019/01/21 | Accepted: 2017/07/3 | Published: 2019/01/21

References
1. 1. Ahani, A., S. Emamgholizade, S.S. Mousavi Nadoushani and K. Azhdari. 2015. Regional Flood Frequency Analysis by Hybrid Cluster Analysis and L-moments. Journal of Watershed Management Research, 6 (12): 11-20
2. Ayers, R.S. and D.W. Westcot. 1985. Water quality for agriculture (Vol. 29). Rome: FAO.
3. Belkhiri, L. and T.S. Narany. 2015. Using multivariate statistical analysis, geostatistical techniques and structural equation modeling to identify spatial variability of groundwater quality. Water Resources Management. 29(6): 2073-2089. [DOI:10.1007/s11269-015-0929-7]
4. Bierman, P., M. Lewis, B. Ostendorf and J. Tanner. 2011. A review of methods for analysing spatial and temporal patterns in coastal water quality. Ecological Indicators, 11(1): 103-114. [DOI:10.1016/j.ecolind.2009.11.001]
5. Darvishi, G., F.G. Kootenaei, M. Ramezani, E. Lotfi and H. Asgharnia. 2016. Comparative Investigation of River Water Quality by OWQI, NSFWQI and Wilcox Indexes (Case study: the Talar River-IRAN). Archives of Environmental Protection, 42(1): 41-48. [DOI:10.1515/aep-2016-0005]
6. Ebrahimi, M., E.L. Gerber and T.D. Rockaway. 2017. Temporal performance assessment of wastewater treatment plants by using multivariate statistical analysis. Journal of Environmental Management, 193: 234-246. [DOI:10.1016/j.jenvman.2017.02.027]
7. Faryadi, S., K. Shahedi and M. Nabatpoor. 2012. Investigation of Water Quality Parameters in Tadjan River using Multivariate Statistical Techniques, 6: 75-92 (In Persian).
8. Gupta, I., D. Shivani and K. Rakesh. 2009. Study of variations in water quality of Mumbai coast through multivariate analysis techniques. Indian Journal of Marine Sciences, 38(2): 170-177.
9. Hajigholizadeh, M. and A.M. Melesse. 2017. Assortment and spatiotemporal analysis of surface water quality using cluster and discriminant analyses. CATENA, 151: 247-258. [DOI:10.1016/j.catena.2016.12.018]
10. Johnson, R.A. and D.W. Wichern. 1992. Applied Multivariate Statistical Analysis. 6 edn. Prentice-Hall International, Englewood Cliffs, New Jersey, USA, 773 pp.
11. Ling, T.Y., C.L. Soo, J.J. Liew, L. Nyanti, S.F. Sim and J. Grinang. 2017. Application of multivariate statistical analysis in evaluation of surface river water quality of a tropical river. Journal of Chemistry, 2017: 1-13. [DOI:10.1155/2017/5737452]
12. Mcneil, V.H., E. Cox and M. Preda. 2005. Assessment of chemical water types and three spatial variation using multi-stage cluster analysis, Queensland, Australia. Journal of Hydrology, 310: 181-200. [DOI:10.1016/j.jhydrol.2004.12.014]
13. Mohamed, I., F. Othman, A.L. Ibrahim, M.E. Alaa-Eldin and R.M. Yunus. 2015. Assessment of water quality parameters using multivariate analysis for Klang River basin, Malaysia. Environmental monitoring and assessment, 187(1): 41-82. [DOI:10.1007/s10661-014-4182-y]
14. Muangthong, S. and S. Shrestha. 2015. Assessment of surface water quality using multivariate statistical techniques: case study of the Nampong River and Songkhram River, Thailand. Environmental monitoring and assessment, 187(9): 548. [DOI:10.1007/s10661-015-4774-1]
15. Noori, R., M.S. Sahabi, A.R. Karbasi, A. Baghvand and H. Taati Zadeh. 2010. Multivariate statistical analysis of source water quality based on correlation and variations in the data set. Desalination, 260: 129-136. [DOI:10.1016/j.desal.2010.04.053]
16. Noshadi, M. and A. Ghafourian. 2016. Groundwater quality analysis using multivariate statistical techniques (case study: Fars province, Iran). Environmental monitoring and assessment, 188(7): 1-13. [DOI:10.1007/s10661-016-5412-2]
17. Organization, W.H. 1993. Guidelines for drinking water quality (2nd ed. Vol. 1). Geneva: World Health Organization.
18. Rahimi. L, A.A. Dehghani and K. Ghorbani. 2016. Comparison of Total flow, Base flow and Water Quality Characteristics Trend in Arazkuseh Hydrometric Station. Journal of Watershed Management Research, 7 (13): 83-91(In persian). [DOI:10.18869/acadpub.jwmr.7.13.91]
19. Raju, N.J., P. Patel, D. Gurung, P. Ram, W. Gossel and P. Wycisk. 2015. Geochemical assessment of groundwater quality in the Dun valley of central Nepal using chemometric method and geochemical modeling. Groundwater for Sustainable Development, 1(1): 135-145. [DOI:10.1016/j.gsd.2016.02.002]
20. Rezaei, A. and M.H. Sayadi. 2015. Long-term evolution of the composition of surface water from the River Gharasoo, Iran: a case study using multivariate statistical techniques. Environmental geochemistry and health, 37(2): 251-261 (In Persian). [DOI:10.1007/s10653-014-9643-2]
21. Roy, P.K., S. Pal, G. Banerjee, M.B. Roy, D. Ray and A. Majumder. 2014. Variation of Water Quality Parameters with Siltation Depth for River Ichamati along International Border with Bangladesh Using Multivariate Statistical Techniques. Journal of the Institution of Engineers (India): Series E, 95(2): 97-103. [DOI:10.1007/s40034-014-0038-9]
22. Shrestha, S. and F. Kazama. 2007. Assessment of surface water quality using multivariate statistical techniques: a case study of the Fuji river basin, Japan. Environmental Modelling and Software. 22: 464-475. [DOI:10.1016/j.envsoft.2006.02.001]
23. Xiao, M., F. Bao, S. Wang and F. Cui. 2016. Water quality assessment of the Huaihe River segment of Bengbu (China) using multivariate statistical techniques. Water Resources, 43(1): 166-176. [DOI:10.1134/S0097807816010115]
24. Yerel, S. 2010. Water quality assessment of Porsuk River, Turkey E-Journal of Chemistry, 7(2): 543-599. [DOI:10.1155/2010/438180]
25. Yidana, S.M. 2010. Groundwater classification using multivariate statistical methods: Southern Ghana. Journal of African Earth Sciences, 58: 455-469. [DOI:10.1016/j.jafrearsci.2009.12.002]
26. Zheng, L.Y., H.B. Yu and Q.S. Wang. 2016. Application of multivariate statistical techniques in assessment of surface water quality in Second Songhua River basin, China. Journal of Central South University. 23: 1040-1051. [DOI:10.1007/s11771-016-0353-z]

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