Groundwater Contaminantion Analysis Using Fuzzy-Topsis, Case Study: ZANJAN Plain

Subject Areas : Water and Environment

Mahmoud Mohammad Rezapour Tabari ¹ , Pouya Salehi Dopolani ²

1 - Associate Prof., Department of Engineering, University of Mazandaran, Mazandaran, Iran* (Corresponding Author)
2 - M.Sc. in Civil Engineering - Hydraulic Structures, Shahrkord University, Shahrekord, Iran

Received: 2016-07-10 Accepted : 2017-06-07 Published : 2020-08-22

Keywords: ZANJAN Plain, Similarity to Ideal Fuzzy Solution, Groundwater, Pollution, Ranking,

Abstract :

Introduction: Assessing the quality of available water is especially important to meet drinking needs. Therefore, it is important to prepare and formulate an approach that can provide a more appropriate evaluation with higher accuracy and taking into account the inaccuracies caused by instrumental errors and measurements. The aim of this study was to evaluate the groundwater quality of Zanjan plain based on a method similar to fuzzy ideal solution. Material and Methods:To achieve this goal,  57 and 59 groundwater quality samples in dry and wet seasons were used and the 28 quality parameters were analyzed in each sample. The relative closeness coefficient of each samples were determined based on fuzzy weight specified by the opinions of experts in this field for each quality parameters and the fuzzy decision matrix based on drinkable water quality standards. By calculating the relative closeness coefficient of each samples, the contamination ranking for each sample was determined. Results and Discussion: The results show that the more polluted areas are mainly concentrated in the center and northwest of the plain and this is in complete harmony with the location of industrial centers (such as lead and zinc complexes) and the hydrogeological characteristics of the aquifer. In order to evaluate the accuracy of the ranking, the results of the proposed method were compared with the definite case of the method similar to the ideal solution. Examination of the qualitative parameters related to rank 1 (lowest pollution rate) shows that most of the parameters in this rank, which should represent the lowest pollution rate, have high values ​​in the definite method compared to the fuzzy method (for example, the amoun of the arsenic parameter located in rank 1 of the definite and fuzzy methods is equal to 0.4 and 0 micrograms per liter, respectively) and this has been repeated in reverse regarding the 59th rank (maximum pollution rate), which indicates the reliability of the rankings and the high accuracy of the proposed method. Conclusion: The results showed that the pollution is concentrated in the central plains and partly in the North and Northwest plain due to the significant polluting industries, pesticides, and manure storage depot in these areas. Based on proposed approach can be calculated the rank pollution of groundwater quality samples with high accuracy, especially in cases where the assessment of water quality for drinking purposes must be considered.

References:

1. Gharibi, H., Mahvi, A. H., Nabizadeh, R., Arabalibeik, H., Yunesian, M., and Sowlat, M. H., 2012. A novel approach in water quality assessment based on fuzzy logic. Journal of Environmental Management, Vol. 112, pp. 87- 95.


2. Brown, R.M., McClelland, N.I., Deininger, R.A., and Tozer, R.G., 1970. A water quality index: do we dare?. Water Sewage Works, Vol. 117, pp. :339–343.


3. Scottish Development Department (S.D.D.), 1975. Towards cleaner water. Edinburgh: HMSO. Report of a River Pollution Survey of Scotland.


4. Nasseri, M., Tajrishy, M., Nikoo, M., Zaherpour, J., 2013. Recognition and Spatial Mapping of Multivariate Groundwater Quality Index using Combined Fuzzy Method. Journal of water and wastewater, Vol. 24(1), pp. 82-93. (In Persian)


5. Avvannavar, S.M., Shrihari, S., 2008. Evaluation of water quality index for drinking purposes for river Netravathi, Mangalore, South India. Environ Monit Assess, Vol. 143, pp. 279–290.


6. Safavi. H.R., Ahmadi, A., Rahmatnia, M., 2015. River Water Quality Zoning Using Combination of Principal Component Analysis (PCA) and Fuzzy Clustering Analysis. Journal of water and wastewater, Vol. 25(5), pp. 21-31. (In Persian)


7. Saberi Nasr, A., Rezaei, M., Dashti Barmaki, M., 2013. Groundwater contamination analysis using Fuzzy Water Quality index (FWQI): Yazd province, Iran. JGeope, Vol. 3 (1), pp. 47-55.


8. Sadat-Noori, S.M., Ebrahimi, K., Liaghat, A.M., 2014. Groundwater quality assessment using the Water Quality Index and GIS in Saveh-Nobaran aquifer, Iran. Environ Earth Science, Vol. 71, pp. 3827–3843.


9. Li, P., Wu, J., Qian, H., Lyu, X., Liu, H., 2014. Origin and assessment of groundwater pollution and associated health risk: a case study in an industrial park, northwest China.  Environ Geochem Health, Vol. 36(4), pp. 693-712.


10. Mohebbia, M.R., Saeedi, R., Montazeri, A., Vaghefi, K.A., Labbafi, S., Oktaie, S., Abtahi, M., Mohagheghian, A., 2013. Assessment of water quality in groundwater resources of Iran using a modified drinking water quality index (DWQI). Ecological Indicators, Vol. 30, pp. 28–34.


11. Noori, R., Sabahi, M.S., Karbassi, A.R., Baghvand, A., Taati Zadeh, H., 2010. Multivariate statistical analysis of surface water quality based on correlations and variations in the data set. Desalination Vol. 260, pp. 129–136.


12. Nikoo, M.R., Kerachian, R., Malakpour-Estalaki, S., Bashi-Azghadi, S.N., Azimi-Ghadikolaee, M.M., 2011. A probabilistic water quality index for river water quality assessment: a case study. Environmental Monitoring Assess, Vol. 181, pp. 465–478.


13. Mohammad Salah, E.A., Turki, A.M., and Al-Othman, E.M., 2012. Assessment of Water Quality of Euphrates River Using Cluster Analysis. Journal of Environmental Protection, Vol. 3, pp. 1629-1633.


14. Mavukkandy, M.O., Karmakar, S., and Harikumar, P.S., 2014. Assessment and rationalization of water quality monitoring network: a multivariate statistical approach to the Kabbini River (India). Environmental Science and Pollution Research, Vol. 21 (17), pp. 10045-10066.


15. Kahraman, C.; Engin, O.; Kabak, O.; Kaya, İ., 2009. Information systems outsourcing decisions using a group decision-making approach, Engineering Applications of Artificial Intelligence, Vol. 22(6), pp. 832–841.


16. Taylan, O., Bafail, A.O., Abdulaal, R.M.S., and Kabli, M.R., 2014. Construction projects selection and risk assessment by fuzzy AHP andfuzzy TOPSIS methodologies. Applied Soft Computing, Vol. 17, pp. 105–116.


17. World Health Organization, (2011). Guidelines for Drinking-water Quality. Fourth edition, ISBN 978 92 4 154815 1.


18. Institute of Standards and Industrial Research of Iran (ISIRI), (2009), “Characteristics of drinking water”, Standard No.1053, Fifth revision.


19. Kaya, I., Kahraman, C., 2014. comparison of fuzzy multicriteria decision making methods for intelligent building assessment. Journal of Civil Engineering and Management, Vol. 20(1), pp. 59–69.


20. Chen, T.C., 2000. Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, Vol. 114(1), pp. 1–9.


21. Management and planning Organization, 2004. Water Sampling Instruction, Bureau of technical criteria, No. 274. (In Persian)