Determining suitable model for zoning drinking water distribution network based on corrosion potential in Sanandaj City, Iran


1 Kurdistan Environmental Health Research Center, Kurdistan University of Medical Sciences Sanandaj, Iran

2 Agricultural and Natural Resources Research Center of Kurdistan, Sanandaj, Iran

3 Department of Civil Engineering, School of Technology, Islamic Azad University, Sanandaj Branch, Sanandaj, Iran


Corrosion in general is a complex interaction between water and metal surfaces and materials in which the water is stored or transported. Water quality monitoring in terms of corrosion and scaling is crucial, and a key element of preventive maintenance, given the economic and health hurts caused by corrosion and scaling in water utilities. The aim of this study is to determine the best model for zoning and interpolation corrosive potential of water distribution networks. For this purpose, 61 points of Sanandaj City distribution network were sampled and using Langelier indices, we investigated corrosivity potential of drinking water. Then, we used geostatistical methods such as ordinary kriging (OK), global polynomial interpolation, local polynomial interpolation, radius-based function, and inverse distance weighted for interpolation, zoning and quality mapping. Variogram analysis of variables was performed to select appropriate models. The results of the calculation of the Langelier index represented scaling potential of drinking water. Suitable model for fitness on exponential variogram was selected based on less (residual sums of squares) and high (R2) value. Moreover, the best method for interpolation was selected using the mean error and root mean square error. Comparison of the results indicated that OK was the most suitable method for drinking water quality zoning.  


  1. van Leeuwen FX. Safe drinking water: the toxicologist's approach. Food Chem Toxicol 2000; 38(1 Suppl): S51-S58.
  2. Wadud A, Chouduri AU. Microbial safety assessment of municipal water and incidence of multi-drug resistant Proteus isolates in Rajshahi, Bangladesh. Current Research in Microbiology and Biotechnology 2013; 1(4): 189-95.
  3. Sander A, Berghult B, Ahlberg E, Broo AE, Johansson EL, Hedberg T. Iron corrosion in drinking water distribution systems-Surface complexation aspects. Corrosion Science 1997; 39(1): 77-93.
  4. Dawoud MA, Darwish MM, El-Kady MM. GIS-Based Groundwater Management Model for Western Nile Delta. Water Resour Manage 2005; 19(5): 585-604.
  5. Tabesh M, Saber H. A Prioritization Model for Rehabilitation of Water Distribution Networks Using GIS. Water Resour Manage 2012; 26(1): 225-41.
  6. Cressie N, Wikle CK. Statistics for Spatio-Temporal Data. New Jersey, NJ: John Wiley & Sons; 2011.
  7. Maselli F, Chiesi M. Evaluation of statistical methods to estimate forest volume in a mediterranean region. Geoscience and Remote Sensing 2006; 44(8): 2239-50.
  8. Zimmerman D, Pavlik C, Ruggles A, Armstrong M. An Experimental Comparison of Ordinary and Universal Kriging and Inverse Distance Weighting. Mathematical Geology 1999; 31(4): 375-90.
  9. Earls J, Dixon B. Spatial interpolation of rainfall data using ArcGIS: A comparative study. Proceedings of the 27th Annual ESRI International User Conference; 2007 Jun 18-22; San Diego, CA.
  10. Naoum S, Tsanis K. Ranking spatial interpolation techniques using a GIS-based DSS. Global Nest: the Int J 2004; 6(1): 1-20.
  11. Shaabani M. Evaluation Geostatistical methods for mapping of groundwater quality and their zoning Case Study: Neyriz Plain, Fars Province. Journal of Natural Geography Lar 2011; 4(13): 93-6. [In Persian].
  12. Taghizadeh Mehrjardi R, Mahmoodi Sh, Heidari A, Sarmadian F. Application of geostastical methods for mapping groundwater quality in Azarbayjan Province, Iran. Am Eurasian J Agric Environ Sci 2008; 3: 726-35.
  13. Piccini C, Marchetti A, Farina R, Francaviglia R. Application of Indicator kriging to Evaluate the Probability of Exceeding Nitrate Contamination Thresholds. International Journal of Environmental Research 2012; 6(4): 853.
  14. Hooshmand A, Delghandi M, Izadi A, Aali A. Application of kriging and cokriging in spatial estimation of groundwater quality parameters. African Journal of Agricultural Research 2011; 6(14): 3402-8.
  15. Maqami Y, Ghazavi R, Abbasali V, Sharafi S. Evaluation of spatial interpolation methods for water quality zoning using GIS Case study, Abadeh Township. Geography and Environmental Planning 2011; 22(2): 171-82.
  16. Al-Mashagbah A, Al-Adamat R, Salameh E. The use of Kriging Techniques with in GIS Environment to Investigate Groundwater Quality in the Amman-Zarqa Basin/Jordan. Research Journal of Environmental and Earth Sciences 2012; 4(2): 177-85.
  17. Li J, Heap AD. A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors. Ecological Informatics 2011; 6(3-4): 228-41.
  18. Ghaneian MT, Ehrampoush MH, Ghanizadeh GH, Amrollahi M. Survey of corrosion and precipitation potential in dual water distribution system in kharanagh district of yazd province. Toloo-E-Behdasht 2008; 7(3-4): 65-72.
  19. Dehghani M, Tex F, Zamanian Z. Assessment of the potential of scale formation and corrosivity of tap water resources and the network distribution system in Shiraz, South Iran. Pak J Biol Sci 2010; 13(2): 88-92.
  20. Clesceri LS, Eaton AD, Greenberg AE. Standard Methods for the Examination of Water and Wastewater. Washington, D.C: American Public Health Association; 1998.
  21. Kumar P, Sanand VS, Santhosh Kumar N, Sreerama Murthy B. Assessment of water quality of thatipudi reservoir of vizianagaram district of andhra pradesh. Innovare Journal of Science 2013; 1(2): 20-4.
  22. Samanta S, Pal D, Lohar D, Pal B. Interpolation of climate variables and temperature modeling. Theor Appl Climatol 2012; 107(1-2): 35-45.
  23. Rawat KS, Mishra AK, Sehgal VK. Identification of
  24. Geospatial Variability of Flouride Contamination in Ground Water of Mathura District, Uttar Pradesh, India. Journal of Applied and Natural Science 2012; 4(1): 117-22.
  25. Babak O, Deutsch C. Statistical approach to inverse distance interpolation. Stoch Environ Res Risk Assess 2009; 23(5): 543-53.
  26. Shi J, Wang H, Xu J, Wu J, Liu X, Zhu H, et al. Spatial distribution of heavy metals in soils: a case study of Changxing, China. Environ Geol 2007; 52(1): 1-10.
  27. Hengl T, Heuvelink GBM, Stein A. A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma 2004; 120(1-2): 75-93.
  28. Mishra U, Lala R, Liuc D, Van Meirvenned M. Predicting the Spatial Variation of the Soil Organic Carbon Pool at a Regional Scale. Soil Science Society of America Journal 01/2010; 74(3) 2010; 74(3): 906-14.
  29. Flipo N, Jeannee N, Poulin M, Even S, Ledoux E. Assessment of nitrate pollution in the Grand Morin aquifers (France): combined use of geostatistics and physically based modeling. Environ Pollut 2007; 146(1): 241-56.
  30. Goovaerts P. Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology 2000; 228(1-2): 113-29.