Environmental Science and Pollution Research

, Volume 26, Issue 26, pp 27225–27238 | Cite as

Evaluation of conventional drinking water treatment plant efficiency according to water quality index and health risk assessment

  • Alper AlverEmail author
Research Article


The objective of this research is to investigate the effluent water quality of a treatment plant in Turkey fed from surface and groundwater, according to water quality index (WOI) and health risk assessment (HRA). In order to achieve this goal, the quality of the influent and effluent water of the treatment plant was monitored monthly from January 2017 to January 2019. Water quality parameter results were compared with the Turkish drinking water standards and the World Health Organization (WHO), revealing that all parameters were within approved limits. Principal component analysis (PCA) was applied to determine the water quality parameter impacts in the overall quality of water and the most attractive parameters were trace elements, heavy metals, NH3-N, NO3, and TKN. To evaluate water quality and the impacts on human health, WQI and HRA, including hazard quotient (HQ) and hazard index (HI), were used. The WQI values were calculated by taking into account PCA results. WQI results demonstrated that the influent and effluent of water treatment plant values have a small number of WQI ranking that expressed the water category was “excellent” for drinking purpose. Finally, metal contamination in influent and effluent waters was assessed and the associated health risks to rural populations were estimated for different age groups, children and adults in the service area of the treatment plant. The health risk assessment with similar to WQI results, the acute, sub-chronic, and chronic risks of trace elements was “negligible” level, i.e., to a level affecting 1 person in 1,000,000 inhabitants.


Drinking water Treatment plant Principal component analysis Water quality index Health risk assessment Non-carcinogens 



I would like to thank Selçuk VAROL for advice, comments, and help in other ways during this paper.


  1. Avigliano E, de Carvalho BM, Invernizzi R, Olmedo M, Jasan R, Volpedo AVJES, Research P (2019) Arsenic, selenium, and metals in a commercial and vulnerable fish from southwestern Atlantic estuaries: distribution in water and tissues and public health risk assessment, pp 1–13Google Scholar
  2. Chen Z, Wu Q, Wu G, Hu H-Y (2017) Centralized water reuse system with multiple applications in urban areas: lessons from China’s experience. Resour Conserv and Recycl 117:125–136CrossRefGoogle Scholar
  3. de Jesus Gaffney V, Almeida CM, Rodrigues A, Ferreira E, Benoliel MJ, Cardoso VV (2015) Occurrence of pharmaceuticals in a water supply system and related human health risk assessment. Water Res 72:199–208CrossRefGoogle Scholar
  4. Dong Z, Kang S, Qin X, Li X, Qin D, Ren J (2015) New insights into trace elements deposition in the snow packs at remote alpine glaciers in the northern Tibetan Plateau, China. Sci Total Environ 529:101–113CrossRefGoogle Scholar
  5. Dong Z, Qin D, Qin X, Cui J, Kang S (2017) Changes in precipitating snow chemistry with seasonality in the remote Laohugou glacier basin, western Qilian Mountains. Environ Sci Pollut Res 24:11404–11414CrossRefGoogle Scholar
  6. ECHA (2019) REACH Annex XVII: REACH Restricted Substance List.
  7. Effendi H (2016) River water quality preliminary rapid assessment using pollution index. Procedia Environ Sci 33:562–567CrossRefGoogle Scholar
  8. EPA US (2018) Regional Screening Levels (RSLs) - User's Guide. National Center for Environmental Assessment c/o - Risk Website.
  9. Ewaid SH, Abed SA, Kadhum SA (2018) Predicting the Tigris River water quality within Baghdad, Iraq by using water quality index and regression analysis. Environ Technol Innov 11:390–398CrossRefGoogle Scholar
  10. Gao L, Wang Z, Shan J, Chen J, Tang C, Yi M, Zhao X (2016) Distribution characteristics and sources of trace metals in sediment cores from a trans-boundary watercourse: an example from the Shima River, Pearl River Delta. Ecotoxicol Environ Saf 134:186–195CrossRefGoogle Scholar
  11. Hahn J, Opp C, Evgrafova A, Groll M, Zitzer N, Laufenberg G (2018) Impacts of dam draining on the mobility of heavy metals and arsenic in water and basin bottom sediments of three studied dams in Germany. Sci Total Environ 640:1072–1081CrossRefGoogle Scholar
  12. Jha DK, Devi MP, Vidyalakshmi R, Brindha B, Vinithkumar NV, Kirubagaran R (2015) Water quality assessment using water quality index and geographical information system methods in the coastal waters of Andaman Sea, India. Mar Pollut Bull 100:555–561CrossRefGoogle Scholar
  13. Jiang Y, Xie Z, Zhang H, Xie H, Cao Y (2017) Effects of land use types on dissolved trace metal concentrations in the Le’an River Basin, China. Environ Monit Assess 189:633CrossRefGoogle Scholar
  14. Kazi T et al (2009) Assessment of water quality of polluted lake using multivariate statistical techniques: a case study. Ecotoxicol Environ Saf 72:301–309CrossRefGoogle Scholar
  15. Kumar M, Ramanathan A, Tripathi R, Farswan S, Kumar D, Bhattacharya P (2017) A study of trace element contamination using multivariate statistical techniques and health risk assessment in groundwater of Chhaprola Industrial Area, Gautam Buddha Nagar, Uttar Pradesh, India. Chemosphere 166:135–145CrossRefGoogle Scholar
  16. Li J, Li F, Liu Q, Zhang Y (2014) Trace metal in surface water and groundwater and its transfer in a Yellow River alluvial fan: evidence from isotopes and hydrochemistry. Sci Total Environ 472:979–988CrossRefGoogle Scholar
  17. Liang B, Han G, Liu M, Yang K, Li X, Liu J (2018) Distribution, sources, and water quality assessment of dissolved heavy metals in the Jiulongjiang River water, southeast China. Int J Environ Res Public Health 15:2752CrossRefGoogle Scholar
  18. Mavukkandy MO, Karmakar S, Harikumar PJES, Research P (2014) Assessment and rationalization of water quality monitoring network: a multivariate statistical approach to the Kabbini River (India). Environ Sci Pollut Res Int 21:10045–10066CrossRefGoogle Scholar
  19. Meng Q, Zhang J, Zhang Z, Wu T (2016) Geochemistry of dissolved trace elements and heavy metals in the Dan River Drainage (China): distribution, sources, and water quality assessment. Environ Sci Pollut Res 23:8091–8103CrossRefGoogle Scholar
  20. World Health Organization (2004) Guidelines for drinking-water quality vol 1, Third edn. World Health Organization, GenevaGoogle Scholar
  21. Ou H-S, Wei C-H, Deng Y, Gao N-Y, Ren Y, Hu YJES, Research P (2014) Principal component analysis to assess the efficiency and mechanism for enhanced coagulation of natural algae-laden water using a novel dual coagulant system. Environ Sci Pollut Res Int 21:2122–2131CrossRefGoogle Scholar
  22. Poonam T, Tanushree B, Sukalyan C (2013) Water quality indices—important tools for water quality assessment: a review. Int J Adv Chem 1:15–28Google Scholar
  23. Ramakrishnaiah C, Sadashivaiah C, Ranganna G (2009) Assessment of water quality index for the groundwater in Tumkur Taluk, Karnataka State. Indian J Chem 6:523–530Google Scholar
  24. Schuster P et al (2008) Mercury and organic carbon dynamics during runoff episodes from a northeastern USA watershed. Water Air Soil Pollut 187:89–108CrossRefGoogle Scholar
  25. Şener Ş, Şener E, Davraz A (2017) Evaluation of water quality using water quality index (WQI) method and GIS in Aksu River (SW-Turkey). Sci Total Environ 584:131–144Google Scholar
  26. Sun W, Xia C, Xu M, Guo J, Sun G (2016) Application of modified water quality indices as indicators to assess the spatial and temporal trends of water quality in the Dongjiang River. Ecol Indic 66:306–312CrossRefGoogle Scholar
  27. US Environmental Protection Agency (2012) Guidelines for Water Reuse. EPA/600/R-12/618. Agency for International Development, Washington, D.CGoogle Scholar
  28. Varol M (2013) Dissolved heavy metal concentrations of the Kralkızı, Dicle and Batman dam reservoirs in the Tigris River basin, Turkey. Chemosphere 93:954–962CrossRefGoogle Scholar
  29. Varol S, Davraz A (2015) Evaluation of the groundwater quality with WQI (Water Quality Index) and multivariate analysis: a case study of the Tefenni plain (Burdur/Turkey). Environ Earth Sci 73:1725–1744CrossRefGoogle Scholar
  30. Vörösmarty CJ, McIntyre PB, Gessner MO, Dudgeon D, Prusevich A, Green P, Glidden S, Bunn SE, Sullivan CA, Liermann CR, Davies PM (2010) Global threats to human water security and river biodiversity. Nature 467:555–561CrossRefGoogle Scholar
  31. Wang J, Liu G, Liu H, Lam PK (2017) Multivariate statistical evaluation of dissolved trace elements and a water quality assessment in the middle reaches of Huaihe River, Anhui, China. Sci Total Environ 583:421–431CrossRefGoogle Scholar
  32. Xiao J, Wang L, Deng L, Jin Z. (2019). Characteristics, sources, water quality and health risk assessment of trace elements in river water and well water in the Chinese Loess Plateau. Sci Total Environ, 650:2004–2012.
  33. Yidana SM, Yidana A (2010) Assessing water quality using water quality index and multivariate analysis. Environ Earth Sci 59:1461–1473CrossRefGoogle Scholar
  34. Zeng X, Liu Y, You S, Zeng G, Tan X, Hu X, Hu X, Huang L, Li F (2015) Spatial distribution, health risk assessment and statistical source identification of the trace elements in surface water from the Xiangjiang River, China. Environ Sci Pollut Res 22:9400–9412CrossRefGoogle Scholar
  35. Zhang Y, Chen J, Wang L, Zhao Y, Ou P, Shi W (2018) Establishing a method to assess comprehensive effect of gradient variation human health risk to metal speciation in groundwater. Environ Pollut 241:887–899CrossRefGoogle Scholar
  36. Zuzolo D, Cicchella D, Catani V, Giaccio L, Guagliardi I, Esposito L, De Vivo B (2017) Assessment of potentially harmful elements pollution in the Calore River basin (Southern Italy). Environ Geochem Health 39:531–548CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Environmental Engineering, Engineering FacultyAksaray UniversityAksarayTurkey

Personalised recommendations