Assessment of spatiotemporal variations in river water quality for sustainable environmental and recreational management in the highly urbanized Danshui River basin

  • Shih-Kai Chen
  • Cheng-Shin JangEmail author
  • Chia-Yu Chou


Rivers are an important urban resource, and water quality influences the use of river water. Thus, analyzing spatiotemporal variations in river water quality is crucial for sustainable use and management of water resources in a highly urbanized region. This study employed river pollution index (RPI) data obtained in 2013 to assess spatiotemporal variations in river water quality for sustainable environmental and recreational management in the highly urbanized Danshui River basin. First, ordinary kriging was adopted to analyze monthly RPI distributions. Subsequently, different percentiles of monthly estimated RPI distributions were probabilistically determined at a river segment. Finally, three measurement methods of local uncertainty, namely—conditional variance, local entropy, and interquartile range—were used to characterize spatiotemporal variations in river water quality in the Danshui River basin. Assessment results revealed that more highly polluted river water quality resulted in higher seasonal variations. Moreover, high and very high seasonal variations were mainly concentrated in urban river segments, whereas low and very low seasonal variations were primarily located in upstream river segments. Thus, to achieve sustainable development goals, artificial wetlands should be established at downstream and midstream urban riverbanks and urban recreational activities should be developed in upstream riverbank parks in the Danshui River basin before the comprehensive improvement of river water quality.


River pollution index Ordinary kriging Local uncertainty River water quality Sustainable development goal 



The authors would like to thank the Taiwan Environmental Protection Administration generously supporting the RPI data in the Danshui River basin, and the Taiwan Ministry of Science and Technology for financially supporting this research under Contract No. MOST 104-2410-H-424-014.


  1. Aminu, M., Matori, A., Yusof, K. W., Malakahmad, A., & Zainol, R. B. (2015). A GIS-based water quality model for sustainable tourism planning of Bertam River in Cameron Highlands, Malaysia. Environmental Earth Sciences, 73(10), 6525–6537.CrossRefGoogle Scholar
  2. Awadallah, A. G. (2012). Selecting optimum locations of rainfall stations using kriging and entropy. International Journal of Civil & Environmental Engineering, 12(1), 36–41.Google Scholar
  3. Bordalo, A. A., Teixeira, R., & Wiebe, W. J. (2006). A water quality index applied to an international Shared River basin: The case of the Douro River. Environmental Management, 38(6), 910–920.CrossRefGoogle Scholar
  4. Cambardella, C. A., Moorman, T. B., Parkin, T. B., Karlen, D. L., Novak, J. M., Turco, R. F., & Konopka, A. E. (1994). Field-scale variability of soil properties in central Iowa soils. Soil Science Society of America Journal, 58, 1501–1511.CrossRefGoogle Scholar
  5. Chen, Y. C., Wei, C., & Yeh, H. C. (2008). Rainfall network design using kriging and entropy. Hydrological Processes, 22(3), 340–346.CrossRefGoogle Scholar
  6. Chen, Y. C., Yeh, H. C., & Wei, C. (2012). Estimation of river pollution index in a tidal stream using kriging analysis. International Journal of Environmental Research and Public Health, 9(9), 3085–3100.CrossRefGoogle Scholar
  7. Cheng, B. Y., Liu, T. C., Shyu, G. S., Chang, T. K., & Fang, W. T. (2011). Analysis of trends in water quality: Constructed wetlands in metropolitan Taipei. Water Science and Technology, 64(11), 2143–2150.CrossRefGoogle Scholar
  8. Deutsch, C. V., & Journel, A. G. (1998). GSLIB: Geostatistical Software Library and User’s Guide. The 2nd Edition. New York: Oxford University Press.Google Scholar
  9. El-Ayouti, A., & Abou-Ali, H. (2013). Spatial heterogeneity of the Nile water quality in Egypt. Journal of Environmental Statistics, 4(8), 1–12.Google Scholar
  10. Goovaerts, P. (1997). Geostatistics for natural resources evaluation. Oxford University Press, New York, pp259–368.Google Scholar
  11. Isaaks, E. H., & Srivastava, R. M. (1989). An introduction to applied Geostatistics (pp. 278–322). New York: Oxford University Press.Google Scholar
  12. Jang, C.S. (2016). Using probability-based spatial estimation of the river pollution index to assess urban water recreational quality in the Tamsui River watershed. Environmental Monitoring and Assessment, 188, Article 36, pp1–17.Google Scholar
  13. Liou, S. M., Lo, S. L., & Wang, S. H. (2004). A generalized water quality index for Taiwan. Environmental Monitoring and Assessment, 96, 35–52.CrossRefGoogle Scholar
  14. Massoud, M. A. (2012). Assessment of water quality along a recreational section of the Damour River in Lebanon using the water quality index. Environmental Monitoring and Assessment, 184(7), 4151–4160.CrossRefGoogle Scholar
  15. Omran, E. E. (2012). A proposed model to assess and map irrigation water well suitability using geospatial analysis. Water, 4(3), 545–567.CrossRefGoogle Scholar
  16. Park, N.W. (2016). Time-series mapping of PM10 concentration using multi-Gaussian space-time kriging: A case study in the Seoul metropolitan area, Korea. Advances in Meteorology, 2016, Article ID 9452080.Google Scholar
  17. Sánchez, E., Colmenarejo, M. F., Vicente, J., Rubio, A., Garcíaa, M. G., Travieso, L., & Borja, R. (2007). Use of the water quality index and dissolved oxygen deficit as simple indicators of watersheds pollution. Ecological Indicators, 7(2), 315–328.CrossRefGoogle Scholar
  18. Semiromi, F. B., Hassani, A. H., Torabian, A., Karbassi, A. R., & Lotfi, F. H. (2011). Evolution of a new surface water quality index for Karoon catchment in Iran. Water Science and Technology, 64(12), 2483–2491.CrossRefGoogle Scholar
  19. Shyu, G. S., Cheng, B. Y., Chiang, C. T., Yao, P. H., & Chang, T. K. (2011). Applying factor analysis combined with kriging and information entropy theory for mapping and evaluating the stability of groundwater quality variation in Taiwan. International Journal of Environmental Research and Public Health, 8, 1084–1109.CrossRefGoogle Scholar
  20. Taiwan Central Weather Bureau (2016). Internet Application of Climate Data. Central Weather Bureau, Ministry of Transportation & Communications, Executive Yuan, Taiwan. Accessed 1 Aug 2017.
  21. Taiwan Environmental Protection Administration (EPA) (2016). Environmental Water Quality Information. Environmental Protection Administration, Executive Yuan, Taiwan. Accessed 1 Aug 2017.
  22. United Nations Development Programme (UNDP) (2015). Sustainable Development Goals (SDGs). United Nations Department of Public Information, United Nations, NY. Accessed 25 Dec 2018.
  23. Wang, Y. B., Liu, C. W., Liao, P. Y., & Lee, J. J. (2014). Spatial pattern assessment of river water quality: Implications of reducing the number of monitoring stations and chemical parameters. Environmental Monitoring and Assessment, 186(3), 1781–1792.CrossRefGoogle Scholar
  24. WHO (World Health Organization) (2003). Guidelines for safe recreational water environments. Vol. 1. Coastal and Fresh Waters. World Health Organization, Geneva, Switzerland, pp159–167.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Civil EngineeringNational Taipei University of TechnologyTaipei CityTaiwan
  2. 2.Department of Leisure and Recreation ManagementKainan UniversityTaoyuan CityTaiwan

Personalised recommendations