Spatiotemporal assessment of water quality monitoring network in a tropical river

  • Moriken Camara
  • Nor Rohaizah JamilEmail author
  • Ahmad Fikri Bin Abdullah
  • Rohasliney binti Hashim


Managers of water quality and water monitoring programs are often faced with constraints in terms of budget, time, and laboratory capacity for sample analysis. In such situation, the ideal solution is to reduce the number of sampling sites and/or monitored variables. In this case, selecting appropriate monitoring sites is a challenge. To overcome this problem, this study was conducted to statistically assess and identify the appropriate sampling stations of monitoring network under the monitored parameters. To achieve this goal, two sets of water quality data acquired from two different monitoring networks were used. The hierarchical agglomerative cluster analysis (HACA) were used to group stations with similar characteristics in the networks, the time series analysis was then performed to observe the temporal variation of water quality within the station clusters, and the geo-statistical analysis associated Kendall’s coefficient of concordance were finally applied to identify the most appropriate and least appropriate sampling stations. Based on the overall result, five stations were identified in the networks that contribute the most to the knowledge of water quality status of the entire river. In addition, five stations deemed less important were identified and could therefore be considered as redundant in the network. This result demonstrated that geo-statistical technique coupled with Kendall’s coefficient of concordance can be a reliable method for water resource managers to identify appropriate sampling sites in a river monitoring network.


Selangor River Water quality Monitoring network Kendall’s W Statistical analysis 


Supplementary material

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ESM 1 (DOCX 50 kb)


  1. Al-Mutairi, N., AbaHussain, A., & El-Battay, A. (2015). Spatial assessment of monitoring network in coastal waters: a case study of Kuwait Bay. Environmental Monitoring and Assessment, 187(10), 621. Scholar
  2. American Public Health Association. (1998). Standard methods for the examination of water and wastewater (20th edition.). American Public Health Association, Washington, DC.Google Scholar
  3. de Amorim, R. C. (2015). Feature rRelevance in Ward’s Hhierarchical Cclustering Uusing the L p nNorm. Journal of Classification, 32(1), 46–62. Scholar
  4. Beveridge, D., St-Hilaire, A., Ouarda, T. B. M. J., Khalil, B., Conly, F. M., & Ritson-Bennett, E. (2012). A geostatistical approach to optimize water quality monitoring networks in large lakes: Application application to Lake Winnipeg. Journal of Great Lakes Research, 38, 174–182. Scholar
  5. Camara, M., Jamil, N. R., Abdullah, A., & Bin, F. (2019). Impact of land uses on water quality in Malaysia: a review. Ecological Processes, 8(1), 10. Scholar
  6. Chapman, D. V., Bradley, C., Gettel, G. M., Hatvani, I. G., Hein, T., & Kovács, J. (2016). Developments in water quality monitoring and management in large river catchments using the Danube River as an example. Environmental Science and Policy, 64, 141–154. Scholar
  7. Chowdhury, S., Othman, F., Jaafar, W. Z. W., Mood, N. C., & Adham, I. (2018). Assessment of pollution and improvement measure of water quality parameters using scenarios modeling for Sungai Selangor Basin. Sains Malaysiana, 47(3), 457–469. Scholar
  8. Department of Environment. (2007). Malaysia Environmental Quality Report, 2006 .
  9. Earle, R. (2008). Presented at the 10 th IWA International Specialized Conference on diffuse pollution and sustainable basin management. Blacklocke / Desalination, 226, 134–142. Scholar
  10. Fulazzaky, M. A., Seong, T. W., & Masirin, M. I. M. (2010). Assessment of water quality status for the Selangor River in Malaysia. Water, Air, and Soil Pollution, 205(1–4), 63–77. Scholar
  11. Gazzaz, N. M., Yusoff, M. K., Aris, A. Z., Juahir, H., & Ramli, M. F. (2012). Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors. Marine Pollution Bulletin, 64(11), 2409–2420. Scholar
  12. International Institute for Sustainable Development. (2015). Water quality monitoring system design.
  13. Jimênez, N., Toro, F. M., Vélez, J. I., & Aguirre, N. (2005). A methodology for the design of quasi-optimal monitoring networks for lakes and reservoirs. Journal of Hydroinformatics, 7(2). Scholar
  14. Karamouz, M., Hafez, B., & Kerachian, R. (2005). Water quality monitoring network for river systems: application of ordinary kriging. In Impacts of Global Climate Change (pp. 1–12). Reston, VA: American Society of Civil Engineers. Scholar
  15. Kendall, M. G., & Smith, B. B. (1939). The problem of m rankings. The Annals of Mathematical Statistics, 10(3), 275–287. Scholar
  16. Kitanidis, P. K. & Peter K. . (1997). Introduction to geostatistics : applications to hydrogeology. Cambridge University Press. = gscholar. Google Scholar
  17. Kusin, F. M., Muhammad, S. N., Zahar, M. S. M., & Madzin, Z. (2016). Integrated river basin management: incorporating the use of abandoned mining pool and implication on water quality status. Desalination and Water Treatment, 57(60), 29126–29136. Scholar
  18. Legendre, P. (2005). Species associations: the Kendall coefficient of concordance revisited. Journal of Agricultural, Biological, and Environmental Statistics, 10(2), 226–245. Scholar
  19. Othman, F., Chowdhury, M. S., Wan Jaafar, W. Z., Faresh, E. M. M., & Shirazi, S. M. (2018). Assessing risk and sources of heavy metals in a tropical river basin: a case study of the Selangor River, Malaysia. Polish Journal of Environmental Studies, 27(4), 1659–1671. Scholar
  20. Ou, C., St-Hilaire, A., Ouarda, T. B. M. J., Conly, F. M., Armstrong, N., Khalil, B., & Proulx-McInnis, S. (2012). Coupling geostatistical approaches with PCA and fuzzy optimal model (FOM) for the integrated assessment of sampling locations of water quality monitoring networks (WQMNs). Journal of Environmental Monitoring, 14(12), 3118. Scholar
  21. Santhi, V. A., & Mustafa, A. M. (2013). Assessment of organochlorine pesticides and plasticisers in the Selangor River basin and possible pollution sources. Environmental Monitoring and Assessment, 185(2), 1541–1554. Scholar
  22. Segura, J. L. A. (2012). Optimisation of monitoring networks for water systems. CRC PRESS Retrieved from
  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. Scholar
  24. Xu, P., Wang, D., Singh, V. P., Wang, Y., Wu, J., Wang, L., Zou, X., Liu, J., Zou, Y., & H. R. (2017). A kriging and entropy-based approach to raingauge network design. Environmental Research, 161, 61–75. Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Moriken Camara
    • 1
  • Nor Rohaizah Jamil
    • 1
    Email author
  • Ahmad Fikri Bin Abdullah
    • 2
  • Rohasliney binti Hashim
    • 3
  1. 1.Department of Environmental Sciences, Faculty of Environmental StudiesUniversiti Putra MalaysiaSerdangMalaysia
  2. 2.Department of Biological and Agricultural Engineering, Faculty of EngineeringUniversiti Putra MalaysiaSerdangMalaysia
  3. 3.Department of Environmental Management, Faculty of Environmental StudiesUniversiti Putra MalaysiaSerdangMalaysia

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