Assessing Water Quality Spatial Heterogeneity from Multiple Pollution Sources in the Boung Cheung Ek Wetland, Phnom Penh, Cambodia

Lelys Bravo, Melissa Lenczewski, Samuel Vesal Mallow, Shantanu Solanki

Research output: Contribution to journalArticlepeer-review


Phnom Penh, the capital of Cambodia, as with many other world megacities, is exposed to multiple major ecological and environmental hazards. Without a proper wastewater treatment facility, it is difficult for local residents to obtain a health-compliant water supply. In this study, a hybrid aggregation method using principal component analysis (PCA) and weighted means was used to calculate a water quality index (WQI) to map the water quality of the entire Boeung Cheung Ek (BCE) wetland region. We used Universal Kriging to map eight water quality parameters: DO, pH, TDS, F, Cl, NO3−, PO43−, and NH4+. The restricted maximum likelihood method was used for model fitting. Data were collected from groundwater and surface water for different rainfall seasons between March 2017 and February 2018. The principal component analysis (PCA) used to compute a water quality index (WQI) is based on the resulting dimensions of the highest variation among all water quality parameters. The results show that the northern part of the study area has a worse water quality than the southern region, which is caused by the discharge of municipal wastewater directly into the BCE wetland area. The results for different rainfall seasons also show that groundwater has a relatively better quality than surface water. The results of this analysis can serve as a supplementary study to support sustainable development goals because they might confirm the need for a wastewater treatment facility being under construction at the time of writing this article with funding from the Japan International Cooperation Agency (JICA).
Original languageEnglish (US)
Article number4
Issue number1
StatePublished - Jan 2024


  • geostatistical analysis
  • Boeng Cheung Ek wetland
  • water quality index
  • principal component analysis (PCA)


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