1. Ratha Chea & Sovan Lek Symposium on Biodiversity and Health 17-18 th November 2014, Phnom Penh Cambodia Spatial analysis of water quality variability in Lower Mekong Basin (LMB) Laboratoire Evolution et Diversité Biologique, UMR 5174, CNRS, Université Paul Sabatier, 118 Route de Narbonne, 31602 Toulouse Cedex 4 France

2. Introduction Mekong river’s water give life to millions people and precious ecosystems of Lower Mekong Basin (LMB). Mekong river is progressively at risk of environmental degradation since the river is faced of rapid industrialization and its impacts of climate changes. Living environment, particularly human health can be affected by low quality of water.

3. Objective The objective of the study is to o assess spatial variability of water quality in LMB based physical & chemical characteristic of water o classify the quality of water according to MRC guidelines. It is expected to identify the zones with Good and Bad quality of water. Seasonal changes of water quality is not considered in this study.

4. Methodology Raw data of water quality (117 monitoring sites, from 1986-2010) Outliers & missing data removals Data Preparation Statistical indicators used to summarize the dataset (Mean & Median) Standardization & Normalization Data transformation Principal component analysis (PCA) K-means clustering & Fuzzy clustering Variable reduction & Clustering modeling Water quality index (WQI) and Water quality guideline adopted by MRC Classification & Evaluation Water quality data used in this study were derived from MRC. Overview of methodology used in this study

5. Variable reduction – PCA Results & Discussion PCA have been performed on 16 variables at 117 sites, 5 components were retained by examining PCA - Scree plot, which explained 87% of the variance in the dataset. Varimax rotation was used to better identify highest loading variables contributing to each factor PCA-Biplot

6. Cluster analysis Five optimal clusters have been identified using Fuzzy and K-means clustering methods according to the retained factors from PCA and CascadeKM. Results & Discussion Fuzzy clusters

7. Results & Discussion Class of water quality ABCDClass Cluster 1410516C Cluster 28324B Cluster 39918B Cluster 471010B Cluster 502018D 2834946117 90% to 100%80% to 90%70% to 80%< 70% High qualityGood qualityModerate qualityPoor quality Classification of water quality Water quality assessment was examined according to the water quality index (WQI) and by comparing to water quality guideline (threshold) adopted by MRC.

8. Poor quality of water Good quality of water

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