Ratha Chea & Sovan Lek

Symposium on Biodiversity and Health

17-18th 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

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.

Objective

The objective of the study is too 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.

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 MRCClassification & Evaluation

Water quality data used in this study were derived from MRC. Overview of methodology used in this study

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

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

Results & Discussion

Class of water qualityA B C D Class

Cluster 1 4 10 5 16 CCluster 2 8 3 2 4 BCluster 3 9 9 1 8 BCluster 4 7 10 1 0 BCluster 5 0 2 0 18 D

28 34 9 46 11790% to 100% 80% to 90% 70% to 80% < 70% High quality Good quality Moderate quality Poor 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.

Poor quality of water

Good quality of water

អរគុ�ណ!!!Thanks for your attention!!!