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!!!