Use of principal component analysis, factor analysis and discriminant analysis to evaluate spatial and temporal variations in water quality of the Mekong River

Sangam Shrestha (corresponding author) Futaba Kazama Takashi Nakamura Department of Ecosocial System Engineering, Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi, 4-3-11, Takeda, Kofu, Yamanashi 400-8511, Japan E-mail: [email protected] Multivariate statistical techniques, such as principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA), were applied for the evaluation of temporal/spatial variations and the interpretation of a large complex water quality dataset of the Mekong River using data sets generated during 6 years (1995–2000) of monitoring of 18 parameters (16,848 observations) at 13 different sites. The results of PCA/FA revealed that most of the variations are explained by dissolved mineral salts along the whole Mekong River and in individual stations. Discriminant analysis showed the best results for data reduction and pattern recognition during both spatial and temporal analysis. Spatial DA revealed 8 parameters (total suspended solids, calcium, sodium, alkalinity, chloride, iron, nitrate nitrogen, total phosphorus) and 12 parameters (total suspended solids, calcium, sodium, potassium, alkalinity, chloride, sulfate, iron, nitrate nitrogen, total phosphorus, silicon, dissolved oxygen) are responsible for significant variations between monitoring regions and countries, respectively. Temporal DA revealed 3 parameters (conductivity, alkalinity, nitrate nitrogen) between monitoring regions; 3 parameters (total suspended solids, conductivity, silicon) in midstream region; and 2 parameters (conductivity, silicon) in upstream, lower stream and delta region which are the most significant parameters to discriminate between the four different seasons (spring, summer, autumn, winter). Thus, this study illustrates the usefulness of principal component analysis, factor analysis and discriminant analysis for the analysis and interpretation of complex datasets and in water quality assessment, identification of pollution sources/factors, and understanding of temporal and spatial variations of water quality for effective river water quality management.