Multivariate analysis of the sediment quality of a tropical coastal lake system

Abstract

AbstractAccurate knowledge of sediment quality is essential because it affects the magnitude and trends of water quality constituents. There are only a few analyses of sediment quality characteristics using multivariate analysis tools. This study utilizes hierarchical cluster analysis (HCA), factor analysis (FA) and multiple regression analysis (MRA) to demonstrate the usefulness of these techniques to analyse sediment quality for Akkulam–Veli Lake, a tropical coastal lake system in Kerala, India. The variation of sediment quality patterns during the premonsoon (PRM), monsoon (MON) and postmonsoon (POM) periods were assessed with cluster analysis. Factor analysis was used to identify prominent factors influencing sediment quality, while the factors influencing heavy metal partitioning in the sediment and overlying water were identified using multiple regression analysis. The study results indicated the sediment in the upstream portion of the lake was polluted during PRM, with the prominent factors being the ‘heavy metal factor’ and the ‘organic pollution factor’, followed by the ‘phosphorus pollution factor’ and the ‘cadmium pollution factor’. The ‘heavy metal factor’ and the ‘organic pollution factor’ are the prominent factors during MON, whereas the ‘heavy metal factor’, ‘organic pollution factor’ and ‘salinity factor’ were prominent POM factors. The salinity of the overlying water above the sediments plays an important role during PRM and POM, whereas the dissolved oxygen content was important during MON.