Descriptive and multivariable analysis of the physico-chemical and biological parameters of Sfax wastewater treatment plant

A set of quantitative descriptive and analytical data from the wastewater treatment plant (WWTP) of Sfax, located on the south-east of Tunisia, has been processed by multivariate statistical techniques in order to investigate the evolution of the wastewater quality over a period of 12 years (1984–1996) for six physico-chemical and biological parameters. The experimental 6×12 matrix was analysed by principal component analysis (PCA). The exploration of the correlation matrix allowed to uncover strong associations between some variables (BODi CODi, SSi BDOo, CODo SSo) as well as a lack of association between the others (Twater and SSo). PCA showed the existence of up to three significant PCs which account for 74% of the variance. The first one assigned to water with relatively low organic load, whereas the second and the third assigned to water with an average and high organic and mineral loads. This study presents necessity and usefulness of multivariate statistical techniques for evaluation and interpretation of large complex data sets with view to get better information about the water quality and design of monitoring network for effective management of water resources.

Water-quality monitoring programs generate complex multidimensional data that need multivariate statistical treatment for their analysis and interpretation of the underlying information. An experimental 6 × 12 matrix was found and analysed by multivariate statistical procedures. The inspection of the correlation matrix of six variables showed the existence of strong correlations between variables. PCA allowed the reduction of the six variables to three PCs to explain 74% of the variance of the original data set. PC1 (33.3 of variance) was found uncorrelated with the SS removal efficiency. This correlation can be attributed to a particular behaviour of the particulate matters consequently a disturbed water ecology. The PC2 (26.9 of variance) can be assigned to seasonal contribution in organic and mineral matters according to the citizen life conditions. PC3 (17% of variance) can be assigned to chemical and biochemical contributions in utilizable particulates. Thus, the multivariable statistical analysis served as an excellent exploratory tool in analysis and interpretation of complex data set on water quality and understanding their temporal and spatial variations.