Source Apportionment of Wastewater Pollutants Using Multivariate Analyses

Abstract

A faster and cost-effective methodology has been developed to estimate the spatial and seasonal variations in wastewater quality and apportion the influencing sources through multivariate statistical techniques, cluster analysis and principal component analysis (PCA). Partially treated or untreated wastewater is released into the river from various industrial and domestic sources, which poses a serious threat to human health. Wastewater samples were collected from five stations along the river bank. PCA performed on overall wastewater samples revealed that in present study all the five sampling stations were influenced by sewage and industrial effluents mixed together. However, the pollutant levels were significantly different in the three groups of wastewater samples, which were confirmed by univariate analysis of principal component (PC) scores. Based on wastewater similarities, cluster analysis identified three groups (central, upstream and downstream) of sampling stations, which further confirmed univariate analysis of PCs scores. Spatial variations in wastewater quality reveled that the highest pollutant concentration was noted for group 1 and lowest for group 2. Seasonal variations in the wastewater quality revealed that highest values of pollutants were observed in low flow and lowest in high flow. Results of the present study obtained through multivariate analyses may be used to classify wastewater and identify the influencing sources of pollutants. The present study may be useful in reducing 11 % of the cost in future investigations. Thus, in future quality estimation of the representative wastewater samples would be faster as well as cost-effective approach.