Multivariate data analysis of key pollutants in sewage samples: a case study Original Research Article

Waste water treatment plants often need detailed information about the sources and levels of pollutants in sewage in order to maintain stable process conditions and to achieve permitted levels for hazardous compounds in their effluents. A high content of pollutants is usually traceable to industrial inputs. In this study the main objective was to study the factors affecting the composition of sewage of domestic origin. Sixty-five domestic sewage samples collected during 9 months at eight different sites in Melbourne, Australia, were analyzed for 83 chemical variables. The data set also included two samples of combined domestic/industrial wastewaters, seven samples from waste water treatment plant influent streams and five domestic water supply samples. The data was studied with multivariate data analysis methods; principal component analysis (PCA) and partial least squares (PLS). With multivariate methods, effects of lifestyle of residents, day of the week and sampling time or weather on the pollutant levels could be determined.