Multivariate statistical analysis of heavy metals pollution in industrial area and its comparison with relatively less polluted area: A case study from the City of Peshawar and district Dir Lower

Multivariate and univariate statistical techniques i.e., cluster analysis PCA, regression and correlation analysis, one way ANOVA, were applied to the metal data of effluents soil and ground water to point out the contribution of different industries towards the metals pollution, their source identification and distribution. The samples were collected from different industries and different downstream points of the main effluents stream and from the relatively less polluted area considered as control area. The samples were analyzed for metal concentration levels by flame atomic absorption spectrophotometer. The metal concentration data in the three media of the polluted area were compared with background data and control data as well as with the WHO safe limits. The results showed that soil has high metals concentration compared to effluents and water. The data also showed elevated levels of Mn and Pb in water that are 8.268 and 2.971 mg/L, respectively. Principal component analysis along with regression analysis showed that the elevated levels of metals in the effluents contaminate adjacent soil and ultimately the ground water. The other elements Co, Cd, Ni and Cu were also found to have correlation in the three media.