A Geospatial Variability Modeling of Soil and Groundwater Contamination in Industrial Zone of Sheikhupora District, Pakistan

Abstract

The rapid population growth, large-scale development, urbanization, and unplanned industrialization has increased the wastewater generation during the past few decades. It has produced serious environmental hazards due to the release of untreated, excessive effluent and solid waste into the drains and cultivated land. The drains carry toxic effluent from multiple types of industries, including chemical industries, pipe and board industries, leather and tannery industries, steel rerolling industries and textile industries etc. These are responsible for environmental degradation which has deteriorated the quality of soil and groundwater. The main objective of this research was to analyse the influence of physicochemical parameters and heavy metal in industrial effluent on the soil, subsoil, and groundwater and to model the integrated environmental hazard in district Sheikhupura. A field survey was conducted to collect samples of industrial effluents (n=53), soil (n=60) and groundwater (n=80). The effluent data was analysed using surface water susceptibility index showing drain channel contamination in 2000 meters buffered area. The physicochemical parameters and heavy metals of soil were assessed using classical and geostatistical methods. Each soil depth exhibits different sensitivity for heavy metals contamination. The concentration order of heavy metal in soil was with a sequence of Cr > Pb > Cd, showing a decreasing trend from the surface to subsurface soil. The principal component analysis and factor analysed the sensitivity of the heavy metal for various physicochemical properties of soil to identify the major carcinogenic source in soil. The critical higher Z-score values for heavy metals in soil were identified in a range of Pb (Z ≥ 30), Cr (≤ 30) and Cd (≥ 20) by boruta algorithm, which showed significant permutations of heavy metals. The geostatistical analysis variogram modelling and kriging technique was performed to evaluate the spatial distribution xiii pattern and hotspots of heavy metals (Cr, Cd, and Pb) concentration using spatial interpolation. The pattern of soil heavy metals distribution was identified using spatial autocorrelation and LISA for clusters of high heavy metals concentration (Cr=08, Cd=17 and Pb=08). The severity of heavy metal contamination in soil was quantified by geo-accumulation index and Improved Nemerow integrated index in six classes and plotted the spatial extent by Empirical Bayesian Kriging. The groundwater was analysed for heavy metals contamination and other physicochemical parameters using USSLS analysis, water quality index, adjusted DRASTIC model and HVF model. The groundwater vulnerability was identified by using different parameters including, aquifer media, net recharge, soil type, hydraulic conductivity, topography and depth to the water table. Effluent percolation occurred by irrigation practices and rainfall to the lower depths of soil and groundwater, which are considered as primary mechanism to transfer the metal contamination. The groundwater quality was classified from good (>100%) to poor (20- 25 %) according to the index classes. The research signified an adaptive and comprehensive approach by analysing the pollution sources, their distribution pattern and quantification of contamination at a spatial scale. The extensive soil and groundwater monitoring and management practices were required to control the future pollution load in the environment.