STUDIES ON THE IMPACT OF INDUSTRIAL WASTEWATER ON WATER QUALITY AND ITS MODELLING

Abstract

Water is vital for sustaining life on earth and social progress. It is increasingly important to conserve water resources, examine the factors responsible for changes in the water environment, and prevent the deterioration of the water quality of rivers. The swift industrial expansion has posed significant environmental challenges. Industrial effluents consist of substantial amounts of harmful pollutants that enter the main rivers via various tapped and untapped drains/local water streams, causing alterations in their physical and chemical properties. Industrial drains, tributaries, and minor water streams absorb and transport contaminants from various point and non-point sources to the main river, resulting in deterioration of the water quality. Water quality is one of the primary environmental concerns globally, especially in regions that have undergone rapid industrialization and urbanization. Industrial effluents can adversely affect the inland surface water, groundwater, and the entire riverine ecosystem, leading to contamination and deterioration of water health. The objectives of the study include the assessment of the impact of industrial discharges on surface water quality, evaluation of industrial effluents from grossly polluting industries (GPIs), assessment of the impact of key parameters on groundwater quality status, application of Streeter-Phelps-based water quality model to analyze the dissolved oxygen (DO) and biochemical oxygen demand (BOD) variation for multipoint sources, and the application of machine learning (ML) algorithms for BOD prediction in the River Ganga. An investigation primarily focuses on evaluating the inland surface water quality in Kashipur city of Uttarakhand state, India, the region affected by industrial pollution. A water quality index (WQI) method based on the analytic hierarchy process (AHP) approach has been conducted. Surface water samples were collected monthly from 12 locations between September 2020 and March 2021, chosen based on their likelihood of receiving the most contaminants from surrounding industries. The ten most commonly used physicochemical parameters were analyzed, namely water temperature, pH (potential of Hydrogen), dissolved oxygen, biochemical oxygen demand, chemical oxygen demand (COD), total dissolved solids (TDS), total suspended solids (TSS), chloride (Cl-), nitrate (NO3 -), and ammonia-nitrogen (NH3-N). Significant spatial variations in parameter concentrations were observed throughout the study area. Two primary factor loadings were revealed by Principal Component Analysis (PCA) results, impacting the surface water chemistry in the study region, where factor 1 represents 50.05% of the variance with six dominant water parameters, and factor 2 represents 33.22% of the total variance.Hierarchical cluster analysis (HCA) classified the sampling locations randomly into two major clusters. Analytic Hierarchy Process results in establishing the weights of the parameters, revealing that dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, and total dissolved solids were the dominant parameters for assessing water quality, with a consistency ratio of 0.097. The water quality index results showed that 8% of the water samples depict “very poor” inland surface water quality, primarily attributed to anthropogenic activities.