Application of Microbial Biotechnology in Wastewater Treatment and Environmental Sustainability

Abstract

Wastewater treatment is a critical component for achieving environmental sustainability, especially in rapidly urbanizing and industrializing regions. Microbial biotechnology has emerged as an effective and eco-friendly approach for improving wastewater treatment efficiency through the use of microorganisms to degrade pollutants and enhance water quality. This study aims to analyze the application of microbial biotechnology in wastewater treatment and to evaluate its impact on environmental sustainability using data-driven approaches. A real-world-inspired dataset consisting of 5,000 samples and 25 relevant physicochemical and biological parameters was utilized in this research. Key features include pH, Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), nutrient concentrations, microbial species, and treatment-related variables. The dataset was preprocessed and divided into training (70%), validation (15%), and testing (15%) subsets to ensure robust model evaluation. Several machine learning and deep learning models were applied. Among the applied models, a 1D Convolutional Neural Network (1D-CNN) demonstrated the best performance. The model effectively captured complex relationships between microbial activity and wastewater quality parameters, achieving a classification accuracy of approximately 90% in predicting water quality status (Good, Moderate, and Poor). The findings indicate that microbial biotechnology can support improved wastewater-treatment outcomes, particularly when combined with advanced deep-learning techniques. The proposed approach supports prediction, monitoring, and decision-making and highlights the potential of integrating microbial biotechnology with artificial intelligence for sustainable wastewater management.