Applications Of Machine Learning In Advanced Pollutant Detection

Abstract

Air quality monitoring is essential for safeguarding both human health and the environment, especially as pollution continues to impact millions of lives globally. Detecting harmful pollutants like nitrogen dioxide (NO₂), carbon dioxide (CO₂), ozone (O₃), and ethanol (C₂H₅OH) is a key step toward addressing this critical challenge. This study employs zinc oxide (ZnO)-based sensors combined with machine learning to classify these pollutants effectively. Using decision tree and random forest algorithms in Python, pollutant types are predicted based on parameters such as concentration, temperature, and sensor response time. The dataset, sourced from prior research, underscores the remarkable sensitivity and stability of ZnO-based sensors, even under varying environmental conditions. Rigorous preprocessing ensured data accuracy, enabling reliable predictions and consistent outcomes. With high classification accuracy, this study demonstrates the transformative potential of integrating ZnO-based sensors with machine learning for real-time air quality monitoring, paving the way for actionable solutions to combat pollution and improve lives worldwide.