ENHANCING AIR POLLUTION CONTROL WITH MACHINE LEARNING IN THE AUTOMATION FIELD

Abstract

The integration of machine learning with real-time data collection offers a transformative approach to optimizing pollution control strategies. This study explores the application of these advanced technologies in various environments, including urban, industrial, coastal, and rural areas. Using predictive machine learning models, significant reductions in pollutants such as PM2.5, SO2, NOx, VOCs, PM10, and NH3 were achieved through targeted and timely interventions. In urban areas, air quality improved notably due to proactive measures informed by high-accuracy predictions. Industrial areas saw a 20% reduction in sulfur dioxide emissions, while coastal areas effectively managed volatile organic compounds. In rural areas, optimizing agricultural practices led to substantial decreases in particulate matter and ammonia emissions. These findings validate the efficacy of machine learning in enhancing pollution control efforts, highlighting its potential to revolutionize air quality management. This study underscores the importance of continued investment in advanced, data-driven approaches to address the growing challenge of air pollution, advocating for more sophisticated, adaptive, and effective strategies to protect public health and the environment.