Real-Time Air Quality Monitoring in Industrial Zones Using IoT-Powered AI Systems

Abstract

Air pollution in industrial zones poses significant risks to human health and the environment, making real-time monitoring essential for effective mitigation. Traditional air quality monitoring methods are deficient mainly in terms of spatial coverage and temporal resolution, making it challenging to trace the dynamic changes in the levels of pollutants. Development and implementation of real-time customized industrial area air quality monitoring using Internet of Things, sensors and artificial intelligence analytics. An IoT-enabled sensor network continuously measures key pollutants: PM2.5 and PM10, NO2, SO2, CO, and VOCs. The data is also processed through AI algorithms and LSTM networks to detect anomalies in pollutant level prediction. The experimental findings were a Root Mean Square Error (RMSE) of 2.2 and an R² of 0.90, better than the regular multivariate linear regression model. The alert mechanism of the system responded within an average of 5 seconds with low false positives below 3%, thus providing reliable real-time monitoring. Users gave a high satisfaction rating (average score of 4.7/5) about the system interface, especially on ease of navigation and clarity of information. These findings suggest a practical approach to monitoring and managing air quality in the industrial environment using IoT and AI technology integration to support improvement in public health and environmental sustainability and ensure minimal deployment challenges and cost-effectiveness.