Integration of Artificial Intelligence and Internet of Things (IoT) for Real-Time Monitoring and Predictive Modeling of Air Quality in Industrial Settings

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) revolutionizes real-time air quality monitoring and predictive modeling in industrial settings, addressing environmental, health, and regulatory challenges. IoTenabled sensors, strategically deployed across industrial sites, continuously collect data on pollutants such as particulate matter (PM2.5, PM10), volatile organic compounds (VOCs), and gases (CO, NOx). These sensors transmit data via protocols like MQTT or LoRaWAN to cloud or edge platforms, where AI algorithms—leveraging machine learning (e.g., Random Forest, LSTM) and deep learning—analyze patterns, detect anomalies, and forecast air quality trends. This synergy enables proactive measures, such as optimizing ventilation or scheduling maintenance, to mitigate pollution risks. Benefits include enhanced worker safety, regulatory compliance, and cost efficiency, though challenges like sensor accuracy, data security, and system scalability persist. Future advancements, including 5G connectivity, explainable AI, and digital twins, promise greater precision and scalability. This integrated approach empowers industries to maintain sustainable operations while safeguarding health and the environment.