Scalable IoT Solutions for Real-Time Monitoring and Mitigation of Industrial Air Pollution Emissions

As the main source of air pollution, industrial emissions provide significant risks to human health and the environment. Conventional monitoring techniques, which frequently depend on irregular manual testing, are unable to deliver the real-time data required for efficient mitigation. The apparel industry is a significant source of carbon emissions, and this study suggests a scalable IoT-based system for real-time air pollution monitoring and mitigation. Using cutting-edge IoT sensors, machine learning algorithms, and Random Forest algorithm-based predictive modeling, the system continuously monitors important pollutants such as CO, NO2, and SO2. Particulate matter identification is further made exact using Convolutional Neural Networks (CNNs). Real-time alerts allow for immediate intervention, promoting adherence to regulations and reducing adverse environmental impacts. Results demonstrate that this method improves that this system enhances both the accuracy and response time of emission monitoring, encouraging eco-friendly industry practices that contribute to the global climate goals.