Real-Time Emission Prediction and Monitoring Using CatBoost and MQTT Protocol

Due to public concern over pollution, there is an increased demand for monitoring systems that will detect emissions in real time and will quantify the changes in the local emissions environment qualitatively and quantitatively. Traditional monitoring systems have been viewed as inefficient in data collection, and data processing, which has delayed response time and mitigation measures. This study proposes an artificial intelligence integrated methodology for emissions analysis and future prediction with the CatBoost machine learning model. This methodology uses data flowing in real time, using message queuing telemetry transport (MQTT), to process emission data, project pollution outbreaks, and display usable data in the web-based interface.The CatBoost model used historic emissions data to predict anticipated quantity of pollutants in specific areas and with the general area.By facilitating instantaneous transmissions of data from numerous Pollution Monitors to AI Models to User Interfaces, MQTT provides a significant increase in the Utility of All Three Types of Technology. This work contributes to the understanding of how Artificial Intelligence can be used in real-time emissions monitoring Systems. The Theory through which this framework was proposed also provides a demonstration of how this newly formed framework may apply to Environmental Management Monitoring of Pollutants and may therefore be of value in Managing Pollution both at an Industrial and Urban level.