Innovative Artificial Intelligence-Based Environmental Monitoring System: Emissions and Air Quality Predictions Using Data from Multiple Sensors and Deep Learning

Abstract

Smart, real-time monitoring is necessary for global environmental pollution. An innovative environmental monitoring system that utilizes artificial intelligence can use sensor data to predict emissions and air quality. Gas, particle matter, meteorological, and satellite data are all inputs into the system, which then uses this information to assess the surrounding environment. In the suggested model, regional and temporal trends in air quality are captured by CNN and RNN deep learning algorithms. Data is preprocessed via edge computing, while models are trained and analyzed by cloud platforms. The prediction is improved with the help of data fusion and advanced feature extraction. Air quality predictions for PM2.5, PM10, CO2, NOx, and SO2 are provided by the system. Urban and environmental planners use visualization dashboards and real-time notifications. The suggested model is more accurate, has lower latency, and is more scalable than statistical and machine learning approaches, according to the experimental results. The system’s resilience is guaranteed by its ability to react dynamically to changing environmental conditions. Proactive pollution management in sustainable, smart cities is made possible by this research. Automated intelligence and data collected from multiple sensors can enhance and expand existing environmental monitoring systems.