Real Time Estimation of GHG Emissions Using IoT Integrated Sensor Fusion

IoT integrated Sensor Fusion is utilised in this work to estimate greenhouse gas (GHG) emissions. A system is proposed that utilizes IoT devices and environmental sensors to monitor and manage environmental impact efficiently in real-time. IoT-enabled sensors are strategically placed in industrial sites, metropolitan centres, and natural landscapes. These sensors measure CO
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, methane, nitrous oxide, and meteorology. Sensor fusion and real time data monitoring are the main contribution of this research work. The technology decreases uncertainty and improves estimation accuracy by integrating sensor data. Machine learning algorithms analyse fused sensor data and create predictive models. These models allow the system to extrapolate emission estimates beyond sensor locations, providing a comprehensive view of GHG emissions across geographies. IoT integration with sensor fusion and digital signal processing (DSP) allows real-time data transmission and remote monitoring. This continuously collects and processes emission data to quickly identify pollution hotspots and conduct emission reduction actions. The system's precision, real-time capabilities, scalability, and adaptability make it ideal for local air quality monitoring, regional emission tracking, and global climate change assessments. Finally, this research uses sensor fusion and IoT technology to estimate GHG emissions. The technology provides accurate, real-time emission estimations from several sensors, aiding environmental monitoring and sustainable development. This strategy could help mitigate climate change and create a green future.