Real-Time Air Quality Monitoring with Edge AI and Machine Learning Algorithm

Real-time air quality monitoring is essential for public health and environmental protection, but typical centralized systems have latency, bandwidth, and privacy concerns. This work describes a novel approach to decentralized air quality monitoring that employs Edge AI and powerful machine learning techniques. IoT sensors outfitted with NVIDIA Jetson devices collect and evaluate air quality data locally. Machine learning models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), analyze pollutants including PM2.5, CO2, and NOx in real time. Implemented across 50 urban sites with varied pollution levels, the technology reduced latency by up to $75 \%$ and bandwidth use by $60 \%$ when compared to typical cloud-based solutions. The pollutant detection accuracy was evaluated against conventional reference monitors and yielded an average correlation coefficient of 0.92. This system also used federated learning to update models based on local data, improving model accuracy by up to $15 \%$ over six months. This new strategy not only increases data processing speed and accuracy, but also protects data privacy and lowers operational costs. Significant improvements in latency, bandwidth efficiency, and model accuracy demonstrate the utility of decentralized air quality monitoring systems in urban settings.