A study in Leveraging Deep Learning and IoT Arrays for Dynamic, Hyper-Local Atmospheric Intelligence - Milind Shivaji Kadam; Vaishnavi Gopal Shirsikar; N. N. Shaikh; Aditi Dinanath Shahane; Dr. Kazi Kutubuddin Sayyad Liyakat (International Journal of Atmosphere, 2025)
The critical demand for high-resolution, actionable atmospheric data is challenged by the high cost and sparse coverage of traditional regulatory monitoring stations. This paper explores the synergistic paradigm shift enabled by integrating low-cost, dense Internet of Things (IoT) sensor arrays with advanced Artificial Intelligence (AI) methodologies, specifically Deep Learning (DL) models. We address the primary limitations of low-cost sensors—inherent bias, sensitivity to environmental drift (temperature/humidity), and calibration inconsistency—by utilizing AI for robust data standardization and enhancement. Deep Neural Networks (DNNs) are employed for predictive calibration, sophisticated noise reduction, and the fusion of heterogeneous data sources (sensor data, meteorological inputs, traffic patterns, satellite imagery). This approach moves atmospheric monitoring from static, generalized reporting to dynamic, hyper-local spatial mapping. The resultant AI-driven atmosphere monitoring system provides unprecedented spatiotemporal resolution, enabling real-time anomaly detection, accurate short-term pollutant forecasting (e.g., ozone and PM2.5), and the identification of previously invisible pollution hotspots. This framework represents a crucial step toward creating reliable, intelligent environmental early warning systems necessary for proactive public health interventions and targeted regulatory efforts
Keywords: Air Quality Monitoring (AQM); Artificial Intelligence (AI); Atmosphere; internet of things (IoT) sensors