Leveraging Artificial Intelligence for the automated processing and analysis of real-time atmospheric data

The volume and complexity of atmospheric data have expanded significantly, driven by the proliferation of low-cost sensor networks, high-fidelity research stations, and multi-platform remote sensing. However, the utility of these datasets is often hindered by inherent noise in low-cost hardware, the necessity for labor-intensive manual analysis, and limited spatial coverage. This work explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) to automate data processing workflows, ensuring high-quality, scalable, and real-time atmospheric insights. 

We present several case studies demonstrating the effectiveness of AI in bridging data gaps and enhancing analytical accuracy. First, we discuss the development of "virtual sensors" for Ozone (O₃) monitoring, designed for deployment within micro-measurement stations where physical chemical sensors may be impractical. Second, we introduce a novel, robust fitting algorithm for Particle Number Size Distributions (PNSD) that operates in near real-time, offering superior reliability over traditional iterative methods. Third, we showcase a predictive model that fuses satellite remote sensing data with ground-level observations to estimate and spatially scale PM_2.5 concentrations, providing high-resolution coverage in previously unmonitored areas. 

Beyond traditional data processing, this work outlines the broader potential of emerging AI technologies to address remaining atmospheric challenges. We explore the implementation of EdgeAI for on-device sensor calibration and the use of Computer Vision to quantify traffic and human activity, thereby providing critical metadata for source apportionment. By integrating these automated technologies, we demonstrate a path toward a more responsive and comprehensive framework for air quality and atmospheric data analysis.