Automated Quantification of Dynamic Aerosol Nucleation Rates via Multi-Modal Data Fusion and AI-Driven Pattern Recognition

**Abstract:** This paper presents a novel framework for the automated and high-resolution quantification of dynamic aerosol nucleation rates – a critical process governing air quality and climate – leveraging multi-modal data fusion and advanced artificial intelligence pattern recognition techniques. Current methods rely on invasive and time-consuming manual analysis, inhibiting real-time monitoring and comprehensive understanding of nucleation events. Our proposed system, termed "AeroQuant," combines microscopy imaging, aerosol mass spectrometry, and environmental sensor data into a unified, AI-driven analysis pipeline, enabling significantly higher throughput, greater accuracy, and real-time feedback for nucleation rate determination. The framework is readily deployable and scalable for both laboratory and field-based aerosol research.