Atmospheric Chemistry

Atmospheric chemistry research focuses on understanding and modeling the complex interactions of chemical species in the atmosphere, aiming to improve air quality predictions and assessments of environmental impacts. Current efforts leverage advanced machine learning techniques, such as neural networks and dynamic mode decomposition, to create faster, more accurate, and interpretable models of atmospheric processes, particularly for trace gases like formaldehyde. These improved models are crucial for enhancing air quality monitoring, forecasting pollution events, and informing policy decisions related to public health and environmental protection.