Gas Plume

Gas plume research focuses on accurately detecting, quantifying, and predicting the behavior of gas emissions from various sources, including industrial facilities, wildfires, and natural seeps. Current research heavily utilizes machine learning, particularly convolutional neural networks (CNNs) and transformer architectures, often integrated with computational fluid dynamics (CFD) models, to improve the speed and accuracy of plume detection and prediction from diverse data sources like satellite imagery and in-situ sensors. This work is crucial for environmental monitoring, climate change mitigation (e.g., methane emission reduction), and safety management in industrial settings, enabling more efficient and effective strategies for pollution control and risk assessment.