Deep learning approaches to probe bioaerosols in the atmosphere
EMSL Project ID
60676
Abstract
Bioaerosols play a key role in cold cloud formation and impacts ice phase precipitation in the atmosphere and ecosystem via deposition of essential micronutrients. EMSL’s micro-spectroscopy instruments could be used to characterize bioaerosols while distinguishing them from other carbonaceous particles to address uncertainties in climate models. However, existing classification and clustering (standard machine learning algorithms) are not sufficient for their accurate identification (~68%). We propose to apply advanced deep learning approaches on single particle imaging and chemical composition data to accurately predict (>~95%) abundance of bioaerosols in the atmosphere. Furthermore, we will apply the model to predict the vertical distribution of bioaerosols under different environmental and cloud conditions. This integrated instrument-software platform will benefit current and future EMSL users by providing accurate quantification of abundance of bioaerosols.
Project Details
Start Date
2023-01-16
End Date
N/A
Status
Active
Released Data Link
Team
Principal Investigator
Co-Investigator(s)
Team Members