Characterizing the contribution of bioaerosols diversity from complex aerosol particle samples.
EMSL Project ID
51821
Abstract
Current global change scenarios expect to significantly increase the contribution of pollen to the total organic aerosol budget. In addition, pollen can burst into smaller fragments that are highly efficient as cloud condensation nuclei. However, current climate models still do not incorporate the effects of these particles due to detection and quantification challenges in complex aerosol mixtures. Chemical biomarkers (i.e. fructose) have commonly been used to trace pollen but this approximation can generate false positive results given the large number of shared compounds between different bioaerosols. A previous pilot EMSL experiment performed on pure pollen samples suggested that using a set of metabolites as a fingerprint of bioaerosols could serve to substantially increase detection accuracy over single biomarker approaches. In this proposal we plan to do develop novel analytical and bioinformatic strategies to characterize and quantify complex pollen mixtures present in the atmosphere. For that, we will use machine learning algorithms to analyze metabolic fingerprints from complex mixtures of different pollen acquired with the next generation ion-mobility spectrometry - mass spectrometry (IMS-MS) capability. The proposed research will lead to a significant advancement in the atmospheric chemistry and provide much needed data to improve the accuracy of climate models.
Project Details
Start Date
2021-01-27
End Date
2023-09-30
Status
Closed
Released Data Link
Team
Principal Investigator
Team Members