Accurately identifying primary biological
aerosol particles (PBAPs)
using analytical techniques poses inherent challenges due to their
resemblance to other atmospheric carbonaceous particles. We present
a study of an enhanced method for detecting PBAPs by combining single-particle
measurement with advanced supervised machine learning (SML) techniques.
We analyzed ambient particles from a variety of environments and lab-generated
standards, focusing on chemical composition for traditional rule-based
and clustering approaches and incorporating morphological features
into the SML approaches, neural networks and XGBoost, for improved
accuracy. This study demonstrates that SML methods outperform traditional
methods in quantifying PBAPs, achieving significant improvements in
precision, recall, F1-score, and accuracy, leading to an increased
number of detected PBAPs by at least 19%. The adaptability of the
proposed XGBoost-based SML model is showcased in comparison to traditional
methods in categorizing PBAPs for blind data sets from different geographical
locations. Two field case studies were investigated, over agricultural
land and Amazonia rain forest, representing relatively low and high
concentrations of PBAPs, respectively, where XGBoost consistently
detected up to 3.5 times more PBAPs than traditional methods. Precise
detection of PBAPs in the atmosphere could significantly improve the
prediction of climatic impacts by them.