Enhanced Detection of Primary Biological Aerosol Particles Using Machine Learning and Single-Particle Measurement

Rahman, Ashfiqur; Lata, Nurun Nahar; Sebben, Bruna Grasielli; Dexheimer, Darielle; Cheng, Zezhen; Godoi, Ricardo Moreton; Bilbao, Aivett; China, Swarup

doi:10.1021/acsestengg.4c00262

Enhanced Detection of Primary Biological Aerosol Particles Using Machine Learning and Single-Particle Measurement

Journal Article · Wed Aug 07 00:00:00 UTC 2024 · ACS ES&T Engineering

DOI:https://doi.org/10.1021/acsestengg.4c00262· OSTI ID:2468643

Rahman, Ashfiqur ^[1]; Lata, Nurun Nahar ^[1]; Sebben, Bruna Grasielli ^[2]; Dexheimer, Darielle ^[3]; ^[1]; ^[2]; ^[1]; ^[1]

Pacific Northwest National Laboratory (PNNL), Richland, WA (United States). Environmental Molecular Sciences Laboratory (EMSL)
Federal University of Paraná (Brazil)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Accurately identifying primary biological aerosol particles (PBAPs) using analytical techniques poses inherent challenges due to their resemblance to other atmospheric carbonaceous particles. Here, 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.

View Accepted Manuscript (DOE)

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: Environmental Molecular Sciences Laboratory (EMSL); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: AC05-76RL01830; FG03-00ER62913; FG03-97ER62338

OSTI ID:: 2468643

Report Number(s):: PNNL-SA--194864

Journal Information:: ACS ES&T Engineering, Journal Name: ACS ES&T Engineering Journal Issue: 10 Vol. 4; ISSN 2690-0645

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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