Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Enhanced Detection of Primary Biological Aerosol Particles Using Machine Learning and Single-Particle Measurement
https://orcid.org/0000-0001-6320-4519
https://orcid.org/0000-0002-4774-4870
https://orcid.org/0000-0003-2985-8249
https://orcid.org/0000-0001-7670-335X
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.
Enhanced Detection of Primary Biological Aerosol Particles Using Machine Learning and Single-Particle Measurement
https://orcid.org/0000-0001-6320-4519
https://orcid.org/0000-0002-4774-4870
https://orcid.org/0000-0003-2985-8249
https://orcid.org/0000-0001-7670-335X
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.
Enhanced Detection of Primary Biological Aerosol Particles Using Machine Learning and Single-Particle Measurement
https://orcid.org/0000-0001-6320-4519
https://orcid.org/0000-0002-4774-4870
https://orcid.org/0000-0003-2985-8249
https://orcid.org/0000-0001-7670-335X
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.
Enhanced Detection of Primary Biological Aerosol Particles Using Machine Learning and Single-Particle Measurement
Rahman, Ashfiqur (Autor:in) / Lata, Nurun Nahar (Autor:in) / Sebben, Bruna Grasielli (Autor:in) / Dexheimer, Darielle (Autor:in) / Cheng, Zezhen (Autor:in) / Godoi, Ricardo Henrique Moreton (Autor:in) / Bilbao, Aivett (Autor:in) / China, Swarup (Autor:in)
ACS ES&T Engineering ; 4 ; 2393-2402
11.10.2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
| British Library Online Contents | 1993
| Wiley | 2011