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Aerosol Science and Technology Volume 57, 2023 - Issue 10

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Original Articles

Machine learning predicts bioaerosol trajectories in enclosed environments: Introducing a novel method

Zhijian LiuDepartment of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding, Hebei, ChinaView further author information

Jiaqi ChuDepartment of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding, Hebei, ChinaView further author information

Zhenzhe HuangDepartment of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding, Hebei, ChinaView further author information

Haochuan LiDepartment of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding, Hebei, ChinaView further author information

Xia XiaoDepartment of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding, Hebei, ChinaView further author information

Junzhou HeDepartment of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding, Hebei, ChinaCorrespondence[email protected]
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Weijie YangDepartment of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding, Hebei, ChinaView further author information

Xuqiang ShaoDepartment of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding, Hebei, ChinaView further author information

Haiyang LiuDepartment of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding, Hebei, ChinaView further author information

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Pages 998-1012 | Received 10 Mar 2023, Accepted 14 Jun 2023, Published online: 20 Jul 2023

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https://doi.org/10.1080/02786826.2023.2230240
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Abstract

The COVID-19 pandemic has sparked a global interest in understanding the mechanism of transmission of bioaerosols in enclosed environments. Swift and accurate calculations of particle trajectories are crucial for predicting the diffusion of bioaerosols. The use of machine learning can expedite these calculations and predictions. However, research focusing on the use of machine learning methods for calculating bioaerosol trajectories remains scarce. A bioaerosol trajectory is a time series with long intervals and delays between different positions; certain machine learning models are well suited for handling time series data. Herein, we aimed to establish a new method we refer to as physics–machine learning (P–ML) that includes a machine learning model for calculating bioaerosol trajectories. To this end, we adopted a lightweight, single-layer long short-term memory (LSTM) model and used supervised learning with mean squared error as an evaluation metric for training. Our findings indicate that disregarding the turbulence diffusion enables us to train the LSTM model by a motion equation. Furthermore, the model accurately predicted trajectories while exhibiting some degree of transferability. However, when considering the turbulence diffusion of bioaerosol, the training data in P–ML method could not be generated using a motion equation with turbulence diffusion model (Discrete Random Walk model). To address this issue, we integrating the fluctuating velocity into the LSTM model input. Consequently, the predicted results were consistent with the motion equation. Our method exhibits considerable potential for expediting trajectory calculation and aiding in early warning and rapid design in enclosed environments.

Editor:

Jing Wang

Disclosure statement

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Additional information

Funding

This work was supported by National Outstanding Youth Science Fund Project of National Natural Science Foundation of China (No. 42122058), National Natural Science Foundation of China (No. 52208107 and 41977368), National Key R&D Program of China (2021YFF0604000), Natural Science Foundation of Hebei Province (No. E2021502046), and Fundamental Research Funds for the Central Universities (No. 2021MS075 and 2022MS088).

Notes on contributors

Zhijian Liu

Zhijian Liu: Funding acquisition, Formal analysis, Supervision, Writing—original draft, Writing—review & editing. Jiaqi Chu: Writing—original draft, Writing—review & editing, Software, Programming, Data curation, Visualization, Data analysis. Zhenzhe Huang, Haochuan Li, and Xiao Xia: Formal analysis, Writing—review & editing, Software, Programming, Data curation. Junzhou He: Writing—original draft, Writing—review & editing, Methodology, Supervision, Project administration, Programming, Formal analysis. Weijie Yang: Formal analysis, Supervision, Funding acquisition. Xuqiang Shao: Programming, Supervision. Haiyang Liu: Writing—review & editing.

Jiaqi Chu

Zhenzhe Huang

Haochuan Li

Xia Xiao

Junzhou He

Weijie Yang

Xuqiang Shao

Haiyang Liu

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Machine learning predicts bioaerosol trajectories in enclosed environments: Introducing a novel method

Notes on contributors

Zhijian Liu

Jiaqi Chu

Zhenzhe Huang

Haochuan Li

Xia Xiao

Junzhou He

Weijie Yang

Xuqiang Shao

Haiyang Liu

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Machine learning predicts bioaerosol trajectories in enclosed environments: Introducing a novel method

Abstract

Disclosure statement

Additional information

Funding

Notes on contributors

Zhijian Liu

Jiaqi Chu

Zhenzhe Huang

Haochuan Li

Xia Xiao

Junzhou He

Weijie Yang

Xuqiang Shao

Haiyang Liu

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