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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. Copyright © 2023 American Association for Aerosol Research

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