An empirical correlation and a set of machine learning (ML) models were developed to estimate droplet size and count distributions over an extended duration after a cough at different relative humidities (RHs), air temperatures and locations within an indoor environment. Experiments covered RHs of 20%–80% and air temperatures of 21 °C–26 °C. Droplet count distributions for 4 size bins (0.3–0.5, 0.5–1, 1–3 and 3–5 μm) were recorded for 70 min within the distance of 2 m from the cough source. Different ML models, including decision tree, random forest and artificial neural network, were trained for each size bin to predict the associated count distribution. Amongst these models, random forest showed a slight superiority in performance. The coefficient of determination for the random forest models ranged from 0.912 to 0.989, indicating robust correlations between the features and the response variables. An empirical correlation was established linking the count distribution of 0.3–0.5 μm droplets to time, RH and distance along the cough direction. Both ML models and the correlation accurately predicted the trends and the distributions, providing valuable data for validating computational simulations and informing indoor environment control systems to reduce the risk of virus transmission.

中文翻译：

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估计室内环境中咳嗽后一段时间内的飞沫大小和计数分布

开发了一种经验相关性和一组机器学习 (ML) 模型，用于估计在不同相对湿度 (RH)、空气温度和室内环境中的位置下咳嗽后较长时间内的液滴大小和计数分布。实验涵盖 20%–80% 的相对湿度和 21 °C–26 °C 的气温。在距咳嗽源 2 m 的距离内记录 70 分钟内 4 个尺寸箱（0.3–0.5、0.5–1、1–3 和 3–5 μm）的飞沫计数分布。针对每个尺寸箱训练不同的机器学习模型，包括决策树、随机森林和人工神经网络，以预测相关的计数分布。在这些模型中，随机森林在性能上表现出轻微的优势。随机森林模型的决定系数范围为 0.912 到 0.989，表明特征和响应变量之间存在稳健的相关性。建立了经验相关性，将 0.3-0.5 μm 飞沫的计数分布与时间、相对湿度和沿咳嗽方向的距离联系起来。机器学习模型和相关性都准确地预测了趋势和分布，为验证计算模拟和通知室内环境控制系统以降低病毒传播的风险提供了有价值的数据。