病毒通过气溶胶传播扩散及感染概率模拟——以地铁车厢为例
Virus aerosol transmission, dispersion, and infection probability simulation: A case study in subway carriages
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摘要:
背景 地铁作为人流密集型场所，极有可能发生病毒的气溶胶传播，但是缺乏具体的传播概率估计。
目的 模拟地铁车厢中呼吸气溶胶的传播和扩散过程，建立模型并计算人群感染概率。
方法 以上海市10号线箱型列车作为研究对象，通过湍流模型对微小颗粒物进行追踪模拟。建立基于封闭地铁车厢内的模型，通过量子发射-感染概率模型对同车厢的乘客感染概率、病毒量子剂量等进行趋势化分析。
结果 在常见的十二孔空调模式下，飞沫气溶胶在车厢内会随着气流快速扩散至同车厢的各个区域，但短时间内不会扩散到其他车厢。同时由于气流不确定性，车厢内的气流可能会在局部地区盘旋汇聚，飞沫气溶胶在进入盘旋汇聚状态后则向外扩散较慢，或很难向外扩散，乘客会加剧这些区域形成的情况。当空调系统运转正常（通风换气率为23.21 h−1），地铁车厢内一名病毒携带者向同车厢其他乘客通过气溶胶传播病毒的概率约为3.8%。但是当空调系统失灵时（通风换气率为0.5 h−1），这一概率将会高达30%。此外，一名超级传播者（病毒喷吐数＞90%普通人），也会将感染概率提升至14.9%。由于车厢内湍流的复杂性，在1/2局部弥散的情况下，感染概率预计扩大至8.9%，或者早晚高峰人流拥挤造成气溶胶浓度上升，感染概率可能会升高至4.7%。普通新冠病毒的地铁传播概率低至0.9%。
结论 结合计算流体力学和感染概率可以看出，地铁作为交通枢纽，在现今常见十二孔空调模式下，虽然人流量大，存在严重的时空交汇问题，但是由于其极好的通风换气性能，具有一定的抵御病毒气溶胶传播的能力。不过由于湍流和乘客位置的影响，飞沫气溶胶可能会在局部地区盘旋，从而导致病毒传播风险增加；同时，存在超级传播者、空调系统的运转状态不佳、早晚人流高峰，也对病毒传播感染概率增加有深刻的影响。
Abstract:Background Subways are typical congregate settings and may facilitate aerosol transmission of viruses. However, quantified transmission probability estimates are lacking.
Purpose To model spread and diffusion of respiratory aerosols in subways by simulation and calculation of infection probabilities.
Methods The internal environment of carriages of Shanghai Metro Line 10 was used to establish a study scene. The movement of tiny particles was simulated using the turbulent model. Trend analysis of infection probabilities and viral quantum doses was conducted in a closed subway carriage scene by a quantum emission-infection probability model.
Results Under a typical twelve-vent air conditioning configuration, respiratory droplet aerosols within a subway carriage dispersed rapidly throughout various regions due to airflow, with limited short-term diffusion to other carriages. Concurrently, owing to the uncertainty of airflow patterns, the airflow might circulate and converge within carriages, causing delayed outward dispersion or hindered dispersion of droplet aerosols upon entry into these zones. Passengers boarding the carriage could exacerbate the formation of these zones. When the air conditioning system functioned adequately (air exchange rate=23.21 h−1), the probability of a virus carrier transmitting the virus to other passengers within the same carriage via aerosol transmission was approximately 3.8%. However, in the event of air conditioning system failure (air exchange rate=0.5 h−1), this probability escalated dramatically to 30%. Furthermore, a super-spreader (with virus spreading exceeding 90% of the average) elevated the infection probability to 14.9%. Additionally, due to the complexity of turbulence within the carriage, if local diffusion occurred in 1/2 zones of a carriage, the anticipated infection probability would increase to 8.9%, or during the morning or evening rush hours leading to elevated aerosol concentrations, the infection probability would rise to 4.7%. The subway transmission probability for common coronaviruses diminished to as low as 0.9%.
Conclusion Combined computational fluid dynamics and infection probability analysis reveals that in the prevalent twelve-vent air conditioning configurations, despite being a major transportation hub with substantial spatial-temporal overlap, the internal space of subway carriages exhibits a certain level of resistance to virus aerosol transmission owing to built-in ventilation capabilities. However, turbulence and passenger positioning may lead to localized hovering of droplet aerosols, thereby increase the risk of virus transmission. Furthermore, super-spreaders, poor operational status of built-in air conditioning system, and high passenger volume at morning or evening peak hours exert profound effects on virus transmission and infection probability.
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