Susceptibility and risk to inhalation of pathogen-laden aerosol in large public spaces: Evidence from Fangcang Shelter Hospitals under multiple ventilation rates

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society Pub Date : 2024-11-22 DOI:10.1016/j.scs.2024.106003

Chuan Jiang , Zhijian Liu , Yongxin Wang , Guangpeng Yao , Junzhou He , Shiyue Li , Rui Rong , Zhenyu Liang , Jiaqi Chu , Jingwei Liu

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引用次数: 0

Abstract

To enhance the prevention and control of pandemic respiratory infections, we constructed an infection risk prediction model for pathogen inhalation based on the dose-response relationship with reference to the long-distance transmission chain of the SARS-CoV-2 pathogen, applying it to the Fangcang Shelter Hospital (FSH). The model quantitatively describes key processes of pathogens shedding, airborne transmission, suspension, inhalation by susceptible individuals, and lung deposition, thus improving the resolution and accuracy of the results. This study considered four ventilation rates and quantitatively assessed their impact on inhalation infection risk. Results indicate that the infection risk within the multi-patient shelter unit is unevenly distributed, with the maximum probability (3.66 %) being more than 30 times higher than the minimum probability (0.10 %) at a ventilation rate of 8 ACH. Poor ventilation (6 ACH) significantly increases average infection probability, with a rise of 35.96 % compared to the average probability (1.14 %) at 8 ACH. However, excessive ventilation (12 ACH) led to diminishing returns on ventilation performance. Lastly, we also found that poor ventilation was a sufficient and non-essential condition for higher infection probability. Our findings can be extended to similar large-scale scenarios, offering positive support for the sustainable development of society.

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大型公共场所吸入病原体气溶胶的易感性和风险：多种通风率下芳草地医院的证据

为加强对大流行性呼吸道传染病的防控，我们参考 SARS-CoV-2 病原体的远距离传播链，基于剂量-反应关系构建了病原体吸入感染风险预测模型，并将其应用于芳草地避难所医院（FSH）。该模型定量描述了病原体脱落、空气传播、悬浮、易感人群吸入和肺沉积等关键过程，从而提高了结果的分辨率和准确性。本研究考虑了四种通风率，并定量评估了它们对吸入感染风险的影响。结果表明，多病人收容单元内的感染风险分布不均，通风率为 8 ACH 时，最大感染概率（3.66%）是最小感染概率（0.10%）的 30 多倍。通风不良（6 ACH）会显著增加平均感染概率，与 8 ACH 时的平均概率（1.14 %）相比，增加了 35.96 %。然而，过度通气（12 ACH）会导致通气性能的收益递减。最后，我们还发现，通风不良是导致感染概率升高的充分条件，但并非必要条件。我们的研究结果可以推广到类似的大规模场景中，为社会的可持续发展提供积极的支持。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Sustainable Cities and Society Social Sciences-Geography, Planning and Development

CiteScore

22.00

自引率

13.70%

发文量

810

审稿时长

27 days

期刊介绍： Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including: 1. Smart cities and resilient environments; 2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management; 3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management); 4. Energy efficient, low/zero carbon, and green buildings/communities; 5. Climate change mitigation and adaptation in urban environments; 6. Green infrastructure and BMPs; 7. Environmental Footprint accounting and management; 8. Urban agriculture and forestry; 9. ICT, smart grid and intelligent infrastructure; 10. Urban design/planning, regulations, legislation, certification, economics, and policy; 11. Social aspects, impacts and resiliency of cities; 12. Behavior monitoring, analysis and change within urban communities; 13. Health monitoring and improvement; 14. Nexus issues related to sustainable cities and societies; 15. Smart city governance; 16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society; 17. Big data, machine learning, and artificial intelligence applications and case studies; 18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems. 19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management; 20. Waste reduction and recycling; 21. Wastewater collection, treatment and recycling; 22. Smart, clean and healthy transportation systems and infrastructure;