北京某高校教室自然通风下新冠感染概率实验和模拟研究
摘要：采用实验和模拟的方法研究了北京某高校教室中学生新冠感染概率的问题。运用Wells-Riley模型计算得出：当量子生成率(quanta值)从14增加到48时，关窗条件下教室人员感染概率从11.22%提高至33.44%,开窗条件下感染概率从5.73%提高至18.37%;换气次数增加至12 h-1时，感染概率为0.51%。利用佩戴口罩模型计算感染概率，关窗条件下不佩戴口罩吸入病毒感染概率为54.79%,佩戴口罩时为13.7%;开窗条件下不佩戴口罩吸入病毒感染概率为29.89%,佩戴口罩时为7.47%。在短时间暴露情况下，关窗条件下佩戴口罩时感染概率降低至23.41%,开窗条件下降低至15.45%,采用机械通风将换气次数增加到5 h-1时，佩戴口罩感染概率降低至0.2%,有效降低了感染概率。
关键词：新型冠状病毒感染概率Wells-Riley模型佩戴口罩模型自然通风换气次数
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参考文献[1] 李瑞彬，吴妍，牛建磊，等.人体呼出颗粒物的传播特性及呼吸道传染病感染概率预测方法[J].暖通空调，2020,50(9):41- 54.
[2] 周永道，董将虎.基于抽样方法估计新型冠状病毒感染人数[J].应用数学学报，2020,43(2):156- 161.
[3] 金启轩.中国新冠肺炎疫情预测建模与理性评估[J].统计与决策，2020(5):11- 14.
[4] 周琦.自然通风病房内通风对呼吸道传染病传播影响的研究[D].南京：东南大学，2016:25- 45.
[5] VILLAFRUELA J M,OLMEDO I,BERLANGA F A,et al.Assessment of displacement ventilation systems in airborne infection risk in hospital rooms[J].PLoS one,2019,14:1- 18.
[6] GAO N P,NIU J L,PERINO M,et al.The airborne transmission of infection between flats in high-rise residential buildings:particle simulation[J].Building and environment,2008,43:1805- 1817.
[7] SHENG Z,ZHANG L.Dilution-based evaluation of airborne infection risk-thorough expansion of Wells-Riley model[J].Building and environment,2021,194:107674.
[8] BUONANNO G,STABILE L,MORAWSKA L.Estimation of airborne viral emission:quanta emission rate of SARS-CoV-2 for infection risk assessment[J].Environment international,2020,141:105794.
[9] DAI H,ZHAO B.Association of the infection probability of COVID-19 with ventilation rates in confined spaces[J].Building simulation,2020,13:1321- 1327.
[10] WANG Z,GALEA E R,ANGUS G,et al.Inflight transmission of COVID-19 based on experimental aerosol dispersion data[J].Journal of travel medicine,2021,28(4):1- 7.
[11] SHAO X,LI X.COVID-19 transmission in the first presidential debate in 2020[J].Physics of fluids,2020,32(11):115125.
[12] LIANG C,JIANG S,SHAO X,et al.Is it safe to reopen theaters during the COVID-19 pandemic?[J].Frontiers in built environment,2021,7:637277.
[13] 钱华，郑晓红，张学军.呼吸道传染病空气传播的感染概率的预测模型[J].东南大学学报(自然科学版),2012,42(3):468- 472.
[14] WELLS W F.Airborne contagion and air hygiene:an ecological study of droplet infection[M].Cambridge:Harvard University Press,1957,38(1):1- 65.
[15] RILEY R L,O'GRADY F.Airborne infection:transmission and control[M].New York:The Macmillan Company,1963:690- 691.
[16] RILEY E C,MURPHY G,RILEY R L.Airborne spread of measles in a suburban elementary school[J].American journal of epidemiology,1978,107(5):421- 432.
[17] 江亿.新冠肺炎病毒传播机理探讨[EB/OL].[2021-10-19].https://mp.weixin.qq.com/s/ECaj1Brrhda XCiEQng7BA.
[18] 朱能，田哲，王侃红.示踪气体跟踪测量方法在空调通风上的应用[J].暖通空调，1999,29(2):58- 62.
[19] SZE-TO G N,CHAO C Y H.Review and comparison between the Wells-Riley and Dose-response approaches to risk assessment of infectious respiratory diseases[J].Indoor air,2009,20(1):2- 16.
[20] QIAN H,LI Y,SETO W H,et al.Natural ventilation for reducing airborne infection in hospitals.[J].Building and environment,2010,45(3):559- 565.
[21] 赵彬.防控COVID-19空气传播所需新风量：以某方舱医院为例的探索[EB/OL].[2021-10-19].https://mp.weixin.qq.com/s/uSzbDCE5RJMTVU8iQ ew.
[2] 周永道，董将虎.基于抽样方法估计新型冠状病毒感染人数[J].应用数学学报，2020,43(2):156- 161.
[3] 金启轩.中国新冠肺炎疫情预测建模与理性评估[J].统计与决策，2020(5):11- 14.
[4] 周琦.自然通风病房内通风对呼吸道传染病传播影响的研究[D].南京：东南大学，2016:25- 45.
[5] VILLAFRUELA J M,OLMEDO I,BERLANGA F A,et al.Assessment of displacement ventilation systems in airborne infection risk in hospital rooms[J].PLoS one,2019,14:1- 18.
[6] GAO N P,NIU J L,PERINO M,et al.The airborne transmission of infection between flats in high-rise residential buildings:particle simulation[J].Building and environment,2008,43:1805- 1817.
[7] SHENG Z,ZHANG L.Dilution-based evaluation of airborne infection risk-thorough expansion of Wells-Riley model[J].Building and environment,2021,194:107674.
[8] BUONANNO G,STABILE L,MORAWSKA L.Estimation of airborne viral emission:quanta emission rate of SARS-CoV-2 for infection risk assessment[J].Environment international,2020,141:105794.
[9] DAI H,ZHAO B.Association of the infection probability of COVID-19 with ventilation rates in confined spaces[J].Building simulation,2020,13:1321- 1327.
[10] WANG Z,GALEA E R,ANGUS G,et al.Inflight transmission of COVID-19 based on experimental aerosol dispersion data[J].Journal of travel medicine,2021,28(4):1- 7.
[11] SHAO X,LI X.COVID-19 transmission in the first presidential debate in 2020[J].Physics of fluids,2020,32(11):115125.
[12] LIANG C,JIANG S,SHAO X,et al.Is it safe to reopen theaters during the COVID-19 pandemic?[J].Frontiers in built environment,2021,7:637277.
[13] 钱华，郑晓红，张学军.呼吸道传染病空气传播的感染概率的预测模型[J].东南大学学报(自然科学版),2012,42(3):468- 472.
[14] WELLS W F.Airborne contagion and air hygiene:an ecological study of droplet infection[M].Cambridge:Harvard University Press,1957,38(1):1- 65.
[15] RILEY R L,O'GRADY F.Airborne infection:transmission and control[M].New York:The Macmillan Company,1963:690- 691.
[16] RILEY E C,MURPHY G,RILEY R L.Airborne spread of measles in a suburban elementary school[J].American journal of epidemiology,1978,107(5):421- 432.
[17] 江亿.新冠肺炎病毒传播机理探讨[EB/OL].[2021-10-19].https://mp.weixin.qq.com/s/ECaj1Brrhda XCiEQng7BA.
[18] 朱能，田哲，王侃红.示踪气体跟踪测量方法在空调通风上的应用[J].暖通空调，1999,29(2):58- 62.
[19] SZE-TO G N,CHAO C Y H.Review and comparison between the Wells-Riley and Dose-response approaches to risk assessment of infectious respiratory diseases[J].Indoor air,2009,20(1):2- 16.
[20] QIAN H,LI Y,SETO W H,et al.Natural ventilation for reducing airborne infection in hospitals.[J].Building and environment,2010,45(3):559- 565.
[21] 赵彬.防控COVID-19空气传播所需新风量：以某方舱医院为例的探索[EB/OL].[2021-10-19].https://mp.weixin.qq.com/s/uSzbDCE5RJMTVU8iQ ew.
Experimental and simulation study on infection rate of SARS-CoV-2 under natural ventilation in classrooms of a university in Beijing
Abstract: Experimental and simulation methods are used to study the SARS-CoV-2 infection rate of students in classrooms of a university in Beijing. The Wells-Riley model is used to calculate and obtain that when the quantum generation rate(quanta) increases from 14 to 48, the infection rate of classroom personnel increases from 11.22% to 33.44% under window closing condition, and from 5.73% to 18.37% under window opening condition. The infection rate is 0.51% when the air change rate is increased to 12 h-1. Using the wearing mask model to calculate the infection rate, when the window is closed, the infection rate of inhaling the virus is 54.79% without a mask and 13.7% with a mask. When the window is open, the infection rate of inhaling the virus is 29.89% without a mask and 7.47% with a mask. In the case of short exposure, when the mask is worn, the infection rate is reduced to 23.41% under window closing condition and 15.45% under window opening condition, and the infection rate is reduced to 0.2% when the air change rate is increased to 5 h-1 by mechanical ventilation, which effectively reduces the infection rate.
Keywords: SARS-CoV-2; infection rate; Wells-Riley model; wearing mask model; natural ventilation; air change rate;
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