Abstract
Ventilation is an effective solution for improving indoor air quality and reducing airborne transmission. Buildings need sufficient ventilation to maintain a low infection risk but also need to avoid an excessive ventilation rate, which may lead to high energy consumption. The Wells-Riley (WR) model is widely used to predict infection risk and control the ventilation rate. However, few studies compared the non-steady-state (NSS) and steady-state (SS) WR models that are used for ventilation control. To fill in this research gap, this study investigates the effects of the mechanical ventilation control strategies based on NSS/SS WR models on the required ventilation rates to prevent airborne transmission and related energy consumption. The modified NSS/SS WR models were proposed by considering many parameters that were ignored before, such as the initial quantum concentration. Based on the NSS/SS WR models, two new ventilation control strategies were proposed. A real building in Canada is used as the case study. The results indicate that under a high initial quantum concentration (e.g., 0.3 q/m3) and no protective measures, SS WR control underestimates the required ventilation rate. The ventilation energy consumption of NSS control is up to 2.5 times as high as that of the SS control.
摘要
通风是一种有效提高室内空气质量和降低疾病通过空气传播的方法。足量的通风可以降低疾病传播风险,但过量的通风会提高建筑能耗。通风策略需要兼顾这两方面。Wells-Riley(WR)模型常用于预测感染风险和控制通风。但很少有研究对比非稳态和稳态WR 模型应用在通风策略上的区别。为了填补这一空白,本文探究了基于非稳态和稳态WR 模型的通风策略对于防疫和建筑能耗的影响。首先,对常用的非稳态和稳态WR 模型进行改进。一些之前被忽略的参数被加入到WR 模型中,例如起始病毒浓度。然后,基于非稳态和稳态WR 模型,提出并对比了两种通风策略。最后,将一栋位于加拿大的建筑物选作案例进行结果展示。案例显示,当起始病毒浓度较高(0.3 q/m3)且室内人员无其他保护措施时,基于稳态WR 模型的通风策略会低估所需要的通风量。此时,非稳态WR 模型通风策略的能耗可达稳态WR 模型通风策略的2.5 倍。
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References
World Health Organization (WHO). Coronavirus disease (COVID-19) pandemic-2021 [OL]. [2021-09-12]. https://www.who.int/emergencies/diseases/novel-coronavirus-2019.
FENG Yu, MARCHAL T, SPERRY T, et al. Influence of wind and relative humidity on the social distancing effectiveness to prevent COVID-19 airborne transmission: A numerical study [J]. Journal of Aerosol Science, 2020, 147: 105585. DOI:https://doi.org/10.1016/j.jaerosci.2020.105585.
SETTI L, PASSARINI F, de GENNARO G, et al. Airborne transmission route of COVID-19: Why 2 meters/6 feet of inter-personal distance could not be enough? [J]. International Journal of Environmental Research and Public Health, 2020, 17(8): 2932. DOI: https://doi.org/10.3390/ijerph17082932.
MORAWSKA L, TANG J W, BAHNFLETH W, et al. How can airborne transmission of COVID-19 indoors be minimised? [J]. Environment International, 2020, 142: 105832. DOI: https://doi.org/10.1016/j.envint.2020.105832.
GREENHALGH T, JIMENEZ J L, PRATHER K A, et al. Ten scientific reasons in support of airborne transmission of SARS-CoV-2 [J]. Lancet (London, England), 2021, 397(10285): 1603–1605. DOI: https://doi.org/10.1016/S0140-6736(21)00869-2.
SHA Hao-han, QI Da-hai. A review of high-rise ventilation for energy efficiency and safety [J]. Sustainable Cities and Society, 2020, 54: 101971. DOI: https://doi.org/10.1016/j.scs.2019.101971.
LUONGO J C, FENNELLY K P, KEEN J A, et al. Role of mechanical ventilation in the airborne transmission of infectious agents in buildings [J]. Indoor Air, 2016, 26(5): 666–678. DOI: https://doi.org/10.1111/ina.12267.
DAI Hui, ZHAO Bin. Association of the infection probability of COVID-19 with ventilation rates in confined spaces [J]. Building Simulation, 2020, 13(6): 1321–1327. DOI: https://doi.org/10.1007/s12273-020-0703-5.
SCHIBUOLA L, TAMBANI C. High energy efficiency ventilation to limit COVID-19 contagion in school environments [J]. Energy and Buildings, 2021, 240: 110882. DOI: https://doi.org/10.1016/j.enbuild.2021.110882.
HOU Dan-lin, KATAL A, WANG L. Bayesian calibration of using CO2 sensors to assess ventilation conditions and associated COVID-19 airborne aerosol transmission risk in schools [J]. MedRxiv, 2021, DOI: https://doi.org/10.1101/2021.01.29.21250791.
REHVA. REHVA COVID-19 guidance document version 4.0.2020 [R].
ZHANG Sheng, LIN Zhang. Dilution-based evaluation of airborne infection risk—Thorough expansion of Wells-Riley model [J]. Building and Environment, 2021, 194: 107674. DOI: https://doi.org/10.1016/j.buildenv.2021.107674.
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, 2010, 20(1): 2–16. DOI: https://doi.org/10.1111/j.1600-0668.2009.00621.x.
AGANOVIC A, BI Yang, CAO Guang-yu, et al. Estimating the impact of indoor relative humidity on SARS-CoV-2 airborne transmission risk using a new modification of the Wells-Riley model [J]. Building and Environment, 2021, 205: 108278. DOI: https://doi.org/10.1016/j.buildenv.2021.108278.
GUO Yong, QIAN Hua, SUN Zhi-wei, et al. Assessing and controlling infection risk with Wells-Riley model and spatial flow impact factor (SFIF) [J]. Sustainable Cities and Society, 2021, 67: 102719. DOI: https://doi.org/10.1016/j.scs.2021.102719.
HARRICHANDRA A, IERARDI A M, PAVILONIS B. An estimation of airborne SARS-CoV-2 infection transmission risk in New York City nail salons [J]. Toxicology and Industrial Health, 2020, 36(9): 634–643. DOI: https://doi.org/10.1177/0748233720964650.
MILLER S L, NAZAROFF W W, JIMENEZ J L, et al. Transmission of SARS-CoV-2 by inhalation of respiratory aerosol in the Skagit Valley Chorale superspreading event [J]. Indoor Air, 2021, 31(2): 314–323. DOI: https://doi.org/10.1111/ina.12751.
YANG Wan, MARR L C. Dynamics of airborne influenza A viruses indoors and dependence on humidity [J]. PLoS One, 2011, 6(6): e21481. DOI: https://doi.org/10.1371/journal.pone.0021481.
SUN Chan-juan, ZHAI Zhi-qiang. The efficacy of social distance and ventilation effectiveness in preventing COVID-19 transmission [J]. Sustainable Cities and Society, 2020, 62: 102390. DOI: https://doi.org/10.1016/j.scs.2020.102390.
PENG Zhe, JIMENEZ J L. Exhaled CO2 as a COVID-19 infection risk proxy for different indoor environments and activities [J]. Environmental Science & Technology Letters, 2021, 8(5): 392–397. DOI: https://doi.org/10.1021/acs.estlett.1c00183.
RUDNICK S N, MILTON D K. Risk of indoor airborne infection transmission estimated from carbon dioxide concentration [J]. Indoor Air, 2003, 13(3): 237–245. DOI: https://doi.org/10.1034/j.1600-0668.2003.00189.x.
ISSAROW C M, MULDER N, WOOD R. Modelling the risk of airborne infectious disease using exhaled air [J]. Journal of Theoretical Biology, 2015, 372: 100–106. DOI: https://doi.org/10.1016/j.jtbi.2
ASHRAE Standard 62.1. Ventilation for acceptable indoor air quality [S].
SHA Hao-han, QI Da-hai. Investigation of mechanical ventilation for cooling in high-rise buildings [J]. Energy and Buildings, 2020, 228: 110440. DOI: https://doi.org/10.1016/j.enbuild.2020.110440.
SHA Hao-han, MOUJAHED M, QI Da-hai. Machine learning-based cooling load prediction and optimal control for mechanical ventilative cooling in high-rise buildings [J]. Energy and Buildings, 2021, 242: 110980. DOI: https://doi.org/10.1016/j.enbuild.2021.110980.
Health Canada. General exposure factor inputs for dietary, occupational, and residential exposure assessments [R]. 2014: 52. DOI: H113-13/2014-1E-PDF.
NG M O, QU Ming, ZHENG Peng-xuan, et al. CO2-based demand controlled ventilation under new ASHRAE Standard 62.1-2010: A case study for a gymnasium of an elementary school at West Lafayette, Indiana [J]. Energy and Buildings, 2011, 43(11): 3216–3225. DOI: https://doi.org/10.1016/j.enbuild.2011.08.021.
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. DOI: https://doi.org/10.1016/j.envint.2020.105794.
Government of Canada. Hours of work 2020 [OL]. [2021-03-07] https://www.canada.ca/en/employment-social-development/programs/employment-standards/work-hours.html.
KATAL A, ALBETTAR M, WANG L. City reduced probability of infection (CityRPI) for indoor airborne transmission of SARS-CoV-2 and urban building energy impacts [J]. MedRxiv, 2021: 2021.01.19.21250046.
WANG Sheng-wei, BURNETT J, CHONG H. Experimental validation of CO2-based occupancy detection for demand-controlled ventilation [J]. Indoor and Built Environment, 1999, 8(6): 377–391. DOI: https://doi.org/10.1177/1420326x9900800605.
ASIF A, ZEESHAN M, JAHANZAIB M. Indoor temperature, relative humidity and CO2 levels assessment in academic buildings with different heating, ventilation and air-conditioning systems [J]. Building and Environment, 2018, 133: 83–90. DOI: https://doi.org/10.1016/j.buildenv.2018.01.042.
AI Z T, MAK C M. Short-term mechanical ventilation of air-conditioned residential buildings: A general design framework and guidelines [J]. Building and Environment, 2016, 108: 12–22. DOI: https://doi.org/10.1016/j.buildenv.2016.08.016.
CHAN W R, LI Xi-wang, SINGER B C, et al. Ventilation rates in California classrooms: Why many recent HVAC retrofits are not delivering sufficient ventilation [J]. Building and Environment, 2020, 167: 106426. DOI: https://doi.org/10.1016/j.buildenv.2019.106426.
RAHMAN M M, RASUL M G, KHAN M M K Energy conservation measures in an institutional building in subtropical climate in Australia [J]. Applied Energy, 2010, 87(10): 2994–3004. DOI: https://doi.org/10.1016/j.apenergy.2010.04.005.
ASHRAE. ASHRAE guideline 14–2014 measurement of energy, demand, and water savings [M]. Atlana, GA, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), 2014.
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SHA Hao-han wrote the first draft of the manuscript. ZHANG Xin conducted the literature review. QI Da-hai provided the concept and edited the draft of manuscript. SHA Hao-han and QI Da-hai replied to reviewers’ comments and revised the final version.
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SHA Hao-han, ZHANG Xin, and QI Da-hai declare that they have no conflict of interest.
Foundation item: Project(RGPIN-2019-05824) supported by the Start-up Fund of Université de Sherbrooke and Discovery Grants of Natural Sciences and Engineering Research Council of Canada (NSERC)
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Sha, Hh., Zhang, X. & Qi, Dh. Impact of mechanical ventilation control strategies based on non-steady-state and steady-state Wells-Riley models on airborne transmission and building energy consumption. J. Cent. South Univ. 29, 2415–2430 (2022). https://doi.org/10.1007/s11771-022-5072-z
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DOI: https://doi.org/10.1007/s11771-022-5072-z