Literature DB >> 36034192

Impact of mechanical ventilation control strategies based on non-steady-state and steady-state Wells-Riley models on airborne transmission and building energy consumption.

Hao-Han Sha1, Xin Zhang1, Da-Hai Qi1.   

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. © Central South University 2022.

Entities:  

Keywords:  Wells-Riley model; airborne transmission; building energy consumption; building ventilation

Year:  2022        PMID: 36034192      PMCID: PMC9399565          DOI: 10.1007/s11771-022-5072-z

Source DB:  PubMed          Journal:  J Cent South Univ        ISSN: 2227-5223            Impact factor:   2.392


  18 in total

1.  Modelling the risk of airborne infectious disease using exhaled air.

Authors:  Chacha M Issarow; Nicola Mulder; Robin Wood
Journal:  J Theor Biol       Date:  2015-02-19       Impact factor: 2.691

2.  Association of the infection probability of COVID-19 with ventilation rates in confined spaces.

Authors:  Hui Dai; Bin Zhao
Journal:  Build Simul       Date:  2020-08-04       Impact factor: 3.751

3.  Estimating the impact of indoor relative humidity on SARS-CoV-2 airborne transmission risk using a new modification of the Wells-Riley model.

Authors:  Amar Aganovic; Yang Bi; Guangyu Cao; Finn Drangsholt; Jarek Kurnitski; Pawel Wargocki
Journal:  Build Environ       Date:  2021-08-23       Impact factor: 6.456

4.  Dynamics of airborne influenza A viruses indoors and dependence on humidity.

Authors:  Wan Yang; Linsey C Marr
Journal:  PLoS One       Date:  2011-06-24       Impact factor: 3.240

5.  How can airborne transmission of COVID-19 indoors be minimised?

Authors:  Lidia Morawska; Julian W Tang; William Bahnfleth; Philomena M Bluyssen; Atze Boerstra; Giorgio Buonanno; Junji Cao; Stephanie Dancer; Andres Floto; Francesco Franchimon; Charles Haworth; Jaap Hogeling; Christina Isaxon; Jose L Jimenez; Jarek Kurnitski; Yuguo Li; Marcel Loomans; Guy Marks; Linsey C Marr; Livio Mazzarella; Arsen Krikor Melikov; Shelly Miller; Donald K Milton; William Nazaroff; Peter V Nielsen; Catherine Noakes; Jordan Peccia; Xavier Querol; Chandra Sekhar; Olli Seppänen; Shin-Ichi Tanabe; Raymond Tellier; Kwok Wai Tham; Pawel Wargocki; Aneta Wierzbicka; Maosheng Yao
Journal:  Environ Int       Date:  2020-05-27       Impact factor: 9.621

6.  Ten scientific reasons in support of airborne transmission of SARS-CoV-2.

Authors:  Trisha Greenhalgh; Jose L Jimenez; Kimberly A Prather; Zeynep Tufekci; David Fisman; Robert Schooley
Journal:  Lancet       Date:  2021-04-15       Impact factor: 79.321

7.  Dilution-based evaluation of airborne infection risk - Thorough expansion of Wells-Riley model.

Authors:  Sheng Zhang; Zhang Lin
Journal:  Build Environ       Date:  2021-02-09       Impact factor: 6.456

Review 8.  Role of mechanical ventilation in the airborne transmission of infectious agents in buildings.

Authors:  J C Luongo; K P Fennelly; J A Keen; Z J Zhai; B W Jones; S L Miller
Journal:  Indoor Air       Date:  2015-12-14       Impact factor: 5.770

9.  Estimation of airborne viral emission: Quanta emission rate of SARS-CoV-2 for infection risk assessment.

Authors:  G Buonanno; L Stabile; L Morawska
Journal:  Environ Int       Date:  2020-05-11       Impact factor: 9.621

10.  An estimation of airborne SARS-CoV-2 infection transmission risk in New York City nail salons.

Authors:  Amelia Harrichandra; A Michael Ierardi; Brian Pavilonis
Journal:  Toxicol Ind Health       Date:  2020-10-21       Impact factor: 2.273

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