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Occupancy impact on the indoor air quality of the monitored open-offices

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Języki publikacji
EN
Abstrakty
EN
Humans spend 90% of their time indoors therefore proper air quality in buildings is crucial for human health and productivity. The problem with poor air quality is usually found in buildings with no mechanical ventilation. Contrary, modern office buildings, which are usually equipped with mechanical ventilation, are often over-ventilated. The reason is usually related to lower-than-designed buildings’ occupancies. In the context of a pandemic, the occupancy of the buildings has significantly decreased, meanwhile the ventilation systems often operate at the design air flow rates thus causing a waste of energy. The paper presents long-term occupancy and CO2 concentration monitoring results for 4 office buildings. All of the buildings showed very low occupancies and over-ventilation of the rooms. Seeking to decarbonize the buildings sector much is done to strengthen the requirements for energy efficiency of buildings, but the results of the study prove once more that the potential of better building energy using systems management is still unexploited.
Słowa kluczowe
Rocznik
Strony
28--35
Opis fizyczny
Bibliogr. 16 poz., tab., wykr.
Twórcy
  • Vilnius Gediminas Technical University, Lithuania
  • Vilnius Gediminas Technical University, Lithuania
  • Vilnius Gediminas Technical University, Lithuania
Bibliografia
  • [1] United Nations Environment Programme. Global status report for buildings and construction. Towards a zero-emissions, efficient and resilient buildings and construction sector. Nairobi: 2021. Retrieved from https://globalabc.org/sites/default/files/2021-10/GABC_Buildings-GSR-2021_BOOK.pdf.
  • [2] European Climate Foundation and the European Alliance to Save Energy (EU-ASE). Building Europe’s net-zero future. 2022. Why the transition to energy efficient and electrified buildings strengthens Europe’s economy we are grateful to the following organisations for contributing their expertise and insight. Retrieved from https://europeanclimate.org/wp-content/uploads/2022/03/ecf-building-emmissions-problem-march2022.pdf.
  • [3] González-Torres M., Pérez-Lombard L., Coronel J.F., Maestre I.R., Yan D., 2022, A review on buildings energy information: Trends, end-uses, fuels and drivers. Energy Reports. 8, pp. 626-637. https://doi.org/10.1016/j.egyr.2021.11.280.
  • [4] López L.R., Dessì P., Cabrera-Codony A., Rocha-Melogno L., Kraakman B., Naddeo V., et al., 2023, CO2 in indoor environments: From environmental and health risk to potential renewable carbon source. Science of The Total Environment. 856 (2):159088. https://doi.org/10.1016/j.scitotenv.2022.159088.
  • [5] Becerra J.A., Lizana J., Gil M., Barrios-Padura A., Blondeau P., Chacartegui R., 2020, Identification of potential indoor air pollutants in schools. Journal of Cleaner Production. 242:118420. https://doi.org/10.1016/j.jclepro.2019.118420.
  • [6] Wei S., Tien P.W., Chow T.W., Wu Y., Calautit J.K., 2022, Deep learning and computer vision based occupancy CO2 level prediction for demand-controlled ventilation (DCV). Journal of Building Engineering. 56:104715. https://doi.org/10.1016/j.jobe.2022.104715.
  • [7] Wang J., Huang J., Feng Z., Cao S.J., Haghighat F., 2021, Occupant-density-detection based energy efficient ventilation system: Prevention of infection transmission. Energy and Buildings. 240:110883. https://doi.org/10.1016/j.enbuild.2021.110883.
  • [8] Bielskus J., Motuzienė V., Vilutienė T., Indriulionis A., 2020, Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model. Energies. 13(15):4033. https://doi.org/10.3390/en13154033.
  • [9] Choi H., Lee J., Yi Y., Na H., Kang K., Kim T., 2022, Deep vision-based occupancy counting: Experimental performance evaluation and implementation of ventilation control. Building and Environment. 223:109496. https://doi.org/10.1016/j.buildenv.2022.109496.
  • [10] Sheikh Khan D., Kolarik J., Anker Hviid C., Weitzmann P., 2021, Method for long-term mapping of occupancy patterns in open-plan and single office spaces by using passive-infrared (PIR) sensors mounted below desks. Energy and Buildings. 230:110534. https://doi.org/10.1016/j.enbuild.2020.110534.
  • [11] Park S., Choi Y., Song D., Kim E.K., 2019, Natural ventilation strategy and related issues to prevent coronavirus disease 2019 (COVID-19) airborne transmission in a school building. Science of The Total Environment. 789:147764. https://doi.org/10.1016/j.scitotenv.2021.147764.
  • [12] Li B., Cai W., 2022, A novel CO2-based demand-controlled ventilation strategy to limit the spread of COVID-19 in the indoor environment. Building and Environment. 219:109232. https://doi.org/10.1016/j.buildenv.2022.109232.
  • [13] Motuzienė V., Bielskus J., Lapinskienė V., Rynkun G., Bernatavičienė J., 2022, Office buildings occupancy analysis and prediction associated with the impact of the COVID-19 pandemic. Sustainable Cities and Society. 77:103557. https://doi.org/10.1016/j.scs.2021.103557.
  • [14] Salimi S., Hammad A., 2019, Critical review and research roadmap of office building energy management based on occupancy monitoring. Energy and Buildings. 182, pp. 214-241. https://doi.org/10.1016/j.enbuild.2018.10.007.
  • [15] Xie X., Lu Q., Herrera M., Yu Q., Parlikad A.K., Schooling J.M., 2021, Does historical data still count? Exploring the applicability of smart building applications in the post-pandemic period. Sustainable Cities and Society. 69:102804. https://doi.org/10.1016/j.scs.2021.102804.
  • [16] Korjenic A., Bednar T., 2012, Validation and evaluation of total energy use in office buildings: A case study. Automation in Construction. 23, pp. 64-70. https://doi.org/10.1016/j.autcon.2012.01.001.
Uwagi
1. This research was funded by a grant (No. S-MIP-20-62) from the Research Council of Lithuania (LMTLT).
2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c7b03407-0dae-4453-bf6b-02b456599d5b
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