Ventilation rate plays a significant role in preventing the airborne transmission of diseases in indoor spaces. Classrooms are a considerable challenge during the COVID-19 pandemic because of large occupancy density and mainly poor ventilation conditions. The indoor CO2 level may be used as an index for estimating the ventilation rate and airborne infection risk. In this work, we analyzed a one-day measurement of CO2 levels in three schools to estimate the ventilation rate and airborne infection risk. Sensitivity analysis and Bayesian calibration methods were applied to identify uncertainties and calibrate key parameters. The outdoor ventilation rate with a 95% confidence was 1.96±0.31ACH for Room 1 with mechanical ventilation and fully open window, 0.40±0.08 ACH for Rooms 2, and 0.79±0.06 ACH for Room 3 with only windows open. A time-averaged CO2 level < 450 ppm is equivalent to a ventilation rate > 10 ACH in all three rooms. We also defined the probability of the COVID-19 airborne infection risk associated with ventilation uncertainties. The outdoor ventilation threshold to prevent classroom COVID-19 aerosol spreading is between 3-8 ACH, and the CO2 threshold is around 500 ppm of a school day (< 8 hr) for the three schools.
Outdoor fresh air ventilation plays a significant role in reducing airborne transmission of diseases in indoor spaces. School classrooms are considerably challenged during the COVID-19 pandemic because of the increasing need for in-person education, untimely and incompleted vaccinations, high occupancy density, and uncertain ventilation conditions. Many schools started to use CO
2
meters to indicate air quality, but how to interpret the data remains unclear. Many uncertainties are also involved, including manual readings, student numbers and schedules, uncertain CO
2
generation rates, and variable indoor and ambient conditions. This study proposed a Bayesian inference approach with sensitivity analysis to understand CO
2
readings in four primary schools by identifying uncertainties and calibrating key parameters. The outdoor ventilation rate, CO
2
generation rate, and occupancy level were identified as the top sensitive parameters for indoor CO
2
levels. The occupancy schedule becomes critical when the CO
2
data are limited, whereas a 15-min measurement interval could capture dynamic CO
2
profiles well even without the occupancy information. Hourly CO
2
recording should be avoided because it failed to capture peak values and overestimated the ventilation rates. For the four primary school rooms, the calibrated ventilation rate with a 95% confidence level for fall condition is 1.96±0.31 ACH for Room #1 (165 m
3
and 20 occupancies) with mechanical ventilation, and for the rest of the naturally ventilated rooms, it is 0.40±0.08 ACH for Room #2 (236 m
3
and 21 occupancies), 0.30±0.04 or 0.79±0.06 ACH depending on occupancy schedules for Room #3 (236 m
3
and 19 occupancies), 0.40±0.32,0.48±0.37,0.72±0.39 ACH for Room #4 (231 m
3
and 8–9 occupancies) for three consecutive days.
A significant amount of energy is consumed on cooling buildings in countries that experience hot/humid climates. The increasing demand for high-rise buildings, with their inherent air conditioning systems, adds extra requirements to electricity grids or local district cooling systems. Thus, this work is structured to identify the influencing factors of cooling energy demand in high-rise buildings that are geographically restricted to countries of these climates. The influence of the factor is quantified as its contribution to cooling energy savings when manipulated or optimized. It is found that the average annual cooling reductions are 12%, 24.7%, 18.3%, and 20% with ranges of 3%-27%, 2.6%-60%, 5.6%-30%, and 11%-29% for building typology, envelope, system, and operation factors, respectively. Environmental factors lack quantification in the literature, although they are considered, however their effect is not quantified. In general, most studies considered building typology and building envelope factors which are related to building design, while few studies considered building operation and building system factors. The aforementioned factors and their importance lead to suggestions of conducting more studies on building operational and building system factors as they significantly contribute in cooling energy savings. Since Urban Heat Island (UHI) can cause a change of a city's microclimate which may double the cooling demand, it is listed as one of the essential environmental factors. This review has shown various aspects that are vital in studying building cooling load demand and its related energy performance.
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