Login / Signup

Assessing the seasonality of occupancy number-associated CO2 level in a Taiwan hospital.

Yi-Chen LiWen-Chang TsengNan-Hung HsiehSzu-Chieh Chen
Published in: Environmental science and pollution research international (2019)
This study enabled the assessment of indoor CO2 levels and evaluated the relationship between occupancy numbers with CO2 levels in a Taiwan hospital. The measurements were conducted over four seasons for five working days (Monday to Friday), with sampling conducted simultaneously from 09:00 am to 5:00 pm and across six locations (for spatial variability): hall (H), registration and cashier (RC), waiting area (WA), occupational therapy room (OT), physical therapy room (PT), and outdoors (O). Based on the analysis, three of the five indoor sampling sites showed significant differences in seasonal CO2 concentrations (p < 0.0001). Based on our result, the physical therapy room had the highest level of CO2 concentration that exceeded the IAQ standard in Taiwan Environmental Protection Agency (EPA) in all seasons, in that the number of occupants contributing to nearly 40% of the variation in CO2 measured. Our results also showed that the indoor/outdoor (I/O) ratios of CO2 concentration for all locations and seasons exceeded 1 in ~ 100% of those locations. The median I/O ratio at sites WA and OT was 2.37 and 2.08 during four seasons, respectively. The highest median I/O ratio was found at site PT, with a calculated range of 2.69 in spring to 3.90 in fall. The highest correlation of occupancy number and CO2 concentration also occurred in PT which correlation coefficients were estimated at 0.47, 0.65, 0.63, and 0.40 in spring, summer, fall, and winter. The findings of the present study can be used to understand occupancy number and its effect on CO2 levels in a hospital environment, as well as the effect of time of day (Monday to Friday) on the number of patients admitted.
Keyphrases
  • air pollution
  • particulate matter
  • healthcare
  • adverse drug
  • risk assessment
  • climate change
  • data analysis
  • water soluble