Can we locate the source of pollution when we are indoors?
Operable windows and indoor air quality
2019
Commission: Academic
Type: Research, Operations guidance
Audience: Facility Managers, Occupants
Team: Ankita Dwivedi, Bethania Lanzaro
Special thanks: Marcella Ucci
Location: UK
Air cannot be contained, it moves imperceptibly between spaces, indoors and out.
This work studies 3 rooms spread across a building to help Facilities Managers identify whether opening windows for ventilation along busy streets is helpful or harmful.
We evaluated the condition of the building including its interior, analysed air pollution data from weather stations around the building and mapped these against datasets from multiple indoor sensors. This highlighted environmental conditions, consequent occupant behaviour and whether occupants were exposed to pollutants due to indoor activity or conditions outdoors.
Work environments in particular are prone to CO2, PM2.5 and VOC build-up, with potentially additional pollutants coming in through windows, such as NOx, and SO2. These contaminants reduce cognitive performance and increase risk of chronic respiratory diseases.
Over the years legislation such as the Clean Air Act and smoking bans in UK have restored air’s invisibility, but has also blinded us to the severity of the threat. As we continue to seal our buildings to protect ourselves from the pollution outside, we concentrate those produced indoors. Healthy air is a luxury in most cities and increasingly in most buildings.
This work will create impact by:
Guiding operational management strategies to minimise exposure to harmful air, whether indoor or outdoor, and reducing energy use at the same time.
Highlighting conflicts in government guidance and industry recommendations on concentrations, dose, exposure limits of pollutants for health impact.
Advising how, where and when air quality, alongside temperature and humidity, should be monitored to predict behaviour in buildings.
Supporting natural ventilation and operable windows in commercial buildings by longitudinal data analysis.