A new study reveals that people may be exposed to unhealthy levels of airborne pollutants inside their homes, even if the outdoor air quality is good.
Researchers at the University of Birmingham used low-cost sensors and innovative techniques over a two-week period to compare particulate matter (PM) in three homes. They discovered that pollution levels in each house were higher and more variable than outdoor levels. The researchers found significant differences in PM levels between the three houses, with one home exceeding the World Health Organisation (WHO) 24-hour PM2.5 limit on nine days.
This highlights the importance of monitoring indoor air quality at a household-specific level.
Published in Scientific Reports, this is the second paper published by McCall MacBain Clean Air Fellows studying the philanthropically funded Master’s degree in Air Pollution Management and Control at the University of Birmingham.
Co-author and Clean Air Fellow Catrin Rathbone commented: “Our study shows the need to monitor indoor air pollution, as people can have unhealthy air at home even if outdoor air is good. PM levels varied significantly between homes, indicating that monitoring just one location isn’t enough.”
The team note that factors such as household location, ventilation, and occupancy patterns influenced particle levels – demonstrating the complexity of indoor air quality.
Co-author and Clean Air Fellow Owain Rose commented: “With more time spent working from home, understanding the factors that affect air quality within households is increasingly important. The methods we used accurately modelled indoor PM levels, helping to improve exposure estimates at a low cost.”
Researchers identified five different factors contributing to PM in indoor spaces – two relating to indoor activities, such as increased movement by residents and three linked to external factors such as a nearby restaurant’s kitchen vent. They found that larger particles (PM10) tended to settle faster compared to smaller particles (PM1, PM2.5).
Researchers used Non-negative Matrix Factorization (NMF) – a powerful tool for uncovering latent patterns in data – to more accurately model indoor PM levels. Using low-cost sensors enabled them to build a more detailed picture of pollutant levels within the properties.
(with inputs from ANI)
