Birmingham Birmingham: A new study suggests that people can come in contact with the unhealthy levels of airborne pollutants inside their homes, even if the external 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 Matt (PM) in three homes. They found that the pollution levels in each house were more and more variable than external levels. Researchers found a significant difference in PM's level between the three houses, in which a house crossed the 24 -hour PM 2.5 border of the World Health Organization (WHO) in nine days.

It highlights the importance of indoor air quality monitoring at home-specific levels. Published in Scientific Reports, it is the second paper published by McCall McBen Clean Air Fellow, who studied the air pollution management at the University of Birmingham and a philanthropically funded master's degree in control. Co-writer and clean air Fellow Catrin Rathbone commented: “Our study indicates the need for monitoring indoor air pollution, because people can have unhealthy air in their homes, even if the outer air is good. The PM is between houses. The level varies greatly, showing that only one location monitoring is not enough. ”

The team noted that factors such as domestic space, ventilation and occupancy patterns affect particle levels – indoor displays the complexity of air quality.

Co-writer and clean air Fellow Owen Rose commented: “With more time spending from home, it is rapidly important to understand the factors affecting air quality inside the houses. The indoor PM levels accurately modeled, helping to improve the risk estimates at low cost. ”

Researchers identified five different factors contributing to PM in indoor locations – to two indoor activities, such as increased movement by residents and three external factors such as nearby restaurant kitchen vents. They found that large particles (PM10) accumulate faster than small particles (PM1, PM2.5).

Researchers used non -negative matrix factorization (NMF) – which is a powerful tool to highlight the pattern hidden in the data – to model indoor PM levels more accurately. Using low -cost sensors helped them to create a more detailed picture of polluting levels within properties. (ANI)