Abstract
Globally, air pollution is a significant concern for human health. Artificial sources of particulate matter (fine dust particles suspended in the air), especially PM2.5, are the significant constituents in indoor air pollution and prominently leads to many lung-related illnesses. Research suggests that around 90% of people spend ~22 hours inside every day, either at a home, office, factory, or restaurant. Plants act as a natural detoxifying agent by taking particulate matter (PM) waste from the atmosphere. Our hypothesis tested whether placing specific plants indoors can reduce levels of PM inside by using principles of biomedical engineering and machine-learning approaches. Our data showed that plants improved the overall quality of the air in the room. Levels of PM2.5 were decreased dramatically in the rooms compared to the average levels measured outside the house. As higher PM exposure in humans is associated with many diseases, our research suggests that plant-based interventions coupled with a sensor and cloud-based application using artificial intelligence may be useful in the long run to reduce indoor air pollution.
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