Pollutant Recognition Based on Supervised Machine Learning for Indoor Air Quality Monitoring Systems

Indoor air may be polluted by various types of pollutants which may come from cleaning

products, construction activities, perfumes, cigarette smoke, water-damaged building materials and

outdoor pollutants. Although these gases are usually safe for humans, they could be hazardous

if their amount exceeded certain limits of exposure for human health. A sophisticated indoor air

quality (IAQ) monitoring system which could classify the specific type of pollutants is very helpful.

This study proposes an enhanced indoor air quality monitoring system (IAQMS) which could

recognize the pollutants by utilizing supervised machine learning algorithms: multilayer perceptron

(MLP), K-nearest neighbour (KNN) and linear discrimination analysis (LDA). Five sources of indoor

air pollutants have been tested: ambient air, combustion activity, presence of chemicals, presence

of fragrances and presence of food and beverages. The results showed that the three algorithms

successfully classify the five sources of indoor air pollution (IAP) with a classification rate of up to

100 percent. An MLP classifier with a model structure of 9-3-5 has been chosen to be embedded into

the IAQMS. The system has also been tested with all sources of IAP presented together. The result

shows that the system is able to classify when single and two mixed sources are presented together.

However, when more than two sources of IAP are presented at the same period, the system will

classify the sources as ‘unknown’, because the system cannot recognize the input of the new pattern.