Evaluation and calibration of low-cost off-the-shelf particulate matter sensors using machine learning techniques

Abstract

The use of inexpensive, lightweight, and portable particulate matter (PM) sensors is increasingly becoming popular in air quality monitoring applications. As an example, these low-cost sensors can be used in surface or underground coal mines for monitoring of inhalable dust, and monitoring of inhalable particles in real-time can be beneficial as it can possibly assist in preventing coal mine related respiratory diseases such as black lung disease. However, commercially available PM sensors are not inherently calibrated, and as a result, they have vague and unclear measurement accuracy. Therefore, they must initially be evaluated and compared with standardised instruments to be ready to be deployed in the fields. In this study, three different types of inexpensive, light-scattering-based widely available PM sensors (Shinyei PPD42NS, Sharp GP2Y1010AU0F, and Laser SEN0177) are evaluated and calibrated with reference instruments. PM sensors are compared with reference instruments in a controlled environment. The calibration is done by means of different machine learning techniques. The results demonstrate that the calibrated response obtained by fusion of sensors has a higher accuracy in comparison to the calibrated response of each individual sensor.