Using Machine Learning for the Calibration of Airborne Particulate Sensors

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 1; p. 99
• Main Authors: Wijeratne, Lakitha O H, Kiv, Daniel R, Aker, Adam R, Talebi, Shawhin, Lary, David J
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 23-12-2019; MDPI
• Subjects: Absenteeism; Air pollution; Airborne instruments; Airborne particulates; Airborne sensing; Asthma; Atmospheric aerosols; Atmospheric chemistry; Automation; Bias; Calibration; Childrens health; Machine learning; Neighborhoods; optical particle counter; Particulates; Probability distribution; Radiation counters; Radiative transfer; Satellites; Sensors; optical particle counter; machine learning; airborne particulates
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20010099

Airborne particulates are of particular significance for their human health impacts and their roles in both atmospheric radiative transfer and atmospheric chemistry. Observations of airborne particulates are typically made by environmental agencies using rather expensive instruments. Due to the expense of the instruments usually used by environment agencies, the number of sensors that can be deployed is limited. In this study we show that machine learning can be used to effectively calibrate lower cost optical particle counters. For this calibration it is critical that measurements of the atmospheric pressure, humidity, and temperature are also made.