Performance characteristics of the low-cost Plantower PMS optical sensor. Issue 2 (1st February 2020)

• Record Type: Journal Article
• Authors: He, Meilu; Kuerbanjiang, Nueraili; Dhaniyala, Suresh
• Is Part Of: Aerosol science and technology. Volume 54:Issue 2(2020)
• Page Start: 232
• Page End: 241
• Publication Date: 2020-02-01
• Subjects: Aerosols -- Periodicals; Aerosol Propellants -- Periodicals; Aerosols -- Periodicals; 660.294515
• DOI: 10.1080/02786826.2019.1696015
• Languages: English
• ISSNs: 0278-6826

Abstract

Abstract: Low-cost sensors have become very popular in recent years for monitoring air pollutants. Commonly, they are calibrated by correlating their signals with reference instrument measurements and using a machine learning model to account for the influence of air properties. As particle properties vary over location, such calibration models are only relevant to measurements made at the calibration location during a limited time period. For a more general operation of these sensors it is critical that their measurement performance is established using the calibration approaches commonly for research grade instruments. Without loss the generality, here we conducted an experimental study with size-classified, composition and concentration varied particles to determine the response function of a popular low-cost sensor, Plantower PMS5003. The sensor response in all the size channels is analyzed using Tikhonov regularization and quadratic programing method with the constraints of nonnegative and monotonic response with particle size. We show that the shape of the response function is closely related to the light scattering response, consistent with what might be expected for an optical sensor. The response function shows that signals in all size channels have a complex dependence on particle material and size distribution. Accurate determination of particle mass and number distributions from the sensor signals in different channels is, thus, not straightforward. The response function calculation is validated by comparing sensor measured and predicted signals using polydispersed particles. The obtained response functions provide critical insight into the operation of a popular low-cost sensor and guidance on interpretation of its results. Copyright © 2019 American Association for Aerosol Research