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Open AccessArticle

Using Machine Learning for the Calibration of Airborne Particulate Sensors

University of Texas at Dallas, 800 W, Campbell Rd, Richardson, TX 75080, USA
Author to whom correspondence should be addressed.
Sensors 2020, 20(1), 99;
Received: 7 November 2019 / Revised: 26 November 2019 / Accepted: 10 December 2019 / Published: 23 December 2019
(This article belongs to the Special Issue Big Data Driven IoT for Smart Cities)
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. View Full-Text
Keywords: optical particle counter; airborne particulates; machine learning optical particle counter; airborne particulates; machine learning
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Wijeratne, L.O.; Kiv, D.R.; Aker, A.R.; Talebi, S.; Lary, D.J. Using Machine Learning for the Calibration of Airborne Particulate Sensors. Sensors 2020, 20, 99.

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