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Using Machine Learning for the Calibration of Airborne Particulate Sensors
2019
Sensors
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
doi:10.3390/s20010099
pmid:31877977
pmcid:PMC6982762
fatcat:dbuztyxtfbamrjnl354edhr3s4