Journal
SENSORS
Volume 20, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/s20010099
Keywords
optical particle counter; airborne particulates; machine learning
Funding
- USAMRMC [W81XWH-18-1-0400]
- National Science Foundation CNS Division of Computer and Network Systems [1541227]
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1541227] Funding Source: National Science Foundation
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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.
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