Developing a Relative Humidity Correction for Low-Cost Sensors Measuring Ambient Particulate Matter

Sensors
Andrea Di AntonioRoderic L Jones

Abstract

There is increasing concern about the health impacts of ambient Particulate Matter (PM) exposure. Traditional monitoring networks, because of their sparseness, cannot provide sufficient spatial-temporal measurements characteristic of ambient PM. Recent studies have shown portable low-cost devices (e.g., optical particle counters, OPCs) can help address this issue; however, their application under ambient conditions can be affected by high relative humidity (RH) conditions. Here, we show how, by exploiting the measured particle size distribution information rather than PM as has been suggested elsewhere, a correction can be derived which not only significantly improves sensor performance but which also retains fundamental information on particle composition. A particle size distribution⁻based correction algorithm, founded on κ -Köhler theory, was developed to account for the influence of RH on sensor measurements. The application of the correction algorithm, which assumed physically reasonable κ values, resulted in a significant improvement, with the overestimation of PM measurements reduced from a factor of ~5 before correction to 1.05 after correction. We conclude that a correction based on particle size distribution, rather t...Continue Reading

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Citations

Oct 11, 2019·Paediatric and Perinatal Epidemiology·Audrey J Gaskins, Jaime E Hart
Mar 12, 2019·Journal of Environmental and Public Health·Michael ZhangDavid F Jativa
Nov 22, 2020·Environmental Science and Pollution Research International·Hongsheng ZhangZhengwei Long
Feb 17, 2021·Journal of the Air & Waste Management Association·Jai PrakashPratim Biswas
Jan 1, 2020·Atmospheric Measurement Techniques·David H Hagan, Jesse H Kroll
Sep 12, 2020··Lin ZhangGuodong Li

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Methods Mentioned

BETA
light scattering
Alphasense

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