Next Article in Journal
Quantification of Axial Abnormality Due to Cerebellar Ataxia with Inertial Measurements
Previous Article in Journal
Colony Fingerprint-Based Discrimination of Staphylococcus species with Machine Learning Approaches
Article

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

1
Centre for Atmospheric Science, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UK
2
Alphasense Ltd., Sensor Technology House, 300 Avenue West, Skyline 120, Great Notley, Essex CM77 7AA, UK
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(9), 2790; https://doi.org/10.3390/s18092790
Received: 29 June 2018 / Revised: 3 August 2018 / Accepted: 22 August 2018 / Published: 24 August 2018
(This article belongs to the Section Chemical Sensors)
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 than PM mass, is required to properly account for RH effects and enable low cost optical PM sensors to provide reliable ambient PM measurements. View Full-Text
Keywords: air pollution; environmental monitoring; low cost sensors; particulate matter; relative humidity correction air pollution; environmental monitoring; low cost sensors; particulate matter; relative humidity correction
Show Figures

Figure 1

MDPI and ACS Style

Di Antonio, A.; Popoola, O.A.M.; Ouyang, B.; Saffell, J.; Jones, R.L. Developing a Relative Humidity Correction for Low-Cost Sensors Measuring Ambient Particulate Matter. Sensors 2018, 18, 2790. https://doi.org/10.3390/s18092790

AMA Style

Di Antonio A, Popoola OAM, Ouyang B, Saffell J, Jones RL. Developing a Relative Humidity Correction for Low-Cost Sensors Measuring Ambient Particulate Matter. Sensors. 2018; 18(9):2790. https://doi.org/10.3390/s18092790

Chicago/Turabian Style

Di Antonio, Andrea, Olalekan A.M. Popoola, Bin Ouyang, John Saffell, and Roderic L. Jones 2018. "Developing a Relative Humidity Correction for Low-Cost Sensors Measuring Ambient Particulate Matter" Sensors 18, no. 9: 2790. https://doi.org/10.3390/s18092790

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop