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http://dx.doi.org/10.22156/CS4SMB.2021.11.08.092

An Asian Dust Compensation Scheme of Light-Scattering Fine Particulate Matter Monitors by Multiple Linear Regression  

Baek, Sung Hoon (Department of Compuer Engineering, Jungwon University)
Publication Information
Journal of Convergence for Information Technology / v.11, no.8, 2021 , pp. 92-99 More about this Journal
Abstract
Light-scattering fine particulate matter monitors can measure particulate matter (PM) concentrations in every second and can be designed in a portable size. They can measure the concentrations of various PM sizes (PM1.0, PM2.5, PM4.0 and PM10) with a single sensor. They measure the number and size of particulate matters and convert them to weight per volume (concentration). These devices show a large error for asian dust. This paper proposes a scheme that compensates the PM2.5 concenstration error for asian dust by multiple linear regression machine learning in light-scattering PM monitors. This scheme can be effective with only two or three types of PM sizes. The experimental results compare a beta-ray PM monitor of national institute of environmental research and a light-scattering PM monitor during a month. The correlation coefficient (R2) of theses two devices was 0.927 without asian dust, but it was 0.763 due to asian dust during the entire experimental period and improved to 0.944 by the proposed machine learning.
Keywords
Machine learning; Fine particulate matter; Yellow dust; Sensor; Convergence;
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