Bu-Yo Kim  This email address is being protected from spambots. You need JavaScript enabled to view it., Joo Wan Cha, Yong Hee Lee 

Research Applications Department, National Institute of Meteorological Sciences, Seogwipo, Jeju 63568, Korea

Received: February 12, 2023
Revised: September 1, 2023
Accepted: October 20, 2023

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.4209/aaqr.230033  

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Cite this article:

Kim, B.Y., Cha, J.W., Lee, Y.H. (2023). Estimation of PM10 and PM2.5 Using Backscatter Coefficient of Ceilometer and Machine Learning. Aerosol Air Qual. Res. 23, 230033. https://doi.org/10.4209/aaqr.230033


  • Particulate matter (PM)10 and PM2.5 were estimated using ceilometer raw data.
  • PM was estimated with meteorological data and machine learning (ML) methods.
  • Comparative analysis of PM estimated using each ML method was performed.
  • Hourly PM estimation is better than linear and exponential regression methods.


Air quality issues, including health and environmental challenges, have recently become more relevant in urban areas with large populations and active industries. Therefore, particulate matter (PM) estimation with high accuracy using various methods is required. In this study, PM10 and PM2.5 in Cheongju city, South Korea, were estimated using the attenuated backscatter coefficient of the ceilometer and meteorological observation data from an automatic weather station with supervised machine learning (ML). The backscatter coefficient data were obtained from the vertical layer with the highest correlation with PM10 and PM2.5. The estimation methods utilized were tree-, vector-, neural-, and regularization-based supervised ML. The extreme gradient boosting method yielded the highest PM estimation accuracy. The estimation of PM10 and PM2.5 for the test data set was more accurate than that in previous studies that used satellite and ground-based meteorological data (bias = 0.10 µg m–3, root mean square error (RMSE) = 14.44 µg m–3, and R = 0.92 for PM10; and bias = 0.12 µg m–3, RMSE = 7.16 µg m–3, and R = 0.91 for PM2.5). Particularly, the correlation coefficient was the highest for the estimation results for strong haze cases (1 km < visibility ≤ 5 km) (R = 0.95 for PM10; R = 0.89 for PM2.5). Therefore, PM estimation using meteorological observation data can help obtain meteorological and PM information simultaneously, making it useful for air quality monitoring.

Keywords: PM10, PM2.5, Ceilometer, Backscatter coefficient, Machine learning, Extreme gradient boosting

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