Preprints
https://doi.org/10.5194/amt-2024-142
https://doi.org/10.5194/amt-2024-142
26 Aug 2024
 | 26 Aug 2024
Status: this preprint is currently under review for the journal AMT.

Estimating hourly ground-level aerosols using GEMS aerosol optical depth: A machine learning approach

Sungmin O, Ji Won Yoon, and Seon Ki Park

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) is the world's first ultraviolet–visible instrument for air quality monitoring in geostationary orbit. Since its launch in 2020, GEMS has provided hourly daytime air quality information over Asia. However, to date, validation and applications of these data are lacking. Here we evaluate the effectiveness of the first 1.5-year GEMS aerosol optical depth (AOD) data in estimating ground-level particulate matter (PM) concentrations at an hourly scale. To do so, we employ random forest models and use GEMS AOD data and meteorological variables as input features to estimate PM10 and PM2.5 concentrations, respectively, in South Korea. The model-estimated PM concentrations are strongly correlated with ground measurements, but they exhibit negative biases, particularly during high aerosol loading months. Our results indicate that GEMS AOD values represent underestimates compared to ground-measured AOD values, possibly leading to negative biases in the final PM estimates. Further, we demonstrate that more training data could significantly improve random forest model performance, thus indicating the potential of GEMS for high-resolution surface PM prediction when sufficient data are accumulated over the coming years. Our results will serve as a reference to aid the evaluation of future GEMS AOD retrieval algorithm improvements and also provide initial guidance for data users.

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Sungmin O, Ji Won Yoon, and Seon Ki Park

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2024-142', Anonymous Referee #1, 16 Sep 2024
  • RC2: 'Comment on amt-2024-142', Anonymous Referee #2, 25 Sep 2024
Sungmin O, Ji Won Yoon, and Seon Ki Park
Sungmin O, Ji Won Yoon, and Seon Ki Park

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Short summary
Air pollutants such as PM10 or PM2.5 can cause adverse public health and environment effects, therefore their regular monitoring is crucial to keep the pollutant concentrations under control. Our study demonstrates the potential of high-resolution aerosol optical depth (AOD) data from the Geostationary Environment Monitoring Spectrometer (GEMS) satellite to estimate ground-level PM concentrations using a machine learning model.