Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast Asia

Seohui Park, Jungho Im, Jhoon Kim, Sang Min Kim

Research output: Contribution to journalArticlepeer-review

Abstract

Rapid economic growth, industrialization, and urbanization have caused frequent air pollution events in East Asia over the last few decades. Recently, aerosol data from geostationary satellite sensors have been used to monitor ground-level particulate matter (PM) concentrations hourly. However, many studies have focused on using historical datasets to develop PM estimation models, often decreasing their predictability for unseen data in new days. To mitigate this problem, this study proposes a novel real-time learning (RTL) approach to estimate PM with aerodynamic diameters of <10 μm (PM10) and <2.5 μm (PM2.5) using hourly aerosol data from the Geostationary Ocean Color Imager (GOCI) and numerical model outputs for daytime conditions over Northeast Asia. Three schemes with different weighting strategies were evaluated using 10-fold cross-validation (CV). The RTL models, which considered both concentration and time as weighting factors (i.e., Scheme 3) yielded consistent improvement for 10-fold CV performance on both hourly and monthly scales. The real-time calibration results for PM10 and PM2.5 were R2 = 0.97 and 0.96, and relative root mean square error (rRMSE) = 12.1% and 12.0%, respectively, and the 10-fold CV results for PM10 and PM2.5 were R2 = 0.73 and 0.69 and rRMSE = 41.8% and 39.6%, respectively. These results were superior to results from the offline models in previous studies, which were based on historical data on an hourly scale. Moreover, we estimated PM concentrations in the ocean without using land-based variables, and clearly demonstrated the PM transport over time. Because the proposed models are based on the RTL approach, the density of in-situ monitoring sites could be a major uncertainty factor. This study identified that a high error occurred in low-density areas, whereas a low error occurred in high-density areas. The proposed approach can be operated to monitor ground-level PM concentrations in real-time with uncertainty analysis to ensure optimal results.

Original languageEnglish
Article number119425
JournalEnvironmental Pollution
Volume306
DOIs
Publication statusPublished - 2022 Aug 1

Bibliographical note

Funding Information:
This work was supported by a grant from the National Institute of Environment Research ( NIER ), funded by the Ministry of Environment (MOE) of the Republic of Korea ( NIER-SP2021-01-02-061 ), by the Fine Particle Research Initiative in East Asia Considering National Differences (FRIEND) Project through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (Grant No.: 2020M3G1A1114615 ), and by Korea Environment Industry & Technology Institute ( KEITI ) through Digital Infrastructure Building Project for Monitoring, Surveying and Evaluating the Environmental Health, funded by Korea Ministry of Environment (MOE) ( 2021003330001(NTIS: 1485017948 )).

Publisher Copyright:
© 2022 Elsevier Ltd

All Science Journal Classification (ASJC) codes

  • Toxicology
  • Pollution
  • Health, Toxicology and Mutagenesis

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