Comparison of pm2.5 in seoul, korea estimated from the various ground-based and satellite aod

Sang Min Kim, Ja Ho Koo, Hana Lee, Jungbin Mok, Myungje Choi, Sujung Go, Seoyoung Lee, Yeseul Cho, Jaemin Hong, Sora Seo, Junhong Lee, Je Woo Hong, Jhoon Kim

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Based on multiple linear regression (MLR) models, we estimated the PM2.5 at Seoul using a number of aerosol optical depth (AOD) values obtained from ground-based and satellite remote sensing observations. To construct the MLR model, we consider various parameters related to the ambient meteorology and air quality. In general, all AOD values resulted in the high quality of PM2.5 estimation through the MLR method: mostly correlation coefficients >~0.8. Among various polar-orbit satellite AODs, AOD values from the MODIS measurement contribute to better PM2.5 estimation. We also found that the quality of estimated PM2.5 shows some seasonal variation; the estimated PM2.5 values consistently have the highest correlation with in situ PM2.5 in autumn, but are not well established in winter, probably due to the difficulty of AOD retrieval in the winter condition. MLR modeling using spectral AOD values from the ground-based measurements revealed that the accuracy of PM2.5 estimation does not depend on the selected wavelength. Although all AOD values used in this study resulted in a reasonable accuracy range of PM2.5 estimation, our analyses of the difference in estimated PM2.5 reveal the importance of utilizing the proper AOD for the best quality of PM2.5 estimation.

Original languageEnglish
Article number10755
JournalApplied Sciences (Switzerland)
Volume11
Issue number22
DOIs
Publication statusPublished - 2021 Nov

Bibliographical note

Funding Information:
Acknowledgments: This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (2021-0-00459, Development of a Service Platform for Improving the Accuracy of Particulate Matter Forecast). This work was also sup-ported by a grant from the National Institute of Environmental Research (NIER), funded by the Ministry of Environment (MOE) of the Republic of Korea (NIER-2021-01-01-100).

Funding Information:
Funding: This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (2021-0-00459, Development of a Service Platform for Improving the Accuracy of Particulate Matter Forecast). This work was also supported by a grant from the National Institute of Environmental Research (NIER), funded by the Ministry of Environment (MOE) of the Republic of Korea (NIER-2021-01-01-100).

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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