Evaluation of correlated Pandora column NO2and in situ surface NO2measurements during GMAP campaign

Lim Seok Chang, Donghee Kim, Hyunkee Hong, Deok Rae Kim, Jeong Ah Yu, Kwangyul Lee, Hanlim Lee, Daewon Kim, Jinkyu Hong, Hyun Young Jo, Cheol Hee Kim

Research output: Contribution to journalArticlepeer-review


To validate the Geostationary Environment Monitoring Spectrometer (GEMS), the GEMS Map of Air Pollution (GMAP) campaign was conducted during 2020-2021 by integrating Pandora Asia Network, aircraft, and in situ measurements. In the present study, GMAP-2020 measurements were applied to evaluate urban air quality and explore the synergy of Pandora column (PC) NO2 measurements and surface in situ (SI) NO2 measurements for Seosan, South Korea, where large point source (LPS) emissions are densely clustered. Due to the difficulty of interpreting the effects of LPS emissions on air quality downwind of Seosan using SI monitoring networks alone, we explored the combined analysis of both PC-NO2 and SI-NO2 measurements. Agglomerative hierarchical clustering using vertical meteorological variables combined with PC-NO2 and SI-NO2 yielded three distinct conditions: synoptic wind-dominant (SD), mixed (MD), and local wind-dominant (LD). These results suggest meteorology-dependent correlations between PC-NO2 and SI-NO2. Overall, yearly daytime mean (11:00-17:00gKST) PC-NO2 and SI-NO2 statistical data showed good linear correlations (RCombining double low lineg1/40.73); however, the differences in correlations were largely attributed to meteorological conditions. SD conditions characterized by higher wind speeds and advected marine boundary layer heights suppressed fluctuations in both PC-NO2 and SI-NO2, driving a uniform vertical NO2 structure with higher correlations, whereas under LD conditions, LPS plumes were decoupled from the surface or were transported from nearby cities, weakening correlations through anomalous vertical NO2 gradients. The discrepancies suggest that using either PC-NO2 or SI-NO2 observations alone involves a higher possibility of uncertainty under LD conditions or prevailing transport processes. However, under MD conditions, both pollution ventilation due to high surface wind speeds and daytime photochemical NO2 loss contributed to stronger correlations through a decline in both PC-NO2 and SI-NO2 towards noon. Thus, Pandora Asia Network observations collected over 13 Asian countries since 2021 can be utilized for detailed investigation of the vertical complexity of air quality, and the conclusions can be also applied when performing GEMS observation interpretation in combination with SI measurements.

Original languageEnglish
Pages (from-to)10703-10720
Number of pages18
JournalAtmospheric Chemistry and Physics
Issue number16
Publication statusPublished - 2022 Aug 23

Bibliographical note

Funding Information:
In mid-2019, the Pandonia Global Network (PGN; https://pandonia-global-network.org , last access: 22 August 2022) was launched, with support from the National Aeronautics and Space Administration (NASA) and European Space Agency (ESA), to facilitate the validation and verification of low-orbit or geostationary environmental satellites. This network is attempting to expand air quality monitoring through integration with existing long-term air quality monitoring stations. Since 2020, the National Institute of Environmental Research, Economic and Social Commission for Asia and the Pacific, and Korea Environment Corporation have been extending the Pandora Asia Network to include 13 Asian countries, with support from the Korea International Cooperation Agency. The Pandora Asia Network is expected to be widely used to study urban air quality in Asia, which is increasingly deteriorating due to rapid economic growth.

Funding Information:
This study was supported by the National Institute of Environmental Research (grant nos. NIER-2021-01-01-052 and NIER-2021-03-03-001), and was partially supported by National Research Foundation of Korea (NRF) funded by the Ministry of Education of the Republic of Korea (grant nos. 2020R1I1A2075417 and 2020R1A6A1A03044834).

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All Science Journal Classification (ASJC) codes

  • Atmospheric Science


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