25 Apr 2022
25 Apr 2022
Status: this preprint is currently under review for the journal ACP.

Evaluation of correlated Pandora column observations and in situ surface air quality measurements during GMAP campaign

Lim-Seok Chang1, Donghee Kim1, Hyunkee Hong1, Deok-Rae Kim1, Jeonga Yu1, Kwangyul Lee1, Hanlim Lee2, Daewon Kim2, Jinkyu Hong3, Hyun-Young Jo4, and Cheol-Hee Kim4,5 Lim-Seok Chang et al.
  • 1Environmental Satellite Center, National Institute of Environmental Research, Incheon, 22689, Republic of Korea
  • 2Department of Spatial Information Engineering, Pukyong National University, Busan, 48547, Republic of Korea
  • 3Department of Atmospheric Sciences, Yonsei University, Seoul, 03722, Republic of Korea
  • 4Institute of Environmental Studies, Pusan National University, Busan, 46241, Republic of Korea
  • 5Department of Atmospheric Sciences, Pusan National University, Busan, 46241, Republic of Korea

Abstract. 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 used a combination of PC and SI measurements, and explored the synergy of this approach through correlation analysis of PC-NO2 and SI-NO2. 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 suggested meteorology-dependent correlations between PC-NO2 and SI-NO2. Overall, yearly daytime mean (11:00–17:00 KST) PC-NO2 and SI-NO2 statistical data showed good linear correlations (R = ~0.73); however, these correlations were dependent on meteorological conditions. SD conditions characterized by higher wind speeds and planetary 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, stack plumes decoupled from LPS or were transported from nearby cities, weakening correlations through anomalous vertical NO2 gradients. 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 toward noon. Thus, Pandora Asia Network observations collected over 13 Asian countries since 2021 can be utilized for investigation of the vertical complexity of air quality in combination with SI measurements. The results of this study also indicate that caution is required when performing GEMS validation using either PC or SI observations alone, particularly under prevailing local wind meteorological conditions or transport processes.

Lim-Seok Chang et al.

Status: open (until 06 Jun 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-170', Anonymous Referee #2, 06 May 2022 reply
  • RC2: 'Comment on acp-2022-170', Anonymous Referee #1, 06 May 2022 reply

Lim-Seok Chang et al.

Lim-Seok Chang et al.


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Short summary
Our study explored the synergy of combined column and surface measurements during the GMAP campaign, and have several points to note for performing GEMS validation, including some cautions. Particularly, under prevailing local wind meteorological conditions, where there would be frequently anomalous vertical structure of pollutants due to strong local circulations, GEMS validation should be done carefully when employing either surface measurement or column measurements alone.