Evaluation of correlated Pandora column NO2 and in situ surface NO2 measurements during GMAP campaign

• Published in: Atmospheric chemistry and physics 2022-08, Vol.22 (16), p.10703-10720
• Main Authors: Lim-Seok, Chang, Kim, Donghee, Hong, Hyunkee, Deok-Rae Kim, Jeong-Ah Yu, Lee, Kwangyul, Lee, Hanlim, Kim, Daewon, Hong, Jinkyu, Hyun-Young, Jo, Cheol-Hee, Kim
• Format: Article
• Language: English
• Subjects: Aerosols; Air pollution; Air quality; Air quality measurements; Aircraft; Boundary layer height; Boundary layers; Cluster analysis; Clustering; Coal-fired power plants; Correlation; Daytime; Driving ability; Emissions; Environmental monitoring; Evaluation; In situ measurement; Industrial plant emissions; Local winds; Meteorological conditions; Meteorology; Nitrogen dioxide; Outdoor air quality; Photochemicals; Photochemistry; Plumes; Point source pollution; Pollution dispersion; Pollution monitoring; Remote sensing; Satellites; Surface wind; Transport processes; Urban air; Urban air quality; Ventilation; Water pollution; Wind; Wind speed
• ISSN: 1680-7316; 1680-7324
• DOI: 10.5194/acp-22-10703-2022

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 (11:00–17:00 KST) PC-NO2 and SI-NO2 statistical data showed good linear correlations (R=∼0.73); 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.