Abstract:This study estimates the influence of anthropogenic emission reductions on nitrogen dioxide (normalNnormalO2) and ozone (O3) concentration changes in Germany during the COVID‐19 pandemic period using in‐situ surface and Sentinel‐5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) satellite column measurements and GEOS‐Chem model simulations. We show that reductions in anthropogenic emissions in eight German metropolitan areas reduced mean in‐situ (& column) normalNnormalO2 concentrations by 23 % (& 16 %) … Show more
“…While, O 3 was significantly (p = 0.02) higher in 2020 than that in 2010–2019, which indicated that O 3 pollution was also affected by those measures. These phenomena were also comparable with previous studies ( Balamurugan et al, 2021 ; Cazorla et al, 2021 ; Sicard et al, 2020 ). Fig.…”
“…While, O 3 was significantly (p = 0.02) higher in 2020 than that in 2010–2019, which indicated that O 3 pollution was also affected by those measures. These phenomena were also comparable with previous studies ( Balamurugan et al, 2021 ; Cazorla et al, 2021 ; Sicard et al, 2020 ). Fig.…”
“…This performance improvement could be attributed to the pronounced seasonal cycle of ozone and weekday-weekend differences. The ozone reaches its maximum in summer and minimum in winter, and due to being in a NO saturated regime, weekend ozone levels are higher than the weekdays 13 . The ML algorithm trained solely with CAMS (“ML_cams”) or in-situ precursors (“ML_insitu”) show poor performance in all terms when compared to ML algorithm trained with the meteorology category alone (“ML_met_ds”) (Fig.…”
Section: Resultsmentioning
confidence: 98%
“…Meanwhile, VOC emissions from volatile chemical products such as cleaning agents and personal care products are becoming more significant 12 . Recent ozone enhancements in urban areas during the COVID-19 lockdown period demonstrate the NO saturated regime’s ozone production chemistry 13 , 14 . Chemical transport models (CTM) are widely used to study the ozone variability 15 – 19 .…”
Surface ozone (O$$_3$$
3
) is primarily formed through complex photo-chemical reactions in the atmosphere, which are non-linearly dependent on precursors. Even though, there have been many recent studies exploring the potential of machine learning (ML) in modeling surface ozone, the inclusion of limited available ozone precursors information has received little attention. The ML algorithm with in-situ NO information and meteorology explains 87% (R$$^{2}$$
2
= 0.87) of the ozone variability over Munich, a German metropolitan area, which is 15% higher than a ML algorithm that considers only meteorology. The ML algorithm trained for the urban measurement station in Munich can also explain the ozone variability of the other three stations in the same city, with R$$^{2}$$
2
= 0.88, 0.91, 0.63. While the same model robustly explains the ozone variability of two other German cities’ (Berlin and Hamburg) measurement stations, with R$$^{2}$$
2
ranges from 0.72 to 0.84, giving confidence to use the ML algorithm trained for one location to other locations with sparse ozone measurements. The inclusion of satellite O$$_3$$
3
precursors information has little effect on the ML model’s performance.
“…The increasing capability of satellite monitoring from space has enabled studies to better characterize pollution patterns, such as analyzing the responses of trace gases to either anthropogenic or biogenic sources (Balamurugan et al., 2021; Pu et al., 2022; R. Zhang et al., 2020) and investigating regional emission trends (DiMaria et al., 2023; Krotkov et al., 2016; J. Li & Wang, 2019). The uncertainties of satellite products consist of both systematic and random components.…”
Section: Introductionmentioning
confidence: 99%
“…Compared with surface measurements, which are limited in spatiotemporal coverages, satellite observations of tropospheric vertical column densities (TVCDs) can provide continuous data sets with a broad spatial range. The Ozone Monitoring Instrument (OMI) and the TROPOspheric Monitoring Instrument (TROPOMI) have provided continuous measurements of NO 2 and HCHO to advance our understanding of the related atmospheric chemical processes (Balamurugan et al., 2021; Lamsal et al., 2010; D. Li et al., 2021; J. Li & Wang, 2019; H. J. R. Wang et al., 2020; Zeng et al., 2008; Y. Zhang et al., 2018). OMI aboard NASA's Aura satellite has been in orbit since 2004.…”
Nitrogen dioxide (NO2) and formaldehyde (HCHO) play vital roles in atmospheric photochemical processes. Their tropospheric vertical column density (TVCD) distributions have been monitored by satellite instruments. Evaluation of these observations is essential for applying these observations to study photochemistry. Assessing satellite products using observations at rural sites, where local emissions are minimal, is particularly useful due in part to the spatial homogeneity of trace gases. In this study, we evaluate OMI and TROPOMI NO2 and HCHO TVCDs using multi‐axis differential optical absorption spectroscopy (MAX‐DOAS) measurements at a rural site in the east coast of the Shandong province, China in spring 2018 during the Ozone Photochemistry and Export from China Experiment (OPECE) measurement campaign. On days not affected by local burning, we found generally good agreement of NO2 data after using consistent a priori profiles in satellite and MAX‐DOAS retrievals and accounting for low biases in scattering weights in one of the OMI products. In comparison, satellite HCHO products exhibited weaker correlations with MAX‐DOAS data, in contrast to satellite NO2 products. However, TROPOMI HCHO products showed significantly better agreement with MAX‐DOAS measurements compared to OMI data. Furthermore, case studies of the vertical profiles measured by MAX‐DOAS on burning days revealed large enhancements of nitrous acid (HONO), NO2, and HCHO in the upper boundary layer, accompanied with considerable variability, particularly in HONO enhancements.
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