MAX-DOAS measurements of tropospheric NO2_{2} and HCHO in Munich and the comparison to OMI and TROPOMI satellite observations

We present two-dimensional scanning Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) observations of nitrogen dioxide (NO$_{2}$) and formaldehyde (HCHO) in Munich. Vertical columns and vertical distribution profiles of aerosol extinction coefficient, NO$_{2}$ and HCHO are retrieved from the 2D MAX-DOAS observations. The measured surface aerosol extinction coefficients and NO$_{2}$ mixing ratios derived from the retrieved profiles are compared to in situ monitoring data, and the surface NO$_{2}$ mixing ratios show a good agreement with in situ monitoring data with a Pearson correlation coefficient (R) of 0.91. The aerosol optical depths (AODs) show good agreement as well (R = 0.80) when compared to sun photometer measurements. Tropospheric vertical column densities (VCDs) of NO$_{2}$ and HCHO derived from the MAX-DOAS measurements are also used to validate Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) satellite observations. Monthly averaged data show a good correlation; however, satellite observations are on average 30 % lower than the MAX-DOAS measurements. Furthermore, the MAX-DOAS observations are used to investigate the spatiotemporal characteristic of NO$_{2}$ and HCHO in Munich. Analysis of the relations between aerosol, NO$_{2}$ and HCHO shows higher aerosol-to-HCHO ratios in winter, which reflects a longer atmospheric lifetime of secondary aerosol and HCHO during winter. The analysis also suggests that secondary aerosol formation is the major source of these aerosols in Munich

Regulated large-scale annual shutdown of Amazonian isoprene emissions?

We perform Empirical Orthogonal Function (EOF) analysis on 12 years of global GOME and SCIAMACHY formaldehyde (HCHO) column observations to determine the most significant spatial and temporal HCHO variations. In most regions, we find that HCHO variability is predominantly driven by seasonal variations of biogenie emissions and biomass burning. However, unusually low HCHO columns are consistently observed over the Amazon rainforest during the transition from the wet-to-dry seasons. We use MODIS leaf area and enhanced vegetation indices, to show variations in vegetation are consistent with the observed decrease in HCHO during this period (correlations of 0.69 and 0.67, respectively). Based on this evidence, we suggest isoprene emitting vegetation experience widespread leaf flushing (new leaf growth) prior to the dry season, resulting in a large-scale annual shutdown of Amazonian isoprene emissions. © 2009

Can a “state of the art” chemistry transport model simulate Amazonian tropospheric chemistry?

We present an evaluation of a nested high-resolution Goddard Earth Observing System (GEOS)-Chem chemistry transport model simulation of tropospheric chemistry over tropical South America. The model has been constrained with two isoprene emission inventories: (1) the canopy-scale Model of Emissions of Gases and Aerosols from Nature (MEGAN) and (2) a leaf-scale algorithm coupled to the Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) dynamic vegetation model, and the model has been run using two different chemical mechanisms that contain alternative treatments of isoprene photo-oxidation. Large differences of up to 100 Tg C yr^(−1) exist between the isoprene emissions predicted by each inventory, with MEGAN emissions generally higher. Based on our simulations we estimate that tropical South America (30–85°W, 14°N–25°S) contributes about 15–35% of total global isoprene emissions. We have quantified the model sensitivity to changes in isoprene emissions, chemistry, boundary layer mixing, and soil NO_x emissions using ground-based and airborne observations. We find GEOS-Chem has difficulty reproducing several observed chemical species; typically hydroxyl concentrations are underestimated, whilst mixing ratios of isoprene and its oxidation products are overestimated. The magnitude of model formaldehyde (HCHO) columns are most sensitive to the choice of chemical mechanism and isoprene emission inventory. We find GEOS-Chem exhibits a significant positive bias (10–100%) when compared with HCHO columns from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) and Ozone Monitoring Instrument (OMI) for the study year 2006. Simulations that use the more detailed chemical mechanism and/or lowest isoprene emissions provide the best agreement to the satellite data, since they result in lower-HCHO columns

Comparison of Cloud Parameters from GOME-2 and Assessment of Cloud Impact on Tropospheric NO2 and HCHO Retrievals

In recent decades, there has been an increasing interest in making use of satellite measurements for identifying trends in atmospheric composition and climate. Instruments like GOME-2 and TROPOMI are dedicated to air-quality and global trace gas monitoring. For the accurate retrieval of columnar information of the trace gases, cloud correction is necessary. This work is meant to examine the quality of the GOME-2 operational cloud product from AC SAF and to propose enhancements of the current dataset to improve the retrieval of the NO2 and HCHO tropospheric gases

Global Monitoring of Volcanic SO2 Degassing Using Sentinel-5 Precursor Tropomi

We present here the TROPOMI SO 2 product, which is publicly available since April 2018. We describe the capabilities and limitations of the product for the monitoring of volcanic SO 2 degassing. With several examples, we illustrate the benefit of a small satellite pixel of 3.5 x 5.5 km 2 . Owing to its improved detection limit, the data can be used to generate time series of SO 2 mass over number of volcanoes, with a large range of SO 2 emissions. We use Nyiragongo as a show case and correlate the SO 2 mass data with lava lake level estimates and local measurements of the seismicity. This paper also presents on-going developments to further improve the performance of the product for weak SO 2 loadings using a new algorithm, COBRA

Bias correction of OMI HCHO columns based on FTIR and aircraft measurements and impact on top-down emission estimates

Spaceborne formaldehyde (HCHO) measurements constitute an excellent proxy for the sources of non-methane volatile organic compounds (NMVOCs). Past studies suggested substantial overestimations of NMVOC emissions in state-of-the-art inventories over major source regions. Here, the QA4ECV (Quality Assurance for Essential Climate Variables) retrieval of HCHO columns from OMI (Ozone Monitoring Instrument) is evaluated against (1) FTIR (Fourier-transform infrared) column observations at 26 stations worldwide and (2) aircraft in situ HCHO concentration measurements from campaigns conducted over the USA during 2012–2013. Both validation exercises show that OMI underestimates high columns and overestimates low columns. The linear regression of OMI and aircraft-based columns gives Ω$_{OMI}$=0,651 Ω$_{airc}$+2,95 x 10$^{15}$, molec. cm$^{-2}$ , with Ω$_{OMI}$ and Ω$_{airc}$ the OMI and aircraft-derived vertical columns, whereas the regression of OMI and FTIR data gives Ω$_{OMI}$= 6,59 Ω$_{FTIR}$ + 2.02 x 10$^{15}$, molec. cm$^{-2}$ . Inverse modelling of NMVOC emissions with a global model based on OMI columns corrected for biases based on those relationships leads to much-improved agreement against FTIR data and HCHO concentrations from 11 aircraft campaigns. The optimized global isoprene emissions ($\sim$ 445 Tgyr$^{-1}$) are 25 % higher than those obtained without bias correction. The optimized isoprene emissions bear both striking similarities and differences with recently published emissions based on spaceborne isoprene columns from the CrIS (Cross-track Infrared Sounder) sensor. Although the interannual variability of OMI HCHO columns is well understood over regions where biogenic emissions are dominant, and the HCHO trends over China and India clearly reflect anthropogenic emission changes, the observed HCHO decline over the southeastern USA remains imperfectly elucidated

TROPOMI–Sentinel-5 Precursor formaldehyde validation using an extensive network of ground-based Fourier-transform infrared stations

TROPOMI (the TROPOspheric Monitoring Instrument), on board the Sentinel-5 Precursor (S5P) satellite, has been monitoring the Earth\u27s atmosphere since October 2017 with an unprecedented horizontal resolution (initially 7 km$^{2}$×3.5 km$^{2}$, upgraded to 5.5 km$^{2}$×3.5 km$^{2}$ in August 2019). Monitoring air quality is one of the main objectives of TROPOMI; it obtains measurements of important pollutants such as nitrogen dioxide, carbon monoxide, and formaldehyde (HCHO). In this paper we assess the quality of the latest HCHO TROPOMI products versions 1.1.(5-7), using ground-based solar-absorption FTIR (Fourier-transform infrared) measurements of HCHO from 25 stations around the world, including high-, mid-, and low-latitude sites. Most of these stations are part of the Network for the Detection of Atmospheric Composition Change (NDACC), and they provide a wide range of observation conditions, from very clean remote sites to those with high HCHO levels from anthropogenic or biogenic emissions. The ground-based HCHO retrieval settings have been optimized and harmonized at all the stations, ensuring a consistent validation among the sites. In this validation work, we first assess the accuracy of TROPOMI HCHO tropospheric columns using the median of the relative differences between TROPOMI and FTIR ground-based data (BIAS). The pre-launch accuracy requirements of TROPOMI HCHO are 40 %–80 %. We observe that these requirements are well reached, with the BIAS found below 80 % at all the sites and below 40 % at 20 of the 25 sites. The provided TROPOMI systematic uncertainties are well in agreement with the observed biases at most of the stations except for the highest-HCHO-level site, where it is found to be underestimated. We find that while the BIAS has no latitudinal dependence, it is dependent on the HCHO concentration levels: an overestimation (+26±5 %) of TROPOMI is observed for very low HCHO levels (8.0×10$^{15}$ molec. cm$^{-2}$). This demonstrates the great value of such a harmonized network covering a wide range of concentration levels, the sites with high HCHO concentrations being crucial for the determination of the satellite bias in the regions of emissions and the clean sites allowing a small TROPOMI offset to be determined. The wide range of sampled HCHO levels within the network allows the robust determination of the significant constant and proportional TROPOMI HCHO biases (TROPOMI =+1.10±0.05 ×10$^{15}$+0.64±0.03 × FTIR; in molecules per square centimetre). Second, the precision of TROPOMI HCHO data is estimated by the median absolute deviation (MAD) of the relative differences between TROPOMI and FTIR ground-based data. The clean sites are especially useful for minimizing a possible additional collocation error. The precision requirement of 1.2×10$^{16}$ molec. cm$^{-2}$ for a single pixel is reached at most of the clean sites, where it is found that the TROPOMI precision can even be 2 times better (0.5–0.8×10$^{15}$ molec. cm$^{-2}$ for a single pixel). However, we find that the provided TROPOMI random uncertainties may be underestimated by a factor of 1.6 (for clean sites) to 2.3 (for high HCHO levels). The correlation is very good between TROPOMI and FTIR data (R=0.88 for 3 h mean coincidences; R=0.91 for monthly means coincidences). Using about 17 months of data (from May 2018 to September 2019), we show that the TROPOMI seasonal variability is in very good agreement at all of the FTIR sites. The FTIR network demonstrates the very good quality of the TROPOMI HCHO products, which is well within the pre-launch requirements for both accuracy and precision. This paper makes suggestions for the refinement of the TROPOMI random uncertainty budget and TROPOMI quality assurance values for a better filtering of the remaining outliers

Air quality impacts of COVID-19 lockdown measures detected from space using high spatial resolution observations of multiple trace gases from Sentinel-5P/TROPOMI

The aim of this paper is to highlight how TROPOspheric Monitoring Instrument (TROPOMI) trace gas data can best be used and interpreted to understand event-based impacts on air quality from regional to city scales around the globe. For this study, we present the observed changes in the atmospheric column amounts of five trace gases (NO2, SO2, CO, HCHO, and CHOCHO) detected by the Sentinel-5P TROPOMI instrument and driven by reductions in anthropogenic emissions due to COVID-19 lockdown measures in 2020. We report clear COVID-19-related decreases in TROPOMI NO2 column amounts on all continents. For megacities, reductions in column amounts of tropospheric NO2 range between 14 % and 63 %. For China and India, supported by NO2 observations, where the primary source of anthropogenic SO2 is coal-fired power generation, we were able to detect sector-specific emission changes using the SO2 data. For HCHO and CHOCHO, we consistently observe anthropogenic changes in 2-week-averaged column amounts over China and India during the early phases of the lockdown periods. That these variations over such a short timescale are detectable from space is due to the high resolution and improved sensitivity of the TROPOMI instrument. For CO, we observe a small reduction over China, which is in concert with the other trace gas reductions observed during lockdown; however, large interannual differences prevent firm conclusions from being drawn. The joint analysis of COVID-19-lockdown-driven reductions in satellite-observed trace gas column amounts using the latest operational and scientific retrieval techniques for five species concomitantly is unprecedented. However, the meteorologically and seasonally driven variability of the five trace gases does not allow for drawing fully quantitative conclusions on the reduction in anthropogenic emissions based on TROPOMI observations alone. We anticipate that in future the combined use of inverse modeling techniques with the high spatial resolution data from S5P/TROPOMI for all observed trace gases presented here will yield a significantly improved sector-specific, space-based analysis of the impact of COVID-19 lockdown measures as compared to other existing satellite observations. Such analyses will further enhance the scientific impact and societal relevance of the TROPOMI mission

From the Ethnic History of Asia – the Dōnghú, Wūhuán and Xiānbēi Proto-Mongolian Tribes

Cilj je ovog članka prikazati povijest protomongolskih plemena Dōnghú, Wūhuán i Xiānbēi od 4. stoljeća pr. Kr. do kraja 3. stoljeća po. Kr. Povijest drevnih nomadskih naroda koji su živjeli sjeverno od Kine zapisana je u kineskim dinastijskim kronikama. Protomongolska plemena 1. tisućljeća pr. Kr. u kineskim se izvorima nazivaju Dōnghú. Najstarije vijesti o njima potječu iz razdoblja Zaraćenih država (4. – 3. st. pr. Kr.), a govore o sukobu sa sjevernim kineskim državama. Druga vrsta izvora za povijest protomongolskih plemena arheološki su nalazi, koji mongolsku etnogenezu povezuju s kulturama pločastih grobova, i Mlađi Xiàjiādiàn. Lingvisti građu za istraživanje mongolske etnogeneze pronalaze u altajskoj jezičnoj porodici, kojoj pripada i mongolski jezik. U radu se na temelju navedenih izvora opisuje promjena političke situacije u stepi krajem 3. stoljeća pr. Kr., kada narod Xiōngnú stvara moćnu državu i pokorava Dōnghúe. Ostatke razbijenih Dōnghúa, koji su najvećim dijelom migrirali na sjever, kineske kronike bilježe pod novim topoetnonimima – Xiānbēi i Wūhuán. Slabljenje i pad države Xiōngnúa omogućili su protomongolskim plemenima ponovni izlazak na povijesnu scenu. Kinesko carstvo Hàn uspostavilo je krajem 1. stoljeća pr. Kr. najprije odnose s plemenima Wūhuán, a sredinom 1. stoljeća po. Kr. i s plemenima Xiānbēi. Oba plemenska saveza u početku su priznavala vrhovnu vlast Kine i obavljala graničarsku službu. Pod vodstvom plemenskih starješina u 2. stoljeću po. Kr. počela su voditi samostalnu politiku i napadati pogranična kineska područja. U zaključnom dijelu rada govori se o vremenu kada su plemena Wūhuán i Xiānbēi bila na vrhuncu moći. No već početkom 3. stoljeća Wūhuáni su potpali pod vlast Kineza i Xiānbēija; plemenski savez Xiānbēi raspao se u drugoj polovini 3. stoljeća.The aim of this paper is to present the history of the Dōnghú, Wūhuán and Xiānbēi Proto-Mongolian tribes in the period from the 4th century B.C. to the end of the 3rd century A.D. The history of the ancient nomadic peoples who lived north of China is written in Chinese dynasty chronicles. Proto-Mongolian tribes from the 1st century B.C. are called Dōnghú in Chinese sources. The earliest news on them originates from the Warring States Period (4th – 3rd century B.C.), and tells of a conflict with the northern Chinese states. Other types of sources on the history of the Proto-Mongolian tribes are archaeological findings, which associate Mongolian ethnogenesis with slab grave cultures and the Lower Xiàjiādiàn. Linguists find the materials for the research on Mongolian ethnogenesis in the Altaic linguistic family, which the Mongolian language belongs to as well. Based on the mentioned sources, the change in the political situation in the steppes at the end of the 3rd century B.C., when the people of Xiōngnú created a powerful state and conquered the Dōnghúes, is described in the paper. The remains of the shattered Dōnghúes, who had mostly migrated to the north, have been recorded in Chinese chronicles under new topoethnonyms: Xiānbēi and Wūhuán. The weakening and fall of the Xiōngnúes’ state enabled the Proto-Mongolian tribes to re-enter the historical scene. At the end of the 1st century B.C. the Chinese Hàn Empire firstly established relations with the Wūhuán tribes and in the middle of the 1st century A.D. with the Xiānbēi tribes, too. In the beginning both tribal alliances acknowledged the supreme authority of China and carried out frontier service. Under the guidance of tribal chiefs the tribes started to run an independent policy and attack China’s border areas during the 2nd century A.D. In the conclusion, the author describes the period when the Wūhuán and Xiānbēi tribes were at the peak of their power. However, already at the beginning of the 3rd century, the Wūhuáns fell under the authorities of China and Xiānbēi, but the Xiānbēi tribal alliance fell apart in the second half of the 3rd century