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Stratospheric bromine compounds are much less abundant than chlorine compounds but their ozone depletion efficiency is roughly 45 times larger than that of chlorine. Among inorganic bromine species, BrO is the most abundant species (around 60 – 70 %), and it can be measured only by UV/Vis remote sensing instruments. Very recent research reported that ozone depleting substances have been strongly reduced, which indicates an ozone recovery. However, the evolution of the BrO concentration in the stratosphere is still worth to be investigated due to the lack of knowledge of the bromine releasing mechanisms and chemical recycling processes. This study shows the temporal variation of stratospheric BrO from 1997 to 2018 and also estimates the total stratospheric bromine budget above Kiruna, Sweden by using ground-based zenith sky DOAS measurements. Kiruna (67°N, 22° E) is located north of the polar circle and is thus well suited for the investigation of polar stratospheric ozone chemistry. The stratospheric bromine budget is hard to determine because of the large uncertainties of the contribution from brominated very short lived species (VSLSs). From this study, the total stratospheric inorganic bromine budget (Bry) was derived from the retrieved BrO to about 21 pptv. Considering the mean age of air, 5.7 years, it seems that the stratospheric BrO load has increased until 2001, and after it has slightly declined. Such a tendency is consistent with the decrease of its tropospheric precursor gases. From the comparison of the derived total stratospheric inorganic bromine load, the expected contribution of VSLS to the total stratospheric inorganic bromine is estimated to around 5 - 6 pptv. [ABSTRACT FROM AUTHOR]

Atmospheric water plays a key role for the Earth’s energy budget and temperature distributionvia radiative effects (clouds and vapour) and latent heat transport and the distribution andtransport of water vapour is closely linked to atmospheric dynamics on all spatio-temporalscales. In this context, global monitoring of the water vapour distribution is essential fornumerical weather prediction, climate modeling and the understanding of climatefeedbacks. In the visible spectral range, total column water vapour (TCWV) was first retrieved usingDifferential Optical Absorption Spectroscopy (DOAS) in the red spectral range (e.g.620-670nm) as implemented in the GOME Data Processor GDP taking advantage of therelatively strong water vapour absorption in that spectral range. However, this method hassome limitations: several corrections e.g. due to non-linear effects (for strong water vapourabsorptions) have to be applied during post-processing and the low ocean surface albedoleads to low sensitivity for near-surface layers. In addition, some of the new satellite sensorsdo not cover the red spectral range, e.g. TROPOMI on board ESA’s Sentinel-5Precursor. Here, we present results applying a new approach using the spectral absorptionstructures of H2O in the blue spectral range (430-450nm). It has the advantage thatnonlinear effects are much weaker and may be neglected. Furthermore, the sensitivityfor near-surface layers over ocean is improved and a smoother spatial distributionis achieved due to the advantageous surface albedo compared to the red spectralrange. We use this new algorithm for retrieving TCWV from TROPOMI as well as OMI andGOME-2 spectra and compare the results with a variety of different reference data sets. [ABSTRACT FROM AUTHOR]

Since two decades, satellite instruments like GOME(-2), SCIAMACHY, and OMI allow theretrieval of tropospheric NO2 on global scale with increasing spatial coverage and resolution.By temporal averaging, spatial patterns of anthropogenic sources can be identified, but arelimited by the satellite ground pixel size. S5p/TROPOMI offers a high spatial resolution of up to 3.5 × 7 km2, which is more than10 times higher as compared to OMI. This allows to detect also weaker sources and topartly resolve the spatial distribution of NOx sources within a megacity or urbanareas. However, at these spatial scales, the impact of wind fluctuations becomescrucial when deducing NOx emission patterns from NO2 column measurements.For instance, plumes from power plants can often clearly be identified in dailyTROPOMI NO2 maps, but are smeared out in temporal mean maps due to changing winddirections. Here we present high-resolution NOx emission maps based on the continuity equation indifferential form: the daily NO2 flux is derived from TROPOMI NO2 column measurements and ECMWF wind fields daily sources (emissions) and sinks (reaction with OH) are given by the divergence of the NO2 flux temporal means are calculated for the resulting emissions instead of the NO2 column densities.By this procedure, the smearing caused by wind fluctuations is prevented. The divergence, i.e. the spatial derivative, is very sensitive to strong gradients as occurringfor strong localized emissions like from power plant stacks. Thus, this approach allows topinpoint NOx point sources. This method clearly reveals stack emissions from power plants and industrial complexes,and also works over polluted background conditions, like the Saudi-Arabian capital Riyadh orthe South African Highveld. [ABSTRACT FROM AUTHOR]

Multi AXis (MAX)-DOAS measurements observe scattered sun light under different elevation angles. From such measurements tropospheric vertical column densities (VCDs) and vertical profiles of different atmospheric trace gases and aerosols can be determined for the lower troposphere. A further advantage of these measurements is the fact that multiple trace gases (e.g. HCHO, CHOCHO, \ce{NO$_2$}, etc.) can be observed with the same measurement setup simultaneously. Together with international partners, we operate five stationary MAX-DOAS instruments located in Mainz/Germany, Bayfordbury/United Kingdom, Greater Noida/India, at the Amazonian Tall Tower Observatory (ATTO) measurement site/Brasil and in Guangzhou/China. Further, campaign based data is available from intensive measurement campaigns which took place at various locations all around the world. These campaign datasets include both stationary and mobile (car and ship MAX-DOAS) measurements. All these measurements were taken with different underlying meteorological and environmental conditions. In this study, we focus on measurements of tropospheric formaldehyde (HCHO). HCHO can be emitted directly by industrial and other anthropogenic and biogenic activities, but it is mainly produced by photochemical reactions from precursor substances (secondary production). Further, it plays an important role in photochemical smog chemistry and \ce{O$_3$} chemistry. As it is an intermediate product of basic oxidation cycles of other hydrocarbons (also referred to as volatile organic compounds (VOCs)) especially in summer, its concentrations are determined by the abundances of VOCs. Therefore, HCHO observations can be used as an indicator for VOCs. Our large data set allows to gain insights into the contributions from different sources and chemical processes covering various geographic and environmental conditions. Here, it is important to note that compared to satellite instruments, MAX-DOAS instruments have a much higher sensitivity to boundary layer HCHO (by a factor of 10 or more).In this study we first retrieve differential slant column densities (dSCDs) of HCHO for our stationary measurements in a consistent way. Further, HCHO profiles are retrieved using the the MAinz Profile Algorithm MAPA (Beirle et al., 2018). Additionally, the HCHO results are combined with the results of other trace gases such as \ce{NO$_2$} and CHOCHO. [ABSTRACT FROM AUTHOR]

Photon path length distributions in the atmosphere are significantly affected by multiple scattering events in the presence of clouds. Here, we present an investigation of photon path length distributions for different cloud situations by combining high resolution space based measurements of the oxygen A-band with radiative transfer simulations. The measured spectra originate from the GOSAT TANSO-FTS instrument whose high spectral resolution allows to almost entirely resolve individual absorption lines, which is a prerequisite to our study. The spectra are compared to radiance simulations from the Monte Carlo Model McArtim. The simulation output provides information on each simulated scattering event which is used to infer photon path length distributions.The agreement of the simulations to the measurement is evaluated using a fit function which considers small wavelength shifts and a correction of the simulated oxygen absorption. To verify the retrieval method, clear sky situations and well characterized cloud situations have been selected using collocated measurements of TANSO-FTS, CALIOP (CALIPSO) and CPR (CloudSat). The selected cloud situations include two one-layer cloud cases and one multiple layer cloud system. We have observed an overestimation of the simulated oxygen absorption by 5-10% for the clear sky measurement over bright surface and the one-layer cloud scenarios. This observed overestimation is currently investigated by considering a larger number of case studies. The multiple cloud layer system yields multiple results, two of the resulting cloud scenarios having a high resemblance to the collocated radar-based cloud profile. This shows that the retrieval can be used to determine basic properties of the cloud system. Overall, the results are promising and demonstrate that oxygen A-band measurements of GOSAT can provide information on the photon path lengh distributions in clouds. [ABSTRACT FROM AUTHOR]

MAX-DOAS measurements are mainly used to retrieve vertical profiles of aerosols and trace gases in the lower troposphere. Due to the measurement principle these observations are sensitive for horizontal distances of about 3-30km, depending on wavelength and atmospheric conditions.Up to now horizontal homogeneous distributions of trace gases and aerosols are usually assumed within the MAX-DOAS profile retrievals. This leads to systematic over and underestimation of the aerosol and trace gas load in the atmosphere, especially close to cities or other sources, where strong horizontal gradients are typically present. Here we show a novel method which considers and retrieves horizontal gradients of aerosols and trace gases from multi-azimuth MAX-DOAS observations.We use the 4-Azimuth MAX-DOAS located in Mainz, Germany, at the Max Planck Institute for Chemistry. This instrument measures simultaneously in four azimuth directions and performs elevation scans. The measurements at the fixed azimuth angles are used together in a combined profile inversion algorithm, which yields a profile above the location of the instrument together with horizontal gradients for every viewing direction.Additionally, the horizontal sensitivity range can be retrieved, animportant information especially when the results are compared to other datasets.The main focus of this presentation are trace gas distributions, namely NO2 and H2O. MAX-DOAS measurements are a perfect link between the satellite measurements, which have typically coarse spatial resolution and the local in situ measurements, which are performed on a regular basis to monitor the air quality. We show comparisons to both datasets. Furthermore, a comparison to a high resolution regional model is performed. [ABSTRACT FROM AUTHOR]

We present results of effective cloud fractions retrieved from GOME-2 (Global Ozone Monitoring Experiment-2) measurements using the MICRU (Mainz Iterative Cloud Retrieval Utilities) algorithm, which has been developed to retrieve small cloud fractions (CF<20%) at high accuracy in order to improve retrievals of tropospheric trace gases (e.g., NO2, HCHO) absorbing in the UV/vis wavelength region. The most important feature of MICRU is the derivation of the minimum reflectance map for a certain satellite sensor and spectral range from the measurements themselves while minimising interferences from surface BRDF effects (mostly solar glitter and shadowing). The algorithm builds on the assumption that the surface is dark compared to clouds. Therefore, it is limited to regions not permanently covered by clouds, ice or snow. Our approach features a minimum LER map parametrised by time, viewing zenith angle, scattering angle, and reflection angle. Hence, a mean standard error smaller than 4% is achieved for small CF. The MICRU results are consistent over the entire swath and between different instrument channels. It is demonstrated that MICRU, compared to the operational cloud fraction algorithms OCRA and FRESCO, significantly improves the determination of cloud fractions with respect to reduced interferences from viewing angle, solar glitter, and shore lines.In this presentation, we investigate the potential of this data set to study spatio-temporal patterns on a global scale, particularly of scenes with small cloud fractions (e.g. broken cloud fields). The correlation with teleconnection anomalies (e.g. El Niño Southern Oscillation) as well as trends of the latitudinal distribution of clouds are discussed. [ABSTRACT FROM AUTHOR]

In urban areas, road traffic is a dominant source of nitrogen oxides (NOx=NO+NO2). Although the emissions from individual vehicles are regulated by the European emission standards, real driving emissions often exceed these limits. In this study, two multi-axis differential optical absorption spectroscopy (MAX-DOAS) instruments on opposite sides of the motorway were used to measure the NO2 absorption caused by road traffic at the A60 motorway close to Mainz, Germany. In combination with wind data, the total NOx emissions for the occurring traffic volume can be estimated. Hereto, the ozone-dependent photochemical equilibrium between NO and NO2 is considered. We show that for 10 May 2019 the measured emissions exceed the maximum expected emissions calculated from the European emission standards for standardised test cycles by a factor of 11±7. One major advantage of the method used here is that MAX-DOAS measurements are very sensitive to the integrated NO2 concentration close to the surface. Thus, all emitted NO2 molecules are detected independently from their altitude, and therefore the whole emission plume originating from the nearby motorway is captured, which is a key advantage compared to other approaches such as in situ measurements. [ABSTRACT FROM AUTHOR]

Bromine monoxide (BrO) is a key radical in the atmosphere, influencing the chemical state of the atmosphere, most notably the abundance of ozone (O3). O3 depletion caused by the release of bromine has been observed and modeled in polar regions, salt pans, and in particular inside volcanic plumes. Furthermore, the molar ratio of BrO and SO2 – which can be detected simultaneously via spectroscopic measurements using the differential optical absorption spectroscopy (DOAS) method – is a proxy for the magmatic composition of a volcano and potentially an eruption forecast parameter. The detection of BrO in volcanic plumes from satellite spectroscopic observations is limited by the precision and sensitivity of the retrieval, which so far only allowed for the detection of BrO during major eruptions. The unprecedented spatial resolution of up to 3.5km×5.5km and the high signal-to-noise ratio of the TROPOspheric Monitoring Instrument (TROPOMI) on board Sentinel-5 Precursor (S-5P) enable observing and monitoring volcanic bromine release globally even for minor eruptions or even quiescent degassing. In this study, we investigate how far the BrO retrieval can be improved using TROPOMI data and how well BrO can be detected, even in small eruptions and during quiescent volcanic degassing. There are two steps for which improvements in accuracy are investigated and applied: the improvement and quantitative determination of (1) the detection limit of the DOAS BrO column retrieval and (2) the correction of the non-volcanic background BrO signal. First, the DOAS retrieval settings are varied, and their influence on accuracy and precision is investigated with respect to the detection limit and potential systematic influences. Based on these results, we propose a dedicated DOAS evaluation scheme optimized for the detection of BrO in volcanic plumes. For the DOAS retrieval, we propose the use of a large fit window from 323–360 nm , yielding a statistical uncertainty lower by a factor of 1.8 compared to previous BrO DOAS algorithms while not enhancing systematic influences. Second, the effect of the background BrO is reduced by a latitude-dependent empirical correction scheme correlated to cloud information as well as information on the O3 column. Via these improvements, the combined statistical and systematic uncertainties in the resulting BrO vertical column density is on the order of 7×1012moleculescm-2. We present a new and accurate retrieval algorithm of BrO columns from TROPOMI observations which allows for the detection of even slightly enhanced BrO amounts inside minor eruptive plumes of bromine-rich volcanoes. While designed specifically for TROPOMI observations, the retrieval algorithm is in general also applicable to other hyperspectral satellite observations. However, some parts might require adaptation. [ABSTRACT FROM AUTHOR]

Launched in October 2017, the TROPOspheric Monitoring Instrument (TROPOMI) aboard Sentinel-5 Precursor provides the potential to monitor air quality over point sources across the globe with a spatial resolution as high as 5.5 km × 3.5 km (7 km × 3.5 km before 6 August 2019). The DLR nitrogen dioxide (NO2) retrieval algorithm for the TROPOMI instrument consists of three steps: the spectral fitting of the slant column, the separation of stratospheric and tropospheric contributions, and the conversion of the slant column to a vertical column using an air mass factor (AMF) calculation. In this work, an improved DLR tropospheric NO2 retrieval algorithm from TROPOMI measurements over Europe is presented. The stratospheric estimation is implemented using the STRatospheric Estimation Algorithm from Mainz (STREAM), which was developed as a verification algorithm for TROPOMI and does not require chemistry transport model data as input. A directionally dependent STREAM (DSTREAM) is developed to correct for the dependency of the stratospheric NO2 on the viewing geometry by up to 2×1014 molec./cm 2. Applied to synthetic TROPOMI data, the uncertainty in the stratospheric column is 3.5×1014 molec./cm 2 in the case of significant tropospheric sources. Applied to actual measurements, the smooth variation of stratospheric NO2 at low latitudes is conserved, and stronger stratospheric variation at higher latitudes is captured. For AMF calculation, the climatological surface albedo data are replaced by geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) obtained directly from TROPOMI measurements with a high spatial resolution. Mesoscale-resolution a priori NO2 profiles are obtained from the regional POLYPHEMUS/DLR chemistry transport model with the TNO-MACC emission inventory. Based on the latest TROPOMI operational cloud parameters, a more realistic cloud treatment is provided by a Clouds-As-Layers (CAL) model, which treats the clouds as uniform layers of water droplets, instead of the Clouds-As-Reflecting-Boundaries (CRB) model, in which clouds are simplified as Lambertian reflectors. For the error analysis, the tropospheric AMF uncertainty, which is the largest source of NO2 uncertainty for polluted scenarios, ranges between 20 % and 50 %, leading to a total uncertainty in the tropospheric NO2 column in the 30 %–60 % range. From a validation performed with ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements, the new DLR tropospheric NO2 data show good correlations for nine European urban/suburban stations, with an average correlation coefficient of 0.78. The implementation of the algorithm improvements leads to a decrease of the relative difference from - 55.3 % to - 34.7 % on average in comparison with the DLR reference retrieval. When the satellite averaging kernels are used to remove the contribution of a priori profile shape, the relative difference decreases further to ∼ - 20 %. [ABSTRACT FROM AUTHOR]

The second Cabauw Intercomparison of Nitrogen Dioxide measuring Instruments (CINDI-2) took place in Cabauw (the Netherlands) in September 2016 with the aim of assessing the consistency of multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements of tropospheric species (NO 2 , HCHO, O 3 , HONO, CHOCHO and O 4). This was achieved through the coordinated operation of 36 spectrometers operated by 24 groups from all over the world, together with a wide range of supporting reference observations (in situ analysers, balloon sondes, lidars, long-path DOAS, direct-sun DOAS, Sun photometer and meteorological instruments). In the presented study, the retrieved CINDI-2 MAX-DOAS trace gas (NO 2 , HCHO) and aerosol vertical profiles of 15 participating groups using different inversion algorithms are compared and validated against the colocated supporting observations, with the focus on aerosol optical thicknesses (AOTs), trace gas vertical column densities (VCDs) and trace gas surface concentrations. The algorithms are based on three different techniques: six use the optimal estimation method, two use a parameterized approach and one algorithm relies on simplified radiative transport assumptions and analytical calculations. To assess the agreement among the inversion algorithms independent of inconsistencies in the trace gas slant column density acquisition, participants applied their inversion to a common set of slant columns. Further, important settings like the retrieval grid, profiles of O 3 , temperature and pressure as well as aerosol optical properties and a priori assumptions (for optimal estimation algorithms) have been prescribed to reduce possible sources of discrepancies. The profiling results were found to be in good qualitative agreement: most participants obtained the same features in the retrieved vertical trace gas and aerosol distributions; however, these are sometimes at different altitudes and of different magnitudes. Under clear-sky conditions, the root-mean-square differences (RMSDs) among the results of individual participants are in the range of 0.01–0.1 for AOTs, (1.5–15) ×1014molec.cm-2 for trace gas (NO 2 , HCHO) VCDs and (0.3– 8)×1010molec.cm-3 for trace gas surface concentrations. These values compare to approximate average optical thicknesses of 0.3 , trace gas vertical columns of 90×1014molec.cm-2 and trace gas surface concentrations of 11×1010molec.cm-3 observed over the campaign period. The discrepancies originate from differences in the applied techniques, the exact implementation of the algorithms and the user-defined settings that were not prescribed. For the comparison against supporting observations, the RMSDs increase to a range of 0.02–0.2 against AOTs from the Sun photometer, (11– 55)×1014molec.cm-2 against trace gas VCDs from direct-sun DOAS observations and (0.8– 9)×1010molec.cm-3 against surface concentrations from the long-path DOAS instrument. This increase in RMSDs is most likely caused by uncertainties in the supporting data, spatiotemporal mismatch among the observations and simplified assumptions particularly on aerosol optical properties made for the MAX-DOAS retrieval. As a side investigation, the comparison was repeated with the participants retrieving profiles from their own differential slant column densities (dSCDs) acquired during the campaign. In this case, the consistency among the participants degrades by about 30% for AOTs, by 180% (40%) for HCHO (NO 2) VCDs and by 90% (20%) for HCHO (NO 2) surface concentrations. In former publications and also during this comparison study, it was found that MAX-DOAS vertically integrated aerosol extinction coefficient profiles systematically underestimate the AOT observed by the Sun photometer. For the first time, it is quantitatively shown that for optimal estimation algorithms this can be largely explained and compensated by considering biases arising from the reduced sensitivity of MAX-DOAS observations to higher altitudes and associated a priori assumptions. [ABSTRACT FROM AUTHOR]

We present the inter-comparison of delta slant column densities (SCDs) and vertical profiles of nitrous acid (HONO) derived from measurements of different multi-axis differential optical absorption spectroscopy (MAX-DOAS) instruments and using different inversion algorithms during the Second Cabauw Inter-comparison campaign for Nitrogen Dioxide measuring Instruments (CINDI-2) in September 2016 at Cabauw, the Netherlands (51.97 ∘ N, 4.93 ∘ E). The HONO vertical profiles, vertical column densities (VCDs), and near-surface volume mixing ratios are compared between different MAX-DOAS instruments and profile inversion algorithms for the first time. Systematic and random discrepancies of the HONO results are derived from the comparisons of all data sets against their median values. Systematic discrepancies of HONO delta SCDs are observed in the range of ±0.3×1015 molec. cm -2 , which is half of the typical random discrepancy of 0.6×1015 molec. cm -2. For a typical high HONO delta SCD of 2×1015 molec. cm -2 , the relative systematic and random discrepancies are about 15 % and 30 %, respectively. The inter-comparison of HONO profiles shows that both systematic and random discrepancies of HONO VCDs and near-surface volume mixing ratios (VMRs) are mostly in the range of ∼±0.5×1014 molec. cm -2 and ∼±0.1 ppb (typically ∼20 %). Further we find that the discrepancies of the retrieved HONO profiles are dominated by discrepancies of the HONO delta SCDs. The profile retrievals only contribute to the discrepancies of the HONO profiles by ∼5 %. However, some data sets with substantially larger discrepancies than the typical values indicate that inappropriate implementations of profile inversion algorithms and configurations of radiative transfer models in the profile retrievals can also be an important uncertainty source. In addition, estimations of measurement uncertainties of HONO dSCDs, which can significantly impact profile retrievals using the optimal estimation method, need to consider not only DOAS fit errors, but also atmospheric variability, especially for an instrument with a DOAS fit error lower than ∼3×1014 molec. cm -2. The MAX-DOAS results during the CINDI-2 campaign indicate that the peak HONO levels (e.g. near-surface VMRs of ∼0.4 ppb) often appeared in the early morning and below 0.2 km. The near-surface VMRs retrieved from the MAX-DOAS observations are compared with those measured using a co-located long-path DOAS instrument. The systematic differences are smaller than 0.15 and 0.07 ppb during early morning and around noon, respectively. Since true HONO values at high altitudes are not known in the absence of real measurements, in order to evaluate the abilities of profile inversion algorithms to respond to different HONO profile shapes, we performed sensitivity studies using synthetic HONO delta SCDs simulated by a radiative transfer model with assumed HONO profiles. The tests indicate that the profile inversion algorithms based on the optimal estimation method with proper configurations can reproduce the different HONO profile shapes well. Therefore we conclude that the features of HONO accumulated near the surface derived from MAX-DOAS measurements are expected to represent the ambient HONO profiles well. [ABSTRACT FROM AUTHOR]

We present different methods for in-field elevation calibration of MAX-DOAS (Multi AXis Differential Optical Absorption Spectroscopy) instruments that were applied and inter-compared during the second Cabauw Intercomparison campaign for Nitrogen Dioxide measuring Instruments (CINDI-2). One necessary prerequisite of consistent MAX-DOAS retrievals is a precise and accurate calibration of the elevation angles of the different measuring systems. Therefore, different methods for this calibration were applied to several instruments during the campaign, and the results were inter-compared. This work first introduces and explains the different methods, namely far- and near-lamp measurements, white-stripe scans, horizon scans and sun scans, using data and results for only one (mainly the Max Planck Institute for Chemistry) instrument. In the second part, the far-lamp measurements and the horizon scans are examined for all participating groups. Here, the results for both methods are first inter-compared for the different instruments; secondly, the two methods are compared amongst each other. All methods turned out to be well-suited for the calibration of the elevation angles of MAX-DOAS systems, with each of them having individual advantages and drawbacks. Considering the results of this study, the systematic uncertainties of the methods can be estimated as ±0.05∘ for the far-lamp measurements and the sun scans, ±0.25∘ for the horizon scans, and around ±0.1∘ for the white-stripe and near-lamp measurements. When comparing the results of far-lamp and horizon-scan measurements, a spread of around 0.9 ∘ in the elevation calibrations is found between the participating instruments for both methods. This spread is of the order of a typical field of view (FOV) of a MAX-DOAS instrument and therefore affecting the retrieval results. Further, consistent (wavelength dependent) offsets of 0.32 ∘ and 0.40 ∘ between far-lamp measurements and horizon scans are found, which can be explained by the fact that, despite the flat topography around the measurement site, obstacles such as trees might mark the visible horizon during daytime. The observed wavelength dependence can be explained by surface albedo effects. Lastly, the results are discussed and recommendations for future campaigns are given. [ABSTRACT FROM AUTHOR]

Since its first confirmed case in December 2019, coronavirus disease 2019 (COVID-19) has become a worldwide pandemic with more than 90 million confirmed cases by January 2021. Countries around the world have enforced lockdown measures to prevent the spread of the virus, introducing a temporal change of air pollutants such as nitrogen dioxide (NO 2 ) that are strongly related to transportation, industry, and energy. In this study, NO 2 variations over regions with strong responses to COVID-19 are analysed using datasets from the Global Ozone Monitoring Experiment-2 (GOME-2) sensor aboard the EUMETSAT Metop satellites and TROPOspheric Monitoring Instrument (TROPOMI) aboard the EU/ESA Sentinel-5 Precursor satellite. The global GOME-2 and TROPOMI NO 2 datasets are generated at the German Aerospace Center (DLR) using harmonized retrieval algorithms; potential influences of the long-term trend and seasonal cycle, as well as the short-term meteorological variation, are taken into account statistically. We present the application of the GOME-2 data to analyze the lockdown-related NO 2 variations for morning conditions. Consistent NO 2 variations are observed for the GOME-2 measurements and the early afternoon TROPOMI data: regions with strong social responses to COVID-19 in Asia, Europe, North America, and South America show strong NO 2 reductions of ∼ 30-50% on average due to restriction of social and economic activities, followed by a gradual rebound with lifted restriction measures., Supplementary Information: The online version contains supplementary material available at 10.1007/s11869-021-01046-2., Competing Interests: Conflict of interestThe authors declare no competing interests., (© The Author(s) 2021.)

Satellite NO 2 observations have been widely used to evaluate emission changes. To determine trends in NO x emission over China, we used a method independent of chemical transport models to quantify the NO x emissions from 48 cities and 7 power plants over China, on the basis of Ozone Monitoring Instrument (OMI) NO 2 observations during 2005 to 2015. We found that NO x emissions over 48 Chinese cities increased by 52% from 2005 to 2011 and decreased by 21% from 2011 to 2015. The decrease since 2011 could be mainly attributed to emission control measures in power sector; while cities with different dominant emission sources (i.e. power, industrial and transportation sectors) showed variable emission decline timelines that corresponded to the schedules for emission control in different sectors. The time series of the derived NO x emissions was consistent with the bottom-up emission inventories for all power plants (r=0.8 on average), but not for some cities (r=0.4 on average). The lack of consistency observed for cities was most probably due to the high uncertainty of bottom-up urban emissions used in this study, which were derived from downscaling the regional-based emission data to cities by using spatial distribution proxies.