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Abstract: In this work we study the ability of the chemistry transport model Lotos-Euros to forecast, with a range of six days, PM10 concentrations and exceedances thereof. For both rural and non-rural locations in The Netherlands and based on one year of data, model performance does not deteriorate up to a lead time of three days. Thereafter the PM10 forecast is increasingly affected by uncertainty in the meteorological forecast. However, up to a lead time of 6 days the forecast still has skill, beats persistence and complies with several performance criteria. The correlation between forecast and observations is between 0.66 and 0.70 for the first half of the forecast and remains above 0.54 until the end of the forecast range. Exceedances of the PM10 concentration over thresholds are also forecasted with reasonable skill up to a forecast range of three days, after which a gradual deterioration sets in. The stability of the forecast displays the same behaviour. Up to a lead time of three days, the forecast remains reasonably stable with more than 80% of forecasted exceedances still present in all later shorter-term forecasts for the same date. Because exceedances can be forecasted with considerable skill a number of days in advance, the forecast can be used for applications that require a range of a few days, such as outdoor activities and the scheduling and implementation of short-term emission reduction measures. [Copyright &y& Elsevier]

We introduce the new ESA Climate Change Initiative TROPOspheric Monitoring Instrument (TROPOMI) global monthly Level 3 (L3) dataset of tropospheric nitrogen dioxide (NO2) for May 2018 to December 2021. The dataset provides spatiotemporally averaged tropospheric NO2 columns, associated averaging kernels and L3 uncertainties at spatial resolutions of 0.2°, 0.5°, and 1.0° on a monthly timescale (https://doi.org/10.21944/CCI-NO2-TROPOMI-L3). To improve our understanding of what fraction of the L2 uncertainty cancels when averaging over space or time (i.e. the random component of the L2 uncertainty) and what fraction persists despite the averaging (systematic component), we first determine spatial and temporal error correlations for all sources of uncertainty in the L2 retrieval. Spatial error correlations arise from the stratosphere-troposphere correction, and from coarse-gridded albedo climatologies used in the L2 air mass factor calculation. We find the temporal error correlation in both the stratospheric uncertainty and the air-mass factor uncertainty to be 30 %. Using these estimates, the L3 uncertainty budget has been established for every grid cell based on input L2 uncertainties and new methods to estimate spatial and temporal representativeness uncertainties and to propagate measurement uncertainties through space and time. The total relative uncertainty in the resulting Level 3 dataset is in the range of 15–20 % in polluted areas, which is significantly lower than in separate Level 2 orbit retrievals, and brings the tropospheric NO2 data to within the GCOS 'goal' and 'breakthrough' requirements. Validation of the (sub-)columns confirms better correlation and reduced dispersion in the differences between satellite and ground-based reference data for the L3 data w.r.t. the underlying L2, albeit with a more pronounced negative bias in the tropospheric columns at pollution hot spots, most probably related to stronger spatial smearing. [ABSTRACT FROM AUTHOR]

The aim of this paper is to apply a new lane separation methodology for the maritime sector emissions attributed to the different vessel types and marine traffic loads in the Mediterranean and the Black Sea defined via the European Marine and Observation Data network (EMODnet), developed in 2016. This methodology is implemented for the first time on the Copernicus Atmospheric Monitoring Service Global Shipping (CAMS-GLOB-SHIP v2.1) nitrogen oxides (NOX) emissions inventory, on the Sentinel-5 Precursor Tropospheric Monitoring Instrument (TROPOMI) nitrogen dioxide (NO2) tropospheric vertical column densities, and on the LOTOS-EUROS (Long Term Ozone Simulation—European Operational Smog) CTM (chemical transport model) simulations. By applying this new EMODnet-based lane separation method to the CAMS-GLOB-SHIP v2.1 emission inventory, we find that cargo and tanker vessels account for approximately 80% of the total emissions in the Mediterranean, followed by fishing, passenger, and other vessel emissions with contributions of 8%, 7%, and 5%, respectively. Tropospheric NO2 vertical column densities sensed by TROPOMI for 2019 and simulated by the LOTOS-EUROS CTM have been successfully attributed to the major vessel activities in the Mediterranean; the mean annual NO2 load of the observations and the simulations reported for the entire maritime EMODnet-reported fleet of the Mediterranean is in satisfactory agreement, 1.26 ± 0.56 × 1015 molecules cm−2 and 0.98 ± 0.41 × 1015 molecules cm−2, respectively. The spatial correlation of the annual maritime NO2 loads of all vessel types between observation and simulation ranges between 0.93 and 0.98. On a seasonal basis, both observations and simulations show a common variability. The wintertime comparisons are in excellent agreement for the highest emitting sector, cargo vessels, with the observations reporting a mean load of 0.98 ± 0.54 and the simulations of 0.81 ± 0.45 × 1015 molecules cm−2 and correlation of 0.88. Similarly, the passenger sector reports 0.45 ± 0.49 and 0.39 ± 0.45 × 1015 molecules cm−2 respectively, with correlation of 0.95. In summertime, the simulations report a higher decrease in modelled tropospheric columns than the observations, however, still resulting in a high correlation between 0.85 and 0.94 for all sectors. These encouraging findings will permit us to proceed with creating a top-down inventory for NOx shipping emissions using S5P/TROPOMI satellite observations and a data assimilation technique based on the LOTOS-EUROS chemical transport model. [ABSTRACT FROM AUTHOR]

In this work, we investigate the ability of a data assimilation technique and space-borne observations to quantify and monitor changes in nitrogen oxides (NOx) emissions over Northwestern Greece for the summers of 2018 and 2019. In this region, four lignite-burning power plants are located. The data assimilation technique, based on the Ensemble Kalman Filter method, is employed to combine space-borne atmospheric observations from the high spatial resolution Sentinel-5 Precursor (S5P) Tropospheric Monitoring Instrument (TROPOMI) and simulations using the LOTOS-EUROS Chemical Transport model. The Copernicus Atmosphere Monitoring Service-Regional European emissions (CAMS-REG, version 4.2) inventory based on the year 2015 is used as the a priori emissions in the simulations. Surface measurements of nitrogen dioxide (NO2) from air quality stations operating in the region are compared with the model surface NO2 output using either the a priori (base run) or the a posteriori (assimilated run) NOx emissions. Relative to the a priori emissions, the assimilation suggests a strong decrease in concentrations for the station located near the largest power plant, by 80% in 2019 and by 67% in 2018. Concerning the estimated annual a posteriori NOx emissions, it was found that, for the pixels hosting the two largest power plants, the assimilated run results in emissions decreased by ~40–50% for 2018 compared to 2015, whereas a larger decrease, of ~70% for both power plants, was found for 2019, after assimilating the space-born observations. For the same power plants, the European Pollutant Release and Transfer Register (E-PRTR) reports decreased emissions in 2018 and 2019 compared to 2015 (−35% and −38% in 2018, −62% and −72% in 2019), in good agreement with the estimated emissions. We further compare the a posteriori emissions to the reported energy production of the power plants during the summer of 2018 and 2019. Mean decreases of about −35% and−63% in NOx emissions are estimated for the two larger power plants in summer of 2018 and 2019, respectively, which are supported by similar decreases in the reported energy production of the power plants (~−30% and −70%, respectively). [ABSTRACT FROM AUTHOR]

Clouds have a strong impact on satellite measurements of tropospheric trace gases in the ultraviolet, visible, and near-infrared spectral ranges from space. Therefore, trace gas retrievals rely on information on cloud fraction, cloud albedo, and cloud height from cloud products. In this study, the cloud parameters from different cloud retrieval algorithms for the Sentinel-5 Precursor (S5P) TROPOspheric Monitoring Instrument (TROPOMI) are compared: the Optical Cloud Recognition Algorithm (OCRA) a priori cloud fraction, the Retrieval Of Cloud Information using Neural Networks (ROCINN) CAL (Clouds-As-Layers) cloud fraction and cloud top and base height, the ROCINN CRB (Clouds-as-Reflecting-Boundaries) cloud fraction and cloud height, the Fast Retrieval Scheme for Clouds from the Oxygen A-band (FRESCO) cloud fraction, the interpolated FRESCO cloud height from the TROPOMI NO 2 product, the cloud fraction from the NO 2 fitting window, the O 2 –O 2 cloud fraction and cloud height, the Mainz Iterative Cloud Retrieval Utilities (MICRU) cloud fraction, and the Visible Infrared Imaging Radiometer Suite (VIIRS) cloud fraction. Two different versions of the TROPOMI cloud products OCRA/ROCINN, FRESCO, and the TROPOMI NO 2 product are included in the comparisons (processor version 1.x and 2.x). Overall, the cloud parameters retrieved by the different algorithms show qualitative consistency in version 1.x and good agreement in version 2.x with the exception of the VIIRS cloud fraction, which cannot be directly compared to the other data. Differences between the cloud retrievals are found especially for small cloud heights with a cloud fraction threshold of 0.2, i.e. clouds that are particularly relevant for tropospheric trace gas retrievals. The cloud fractions of the different version 2 cloud products primarily differ over snow- and ice-covered pixels and scenes with sun glint, for which only MICRU includes an explicit treatment. All cloud parameters show some systematic problems related to the across-track dependence, where larger values are found at the edges of the satellite view. The consistency between the cloud parameters from different algorithms depends strongly on how the data are filtered for the comparison, for example, what quality value is used or whether snow- and ice-covered pixels are excluded from the analysis. In summary, clear differences were found between the results of various algorithms, but these differences are reduced in the most recent versions of the cloud data. [ABSTRACT FROM AUTHOR]

Aggressive measures were instituted by the Beijing municipal authorities to restrict vehicular traffic in the Chinese capital during the recent Sino-African Summit. We show that reductions in associated emissions of NOx were detected by the Dutch-Finnish Ozone Monitoring Instrument (OMI) aboard the Aura satellite. Interpretation of these data using a 3-dimensional chemical transport model indicates that emissions of NOx were reduced by 40% over the period of November 4 to 6, 2006, for which the restrictions were in place., Engineering and Applied Sciences, Version of Record

This study gives a systematic comparison of the Tropospheric Monitoring Instrument (TROPOMI) version 1.2 and Ozone Monitoring Instrument (OMI) QA4ECV tropospheric NO2 column through global chemical data assimilation (DA) integration for the period April–May 2018. DA performance is controlled by measurement sensitivities, retrieval errors, and coverage. The smaller mean relative observation errors by 16 % in TROPOMI than OMI over 60 ∘ N–60 ∘ S during April–May 2018 led to larger reductions in the global root-mean-square error (RMSE) against the assimilated NO2 measurements in TROPOMI DA (by 54 %) than in OMI DA (by 38 %). Agreements against the independent surface, aircraft-campaign, and ozonesonde observation data were also improved by TROPOMI DA compared to the control model simulation (by 12 %–84 % for NO2 and by 7 %–40 % for ozone), which were more obvious than those by OMI DA for many cases (by 2 %–70 % for NO2 and by 1 %–22 % for ozone) due to better capturing spatial and temporal variability by TROPOMI DA. The estimated global total NOx emissions were 15 % lower in TROPOMI DA, with 2 %–23 % smaller regional total emissions, in line with the observed negative bias of the TROPOMI version 1.2 product compared to the OMI QA4ECV product. TROPOMI DA can provide city-scale emission estimates, which were within 10 % differences with other high-resolution analyses for several limited areas, while providing a globally consistent analysis. These results demonstrate that TROPOMI DA improves global analyses of NO2 and ozone, which would also benefit studies on detailed spatial and temporal variations in ozone and nitrate aerosols and the evaluation of bottom-up NOx emission inventories. [ABSTRACT FROM AUTHOR]

An improved divergence method has been developed to estimate annual methane (CH 4) emissions from TROPOspheric Monitoring Instrument (TROPOMI) observations. It has been applied to the period of 2018 to 2021 over the Middle East, where the orography is complicated, and the mean mixing ratio of methane (XCH 4) might be affected by albedos or aerosols over some locations. To adapt to extreme changes of terrain over mountains or coasts, winds are used with their divergent part removed. A temporal filter is introduced to identify highly variable emissions and to further exclude fake sources caused by retrieval artifacts. We compare our results to widely used bottom-up anthropogenic emission inventories: Emissions Database for Global Atmospheric Research (EDGAR), Community Emissions Data System (CEDS), and Global Fuel Exploitation Inventory (GFEI) over several regions representing various types of sources. The NO x emissions are from EDGAR and Daily Emissions Constrained by Satellite Observations (DECSO), and the industrial heat sources identified by Visible Infrared Imaging Radiometer Suite (VIIRS) are further used to better understand our resulting methane emissions. Our results indicate possibly large underestimations of methane emissions in metropolises like Tehran (up to 50 %) and Isfahan (up to 70 %) in Iran. The derived annual methane emissions from oil/gas production near the Caspian Sea in Turkmenistan are comparable to GFEI but more than 2 times higher than EDGAR and CEDS in 2019. Large discrepancies in the distribution of methane sources in Riyadh and its surrounding areas are found between EDGAR, CEDS, GFEI, and our emissions. The methane emission from oil/gas production to the east of Riyadh seems to be largely overestimated by EDGAR and CEDS, while our estimates as well as GFEI and DECSO NO x indicate much lower emissions from industrial activities. On the other hand, regions like Iran, Iraq, and Oman are dominated by sources from oil and gas exploitation that probably include more irregular releases of methane, with the result that our estimates, which include only invariable sources, are lower than the bottom-up emission inventories. [ABSTRACT FROM AUTHOR]

The TROPOspheric Monitoring Instrument (TROPOMI) is a spectrometer measuring in the UV, visible, near-infrared and short-wave infrared, which allows the retrieval of trace gas species like O3, NO2, HCHO, SO2, CO, CH4 and aerosol aspects like the aerosol index. The Copernicus Sentinel-5P satellite, with TROPOMI as payload, was successfully launched on 13 October 2017. TROPOMI has a full global coverage each day, but with a much-improved resolution (3.5 x 7 km2) compared to the Ozone Monitoring Instrument (OMI) which is providing measurements since 2004. Because of the fine resolution, the TROPOMI observations are expected to be of great importance for estimating pollutant concentrations and emissions at the scale of smaller towns, individual power plants, wildfires and major infrastructures. Operational data products, including NO2, have become available July 2018 (doi: 10.5270/S5P-s4ljg54). The Nitrogen Dioxide (NO2) tropospheric columns are retrieved using the DOAS algorithm combined with an integrated modelling-retrieval-assimilation approach to derive the air-mass factors and to estimate the stratospheric column. This latter component is based on the TM5-MP chemistry-transport model operated at a resolution of 1x1 degree. Developments from the EU QA4ECV project (www.qa4ecv.eu) have been included in the retrieval software to ensure consistency with the datasets from this project, including OMI NO2. More information can be found at http://www.tropomi.eu/data-products/nitrogen-dioxide.In our contribution we will provide an overview of the TROPOMI NO2 retrieval with a focus on recent developments and plans. An assessment of the quality of the NO2 product will be provided, and the stability of the NO2 product since launch will be reported. Comparisons will be presented with surface remote sensing observations (e.g. MAX-DOAS), the OMI QA4ECV NO2 products, and with CAMS global and regional air quality models. At the time of the EGU, TROPOMI will have accumulated a full year of nearly continuous observations (April 2018 - March 2019). The benefits of the high-resolution TROPOMI NO2 observations for the monitoring of sources and air quality will be demonstrated. [ABSTRACT FROM AUTHOR]

NOx is an important primary air pollutant of major environmental concern which is predominantly produced by anthropogenic combustion activities. NOx needs to be accounted for in national emission inventories, according to international treaties. Constructing accurate inventories requires substantial time and effort, resulting in reporting delays of 1 to 5 years. In addition to this, difficulties can arise from temporal and country-specific legislative and protocol differences. To address these issues, satellite-based atmospheric composition measurements offer a unique opportunity for the independent and large-scale estimation of emissions in a consistent, transparent, and comprehensible manner. Here we test the multi-source plume method (MSPM) to assess the NOx emissions over Germany in the COVID-19 period from 2019–2021. For the years where reporting is available, the differences between satellite estimates and inventory totals were within 75–100 kt (NO2) NOx (<10 % of inventory values). The large reduction in the NOx emissions (∼15 %) concurrent with the COVID-19 lockdowns was observed in both the inventory and satellite-derived emissions. The recent projections for the inventory emissions of 2021 pointed to a recovery of the 2021 emissions towards pre-COVID-19 levels. In the satellite-derived emissions, however, such an increase was not observed. While emissions from the larger power plants did rebound to pre-COVID-19 levels, other sectors such as road transport did not, and the change in emissions is likely due to a reduction in the number of heavier transport trucks compared to the pre-COVID-19 numbers. This again illustrates the value of having a consistent satellite-based methodology for faster emission estimates to guide and check the conventional emission inventory reporting. The method described in this work also meets the demand for independent verification of the official emission inventories, which will enable inventory compilers to detect potentially problematic reporting issues, bolstering transparency and comparability, which are two key values for emission reporting. [ABSTRACT FROM AUTHOR]

Meteorological fields calculated by numerical weather prediction (NWP) models drive offline chemical transport models (CTMs) to solve the transport, chemical reactions, and atmospheric interaction over the geographical domain of interest. HARMONIE (HIRLAM ALADIN Research on Mesoscale Operational NWP in Euromed) is a state-of-the-art non-hydrostatic NWP community model used at several European weather agencies to forecast weather at the local and/or regional scale. In this work, the HARMONIE WINS50 (cycle 43 cy43) reanalysis dataset at a resolution of 0.025° × 0.025° covering an area surrounding the North Sea for the years 2019–2021 was coupled offline to the LOTOS-EUROS (LOng-Term Ozone Simulation-EURopean Operational Smog model, v2.2.002) CTM. The impact of using either meteorological fields from HARMONIE or from ECMWF on LOTOS-EUROS simulations of NO2 has been evaluated against ground-level observations and TROPOMI tropospheric NO2 vertical columns. Furthermore, the difference between crucial meteorological input parameters such as the boundary layer height and the vertical diffusion coefficient between the hydrostatic ECMWF and non-hydrostatic HARMONIE data has been studied, and the vertical profiles of temperature, humidity, and wind are evaluated against meteorological observations at Cabauw in The Netherlands. The results of these first evaluations of the LOTOS-EUROS model performance in both configurations are used to investigate current uncertainties in air quality forecasting in relation to driving meteorological parameters and to assess the potential for improvements in forecasting pollution episodes at high resolutions based on the HARMONIE NWP model. [ABSTRACT FROM AUTHOR]

Meteorological fields calculated by Numerical Weather Prediction (NWP) Models drive offline Chemical Transport Models (CTM) to solve the transport, chemical reactions, and atmospheric interaction over the geographical domain of interest. In this way, forecasts and (re-)analyses provided by NWP can be used for air quality forecasting, climate modeling, and environmental studies. The more precise the meteorological input data represents the atmospheric dynamics, the better the CTM represents pollutant transport, mixing, and the subsequent impact on surface air quality. HARMONIE (HIRLAM ALADIN Research on Mesoscale Operational NWP in Euromed) is a state-of-the-art non-hydrostatic NWP community model used at several European weather agencies to forecast weather at the local and/or regional scale. In this work, the HARMONIE WINS50 (cycle 43 cy43) reanalysis data set at a resolution of 0.025° ×0.025° covering an area surrounding the North Sea for the years 2019-2021 was offline coupled to the state-of-the-art model LOTOS-EUROS (v2.2.002), which is a CTM that is one of the members of the Copernicus Atmosphere Monitoring Service (CAMS), an ensemble of CTMs that is used to produce operational air quality forecasts over Europe and at a higher resolution also over the Netherlands. The impact on simulated NO2 concentrations of using meteorological fields from HARMONIE in LOTOS-EUROS compared to the use of fields from ECMWF (here used at 0.7°×0.7°) is evaluated against ground-level sensors and TROPOMI tropospheric NO2 vertical columns. Furthermore, the difference between crucial meteorological input parameters such as the boundary layer height and the vertical diffusion coefficient between the hydrostatic (ECMWF) and non-hydrostatic (HARMONIE) model fields is studied, and the vertical profiles of temperature, humidity, and wind are evaluated against meteorological vertical profile observations at Cabauw in The Netherlands. The results of these first evaluations of the LOTOS-EUROS model performance in both configurations are used to investigate current uncertainties in air quality forecasting in relation to driving meteorological parameters and to assess the potential for improvements in high-resolution air quality forecasting episodes based on the HAR MONIE NWP model. [ABSTRACT FROM AUTHOR]

NOx is an important primary air pollutant, dominantly produced by anthropogenic, mostly combustion based, activities from sectors such as industry, traffic and transport. NOx is directly linked to negative health and environmental impacts. Currently, the construction of emission inventories to keep track of NOx emissions is based on official national reported emissions and prOxies such as activity data as well as direct measurements. The effort to properly construct an accurate inventory is significant and time consuming which causes a reporting offset between one and five years with respect to the current date. Next to this temporal lag difficulties in composed inventories can arise from legislative and protocol differences between countries and over time in reporting of emissions. Satellite based atmospheric composition measurements provide a unique opportunity to fill this gap and independently estimate emissions on a large scale in a consistent, transparent and comprehensible way. They give the possibility to check for compliance with emission reduction targets in a timely manner as well as to observe rapid emission reductions such as experienced during the COVID-19 lock-downs. In this study we apply a consistent methodology to derive NOx emissions over Germany for the years of 2019-2021. For the years where reporting is available differences between satellite estimates and inventory totals were within 100kt. The large reduction of NOx emissions related to the COVID-19 lock-downs were observed in both the inventory and satellite derived emissions. The recent projections for the inventory emissions pointed to a recovery of the emissions towards pre-COVID19 levels this increase was not observed. While emissions from the larger power-plants did rebound to earlier levels, others sectors such as road transport and shipping did not and could be linked to a reduction in the number of heavier transport trucks. This again illustrates the value of having a consistent satellite based methodology for faster projections to guide and check the conventional emission inventory reporting. The method described in this manuscript also meet the demand for independent verification of the official emission inventories, which will enable inventory compilers to detect potentially problematic reporting issues. Transparency and comparability, two key values for emission reporting, are thus bolstered by this technique. [ABSTRACT FROM AUTHOR]

Lightning, one of the major sources of nitrogen oxides (NO x) in the atmosphere, contributes to the tropospheric concentration of ozone and to the oxidizing capacity of the atmosphere. Lightning produces between 2 and 8 Tg N yr -1 globally and on average about 250 ± 150 mol NO x per flash. In this work, we estimate the moles of NO x produced per flash (LNO x production efficiency) in the Pyrenees (Spain, France and Andorra) and in the Ebro Valley (Spain) by using nitrogen dioxide (NO 2) and cloud properties from the TROPOspheric Monitoring Instrument (TROPOMI) as well as lightning data from the Earth Networks Global Lightning Network (ENGLN) and from the EUropean Co-operation for LIghtning Detection (EUCLID). The Pyrenees are one of the areas in Europe with the highest lightning frequencies, which, along with their remoteness as well as their very low NO x background, enables us to better distinguish the LNO x signal produced by recent lightning in TROPOMI NO 2 measurements. We compare the LNO x production efficiency estimates for eight convective systems in 2018 using two different sets of TROPOMI research products provided by the Royal Netherlands Meteorological Institute (KNMI) and the Deutsches Zentrum für Luft- und Raumfahrt (DLR). According to our results, the mean LNO x production efficiency in the Pyrenees and in the Ebro Valley, using a 3 h chemical lifetime, ranges between 14 and 103 mol NO x per flash from the eight systems. The mean LNO x production efficiency estimates obtained using both TROPOMI products and ENGLN lightning data differ by ∼ 23 %, while they differ by ∼ 35 % when using EUCLID lightning data. The main sources of uncertainty when using ENGLN lightning data are the estimation of background NO x that is not produced by lightning and the time window before the TROPOMI overpass that is used to count the total number of lightning flashes contributing to freshly produced LNO x. The main source of uncertainty when using EUCLID lightning data is the uncertainty in the detection efficiency of EUCLID. [ABSTRACT FROM AUTHOR]

The aim of this paper is to evaluate the surface concentration of nitrogen dioxide (NO2) inferred from the Sentinel-5 Precursor Tropospheric Monitoring Instrument (S5P/TROPOMI) NO2 tropospheric column densities over Central Europe for two time periods, summer 2019 and winter 2019–2020. Simulations of the NO2 tropospheric vertical column densities and surface concentrations from the Long-Term Ozone Simulation–European Operational Smog (LOTOS-EUROS) chemical transport model are also applied in the methodology. More than two hundred in situ air quality monitoring stations, reporting to the European Environment Agency (EEA) air quality database, are used to carry out comparisons with the model simulations and the spaceborne inferred surface concentrations. Stations are separated into seven types (urban traffic, suburban traffic, urban background, suburban background, rural background, suburban industrial and rural industrial) in order to examine the strengths and shortcomings of the different air quality markers, namely the NO2 vertical column densities and NO2 surface concentrations. S5P/TROPOMI NO2 surface concentrations are inferred by multiplying the fraction of the satellite and model NO2 vertical column densities with the model surface concentrations. The estimated inferred TROPOMI NO2 surface concentrations are examined further with the altering of three influencing factors: the model vertical leveling scheme, the versions of the TROPOMI NO2 data and the air mass factors applied to the satellite and model NO2 vertical column densities. Overall, the inferred TROPOMI NO2 surface concentrations show a better correlation with the in situ measurements for both time periods and all station types, especially for the industrial stations (R > 0.6) in winter. The calculated correlation for background stations is moderate for both periods (R~0.5 in summer and R > 0.5 in winter), whereas for traffic stations it improves in the winter (from 0.20 to 0.50). After the implementation of the air mass factors from the local model, the bias is significantly reduced for most of the station types, especially in winter for the background stations, ranging from +0.49% for the urban background to +10.37% for the rural background stations. The mean relative bias in winter between the inferred S5P/TROPOMI NO2 surface concentrations and the ground-based measurements for industrial stations is about −15%, whereas for traffic urban stations it is approximately −25%. In summer, biases are generally higher for all station types, especially for the traffic stations (~−75%), ranging from −54% to −30% for the background and industrial stations. [ABSTRACT FROM AUTHOR]

TROPOMI (TROPOspheric Monitoring Instrument) measurements of tropospheric NO2 columns provide powerful information on emissions of air pollution by ships on open sea. This information is potentially useful for authorities to help determine the (non-) compliance of ships with increasingly stringent NOx emission regulations. We find that the information quality is improved further by recent upgrades in the TROPOMI cloud retrieval and an optimal data selection. We show that the superior spatial resolution of TROPOMI allows for the detection of several lanes of NO2 pollution ranging from the Aegean Sea near Greece to the Skagerrak in Scandinavia, which have not been detected with other satellite instruments before. Additionally, we demonstrate that under conditions of sun glint TROPOMI's vertical sensitivity to NO2 in the marine boundary layer increases by up to 60 %. The benefits of sun glint are most prominent under clear-sky situations when sea surface winds are low but slightly above zero (±2 m s -1). Beyond spatial resolution and sun glint, we examine for the first time the impact of the recently improved cloud algorithm on the TROPOMI NO2 retrieval quality, both over sea and over land. We find that the new FRESCO + (Fast Retrieval Scheme for Clouds from the Oxygen A band) wide algorithm leads to 50 hPa lower cloud pressures, correcting a known high bias, and produces 1– 4×1015 molec. cm -2 higher retrieved NO2 columns, thereby at least partially correcting for the previously reported low bias in the TROPOMI NO2 product. By training an artificial neural network on the four available periods with standard and FRESCO + wide test retrievals, we develop a historic, consistent TROPOMI NO2 data set spanning the years 2019 and 2020. This improved data set shows stronger (35 %–75 %) and sharper (10 %–35 %) shipping NO2 signals compared to co-sampled measurements from OMI. We apply our improved data set to investigate the impact of the COVID-19 pandemic on ship NO2 pollution over European seas and find indications that NOx emissions from ships reduced by 10 %–20 % during the beginning of the COVID-19 pandemic in 2020. The reductions in ship NO2 pollution start in March–April 2020, in line with changes in shipping activity inferred from automatic identification system (AIS) data on ship location, speed, and engine. [ABSTRACT FROM AUTHOR]

We present tropospheric nitrogen dioxide (NO2) changes observed by the Canadian Pandora measurement program in the Greater Toronto Area (GTA), Canada, and compare the results with surface NO2 concentrations measured via in situ instruments to assess the local emission changes during the first two years of the COVID-19 pandemic. In the City of Toronto, the first lockdown period started on 15 March 2020, and continued until 24 June 2020. ECMWF Reanalysis v5 (ERA-5) wind information was used to facilitate the data analysis and reveal detailed local emission changes from different areas of the City of Toronto. Evaluating seven years of Pandora observations, a clear NO2 reduction was found, especially from the more polluted downtown Toronto and airport areas (e.g., declined by 35% to 40% in 2020 compared to the 5-year mean value from these areas) during the first two years of the pandemic. Compared to the sharp decline in NO2 emissions in 2020, the atmospheric NO2 levels in 2021 started to recover, but are still below the mean values in pre-pandemic time. For some sites, the pre-pandemic NO2 local morning rush hour peak has still not returned in 2021, indicating a change in local traffic and commuter patterns. The long-term (12 years) surface air quality record shows a statistically significant decline in NO2 with and without April to September 2020 observations (trend of −4.1%/yr and −3.9%/yr, respectively). Even considering this long-term negative trend in NO2, the observed NO2 reduction (from both Pandora and in situ) in the early stage of the pandemic is still statistically significant. By implementing the new wind-based validation method, the high-resolution satellite instrument (TROPOMI) can also capture the local NO2 emission pattern changes to a good level of agreement with the ground-based observations. The bias between ground-based and satellite observations during the pandemic was found to have a positive shift (5–12%) than the bias during the pre-pandemic period. [ABSTRACT FROM AUTHOR]

In recent decades, satellite instruments have been providing observations of air pollutants such as nitrogen dioxide (NO 2) with global coverage. Since late 2018, the TROPOspheric Monitoring Instrument (TROPOMI) on-board the Copernicus Sentinel-5 Precursor satellite has produced NO 2 vertical column densities (VCDs) with the best spatial resolution (currently 5.5 × 3.5 km2 at nadir). In this paper, we test and adapt two previously published methods to estimate surface-level NO 2 concentrations from TROPOMI VCD retrievals in the specific conditions of Finland. Satellite-based estimates show good correlation with co-located surface NO 2 measurements from the Finnish AQ network, although not accounting for the level of NO 2 mixing within the boundary layer leads to underestimation. We also use a linear relation between in situ measurements and surface-level estimates to correct for the observed negative bias in the estimates. Finally, we use chemical transport model simulations to estimate the ratio of the annual mean surface concentrations to the values sampled according to TROPOMI observation times, and use it to correct our surface-level NO 2 estimates for compatibility with annual limit values defined in AQ legislation. Overall, the results provide new information to complement traditional ground-based AQ measurements, especially over areas where the surface AQ networks are sparse, and to support national environmental authorities in air quality assessment and reporting. • Surface-level concentrations of NO 2 estimated from satellite observations in Finland. • The satellite estimates show high correlation with ground-based data. • Annual average estimates calculated over air quality monitoring regions in Finland. • The results are useful to complement air quality network measurements. [ABSTRACT FROM AUTHOR]

Global lockdown measures to prevent the spread of the coronavirus disease 2019 (COVID-19) led to air pollutant emission reductions. While the COVID-19 lockdown impacts on both trace gas and total particulate pollutants have been widely investigated, secondary aerosol formation from trace gases remains unclear. To that end, we quantify the COVID-19 lockdown impacts on NO x and SO 2 emissions and sulfate-nitrate-ammonium aerosols using multiconstituent satellite data assimilation and model simulations. We find that anthropogenic emissions over major polluted regions were reduced by 19 to 25% for NO x and 14 to 20% for SO 2 during April 2020. These emission reductions led to 8 to 21% decreases in sulfate and nitrate aerosols over highly polluted areas, corresponding to >34% of the observed aerosol optical depth declines and a global aerosol radiative forcing of +0.14 watts per square meter relative to business-as-usual scenario. These results point to the critical importance of secondary aerosol pollutants in quantifying climate impacts of future mitigation measures.

The Arctic region is experiencing notable warming as well as more lightning. Lightning is the dominant source of upper tropospheric nitrogen oxides (NO x ), which are precursors for ozone and hydroxyl radicals. In this study, we combine the nitrogen dioxide (NO 2 ) observations from the TROPOspheric Monitoring Instrument (TROPOMI) with Vaisala Global Lightning Dataset 360 to evaluate lightning NO 2 (LNO 2 ) production in the Arctic. By analyzing consecutive TROPOMI NO 2 observations, we determine the lifetime and production efficiency of LNO 2 during the summers of 2019-2021. Our results show that the LNO 2 production efficiency over the ocean is ∼6 times higher than over continental regions. Additionally, we find that a higher LNO 2 production efficiency is often correlated with lower lightning rates. The summertime lightning NO x emission in the Arctic (north of 70° N) is estimated to be 219 ± 116 Mg of N, which is equal to 5% of anthropogenic NO x emissions. However, for the span of a few hours, the Arctic LNO 2 density can even be comparable to anthropogenic NO 2 emissions in the region. These new findings suggest that LNO 2 can play an important role in the upper-troposphere/lower-stratosphere atmospheric chemical processes in the Arctic, particularly during the summer.

China's policy interventions to reduce the spread of the coronavirus disease 2019 have environmental and economic impacts. Tropospheric nitrogen dioxide indicates economic activities, as nitrogen dioxide is primarily emitted from fossil fuel consumption. Satellite measurements show a 48% drop in tropospheric nitrogen dioxide vertical column densities from the 20 days averaged before the 2020 Lunar New Year to the 20 days averaged after. This decline is 21 ± 5% larger than that from 2015 to 2019. We relate this reduction to two of the government's actions: the announcement of the first report in each province and the date of a province's lockdown. Both actions are associated with nearly the same magnitude of reductions. Our analysis offers insights into the unintended environmental and economic consequences through reduced economic activities., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

The TROPOspheric Monitoring Instrument (TROPOMI) is used to derive top-down NO X emissions for two large power plants and three megacities in North America. We first re-process the vertical column NO 2 with an improved air mass factor to correct for a known systematic low bias in the operational retrieval near urban centers. For the two power plants, top-down NO X emissions agree to within 10% of the emissions reported by the power plants. We then derive top-down NO X emissions rates for New York City, Chicago, and Toronto, and compare them to projected bottom-up emissions inventories. In this analysis of 2018 NO X emissions, we find a +22% overestimate for New York City, a -21% underestimate in Toronto, and good agreement in Chicago in the projected bottom-up inventories when compared to the top-down emissions. Top-down NO X emissions also capture intraseasonal variability, such as the weekday versus weekend effect (emissions are +45% larger on weekdays versus weekends in Chicago). Finally, we demonstrate the enhanced capabilities of TROPOMI, which allow us to derive a NO X emissions rate for Chicago using a single overpass on July 7, 2018. The large signal-to-noise ratio of TROPOMI is well-suited for estimating NO X emissions from relatively small sources and for sub-seasonal timeframes.

TROPOMI, on-board the Sentinel-5 Precursor satellite is a nadir-viewing spectrometer measuring reflected sunlight in the ultraviolet, visible, near-infrared, and shortwave infrared spectral range. From these spectra several important air quality and climate-related atmospheric constituents are retrieved at an unprecedented high spatial resolution, including nitrogen dioxide (NO 2 ). We present the first retrievals of TROPOMI NO 2 over the Canadian Oil Sands, contrasting them with observations from the OMI satellite instrument, and demonstrate its ability to resolve individual plumes and highlight its potential for deriving emissions from individual mining facilities. Further, the first TROPOMI NO 2 validation is presented, consisting of aircraft and surface in-situ NO 2 observations, as well as ground-based remote-sensing measurements between March and May 2018. Our comparisons show that the TROPOMI NO 2 vertical column densities are highly correlated with the aircraft and surface in-situ NO 2 observations, and the ground-based remote-sensing measurements with a low bias (15-30 %) over the Canadian Oil Sands., Plain Language Summary: Nitrogen dioxide (NO 2 ) is a pollutant that is linked to respiratory health issues and has negative environmental impacts such as soil and water acidification. Near the surface the most significant sources of NO 2 are fossil fuel combustion and biomass burning. With a recently launched satellite instrument (TROPOspheric Monitoring Instrument; TROPOMI) NO 2 can be measured with an unprecedented combination of accuracy, spatial coverage, and resolution. This work presents the first TROPOMI NO 2 measurements near the Canadian Oil Sands and shows that these measurements have an outstanding ability to detect NO 2 on a very high horizontal resolution that is unprecedented for satellite NO 2 observations. Further, these satellite measurements are in excellent agreement with aircraft and ground-based measurements.

Top-down estimates of surface NO X emissions were derived for 23 European cities based on the downwind plume decay of tropospheric nitrogen dioxide (NO₂) columns from the LOTOS-EUROS (Long Term Ozone Simulation-European Ozone Simulation) chemistry transport model (CTM) and from Ozone Monitoring Instrument (OMI) satellite retrievals, averaged for the summertime period (April⁻September) during 2013. Here we show that the top-down NO X emissions derived from LOTOS-EUROS for European urban areas agree well with the bottom-up NO X emissions from the MACC-III inventory data (R² = 0.88) driving the CTM demonstrating the potential of this method. OMI top-down NO X emissions over the 23 European cities are generally lower compared with the MACC-III emissions and their correlation is slightly lower (R² = 0.79). The uncertainty on the derived NO₂ lifetimes and NO X emissions are on average ~55% for OMI and ~63% for LOTOS-EUROS data. The downwind NO₂ plume method applied on both LOTOS-EUROS and OMI tropospheric NO₂ columns allows to estimate NO X emissions from urban areas, demonstrating that this is a useful method for real-time updates of urban NO X emissions with reasonable accuracy.