Tropomi (Tropospheric Monitoring Instrument) is the payload on board of the European Copernicus Sentinel 5 Precursor satellite, dedicated to atmospheric composition monitoring. Tropomi is an imaging spectrometer developed by The Netherlands in cooperation with ESA, measuring in the UV, visible, near infrared and shortwave infrared, with a spectral resolution in the range of 0.25 to 0.5 nm. The spatial sampling of Tropomi is approximately 3.5 x 5.5 km² for most of the bands and 3.5 x 7.0 km² in the shortwave infrared. The high spatial resolution in combination with the daily global coverage, are key features for the uptake of the Tropomi data for air quality and climate research, as well as for operational applications. The nominal lifetime of the Tropomi mission is 7 years, however there are currently no technical limitations to extent the lifetime many years longer.

Tropomi was launched in 2017 and the operational data recors starts in April 2018. All operational data products have recently been reprocessed to provide a 5-year consistent data record. The operational data products include short lived pollutants (NO₂, SO₂, CO, tropospheric O₃ and HCHO), O₃ total column and profiles, greenhouse gases (CH₄ and tropospheric O₃), and cloud and aerosol parameters. Furthermore, there are several research products for additional trace gases (CHOCHO, HONO and OClO), aerosol optical thickness, surface parameters (reflectance and vegetation fluorescence) and ocean color.

Tropomi already observed many important events, for example the monitoring of the reduced emissions due to global COVID-19 lockdowns, the discovery of several methane leaks of the oil and gas industry and landfills, the monitoring of the ash cloud of the Hunga Tonga volcanic eruption and the observation of large wildfire plumes from Australia, North and South America, Africa and Siberia.

In this contribution we present the current status, achievements and outlook of the Tropomi mission.

How to cite: Veefkind, J. P. and Team, T. T.: Tropomi on Sentinel 5 Precursor: 5 Years Data Record of the Atmospheric Composition for Air Quality, Climate and Ozone Layer Monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6815, https://doi.org/10.5194/egusphere-egu23-6815, 2023.

New remote sensing satellites for Earth Observation provide information to enhance situational awareness during exceptional socio-economic and natural events. Specifically, satellite-based observations of air pollutants, when combined with auxiliary (spaceborne and not) data, can provide new insights on societal and economic changes taking place around the world. Here we present a few applications of space-based atmospheric observations to support Finnish authorities. A recent history of such episodes includes: monitoring air quality changes in Helsinki during the COVID-19 pandemic; assessing methane emissions from the NordStream pipeline leakage over the Baltic Sea; assessing the environmental and social effects of the war in Ukraine as part of Finnish international cooperation projects. We integrate multiple data sources acquired by different spaceborne sensors including, e.g., the nitrogen dioxide (NO2) retrievals from the Copernicus Sentinel-5 Precursor/TROPOMI (TROPOspheric Monitoring Instrument), Sentinel-2 false color imagery, NASA VIIRS fire products as well as models and other environmental data.

How to cite: Ialongo, I., Virta, H., Hakkarainen, J., Szelag, M., Sundström, A.-M., and Tamminen, J.: Enhanced situational awareness in Finland using atmospheric (and other) space-based observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12496, https://doi.org/10.5194/egusphere-egu23-12496, 2023.

Air Quality (AQ) monitoring in Belgium has hitherto been relying mostly on in-situ measurements of surface concentration, with geographical gaps between observations filled in with numerical modelling ingesting (proxies for) bottom-up emission estimates.   However, a new generation of satellite sounders on sun-synchronous Low Earth Orbits (LEO) – like the Copernicus Sentinel-5(p) series – performs now daily global mapping of atmospheric composition down to the 3-km scale. Soon this daily global mapping will be complemented with geostationary instruments (GEO, e.g. Sentinel-4) observing the diurnal cycle in trace gas concentrations, although over the limited geographical area accessible from a geostationary viewpoint. This new constellation of satellite sounders is built to support detailed monitoring of AQ on the different relevant scales: from point-like emissions to intercontinental transport, and from city-level to international regulations established by public authorities to manage AQ in their area of responsibility. Nevertheless, uptake of these new satellite AQ data by the various Belgian stakeholders is not guaranteed.  Indeed, to realize the full complementary impact of this constellation of LEO and GEO satellites, i.e. to make their observations fit-for-purpose for air quality applications at the different scales, several challenges need to be addressed. These include (1) the need to enhance to sub-city scales the resolution of satellite data to make them better suited for the monitoring of e.g. the impact of the Low Emission Zones enforced in several European cities, (2) to characterize the non-trivial relation between the column amount of the pollutant measured by a satellite and the near-surface concentrations measured by in-situ networks, and (3) to determine how the different LEO and GEO vantage points lead to a different perception of atmospheric and surface details and how we can benefit from - or correct for - these differences.

Work on these challenges is taking place in the dedicated Belgian federal research project LEGO-BEL-AQ (2020-2023, https://lego-bel-aq.aeronomie.be/index.php) funded by BELSPO, with a particular focus on AQ in Belgium.  In this contribution, we demonstrate that a combination of temporal aggregation, careful data selection, and horizontal oversampling can produce a meaningful increase in horizontal resolution in S5P tropospheric NO₂ column maps, revealing policy-relevant features in the NO₂ distribution over Belgium’s major cities. Comparisons between our high-resolution S5P NO₂ maps and the near-surface in-situ observations as procured by the Belgian authorities, reveal high correlation when considering longer time scales (seasonal and annual), allowing a pragmatic conversion from tropospheric columns to near-surface concentrations over the complete Belgian domain, and consequently also a confrontation to WHO annual thresholds at the level of individual Belgian municipalities.

Acknowledgements

This work has been supported by the BELSPO BRAIN-be 2.0 project LEGO-BEL-AQ (https://lego-bel-aq.aeronomie.be)

How to cite: Verhoelst, T., Compernolle, S., Lambert, J.-C., Fierens, F., and Vanpoucke, C.: Monitoring Belgian air quality with LEO and GEO atmospheric composition data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12135, https://doi.org/10.5194/egusphere-egu23-12135, 2023.

The Sentinel-5P TROPOMI instrument provides unique observations of atmospheric composition at a high spatial resolution of about 5 km with near-daily global coverage. A new mission reprocessing of all official data products for the full operational phase (30 April 2018 until the present) is currently being generated, co-ordinated by ESA, and is expected to become available in March/April 2023. 

For NO2 this reprocessing will result in a uniform dataseries for the full mission based on processor version 2.4.0 and on version 2 level-1B input data. The reprocessing is replacing the currently available data record for NO2, based on processor versions 1.2 x, 1.3.x, 1.4.0, 2.2.0 and 2.3.1. The upgrades to v1.4 and v2.2 involved major upgrades. Validation activities indicated that the older versions 1.2 and 1.3 had a low bias, a problem which was (at least partly) resolved by introducing v1.4 and v2.y. However, this also implies that trend studies are severely hampered by the jumps in the data series resulting from this sequence of updates. In order to support COVID-19 lockdown air pollution studies an intermediate "PAL" reprocessing of NO2 was made available in December 2021 based on the v2.3.1 NO2 processor and v1 L-1B data (https://data-portal.s5p-pal.com/products/no2.html).  The new official reprocessing with v2.4.0 and v2 L-1B will replace this PAL dataset. The new official reprocessing is expected to be of great use for studying the inter-annual variability in NO2 in recent years.  

The major change in v2.4.0 compared to v2.3.1 is the replacement of the surface albedo datasets with a directional (viewing-angle dependent) Lambertian equivalent reflectivity (DLER) database derived from TROPOMI observations. Before the TROPOMI NO2 processor made use of the OMI (for the NO2 fitting window) and GOME-2 (for the O-2A band spectral region used for the cloud retrieval) LER. The use of the TROPOMI DLER is especially important for the cloud fraction and cloud pressure retrievals, because the clear-sky reflectivity in the NIR is very sensitive to the (viewing) geometry. 

In our contribution we will present the new reprocessed TROPOMI NO2 v2.4 dataset and quantify the impact of the use of the TROPOMI DLER on the cloud properties, air-mass factor and tropospheric NO2 column. 

How to cite: Eskes, H., van Geffen, J., Boersma, K. F., Tilstra, G., Sneep, M., and Veefkind, P.: Sentinel-5P TROPOMI NO2 reprocessing v2.4.0: Impact of the surface albedo, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15936, https://doi.org/10.5194/egusphere-egu23-15936, 2023.

Scattering of light in the atmosphere and low sea surface albedo decrease the sensitivity of satellites to air pollution close to the sea surface. To reliably retrieve tropospheric nitrogen dioxide (NO₂) columns using the TROPOspheric Monitoring Instrument (TROPOMI), it is therefore necessary to have good a priori knowledge of the vertical distribution of NO₂. In this study, we used an aircraft of the Royal Belgian Institute of Natural Sciences, part of the Belgian coastguard structure, which was already equipped with a sniffer sensor system, measuring CO₂, NO_x and SO₂. This instrumentation enables us (1) to capture pollution plumes originating from ships sailing within an Emission Control Area, and (2) to validate TROPOMI tropospheric NO₂ columns over the polluted North Sea in summer 2021 and (3) to evaluate vertical profile shapes from several chemical models. We observe multiple clear signatures of ship plumes from seconds after emission to multiple kilometers downwind. Besides that, our results show that the chemical transport model TM5, which is used in the retrieval of the operational TROPOMI data, tends to underestimate surface level pollution while overestimating NO₂ at higher levels over the polluted North Sea. The higher horizontal resolutions in the regional CAMS ensemble mean and LOTOS EUROS improve the surface level pollution estimates, but the models still systematically overestimate NO₂ levels at higher altitudes, indicating exaggerated vertical mixing in the models. When replacing the TM5 a priori NO₂ profiles with the aircraft-measured NO₂ profiles in the air mass factor (AMFs) calculation, we find that recalculated AMFs reduce, and the retrieved NO₂ columns increase by 20%. This indicates a significant low bias in TROPOMI tropospheric NO₂ measurements over the North Sea. This low bias has important implications for estimating emissions over the sea. While TROPOMI NO₂ low biases caused by the TM5 a priori profiles have previously also been reported over land, the reduced vertical mixing and smaller surface albedo over sea makes this issue especially relevant over sea and coastal regions. 

How to cite: Rieß, T. C. V. W., van Vliet, J., Van Roy, W., de Laat, J., Dammers, E., and Boersma, F.: Aircraft validation reveals a 20% low bias in TROPOMI NO2 over sea caused by TM5 a priori profiles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13654, https://doi.org/10.5194/egusphere-egu23-13654, 2023.

Observations of the vertical distribution of nitrogen oxides (NO_x ≡ NO + NO₂) in the troposphere are severely limited, despite its influence on ozone formation. Here, we derive vertical profiles of the NO_x component NO₂ by applying cloud-slicing to partial columns of NO₂ from the space-based TROPOMI instrument. This yields seasonal means of NO₂ volume mixing ratios at ~100 km resolution for multiple years (March 2018 to February 2022) on a global scale in the upper troposphere (180-320 hPa and 320-450 hPa), the middle troposphere (450-600 hPa and 600-800 hPa) and the boundary layer (800 hPa to the Earth’s surface). We evaluate our product against in situ NO₂ measurements from NASA DC-8 aircraft campaigns over Canada (ARCTAS, ATom, INTEX-A), the Eastern US (ATom, SEAC4RS, INTEX-A), the North and South Atlantic (ATom), and the Central and South Pacific (ATom) and use our validated dataset to assess state-of-knowledge of global tropospheric NO_x as simulated by GEOS-Chem.  In the middle troposphere, cloud-sliced NO₂ has a mean value of 20-40 pptv and deviates by < 5 pptv where NO₂ from aircraft observations exceeds the instrument detection limit. The consistency between cloud-slicing results and aircraft observations here is due to high sampling frequency and ideal conditions for cloud-slicing. Differences with aircraft observations are larger (up to 120 pptv) in the upper troposphere between 320-180 hPa where aircraft observations may be susceptible to biases and where cloud-sliced NO₂ data are relatively sparse. In the boundary layer, retrievals consistent with the aircraft observations are only possible over marine environments where NO₂ concentrations differ by < 35 pptv compared to > 450 pptv over terrestrial regions. This is because large land-based NO_x sources cause steep vertical NO₂ gradients that are problematic for cloud-slicing which assumes NO₂ is well mixed throughout the troposphere. We find that NO₂ concentrations above the Eastern US differ by < 20 pptv when comparing cloud-sliced tropospheric vertical profiles to simulated vertical profiles from the GEOS-Chem chemical transport model. However, GEOS-Chem consistently underestimates concentrations of NO₂ in the remote troposphere, simulating concentrations that are 50% less than the mean cloud-sliced NO₂ observations. This is a result of the limited number of current NO₂ observations used to validate models like GEOS-Chem which are limited in both time and space. By deriving tropospheric vertical profiles from cloud-slicing satellite observations there is an opportunity to obtain routine NO₂ observations which can then be compared to aircraft measurements and simulations from the GEOS-Chem model. From this, we can determine the environmental factors that impact tropospheric NO_x on a global scale and address long-standing uncertainties in our understanding of NO_x in the troposphere.

How to cite: Horner, R., Marais, E., and Wei, N.: Retrieval and validation of global tropospheric nitrogen dioxide (NO2) vertical profiles obtained via cloud-slicing TROPOMI partial columns, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5358, https://doi.org/10.5194/egusphere-egu23-5358, 2023.

Over the last 20 years, low-Earth orbit (LEO) atmospheric composition observations have provided amazing satellite measurements of atmospheric pollutants, mainly at continental-to-global, weekly-to-seasonal scales. The new-generation geostationary (GEO) satellite perspective, with high spatial resolution and hourly measurements, represents a major step forward in capability for understanding how air quality processes change diurnally at the local scale. South Korea's Geostationary Environment Monitoring Spectrometer (GEMS) was launched in February 2020 over Asia and is the first member of the GEO constellation that will eventually include the Tropospheric Emissions: Monitoring Pollution (TEMPO) mission over North America, and Sentinal-4 over Europe. The measurement hourly time resolution is truly the new perspective that the GEO platform provides, and in this presentation, we use a combination of satellite observations from GEMS and chemical transport model simulations to investigate the diurnal variation of pollution over several Asian regions. When considering the GEMS whole-Asia field-of-regard, the most striking impression of the NO2 diurnal variation is of how large it is in magnitude as well as how much the spatial distribution changes hour-by-hour. This questions our understanding of the distributions of reactive species based on the representativeness of once-a-day LEO observations. To help understand daily differences in diurnal patterns at regional and local scales, we use the Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA-V0). This uses a global modeling framework with regional grid refinement to resolve chemistry at emission and exposure relevant scales. The model shows reasonable agreement with the GEMS data and captures the different diurnal patterns at the different spatial scales and the degree of day-to day variability. The model also allows the drivers of variability due to emissions, meteorology, and photochemistry to be considered separately. The results of this analysis are further compared with the NO2 diurnal variability observed by PANDORA sun spectrometer measurements at polluted and less-polluted Korean and other Asian sites. We investigate spatial scale, including the city-scale within Seoul, at which GEMS captures the differences in diurnal variability between the PANDORAs.

How to cite: Edwards, D., Martinez-Alonso, S., Jo, D., Ortega, I., Emmons, L., Worden, H., and Kim, J.: The diurnal variation of pollutant distributions over Asia using observations from the Geostationary Environment Monitoring Spectrometer (GEMS), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9634, https://doi.org/10.5194/egusphere-egu23-9634, 2023.

Agricultural emissions of nitric oxide (NO) from soils can cause formation of tropospheric ozone and particulate matter pollution, and in the presence of ozone NO is rapidly transformed nitrogen dioxide (NO2), itself an air pollutant. Emissions of NO from soils can be highly episodic, with a large proportion of annual emissions occurring in pulses following fertilization or wetting of dry soils. Freeze-thaw events may also be an important source of NO pulses, but the magnitude of these emissions is poorly understood, as are the mechanisms underlying freeze-thaw NO pulses and their influence on atmospheric composition and air quality.  Here we use daily observations of NO₂ and air temperature for 2018-2021 from atmospheric monitoring stations in the Corn Belt of the midwestern United States to evaluate the potential influence of freeze/thaw events on atmospheric NO_x.  We supplement these data with retrievals of NO₂ from the Tropospheric Pollution Monitoring Instrument (TROPOMI) screened to include acceptable retrievals over snow, retrievals of soil freeze/thaw status from the Soil Moisture Active Passive project (SMAP), and observed and reanalyzed soil temperature.  We find evidence for elevated NO₂ concentrations during winter months, including instances of elevated concentrations at the onset of spring thaw.  Freezing degree days—the accumulation of average daily temperature for days with soil temperature maxima of 0°C or less—fail to act as a clear predictor of the magnitude of post-thaw concentrations.  These results will be integrated with complementary high temporal-resolution eddy covariance flux measurements at a long-term agricultural research station in Nebraska, along with soil core incubations involving biogeochemical and molecular analyses, to provide insights into the magnitude of freeze/thaw fluxes and their underlying mechanisms.

How to cite: Hickman, J., Dammers, E., Pusede, S., Miles, M., Suyker, A., Awada, T., Maul, J., and Groffman, P.: Investigating Freeze/Thaw soil emissions of nitric oxide using in situ and Tropomi observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10465, https://doi.org/10.5194/egusphere-egu23-10465, 2023.

Ships are important emission sources of nitrogen oxides (NOx), which are relevant pollutants in the atmosphere affecting the environment and human health. Global shipping plays a big role in transporting goods around the world. For decades, some of the busiest shipping lanes have been tracked by satellites from space. With TROPOMI aboard the Sentinel 5-Precursor (S5P), the potential for detecting shipping emissions has increased due to its low noise and high spatial resolution of 5.5 x 3.5 km². Previous studies have shown that even individual ship plumes can be identified from TROPOMI data.

In this study, we use different filtering methods to identify on a global scale as many shipping emission signals as possible from the TROPOMI data. One important aspect is to focus on finding real shipping signals and avoiding inadvertently interpreting a priori information. The aim of this study is to contribute to the progress of satellite remote sensing of shipping emissions and to better understand air pollution caused by the shipping sector and their effect on the environment.

How to cite: Latsch, M., Richter, A., and Burrows, J. P.: Improving the detection of global NOx emissions from shipping in S5P/TROPOMI data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5353, https://doi.org/10.5194/egusphere-egu23-5353, 2023.

Nitrogen oxides (NO_x) emissions play an important role in air quality, the nitrogen cycle, and as precursor for climate gasses. The most important sources of NO_x emissions are fossil fuel burning (industry and traffic) and the release from soil.

With the inversion algorithm DECSO (Daily Emissions Constrained by Satellite Observations) we derive quantitative NO_x emissions on a 5 to 20 km resolution from TROPOMI (on Sentinel 5p) observations of NO₂, taking advantage of the fine spatial resolution (5 x 3.5 km) of the TROPOMI instrument. DECSO is a full inversion algorithm based on data assimilation of satellite observation and the Chemical-transport model CHIMERE. For the data assimilation a Kalman Filter technique is used. For the inversion no apriori information of the NO_x emissions is needed and for this reason new sources can be detected. In the postprocessing we use the seasonal cycle to distinct between soil emissions (having strong seasonal cycle with summer peak) and anthropogenic emissions (having low variability over the year).

To assess the quality of satellite-derived NO_x emissions on various scales, i.e. national, regional, city and points-sources, they are compared to various bottom-up inventories. For bottom-up emissions we selected NEC (National Emission Ceilings Directive inventory), LRTAP (LRTAP convention data), the CAMS (Copernicus Atmosphere Monitoring Service) regional anthropogenic emission database and the high-resolution emission inventory HERMES (High-Elective Resolution Modelling Emission System) for Catalonia. Detailed results will be shown, including the spatial and temporal variation per emission category.

How to cite: van der A, R., Ding, J., Mijling, B., Eskes, H., and Guevara, M.: Evaluation of satellite-derived NOx emissions over Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8272, https://doi.org/10.5194/egusphere-egu23-8272, 2023.

We have analyzed TROPOMI NO₂ data over the Copperbelt, a mining region which straddles the Democratic Republic of Congo and Zambia. While the ore mined there is primarily copper, this region is currently of great strategic interest because it is the world’s biggest producer of cobalt. Demand for cobalt, key to clean energy technologies (e.g., electric car batteries), is increasing worldwide and cobalt control is becoming a matter of national and global energy security. The impact of increasing mining-related activities on local air quality (high NO_x is harmful to respiratory systems and crops) is unknown.

TROPOMI, onboard ESA’s Sentinel-5 Precursor, is an imaging spectrometer in a sun-synchronous orbit at 824 km of altitude which measures concentrations of relevant atmospheric species (trace gases, aerosols, cloud) with quasi-global daily coverage and at high spatial resolution (~ 3.5 x 5.5 km² in the case of NO₂).

We show that mining-related activities (such as extraction, smelting, and refining) can be remotely detected based on their TROPOMI NO₂ signature, even in the presence of high background NO₂ from biomass burning. Annual TROPOMI NO₂ means for 2019, 2020, and 2021 show local enrichments consistent with point sources spatially collocated with both mines and large cities where mining-related activities take place. We have identified temporal trends in NO₂ from these point sources and, when possible, we have compared those to production figures from the mining companies involved. We have quantified top-down annual NO_x (NO+NO₂) emissions for each of the point sources identified by applying the divergence method to the TROPOMI retrievals, using ancillary ERA5 meteorological data. Because in situ NO_x measurements are not available, we contrast our emission results with emissions from the CAMS-GLOB-ANT v5.1 inventory.

Our results show that NO_x emissions from mining-related activities can be quantified remotely, which is important in the absence of local air quality monitoring. They also demonstrate that NO₂ trend analysis can be a good indicator of mine production. This is particularly relevant for non-publicly traded mining companies, which are not required to publish their production figures. Lack of TROPOMI SO₂ enhancements colocated with our NO₂ point sources is consistent with SO₂ capture and transformation into H₂SO₄, which is then used in mining-related processes or commercialized.

How to cite: Martínez-Alonso, S., Veefkind, P., Dix, B., Gaubert, B., Granier, C., Soulié, A., Darras, S., Theys, N., Emmons, L., Eskes, H., Tang, W., Worden, H., deGouw, J., and Levelt, P.: Emissions from Mining-related Activities in Africa using TROPOMI Satellite Observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9659, https://doi.org/10.5194/egusphere-egu23-9659, 2023.

We map high-resolution nitrogen oxides (NO_x) emissions in US cities from the retrieved TROPOspheric Monitoring Instrument (TROPOMI) tropospheric nitrogen dioxide (NO₂) columns. A new database of gridded emissions at a horizontal spatial resolution of 0.05°×0.05° has been developed using our newly-developed CTM-Independent SATellite-derived Emission estimation Algorithm for Mixed-sources (MISATEAM). We validate the accuracy of MISATEAM using synthetic NO₂ observations derived from the NASA-Unified Weather Research and Forecasting (NU-WRF) model at a horizontal spatial resolution of 4 km × 4 km. The validation results demonstrate the excellent agreement between the inferred emissions magnitudes and the NU-WRF given ones with a correlation coefficient (R) of 0.99 and a normalized mean bias (NMB) of -0.08. They also show a consistent spatial pattern with R of 0.88 ± 0.06 for all investigated cities when comparing inferred and given emissions at grid level. The TROPOMI-based database derived in this study includes annual emission maps for 39 US large cities from 2018 to 2021. While there is a good agreement with national emission inventory (NEI) in general, there are noticeable differences in spatial pattern in some cases. The satellite-derived spatiotemporal patterns of NO_x emissions complement information difficult to capture in the conventional emission inventories compiled with “bottom-up” methods by suggesting the misallocation of emissions and/or missing sources. We expect to extend the database globally and also include estimates based on NO₂ observations from OMI to provide a longer time record. The method could also be applied to data from future geostationary satellites, such as Geostationary Environment Monitoring Spectrometer (GEMS) or the Tropospheric Emissions: Monitoring Pollution (TEMPO) instrument, to provide diurnal variations in NO_x emissions.

How to cite: Liu, F., Beirle, S., Joiner, J., Choi, S., Tao, Z., Knowland, K. E., Smith, S., Tong, D. Q., and Wagner, T.: High-resolution mapping of nitrogen oxides emissions in US large cities from TROPOMI retrievals of tropospheric nitrogen dioxide columns, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1606, https://doi.org/10.5194/egusphere-egu23-1606, 2023.

We present an updated (v2) catalog of NO_x emissions from point sources 
as derived from TROPOMI measurements of NO₂ (PAL product) combined with wind fields from ERA5.
Several improvements have been introduced to the algorithm. 
Most importantly, several corrections are applied, 
accounting for the effects of plume height on satellite sensitivity, 3D topographic effects, 
and the chemical loss of NO_x, 
resulting in considerably higher and more accurate NO_x emissions. 
In addition, error estimates are provided for each point source.
        
The catalog v2 is based on a fully automated iterative detection algorithm of point sources worldwide.
It lists 1139 locations that have been found to be significant NO_x sources.
The majority of these locations match to power plants listed in the global power plant database.
Other NO_x point sources correspond to cement plants, metal smelters, industrial areas, or medium-sized cities.
        
The emissions listed in v2 of the catalog show good agreement (within 20% on average) 
to emissions reported by German Environment Agency (Umweltbundesamt, UBA) 
as well as the United States Environmental Protection Agency (EPA). 

How to cite: Beirle, S., Borger, C., Jost, A., and Wagner, T.: Improved catalog of NOx point source emissions (version 2), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5029, https://doi.org/10.5194/egusphere-egu23-5029, 2023.

Chemical transport models (CTMs) are commonly used to model air pollutant concentrations. CTMs, on the other hand, require a lot of computing power and sometimes yield biased findings that result from emission inventories and chemical mechanisms employed. Machine learning algorithms are used in a wide range of fields, including Earth system science. Its popularity stems from its ability to learn complex non-linear relationships. As a follow-up of our previous study [1], we attempted to deduce the capability of Machine Learning (ML) in modelling air pollutant concentrations.

In this study, we employed the Gradient Boosted Tree (GBT) algorithm to model near-surface NO₂ and O₃ over Germany at 0.1 degree resolution and daily intervals. The GBT model is trained using TROPOMI satellite column NO₂, O₃, HCHO data, as well as meteorology and road density as an information for NO_X emission sources. Government air quality (NO₂ and O₃) observations from urban, suburban, and background stations are used as target variables; 321 stations are considered for NO₂ ML model training and 256 stations are considered for O₃ ML model training. The GBT model trained for near-surface NO₂ explains 68-88% of observed concentrations, whereas, for near-surface O₃, the GBT model explains 74-92% of observed concentrations. 

Road density and TROPOMI NO₂ data are the most important features in the fitted model for near-surface NO₂. This is due to the fact that road density (a proxy for traffic) is the main source of near-surface NO_X emission, and the TROPOMI tropospheric NO₂ column is a good representation of near-surface NO₂ concentration. The downward UV radiation (DUV) at the surface and temperature are the most important features in the fitted model for near-surface O₃. Since O₃ is formed from the photolysis of NO₂, DUV plays an important role in the fitted model for O₃. Temperature is the driver of biogenic Volatile Organic Compounds (VOCs), which are an important precursor to O₃. 

In all cases, the GBT model outperforms feed-forward neural networks. Furthermore, the developed GBT model for near-surface O₃ is reliably transferable to other locations and countries (R²=0.87-0.94), whereas the developed model for near-surface NO₂ is moderately transferable (R²=0.32-0.68). The reason could be that the road density is not the best representative of traffic NO_X emissions and can be improved in a future study. Overall, we developed a new machine learning model to cost-effectively model the near-surface NO₂ and O₃ concentrations, which could help us to better understand the air pollution distribution at a moderate resolution.

References:

Balamurugan, V., Balamurugan, V. and Chen, J., 2022. Importance of ozone precursors information in modelling urban surface ozone variability using machine learning algorithm. Scientific reports, 12(1), pp.1-8.

How to cite: Balamurugan, V., Chen, J., Wenzel, A., and Keutsch, F. N.: Modelling of near-surface NO2 and O3 concentration over Germany using machine learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8715, https://doi.org/10.5194/egusphere-egu23-8715, 2023.

Nitrogen dioxide (NO₂) is among the major air pollutants in Europe posing severe hazard to environmental and human health. The concentrations of surface NO₂ are measured by ground monitoring stations which are fairly limited in representation and distribution. While NO₂ estimates from chemical transport models are realistic, their complexity makes them computationally intensive. Satellite observations from instruments such as TROPOMI provide high spatiotemporal distribution of NO₂. However, these instruments capture NO₂ density only along the tropospheric column and not on the surface. Exploiting the availability of ground station measurements and spatially continuous information from TROPOMI, this study estimates surface NO₂ concentrations over Europe at 1km spatial resolution for 2019-2021 using XGBoost machine learning model. While ground measurements are used as target reference features, satellite observations such as tropospheric column density of NO₂ (from TROPOMI), night light radiance (from VIIRS), NDVI (from MODIS) and modelled meteorological parameters such as planetary boundary layer height, wind velocity, temperature are used as input features to the model. We find an overall mean absolute error of 7.87µg/m3, mean bias of -3.13µg/m3 and spearman correlation of 0.61 during model validation. We found that the performance of the model is influenced by NO₂ concentration levels and is most reliable for predictions at concentration levels <40µg/m3 with a relative bias of <40%. The spatial error analysis also indicates the spatial robustness of the model across the study area. The importance of input features is evaluated using SHapley Additive exPlanations (SHAP), which shows TROPOMI NO₂ being the most important source for the modelled NO₂ predictions. Furthermore, SHAP values also highlight the role of VIIRS night light radiance in deriving finer detailed spatial patterns of surface NO₂ estimates. Despite the complex non-linear relationship of the input features, the trained XGBoost model requires an average of 570 seconds to predict single day surface NO₂ concentrations for the large study area of continental scale. Thus, this work evaluates the importance of TROPOMI data and reliability of machine learning models for estimating surface NO₂ concentrations on a larger spatial scale.

How to cite: Shetty, S., Schneider, P., Stebel, K., Kylling, A., Koren Berntsen, T., and Hamer, P.: Evaluation of TROPOMI observations for estimating surface NO2 concentrations over Europe using XGBoost Model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7796, https://doi.org/10.5194/egusphere-egu23-7796, 2023.

To support the ambition of national and EU legislators to substantially lower greenhouse gas (GHG) emissions as ratified in the Paris Agreement on Climate Change, an observation-based "top-down" GHG monitoring system is needed to complement and support the legally binding "bottom-up" reporting in national inventories. For this purpose, the European Commission is establishing an operational anthropogenic GHG emissions Monitoring and Verification Support (MVS) capacity as part of its Copernicus Earth observation programme. A constellation of up to three CO₂, NO₂, and CH₄ monitoring satellites (CO2M) will be at the core of this MVS system. The satellites, to be launched from 2026, will provide images of CO₂, NO₂, and CH₄ at a resolution of about 2 km  2 km along a 250-km wide swath. This will not only allow observing the large-scale distribution of the two most important GHGs (CO₂ and CH₄), but also capturing the plumes of individual large point sources and cities.

The divergence method can be used to estimate point source emissions from satellite images, using fewer assumptions than other light-weight plume quantification methods (e.g., no a-priori source locations have to be known). However, the method only uses a few pixels near a point source, while pixels downstream of the source are implicitly excluded. Combined with the high noise of, in particular, CO₂ satellite images, the divergence computed from a single overpass image is usually too noisy for CO₂ emission quantification. In order to improve the information content in divergence maps, it is therefore common to average the map over many images (e.g., computing monthly or yearly averages) to get better emission estimates. As a result, the temporal resolution of the divergence method is limited.

In this work, we present a novel approach to improve the information content in CO₂ divergence maps, by exploiting the joint information content present in the simultaneously acquired CO₂ and NO₂ images. The purpose of this approach is to allow us to compute accurate divergence maps based on fewer images than typically required for the divergence method, and thus to obtain a finer temporal resolution of emission estimates. The method assumes that the signal-to-noise ratio of NO₂ images is better than that of CO₂ images, while both images contain a similar set of plumes related to emission point sources. Based on the signal-to-noise ratio in the CO₂ and NO₂ images and their covariances, we can estimate the optimal CO₂ image and optimal CO₂ divergence map in the minimum mean square error (MMSE) sense using a linear MMSE estimator. We demonstrate the effectiveness of this estimator on examples from the SMARTCARB dataset (Kuhlmann et al., 2020), and show that an about +20 dB boost in the peak signal-to-noise ratio (PSNR) can be achieved for individual overpass CO₂ divergence maps, which is roughly equivalent to the PSNR improvement otherwise obtained by averaging 10 images. Our approach therefore allows us to estimate CO₂ emissions over shorter observation periods and increases the emission estimation accuracy of weaker sources.

How to cite: Koene, E., Kuhlmann, G., Emmenegger, L., and Brunner, D.: Bayesian estimation of CO2 flux divergence maps using joint (CO2M-like) NO2 and CO2 images, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6463, https://doi.org/10.5194/egusphere-egu23-6463, 2023.

This study presents a comprehensive analysis of the spatio-temporal variability of TROPOMI NO2 tropospheric columns (TrC-NO₂) over the Iberian Peninsula over the period 2018-2021 (using the recently released PAL product to ensure consistency).

A first exploration of the impact of cloud cover on the availability of TROPOMI TrC-NO2 observations indicates that data gaps range between 20-30% in summer to 55-70% in April and November, with substantial spatial differences between northern and southern and/or arid areas. The spatial distribution of TrC-NO₂ highlights strong hotspots over urban areas (especially Madrid and Barcelona), with additional enhancements along international maritime routes and major highways. A reasonable correlation with surface NO₂ mixing ratios is found, around 0.7-0.8 depending on the averaging time.

The weekly and monthly variability of TrC-NO₂ over the peninsula is then analyzed at the light of the urban cover fraction (taken from the Copernicus Land Monitoring Service). From least to most urbanized areas, the weekend reduction is found to range from -10 to -40%. A detailed analysis at the intra-agglomeration scale highlights that strongest weekend effects do not always peak in the center but sometimes in surrounding cities, which suggests a larger contribution of commuting to total NOx anthropogenic emissions. Similarly, the monthly profiles strongly change depending on the level of urbanization, from -40%/+26% in summer/winter in most urbanized areas, to -10%/+20% in least urbanized ones. Interestingly, the same analysis applied to cropland fraction highlight an enhancement in June-July that could be due to natural soil NO emissions that are known to peak during the warm season. Beyond some specific discrepancies, a generally good consistency is found between the variability of NO₂ seen from space with TROPOMI and the one observed at the surface.

Our study thus illustrates the potential of TROPOMI TrC-NO₂ to provide a valuable complement to surface monitoring network, especially in agricultural and maritime areas where surface NO₂ observations are missing but yet crucial for better understanding the impact of local NOx emissions, especially for the production of tropospheric ozone.

Petetin, H., Guevara, M., Compernolle, S., Bowdalo, D., Bretonnière, P.-A., Enciso, S., Jorba, O., Lopez, F., Soret, A., and Pérez García-Pando, C.: Potential of TROPOMI for understanding spatio-temporal variations in surface NO₂ and their dependencies upon land use over the Iberian Peninsula, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1056, 2022.

How to cite: Petetin, H., Guevara, M., Compernolle, S., Bowdalo, D., Bretonnière, P.-A., Enciso, S., Jorba, O., Lopez, F., Soret, A., and Pérez García-Pando, C.: Potential of space-based TROPOMI observations for understanding the spatial  and temporal variability of surface NO2 and its dependencies upon land use over south-western Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6310, https://doi.org/10.5194/egusphere-egu23-6310, 2023.

Remote sensing from hyperspectral satellite instruments, such as OMI, TROPOMI and OMPS, can simultaneously obtain the spatio-temporal distribution of several species of trace gases, which has been widely used to study the emissions, regional transport and physical and chemical evolution of trace gases. Nevertheless, there were very few relevant studies using Chinese satellite instruments, because the poor spectral quality makes it extremely difficult to retrieve data from the spectra of the Environmental Trace Gases Monitoring Instrument (EMI), the first Chinese satellite-based ultraviolet–visible spectrometer monitoring air pollutants. In this study, we performed on-orbit wavelength calibration to calculate daily instrumental spectral response functions (ISRFs) and wavelength shifts to diminish the fitting residuals. For the retrieval under the low signal-to-noise ratio (SNR) of EMI, an adaptive iterative retrieval algorithm is set up to select the retrieval setting best with minimum uncertainty. Besides, we used simulated irradiance instead of measured irradiance to obtain the requisite daily solar spectrum for the following retrieval algorithm, because EMI only provides the solar spectrum once every six months. Through these algorithm updates, several trace gases, such as Ozone (O₃), nitrogen dioxide (NO₂), sulfur dioxide (SO₂) and formaldehyde (HCHO), were retrieved from EMI with comparable accuracy of OMI and TROPOMI. The retrieval results from EMI were used to locate emission sources, evaluate regional transport and trace the change of air quality due to important events, such as COVID-19 pandemic, China International Import Expo and Beijing Winter Olympic Games.

How to cite: Liu, C., Zhang, C., Su, W., Hu, Q., Zhao, F., Li, Z., Xing, C., Liu, H., and Tan, W.: Remote sensing of trace gases with Chinese satellite instruments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4767, https://doi.org/10.5194/egusphere-egu23-4767, 2023.

Contributing to the European Union’s Copernicus Earth Observation programme since October 2017, the Sentinel-5 Precursor (S5P) satellite mission is dedicated to global atmospheric composition measurements for the monitoring and study of air quality and climate. On board of the S5P early afternoon polar satellite, the imaging spectrometer TROPOMI (TROPOspheric Monitoring Instrument) performs nadir measurements of the Earth’s radiance from the UV-visible to the short-wave-infrared spectral ranges at a much finer spatial resolution than its predecessors do, and from which the global distribution of several atmospheric trace gases is retrieved daily, including ozone.

Ozone in the troposphere is the third most important anthropogenic contributor to greenhouse radiative forcing. The distribution of tropospheric ozone is highly variable over a wide range of spatial and temporal scales due to a complex interplay between dynamical, chemical, and radiative processes. Global measurement systems are faced with the challenge of accurately capturing this variability at the scale of interest. In this contribution, we therefore present a comprehensive quality assessment of the recently reprocessed and hence homogenous TROPOMI tropospheric ozone data records, and demonstrate their application. A distinction is made between the tropospheric column and profile products.

The Convective Cloud Differential technique (CCD) is applied to derive three-day running mean ozone columns between surface and 270 hPa over the tropical belt, covering nearly five years of TROPOMI data. These data are characterized primarily by analysing comparisons to SHADOZ ozonesonde and other, currently operating GOME-type sounders (EOS-Aura OMI, Metop-B GOME-2). We will show that the TROPOMI bias varies somewhat with reference instrument, but generally remains below about four Dobson Units. We find signs of a weak latitudinal pattern and a moderate seasonal pattern in the mean differences, again, depending on the reference instrument.

TROPOMI’s operational ozone profile retrieval algorithm is based on the optimal estimation method and was implemented in November 2021, now covering over one year of data. Validation results are collected from both the ESA/Copernicus Atmospheric Mission Performance Cluster/Validation Data Analysis Facility (ATM-MPC/VDAF) and from the S5P Validation Team (S5PVT) AO project CHEOPS-5p. The quality assessment relies on the analysis of retrieval diagnostics in terms of data and information content studies, and on the comparison of TROPOMI data with ground-based measurements. The latter are acquired by ozonesondes and lidars taking part in WMO's Global Atmosphere Watch and its contributing networks NDACC and SHADOZ, and are collected in a harmonized formatting from the ESA Atmospheric Validation Data Centre (EVDC). With a mean comparison bias below 10 % throughout the entire profile and a dispersion of the order of 20 % in the troposphere, the mission requirements are mostly met.

Finally, our verification of the presence of known geophysical structures and cycles confirms that TROPOMI tropospheric ozone data meet the needs of the atmospheric research community. Ultimately, this work paves the way for a more comprehensive evaluation of tropospheric ozone data records contributing to the ongoing tropospheric ozone assessment report (TOAR).

How to cite: Keppens, A., Hubert, D., Lambert, J.-C., Di Pede, S., Veefkind, P., Heue, K.-P., Loyola, D., and Dehn, A. and the S5P MPC VAL team, CHEOPS-5p validation team, and the SHADOZ ozonesonde station PIs and staff: TROPOMI tropospheric ozone data: Quality assessment and application, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5653, https://doi.org/10.5194/egusphere-egu23-5653, 2023.

The past two decades have been the golden age of tropospheric composition sounding with instruments like the Tropospheric Emission Spectrometer (TES), the Infrared Atmospheric Sounding Interferometer (IASI), SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY),  the Ozone Mapping Interferometer (OMI) harnessing spectral radiation from the thermal infrared to the ultraviolet to pioneer new products such as tropospheric ozone, ammonia, methane, nitrogen dioxide, and water vapor deuterium.  As a new generation of sounders that include both geostationary and low earth orbiting satellites become the anchor of a global air quality monitoring system, there is an urgent need to shift away from an instrument focus, which is a confined to a  band of frequencies,  to a measurement focus, which incorporates the best available information.  The TROPESS project is a new measurement focused approach that uses a common retrieval algorithm applied to a suite of instruments either singularly or in combination.  Building on the heritage of TES, we show how the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES) algorithm has been applied to produce ozone from TES, AIRS, and OMI as well as its potential to combine CrIS and TROPOMI radiances.  Employing proven optimal estimation techniques, we further show how data produced from MUSES can be ingested into chemical data assimilation providing a comprehensive understanding of global atmospheric chemistry and its evolution.  The suite of products and their scientific impact on atmospheric composition are surveyed.  TROPESS points to a new paradigm that can effectively harness a constellation of data to quantify the rapid changes in the landscape of emissions and their impact on air quality and climate. 

How to cite: Bowman, K.: Towards the next generation of tropospheric composition sounding: the NASA TRopospheric Ozone and its Precursors from Earth System Sounding (TROPESS), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10973, https://doi.org/10.5194/egusphere-egu23-10973, 2023.

Geostationary Environment Monitoring Spectrometer (GEMS), the world's first geostationary environmental senor onboard Geo-Kompsat 2B, was launched in February, 2020 to monitor atmospheric pollutants (such as -> e.g. Aerosol properties, Nitrogen dioxide, Sulphur dioxide, Formaldehyde and Ozone) with high temporal and spatial resolution over ASIA. Environmental Satellite Center (ESC) of National Institute of Environmental Research has distributed these data since March, 2021 after in-orbit test was completed. 
We performed the accuracy validation of GEMS atmospheric pollutants retrieval algorithm using other environmental satellite (such as TROPOMI, OMPS, etc.) and ground-based measurements (such as Pandora, Max-DOAS, etc.) data through GEMS Map of Air Pollution (GMAP) and Satellite Integrated Joint monitoring of Air Quality (SIJAQ) campaign. 
We validated the accuracy of GEMS atmospheric-pollutants-retrieval algorithm using the data from other environmental satellites (e.g. TROPOMI, OMPS, etc.) and ground-based measurements data (e.g. Pandora, Max-DOAS, etc.).
After that, we improved the accuracy of retrieval algorithm and released GEMS version two data in November last year. In this version two data, we found improvements needed in a priori data, cloud data, and surface reflectance data. In this present study, we introduce the difference and improvements GEMS version two and one data.

How to cite: Hong, H., Park, J., Lee, H., Lee, W., Kim, D., Kim, J., and Lee, D.: Introduction to GEMS version two air pollutants retrieval algorithm, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2972, https://doi.org/10.5194/egusphere-egu23-2972, 2023.

Satellite remote sensing techniques can provide detailed information on the spatial and temporal distribution of nitrogen dioxide (NO₂) in the troposphere, which enables us to monitor changes in NO₂ levels over time and assess the effectiveness of emissions reduction measures. The TROPOspheric Monitoring Instrument (TROPOMI/Sentinel-5P) is particularly useful for identifying pollution sources within individual urban areas, as it has a higher spatial resolution with daily global coverage. In this study, we first compared the S5P-PAL reprocessing data with offline products (processor versions are earlier than v2.3.1) and used ground-based Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) observations in Xuzhou (a city in eastern China) to evaluate the performance. It was found that the size of the footprint can impact the validation results, and the smaller pixels (<29 km²) have a higher correlation with MAX-DOAS (R=0.91). Then we explored its serendipitous capability with the advantages of orbital characteristics and the high spatial resolution of the sensor. Using the overlapping orbits of TROPOMI at high latitudes, we applied a model-free inversion approach to derive diurnal NOx emissions. The orbits of Sentinel-5P allow for a greater than 20% probability of being observed twice within a 100-minute interval on the same day within a range from 35° to high latitudes.  Besides that, we proposed a downscaling method to generate high-resolution (0.05°) NO₂ columns from the Ozone Monitoring Instrument (OMI/Aura) retrievals, which has provided continuous measurements since 2004 while having the limitation of relatively low spatial resolution. This model used data from the common observation period of TROPOMI and OMI after 2018 to derive the relationship between high- and low-resolution NO₂ concentrations and applied to the historical dataset. Overall, this study demonstrates the utility of overlapping NO₂ columns for investigating diurnal variability and highlights the importance of the spatial scale when analyzing and interpreting NO₂ data.

How to cite: He, Q., Qin, K., and Cohen, J. B.: Exploring the utility of global high-resolution NO2 columns from TROPOMI for investigating the diurnal variability and downscaling historical measurements, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2616, https://doi.org/10.5194/egusphere-egu23-2616, 2023.

Atmospheric water vapour plays a key role for the Earth's energy budget and temperature distribution via radiative effects and latent heat transport. Moreover, the distribution and transport of water vapour is closely linked to atmospheric dynamics on all spatiotemporal scales. In this context, monitoring of the water vapour distribution is essential for numerical weather prediction as well as for climate modelling.

The Geostationary Environment Monitoring Spectrometer (GEMS) instrument on board the GEO-KOMPSAT-2B satellite offers new opportunities for observing and investigating the regional water vapour distribution over East Asia, especially phenomena such as typhoons or atmospheric rivers, and could thus represent another valuable data source, e.g. for nowcasting systems of natural hazards.

In this study, we show the first total column water vapour (TCWV) results retrieved from GEMS UV/vis spectra based on the algorithm of Borger et al. (2020). In addition, we also present an update of the existing algorithm, which, for example, replaces the previous simplified determination of the a priori water vapour profile with a deep neural network. We also compare our results to different reference data sets from ground-based in situ and remote sensing observations, reanalysis models, and satellite measurements.

How to cite: Borger, C., Beirle, S., and Wagner, T.: Total Column Water Vapour Retrieval from GEMS, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7191, https://doi.org/10.5194/egusphere-egu23-7191, 2023.

The Geostationary Environment Monitoring Spectrometer (GEMS) onboard the Geostationary Korea Multi-Purpose Satellite-2B (GEO-KOMPSAT-2B) satellite observes the hourly volcanic SO₂ over Asia. In this study, the various physical characteristics of volcanic plumes have been investigated based on hourly volcanic SO₂ measurements. The transport direction, path and speed, and altitude of volcanic SO₂ plume emitted from Nishinoshima in Japan, Etna in Italy, and Dukono located in Halmahera, Indonesia were calculated. The SO₂ plume from Nishinoshima, Japan, moved westward at a maximum speed of 57 km/h on August 4, 2020. The SO₂ plume generated from Etna was observed to move over China using both GEMS and TROPOMI, and moved at an altitude of 11–14 km and a speed of 162–190 km/h. In the case of the SO₂ plume from the Dukono volcano flowed into an average of 3.6 Mg of SO₂ per hour to the cities of nearby islands. GEMS can be utilized for an improvement in the prediction accuracy of SO₂ plume transport using a chemical transport model due to the availability of hourly volcanic SO₂ height information. In addition, hourly observations of SO₂ concentrations are expected to protect SO₂ exposure through rapid forecasting for people in cities around the volcano.

How to cite: Park, J., Lee, H., Yang, J., Hong, H., Kim, J., Roozendael, M. V., Theys, N., Li, C., Ahn, M.-H., Lee, D., Park, J., Choi, W., Park, R., and Kim, D.: The hourly volcanic SO2 column density and physical characteristics using Geostationary Environment Monitoring Spectrometer (GEMS) measurements, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10841, https://doi.org/10.5194/egusphere-egu23-10841, 2023.

The Geostationary Environment Monitoring Satellite (GEMS) retrieves several species of trace gases and aerosol properties. For the aerosol property, retrieval results from the GEMS can be used for the surface air quality analysis and aerosol effect for the airmass factor (AMF) calculation. To provide accurate information on aerosol, in addition, aerosol vertical information is also retrieved from the GEMS defined by the aerosol effective height (AEH). The AEH can help to estimate the AMF for tropospheric trace gases and surface concentration of particulate matter (PM). 

The aerosol vertical distribution is relatively difficult to retrieve compared to those of clouds, because the optical property of aerosol is various due to the various aerosol types in the atmosphere. For the UV-visible hyperspectral observation, the aerosol vertical distribution can estimate from the absorption bands based on the Oxygen molecules, such as O2-A, O2-B, and O2-O2 absorption. Because of the limitation for the spectral coverage from 300~500 nm, however, GEMS is only available to use O2-O2 absorption bands. For the possibility of the AEH retrieval algorithm from GEMS, Park et al. (2016) investigated the theoretical sensitivity test of the AEH retrieval by solely using the O2-O2 absorption band with considering the aerosol and surface properties. Based on the previous studies, we introduce the operational retrieval algorithm for AEH with the theoretical basement. Also, we showed the performance of the operational AEH algorithm from GEMS based on case studies and the validation study using Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP).

How to cite: Park, S. S., Kim, J., Cho, Y., and Park, J.: Operational Algorithm of Aerosol Effective Height from the Geostationary Environment Monitoring Spectrometer (GEMS), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1826, https://doi.org/10.5194/egusphere-egu23-1826, 2023.

The NASA S5P-TROPOMI (Sentinel 5 Precursor-Tropospheric Monitoring Instrument) aerosol algorithm (N-TropOMAER) extends the continuous 17-year record of near UV aerosol properties started by the Aura Ozone Monitoring Instrument (OMI) in 2005.  Aerosol optical depth (AOD) and single scattering albedo (SSA) for clear sky conditions and above-cloud aerosol optical depth (ACAOD) are retrieved.  In this presentation we will describe a second-generation UV-VIS algorithm that includes retrieval capability in the visible, and derivation of aerosol layer height (ALH) using TROPOMI O₂B band observations. We will also discuss recent theoretical advances that allow extending retrieved aerosol absorption information in the ultraviolet to the visible. Initial results and comparison to other satellite and ground-based observations will be presented.

How to cite: Torres, O., Ahn, C., Jethva, H., Kayetha, V., and Loyola, D.: A new generation UV-VIS TROPOMI Aerosol Algorithm, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7964, https://doi.org/10.5194/egusphere-egu23-7964, 2023.

Sulfur dioxide (SO₂) is released into the Earth’s atmosphere through natural and anthropogenic processes and the latter category amounts to the majority of the global SO₂ emissions. Satellite SO₂ observations have been used to monitor SO₂ emissions at different regional and global scales and to detect large-point sources of SO₂ emissions of diverse origins. In this study, we conducted atmospheric inversions at the global scale to estimate daily SO₂ emissions at 1.26^o×2.5^o (latitude×longitude) spatial resolution over polluted regions for two years 2020 and 2021 using satellite SO₂ total vertical column densities (TVCDs) obtained by the Sentinel 5p TROPOspheric Monitoring Instrument (TROPOMI) and AURA Ozone Monitoring Instrument (OMI). We used the global chemistry coupled transport model LMDz-INCA with 1.26^o×2.5^o (latitude×longitude) horizontal resolution and 79 hybrid σ-p vertical levels extending to the stratosphere to simulate the model SO₂ TVCDs. The model uses a priori monthly global anthropogenic emission inventories from the open-source Community Emissions Data System (CDES). As the TROPOMI operational offline L2 SO₂ data product has high noise levels, we used the TROPOMI COBRA SO₂ data product in this study which has comparatively smaller noise. First, we evaluated the SO₂ TVCDs from the LMDz-INCA model simulations for a reference year 2019 with the observed SO₂ TVCDs from TROPOMI and OMI. The daily average (10-day running average) of the model simulated SO₂ TVCDs over the major polluted regions like India, China, and the Middle East, and over less polluted regions like South Africa, and South America mostly follow the trend of the observed SO₂ TVCDs from both TROPOMI and OMI. The model overestimates SO2 TVCDs over India and the Middle East and underestimates them over China, South Africa, and South America. For Europe and North America, the noise levels in the daily averaged TVCDs from both TROPOMI and OMI are too high for a meaningful comparison. In order to estimate anthropogenic SO₂ emissions, we used a recently developed inversion approach (Zheng et al., 2020), which was previously used to estimate anthropogenic NO_x emissions over China. We performed the model simulation for 2019 with 40% reduced anthropogenic SO₂ emission to calculate the gridded local sensitivities of the TVCDs to the change in the anthropogenic SO₂ emissions. The inversion approach combines these gridded local sensitivities and the relative change of the observed satellites and the modelled TVCDs to derive the relative change of anthropogenic SO₂ emissions from the reference year 2019 to the inversion years 2020 and 2021. The estimated total SO₂ emissions from TROPOMI and OMI observations for 2020 and 2021 are mostly higher compared to the reference year total emissions over the world and over the selected regions. The total SO₂ emissions from TROPOMI and OMI observations at the common model grids for both inversion years are consistent with each other.

How to cite: Kumar, P., Ciais, P., Halder, S., Broquet, G., Hauglustaine, D., and Theys, N.: SO2 emission estimates using satellite observations from TROPOMI and OMI and the global chemistry transport model LMDz-INCA, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12195, https://doi.org/10.5194/egusphere-egu23-12195, 2023.

Early versions of satellite nadir-viewing UV SO₂ data products assumed snow-free surface conditions. Snow covered terrain, with its high reflectance in the UV, typically enhances satellite sensitivity to boundary layer pollution. However, a significant fraction of high-quality cloud-free measurements over snow is currently excluded from analyses.  This leads to increasing the uncertainties of the satellite emissions estimates and introducing potential seasonal biases due to the lack of data in winter months for some high-latitudinal sources. In this study, we investigated how OMI and TROPOMI satellite SO₂ measurements over snow-covered surfaces could be used to improve the annual emissions reported in our SO₂ emissions catalogue (version 2, Fioletov et al., 2023). Although only 100 out of 759 sources listed in the catalogue have 10% or more of the observations over snow, for 40 high-latitude sources more than 30% of measurements suitable for emission calculations were made over snow-covered surfaces. For example, in the case of Norilsk, the world’s largest SO₂ emissions point source, annual emissions estimates in the SO₂ catalogue were based only on 3-4 summer months, while addition of data for snow conditions extends that period to 7 months.

Emissions in the SO₂ catalogue were based on satellite measurements of SO₂ slant column densities (SCDs) that were converted to vertical column densities (VCDs) using site-specific clear-sky air mass factors (AMFs), calculated for snow-free conditions. The same approach was applied to measurements with snow on the ground whereby a new set of constant, site-specific, clear-sky with snow AMFs was created, and these were applied to the measured SCDs. Annual emissions were then estimated for each source considering (i) only snow-free days, (ii) only clear-sky with snow days and (iii) a merged dataset (snow and no snow conditions). For individual sources, the difference between emissions estimated for snow and snow-free conditions is within ±20% for three quarters of smelters and oil and gas sources, and with practically no systematic bias. This is excellent consistency given that there is typically a 3-5 times difference between AMFs for snow and snow-free conditions. For coal-fired power plants, however, emissions estimated for snow conditions are on average 25% higher than for no snow conditions; this difference is likely real and due to larger production (consumption of coal) and emissions in wintertime.

Reference:

Fioletov, V. E., McLinden, C. A., Griffin, D., Abboud, I., Krotkov, N., Leonard, P. J. T., Li, C., Joiner, J., Theys, N., and Carn, S.: Version 2 of the global catalogue of large anthropogenic and volcanic SO₂ sources and emissions derived from satellite measurements, Earth Syst. Sci. Data, 15, 75–93, https://doi.org/10.5194/essd-15-75-2023, 2023.

How to cite: Fioletov, V., McLinden, C., Griffin, D., Krotkov, N., Li, C., Joiner, J., Theys, N., and Carn, S.: Estimation of anthropogenic and volcanic SO2 emissions from satellite data in the presence of snow/ice on the ground, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3543, https://doi.org/10.5194/egusphere-egu23-3543, 2023.

Bromine monoxide (BrO) is a halogen radical influencing atmospheric chemical processes, in particular the abundance of ozone, e. g. in the polar boundary layer and above salt lakes, in the stratosphere as well as in volcanic plumes. Furthermore, the molar bromine to sulphur ratio in volcanic gas emissions is a proxy for the magmatic composition of a volcano and potentially an eruption forecast parameter.

The high spatial resolution combined with the better signal-to-noise ratio of the S-5P/TROPOMI instrument (up to 3.5x5.5km²) and its daily global coverage offer the potential to detect BrO and its corresponding ratio with sulphur dioxide (BrO/SO₂) even during minor eruptions and for continuous passive degassing volcanoes.

Here, we present a global overview of BrO/SO₂ molar ratios in volcanic dispersion gas plumes derived from a systematic long-term investigation covering four years (January 2018 to December 2021) of TROPOMI data.

We retrieved column densities of BrO and SO₂ using Differential Optical Absorption Spectroscopy (DOAS) and calculated mean BrO/SO₂ molar ratios for various volcanoes. The calculated BrO/SO₂ molar ratios differ strongly ranging from several 10^-5 up to several 10^-4 both between different volcanoes, but also between measurements at one volcano at different points in time. In our four-year study of S-5P/TROPOMI data we successfully recorded 4232 volcanic plumes, 3063 of which can be clearly assigned to 43 volcanoes. Subsequently, the mean BrO/SO₂ ratio is calculated for these plumes - increasing the global data-base of reported BrO/SO₂ ratios from 28 to 60 volcanoes. For the first time, BrO/SO₂ ratios were successfully determined for six hot spot volcanoes - all of which yield low BrO/SO₂ ratios between 2-5x10^-5, in contrast to 2-16x10^-5 for subduction zone volcanoes, suggesting a depletion of bromine in the Earth’s mantle.

In addition, time-series of the BrO/SO₂ ratio were derived for 19 volcanoes, with more than 200 daily measurements of the BrO/SO₂ ratio at the five volcanoes Mt. Etna, Italy, Dukono, Indonesia, Popocatepetl, Mexico, Nevado del Ruiz, Colombia, and Sangay, Ecuador.

How to cite: Warnach, S., Borger, C., Bobrowski, N., Sihler, H., Schöne, M., Beirle, S., Platt, U., and Wagner, T.: Bromine monoxide composition in volcanic plumes measured by S-5P/TROPOMI – Global survey of magmatic composition, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8160, https://doi.org/10.5194/egusphere-egu23-8160, 2023.

Usually, horizontally homogenous atmospheric properties are assumed for the analysis of satellite observations of atmospheric trace gases. While for most atmospheric quations, this simplification causes only small to moderate errors, for the observation of volcanic plumes this neglecting 3D effects can lead to very large errors. These errors (3D effects) can become especially important for satellite observations with high spatial resolution like TROPOMI on Sentinel-5 Precursor.

Different 3D effects were recently investigated for volcanic plumes by Wagner et al. (2022). It was found that especially the so-called light mixing effect can lead to a strong underestimation of the true trace gas amount of volcanic plumes if 1D atmospheric properties were assumed in the retrieval. For strong absorbers like SO₂, the underestimation can further be increased by the saturation effect. In that study, different 3D effects were separately studied for idealised plumes.

Here we investigate the combined 3D effects for realistic volcanic plumes using radiative transfer simulations. We focus on two scenarios: first on observations of the ascending part of the plume above a volcano and second on the horizontally advected plume at a distance from the volcanic vent. In addition to the 3D effect of the volcanic plume (trace gases and aerosols), also the influence of the surface elevation is investigated.

Wagner, T., Warnach, S., Beirle, S., Bobrowski, N., Jost, A., Puķīte, J., and Theys, N.: Investigation of 3D-effects for UV/vis satellite and ground based observations of volcanic plumes, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-253, in review, 2022.

How to cite: Wagner, T., Warnach, S., Beirle, S., Bobrowski, N., Puķīte, J., Roberts, T., Surl, L., and Theys, N.: Investigation of 3D-effects for UV/vis satellite observations of volcanic plumes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8772, https://doi.org/10.5194/egusphere-egu23-8772, 2023.

Nitrous acid (HONO) plays a key role in atmospheric chemistry as a major source – through rapid photolysis – of the hydroxyl radical (OH), the primary oxidant in the Earth's atmosphere. However, significant uncertainties remain on the spatial and temporal variability of HONO, on its formation pathways in the atmosphere, and on the contribution of its primary emissions over its secondary formation. Recently, spaceborne measurements in the UV-Vis spectral domain, taken in the early afternoon with the S5P/TROPOMI instrument, have provided a first global picture of HONO in fresh biomass burning plumes, demonstrating the importance of satellite data for improving our representation of atmospheric HONO. With daily overpass times in the early morning and early evening, the polar-orbiting IASI/Metop instruments and their global measurements of the Earth's radiance in the thermal infrared, have the potential to contribute to tackling remaining uncertainties on HONO and to complement the TROPOMI measurements.

So far detected by infrared satellite sounders in the exceptional 2009 and 2019/2020 Australian bushfires only, we use a sensitive detection method to demonstrate that unambiguous HONO enhancements can also be identified in IASI spectra recorded in concentrated fire plumes worldwide. With this method, we analyse the long, unique observational timeseries (2007-2022) of IASI and we report a 15-year record of fire events in which HONO has been detected. This dataset reveals first that HONO is primarily captured by IASI at the Northern Hemisphere mid and high latitudes, and secondly that the IASI evening measurements allow a significantly higher number of HONO detections than during daytime despite the overall weaker thermal contrast and lower measurement sensitivity affecting such night-time observations. We discuss different factors that can explain these features, such as the sharp intra-day variability of HONO, the links with the diurnal variations and intensity of fires, and the vertical sensitivity of the IASI measurements. We apply a retrieval approach based on an artificial neural network to quantify the vertical abundance of HONO in IASI measurements. For selected fires, we analyse the temporal evolution of the HONO total columns along with TROPOMI data. 

How to cite: Franco, B., Clarisse, L., Theys, N., Hadji-Lazaro, J., Clerbaux, C., and Coheur, P.: 15 years of pyrogenic HONO plumes observed with IASI, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12469, https://doi.org/10.5194/egusphere-egu23-12469, 2023.

Large cities can experience high levels of fine particulate matter (PM_2.5) pollution linked to ammonia (NH₃) mainly emitted from agricultural activities. Using a combination of PM_2.5 and NH₃ measurements from in situ instruments, satellite infrared spectrometers, and atmospheric model simulations, we demonstrate the role of atmospheric NH₃ and meteorological conditions in pollution events occurring in Paris, Toronto, and Mexico City.

Ten years of measurements from the Infrared Atmospheric Sounding Interferometer (IASI) are used to assess the spatio-temporal NH₃ variability over and around the three cities. The three regions are subject to long range transport of NH₃, as shown using HYSPLIT cluster back-trajectories. The results show that the NH₃ variability is mainly driven by meteorology, and interestingly, we can detect the fertilizers application period by looking at the NH₃ – temperature relationship. To check how well chemistry transport models perform during pollution events, we evaluate simulations made using the GEOS-Chem model for March 2011. In these simulations we find that NH₃ concentrations are overall underestimated, though day-to-day variability is well represented. PM_2.5 is generally underestimated over Paris and Mexico, but overestimated over Toronto.

We use complementary information derived from IASI, and ground-based open-path measurements over Paris. We, therefore, assess the NH₃ temporal variabilities at different timescales (diurnal, seasonal, and interannual), to unravel NH₃ sources (agriculture and traffic) in Paris.

How to cite: Viatte, C., Abeed, R., Yamanouchi, S., Porter, W., Safieddine, S., Van Damme, M., Clarisse, L., Herrera, B., Grutter, M., Coheur, P.-F., Strong, K., and Clerbaux, C.: NH3 spatio-temporal variability over Paris, Mexico and Toronto and its link to PM2.5 during pollution events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5209, https://doi.org/10.5194/egusphere-egu23-5209, 2023.

Over the past century ammonia (NH₃) emissions have increased with human population growth and fertilizer usage. The abundant NH₃ emissions lead to climate change, reduction in biodiversity and affect the human health. Up-to-date information of NH₃emissions are essential to better understand the impact of NH₃. In this study we adapted the existing DECSO (Daily Emissions Constrained by Satellite Observations) algorithm for use of NH₃ observations from the Cross-track Infrared Sounder (CrIS) to estimate NH₃ emissions. By considering the interaction between NH₃ and NO_x, we implemented DECSO to estimate NO_x and NH₃ emissions simultaneously on 20 km resolution over European domain. NH₃ and NO_x emissions over Europe are derived for 2020 on a daily basis from CrIS and TROPOMI (on Sentinel 5p). Due to the sparseness of daily satellite observations of NH₃, monthly emissions of NH₃ are constructed and analysed.  The comparison of these emissions with other existing emission inventories will be presented.

How to cite: Ding, J., van der A, R., Eskes, H., Dammers, E., and Shephard, M.: NH3 emissions derived from CRIS observations over Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13487, https://doi.org/10.5194/egusphere-egu23-13487, 2023.

The Geostationary Interferometric Infrared Sounder (GIIRS) onboard FengYun-4 series satellites is the world’s first geostationary hyperspectral infrared sounder. With hyperspectral measurement covering the carbon monoxide (CO) and ammonia (NH₃) absorption windows around 2150 cm^-1 and 9150 cm^-1, respectively, GIIRS provides a unique opportunity for monitoring the diurnal variabilities of atmospheric CO and NH₃ over East Asia. In this study, we develop the FengYun Geostationary satellite Atmospheric Infrared Retrieval (FY-GeoAIR) algorithm to retrieve the CO and NH₃ profiles from FY-4B/GIIRS data and provide CO and NH₃ maps at a spatial resolution of 12 km and a temporal resolution of 2 hours. The performance of the algorithm is first evaluated by conducting retrieval experiments using simulated synthetic spectra. The result shows that the GIIRS data provide significant information for constraining CO and NH₃ profiles. The degree of freedom for signal (DOFS) and retrieval error are both significantly correlated with thermal contrast (TC), the temperature difference between the surface and the lower atmosphere. Retrieval results from one month of GIIRS spectra in July 2022 show that the DOFS for the majority is between 0.6 and 1.2 for the CO total column and between 0 and 1.0 for the NH₃ total column. Consistent with retrievals from low-earth-orbit (LEO) infrared sounders, the largest observation sensitivity, as quantified by the averaging kernel (AK), is in the free troposphere for CO and in the lower troposphere for NH₃. The diurnal changes in DOFS and vertical sensitivity of observation are primarily driven by the diurnal TC variabilities. This study demonstrates the capability of GIIRS in observing the diurnal CO and NH₃ changes in East Asia, which will have great potential in improving local and global air quality and climate research.

How to cite: Zeng, Z.-C., Lee, L., and Qi, C.: Observing Tropospheric Carbon Monoxide and Ammonia from the Geostationary Interferometric Infrared Sounder (GIIRS) onboard FengYun-4B, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6705, https://doi.org/10.5194/egusphere-egu23-6705, 2023.

How to cite: Tournadre, B., Dupont, H., Pontet, S., Vagnot, M.-P., Cozic, J., Chappaz, C., and Socquet-Juglard, S.: Monitoring of atmospheric ammonia (NH3) in Auvergne-Rhône-Alpes region (France): comparison between satellite, ground-based observations, and simulations from a chemistry-transport model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15585, https://doi.org/10.5194/egusphere-egu23-15585, 2023.