Sentinel-5 Precursor Satellite Research Articles

Monitoring European anthropogenic NOx emissions from space

Abstract. Since the launch of TROPOMI on the Sentinel-5 Precursor (S5P) satellite, NO2 observations have become available with a resolution of 3.5× 5 km, which makes monitoring NOx emissions possible at the scale of city districts and industrial facilities. For Europe, emissions are reported on an annual basis for country totals and large industrial facilities and made publicly available via the European Environment Agency (EEA). Satellite observations can provide independent and more timely information on NOx emissions. A new version of the inversion algorithm DECSO (Daily Emissions Constrained by Satellite Observations) has been developed for deriving emissions for Europe on a daily basis, averaged to monthly mean maps. The estimated precision of these monthly emissions is about 25 % for individual grid cells. These satellite-derived emissions from DECSO have been compared to the officially reported European emissions and spatial–temporal disaggregated emission inventories. The country total DECSO NOx emissions are close to the reported emissions and the emissions compiled by the Copernicus Atmosphere Monitoring Service (CAMS). Comparison of the spatially distributed NOx emissions of DECSO and CAMS showed that the satellite-derived emissions are often higher in cities, while they are similar for large power plants and slightly lower in rural areas.

A Bayesian Framework to Quantify Uncertainty in Aerosol Optical Model Selection Applied to TROPOMI Measurements

This article presents a method within a Bayesian framework for quantifying uncertainty in satellite aerosol remote sensing when retrieving aerosol optical depth (AOD). By using a Bayesian model averaging technique, we take into account uncertainty in aerosol optical model selection and also obtain a shared inference about AOD based on the best-fitting optical models. In particular, uncertainty caused by forward-model approximations has been taken into account in the AOD retrieval process to obtain a more realistic uncertainty estimate. We evaluated a model discrepancy, i.e., forward-model uncertainty, empirically by exploiting the residuals of model fits and using a Gaussian process to characterise the discrepancy. We illustrate the method with examples using observations from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite. We evaluated the results against ground-based remote sensing aerosol data from the Aerosol Robotic Network (AERONET).

Quantifying instantaneous nitrogen oxides emissions from power plants based on space observations

Thermal power plants are significant contributors to nitrogen oxides (NOx), impacting global atmospheric conditions and human health. Satellite observations, known for their continuity and global coverage, have become an effective means of quantifying power plant emissions. Previous studies, often accumulating long temporal data into integrated plumes, resulted in substantial errors in annual emissions at the individual power plant level due to neglecting variations in emissions and diffusion conditions. This study presents, for the first time, the quantification of instantaneous NOx emissions based on single overpass observations from the Tropospheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite. By addressing the temporal variability of power plant emissions, it effectively reduces annual estimation errors. Comparative analysis between the Exponentially-Modified Gaussian (EMG) and Gaussian Plume Model (GPM) simulations demonstrates the capability of EMG to provide instantaneous emission estimates based on actual plumes, exhibiting closer proximity to actual monitoring values than GPM. Applying the EMG method, we quantify the instantaneous emission rates of six power plants in the United States. Comparing annual emission estimations at individual power plants with traditional integrated plume results, our method demonstrates a 63.7 % improvement in annual emission estimations. This study offers more detailed data on power plant emissions, providing a new avenue for better understanding the emission behavior of thermal power plants.

Quantifying Urban Daily Nitrogen Oxide Emissions from Satellite Observations

Urban areas, characterized by dense anthropogenic activities, are among the primary sources of nitrogen oxides (NOx), impacting global atmospheric conditions and human health. Satellite observations, renowned for their continuity and global coverage, have emerged as an effective means to quantify pollutant emissions. Previous bottom-up emission inventories exhibit considerable discrepancies and lack a comprehensive and reliable database. To develop a high-precision emission inventory for individual cities, this study utilizes high-resolution single-pass observations from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite to quantify the emission rates of NOx. The Exponentially Modified Gaussian (EMG) model is validated for estimating NOx emission strength using real plumes observed in satellite single-pass observations, demonstrating good consistency with existing inventories. Further analysis based on the results reveals the existence of a weekend effect and seasonal variations in NOx emissions for the majority of the studied cities.

Propagation of NO2 originated in intense fires in the Paraná River Delta analyzed from satellite observations

Wildfires in the Paraná River Delta started to be a common situation in the year 2008 and during the first years of the 2020́s decade: 2020, 2021, 2022. These fires affected natural areas, primarily the islands within the delta, situated in front of Rosario city (32.95 °S, 60.67 °W, 25 meters above sea level (m.a.s.l.)), Argentina. They mostly occur during the period between June and September each year and are more intense when a strong La Niña event is present. The aim of this study is to examine the association between the propagation of high intensity nitrogen dioxide (NO2) plumes and the significant fire events that occurred in the Paraná River Delta from 6 to 17 September 2022. Satellite data of NO2 obtained from the TROPOspheric Monitoring Instrument (TROPOMI) on board the European Space Agency (ESA) Sentinel-5 Precursor satellite, air mass forward trajectories performed with The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT), Fire Radiative Power (FRP) data provided by Visible Infrared Imaging Radiometer Suite (VIIRS) on board The National Oceanic and Atmospheric Administration - 20 (NOAA-20) and Suomi National Polar-orbiting Partnership (S-NPP) satellites, Aerosol Optical Depth (AOD) from VIIRS S-NPP and Smoke Injection Height from Multi-Angle Implementation of Atmospheric Correction (MAIAC) Thermal Technique were employed. The trajectories followed by the air masses coming from the major fire sources locations nearby Rosario city and its surrounding area, were analyzed at altitudes of 20, 500, 1000, 1500 and 2000 m above ground level (AGL) in order to evaluate the impact of the NO2 clouds present in the atmosphere and carried by the winds, at different sites of Rosario and its adjacent region. The NO2 plumes has the potential to pose a significant health risk for this region, inhabited by an approximate population of one and a half million individuals. This risk is contingent upon the altitude where the highest levels of NO2 in the tropospheric column are found. Tropospheric vertical column densities (VCDs) of NO2 in the range of 4.68×1014 to 1.66×1016 molecules/cm2 were determined. The results indicate a robust correlation between elevated tropospheric NO2 VCD values and intense fires, as characterized by their FRP. Additionally, high tropospheric NO2 VCDs were observed to impact cities and towns located in close proximity to the fires. In particular, the unusually high tropospheric NO2 VCDs determined for the towns of Fighiera and Pueblo Esther, situated along the coast and near the Paraná River Delta, are noteworthy. This is uncommon considering they are small towns in rural areas that typically have good air quality. The results regarding FRP, provided by VIIRS on board NOAA-20 and S-NPP satellites, indicate that the fire characterization of hotspots during the considered period had a mean value in the range of 20.17–21.47 MW. On specific days (12, 13 and 16 September 2022), the cumulative (total) daily values were very high, ranging from 2000 to 10000 MW. Furthermore, on 12 and 13 September 2022, the smoke aerosols did not reach significant altitudes in the atmosphere, remaining within the range of 0 to 1500 m AGL. During the fire period, AOD values close to 1 were observed within the fire plume. Notably, Puerto San Martín (0.27), San Lorenzo (0.22), and Ibarlucea (0.20) recorded higher average AOD values compared to other sites. The remaining sites generally exhibited lower average AOD values, with a slight distinction between rural locations (Pueblo Esther: 0.14 and Fighiera: 0.14) and the city center of Rosario (0.09).

Efficient Data-Driven Gap Filling of Satellite Image Time Series Using Deep Neural Networks with Partial Convolutions

Abstract The abundance of gaps in satellite image time series often complicates the application of deep learning models such as convolutional neural networks for spatiotemporal modeling. Based on previous work in computer vision on image inpainting, this paper shows how three-dimensional spatiotemporal partial convolutions can be used as layers in neural networks to fill gaps in satellite image time series. To evaluate the approach, we apply a U-Net-like model on incomplete image time series of quasi-global carbon monoxide observations from the Sentinel-5 Precursor (Sentinel-5P) satellite. Prediction errors were comparable to two considered statistical approaches while computation times for predictions were up to three orders of magnitude faster, making the approach applicable to process large amounts of satellite data. Partial convolutions can be added as layers to other types of neural networks, making it relatively easy to integrate with existing deep learning models. However, the approach does not provide prediction uncertainties and further research is needed to understand and improve model transferability. The implementation of spatiotemporal partial convolutions and the U-Net-like model is available as open-source software. Significance Statement Gaps in satellite-based measurements of atmospheric variables can make the application of complex analysis methods such as deep learning approaches difficult. The purpose of this study is to present and evaluate a purely data-driven method to fill incomplete satellite image time series. The application on atmospheric carbon monoxide data suggests that the method can achieve prediction errors comparable to other approaches with much lower computation times. Results highlight that the method is promising for larger datasets but also that care must be taken to avoid extrapolation. Future studies may integrate the approach into more complex deep learning models for understanding spatiotemporal dynamics from incomplete data.

Trans-boundary spatio-temporal analysis of Sentinel 5P tropospheric nitrogen dioxide and total carbon monoxide columns over Punjab and Haryana Regions with COVID-19 lockdown impact.

This study conducts a spatio-temporal analysis of tropospheric nitrogen dioxide (NO2) and total carbon monoxide (CO) concentrations in the Punjab and Haryana regions of India and Pakistan, using datasets from the Sentinel 5-Precursor (S5P) satellite. These regions, marked by diverse economic growth factors including population expansion, power generation, transportation, and agricultural practices, face similar challenges in atmospheric pollution, particularly evident in major urban centers like Delhi and Lahore, identified as pollution hotspots. The study also spotlights pollution associated with power plants. In urban areas, tropospheric NO2 levels are predominantly elevated due to vehicular emissions, whereas residential activities mainly contribute to CO pollution. However, precisely attributing urban CO sources is complex due to its longer atmospheric residence time and intricate circulation patterns. Notably, the burning of rice crop residue in November significantly exacerbates winter pollution episodes and smog, showing a more pronounced correlation with total CO than with tropospheric NO2 levels. The temporal analysis indicates that the months from October to December witness peak pollution, contrasted with the relatively cleaner period during the monsoon months of July to September. The severe pollution in the OND quarter is attributed to factors such as variations in boundary layer height and depletion of OH radicals. Furthermore, the study highlights the positive impact of the COVID-19 lockdown on air quality, with a significant decrease in NO2 concentrations during April, 2020 (Delhi: 59%, Lahore: 58%). However, the reduction in total CO columns was less significant. The study also correlates lockdown stringency with tropospheric NO2 columns (R2: 0.37 for Delhi, 0.25 for Lahore, 0.22 for Rawalpindi/Islamabad), acknowledging the influence of various meteorological and atmospheric variables. The research highlights the significant impact of crop residue burning on winter pollution levels, particularly on total CO concentrations. The study also shows the notable effect of the COVID-19 lockdown on air quality, significantly reducing NO2 levels. Additionally, it explores the correlation between lockdown stringency and tropospheric NO2 columns, considering various meteorological factors.

Zonal variability of methane trends derived from satellite data

Abstract. The Tropospheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor (S5P) satellite is part of the latest generation of trace gas monitoring satellites and provides a new level of spatio-temporal information with daily global coverage, which enables the calculation of daily globally averaged CH4 concentrations. To investigate changes in atmospheric methane, the background CH4 level (i.e. the CH4 concentration without seasonal and short-term variations) has to be determined. CH4 growth rates vary in a complex manner and high-latitude zonal averages may have gaps in the time series, and thus simple fitting methods do not produce reliable results. In this paper we present an approach based on fitting an ensemble of dynamic linear models (DLMs) to TROPOMI data, from which the best model is chosen with the help of cross-validation to prevent overfitting. This method is computationally fast and is not dependent on additional inputs, allowing for fast and continuous analysis of the most recent time series data. We present results of global annual methane increases (AMIs) for the first 4.5 years of S5P/TROPOMI data, which show good agreement with AMIs from other sources. Additionally, we investigated what information can be derived from zonal bands. Due to the fast meridional mixing within hemispheres, we use zonal growth rates instead of AMIs, since they provide a higher temporal resolution. Clear differences can be observed between Northern Hemisphere and Southern Hemisphere growth rates, especially during 2019 and 2022. The growth rates show similar patterns within the hemispheres and show no short-term variations during the years, indicating that air masses within a hemisphere are well-mixed during a year. Additionally, the growth rates derived from S5P/TROPOMI data are largely consistent with growth rates derived from Copernicus Atmospheric Monitoring Service (CAMS) global-inversion-optimized (CAMS/INV) data, which use surface observations. In 2019 a reduction in growth rates can be observed for the Southern Hemisphere, while growth rates in the Northern Hemisphere stay stable or increase. During 2020 a strong increase in Southern Hemisphere growth rates can be observed, which is in accordance with recently reported increases in Southern Hemisphere wetland emissions. In 2022 the reduction in the global AMI can be attributed to decreased growth rates in the Northern Hemisphere, while growth rates in the Southern Hemisphere remain high. Investigations of fluxes from CAMS/INV data support these observations and suggest that the Northern Hemisphere decrease is mainly due to the decrease in anthropogenic fluxes, while in the Southern Hemisphere, wetland fluxes continued to rise. While the continued increase in Southern Hemisphere wetland fluxes agrees with existing studies about the causes of observed methane trends, the difference between Northern Hemisphere and Southern Hemisphere methane increases in 2022 has not been discussed before and calls for further research.

Status of atmospheric air pollution in Ukraine prior to the full-scale russian invasion. Part 2: Pollutants total content according to the satellite data

The paper describes the main features of pollutant total content distribution over Ukraine that can be used as baseline air quality data observed before the full-scale russian invasion in Ukraine. The study is based on the data derived from the TROPOspheric Monitoring Instrument (TROPOMI) onboard of the Sentinel-5 Precursor (5P) satellite that indicates nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO) and formaldehyde (CH2O) levels. We defined the characteristics of pollutants spatial distribution with full coverage of Ukrainian territory. Despite the increasing role of automotive emissions, the most polluted air in Ukraine was still observed over large industrial cities and smaller settlements having the biggest thermal power plants (TPP). The high level of pollutants content over these locations negatively affects air quality in suburban and rural areas by the prevailing wind. They form relatively stable polluted spots over larger areas. Hence, main polluted areas include: Donetsk Region; territories in the central part of Ukraine along the Dnipro River and near the destroyed Kakhovka reservoir; Kharkiv and Zmiiv TPPs; Kyiv and Trypillia TPPs; and the territories in the western part, including Lviv, Dobrotvir and Burshtyn TPPs. The polluted air from these territories determines the air quality depending on a prevailing wind. In case of high wind speeds polluted air can be distributed from urban areas towards relatively clean territories such as Carpathian and Crimean Mountains, northern Polissia, and the Medobory National Park of Podillia. We determined quantitative parameters of wind speed and direction for every pollutant that causes higher total content over relatively clean territories. Pollutants dispersion in the atmosphere varies depending on a boundary layer height (BLH). It was found that NO2, CO and SO2 content significantly increased when the BLH was below 500 m over both urban areas and clean territories with no emission sources available. The inverse dependence on the BLH was identified for CH2O. This can be explained by a more intense photochemical production at higher altitudes. The detected baseline air pollution conditions can be used in order to assess the impact of the war on air quality in Ukraine and come up with relevant post-war development measures.

Satellite-based assessment of national carbon monoxide concentrations for air quality reporting in Finland

Carbon monoxide (CO) has negative health effects, especially on the respiratory system, when present in large concentrations in ambient air. Therefore, it is one of the 12 air pollutants with monitoring and assessment obligations set by the European Union Ambient Air Quality Directives. With low CO concentrations in Finland, continuous surface measurements are not compulsory but the monitoring and assessment obligation remains. Currently, there are no air quality stations equipped with CO measurements in Finland, and the assessment can only be based on emission estimates.In this work, we demonstrate the use of satellite-based CO measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on-board Sentinel-5 Precursor satellite (S5P) to strengthen the assessment with actual measurements. Averaging satellite measurements over several years reveals the spatial distribution of CO with nationwide coverage in city-scale resolution. Individual satellite measurements, in turn, are used to estimate the surface concentrations and their annual variability in different regions in Finland.For the surface estimates, we used a simple and robust linear model between the satellite measurements and ground-level in-situ measurements. We found that the linear model produces reasonable surface concentration estimates to be used in air quality assessments where the objective is to evaluate whether the monitoring threshold set by the EU is exceeded on a yearly scale. It is noted that cloudiness can cause local data gaps of several days and the low solar elevation angles prevent satellite measurements from 9 to 18 weeks during the winter time in Finland.In Finland, the average total columns show low spatial variability, with only a few high-concentration areas identified. For the last three years, the estimated surface concentrations have stayed well below the lower assessment threshold set by the Air Quality Directive.

TROPOMI NO2 Sentinel-5P data in the Community of Madrid: A detailed consistency analysis with in situ surface observations

Excess reactive nitrogen emissions from anthropogenic activities can negatively impact the natural environment. The sensor TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor (Sentinel-5P) satellite is capable of providing data on tropospheric NO2 vertical column densities (VCDs). This work investigates NO2 data retrieved from TROPOMI to study the spatial and temporal patterns of NO2 pollution compared with surface-level concentration measurements in the Community of Madrid in 2022. A high linear correlation coefficient (r) was obtained by comparing daily Level-3 tropospheric TROPOMI NO2 data with ground-based data from air quality monitoring stations at satellite overpass time (r = 0.78). Since the overpasses of Sentinel-5P over the region consistently occur between 12:00 and 4:00 p.m. local time, a second correlation analysis was carried out by averaging all available data from both sources within that interval, resulting in an even higher coefficient (r = 0.81). Results showed a lower correlation at rural sites compared to urban and suburban sites. Examining the monthly and weekly variations of NO2 concentration from satellite data and in situ measurements, similar tendencies in NO2 concentration were observed from both sources. In agreement with previous studies, the cold months and weekdays exhibited higher NO2 concentrations. It was concluded that TROPOMI NO2 data and in situ measurements yield a strong correlation for the Community of Madrid target area, and TROPOMI data can be further investigated to propose pollution mitigation policies.

Latitudinal distribution and sources analysis of greenhouse gases and air pollutants observed during the 2021 Yellow Sea Air Quality campaign aboard a research vessel

The Yellow Sea serves as a pathway for air pollution transport from the Asian continent into Korea and is annually monitored by the Korean Meteorological Administration (KMA) research vessel during the spring season. This study investigated the spatio-temporal variation of long-range transported greenhouse gases (CO2 and CH4) and air pollutants (CO, NOx, SO2, O3) in the Yellow Sea during the Yellow Sea Air Quality (YES-AQ) campaign. Our analysis reveals that GHGs and air pollutants exhibit a clear latitudinal distribution. Lower concentrations are observed in the range of 31.2–34.5°N, primarily influenced by air masses traversing the ocean and the western part of Korean Peninsula. In contrast, higher concentrations occur more frequently at 34.5–37.8°N, primarily affected by continental air masses. The regression slopes of CO and CO2 vary depending on regional characteristics, particularly in air originating from Korea and China. Regression slope analysis between CO, CO2, and CH4, except for high plume of CH4 (>2.05 ppm), indicates that fossil fuel sources play a significant role. A high plume of CH4 (>2.05 ppm) was observed on April 22nd, coinciding with the maximum O3 concentrations during the period of highest temperatures throughout the voyage. The potential source regions were identified in the west-southern coast of Korea, using footprint analysis from Stochastic Time-Inverted Lagrangian Transport (STILT) and column-averaged dry-air mole fraction of CH4 (XCH4) retrieval from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor (S5P) satellite. The high CH4 enhancements are most likely due to biogenic emissions triggered by favorable near-surface temperatures for microbial activities in soil. Furthermore, the high CH4 enhancements were not correlated with CO and CO2, indicating that the source was not related to fossil fuel combustion. On March 29–30, a high plume of NOx was detected and is likely attributed to the Bohai Sea, which is located just above the Yellow Sea and surrounded by highly industrialized and semi-enclosed areas.

Implementation of a satellite-based tool for the quantification of CH4 emissions over Europe (AUMIA v1.0) – Part 1: forward modelling evaluation against near-surface and satellite data

Abstract. Methane is the second-most important greenhouse gas after carbon dioxide and accounts for around 10 % of total European Union greenhouse gas emissions. Given that the atmospheric methane budget over a region depends on its terrestrial and aquatic methane sources, inverse modelling techniques appear as powerful tools for identifying critical areas that can later be submitted to emission mitigation strategies. In this regard, an inverse modelling system of methane emissions for Europe is being implemented based on the Weather Research and Forecasting (WRF) model: the Aarhus University Methane Inversion Algorithm (AUMIA) v1.0. The forward modelling component of AUMIA consists of the WRF model coupled to a multipurpose global database of methane anthropogenic emissions. To assure transport consistency during the inversion process, the backward modelling component will be based on the WRF model coupled to a Lagrangian particle dispersion module. A description of the modelling tools, input data sets, and 1-year forward modelling evaluation from 1 April 2018 to 31 March 2019 is provided in this paper. The a posteriori methane emission estimates, including a more focused inverse modelling for Denmark, will be provided in a second paper. A good general agreement is found between the modelling results and observations based on the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite. Model–observation discrepancies for the summer peak season are in line with previous studies conducted over urban areas in central Europe, with relative differences between simulated concentrations and observational data in this study ranging from 1 % to 2 %. Domain-wide correlation coefficients and root-mean-square errors for summer months ranged from 0.4 to 0.5 and from 27 to 30 ppb, respectively. On the other hand, model–observation discrepancies for winter months show a significant overestimation of anthropogenic emissions over the study region, with relative differences ranging from 2 % to 3 %. Domain-wide correlation coefficients and root-mean-square errors in this case ranged from 0.1 to 0.4 and from 33 to 50 ppb, respectively, indicating that a more refined inverse analysis assessment will be required for this season. According to modelling results, the methane enhancement above the background concentrations came almost entirely from anthropogenic sources; however, these sources contributed with only up to 2 % to the methane total-column concentration. Contributions from natural sources (wetlands and termites) and biomass burning were not relevant during the study period. The results found in this study contribute with a new model evaluation of methane concentrations over Europe and demonstrate a huge potential for methane inverse modelling using improved TROPOMI products in large-scale applications.

Vicarious calibration of the Tropospheric Monitoring Instrument (TROPOMI) short-wave infrared (SWIR) module over the Railroad Valley Playa

Abstract. The short-wave infrared (SWIR) module of the Tropospheric Monitoring Instrument (TROPOMI) on board the ESA's Sentinel-5 precursor (S5p) satellite has been very stable during its 5 years in orbit. Calibration was performed on the ground, complemented by measurements during in-flight instrument commissioning. The radiometric response and general performance of the SWIR module are monitored by on-board calibration sources. We show that after 5 years in orbit, TROPOMI-SWIR has continued to show excellent performance with degradation of at most 0.1 % in transmission and having lost less than 0.3 % of the detector pixels. Independent validation of the instrument calibration, via vicarious calibration, can be done through comparisons with ground-based reflectance data. In this work, ground measurements at the Railroad Valley Playa, a valley in central Nevada that is often used as a reference for satellite measurements, are used to perform vicarious calibration of the TROPOMI-SWIR measurements. This is done using dedicated measurement campaigns as well as automated reflectance measurements within the RADCALNET programme. As such, TROPOMI-SWIR is an excellent test case to explore the methodology of vicarious calibration applied to infrared spectroscopy. Using methodology developed for the vicarious calibration of the OCO-2 and GOSAT missions, the absolute radiometry of TROPOMI-SWIR performance is independently verified to be stable down to ∼ 6 %–10 % using the Railroad Valley when both the absolute and relative radiometric calibrations are applied. Differences with the on-board calibration originate from the bidirectional reflection distribution function (BRDF) effects of the desert surface, the large variety in viewing angles, and the different sizes of footprints of the TROPOMI pixels. Vicarious calibration is shown to be an additional valuable tool in validating radiance-level performances of infrared instruments such as TROPOMI-SWIR in the field of atmospheric composition. It remains clear that for instruments of similar design and resolution to TROPOMI-SWIR, on-board calibration sources will continue to provide superior results due to the limitations of the vicarious calibration method.

Monitoring spatiotemporal changes of NO2 using TROPOMI and sentinel-5 images for Dhaka city and its surrounding areas of Bangladesh

Introduction: Discernable air pollution occurs in most developing countries due to rapid urbanization which can be parameterized by air, humidity, population density, temperature, contaminants, exorbitant fossil fuel consumption, and inadequate transportation. Nitrogen dioxide (NO2), one of the most widely recognized air pollutants, has a detrimental impact on human health explicitly or implicitly and considerably influences on atmospheric composition.
 Materials and methods: In this study, NO2 intensity was analyzed from 2018 with aiming to monitor spatiotemporal changes in Dhaka and its surrounding areas with the Tropospheric Monitoring Instrument (TROPOMI) sensor data. Copernicus Sentinel-5 Precursor satellite data was used in the Google Earth Engine platform to get the result.
 Results: The results revealed a strong relationship (R2 =0.9478) between the NO2 concentration and high population density and the temporal variation is higher during the pre-monsoon than throughout the post-monsoon. The reason behind is the lack of sunlight and the difficulty to break down the NO2, which causes the removal of NO2 from the atmosphere to proceed more slowly. In contrast, Land Use and Land Cover (LULC) are also impacted by the high concentration which is remains in the built-up area.
 Conclusion: This research mainly considered that how NO2 concentration measured from satellite images with temporal variation within a year and what factors strongly influence raising NO2 levels. This model can be used for policy-making to take proper initiatives to reduce NO2 concentrations. The result showed significant uses of TROPOMI with relating population density and LULC in Dhaka and its surrounding areas of Bangladesh.

Plume detection and emission estimate for biomass burning plumes from TROPOMI carbon monoxide observations using APE v1.1

Abstract. This paper presents the automated plume detection and emission estimation algorithm (APE), developed to detect CO plumes from isolated biomass burning events and to quantify the corresponding CO emission rate. APE uses the CO product of the Tropospheric Monitoring Instrument (TROPOMI) on board the Copernicus Sentinel-5 Precursor (S5P) satellite, launched in 2017, and collocated active fire data from the Visible Infrared Imaging Radiometer Suite (VIIRS), the latter flying 3 min ahead of S5P. After identifying appropriate fire events using VIIRS data, an automated plume detection algorithm based on traditional image processing algorithms selects plumes for further data interpretation. The approach is based on thresholds optimized for data over the United States in September 2020. Subsequently, the CO emission rate is estimated using the cross-sectional flux method, which requires horizontal wind fields at the plume height. Three different plume heights were considered, and the ECMWF Reanalysis v5 (ERA5) data were used to compute emissions. A varying plume height in the downwind direction based on three-dimensional Lagrangian simulation was considered appropriate. APE is verified for observations over Australia and Siberia. For all fire sources identified by VIIRS, only 16 % of the data corresponded to clear-sky TROPOMI CO data with plume signature. Furthermore, the quality filters of APE resulted in emission estimations for 26 % of the TROPOMI CO data with plume signatures. Visual filtering of the APE's output showed a true-positive confidence level of 97.7 %. Finally, we provide an estimate of the emission uncertainties. The greatest contribution of error comes from the uncertainty in Global Fire Assimilation System (GFAS) injection height that leads to emission errors <100 %, followed by systematic errors in the ERA5 wind data. The assumption of constant emission during plume formation and spatial under-sampling of CO column concentration by TROPOMI yields an error of <20 %. The randomized errors from the ensemble ERA5 wind data are found to be less than 20 % for 97 % of the cases.

Estimating surface-level nitrogen dioxide concentrations from Sentinel-5P/TROPOMI observations in Finland

In recent decades, satellite instruments have been providing observations of air pollutants such as nitrogen dioxide (NO2) with global coverage. Since late 2018, the TROPOspheric Monitoring Instrument (TROPOMI) on-board the Copernicus Sentinel-5 Precursor satellite has produced NO2 vertical column densities (VCDs) with the best spatial resolution (currently 5.5 × 3.5km2 at nadir). In this paper, we test and adapt two previously published methods to estimate surface-level NO2 concentrations from TROPOMI VCD retrievals in the specific conditions of Finland. Satellite-based estimates show good correlation with co-located surface NO2 measurements from the Finnish AQ network, although not accounting for the level of NO2 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 NO2 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.

Estimating ground-level CH4 concentrations inferred from Sentinel-5P

ABSTRACT Methane (CH4) is an important greenhouse gas; however, there is a lack of large-scale studies on ground-level CH4 concentrations. We estimated global ground-level CH4 concentrations based on the CH4 columns from the Copernicus Sentinel-5 precursor satellite (S5P) and vertical profiles of CH4 concentrations simulated from the Atmospheric Chemical Transport Model (GEOS-Chem). The proposed approach had achieved a high predictive accuracy for monthly ground-level CH4 concentrations, with a correlation coefficient of 0.93 (p < 0.01) and RMSE of 29.93 ppb between the estimated CH4 concentrations and those of ground measurements from the World Data Centre for Greenhouse Gases (WDCGG). Compared with the S5P CH4 columns, the estimated ground-level CH4 concentration has a close spatial relationship with emissions. The high CH4 concentrations occurred in eastern China, northern India, western Russia, eastern U.S., and central Europe. Furthermore, the estimated ground-level CH4 concentrations could reflect the seasonal variations of the observations, with correlation coefficients from 0.14 to 0.92. Our findings highlight the importance of satellite observations on atmospheric CH4 in understanding the spatial and temporal emissions.

A Systematic Approach to Identify Shipping Emissions Using Spatio-Temporally Resolved TROPOMI Data

Stringent global regulations aim to reduce nitrogen dioxide (NO2) emissions from maritime shipping. However, the lack of a global monitoring system makes compliance verification challenging. To address this issue, we propose a systematic approach to monitor shipping emissions using unsupervised clustering techniques on spatio-temporal georeferenced data, specifically NO2 measurements obtained from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Copernicus Sentinel-5 Precursor satellite. Our method involves partitioning spatio-temporally resolved measurements based on the similarity of NO2 column levels. We demonstrate the reproducibility of our approach through rigorous testing and validation using data collected from multiple regions and time periods. Our approach improves the spatial correlation coefficients between NO2 column clusters and shipping traffic frequency. Additionally, we identify a temporal correlation between NO2 column levels along shipping routes and the global container throughput index. We expect that our approach may serve as a prototype for a tool to identify anthropogenic maritime emissions, distinguishing them from background sources.

Spatial and Temporal Variations of Atmospheric CH4 in Monsoon Asia Detected by Satellite Observations of GOSAT and TROPOMI

Space-based measurements, such as the Greenhouse gases Observing SATellite (GOSAT) and the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite, provide global observations of the column-averaged CH4 concentration (XCH4). Due to the irregular observations and data gaps in the retrievals, studies on the spatial and temporal variations of regional atmospheric CH4 concentrations are limited. In this paper, we mapped XCH4 data over monsoon Asia using GOSAT and TROPOMI observations from April 2009 to December 2021 and analyzed the spatial and temporal pattern of atmospheric CH4 variations and emissions. The results show that atmospheric CH4 concentrations over monsoon Asia have long-term increases with an annual growth rate of roughly 8.4 ppb. The spatial and temporal trends of XCH4 data are significantly correlated with anthropogenic CH4 emissions from the bottom-up emission inventory of EDGAR. The spatial pattern of gridded XCH4 temporal variations in China presents a basically consistent distribution with the Heihe–Tengchong Line, which is mainly related to the difference in anthropogenic emissions in the eastern and western areas. Using the mapping of XCH4 data from 2019 to 2021, this study further revealed the response of atmospheric CH4 concentrations to anthropogenic emissions in different urban agglomerations. For the urban agglomerations, the triangle of Central China (TCC), the Chengdu–Chongqing City Group (CCG), and the Yangtze River Delta (YRD) show higher CH4 concentrations and emissions than the Beijing–Tianjin–Hebei region and nearby areas (BTH). The results reveal the spatial and temporal distribution of CH4 concentrations and quantify the differences between urban agglomerations, which will support further studies on the drivers of methane emissions.