TROPOspheric Monitoring Instrument Data Research Articles

The Greenhouse gas Observations of Biospheric and Local Emissions from the Upper sky (GOBLEU): a mission overview, instrument description, and results from the first flight

BackgroundThe Greenhouse gas Observations of Biospheric and Local Emissions from the Upper sky (GOBLEU) is a new joint project by Japan Aerospace Exploration Agency (JAXA) and ANA HOLDING INC. (ANAHD), which operates ANA flights. GOBLEU aims to visualizes our climate mitigation effort progress in support of subnational climate mitigation by collecting greenhouse gas (GHG) data as well as relevant data for emissions (nitrous dioxide, NO2) and removals (Solar-Induced Fluorescence, SIF) from regular passenger flights. We developed a luggage-sized instrument based on the space remote-sensing techniques that JAXA has developed for Japan’s Greenhouse gas Observing SATellite (GOSAT). The instrument can be conveniently installed on a coach-class passenger seat without modifying the seat or the aircraft.ResultsThe first GOBLEU observation was made on the flight from the Tokyo Haneda Airport to the Fukuoka Airport, with only the NO2 module activated. The collected high-spatial-resolution NO2 data were compared to that from the TROPOspheric Monitoring Instrument (TROPOMI) satellite and surface NO2 data from ground-based air quality monitoring stations. While GOBLEU and TROPOMI data shared the major concentration patterns largely driven by cities and large point sources, regardless of different observation times, we found fine-scale concentration pattern differences, which might be an indication of potential room for GOBLEU to bring in new emission information and thus is worth further examination. We also characterized the levels of NO2 spatial correlation that change over time. The quickly degrading correlation level of GOBLEU and TROPOMI suggests a potentially significant impact of the time difference between CO2 and NO2 as an emission marker and, thus, the significance of co-located observations planned by future space missions.ConclusionsGOBLEU proposes aircraft-based, cost-effective, frequent monitoring of greenhouse emissions by GOBLEU instruments carried on regular passenger aircraft. Theoretically, the GOBLEU instrument can be installed and operated in most commercially used passenger aircraft without modifications. JAXA and ANAHD wish to promote the observation technique by expanding the observation coverage and partnership to other countries by enhancing international cooperation under the Paris Agreement.

First-time calculation of the spatial distribution of concentration and air quality index over South Africa using TROPOMI data

ABSTRACT The release of toxic gases into the atmosphere may reach concentrations that can cause undesirable health, economic, or aesthetic effects. It is therefore important to monitor the amounts of pollutants injected into the atmosphere from various sources. Most countries have a ground network with multiple measuring sites and instruments, that can measure the air quality index (AQI). However, the main challenge with the networks is the low spatial coverage. In this work, satellite data is used to calculate for the first time the spatial distribution of AQI and pollutant concentration over South Africa. The TROPOspheric Monitoring Instrument (TROPOMI) onboard Sentinel-5P data is used to calculate AQI from carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2) gases. The results that the month of June has the worst air quality distribution throughout the country, while March has the best air quality distribution. Overall, the results clearly show that TROPOMI has the capability to measure air quality at a country and city level. Implications: In this work, satellite data is used to calculate for the first time the spatial distribution of the air quality index (AQI) and pollutant concentration over South Africa. The TROPOspheric Monitoring Instrument (TROPOMI) onboard Sentinel-5P data is used to calculate AQI from carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2) gases. Currently, South Africa has a ground network of instruments that measure AQ, however, the network does not cover the whole country. In this work, we show that the use of TROPOMI can compliment the current network and provide data for the areas not covered.

An intercomparison of satellite, airborne, and ground-level observations with WRF–CAMx simulations of NO2 columns over Houston, Texas, during the September 2021 TRACER-AQ campaign

Abstract. Nitrogen dioxide (NO2) is a precursor of ozone (O3) and fine particulate matter (PM2.5) – two pollutants that are above regulatory guidelines in many cities. Bringing urban areas into compliance of these regulatory standards motivates an understanding of the distribution and sources of NO2 through observations and simulations. The TRACER-AQ campaign, conducted in Houston, Texas, in September 2021, provided a unique opportunity to compare observed NO2 columns from ground-, airborne-, and satellite-based spectrometers. In this study, we investigate how these observational datasets compare and simulate column NO2 using WRF–CAMx with fine resolution (444 × 444 m2) comparable to the airborne column measurements. We compare WRF-simulated meteorology to ground-level monitors and find good agreement. We find that observations from the GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) instrument were strongly correlated (r2 = 0.79) to observations from Pandora spectrometers with a slight high bias (normalized mean bias (NMB) = 3.4 %). Remote sensing observations from the TROPOspheric Monitoring Instrument (TROPOMI) were generally well correlated with Pandora observations (r2 = 0.73) with a negative bias (NMB = −22.8 %). We intercompare different versions of TROPOMI data and find similar correlations across three versions but slightly different biases (from −22.8 % in v2.4.0 to −18.2 % in the NASA MINDS product). Compared with Pandora observations, the WRF–CAMx simulation had reduced correlation (r2 = 0.34) and a low bias (−21.2 %) over the entire study region. We find particularly poor agreement between simulated NO2 columns and GCAS-observed NO2 columns in downtown Houston, an area of high population and roadway densities. These findings point to a potential underestimate of NOx emissions (NOx = NO + NO2) from sources associated with the urban core of Houston, such as mobile sources, in the WRF–CAMx simulation driven by the Texas state inventory, and further investigation is recommended.

Satellite-Derived Estimate of City-Level Methane Emissions from Calgary, Alberta, Canada

Cities are important sources of anthropogenic methane emissions. Municipal governments can play a role in reducing those emissions to support climate change mitigation, but they need information on the emission rate to contextualize mitigation actions and track progress. Herein, we examine the application of satellite data from the TROPOspheric Monitoring Instrument (TROPOMI) to estimate city-level methane emission rates in a case study of the City of Calgary, Alberta, Canada. Due to low and variable annual observational coverage, we integrated valid TROPOMI observations over three years (2020–2022) and used mass balance modeling to derive a long-term mean estimate of the emission rate. The resulting column-mean dry-air mole fraction (XCH4) enhancement over Calgary was small (4.7 ppb), but within the city boundaries, we identified local hot spots in the vicinity of known emission sources (wastewater treatment facilities and landfills). The city-level emission estimate from mass balance was 215.4 ± 132.8 t CH4/d. This estimate is approximately four times larger than estimates from Canada’s gridded National Inventory Report of anthropogenic CH4 emissions and six times larger than the Emissions Database for Global Atmospheric Research (EDGAR v8.0). We note that valid TROPOMI observations are more common in warmer months and occur during a narrow daily overpass time slot over Calgary. The limited valid observations in combination with the constrained temporal observational coverage may bias the emission estimate. Overall, the findings from this case study highlight an approach to derive a screening-level estimate of city-level methane emission rates using TROPOMI data in settings with low observational coverage.

Comparative Analysis and High−Precision Modeling of Tropospheric CH4 in the Yangtze River Delta of China Obtained from the TROPOMI and GOSAT

Remote sensing satellite monitoring involving the use of shortwave infrared (SWIR) solar backscatter radiation to measure atmospheric CH4 column concentrations provides wide−ranging and accurate data for quantitatively determining atmospheric CH4 emissions and is highly important for human studies of atmospheric composition and environmental protection. The ESA−launched Sentinel−2 satellite equipped with a tropospheric monitoring instrument (TROPOMI) can provide the concentration of CH4 columns in every piece of the global atmosphere every day. However, these data may be affected by surface albedo, SWIR, aerosols, cirrus cloud scattering, and other factors. The greenhouse gas observing satellite (GOSAT) launched by Japan has fairly accurate data that are minimally affected by the aforementioned factors; however, its data density is much less than that of the TROPOMI. In this study, we propose a CH4 model that combines the TROPOMI and GOSAT data. We construct the model by analyzing the data from the TROPOMI and GOSAT at the same location at the same time. Then, we apply the proposed model to a certain location at a certain time with TROPOMI data but without GOSAT data to obtain a large range of high−precision CH4 data. The most developed urban agglomeration in the Yangtze River Delta in China was selected for model construction and the correlations between the TROPOMI and GOSAT data and their spatial and temporal trends were analyzed. First, we analyzed the CH4 concentrations in the same area measured by both models. The results revealed a high degree of temporal and spatial correlation in the YRD region. The correlation coefficient reached 0.71 in the metropolitan area of the YRD. At the small−city scale, the correlation is much more significant, with the correlation reaching 0.80, 0.79, and 0.71 for Nanjing, Shanghai, and Ningbo, respectively. The most accurate model was screened through comparative construction to calibrate the TROPOMI data and high−precision and high−coverage CH4 concentration information was obtained for the study area. Five models (linear model, quadratic term model, cubic term model, lognormal model, and logistic model) were used to select the best−fitting model. The magnitudes of the differences in the CH4 concentrations calculated by each model were compared. The final results showed that the linear model, as the prediction model, had the highest accuracy, with a coefficient of determination (R22) of 0.542. To avoid the specificity of the constructed model, we used the same method in several simulations to validate. The coefficient of determination of the model constructed with different stochastic data was greater than 0.5. Subsequently, we used Nanjing as the study area and applied the same method to construct the model. The coefficient of determination of the model (R22) was approximately 0.601. The model constructed in this research can be used not only for data conversion between the same products from different sensors to obtain high−precision data products but also for calibrating newly developed satellite data products that utilize mature data products.

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.

Impact of the implementation of Madrid's low emission zone on NO2 concentration using Sentinel-5P/TROPOMI data

In recent years, several measures and policies have been introduced to safeguard healthy NO2 levels in urban areas, including the creation of low emission zones (LEZs). In November 2018, a traffic-restricted area known as the Special Protection Low Emission Zone (SPLEZ) was established in the core of the city of Madrid, embracing most of its central district. This study aims to evaluate whether the implementation of the LEZ has led to improvements in air quality by analyzing NO2 concentrations in various case scenarios. For this purpose, data from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 precursor (Sentinel-5P) were used. The pre- and post-LEZ implementation seasonal analysis (from July 2018 to May 2019) and the entirety of 2022 indicated that the epicenter of transport-related NO2 concentrations did not lie within its boundaries. However, NO2 levels within the SPLEZ peaked during winter, with heating and meteorological factors playing a significant role. Additionally, the findings from the case study of the COVID-19 pandemic demonstrated the influence of human activities on the NO2 concentration in both the SPLEZ and the entire metropolitan area. NO2 emissions decreased during the strict lockdown period and steadily increased as activities resumed. Satellite data was compared to surface-level measurements in each case study, resulting in a linear correlation coefficient of 0.72. TROPOMI data was found to closely align with surface-level observations as pollution levels increased, contrasting with better air quality scenarios. Despite certain limitations, it can serve as a valuable tool for providing complementary information to surface-level data, particularly in characterizing changes in pollution distribution patterns.

Global Observations of Tropospheric Bromine Monoxide (BrO) Columns From TROPOMI

AbstractBromine monoxide (BrO) plays an important role in tropospheric chemistry. The state‐of‐the‐science TROPOspheric Monitoring Instrument (TROPOMI) offers the potential to monitor atmospheric composition with a fine spatial resolution of up to 5.5 × 3.5 km2. We present here the retrieval of tropospheric BrO columns from TROPOMI. We implement a stratospheric correction scheme using a climatological approach based on the latest GEOS‐Chem High Performance chemical transport model, and improve the tropospheric air mass factor calculation with TROPOMI surface albedo data accounting for the geometrical dependency. Our product presents a good level of consistency in comparison with measurements from ground‐based zenith‐sky differential optical absorption spectroscopy (r = 0.67), aircrafts (r = 0.46), and satellites (similar spatial distributions of BrO columns). Furthermore, our retrieval captures BrO enhancements in the polar springtime with values up to 7.8 × 1013 molecules cm−2 and identifies small‐scale emission sources such as volcanoes and salt marshes. Based on TROPOMI data, we probe a blowing snow aerosol bromine mechanism in which the snow salinity is reduced to better match simulation and observation. Our TROPOMI tropospheric BrO product contributes high‐resolution global information to studies investigating atmospheric bromine chemistry.

A new accurate retrieval algorithm of bromine monoxide columns inside minor volcanic plumes from Sentinel-5P TROPOMI observations

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

Evaluation of total ozone measurements from Geostationary Environmental Monitoring Spectrometer (GEMS)

Abstract. The continued interest in air pollution and stratospheric ozone variability has motivated the development of a Geostationary Environmental Monitoring Spectrometer (GEMS) for hourly ozone monitoring. This paper provides the atmospheric science community with the world's first assessment of GEMS total column ozone (TCO) retrieval performance and diurnal ozone variation. The algorithm used for GEMS is a more advanced version of its predecessor, the Total Ozone Mapping Spectrometer (TOMS) V8, that incorporates several improvements, including a new lookup table, a simple Lambertian-equivalent reflectivity model, and a spectral dependence correction. The GEMS algorithm also uses the optimal estimation method (OEM) to make error analysis more accessible and robust. The estimated retrieval errors range from 1.5 to 2 DU in September and 2 DU in December, with a constant degree of freedom of the signal (DFS) of 1 in September and a variable DFS of 1.25 to 1.4 in December throughout the day, depending on solar zenith angle (SZA). To assess the performance of the GEMS algorithm, the hourly GEMS total ozone was compared with ground-based measurements from Pandora instruments and other satellite platforms from TROPOMI (TROPOspheric Monitoring Instrument) and OMPS (Ozone Mapping and Profiler Suite Nadir Mapper). GEMS has a high correlation of 0.97 and small RMSE values compared to Pandora TCO at Busan and Seoul in South Korea. It is notable that despite exhibiting seasonal dependence in the mean bias of GEMS with Pandora, GEMS is capable of observing daily variations in ozone that are highly consistent with Pandora measurements, with a bias of approximately 1 %. The comparison of GEMS TCO data with TROPOMI and OMPS TCO data shows a high correlation of 0.99 and low RMSE compared to TROPOMI and OMPS TCO data, but the data have a negative bias of −2.38 % and −2.17 %, with standard deviations of 1.33 % and 1.57 %, respectively. Similar to OMPS, the influence of SO2 from volcanic eruptions is not properly removed in some regions, leading to GEMS overestimating TCO in those areas. The mean biases of GEMS TCO data with TROPOMI and OMPS TCO are within ±1 % at low latitudes but become negative at midlatitudes, with an increasingly negative dependence on latitude. Furthermore, this dependence becomes more prominent from summer to winter. The empirical correction applied to the GEMS irradiance data improves the dependence of the mean bias on season and latitude, but a consistent bias still remains, and a marginal positive trend was observed in December. Therefore, further investigation into correction methods is needed. The results are a meaningful scientific advance by providing the first validated, hourly UV ozone retrievals from a satellite in geostationary orbit. This experience can be used to advance research with future geostationary environmental satellite missions, including the incoming TEMPO (Tropospheric Emissions: Monitoring of Pollution) and Sentinel-4.

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.

An assessment of air quality indicators Sentinel-5P TROPOMI data were used to examine the NO2 SO2 O3 AEROSOL levels in Uttar Pradesh before, after, and during the COVID 19 phase

Significantly affecting human activities and economic sectors were the COVID-19 pandemic and related lockdowns. Nevertheless, despite these difficulties, the environment saw some improvement, especially in terms of the air quality. A dearth of thorough research comparing air quality indicators before, during, and after the epidemic has been noted despite the many studies that have emphasized the decrease in pollutants across India during the early phases of the pandemic. In-depth analyses of four important air quality indicators nitrogen dioxide (NO2), sulphur dioxide (SO2), ultraviolet aerosol index (UVAI) and ozone (O2) are conducted in this work. We examined the patterns on the Google Earth Engine (GEE) platform, specifically for the state of Uttar Pradesh in India, utilizing data from the TROPOMI (Tropospheric Monitoring Instrument) during a five-year period, focusing on the same three-month timeline each year. For several atmospheric variables (NO2,SO2,O2, Aerosol,), the given data from 2019 to 2023 includes monthly averages and standard deviations. The overall trend from 2019 to 2023 shows varying quantities of pollutants, with some, like NO2, rebounding after an initial decrease and others, like aerosols, exhibiting considerable annual changes, underscoring the dynamic character of environmental circumstances over time.

Estimate of near-surface NO2 concentrations in Fenwei Plain, China, based on TROPOMI data and random forest model.

Nitrogen dioxide (NO2) concentration is a crucial indicator of ground-level air quality, and elevated concentrations can adversely affect human health and the atmospheric environment. In this study, we utilized Tropospheric Monitoring Instrument (TROPOMI) tropospheric NO2 vertical column density data (VCD) and multi-source geographic data to establish a random forest regression (RF) model that accurately estimates NO2 concentrations near the ground in the Fenwei Plain. The model addresses the inherent limitations of traditional ground-based monitoring and provides data support for analyzing regional pollution spatial and temporal characteristics. (1) The RF model based on TROPOMI and geographic data demonstrates high estimation accuracy, with monthly average RF model fit and validation coefficient of determination (R2) reaching 0.949 and 0.875, respectively. (2) A complex nonlinear relationship exists between near-surface NO2 concentration and multi-source geographic data. The RF model's estimations reveal clear seasonal and regional variations in near-surface NO2 concentration. Concentrations are generally highest in winter, followed by spring and autumn, and lowest in summer. The high NO2 concentrations are primarily mainly distributed in the plains and river valleys with low elevation and dense population density. The model estimation results also indicate that the estimated effect is better when the NO2 concentration fluctuates less and anthropogenic emission reduction measures significantly impact the NO2 concentration near the ground. (3) The population exposure risk results indicate that most cities in the Fenwei Plain face varying exposure risks. These findings offer valuable insights for regional NO2 pollution management.

A blended TROPOMI+GOSAT satellite data product for atmospheric methane using machine learning to correct retrieval biases

Abstract. Satellite observations of dry-column methane mixing ratios (XCH4) from shortwave infrared (SWIR) solar backscatter radiation provide a powerful resource to quantify methane emissions in service of climate action. The TROPOspheric Monitoring Instrument (TROPOMI), launched in October 2017, provides global daily coverage at a 5.5 × 7 km2 (nadir) pixel resolution, but its methane retrievals can suffer from biases associated with SWIR surface albedo, scattering from aerosols and cirrus clouds, and across-track variability (striping). The Greenhouse gases Observing SATellite (GOSAT) instrument, launched in 2009, has better spectral characteristics and its methane retrieval is much less subject to biases, but its data density is 250 times sparser than TROPOMI. Here, we present a blended TROPOMI+GOSAT methane product obtained by training a machine learning (ML) model to predict the difference between TROPOMI and GOSAT co-located measurements, using only predictor variables included in the TROPOMI retrieval, and then applying the correction to the complete TROPOMI record from April 2018 to present. We find that the largest corrections are associated with coarse aerosol particles, high SWIR surface albedo, and across-track pixel index. Our blended product corrects a systematic difference between TROPOMI and GOSAT over water, and it features corrections exceeding 10 ppb over arid land, persistently cloudy regions, and high northern latitudes. It reduces the TROPOMI spatially variable bias over land (referenced to GOSAT data) from 14.3 to 10.4 ppb at a 0.25∘ × 0.3125∘ resolution. Validation with Total Carbon Column Observing Network (TCCON) ground-based column measurements shows reductions in variable bias compared with the original TROPOMI data from 4.7 to 4.4 ppb and in single-retrieval precision from 14.5 to 11.9 ppb. TCCON data are all in locations with a SWIR surface albedo below 0.4 (where TROPOMI biases tend to be relatively low), but they confirm the dependence of TROPOMI biases on SWIR surface albedo and coarse aerosol particles, as well as the reduction of these biases in the blended product. Fine-scale inspection of the Arabian Peninsula shows that a number of hotspots in the original TROPOMI data are removed as artifacts in the blended product. The blended product also corrects striping and aerosol/cloud biases in single-orbit TROPOMI data, enabling better detection and quantification of ultra-emitters. Residual coastal biases can be removed by applying additional filters. The ML method presented here can be applied more generally to validate and correct data from any new satellite instrument by reference to a more established instrument.

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.

Generating high-resolution total canopy SIF emission from TROPOMI data: Algorithm and application

Solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in modeling global terrestrial gross primary production (GPP). Canopy total SIF emissions (SIFtotal) are mechanistically linked to the plant photosynthesis, and can be estimated from satellite observed SIF (SIFobs) through radiative transfer modeling. However, the current satellite SIFobs and thus SIFtotal are available only at coarse spatial resolutions from several kilometers to tens of kilometers, inhibiting the application at fine spatial scales. Here, we proposed an algorithm to generate both global high-resolution SIFtotal (HSIFtotal) and high-resolution SIFobs (HSIFobs) at 1 km from low-resolution SIFobs (LSIFobs) from the TROPOspheric Monitoring Instrument (TROPOMI), which has a spatial resolution at nadir of 3.5 km by 5.6–7 km. Our statistical method is based on the law of energy conservation and uses satellite derived fraction of absorbed photosynthetically active radiation, fluorescence efficiency, and the escape probability of fluorescence. We evaluated the accuracy of our HSIFtotal using the Orbiting Carbon Observatory-2 SIF (R2 = 0.78). We found that the spatial resolution had clear effects on the relationship between HSIFtotal and GPP. We also compared HSIFtotal to 8-day averaged tower GPP from 135 flux sites and found that they were better correlated when HSIFtotal was averaged over a 1-km radius around the tower than when averaged over a larger radius. Our study provided a unique high-resolution HSIFtotal product, which will advance the estimation of GPP by extrapolating site-level relationships to the global scale.

Satellite-based, top-down approach for the adjustment of aerosol precursor emissions over East Asia: the TROPOspheric Monitoring Instrument (TROPOMI) NO2 product and the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol optical depth (AOD) data fusion product and its proxy

Abstract. In response to the need for an up-to-date emissions inventory and the recent achievement of geostationary observations afforded by the Geostationary Environment Monitoring Spectrometer (GEMS) and its sister instruments, this study aims to establish a top-down approach for adjusting aerosol precursor emissions over East Asia. This study involves a series of the TROPOspheric Monitoring Instrument (TROPOMI) NO2 product, the GEMS aerosol optical depth (AOD) data fusion product and its proxy product, and chemical transport model (CTM)-based inverse modeling techniques. We begin by sequentially adjusting bottom-up estimates of nitrogen oxides (NOx) and primary particulate matter (PM) emissions, both of which significantly contribute to aerosol loadings over East Asia to reduce model biases in AOD simulations during the year 2019. While the model initially underestimates AOD by 50.73 % on average, the sequential emissions adjustments that led to overall increases in the amounts of NOx emissions by 122.79 % and of primary PM emissions by 76.68 % and 114.63 % (single- and multiple-instrument-derived emissions adjustments, respectively) reduce the extents of AOD underestimation to 33.84 % and 19.60 %, respectively. We consider the outperformance of the model using the emissions constrained by the data fusion product to be the result of the improvement in the quantity of available data. Taking advantage of the data fusion product, we perform sequential emissions adjustments during the spring of 2022, the period during which the substantial reductions in anthropogenic emissions took place accompanied by the COVID-19 pandemic lockdowns over highly industrialized and urbanized regions in China. While the model initially overestimates surface PM2.5 concentrations by 47.58 % and 20.60 % in the North China Plain (NCP) region and South Korea (hereafter referred to as Korea), the sequential emissions adjustments that led to overall decreases in NOx and primary PM emissions by 7.84 % and 9.03 %, respectively, substantially reduce the extents of PM2.5 underestimation to 19.58 % and 6.81 %, respectively. These findings indicate that the series of emissions adjustments, supported by the TROPOMI and GEMS-involved data fusion products, performed in this study are generally effective at reducing model biases in simulations of aerosol loading over East Asia; in particular, the model performance tends to improve to a greater extent on the condition that spatiotemporally more continuous and frequent observational references are used to capture variations in bottom-up estimates of emissions. In addition to reconfirming the close association between aerosol precursor emissions and AOD as well as surface PM2.5 concentrations, the findings of this study could provide a useful basis for how to most effectively exploit multisource top-down information for capturing highly varying anthropogenic emissions.

Vertical information of CO from TROPOMI total column measurements in context of the CAMS-IFS data assimilation scheme

Abstract. Since 2017 the Tropospheric Monitoring Instrument (TROPOMI) on board ESA's Copernicus Sentinel-5 satellite (S5-P) has provided the operational carbon monoxide (CO) data product with daily global coverage on a spatial resolution of 5.5×7 km2. The European Centre for Medium-Range Weather Forecasts (ECMWF) plans to assimilate the retrieved total columns and the corresponding vertical sensitivities in the Copernicus Atmosphere Monitoring Service Integrated Forecasting System (CAMS-IFS) to improve forecasts of the atmospheric chemical composition. The TROPOMI data will primarily constrain the vertical integrated CO field of CAMS-IFS but to a lesser extent also its vertical CO distribution. For clear-sky conditions, the vertical sensitivity of the TROPOMI CO data product is useful throughout the atmosphere, but for cloudy scenes it varies due to cloud shielding and light scattering. To assess the profile information, we deploy an a posteriori profile retrieval that combines an ensemble of TROPOMI CO column retrievals with different vertical sensitivities to obtain a vertical CO profile that is then a representative average for the chosen spatial and temporal domain. We demonstrate the approach on three CO pollution cases. For the so-called “Rabbit Foot Fire” in Idaho on 12 August 2018, we estimate a CO profile showing the pollution at an altitude of about 5 km in good agreement with airborne in situ measurements of the Biomass Burning Flux Measurements of Trace Gases and Aerosol (BB-FLUX) field campaign. The distinct CO enhancement in a plume aloft (length =212 km, width =34 km), decoupled from the ground, is sensed by TROPOMI but is not present in the CAMS-IFS model. For a large-scale event, we analyzed the CO pollution from Siberian wildfires that took place from 14 to 17 August 2018. The TROPOMI data estimate the height of the pollution plume over Canada at 7 km in agreement with CAMS-IFS. However, CAMS-IFS underestimates the enhanced CO vertical column densities sensed by TROPOMI within the plume by more than 100 ppb. Finally, we study the seasonal biomass burning in the Amazon. During the burning season the CO profile retrieved from the TROPOMI measurements (1–15 August 2019) agrees well with the one of CAMS-IFS with a similar vertical shape between ground and 14 km altitude. Hence, our results indicate that assimilating TROPOMI CO retrieval with different vertical sensitivities (e.g., under clear-sky and cloudy conditions) provides information about the vertical distribution of CO.

Sulfur dioxide flux measurement at Mount Tokachi, Japan, with TROPOspheric Monitoring Instrument

Introduction: Monitoring the volcanic activity of a potentially hazardous volcano is essential for eruption warning and hazard mitigation. The SO2 flux from the volcano is one of the most important measures to understand its activity. Mount Tokachi, in Japan, is an active volcano that experienced three magmatic explosive eruptions in the 20th century (in 1926, 1962, and 1988–1989). Since 2006, geodetic observations have captured ground deformation, suggesting an inflation beneath the main crater. Moreover, since 2020 daily visual observations have detected the increase in plume heights and the occurrence of volcanic glow at the main crater. The high-time-resolution estimation of SO2 flux will help monitor the activity of Mount Tokachi and clarify the associating mechanisms. Furthermore, satellite remote sensing can estimate the vertical column density (VCD) of sulfur dioxide (SO2), enabling the daily determination of SO2 flux without the need to visit the site. Due to the improved spatial resolution, the TROPOspheric Monitoring Instrument (TROPOMI) has advanced satellite-based volcanic gas flux measurements.Methods: We have analyzed the available TROPOMI data and conducted ground-based observations to estimate the SO2 flux from Mount Tokachi at 100–2,000 tons/day.Result and Discussion: The average annual SO2 flux has been increasing since 2021, on par with the increase in plume height and volcanic glow. TROPOMI data enabled quantification of the annual SO2 flux, with a sufficient temporal resolution to monitor the volcanic activity at Mount Tokachi. However, a high flux, such as in excess of 2,000 tons/day, was observed in the winter season. The flux from the satellite data was similar to that from ground-based observations during the summer. However, a seasonal change in flux from the satellite data was observed in winter, with the flux being larger than that in summer, possibly because the flux increases during winter. Another possible reason is the influence of snow cover on satellite observations due to its high surface reflectance. We reanalyzed some TROPOMI data during the winter, in which the ground snow cover was misidentified as clouds at low altitudes. This procedure suppresses the error caused by the high surface reflectance due to winter snow coverage and improves the quality of the annual SO2 flux from satellite observations. This methodology is applicable to other high-latitude or high-altitude volcanoes during specific seasons characterized by frequent fluctuations in snow cover conditions.

A machine learning-based approach for fusing measurements from standard sites, low-cost sensors, and satellite retrievals: Application to NO2 pollution hotspot identification

While low-cost sensors (LCSs) and satellite retrievals are valuable supplements to regulatory air quality monitoring stations (AQMs), measurements from LCSs and satellite retrievals suffer from considerable bias and uncertainty. Here, we proposed a machine learning-based approach named the Fusion-Imputation-Gradient-Boosting-Machine (FI-GBM) model which fused the NO2 measurements from AQM, LCS, and the TROPOspheric Monitoring Instrument (TROPOMI) for mapping hourly ground-level NO2 at 1 km resolution. Based on the machine-learned relationships among AQM, LCS, TROPOMI measurements, and environmental covariates, the LCS and TROPOMI data were assimilated into AQM data. We selected Tangshan, an industrial city in North China, for the demonstration. The FI-GBM model showed high predictive performance in the sample-based cross-validation (R2 = 0.89). The R2 values of the cell-, area-, and month-based cross-validations were 0.67, 0.59, and 0.64, respectively. Fusing LCS and TROPOMI data improved the predictive performance compared to the benchmark models using neither or only one of them. The FI-GBM model showed decent utilization of the strengths of TROPOMI and LCS in regional and local-scale monitoring, respectively. It is noteworthy that the FI-GBM model could automatically filter noisy samples from LCS data, which was critical for discriminating between true and false-positive pollution hotspots. This study provides a data-noise-reduction approach for fusing multisource measurements in order to identify pollution hotspots and trace pollutant sources, thereby promoting cleaner production.