This study analyzed the relationship between late Harmattan dust in February-March and subsequent early rainfall in April-May in West Africa, focusing around the high cocoa-producing regions. The impact of Harmattan was evaluated using high-resolution Sentinel-5P TROPOMI data. Strong positive correlations were found in the southwestern regions of Côte d'Ivoire and southern Liberia where cocoa production is prominent. This is consistent with anecdotal evidence obtained from a local farmer in Gagnoa, which falls under the positively correlated area found in this study. The findings suggest that late Harmattan dust may serve as a potential indicator of early rainfall. This could help better agricultural planning for local farming communities, given the significant influence of rainfall amount on cocoa growth.

In recent years, record-breaking wildfires have occurred globally, with projections indicating a dramatic increase in their frequency and intensity in the future. These wildfires present serious risks to the environment by releasing harmful pollutants and various greenhouse gases, which significantly contribute to air pollution and climate change. To accurately predict emissions of such gases, a comprehensive understanding of combustion efficiency is essential. Due to TROPOMI’s capability to measure trace gases such as NO2 and CO with high spatial resolution and global coverage, it has been used in various studies to analyse combustion efficiency. The study used NO2 and CO column concentrations measured by TROPOMI to estimate Mole Density Ratio (MDR), which is a proxy of combustion efficiency, over two devastating wildfires that occurred in California in 2020. By using TROPOMI data, aggregated to various resolutions using the super-observation approach, the study assessed the spatial and temporal limits of TROPOMI-derived MDR. It evaluated changes in MDR values across various vegetation types by integrating higher resolution land classification data from MODIS. Additionally, it explored the relationship between MDR and environmental indicators such as drought conditions and soil moisture. Super-observations resulted in significantly different MDR values with those estimated at TROPOMI resolution. The findings indicated that there was loss of information regarding MDR when super-observations were used. Furthermore, there was no clear link found on the impact of environmental factors such as soil moisture and drought conditions on MDR. Finally, a detailed land use characterisation provided deeper insights into the effect of burning various types of vegetation on the MDR. However, to be able to fully interpret the effect of super-observations and environmental factors
on MDR, a more extensive analysis is suggested.

During a solar eclipse, sunlight incident on the Earth is reduced due to the (partial) shadow of the Moon. Atmospheric trace gas concentrations which are influenced by the amount of available sunlight, such as nitrogen dioxide (NO₂), may be affected due to the disrupted photolysis processes. Large-scale observations of the increased NO₂ concentrations caused by the solar eclipse would improve our understanding of the sensitivity of NO₂ in the atmosphere to short-term variations in sunlight. Spaceborne
measurements can provide valuable information about the large-scale spatial distribution of NO₂, which is provided daily by the TROPOMI instrument aboard the Sentinel-5 Precursor satellite by measuring and retrieving locally reflected sunlight. However, the TROPOMI NO₂ retrieval is unable to derive reliable concentrations during a solar eclipse, as solar eclipses are not taken into account in its retrieval algorithm. In this research, we have adjusted the NO₂ retrieval of TROPOMI such that it can handle solar eclipses and study the large-scale response of NO₂ during two solar eclipses over Europe in 2021 and 2022. We found a large-scale increase of NO₂ in the adjusted measurements, which linearly correlated with the degree of obscuration. We compared the measured NO₂ increase with the values from the atmospheric chemistry model TM5 including an applied eclipse implementation and we found a close agreement in most areas that are not highly polluted. Our measurements and model predict a NO₂ increase of 60%±12% and 70%±7% for an obscuration fraction of 1, respectively. More advanced chemistry modelling work is needed to explain the measurements in highly populated areas. We conclude that our results demonstrate that the TROPOMI algorithm is capable of correctly measuring NO₂ after an adjustment of the NO₂ retrieval. We have shown that it is possible to adjust an atmospheric trace gas retrieval for the influence of a solar eclipse. Moreover, we are the first to provide evidence for an increase in NO₂ during a solar eclipse using space-based measurement techniques and to quantify this increase on a large scale with the same instrument. Our measurements can be used to test atmospheric chemistry models, possibly improving their sensitivity to solar eclipses but also artificial shadows on the Earth induced by sunlight-intercepting geoengineering approaches.

The Sentinel-5 Precursor satellite has a payload of the TROPOspheric Monitoring Instrument, TROPOMI.The satellite was launched in 2017 by ESA with the intended goal of measuring trace gases in the atmosphere.One of the products of TROPOMI is the Tropospheric NO2 column. This product is based on thespectral measurements to obtain the column abundance of NO2 in the troposphere. This product alsorelies on a-priori data and one of  these a-priori datasets is the albedo dataset.The currently used dataset has a resolution of 0.5°x 0.5°, which corresponds to approximately 55 kmx 34 km at mid-latitudes. The TROPOMI pixel size is significantly smaller, 3.5 km x 7 km. Due to this large difference in resolution the discussion arises if this used dataset is sufficient for accurate results. This researchmakes a comparison between the current a-priori dataset and possible replacements.
This paper makes this comparison by calculating Air Mass Factors (AMFs) using the OMI LERalbedo climatology as a reference and the two alternative high resolution surface reflectance datasets,Sentinel-2 and Landsat-8. These surface reflectance datasets were regridded and averaged on the corresponding TROPOMI grid. The focus area of this paper is the Greater Rotterdam region in the Nether-lands.Before these AMF calculations were done, a comparison between Sentinel-2 and Landsat-8 surfacereflectance datasets is made. This is done both on their own high resolution and regridded onto theTROPOMI grid. Above water surfaces and land covered by vegetation a bias of approximately 0.01 was present between the two high resolution surface reflectance datasets. These differences are relatively small. The differences calculated for the datasets regridded to the TROPOMI grid were also relatively small, with a bias of 0.01 above the water and vegetation surfaces.
Two cases were studied during this research: the 21st of April 2018 and the 6th/7th of May 2018. Theresults show that significant improvements can be made by using a higher resolution surface reflectancedataset. A median bias of -10.4% (-15.6%) was calculated for the 21st of April for Sentinel-2 (Landsat-8)compared to the AMFs based on the OMI albedo dataset. For May this was -3.9% (-9.3%). Furthermorethis study showed extreme AMF-biases of 68.0% overestimation and 39.8% underestimation by the OMIalbedo dataset compared to Sentinel-2, where the overestimation was observed over the greenhouses inthe Westland region and the underestimation in the rural region to the East of the domain in April.For May the underestimation was mostly observed to the West (North Sea), indicating that over regionswith a low surface reflectance the atmospheric correction greatly influences the AMF. The comparisonbetween Landsat-8 and OMI showed similar results in the AMF differences.These findings are supported further by a recent Sentinel-5P validation study, which comparedground based observations to the TROPOMI observations. This project found an NO2 underestimationof approximately 20% for many different stations. This research suggest that, at least partly, this difference can be explained by the coarse resolution of the a-priori albedo dataset used. 

Horizontal drilling and hydraulic fracturing have increased unconventional oil and gas extraction from shale reserves in the U.S. in the last decade, making up more than half of total U.S. oil and gas production at present. This activity results in NOx emissions in the extraction regions that are measurable from space using the Ozone Monitoring Instrument (OMI) on the NASA Aura satellite. The NOx emissions are a result of two different activities: (1) the drilling and hydraulic fracturing of new wells, and (2) the extraction of oil and gas after the well is completed. To separate the NOx emissions from drilling and extraction, a multiple linear regression to the NO2 columns as a function of time is calculated for 9 extraction regions using the number of drilling rigs and the oil and gas extraction data from 2007 until 2018. In 3 regions (Permian, Bakken, Eagle Ford) a significant correlation between measured and modeled NO2 columns is found, of which the Permian region shows the highest correlation. The analysis shows that half of the total NOx emissions in the Permian region can be attributed to emissions from oil and gas activities, and that both the drilling and extraction activities have an equal share in the emissions. A fuel-based oil and gas emission inventory shows a different split for NOx emissions from drilling and extraction in the Permian region, indicating drilling as the larger source. In other extraction regions, NO2 columns show poor correlation with the oil and gas activities due to the proximity of urban areas (Barnett, Denver-Julesburg, Haynesville regions), power plants (San Juan) or variations in the drilling and extraction activity over time that are too small (Uintah, Upper Green River).

Chlorophyll (Chl) and Total Suspended Matter (TSM) are both important water quality parameters since they influence the amount of oxygen & amount of light penetrating the water. Oxygen and light are vital in marine ecosystems. The Dutch governmental organisation, Rijkswaterstaat (RWS), has been monitoring the water quality of the Dutch part of the North Sea for the last 35 years. A research vessel takes off to sample parameters such as chlorophyll and TSM every few weeks at fixed locations. Recently, Sentinel-3 satellites started to provide satellite-based information on air or water quality. It is expected that products from the Ocean Land Colour Instrument (OLCI) sensor, on board of Sentinel-3, can greatly improve both the geographical and temporal coverage of these parameters. For the Dutch coastal waters this is challenging, because they predominantly consist of complex coastal waters. This study focusses on the validation of water quality parameters (Chl and TSM) available from Sentinel-3A OLCI observations. To verify the processing line of the OLCI data products it was desired to evaluate the following variables as well: 1) the aerosol optical thickness used in the atmospheric aerosol correction process, which is an important step for deriving the water-leaving reflectance, and 2) the water-leaving reflectance itself used as the main signal for deriving Chl and TSM. OLCI water quality data products were compared to Rijkswaterstaat in-situ measurements for the months May until September of 2017. Furthermore, OLCI’s Chl and TSM were compared with climatologies of MERIS data. The OLCI water-leaving reflectance and aerosol optical thickness data products were compared with observations from the Belgian AERONET-OC station Thornton. To evaluate the spatial distribution of OLCI's aerosol optical thickness comparisons with nearly coincident MODIS-AQUA observations were made. To evaluate the spatial variability of OLCI's data products boxplots were created of Chl, TSM and the aerosol optical thickness. The water quality products of OLCI consist of a Chl product determined by the OC4Me algorithm and a Chl & TSM product derived from a neural network. OLCI Chl obtained from the OC4Me algorithm showed an overestimation of a factor 2 compared to the in-situ measurements. The Chl results of the neural network compared well with the in-situ measurements showing a correlation coefficient of 0.77. OLCI TSM showed an unrealistic underestimation of a factor 4 compared to in-situ measurements. Boxplots showed that the largest spatial variability is found at stations <50 km from the coast for the three water quality products. This unrealistic underestimation of scattering TSM would imply an underestimation of the water-leaving reflectance in all the bands. Comparing OLCI's water-leaving reflectance with AERONET's showed underestimations in the blue and green bands only. OLCI’s water-leaving reflectance of the red and near-infra-red (NIR) bands correlated well with the AERONET-OC measurements. The aerosol optical thickness data product showed unrealistic overestimations of OLCI compared to AERONET-OC, but had a correlation coefficient of 0.58 when comparing it to MODIS aerosol optical thickness product. The spatial variability of OLCI's aerosol optical thickness is very high with differences of more than 40% per kilometre. In general, all products seem to have unrealistic values around clouds and in coastal areas, especially the aerosol optical thickness product. The pixels in those regions are different from other pixels. These results imply that further research into the software implementation of the radiative transfer models, lookup tables, vicarious calibrations and Neural Networks is needed to understand how retrievals of Chl and TSM concentrations are influenced. Such a fundamental understanding is ultimately also of interest for end users and all parties providing products and services for marine applications.