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.

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.

The main objective of this thesis is to design a new aerosol layer height retrieval in order to improve the operational NO2 retrieval, both in the troposphere, from space-borne instruments for highly polluted events and under cloud-free conditions. This thesis focuses on the exploitation of the OMI satellite measurements acquired in the visible wavelength range (405-490 nm). In addition, we develop numerical methods and tools (e.g. machine learning) in order to support the operational processing of big data amounts from the forthcoming new-generation satellite instruments for air quality and climate research.@en

Atmospheric aerosols are solid or liquid particles suspended in the air. The majority of them are produced by natural processes, including sea salt from oceans, mineral dust from (semi-)arid regions, carbon containing particles from wildfires, and sulfates and ash from volcanic activities. Anthropogenic aerosols are produced by industrial activities, power generation, transportation, agriculture, and human-induced biomass burning events. Depending on the meteorological conditions, aerosol particles can stay in the atmosphere for several hours to several months and can be transported over long distances, causing adverse effects on human health, visibility and climate. This thesis focuses on the aerosol optical properties, particularly the light absorption of the aerosol particles that has significant effects on the Earth’s climate system. This thesis starts with a general introduction of atmospheric aerosols, including its sources, categories, physical properties and measurement techniques (Chapter 1). Next, the Ultra-Violet Aerosol Index (UVAI) is introduced, which is calculated from satellite measurements of the radiance at two wavelengths in the UV. UVAI contains information of aerosol absorption, and it has a very long and almost continuous data record starting in 1978. Direct use of UVAI is challenging because it is not a geophysical quantity, but a numerical index. The objective of this thesis is to derive quantitative properties on aerosol absorption from the UVAI (e.g. single scattering albedo, absorption aerosol optical depth) that can be directly used in aerosol radiative transfer assessments. Two types of methods have been developed, i.e. physically-based methods and statistically-based methods. The first compares the observed UVAI to the one simulated by radiative transfer models. The second uses Machine Learning algorithms trained by existing data sets. The physically-based methods have been applied to quantify aerosol absorption of several large scale wildfires (Chapter 2 and 3). An important challenge of these method is that assumptions have to be made on the aerosol micro-physical properties, leading to significant uncertainties in the results, whereas theMachine Learning-based methods can avoid this kind of assumptions. Chapter 3 investigates the feasibility to quantify aerosol absorption from UVAI using a Machine Learning algorithm. Despite the higher computational efficiency and better results, the application of such data-driven methods is still restricted by the limited data on the aerosol vertical distribution. Therefore, in Chapter 4, a database of aerosol height is created from a chemistry transport model. This database is applied in Chapter 5, where a Deep Neural Network method is used to derive the quantitative aerosol absorptive properties from the OMI/Aura UVAI for the period from 2006 to 2019. In comparison to ground-based observations, the results of the Deep Neural Network agree better than satellite retrievals and also better than chemistry transport model simulations. This thesis demonstrates the feasibility of deriving quantitative aerosol absorptive properties from the satellite retrieved UVAI.We use traditional radiative transfer simulations meanwhile investigating the new possibilities of data-driven methods in aerosol remote sensing. Although the retrieval results are encouraging, there remain limitations and challenges which need to be addressed. These are discussed in Chapter 6 with corresponding suggestions and prospects. Despite the challenges, it is expected that a synthetic database of global aerosol absorption can be derived fromUVAI observations provided by multiple satellite products. Such a data set will make great contributions to quantify the effect of absorbing aerosols on the climate system.@en

This thesis investigates TROPOMI NO2 observations over low clouds and fog. Such scenes present unique opportunities due to the increased reflectivity of clouds in the lower troposphere, which enhances sensor sensitivity. However, in order to accurately estimate vertical NO2density in these scenarios, a better understanding of the retrieval process in such conditions is imperative. Two NO2 retrieval case studies, both exhibiting a spurious retrieval in the presence of low clouds and fog above the North Sea, are examined: February 14𝑡ℎ, 2023, and April 9𝑡ℎ, 2023. The main focus was on the influence of the air-mass factor on the retrieval. This factor converts measured slant column data along the satellite path into vertical tropospheric columns. Spurious spatial patterns are strongly amplified after the application of the air-mass factor. Key dependencies influencing the conversion from slant to vertical column are identified, being the the a-priori NO2 profile, air mass factor calculation, and cloud characterization. Notably, the low spatial resolution of the a-priori model and its pronounced peak near the surface contribute to inflated NO2 values over clouds. The majority of the a-priori NO2 profile is simulated underneath the cloud field, leading the retrieval algorithm to assume it did not capture a significant portion of the concentration, thus strongly increasing retrieved NO2  densities over clouds. Furthermore, spatial reflectivity patterns in the North Sea are not always accounted for in the surface reflectivity climatology. This results in misinterpreted clouds with a low cloud fraction. Misinterpreted clouds over open sea areas, combined with low cloud pressures, result in downward corrections over clear-sky seas. These effects culminate in an exacerbated cloudy-clear contrast in NO2 densities, aligning with the outlines of low cloud fields. Additionally, the heterogeneous cloud field challenges the assumption of a fixed-height, fixed-albedo Lambertian reflector. This inflation effect is modulated by the cloud height. The uncertain height of the Lambertian cloud in the lowest troposphere, combined with an assumed NO2 profile at the same height, dominates the air-mass factor calculation . Four recommendations to enhance NO2 retrieval accuracy in the presence of low clouds and fog over the North Sea are presented, which include a Cloud-as-Layers characterization, high-resolution simulations and in-situ measurements of vertical NO2 profiles in the presence of low clouds, an a-priori correction of the NO2 profile in the presence of low clouds and implementing auxiliary data relating to spatial patterns in sea-colour of the North Sea.

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.

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.