Abstract: Exposure to air pollution, such as particulate matter (PM), causes adverse health effects, particularly to the respiratory tract and cardiovascular system. PM is the collective name for all kinds of particles ranging from small particles and liquid droplets, which contain organic compounds, acids and metals, to soil or dust particles. One distinguishes PM10, PM2.5 and PM0.1, which have aerodynamic particle sizes smaller than 10, 2.5 and 0.1 µm, respectively. It is mainly the latter that is the most harmful, as PM0.1 penetrates deep into the respiratory system and carries relatively more toxic substances than the other PM fractions. Over a 15-year period, PM concentrations in European member states have fallen by about 30%. Nevertheless, the World Health Organisation (WHO) air quality guidelines, which became stricter in 2021, are exceeded in most places around the world. Particularly in cities, excessive levels of PM are measured and it is here that PM mitigation should be investigated. For this, the implementation of urban green infrastructure, including trees, shrubs, green roofs and green walls, is being looked at. Plants hinder airflow and remove PM from the air by deposition on their leaves and branches. This process is known as dry deposition. Plants can capture PM very efficiently, due to their complex structure of leaves and branches. Green walls offer significant advantages over other types of urban green infrastructure because they can grow on the huge available wall area and, because they do not hinder air circulation, as we sometimes see with trees. Green walls are believed to have a much greater, untapped potential to reduce PM pollution. However, a literature review showed that we do not know the quantitative impact of green walls and lack the tools and/or general methodology to do so. The objective of this thesis is therefore to develop a method for assessing PM removal by green walls, based on predictive models and based on relevant parameters that are experimentally determined. Computational fluid dynamics (CFD) is a numerical method to simulate airflow in complex environments such as cities. These models can also simulate the vegetation-wind interaction in detail and are interesting tools to assess the effect of green walls on PM concentrations in real environments. It is important to first study the aerodynamic effect of green walls and parameterise it correctly in CFD models. Plants decrease the wind speed and create turbulence through a combination of viscous and form drag, which are determined by the permeability (K) and drag coefficient (Cd), respectively. Wind tunnel experiments were conducted with three commonly found climbers (Hedera helix, Parthenocissus tricuspidata and Parthenocissus quinquefolia) and the variation of leaf area density was investigated for two of them. It was observed that the air resistance depended on plant species, leaf area density and wind speed. The difference between the plant species was assigned to the functional leaf size (FLS), the ratio of the largest circle within the boundaries of the leaf to the total leaf area. FLS is likely associated with other morphological characteristics of plants that, when considered collectively, provide a more comprehensive representation of leaf complexity. The pressure and velocity measurements obtained were used to optimise the permeability and drag coefficient in a CFD model. At wind speeds below 0.6 m s-1, the resistance was mainly determined by viscous drag and a larger leaf size resulted in a higher viscous drag. At wind speeds above 1.5 m s-1, form drag was dominant and the parameterised Cd decreased with increasing wind speed due to the sheltering effect of successive plant elements. The leaf area density had a significant effect on K and Cd and, is therefore an important plant parameters in CFD models. The main conclusion here is that the common practice of using a constant Cd to model the influence of plants on the air flow leads to deviations from reality. Wind tunnels are highly suitable to study the impact of green walls on PM concentration under controlled environmental conditions. For this purpose, a new wind tunnel setup was built and great attention was paid to obtaining a uniform air flow. Thus, based on CFD models, appropriate flow controllers were chosen, consisting of honeycombs and screens with different mesh sizes. New PM generation devices and measuring equipment were installed and set up appropriately. Devices were available for generating and measuring ultrafine dust (<0.1 µm, i.e. PM0.1) and fine dust (<0.3 µm, i.e. PM0.3) consisting of soot particles, and, on the other hand, fine dust with particle sizes smaller than 2.5 (PM2.5) and 10 µm (PM10) consisting of 'Arizona fine test dust'. With the new wind tunnel setup, it was possible to measure the influence of Hedera helix (common ivy), grown in a planter against a climbing aid, on the PM concentration and this was expressed by a collection efficiency, i.e. the difference in concentration in front and behind the plants normalised for the incoming concentration. The collection efficiency of H. helix depended on the particle size of the PM and wind speed. The collection efficiency decreased when the particle size increased from 0.02 to 0.2 µm and increased again for particle sizes above 0.3 µm. The collection efficiency also increased with increasing wind speed, especially for particle sizes > 0.03 µm. On the other hand, relative humidity and the type of PM (soot or dust) did not significantly affect the collection efficiency. The main objective of this study was to obtain an optimised size-resolved deposition model. Dry deposition occurs through several mechanisms, in particular gravity, diffusion, impaction and interception, and the subsequent resuspension of deposited PM back to the environment. The modelling of these mechanisms was described by \citet{Zhang2001} and \citet{Petroff2010}. The data obtained from the wind tunnel experiments allowed validating these deposition models. It was for the first time that deposition of real PM on green walls was studied. The different PM deposition mechanisms were found to be strongly dependent on particle size and wind speed. The models of \citet{Zhang2001} and \citet{Petroff2010} each matched PM concentration measurements for only certain particle sizes. Therefore, a combination of the two models was investigated and the root mean square error was lower by on average 3.5% (PM < 0.03 µm) and 46% (PM > 0.03 µm) compared to the original models at wind speeds greater than 1.5 m s-1. For wind speeds less than 1.5 m s-1, the optimised model did not differ from the original models. The optimised model was able to meet the imposed criteria for air quality models, where a correct model exhibits low deviation from measurements ('normalised mean square error' < 1.5), low bias ('fractional bias' between -0.3 and 0.3) and high R2. In comparison, the R$2$ of the optimised model was 0.57, while that of Zhang et al. (2001) and Petroff et al. (2010) was 0.23 and 0.31, respectively. The optimised model was however characterised by a high scatter, with the fraction of modeled results located within a factor of two of the measurements being lower than 50. A model study with a green façade oriented parallel to the incoming airflow showed that deposition by interception and impaction reduced remarkably, but that the orientation had no effect on deposition by Brownian diffusion. A promising green wall form for PM mitigation is the living wall system (LWS). LWS consist of supporting structures with substrate to grow plants in and can be planted with a variety of plant species. This allows to select plant species with optimal characteristics to achieve PM deposition. These characteristics refer to the macro- and microstructure of the leaves, and research has been conducted mainly on these. On the other hand, the influence of the supporting structure and substrate on PM concentrations has rarely been studied. With the new wind tunnel setup, LWS from different manufacturers were tested for their ability to capture PM. The setups were subjected for three hours to an air flow with a low PM concentration (resuspension phase) and then for three hours to an air flow to which additional PM was added (deposition phase). Some setups were able to decrease the PM concentration during both phases, while others just caused the concentration to increase. Some systems were able to reduce particulate matter concentration during both phases, namely LWS consisting of planters (-2% and -4% for PM0.1 and PM2.5, respectively) and textile cloths (-23% and -5% for PM0.1 and PM2.5, respectively). While other systems actually resulted in an increase in concentration especially LWS existing textile fabrics consisting of geotextiles (+11% for both PM fractions) and with moss as substrate (+2% and +5% for PM0.1 and PM2.5, respectively). This highlights the importance of careful selection of suspension systems to reduce particulate matter concentrations. Further research is therefore needed on the materials used in these systems in relation to their particulate content, as well as on plant development in these systems. In addition to air measurements, measurements were taken of the amount of PM deposited on the leaves and suspension system of LWS. This allowed the difference in PM resuspension and deposition between plant species to be investigated. The amount of deposited particulate matter was determined based on 'saturation isothermal remanent magnetisation' (SIRM), a measure of magnetisable particulate matter. This was possible because the added 'Arizona fine test dust' contained iron oxide. However, no significant difference was observed between the SIRM values measured before the wind tunnel experiment, after resuspension and after deposition. This suggested that the iron oxide content in the Arizona fine test dust was too low to measure a significant difference in the SIRM values on leaves after three hours. The plant species did give rise to different SIRM values ranging between 5 and 260 µ A. In particular, SIRM values above 26 µ A were observed for the plant species that were grouped due to their significantly higher accumulation of PM. 'Specific leaf area' (SLA), specifically the ratio of the one-sided 'fresh' leaf area to its dry mass, was the significant leaf characteristic. SLA correlated with leaf complexity. In particular, plant species with elongated leaves were characterized by low SLA, high FLS and high complexity and showed significantly higher SIRM values. Finally, the optimised size-resolved deposition model was also tested in an urban model to get an idea of the impact of a green wall on PM concentrations in a so-called 'street canyon'. These are narrow streets with high buildings on both sides, making air pollution more persistent. To this end, an ideal scenario was tested in which a green wall was introduced along both sides of the street over a length of about 270 m. The model result showed a decrease in PM2.5 and PM10 of 46 ± 12% and 52 ± 14%. This result is of course for a very optimal scenario where the green wall covers the entire building façades. Since this is not feasible in reality, other ways of promoting contact between green walls and polluted air can be explored. The insights obtained illustrate that the use of climbing plants can be a cost-effective and environmentally friendly solution to reduce PM concentrations. Moreover, the findings showed that models can be used to investigate the impact of green walls on PM levels. These findings fit within the broader context of designing healthy and sustainable urban environments and developing innovative solutions based on solid scientific knowledge.

Abstract: This dissertation focuses on the materials and techniques used by the Brussels artists Michael Sweerts (1618-1664) and Michaelina Wautier (1614-1689). It tries to answer the following research questions: 1.Did 17th-century Brussels-based artists use idiosyncratic painting materials and techniques, and can their work be distinguished from paintings created in other artistic centers based on these materials and techniques? 2.How did Sweerts and Wautier influence each other’s work? In order to answer the research questions, a selection of relevant and representative paintings from Sweerts’ and Wautier’s oeuvre has been studied with MA-XRF scanning and microscopic examination. The results are discussed in volume 2 of this dissertation, and form the basis upon which the chapters in volume 1 are built. MA-XRF scanning is a non-destructive imaging technique based on the technique of X-ray fluorescence (XRF). The technique allows for the collection of elemental information about the materials present in a work of art. The work presented in this dissertation has shed more light on the working methods of Sweerts and Wautier. It has shown they both showed an openness and willingness to continue learning and developing as an artist. Wautier did not feel constrained to work only in one genre and apparently carried on developing her skills throughout her career, as evidenced by Flower Garland with Butterfly. She might have trained with a specialized artist to master their idiosyncratic working methods and this diverse genre. This suggests that Wautier had an interest in art education. Sweerts too had a passion for art education: he was involved in the drawing academy of Camillo Pamphilj in Rome, and later established his own academy for life drawing in Brussels. Sweerts’ work clearly shows the influence of his Italian contemporaries. Whilst living in Rome, he adapted his working methods to local customs such as working on brightly colored grounds. Wautier’s oeuvre shows that she was open to explore different genres and that she took inspiration from her contemporaries, including Theodoor van Loon and Daniël Seghers. In doing so, she furthered the development of different genres as she added original elements to her compositions. It is the conclusion of this dissertation that Michaelina Wautier can thus be considered one of the innovative artists of mid-17th-century Flemish painting, who inspired other artists in the Low Countries, including Michael Sweerts.

Abstract: Phenolic compounds can be found everywhere in our daily lives but exhibit high toxicity, low (bio)degradability and hormone-disrupting effects when they are released in the environment. It is for this reason imperative to develop detection strategies for these pollutants. A promising approach involves the use of a photoelectrochemical (PEC) sensor. In this sensor, a photosensitiser (PS) type II, which generates 1O2 under illumination, is used to oxidise phenolic compounds present in the sample. The oxidised phenols are reduced at the electrode surface leading to the generation of an electrocatalytic redox cycle. In this thesis, an in-depth understanding, through the identification of the reactive oxygen species (ROS) in the PEC sensing mechanism, is obtained. The detection strategy is optimised by choosing the PS with the highest 1O2 production and by optimising the detection parameters so that the PEC sensor can be successfully applied for the detection of phenols in industrial samples. First, it was determined that the use of highly fluorinated zinc phthalocyanine derivatives, F52PcZn and F64PcZn, as photocatalysts was optimal for the sensing of phenol due to their high 1O2 production and improved single-site isolation. However, next to 1O2, it was shown that the ROS O2•- and H2O2 were also generated in the PEC sensor. Their contribution to the photocurrent response was studied by rotating disk electrode measurements in function of the pH and applied potential. After this, the PEC detection strategy was optimised in terms of pH and applied potential for the detection of doxycycline, cefadroxil, and phenol. It was found that the use of alkaline pH-levels led to nmol L-1-level detection limits. The combination with square wave voltammetry (SWV) was, also, proposed to allow the quantification and identification of phenolic compounds in a specific sample. At last, the developed PEC and SWV sensors were applied for the measurement of phenolic compounds in industrial water samples. The PEC sensor could follow the decrease of the phenolic concentration throughout the wastewater treatment process while the SWV sensor provided the electrochemical fingerprints of these samples. The thesis concluded that the use of the PEC sensor was advantageous in the measurement of lower concentrated phenolic samples due to its high sensitivity and fast measurement time in comparison to commercial test kits.

Abstract: Today, illicit drugs are omnipresent in society. Clandestine markets are growing faster than ever before, record amounts of cocaine are seized in seaports and airports, while the associated violence is spiralling out of control. In addition, drug monitoring centres worldwide are warning for the increasing complexity of the drug markets, as the traditionally popular drugs are joined by countless new synthetic variants, while medical drugs are also increasingly being abused. In order to provide services confronted with illicit drug samples (police, customs, forensic scientists, first responders, …) with important information on the identity of an unknown sample, suitable analytical tests are required. While these exist for laboratory environments, on-site applicable tests are important to accelerate the decision-making process. Electrochemical sensors have all the advantages required for such on-site tests: they are fast, portable, easy-to-use and reliable. Furthermore, they are not influenced by colours, which are frequently added to drug samples to deceive the existing tests. Previous work has mainly focussed on the detection of a single drug per analysis. However, many drugs could be encountered due to the diversity of the drug markets. Therefore, this project developed electrochemical strategies for the detection of multiple drugs simultaneously. First, the electrochemical behaviour of the individual drugs was studied in different measuring conditions (assessing the influence of pH, concentration and temperature). Then, all findings and strategies were combined to detect multiple targets simultaneously. An electrochemical sensor was developed for the four most popular drugs at music festivals: cocaine, MDMA, amphetamine and ketamine. This sensor generates a so-called ‘superfingerprint’ of the sample, which is then automatically interpreted by a developed algorithm in order to produce a straightforward output. Finally, a pill analysis sensor was developed in the context of drug checking services, where a consumer can anonymously have a sample chemically analysed to obtain information on the composition, dose and potentially harmful additives. The sensor achieved an outstanding accuracy in identifying the main component and provided the option to quantify, as well as an indication on the presence of other substances in the sample. The project’s findings demonstrate the potential for electrochemistry in illicit drug detection and provide a basis for the development of new sensors, targeting other drug combinations.

Abstract: It is standard practice in virtual reality applications to synthesize binaural audio based on a discrete set of directionally-dependent head-related impulse responses (HRIRs). This set of HRIRs is often time-aligned in a pre-processing step, to allow for high-fidelity interpolation between HRIRs corresponding with neighbouring directions. The fidelity of this operation depends on the similarity of neighbouring aligned HRIRs. The pairwise quality of similarity makes it a difficult criterion to optimize globally and consequently one often resorts to alignment methods based on a specific feature that can be extracted for each HRIR separately, e.g., the first-onset of the peak or the group delay. However, such proxies for similarity are very sensitive to noise and therefore require a high signal-to-noise ratio, which makes them less suitable for processing HRIRs acquired outside an anechoic room. In this paper, we advance a novel alignment method, which maximizes the similarity – defined as the correlation between the full-length HRIRs – between neighbouring aligned HRIRs for all directions at once. We show that this correlation-based alignment procedure outperforms the first-onset alignment with regards to the fidelity of the spherical harmonics representation of both the spectral and interaural time difference (ITD) information, when tested on the KEMAR HRIR and six human HRIRs. Finally, we show that the correlation-based alignment is more robust to noise.

Abstract: Plasma-based CO2 conversion is worldwide gaining increasing interest. The aim of this work is to find potential pathways to improve the energy efficiency of plasma-based CO2 conversion beyond what is feasible for thermal chemistry. To do so, we use a combination of modeling and experiments to better understand the underlying mechanisms of CO2 conversion, ranging from non-thermal to thermal equilibrium conditions. Zero-dimensional (0D) chemical kinetics modelling, describing the detailed plasma chemistry, is developed to explore the vibrational kinetics of CO2, as the latter is known to play a crucial role in the energy efficient CO2 conversion. The 0D model is successfully validated against pulsed CO2 glow discharge experiments, enabling the reconstruction of the complex dynamics underlying gas heating in a pure CO2 discharge, paving the way towards the study of gas heating in more complex gas mixtures, such as CO2 plasmas with high dissociation degrees. Energy-efficient, plasma-based CO2 conversion can also be obtained upon the addition of a reactive carbon bed in the post-discharge region. The reaction between solid carbon and O2 to form CO allows to both reduce the separation costs and increase the selectivity towards CO, thus, increasing the energy efficiency of the overall conversion process. In this regard, a novel 0D model to infer the mechanism underlying the performance of the carbon bed over time is developed. The model outcome indicates that gas temperature and oxygen complexes formed at the surface of solid carbon play a fundamental and interdependent role. These findings open the way towards further optimization of the coupling between plasma and carbon bed. Experimentally, it has been demonstrated that “warm” plasmas (e.g. microwave or gliding arc plasmas) can yield very high energy efficiency for CO2 conversion, but typically only at reduced pressure. For industrial application, it will be important to realize such good energy efficiency at atmospheric pressure as well. However, recent experiments illustrate that the microwave plasma at atmospheric pressure is too close to thermal conditions to achieve a high energy efficiency. Hence, we use a comprehensive set of advanced diagnostics to characterize the plasma and the reactor performance, focusing on CO2 and CO2/CH4 microwave discharges. The results lead to a deeper understanding of the mechanism of power concentration with increasing pressure, typical of plasmas in most gases, which is of great importance for model validation and understanding of reactor performance.

Abstract: Onze relatie met de natuur om ons heen is, op zijn zachtst gezegd, ingewikkeld. Mensen gingen niet plots een bedreiging vormen voor het leven op aarde nadat ze er eeuwenlang mee in harmonie hadden geleefd. Verschillende ideeën over de omgang met de natuurlijke omgeving – sommige duurzaam, andere ronduit desastreus – hebben altijd naast elkaar bestaan. We waren er ons al verrassend vroeg van bewust dat ons handelen een nefaste impact kon hebben op de natuur. Maar die bezwaren werden geregeld aan de kant geschoven. Deze inleiding tot de milieugeschiedenis helpt de lezer te begrijpen hoe onze hedendaagse problematische omgang met de natuur en ons milieu tot stand is gekomen. Het is een introductie tot het recente onderzoek naar de relatie tussen mens en natuur doorheen de eeuwen, in de Lage Landen en ver daarbuiten.

Abstract: Climate change is causing alterations in precipitation patterns, leading to adverse ecological consequences in many ecosystems. Recently, an increasingly persistent weather pattern has emerged, characterized by lengthening the duration of alternating dry and wet periods, which is more complex than exclusively drought or increasing precipitation. It is currently unclear how soil microbial communities respond to these new regimes in relation to their interactions with plants, especially in precipitation-sensitive ecosystems, such as tropical rainforests and grasslands. In this thesis, we explored responses of soil bacterial and fungal communities to increasing weather persistence in rainforests and grasslands, using high throughput sequencing technology. We firstly investigated the resistance and resilience of microbial communities to prolonged drought in a mature seasonal tropical rainforest which experiences unusually intensive dry seasons in the current century. Through excluding rainfall during and after the dry season, a simulated prolongation of the dry season by five months was compared to the control. Our results indicate that as rain exclusion progressed, the microbial communities increasingly diverged from the control, indicating a moderate resistance to prolonged drought. However, when the drought ceased, the composition and co-occurrence patterns of soil microbial communities immediately recovered to that in the control, implying a high resilience. To further investigate the ecological roles of soil microbial communities in response to increasing weather persistence, we set up grassland mesocosm experiments. In these experiments, precipitation frequency was adjusted along a series, ranging from 1 to 60 consecutive days alternating of dry and wet periods, while keeping the total precipitation constant. Our results show that microbial community assembly tended to be more stochastic processes at intermediate persistence of dry and wet alternations while more deterministic processes dominated at low and high persistence within 120 days regime exposure. Moreover, more persistent precipitation reduced the fungal diversity and network connectivity but barely impacted that of bacterial communities. The prior experiences of persistent weather events for one year caused legacy effects. The soil microbial legacy induced by soil microbial communities subjected to prior persistent weather events was more enduring in subsequent fungal communities than bacterial communities, likely due to slower growth of fungi compared to bacteria. However, a minor effect of soil microbial legacy  was observed on plant performance. In addition, we kept the grassland mesocosm experiment for two growing seasons. The effects of precipitation persistence on soil microbial communities increased in the second year. The dissimilarities of microbial communities between the first and second year were less with more persistent precipitation, potentially resulting in more vulnerable microbial communities, due to some taxa disappearing and a reduction in functional redundancy under more persistent weather. To conclude, our findings provide a comprehensive theoretical understanding of soil microbial communities in response to the current and future climate change, drawing from both natural and experimental systems. It helps in predicting and managing the impacts of future climate change on ecosystems mediated by microbial communities. Additionally, the findings of microbe-mediated legacy effects on grassland ecosystems can provide practical guidance for their application in agriculture, specifically for using an inoculum to mitigate the impacts of climate change.