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The climate impact of carbon capture and storage depends on how much CO2 is stored underground, yet databases of industrial-scale projects report capture capacity as a measure of project size. We review publicly available sources to estimate the amount of CO2 that has been stored by facilities since 1996. We organize these sources into three categories corresponding to the associated degree of assurance: (1) legal assurance, (2) quality assurance through auditing, and (3) no assurance. Data were found for 20 facilities, with an aggregate capture capacity of 36 Mt of CO2 year–1. Combining data from all categories, we estimate that 29 Mt of CO2 was geologically stored in 2019 and there was cumulative storage of 197 Mt over the period of 1996–2020. These are climate relevant scales commensurate with recent cumulative and ongoing emissions impacts of renewables in some markets, e.g., solar photovoltaics in the United States. The widely used capture capacity is in aggregate 19–30% higher than storage rates and is not a good proxy for estimating storage volumes. However, the discrepancy is project-specific and not always a reflection of project performance. This work provides a snapshot of storage amounts and highlights the need for uniform reporting on capture and storage rates with quality assurance.

Building on the dynamic capabilities literature and natural-resource-based view, the paper examines whether firms can attain sales growth through a range of sustainability-oriented dynamic capabilities including (1) internal green supply chain management capabilities, (2) external green supply chain management capabilities and (3) green political capabilities. Based on a dataset of 277 public US firms between 2010 and 2020, a panel quantile model of firm growth showcases that while internal green supply chain capabilities and green political capabilities affect firms' growth performance positively, external green supply chain capabilities are associated with slower growth. Importantly, the results indicate that the positive growth effects of green political capabilities are short-lived, while those of internal green supply chain capabilities are long-lived. The study contributes to the sustainability-oriented dynamic capabilities literature by showing that different capabilities have different implications for firm growth depending on the firm's base performance and the time periods under consideration.

There is a general agreement within the wildfire community that exclusively top down approaches to policy making and management are limited and that we need to build governance capacity to cooperatively manage across jurisdictional boundaries. Accordingly, the concept of co-management has grown in popularity as a theoretical lens through which to understand cross-boundary response to wildland fires. However, definitional ambiguity has led to on-going debates about what co-management is. Further, there is limited understanding about the nature of co-management during crisis events. This had led to scholars posing the question: What is co-management in the context of jurisdictionally complex wildfire? In this paper, we seek to address this question based on interviews with leaders engaged in the management of jurisdictionally complex wildfire incidents. We propose a multi-level framework that defines co-management as strategic efforts of individual actors to cooperatively manage perceived interdependencies with others through one or more formal or informal institutional arrangements. Our findings reveal different strategic orientations to co-management that have management implications. We then demonstrate the value of the proposed framework in its ability to organize a series of diagnostic questions for diagnosing co-management situations within the context of jurisdictionally complex wildfires. Accepted version

Peer reviewed: True, Funder: Leverhulme Trust, Indoor, airborne, transmission of SARS-CoV-2 is a key infection route. We monitored fourteen different indoor spaces in order to assess the risk of SARS-CoV-2 transmission. PM2.5 and CO2 concentrations were simultaneously monitored in order to understand aerosol exposure and ventilation conditions. Average PM2.5 concentrations were highest in the underground station (261 ± 62.8 μgm−3), followed by outpatient and emergency rooms in hospitals located near major arterial roads (38.6 ± 20.4 μgm−3), the respiratory wards, medical day units and intensive care units recorded concentrations in the range of 5.9 to 1.1 μgm−3. Mean CO2 levels across all sites did not exceed 1000 ppm, the respiratory ward (788 ± 61 ppm) and the pub (bar) (744 ± 136 ppm) due to high occupancy. The estimated air change rates implied that there is sufficient ventilation in these spaces to manage increased levels of occupancy. The infection probability in the medical day unit of hospital 3, was 1.6-times and 2.2-times higher than the emergency and outpatient waiting rooms in hospitals 4 and 5, respectively. The temperature and relative humidity recorded at most sites was below 27 °C, and 40% and, in sites with high footfall and limited air exchange, such as the hospital medical day unit, indicate a high risk of airborne SARS-CoV-2 transmission.

Advantages of ionic liquids (ILs) over volatile organic solvents in chemical processes include no or negligible evaporative losses and high tunability. However, the conventional production of aprotic ILs via metathesis can be unattractive (both economically and environmentally) because of its high complexity, while the performance of other synthesis routes remains unclear. Existing life-cycle assessments furthermore fail to combine the production and use phases of these solvents, leading to erroneous conclusion about their sustainability credentials. This paper compares a one-pot, halide-free production route to 1-butyl-3-methylimidazolium tetrafluoroborate [BMIM][BF4] against metathesis and two conventional fuel desulfurization solvents, namely, acetonitrile and dimethylformamide (DMF). Halide-free synthesis is predicted to reduce the cost and environmental impacts associated with the production of [BMIM][BF4] by 2–5-fold compared to metathesis. Upon including the use phase of the solvents in fuel desulfurization and accounting for the uncertainty in background data, halide-free [BMIM][BF4] consistently presents the lowest cost and environmental impacts, while DMF is the worst in class. As well as exemplifying the importance of synthesis routes of ILs on their sustainability, these results highlight the need to include the use phase of solvents for more comprehensive life-cycle assessments.

Estimates of economic implications of climate policy are important inputs into policy-making. Despite care to contextualize quantitative assessments of mitigation costs, one strong view outside academic climate economics is that achieving Paris Agreement goals implies sizable macroeconomic losses. Here, we argue that this notion results from unwarranted simplification or omission of the complexities of quantifying mitigation costs, which generates ambiguity in communication and interpretation. We synthesize key factors influencing mitigation cost estimates to guide interpretation of estimates, for example from the Intergovernmental Panel on Climate Change, and suggest ways to improve the underlying models. We propose alternatives for the scenario design framework, the framing of mitigation costs and the methods used to derive them, to better inform public debate and policy.

The environmental impacts of food are currently at unsustainable levels. Consumers undoubtedly play a central role in reducing the impacts of the food system to more sustainable levels via dietary changes and food waste reduction. Mathematical optimisation is one approach to identifying less environmentally impactful dietary patterns. A limited number of studies, however, have assessed whether impact reductions offered by optimised diets are enough to remain within planetary boundaries (i.e. attain ‘absolute’ environmental sustainability). Using UK food consumption as a case study, here we employ linear programming to identify nutritionally adequate diets that meet sociocultural acceptability criteria whilst minimising (a) environmental impact transgressions of their allocated share of the safe operating space (SoSOS) for nine planetary boundaries (PBs), (b) cost, or (c) deviation from the current diet. We show that the current diet is unsustainable as it transgresses six or seven PBs, depending on the SoSOS allocation principle. Optimising for minimum SoSOS transgressions yields diets offering significant impact reductions (66 - 95% reduction across all PBs) compared to the current average dietary pattern, but whether they completely mitigate SoSOS transgressions depends on the sharing principle adopted to assign the SoSOS to national food consumption. Additionally, by comparing least-cost and least-transgression solutions, we find a trade-off between cost and environmental sustainability indicating that more sustainable dietary patterns are not currently incentivised by the relative prices of food items in the UK. Our work demonstrates the value in embedding ‘absolute’ sustainability in diet optimisation so that solutions inherently provide a more clear-cut understanding of their broad implications on the environment., Sustainable Production and Consumption, 28, ISSN:2352-5509

In remote sensing (RS), shadows play an important role, commonly affecting the quality of data recorded by remote sensors. It is, therefore, of the utmost importance to detect and model the shadow effect in RS data as well as the information that is obtained from them, particularly when the data are to be used in further environmental studies. Shadows can generally be categorized into four types based on their sources: cloud shadows, topographic shadows, urban shadows, and a combination of these. The main objective of this study was to review the recent literature on the shadow effect in remote sensing. A systematic literature review was employed to evaluate studies published since 1975. Various studies demonstrated that shadows influence significantly the estimation of various properties by remote sensing. These properties include vegetation, impervious surfaces, water, snow, albedo, soil moisture, evapotranspiration, and land surface temperature. It should be noted that shadows also affect the outputs of remote sensing processes such as spectral indices, urban heat islands, and land use/cover maps. The effect of shadows on the extracted information is a function of the sensor–target–solar geometry, overpass time, and the spatial resolution of the satellite sensor imagery. Meanwhile, modeling the effect of shadow and applying appropriate strategies to reduce its impacts on various environmental and surface biophysical variables is associated with many challenges. However, some studies have made use of shadows and extracted valuable information from them. An overview of the proposed methods for identifying and removing the shadow effect is presented.

1. Understanding the role of different species in the transmission of multi-host pathogens, such as rabies virus, is vital for effective control strategies. Across most of sub-Saharan Africa domestic dogs Canis familiaris are considered the reservoir for rabies, but the role of wildlife has been long debated. Here we explore the multi-host transmission dynamics of rabies across south-east Tanzania. 2. Between January 2011 and July 2019, data on probable rabies cases were collected in the regions of Lindi and Mtwara. Hospital records of animal-bite patients presenting to healthcare facilities were used as sentinels for animal contact tracing. The timing, location and species of probable rabid animals were used to reconstruct transmission trees to infer who infected whom and the relative frequencies of within- and between-species transmission. 3. During the study, 688 probable human rabies exposures were identified, resulting in 47 deaths. Of these exposures, 389 were from domestic dogs (56.5%) and 262 from jackals (38.1%). Over the same period, 549 probable animal rabies cases were traced: 303 in domestic dogs (55.2%) and 221 in jackals (40.3%), with the remainder in domestic cats and other wildlife species. 4. Although dog-to-dog transmission was most commonly inferred (40.5% of transmission events), a third of inferred events involved wildlife-to-wildlife transmission (32.6%), and evidence suggested some sustained transmission chains within jackal populations. 5. A steady decline in probable rabies cases in both humans and animals coincided with the implementation of widespread domestic dog vaccination during the first 6 years of the study. Following the lapse of this program, dog rabies cases began to increase in one of the northernmost districts. 6. Synthesis and applications. In south-east Tanzania, despite a relatively high incidence of rabies in wildlife and evidence of wildlife-to-wildlife transmission, domestic dogs remain essential to the reservoir of infection. Continued dog vaccination alongside improved surveillance would allow a fuller understanding of the role of wildlife in maintaining transmission in this area. Nonetheless, dog vaccination clearly suppressed rabies in both domestic dog and wildlife populations, reducing both public health and conservation risks and, if sustained, has potential to eliminate rabies from this region.

Fire has long been a part of the Australian environment and remains a potent force in shaping the adaptations of species, the dynamics of populations, and the structure of ecological communities. However, the fire regime is changing. Fire seasons are longer, wet vegetation types that do not usually burn are now at risk, and fire intensity and severity are greater than in the recent past. A particularly widespread event occurred in the forest and woodland regions of Australia over the summer of 2019-2020. Termed the "Black Summer" bushfires, remotely sensed data indicate that over 30 million hectares of vegetation were burned, including an unprecedented large area of forest. The extraordinary geographical scale of the Black Summer fires, including the intensity and speed of fire spread, has led to widespread concern about the ecological damage that occurred. Recent estimates suggest that almost three billion vertebrates and up to 240 trillion invertebrates were affected by the fires. Fires were experienced in part of the geographical ranges of 832 vertebrate species and 37 threatened ecological communities, with some of these entities feared to be at risk of extinction. Field assessments of ecological recovery have been slowed by COVID-19 restrictions and by a diminution in ecological monitoring capacity that occurred before the last fire season. This paper assesses the dire ecological consequences of the Black Summer bushfires and proposes a series of steps to help achieve recovery of biodiversity and mitigate the effects of future mega-fires. Integr Environ Assess Manag 2021;17:1162-1167. © 2021 SETAC.

Monitoring cognitive workload has the potential to improve both the performance and fidelity of human decision making. However, previous efforts towards discriminating further than binary levels (e.g., low/high or neutral/high) in cognitive workload classification have not been successful. This lack of sensitivity in cognitive workload measurements might be due to individual differences as well as inadequate methodology used to analyse the measured signal. In this paper, a method that combines the speech signal with cardiovascular measurements for screen and heartbeat classification is introduced. For validation, speech and cardiovascular signals from 97 university participants and 20 airline pilot participants were collected while cognitive stimuli of varying difficulty level were induced with the Stroop colour/word test. For the trinary classification scheme (low, medium, high cognitive workload) the prominent result using classifiers trained on each participant achieved 15.17 ± 0.79% and 17.38 ± 1.85% average misclassification rates indicating good discrimination at three levels of cognitive workload. Combining cardiovascular and speech measures synchronized to each heartbeat and consolidated with short-term dynamic measures might therefore provide enhanced sensitivity in cognitive workload monitoring. The results show that the influence of individual differences is a limiting factor for a generic classification and highlights the need for research to focus on methods that incorporate individual differences to achieve even better results. This method can potentially be used to measure and monitor workload in real time in operational environments.

Actinobacillus pleuropneumoniae is an important respiratory pig pathogen that causes substantial losses in the worldwide swine industry. Chronic or subclinical infection with no apparent clinical symptoms poses a challenge for preventing transmission between herds. Rapid diagnostics is important for the control of epidemic diseases. In this study, we formulated an A. pleuropneumoniae species-specific apxIVA-based CRISPR/Cas12a-assisted rapid detection platform (Card) that combines recombinase polymerase amplification (RPA) of target DNA and subsequent Cas12a ssDNase activation. Card has a detection limit of 10 CFUs of A. pleuropneumoniae, and there is no cross-reactivity with other common swine pathogens. The detection process can be completed in 1 h, and there was 100% agreement between the conventional apxIVA-based PCR and Card in detecting A. pleuropneumoniae in lung samples. Microplate fluorescence readout enables high-throughput use in diagnostic laboratories, and naked eye and lateral flow test readouts enable use at the point of care. We conclude that Card is a versatile, rapid, accurate molecular diagnostic platform suitable for use in both laboratory and low-resource settings.

The RNA chaperone Hfq promotes the association of small RNAs (sRNAs) with cognate mRNAs, controlling the expression of bacterial phenotype. Actinobacillus pleuropneumoniae hfq mutants strains are attenuated for virulence in pigs, impaired in the ability to form biofilms, and more susceptible to stress, but knowledge of the extent of sRNA involvement is limited. Here, using A. pleuropneumoniae strain MIDG2331 (serovar 8), 14 sRNAs were identified by co-immunoprecipitation with Hfq and the expression of eight, identified as trans-acting sRNAs, were confirmed by Northern blotting. We focused on one of these sRNAs, named Rna01, containing a putative promoter for RpoE (stress regulon) recognition. Knockout mutants of rna01 and a double knockout mutant of rna01 and hfq, both had decreased biofilm formation and hemolytic activity, attenuation for virulence in Galleria mellonella, altered stress susceptibility, and an altered outer membrane protein profile. Rna01 affected extracellular vesicle production, size and toxicity in G. mellonella. qRT-PCR analysis of rna01 and putative cognate mRNA targets indicated that Rna01 is associated with the extracytoplasmic stress response. This work increases our understanding of the multilayered and complex nature of the influence of Hfq-dependent sRNAs on the physiology and virulence of A. pleuropneumoniae.

The purpose of this research is to demystify literature on aerotropolis using systematic review. Literature on aerial life and aeronautical studies suggests that airports are frequently cited outside urban centres. However, recent events surrounding the growth of aerotropolis contradicts existing realities. In fact, the pull and push factors constitute the life cycle of aerotropolis in urban enclaves. In generating data for this study, Dimensions, an artificial intelligence databank, was adopted, and a hybrid method which combines both VOSviewer and Citespace software was the preferred analytical tool for analysis. Key findings were imperative in establishing certain parameters regarding aerial life, including but not limited to knowledge about the technologies adopted, quality of stakeholders, in addition to existing relationships of urban space, urbanisation, and geography. Furthermore, two recurrent themes were identified, such as the development in ICT, and smart technologies, which corresponds with the multiple potentials that exist for developing sustainable airports, such as eco-innovation, greenovation, and social innovation. This study contributes to the concept of transit-bound tourism, a concept we coined to depict the role tourism can play in transit philosophy and economics.

This study identifies the spatial and temporal trends, as well as the drivers, of fire dynamics in the Bolivian Amazon basin. Bolivia ranks in the top ten countries in terms of total annual burnt, with fires affecting an estimated 2.3 million hectares of forest in 2020. However, in comparison to the Brazilian Amazon, there has been little research into the fire regime in Bolivia. The sparse research and the limited literature on the subject indicate that fire activity is higher in the Bolivian Amazon basin’s dry forests and flooded savanna zones, and that agriculture and drought are the primary causes of fire activity. In this study, trend analysis and emerging hotspot analysis are deployed to identify the spatial and temporal patterns of fire activity and boosted regression tree models to identify the drivers of forest fire within each ecoregion of the Bolivian Amazon basin. Comparable to most of the Brazilian literature, this study finds that fire activity and fire season length is higher in the flooded Beni Savanna, and Chiquitano seasonally dry tropical forests than in the Bolivian Amazon ecoregion. This study also identifies moisture stress and human activity as the main drivers of fire dynamics within the region. It is intended that this research will offer a foundation for future research and conservation activities aimed at better understanding the fire regime of the Bolivian Amazon basin.

Pseudomonas aeruginosa is a Gram-negative bacterium that is able to survive and adapt in a multitude of niches as well as thrive within many different hosts. This versatility lies within its large genome of ca. 6 Mbp and a tight control in the expression of thousands of genes. Among the regulatory mechanisms widespread in bacteria, cyclic-di-GMP signaling is one which influences all levels of control. c-di-GMP is made by diguanylate cyclases and degraded by phosphodiesterases, while the intracellular level of this molecule drives phenotypic responses. Signaling involves the modification of enzymes’ or proteins’ function upon c-di-GMP binding, including modifying the activity of regulators which in turn will impact the transcriptome. In P. aeruginosa, there are ca. 40 genes encoding putative DGCs or PDEs. The combined activity of those enzymes should reflect the overall c-di-GMP concentration, while specific phenotypic outputs could be correlated to a given set of dgc/pde. This notion of specificity has been addressed in several studies and different strains of P. aeruginosa. Here, we engineered a mutant library for the 41 individual dgc/pde genes in P. aeruginosa PAO1. In most cases, we observed a significant to slight variation in the global c-di-GMP pool of cells grown planktonically, while several mutants display a phenotypic impact on biofilm including initial attachment and maturation. If this observation of minor changes in c-di-GMP level correlating with significant phenotypic impact appears to be true, it further supports the idea of a local vs global c-di-GMP pool. In contrast, there was little to no effect on motility, which differs from previous studies. Our RNA-seq analysis indicated that all PAO1 dgc/pde genes were expressed in both planktonic and biofilm growth conditions and our work suggests that c-di-GMP networks need to be reconstructed for each strain separately and cannot be extrapolated from one to another., Frontiers in Microbiology, 13, ISSN:1664-302X

The application of water, or water mixed with suppressants, to combat wildfires is one of the most common firefighting methods but is rarely studied for smouldering peat wildfire, which is the largest type of fire worldwide in term of fuel consumption. We performed experiments by spraying suppressant to the top of a burning peat sample inside a reactor. A plant-based wetting agent suppressant was mixed with water at three concentrations: 0% (pure water), 1% (low concentration), and 5% (high concentration), and delivered with varying flowrates. The results showed that suppression time decreased non-linearly with flow rate. The average suppression time for the low-concentration solution was 39% lower than with just water, while the high-concentration solution reduced suppression time by 26%. The volume of fluid that contributes to the suppression of peat in our experiments is fairly constant at 5.7 ± 2.1 L kg−1 peat despite changes in flow rate and suppressant concentration. This constant volume suggests that suppression time is the duration needed to flood the peat layer and that the suppressant acts thermally and not chemically. The results provide a better understanding of the suppression mechanism of peat fires and can improve firefighting and mitigation strategies.

Given that many people typically spend the majority of their time at home, accurate measurement and modelling of the home environment is critical in estimating their exposure to air pollution. This study investigates the fate and impact on human exposure of outdoor and indoor pollutants in London homes, using a combination of sensor measurements, outdoor air pollution estimated from the CMAQ-urban model and indoor mass balance models. Averaged indoor concentrations of PM2.5, PM10 and NO2 were 14.6, 24.7 and 14.2 μg m−3 while the outdoor concentrations were 14.4, 22.6 and 21.4 μg m−3, respectively. Mean infiltration factors of particles (0.6–0.7) were higher than those of NO2 (0.4). In contrast, higher loss rates were found for NO2 (0.5–0.8 h−1) compared to those for particles (0.1–0.3 h−1). The average concentrations of PM2.5, PM10 and NO2 in kitchen environments were 22.0, 33.7 and 20.8 μg m−3, with highest hourly concentrations (437, 644 and 136 μg m−3, respectively) during cooking times (6–7 pm). Indoor sources increased the indoor concentrations of particles and NO2 by an average of 26–37% in comparison to the indoor background level without indoor sources. Outdoor and indoor air exchange plays an important role in reducing air pollution indoors by 65–86% for particles and 42–65% for NO2.

Changes in rainfall onset and cessation dates are critical for improving decision making and adaptation strategies in numerous socio-economic sectors. An objective method of determining onset and cessation date is employed over Southern West Africa (SWA) in this study. The method is applied over 34 years of the quasi-global rainfall dataset from the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) and five High Resolution Model Intercomparison Project (HighResMIP) model datasets under the Coupled Model Intercomparison Project Phase 6 (CMIP6) experiment. Generally, a strong agreement exists between CHIRPS and the HighResMIP models in capturing the behaviour of seasonal rainfall over SWA, with models able to capture the bimodal rainfall season. The ability of models in capturing onset and cessation dates as observed in CHIRPS shows the strength of these models in representing the short break between the two wet seasons that is otherwise known as the ‘Little Dry Season’. Patterns observed in the onset and cessation dates over the SWA region are consistent with the northward and southward displacement of the Intertropical Convergence Zone (ITCZ). The seasonal timing of the models shows good agreement with observations such that most mean onset/cessation dates agree within 26 days. While IPSL-CM6A-ATM-HR, a model among the five HighResMIPs used in the study, best agrees with CHIRPS in representing onset and cessation dates during the unimodal rainfall season, no one model best agrees with CHIRPS during the bimodal season, with models outperforming each other in representing onset/cessation dates with little variation.

The recently developed polyoxometalate (POM)–ionosolv concept offers an interesting strategy to generate two valuable product streams from lignocellulosic biomass, a solid cellulose-rich pulp and short-chain carboxylic acids like formic acid and acetic acid in a simple and cost-efficient manner. This study aimed to find optimum parameters for the two steps of the transformation by performing a sensitivity analysis on the initial ionosolv fractionation step as well as kinetic investigations of the following POM-catalyzed oxidation step. The results were transferred to the POM–ionosolv concept to find the overall process optimum. Beech wood was used as an industrially relevant substrate for ionosolv fractionation with the low-cost ionic liquid triethylammonium sulfate, [TEA][HSO4], and the HPA-5 [H8PV5Mo7O40] POM catalyst for the oxidation of the dissolved components in an oxygen atmosphere. As the most seminal finding, we defined optimum conditions of 125 °C, 1200 rpm, 30 bar oxygen, and 24 h reaction time in ionic liquid containing 70% water, achieving 72% xylose extraction from beech wood, which resulted in a 39% formic acid yield. We suggest that the fractionation and catalytic conversion are carried out at different water contents for maximum conversion efficiency for each step.

Present-day intelligent healthcare applications offer digital healthcare services to users in a distributed manner. The Internet of Healthcare Things (IoHT) is the mechanism of the Internet of Things (IoT) found in different healthcare applications, with devices that are attached to external fog cloud networks. Using different mobile applications connecting to cloud computing, the applications of the IoHT are remote healthcare monitoring systems, high blood pressure monitoring, online medical counseling, and others. These applications are designed based on a client–server architecture based on various standards such as the common object request broker (CORBA), a service-oriented architecture (SOA), remote method invocation (RMI), and others. However, these applications do not directly support the many healthcare nodes and blockchain technology in the current standard. Thus, this study devises a potent blockchain-enabled socket RPC IoHT framework for medical enterprises (e.g., healthcare applications). The goal is to minimize service costs, blockchain security costs, and data storage costs in distributed mobile cloud networks. Simulation results show that the proposed blockchain-enabled socket RPC minimized the service cost by 40%, the blockchain cost by 49%, and the storage cost by 23% for healthcare applications.

Negative emission technologies (NETs) could play a key role in ensuring net-zero and longer-term net negative emission ambitions are met. However, greenhouse gas emissions (and other pollutants) will occur over the life cycle of a NET and will need to be taken into consideration when developing schemes to roll out their use. We compare five NETs: afforestation/reforestation (AR), enhanced weathering (EW), mangrove restoration (MR), bioenergy and direct air capture with carbon storage (BECCS and DAC), using life cycle assessment to determine their environmental impacts (global warming, freshwater, toxicity etc.). We find that there is a wide range in the environmental impacts estimated across the NETs and the context in which they are used will directly impact which NET has low or high environmental impacts. This is an important aspect to consider when deciding which NET to prioritise in strategies to roll out their use on large scales. If consistent removal of CO2 from the atmosphere is the goal, then AR and MR have the lowest environmental impacts. However, if large and quick CO2 removal is the goal then EW, DAC and BECCS have similar, if not lower, environmental impacts.

This paper proposes a new method of impact classification for a Structural Health Monitoring system through the use of Self-Attention, the central building block of the Transformer neural network. As a topical and highly promising neural network architecture, the Transformer has the potential to greatly improve the speed and robustness of impact detection. This paper investigates the suitability of this new network, confronting the advantages and disadvantages offered by the Transformer and a well-known and established neural network for impact detection, the Convolutional Neural Network (CNN). The comparison is undertaken on performance, scalability, and computational time. The inputs to the networks were created using a data transformation technique, which transforms the raw time series data collected from the network of piezoelectric sensors, installed on a composite panel, through the use of Fourier Transform. It is demonstrated that the Transformer method reduces the computational complexity of the impact detection significantly, while achieving excellent prediction results.