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Landfill site trees: Potential source or sink of greenhouse gases?
Tree stems can transport greenhouse gases (GHGs) produced belowground to the atmosphere. Previous studies in natural wetland and upland ecosystems have quantified tree stem fluxes of methane (CH4), carbon dioxide (CO2) and nitrous oxide (N2O). However, tree stem GHG fluxes have not previously been measured in the context of managed environments. The work presented in this thesis aimed to quantify GHG fluxes from tree stems on closed landfill sites.
To investigate the potential for trees growing on closed landfill sites to act as conduits for GHGs produced belowground to the atmosphere, GHG fluxes were measured from tree stem and soil surfaces. In situ measurements from a closed landfill site in the UK were examined for spatial and temporal patterns and evaluated against data from a comparable non-landfill area. Measurements were also conducted from landfill sites in the UK with varying management practices and different tree species present. The resulting flux values were scaled up to estimate the magnitude of tree stem GHG fluxes from closed landfills at a national level.
The findings presented here show evidence of tree mediated GHG transport on closed landfill sites and temporal variations in fluxes from tree stems were also observed, with generally higher fluxes in the summer months. Stem CH4 fluxes varied between trees growing on landfill sites with different management practices. Additionally, stem N2O fluxes displayed spatial patterns, with decreasing emissions at increased height from the forest floor, indicating an underground source. Evidence suggested that GHG fluxes from closed landfills are influenced by factors including the quantity of GHG produced in the waste (linked to the age of the site), the susceptibility of the area to waterlogging and landfill management techniques put in place upon closure (for example, clay caps, cover soils and gas extraction). Upscaled CH4 and N2O flux values from tree stems on closed landfill sites corresponded to less than 1% of the total CH4 and N2O emissions reported from UK landfills in 2020.
Overall, results indicated that measuring soil fluxes alone from forested landfill sites would result in an underestimation of the total surface fluxes. However, the emission rates from tree stems on closed landfills observed in this thesis do not exceed those in natural ecosystems. Therefore, with careful planning and management, the recommendation is that trees can be planted on closed landfill sites in the UK without emitting atypical levels of GHGs. However, including gas fluxes from tree stems on closed landfills would increase the accuracy of GHG budgets at national and global levels
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Rapid detection of soil carbonates by means of NIR spectroscopy, deep learning methods and phase quantification by powder Xray diffraction
Soil NIR spectral absorbance/reflectance libraries are utilized towards
improving agricultural production and analysis of soil properties which are key
prerequisite for agroecological balance and environmental sustainability.
Carbonates in particular, represent a soil property which is mostly affected
even by mild, let alone extreme, changes of environmental conditions during
climate change. In this study we propose a rapid and efficient way to predict
carbonates content in soil by means of FT NIR reflectance spectroscopy and by
use of deep learning methods. We exploited multiple machine learning methods,
such as: 1) a MLP Regressor and 2) a CNN and compare their performance with
other traditional ML algorithms such as PLSR, Cubist and SVM on the combined
dataset of two NIR spectral libraries: KSSL (USDA), a dataset of soil samples
reflectance spectra collected nationwide, and LUCAS TopSoil (European Soil
Library) which contains soil sample absorbance spectra from all over the
European Union, and use them to predict carbonate content on never before seen
soil samples. Soil samples in KSSL and in TopSoil spectral libraries were
acquired in the spectral region of visNIR, however in this study, only the NIR
spectral region was utilized. Quantification of carbonates by means of Xray
Diffraction is in good agreement with the volumetric method and the MLP
prediction. Our work contributes to rapid carbonates content prediction in soil
samples in cases where: 1) no volumetric method is available and 2) only NIR
spectra absorbance data are available. Up till now and to the best of our
knowledge, there exists no other study, that presents a prediction model
trained on such an extensive dataset with such promising results on unseen
data, undoubtedly supporting the notion that deep learning models present
excellent prediction tools for soil carbonates content.Comment: 39 pages, 5 figure
Human wellbeing responses to speciesâ traits
People rely on well-functioning ecosystems to provide critical services that underpin human health and wellbeing. Consequently, biodiversity loss has profound negative implications for humanity. Human-biodiversity interactions can deliver individual-level wellbeing gains, equating to substantial healthcare cost-savings when scaled-up across populations. However, critical questions remain about which species and/or traits (e.g. colours, sounds, smells) elicit wellbeing responses. The traits that influence wellbeing can be considered âeffectâ traits. Using techniques from community ecology, we analyse a database of speciesâ effect traits articulated by people, to identify those that generate different types of wellbeing (physical, emotional, cognitive, social, spiritual and âglobalâ wellbeing, the latter being akin to âwhole-person healthâ). Effect traits have a predominately positive impact on wellbeing, influenced by the identity and taxonomic kingdom of each species. Different sets of effect traits deliver different types of wellbeing. However, traits cannot be considered independently of species because multiple traits can be supported by a single species. Indeed, we find numerous effect traits from across the ecological community can elicit multiple types of wellbeing, illustrating the complexity of biodiversity experiences. Our empirical approach can help implement interdisciplinary thinking for biodiversity conservation and nature-based public health interventions designed to support human wellbeing
Improved accuracy and spatial resolution for bio-logging-derived chlorophyll a fluorescence measurements in the Southern Ocean
The oceanâs meso- and submeso-scales (1-100 km, days to weeks) host features like filaments and eddies that have a key structuring effect on phytoplankton distribution, but that due to their ephemeral nature, are challenging to observe. This problem is exacerbated in regions with heavy cloud coverage and/or difficult access like the Southern Ocean, where observations of phytoplankton distribution by satellite are sparse, manned campaigns costly, and automated devices limited by power consumption. Here, we address this issue by considering high-resolution in-situ data from 18 bio-logging devices deployed on southern elephant seals (Mirounga leonina) in the Kerguelen Islands between 2018 and 2020. These devices have submesoscale-resolving capabilities of light profiles due to the high spatio-temporal frequency of the animalsâ dives (on average 1.1 +-0.6 km between consecutive dives, up to 60 dives per day), but observations of fluorescence are much coarser due to power constraints. Furthermore, the chlorophyll a concentrations derived from the (uncalibrated) bio-logging devicesâ fluorescence sensors lack a common benchmark to properly qualify the data and allow comparisons of observations. By proposing a method based on functional data analysis, we show that a reliable predictor of chlorophyll a concentration can be constructed from light profiles (14 686 in our study). The combined use of light profiles and matchups with satellite ocean-color data enable effective (1) homogenization then calibration of the bio-logging devicesâ fluorescence data and (2) filling of the spatial gaps in coarse-grained fluorescence sampling. The developed method improves the spatial resolution of the chlorophyll a field description from ~30 km to ~12 km. These results open the way to empirical study of the coupling between physical forcing and biological response at submesoscale in the Southern Ocean, especially useful in the context of upcoming high-resolution ocean-circulation satellite missions
A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling
The accurate prediction of short-term electricity prices is vital for
effective trading strategies, power plant scheduling, profit maximisation and
efficient system operation. However, uncertainties in supply and demand make
such predictions challenging. We propose a hybrid model that combines a
techno-economic energy system model with stochastic models to address this
challenge. The techno-economic model in our hybrid approach provides a deep
understanding of the market. It captures the underlying factors and their
impacts on electricity prices, which is impossible with statistical models
alone. The statistical models incorporate non-techno-economic aspects, such as
the expectations and speculative behaviour of market participants, through the
interpretation of prices. The hybrid model generates both conventional point
predictions and probabilistic forecasts, providing a comprehensive
understanding of the market landscape. Probabilistic forecasts are particularly
valuable because they account for market uncertainty, facilitating informed
decision-making and risk management. Our model delivers state-of-the-art
results, helping market participants to make informed decisions and operate
their systems more efficiently
SpaceâScale Resolved Surface Fluxes Across a Heterogeneous, MidâLatitude Forested Landscape
The Earth\u27s surface is heterogeneous at multiple scales owing to spatial variability in various properties. The atmospheric responses to these heterogeneities through fluxes of energy, water, carbon, and other scalars are scale-dependent and nonlinear. Although these exchanges can be measured using the eddy covariance technique, widely used tower-based measurement approaches suffer from spectral losses in lower frequencies when using typical averaging times. However, spatially resolved measurements such as airborne eddy covariance measurements can detect such larger scale (meso-ÎČ, meso-Îł) transport. To evaluate the prevalence and magnitude of these flux contributions, we applied wavelet analysis to airborne flux measurements over a heterogeneous mid-latitude forested landscape, interspersed with open water bodies and wetlands. The measurements were made during the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors intensive field campaign. We ask, how do spatial scales of surface-atmosphere fluxes vary over heterogeneous surfaces across the day and across seasons? Measured fluxes were separated into smaller-scale turbulent and larger-scale mesoscale contributions. We found significant mesoscale contributions to sensible and latent heat fluxes through summer to autumn which would not be resolved in single-point tower measurements through traditional time-domain half-hourly Reynolds decomposition. We report scale-resolved flux transitions associated with seasonal and diurnal changes of the heterogeneous study domain. This study adds to our understanding of surface-atmospheric interactions over unstructured heterogeneities and can help inform multi-scale model-data integration of weather and climate models at a sub-grid scale
Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse
This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses.
This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups.
In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in usersâ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018â6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena
Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control
This paper provides an overview of the current state-of-the-art in selective
harvesting robots (SHRs) and their potential for addressing the challenges of
global food production. SHRs have the potential to increase productivity,
reduce labour costs, and minimise food waste by selectively harvesting only
ripe fruits and vegetables. The paper discusses the main components of SHRs,
including perception, grasping, cutting, motion planning, and control. It also
highlights the challenges in developing SHR technologies, particularly in the
areas of robot design, motion planning and control. The paper also discusses
the potential benefits of integrating AI and soft robots and data-driven
methods to enhance the performance and robustness of SHR systems. Finally, the
paper identifies several open research questions in the field and highlights
the need for further research and development efforts to advance SHR
technologies to meet the challenges of global food production. Overall, this
paper provides a starting point for researchers and practitioners interested in
developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic
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