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Kinetics of plasticiser release and degradation in soils
Get PDFDespite the increasing use of emerging phthalate and non-phthalate plasticisers as replacements for restricted phthalates, few studies have investigated their rates of entry and persistence in soils. We investigated release of the emerging plasticiser diethyl hexyl terephthalate (DEHTP) from polyvinyl chloride microplastics (PVC; 4 mm diameter; 21% DEHTP w/w) in soils in a 3-month laboratory study. DEHTP was released rapidly, with 6.6–12.1 ng DEHTP released per mg PVC within 100 days. Plasticiser half-lives in soils were significantly positively correlated with logKOW. Degradation was typically slower in acidic heathland (pH 3.8; organic matter 3.7%), than in alkaline grassland (pH 7.3; OM 16%) or sandy loam agricultural (pH 5.3; OM 5%) soils. Rapid release and potential persistence of some emerging plasticisers in soils indicates that presence of these contaminants may increase in the future
Stakeholder perceptions of drought resilience using government drought compensation in Thailand
Get PDFIn the context of escalating climate challenges in Southeast Asia, this study investigates the dynamics of disaster budget allocation in Thailand and examines farmers’ perceptions of drought compensation, focusing on the Ping catchment situated in the Northwest of the country. The main objective of the study was to gauge stakeholders’ awareness and views on government drought compensation and evaluate its effectiveness. Using government budget data, drought indicators, and a comprehensive survey in Chiang Mai and Tak provinces, the study explores correlations between budget allocation, drought indicators, and farmers’ experiences. A correlation analysis unveils stronger links between compensation and Vegetation Condition Index (VCI) as compared to Drought Severity Index (DSI), with regional variations and the impact of irrigation practices. Compensation shows positive correlations with drought severity, suggesting support to farmers occurs when they suffer severe crop damage. We investigate drought occurrences and their impacts along with farmer’s awareness and experiences of drought compensation schemes to uncover disparities in awareness, application rates, and satisfaction levels, providing insights into farmers’ views on compensation effectiveness. The study concludes by proposing policy adjustments, tailored regional approaches, and feedback mechanisms to enhance the effectiveness of drought compensation strategies. Despite limitations in sample size and potential biases, this study contributes valuable insights into the complex dynamics of disaster budget allocation, drought compensation, and farmers’ perspectives in Thailand, laying a foundation for refining policies and fostering sustainable agricultural practices amidst increasing climate challenges
Investigating the Impact of Feature Reduction for Deep Learning-based Seasonal Sea Ice Forecasting
Get PDFWith the state-of-the-art IceNet model, deep learning has contributed to an important aspect of climate research by leveraging a range of climate inputs to provide accurate forecasts of Arctic sea ice concentration (SIC). The deep learning subfield of eXplainable AI (XAI) has gained enormous attention in order to gauge feature importance of neural networks, for instance by leveraging network gradients. In recent work, an XAI study of the IceNet was conducted, using gradient saliency maps to interrogate its feature importance. A majority of XAI studies provide information about feature importance as revealed by the XAI method, but rarely provide thorough analysis of effects from reducing the number of input variables. In this paper, we train versions of the IceNet with drastically reduced numbers of input features according to results of XAI and investigate the effects on the sea ice predictions, on average and with respect to specific events. Our results provide evidence that the model generally performs better when less features are used, but in case of anomalous events, a larger number of features is beneficial. We believe our thorough study of the IceNet in terms of feature importance revealed by XAI may give inspiration for other deep learning-based problem scenarios and application domains
Very high fire danger in UK in 2022 at least 6 times more likely due to human-caused climate change
Get PDFThe UK experienced an unprecedented heatwave in 2022, with temperatures reaching 40 °C for the first time in recorded history. This extreme heat was accompanied by widespread fires across London and elsewhere in England, which destroyed houses and prompted evacuations. While attribution studies have identified a strong human fingerprint contributing to the heatwave, no studies have attributed the associated fires to anthropogenic influence. In this study, we assess the contribution of human-induced climate change to fire weather conditions over the summer of 2022 using simulations from the HadGEM3-A model with and without anthropogenic emissions and apply the Canadian Fire Weather Index. Our analysis reveals at least a 6-fold increase in the probability of very high fire weather in the UK due to human influence, most of which is driven by high fire conditions across England. These findings highlight the significant role of human-induced climate change in emerging UK wildfires. As we experience more hotter and drier summers as temperatures continue to rise the frequency and severity of fires are likely to increase, posing significant risks to both natural ecosystems and human populations. This study underscores the need for further research to quantify the changing fire risk due to our changing climate and the urgent requirement for mitigation and adaptation efforts to address the growing wildfire threat in the UK
The role of substrate characteristics and temperature for potential non-native plant establishment in Maritime Antarctic ecosystems
Get PDFPolar ecosystems are threatened by non-native plants, and this risk will increase with climate warming. Non-native plant growth depends on Antarctic environmental conditions and substrates, but these influences are poorly quantified. Under laboratory conditions we quantified the growth of Holcus lanatus, Trifolium repens and Taraxacum officinale across nine sub-Antarctic and Maritime Antarctic substrates with varying characteristics. This included, among others, variation in carbon (0.2–27.0%), nitrogen (0.03–2.1%) and phosphorus (0.04–0.54%) contents, under simulated Antarctic conditions (2°C) and a warming scenario. Legacy effects from an established non-native chironomid midge (Eretmoptera murphyi) and non-native grasses were included. H. lanatus and T. repens grew best in organic- and nutrient-rich substrates, while T. officinale growth was poorly correlated with substrate characteristics. Warming increased plant size by one to three times, but inconsistently across species and substrates, suggesting that climate change impacts on plant growth will vary across the Maritime Antarctic. A variable response was also observed in the legacy effects of E. murphyi, while non-native grasses increased H. lanatus and T. repens plant size, but not that of T. officinale. Plant growth was positively correlated with substrate organic and phosphorus content, and this information was used to trial a novel approach to identifying sites ‘at risk’ from plant invasions in the Maritime Antarctic
Use of excess meltwater from continuous flow analysis systems for the analysis of low concentration insoluble microparticles in ice cores
Get PDFLow-concentration insoluble microparticles that are preserved in ice cores offer valuable information for reconstructing past environmental changes. However, their low concentrations and limited sample availability present challenges for extraction and recovery while ensuring representativeness of results. The analysis of ice cores using continuous flow analysis systems generates large volumes of excess meltwater as a by-product with the potential to improve the acquisition of targeted low-concentration insoluble microparticle samples. Here, we present Antarctic ice core diatom records, representative of targeted low-concentration insoluble microparticle records, recovered from excess meltwater generated from a continuous flow analysis system. We analyse these records to evaluate the feasibility of using this excess meltwater to generate replicable and representative results. Our results demonstrate that diatom records obtained from a continuous flow analysis system exhibit high recovery percentages and replicability, with minor quantifiable loss and memory effects in the system. Our multi-outlet sampling assessment highlights that the waste lines of the continuous flow analysis system are an optimal source for sampling excess meltwater. Additionally, the analysis of diatom spatial distribution in filters suggest a lower threshold for applying analytical methods which assume targeted microparticles are homogeneously distributed. These results confirm that a continuous flow analysis system can be used to extract targeted low-concentration insoluble microparticles from ice core samples, yielding representative and reproducible results
Novel management strategies for optimizing shallow geothermal energy exploitation: a European urban experience perspective
Get PDFThe intensive exploitation of urban aquifers by shallow geothermal systems can affect the thermal balance of urban aquifers, thus reducing their renewability. This paper proposes a new management strategy for the sustainable use of shallow geothermal energy resources, based on imposing new constraints related to system exploitation regimes. To achieve this objective, a novel methodology was introduced for optimizing the operation of geothermal systems, by adjusting the flow rate and/or temperature change to maintain the existing thermal energy demand. The methodology was applied to a 1.8 million real operational data set from 24 shallow groundwater heat pump systems (GWHP), which are large and medium scale systems. The investigated GWHPs are located in five European cities. Two management alternatives for the optimization of geothermal energy resources use are presented in this work: (1) prioritizing higher flow rates over lower temperature changes, which tended to relatively decrease the discharge temperature by 1.48 °C on average, and (2) prioritizing higher temperature changes over lower flow rates, which tended to relatively decrease flow rates down to 8.09 L s−1 on average. The results show that GWHPs operating in European cities with the highest thermal power demand and flow rates achieved the highest flow rate reduction
Characterisation of precursory seismic activity towards early warning of landslides via semi-supervised learning
Get PDFThis study demonstrates that machine learning from seismograms, obtained from commonly deployed seismometers, can identify the early stages of slope failure in the field. Landslide hazards negatively impact the economy and public through disruption, damage of infrastructure and even loss of life. Triggering factors leading to landslides are broadly understood, typically associated with rainfall, geological conditions and steep topography. However, early warning at slope scale of an imminent landslide is more challenging. Through semi-supervised learning for seismic event detection from continuous seismic recordings over a period of about 10 years, we demonstrate that timely landslide induced displacement prediction is possible, providing the basis for landslide early warning systems. Our proposed methodology detects and characterises seismic precursors to landslide events making use of seismic recordings near an active slow moving earth slide-flow using a semi-supervised Siamese network. This data driven methodology identifies increase in microseismicity, and the change in the frequency spectrum of that microseismicity which identify key stages prior to a failure: ‘rest’, ‘precursor’ and ‘active’. Due to the semi-supervised nature of Siamese networks, the methodology is adaptable to discovering new types of distinct events, making it an ideal solution for precursor detection at new sites
Predicting climate-change impacts on the global glacier-fed stream microbiome
Get PDFThe shrinkage of glaciers and the vanishing of glacier-fed streams (GFSs) are emblematic of climate change. However, forecasts of how GFS microbiome structure and function will change under projected climate change scenarios are lacking. Combining 2,333 prokaryotic metagenome-assembled genomes with climatic, glaciological, and environmental data collected by the Vanishing Glaciers project from 164 GFSs draining Earth’s major mountain ranges, we here predict the future of the GFS microbiome until the end of the century under various climate change scenarios. Our model framework is rooted in a space-for-time substitution design and leverages statistical learning approaches. We predict that declining environmental selection promotes primary production in GFSs, stimulating both bacterial biomass and biodiversity. Concomitantly, predictions suggest that the phylogenetic structure of the GFS microbiome will change and entire bacterial clades are at risk. Furthermore, genomic projections reveal that microbiome functions will shift, with intensified solar energy acquisition pathways, heterotrophy and algal-bacterial interactions. Altogether, we project a ‘greener’ future of the world’s GFSs accompanied by a loss of clades that have adapted to environmental harshness, with consequences for ecosystem functioning
Sustainability Nexus AID: soil health
Get PDFThe Sustainability Nexus Analytics, Informatics, and Data (AID) Programme of the United Nations University (UNU), aims to provide information, data, computational, and analytical tools to support the sustainable management and long-term security of natural resources using a nexus approach. This paper introduces the Soil Health Module of the Sustainability Nexus AID Programme. Healthy soil is crucial for life on Earth, and it is essential for ecosystem services and functioning, access to clean water, socioeconomic structure, biodiversity, and food security for the growing population of the world. Healthy soils contribute to mitigating the effects of climate change and reduce the consequences of extreme events such as flooding and drought. Healthy soils influence the hydrologic cycle by regulating transpiration, water infiltration, and soil water evaporation affecting land–atmosphere interactions. The Soil Health Module of the UNU Sustainability Nexus AID Programme aims to evolve into the ultimate focal point, supporting a diverse array of stakeholders with state-of-the-art data and tools that are essential for soil health monitoring and projection. This paper discusses the importance of adopting a nexus approach for ensuring soil health, explores the AID tools currently at our disposal for quantifying and predicting soil health, and concludes with recommendations for future effort and direction within the Sustainability Nexus AID Programme concerning soil health
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