Abstract: Urban park vitality serves as a critical metric for assessing park service performance, drawing substantial scholarly interest. However, existing studies often neglect the temporal variability in vitality patterns and thus failing to accurately reflect actual park performance. This study constructs a "temporal behavior-spatial attributes-park typology" framework using high-precision (50m) mobile signaling data to capture hourly vitality fluctuations in 59 parks of Hangzhou’s Gongshu District. By applying dynamic time warping-optimized K-means clustering, the research identifies vitality types and employs geographical detectors to analyze multivariate interaction effects influencing park vitality. We find there were three vitality types based on visitor temporal patterns: Morning Exercise-Dominated, All-Day Balanced and Evening Aggregation. Community parks show weekday morning dominance, while comprehensive parks demonstrate weekend all-day equilibrium. These vitality types show varying degrees of correlation with spatial attributes at different times: weekend morning vitality is synergistically driven by park area and surrounding POI density, whereas weekday evening vitality is primarily influenced by interactions between park service facility density and surrounding residential population density. The three vitality types highlight transportation accessibility and commercial amenities regulate temporal vitality, and time-specific management can enhance park recreational performance. Findings offer references for optimizing urban park vitality in other regions.

Abstract: The Mindong-Changchunling region is situated in the central portion of the Songliao Basin within China. The principal target stratum in this area is the Fuyu oil layer from the lower Cretaceous Quan 4th member. The reservoir is distinguished by its exceptionally fine lithology, predominantly comprising fine sandstone and siltstone, with minor intercalations of medium sandstone. The distribution of high-quality reservoirs in the Mindong-Changchunling area is constrained by significant differences in provenance, sedimentation, diagenesis, and other factors. The sand body distribution in the Changchunling area is influenced by the eastern near-source provenance. The reservoir properties of these sand bodies are impacted by the poor sorting and high mud content typical of near-source delta sand bodies. Nonetheless, the reservoir properties are enhanced by late uplift and surface water dissolution eluviation. In contrast, the distribution of sand bodies in the Mindong area is governed by the southwestern far-source provenance. Far-source delta sand bodies are characterized by better sorting and high mud content, but their reservoir properties are primarily diminished by carbonate cementation. During the early middle diagenetic stage, the dissolution of feldspar by organic acids improves the reservoir properties of the sand bodies. Three advantageous reservoir zones with distinct genetic types have emerged in the Mindong-Changchunling area due to variations in sedimentation and diagenesis. These zones include the Chang107-Chang104-Chang52 well area, the Fu155-Fu161-Fu157 well area, and the Min103-Min31 well area.

Abstract: Understanding the relationship between geological and geomorphological processes is essential for reconstructing landscape evolution. This study investigates the influence of geology and geomorphology on landscape development in central Brazil, specifically within the Natividade Group distribution area. To achieve this, we integrated remote sensing data (Sentinel-2 and SRTM imagery) with geospatial analyses to generate two key maps: (i) a pedogeomorphological map, which classifies landforms and soil-landscape relationships, and (ii) a predictive geological-geomorphological map, which models spatial patterns of geological and geomorphological influence using machine-learning techniques. The identified pedoforms were grouped into three major slope classes, each reflecting distinct relationships among lithology, relief, and soil development. This classification structure enhances the interpretation of landscape evolution by linking physical terrain characteristics to underlying geological controls. The pedogeomorpho-logical map identified nine distinct pedoforms, reflecting variations in drainage density and patterns, relief and lithology. The spatial distribution of these parameters indicates strong correlations between soil development, geomorphological processes, and the underlying geological framework. Pedoforms associated with resistant lithology, such as quartzite-rich metasedimentary rocks, are linked to shallow, poorly developed soils, especially related to the Natividade Group, whereas areas with phyllite, schist, and Paleoproterozoic basement rocks of the Almas and Aurumina Terranes exhibit deeper, chemically weathered soils. These findings highlight the role of soil formation as a key indicator of landscape evolution in tropical climate regions. The predictive mapping approach employed a Random Forest classifier trained with 15 environmental predictors derived from remote sensing datasets. This analysis resulted in six landscape classes, revealing the ongoing interaction between geology, geomorphology, and surface pro-cesses. While the predictive model effectively delineated geological-geomorphological units, its moderate accuracy suggests that additional variables, such as geophysical data, could enhance classification. The results confirm that the interplay between geology and geomorphology significantly influences landscape evolution, though other factors, such as climate and vegetation, also play crucial roles. Tectonic events, including the Rhyacian amalgamation (~2.2 Ga), Statherian taphrogenesis (~1.6 Ga), Neoproterozoic orogeny (~600 Ma), and the formation of the Sanfranciscana Basin (~100 Ma), have left imprints on the region’s current landscape. The combination of remote sensing techniques with ge-ological and geomorphological studies provides a comprehensive framework for un-derstanding landscape evolution and supports land-use planning, environmental con-servation, and risk assessment in geologically complex regions.

Abstract: Since precipitation analysis reveals critical statistical characteristics, temporal patterns, and spatial distributions of rainfall and snowfall events, it plays an important role in planning urban drainage systems, flood forecasting, hydrological modeling, and climate studies. It helps engineers design climate-resilient infrastructure capable of withstanding extreme weather events, which is becoming increasingly important as precipitation patterns change over time. With precipitation analysis, multiple valuable information can be determined, such as storm intensity, duration, and frequency. To enhance understanding of precipitation data and analysis results, researchers often use graphical representation methods to show the data in visual formats. Although existing precipitation analysis and basic visualization methods visual representations are helpful, it is critical to have a comprehensive analysis and visualization system to detect significant patterns and anomalies in high-resolution temporal precipitation data more effectively. This study presents a visual analytics system enabling interactive analysis of hourly precipitation data across all U.S. states. Multiple coordinated visualizations are designed to support both single and multiple-station analysis. These visualizations allow users to directly examine temporal patterns, spatial distributions, and statistical characteristics of precipitation events directly within visualizations. Case studies demonstrate the usefulness of the designed system by evaluating various historical storm events.

Abstract: In view of the direct and indirect impacts of disasters on communities and their livelihoods, governments, the private sector and civil society organizations have been faced with tremendous challenges in identifying best practice solutions for revamping community functions in the aftermath of disasters. Although there are myriads of approaches for ensuring communities get back to normal including the ‘bouncing back’ or ‘building back better’ mantra, contemporary studies have scarcely discussed transitions from the traditional modes of livelihood recovery to digitization and other forms of ‘self-protection’ and ‘social protection’ where individuals and communities are empowered to reduce risks and their own vulnerability in response to shocks and amidst an ailing global or local economy. This scholarly piece therefore explores sustainable livelihoods recovery in the context of rethinking a systemic approach to recovery that is more inclusive, empathic and considers neutrality in humanitarian interventions. It builds on the foundational and contemporary works of founding and contemporary disaster risk management researchers and risk governance policy experts to shed more light on facilitating accessibility to improved and sustainable humanitarian interventions particularly in rapid and slow-onset disaster recovery.

Abstract: Mapping and profiling of clay resources were essential for numerous industries due to clay's unique properties and widespread availability. This study employed GPS and QGIS to map clay resources in Brgy. Bugas-Bugas, Placer, and Brgy. Cabugo, Claver, Surigao del Norte. Laboratory analysis was conducted to evaluate elemental and mineral composition, plasticity, shrinkage, water absorption, porosity, color characteristics, and flexural strength of the clay samples. Significant variations in clay properties were observed, with chemical analysis revealing higher silica content in Claver and Placer clay, indicating potential strength and lower shrinkage. Conversely, Kauswagan clay exhibited higher levels of aluminum oxide and iron oxide, suggesting increased water absorption and darker coloration after firing. XRD analysis identified montmorillonite as the main mineral found in the Claver clay sample, while the Placer clay sample from Brgy. Bugas-Bugas was identified as dickite clay, containing significant quartz and anorthite, a variety of plagioclase, indicative of a siliceous secondary clay. Plasticity tests demonstrated high plasticity for Kauswagan and moderate plasticity for Claver and Placer, while shrinkage tests indicated low drying shrinkage for Placer and high total linear shrinkage for Kauswagan. Kauswagan clay exhibited the highest water absorption rates, whereas Placer and Claver clays were suitable for tile manufacturing due to their lower water absorption rates. Porosity was highest in Kauswagan, followed by Placer. Color analysis revealed that fired Placer samples were lighter in color compared to Claver samples, with both showing a slight shift towards green and a more yellowish hue in Placer. Placer exhibited higher flexural strength compared to Kauswagan. These findings significantly contributed to understanding clay resources in the area, facilitating informed decision-making for their development and utilization of clay across a wide range of industries vital for efficient and effective resource management.

Abstract: This study utilizes ENVI-met simulations to examine the effectiveness of nature-based urban design strategies in enhancing outdoor thermal comfort for senior citizens during Mediterranean heat waves. The research focuses on a high-density, post-refugee neighborhood in Greater Athens, assessing both baseline and optimized urban scenarios featuring mature trees and water elements. Simulations conducted on July 23, 2023—the hottest recorded day of the year—demonstrate substantial reductions in Physiologically Equivalent Temperature (PET), with improvements ranging from 11.17 K to 22.45 K in the morning, 2.17 K to 14.55 K in the afternoon, and 0.56 K to 4.78 K in the evening. Furthermore, dynamic comfort analysis reveals a reduction in energy balance of up to 191.92 W during peak heat hours, alleviating thermal strain on elderly individuals. These findings highlight the pivotal role of shading and evaporative cooling strategies in mitigating urban heat stress, particularly for vulnerable populations. The study underscores the urgency of integrating climate-responsive urban interventions into policy frameworks to enhance resilience against extreme heat events.

Abstract: Positional accuracy in cadastral data is fundamental for secure land tenure and efficient land administration. However, many land administration systems, experience difficulties to meet accuracy standards, particularly in areas with digitized historical maps, leading to disruptions in land transactions. This study investigates the use of unsupervised clustering algorithms in order to identify and characterize systematic spatial error patterns in cadastral maps. We compare Fuzzy c-means (FCM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Gaussian Mixture Models (GMM) in clustering error vectors derived from 500 homologous points. These points were obtained by comparing cadastral data with a higher-accuracy land survey within a 7 km² area in Ioannina, Greece, known for its inaccuracies in the Greek National Cadastre. The optimal number of clusters for each algorithm was determined. Results show that DBSCAN and GMM successfully captured a central area of random errors surrounded by a region exhibiting a systematic, counter-clockwise rotational error, whereas FCM did not capture this pattern. DBSCAN, with its ability to isolate noise points in the center of the study area, provided the most interpretable results. This clustering approach can be integrated into automated cadastral map improvement methods, contributing to progressive cadastral renewal efforts.

Abstract: Although Bangladesh is frequently regarded as 'ground zero' for climate change, the Chittagong Hill Tracts (CHT) have only recently been recognized for their environmental vulnerabilities, particularly following the severe rainfall and landslides of 2017. However, attributing these risks solely to climate change oversimplifies the complex interplay of environmentally unsustainable 'development' projects, extensive (il)legal deforestation, and localized socio-political dynamics. Using a mixed-method approach, this study investigates the convergence of climate trends with these broader drivers of environmental risk, drawing on meteorological data and qualitative insights from diverse local communities (n=400). The findings reveal a critical gap in climate-specific data for CHT, complicating the attribution of observed changes solely to climatic factors. Instead, the evidence underscores significant risks stemming from the combined effects of land degradation, water scarcity, flash floods, landslides, and other disturbances tied to deforestation and neoliberal development. This paper advances theoretical discussions by challenging climate-only narratives and emphasizing the need to analyze environmental risks within their localized socio-ecological contexts. It contributes to the growing body of scholarship advocating for context-sensitive frameworks that move beyond generalized attributions of climate change as the root cause of all environmental vulnerabilities, thereby promoting more nuanced, equitable, and regionally attuned policy responses.

Abstract:

Artificial Intelligence (AI) and digital governance possess the ability to impact societies benefiting all people and nature especially in the context of rural regions in India. The presence of AI technologies available in the administration of regions and advancement of rural development suggests that there are great opportunities in agriculture, healthcare, education, and resource management. Integrating AI in governance has the possibility of integrating technology, improving rural livelihood via access to healthcare and the precision of agricultural practices, and even achieving sustainable development goals (SDGs). Nevertheless, better possibilities of employment of Ai are precluded by barriers such as lack of technological capabilities, deficits in the level of education and restrictions within the policies. Due to the effectiveness of AI in changing environments in rural areas, a mix of policy frameworks, enhancing resources on education, and collaboration between government bodies, business groups, and community organizations is practiced. Once implemented, such a strategy can further facilitate the embedding of AI in rural development, preparing the ground for future research and policy development.

Abstract: The integration of Machine Learning (ML) into autonomous spraying systems (1) is one of the major developments in digital precision agriculture (2) that is significantly improving resource efficiency, sustainability, and production. This study looks at current advancements in machine learning applications for automated spraying in agricultural mechanization (3), emphasising new innovations, difficulties, and prospects. The study provides an in-depth analysis of the three main categories of autonomous sprayers—drones, ground-based robots, and tractor-mounted systems—that incorporate machine learning techniques. A comprehensive review of research published between 2014 and 2024 was conducted using Web of Science and Scopus, selecting relevant studies on agricultural robotics (4), sensor integration, and ML-based spraying automation. The results indicate that supervised, unsupervised and deep learning models increasingly contribute to improved real-time decision making, performance in pest and disease detection (5) as well as accurate application of plant protection products. By utilising cutting-edge technology like multispectral sensors, LiDAR, and sophisticated neural networks, these systems significantly increase spraying operations' efficiency while cutting waste and significantly minimising their negative effects on the environment. Notwithstanding significant advancements, issues still exist, such as the requirement for high-quality datasets, system calibration, and flexibility in a range of field circumstances. This study highlights important gaps in the literature and suggests future areas of inquiry to develop ML-driven autonomous spraying even more, assisting in the shift to more intelligent and environmentally friendly farming methods.

Abstract: Agriculture is responsible for 37.8% of Ireland’s greenhouse gas (GHG) emissions, with a target to reduce overall emissions by 42% by 2030. For this, aligning with the Climate Action Plan and the European Green Deal, many agri-environmental schemes have been employed. Several models are also available for accounting and reporting but mostly fail to report on a mixed farm level. HOLOS-IE, an agricultural system-based digital platform enabling the simulation of the sectoral GHGs and net farm carbon footprint, is in development to combat this. To ensure the model's development, we engaged with various stakeholders through interviews, workshops, conferences, and online surveys. These engagements revealed several key points, including improving soil health, offering financial support for transitioning to sustainable practices and adopting biodiversity initiatives to improve farms’ environmental impact. When discussing the HOLOS-IE model, 84.13% of stakeholders thought it would be beneficial, 9.52% were neutral, and 6.35% did not believe it would be helpful. Stakeholders recommended enhancing accuracy, improving user-friendliness and accessibility, considering profitability, and ensuring data confidentiality to improve the model. These will guide the model’s refinement, focusing on developing a reliable, user-centred tool to assess farm carbon footprint, and report on and plan climate-resilient agricultural practices.

Abstract: Brazil is globally recognized for its abundance of water. However, the geographical distribution of its population and the environmental, socioeconomic and cultural differences between its regions lead to more complex water management. Furthermore, climate change has posed additional challenges that require more modern and committed management. The north of the country, known for its humid climate and for hosting the largest river in the world (Amazonas), was impacted in 2024 the worst drought in its history. The implementation of water reuse infrastructure to guarantee regional water security has already been considered innovative, in the past. Currently, more assertive actions must also involve the transfer of responsibilities in relation to the impacts and expected risks from climate change, losses resulting from disasters, in addition to their prevention. Innovative climate insurance is gaining prominence as it finances loss prevention measures and creates resilience to climate risks arising from severe droughts. The international scientific community indicates that Brazil still does not have studies to address these issues. The present study was carried out to confirm this hypothesis. The results present an important gap to be explored by the national scientific community, as well as other developing countries that present a similar scenario.

Abstract:

Lithium-cesium-tantalum (LCT) pegmatites account for circa one-third of global lithium resources and two-thirds of global lithium production. Western Australia, the world's largest supplier of hardrock lithium ores, and Ontario, an emerging lithium mining jurisdiction, have significant endowments that will be critical to the ‘green revolution’ given the predicted transition to lithium-based electromobility. In addition, both jurisdictions show excellent potential for future lithium discoveries given they cover large areas of favorable geology that, by and large, have recorded only limited lithium exploration. Here, we developed holistic LCT pegmatite targeting models for these important jurisdictions, informed by a detailed review of this deposit type and framed in the context of a mineral systems approach. Artificial intelligence (AI)-powered mineral potential modelling (MPM), using multiple, complimentary techniques and guided by the mappable elements of the LCT pegmatite genetic model, not only delivered the first regional scale views of lithium potential across the Archean to Proterozoic terrains of Western Australia and Ontario but also delivered compelling targets for future exploration and though-provoking insights, such as the statistically verifiable proximity relationship between lithium, gold and nickel occurrences. Overall, this study also served to demonstrate the power of precompetitive, high-quality geoscience data, not only for regional scale targeting but also the development of camp-scale targets that are concise enough to be investigated using conventional prospecting techniques.

Abstract: The Dual Process Model of Coping with Bereavement (DPM) is a well-known framework in contemporary grief research and counselling. It depicts how mourners oscillate between various tasks and reactions. There’s a need to engage more with the intense feelings of loss (Loss-Oriented tasks), but also with other things in life and other parts of the adjustment process after a loss (Restoration-Oriented tasks). This interdisciplinary article applies the framework to ecological grief and extends it to collective levels. While the DPM has been broadened to family dynamics (Stroebe and Schut 2015), many subjects of grief are even more collective and require mourning from whole communities or societies. Religious communities can be in an important role in this. The article provides a new application called the DPM-EcoSocial and discusses the various tasks named in it, which are ultimately based on grief researcher Worden’s work. The particularities of ecological grief are discussed, such as the complications caused by guilt dynamics, climate change denial, attribution differences about climate disasters, and nonfinite losses. Grief and grievance are intimately connected in ecological grief, and (religious) communities have important tasks for remembrance, mourning, and witness. The collective processes can lead to meaning reconstruction, transilience, and adversarial growth.

Abstract:

Greenhouse gas accounting conventions were first devised in the 1990’s to assess and compare emissions. Several assumptions were made when framing conventions that remain in practice, however recent advances offer potentially more consistent and inclusive accounting of greenhouse gases. We apply these advances, namely: gross accounting of CO₂ sources; linking land use emissions with sectors; using Effective Radiative Forcing (ERF) rather than Global Warming Potentials (GWPs) to compare emissions; including both warming and cooling emissions, and including loss of additional sink capacity (LASC). We compare these results with conventional accounting and find that this approach boosts perceived carbon emissions from deforestation, and finds agriculture, the most extensive land user, to be the leading emissions sector and to have caused 60% (32%-87%) of ERF change since 1750. We also find that fossil fuels are responsible for 18% of ERF, a reduced contribution due to masking from cooling co-emissions. We test the validity of this accounting and find it useful for determining sector responsibility for present-day warming and for framing policy responses, while recognising the dangers of assigning value to cooling emissions, due to health impacts and future warming.

Abstract:

Drought has crucial impacts on socioeconomic sectors. Unlike other natural hazards (e.g., flood), drought has a creepy nature (initiates, propagates, and terminates gradually). Drought has significant impacts on socioeconomic sectors, yet its gradual and prolonged nature makes its effects challenging to quantify, particularly on financial markets. Consequently, its impacts on the stock market have been poorly quantified. This study aimed to analyze the relationship between drought severity, calculated using the Drought Severity and Coverage Index (DSCI), and stock performance in key sectors, including agriculture, water management, industrial manufacturing, and food services. Data for drought and stock market in the U.S. were obtained from the U.S. drought Monitor (USDM) and Nasdaq, respectively. Using Pearson correlation coefficients, we examined the relationship across monthly, quarterly, and yearly periods, considering no lag and other different lag times up to five years to account for delayed effects. The results revealed weak to strong correlations between drought severity and stock indices, varying with drought intensity and sector-specific characteristics. In particular, the agriculture and water management sectors showed strong negative correlations, with peak impacts observed after a 3-year lag. In contrast, industrial and food service sectors displayed weaker correlations due to their global operations and diversified supply chains. The weak correlation can be attributed to the impacts of the COVID-19 pandemic, as drought trends generally align with stock market performance, but this relationship was disrupted in 2019. These findings fill a critical gap in understanding the economic consequences of drought on financial markets and offer valuable information for forecasting stock market trends and helping businesses, investors, and policymakers better understand and address the financial risks associated with droughts.

Abstract: This study explores the creation of a digital twin for Kyrenia, Cyprus, focusing on the challenges and opportunities presented by this innovative urban planning tool. We discuss the integration of real-time data, citizen engagement, scalability, environmental sustainability, and the economic and social impacts of implementing a digital twin. The research identifies key gaps in current literature and proposes future research directions to enhance the utility of digital twins in urban development.

Abstract: Understanding the changes in land use and land cover (LULC) in national parks and their corresponding ecosystem service value (ESV) shifts are crucial for shaping future management policies and directions. However, comprehensive analyses in this research area that integrate tourism development perspectives are lacking. Therefore, this interdisciplinary study considers Akan-Mashu National Park in Japan as a case study. Using remote sensing data, LULC maps for the past 10 years were generated using the Google Earth Engine. The benefit transfer method was employed to calculate the corresponding ESV for each year, followed by a qualitative analysis of local tourism policy documents to explore how the park ecosystem has changed in the context of promoting tourism development. The results showed that LULC changes in Akan-Mashu National Park have been relatively stable over the past decade, with the most noticeable changes occurring in built-up areas. The results also confirm that tourism development has not had a significant negative impact on the ESV of the Akan-Mashu National Park. The recommendations proposed in this study can also be applied to other similar national parks or protected areas worldwide to achieve a dynamic balance between environmental protection and tourism development.

Abstract: Microplastics and electromagnetic/magnetic fields (EMF/MF) represent two emerging environmental health concerns. Microplastics are widely distributed in the environment and have permeated human tissues, including the bloodstream, where their presence can provoke toxicological effects, such as inflammation and oxidative stress. Meanwhile, household EMF/MF exposure derives from the increased use of electronic devices and is linked to physiological stress and cellular disruption, with possible long-term health risks. For instance, research is being carried out on the effect of microplastics and EMF/MF on human health, but these are being done in isolation. The present review deals with the possible synergy between microplastics present in the circulatory system and exposure to EMF/MF. Despite a direct study of this interaction not being conducted, separate studies on their influences suggest that the way of interaction goes through oxidative stress and inflammation. The paper describes relevant biological mechanisms, gives an overview of the currently available evidence, and highlights the deficiency in research regarding the joint effects of microplastics together with EMF/MF on human health.