Abstract: Severe Clear-Air Turbulence (severe CAT) remains a relevant hazard to aviation safety, often occurring without visible atmospheric indicators. This study presents a hybrid forecasting framework that integrates outputs from the Global Forecast System (GFS025) with advanced machine learning (ML) algorithms to predict severe CAT events over Southeast Brazil, within the region bounded by 43°W to 49°W and 19°S to 25°S, from January 2018 to December 2021. To enhance predictive performance and reduce model complexity, a statistically robust dimensionality reduction technique was applied using p-value filtering and False Discovery Rate (FDR) control, resulting in a refined set of 13 physically interpretable predictors. Key turbulence indices, such as Ellrod’s index (ELL2) and Brown’s index (BROWN), emerged as the most relevant features for classification. Nine ML algorithms were tested and evaluated through Receiver Operating Characteristic (ROC) analysis and Area Under the Curve (AUC) scores. The Multi-Layer Perceptron (MLP) model, with a single hidden layer of 10 neurons, achieved the highest AUC (0.95), followed closely by Random Forest (0.94), demonstrating the effectiveness of relatively simple architectures when coupled with feature selection. These findings underscore the value of combining physically consistent diagnostics with data-driven methods for regional severe CAT forecasting. The proposed approach offers a scalable and adaptable framework that supports enhanced aviation safety and provides a solid foundation for the continued development of operational turbulence prediction tools.

Abstract: Ocean surface heat fluxes play a significant role in the development of various weather and climate phenomena at various spatial and temporal scales. The initial development of the Cyclone Global Navigation Satellite System (CYGNSS) Ocean Surface Heat Flux Product, shortly after the satellite mission began, became a valuable tool for analyzing and observing latent and sensible heat fluxes over the tropical and subtropical oceans, aiding in analyzing their impact on tropical and extratropical cyclones, tropical convection, atmospheric rivers, and more. While in-situ measurements from buoys and flux towers remain the preferred observational tool and standard for estimating ocean surface heat fluxes, satellite products like CYGNSS can fill gaps where in-situ observations are lacking and provide higher spatial resolution observations than reanalysis datasets. This paper describes the updates and changes made to the CYGNSS Fluxes since its initial development, how the current dataset compares to in situ data, and CYGNSS’s long-term observations of ocean surface heat fluxes over the tropical and subtropical oceans.

Abstract: Environmental services of mires and peatlands and negative impacts of their alteration are comprehensively documented. A spatial detection of these organic soils is therefore essential. This paper examines the detectability of organic soils in forests using open ge-ospatial and remote sensing data combined with mapped soil and water level infor-mation in two Random Forest (RF) approaches. Either surrounded in or covered by for-est, organic soils of the study region exhibit elevated soil water content reaching satura-tion during the hydrological winter. Consequently, terrain indices from Digital Elevation Models (DEM) and soil moisture from L-band ALOS PALSAR signals are used as pre-dictors in RF algorithms. CORINE Land Cover data help assess how different forest cov-er types (FCT) influence RF models. Substantial agreement is reached in the target classi-fication when FCT is included and when using the higher spatial resolution DEM. The Boolean approach is less affected by different compositions of predictor variables, but is more sensitive to the level of imbalance in the reference data. This becomes evident when comparing the “event error” and “no event error”. In all RF models, the variable importance of soil moisture pixel values retrieved from L-band ALOS PALSAR is the highest when FCT is not in-cluded.

Abstract: Rationale: The Niger Delta, one of the world’s most resource-rich regions, has long borne the negative impacts of oil extraction, resulting in persistent pollution, ecosystem collapse, and infrastructural neglect. These issues, while well-documented, are rarely framed as a public health emergency requiring systemic intervention. The region’s environmental degradation is not merely an ecological concern but a slow-moving public health crisis, driven by governance failures, regulatory fragmentation, and economic inequalities embedded in the extractive system. Objective: This study aims to reframe environmental degradation in the Niger Delta as a public health emergency and propose the Integrated Environmental-Health Accountability Framework (IEHAF) to address the multi-dimensional health impacts of pollution. The objective is to offer a new approach that links ecological damage to morbidity, mortality, and socioeconomic hardship, while emphasizing interdisciplinary research and policy reform. Method(s): Drawing from environmental epidemiology, hydrochemistry, biodiversity loss, and health systems research, this paper synthesizes existing knowledge to develop the IEHAF framework. It focuses on three domains: (1) environmental toxicity and ecological collapse, (2) human exposure, vulnerability, and adaptation, and (3) institutional accountability and policy inertia. These domains guide an analysis of how pollution exposure leads to health inequities and long-term socioeconomic consequences. Results: The IEHAF framework emphasizes that pollution is not only an environmental issue but a central determinant of health inequity. By linking environmental, health, and economic systems, the framework provides a holistic view of the long-term effects of extractive practices. It reveals how institutional inertia and policy fragmentation have worsened the crisis, deepening intergenerational poverty. Conclusions: Reframing environmental degradation as a public health emergency is vital for shifting policy responses from reactive to proactive. IEHAF offers a structured approach to integrate environmental governance, health surveillance, and social protection, essential for sustainable development in the region. Recommendations: Policymakers must adopt IEHAF in regulatory frameworks, integrate health outcomes into extractive licensing, and ensure systematic health screenings for affected communities. Researchers should prioritize long-term evaluations of IEHAF, while civil society should advocate for community-led environmental monitoring and corporate accountability. Significant Health Statement: Chronic exposure to pollutants in the Niger Delta leads to long-term health burdens, including gastrointestinal disorders, respiratory diseases, and neurological damage. The IEHAF framework emphasizes the need for early intervention, health surveillance, and integrated policies to protect vulnerable populations, making the recognition of pollution as a central health determinant both a policy necessity and a moral imperative.

Abstract: This study presents a comparative evaluation of Environmental Product Declarations (EPDs) within the traditional ceramic industry, emphasizing the intersection of energy use, environmental performance, and policy-relevant data structures. Four product categories—ceramic tiles, sanitary ware, clay bricks, and clay roof tiles—were analyzed using datasets from One Click LCA and the International EPD System. Environmental indicators assessed include fossil-based and total Global Warming Potential (GWP), freshwater consumption, and energy demand, standardized per 1 kg of product. The analysis reveals significant discrepancies in data provenance and methodological consistency across platforms, with One Click LCA offering harmonized datasets for tiles and sanitary ware, while the International EPD System permits variable sources, particularly evident in brick EPDs. These inconsistencies hinder comparability and dilute the strategic value of EPDs in guiding low-carbon market transitions. The study highlights that energy-intensive production stages in tiles and sanitary ware contribute to elevated environmental burdens, underscoring the need for harmonized software tools, transparent reporting formats, and standardized background databases. Confidentiality regarding energy sources and firing temperatures remains a barrier to optimization, while the absence of fossil fuel-specific GWP guidance and high EPD development costs limit broader market uptake. By expanding comparative assessments across production sites and product types, the research supports the development of robust benchmarks and tailored Product Category Rules (PCRs), reinforcing the role of EPDs as actionable instruments in energy policy and sustainable market design.

Abstract: Field-scale nitrogen migration mechanisms in small watersheds remain poorly quantified due to insufficient representation of microtopographic heterogeneity. This study investigates nitrogen transport dynamics in a 1.27 km² agricultural watershed in China's Jianghuai region using UAV-derived 0.1 m DEMs and coupled hydrological-erosion modeling. The SCS-CN and MUSLE models quantified nitrogen output loads, while the multi-flow direction algorithm simulated migration trajectories for total nitrogen (TN), ammonium, and nitrate. Results revealed strong spatial heterogeneity in nitrogen exports (watershed mean: 29.66 kg TN/km²·a), with bare land and greenhouses exhibiting the highest outputs (448.54 and 363.41 kg/km²·a) and forested areas showing minimal loss (<6.1 kg/km²·a). Nitrogen migration was predominantly controlled by topographic gradients, with microtopographic features—field ridges, ditches, and buildings—physically redirecting flows and creating critical export nodes at field boundaries. DEM resolution critically affected simulation accuracy: erosion intensity displayed a non-monotonic response with an inflection point near 1 m resolution, corresponding to the median elevation difference (1.2 m) of field ridges. Structural equation modeling confirmed that high-resolution DEMs (0.1–2 m) maintained topographic control over nitrogen migration (~80% contribution), whereas 30 m DEMs reduced this influence to 30%, inducing spurious meteorological dominance. This study demonstrates that decimeter-scale DEMs are essential for accurately capturing microtopographic regulation of nitrogen transport, providing a methodological basis for precision management of agricultural non-point source pollution.

Abstract: This article addresses the limitations of traditional petrophysical interpretation and lithofacies analysis methods used in commercial software solutions, such as sub-jectivity, insufficient detail, and reliability, particularly in cases of complex reservoir structures. Accordingly, the development of automated lithofacies analysis tools using Artificial Intelligence (AI) and Machine Learning (ML) is a relevant objective for en-hancing the reliability of geological modeling and reservoir evaluation. The authors have developed an innovative methodological approach for auto-mated lithofacies classification of well logging data, demonstrated via case study of Gran Field. The methodology is centered on the k-means unsupervised clustering al-gorithm, specifically adapted for comprehensive petrophysical data analysis. It is demonstrated that the proposed approach effectively partitions the geological section into lithofacies and ensures the reliability of petrophysical interpretation re-sults. The optimal number of clusters (k=3) was determined using the Silhouette Coef-ficient, and the results were visualized using the Principal Component Analysis (PCA) method, confirming that the identified groups correspond to petrophysical patterns. The clustering results, incorporating PCA, showed clear separation into clay, silt-stone, and sandstone lithofacies. The k-means-based approach mitigates the primary limitations of traditional methods reliant on the subjective selection of cut-off values and forms a reliable foundation for building advanced geological and hydrodynamic models. To facilitate practical application, a Python-based web interface was developed using the Streamlit framework. This application offers a user-friendly interface for preprocessing well-log data, performing clustering, and visualizing results, bridging the gap between advanced ML algorithms and specialists without programming ex-pertise. Comparative analysis reveals that the k-means algorithm outperforms alternative methods across several key metrics, notably in interpretability and the structural co-herence of the results. Future development prospects include the integration of densi-ty-based clustering algorithms, such as DBSCAN, to increase the system's adaptability in complex geological sections. This will open new possibilities for intelligent analyti-cal systems in the field of reservoir evaluation and resource assessment.

Abstract: Urban flood modelling in infrastructure-dense and heavily modified catchments requires enhanced process realism, operational applicability, robust diagnostic and scenario-based evaluation to reliably capture complex system interactions and support decision-making under extreme and failure conditions. This study employs a TELEMAC-2D rain-on-grid approach to simulate pluvial flood dynamics in two urban sub-catchments of the Emscher River (North Rhine-Westphalia, Germany). A stepwise model development and calibration workflow is implemented, combining and adjustments of land-use-based roughness, re-finement of SCS Curve Numbers, and the progressive integration of key hydraulic and op-erational components, including culverts, bridges, retention basins, and pumping stations. Model performance is evaluated based on hydrograph shape and volume, peak discharge and its timing, and inundation extent, with a specific focus on the relative contributions of (i) surface parameter calibration (friction coefficient-Manning’s n and run-off- Curve Numbers), (ii) explicit representation of hydraulic structures, and (iii) operational control rules under varying rainfall scenarios and antecedent moisture conditions (AMC). The analysis tests the hypothesis that structural and operational realism can contribute as much as traditional surface calibration to improve model performance and that their effec-tiveness is strongly influenced by prior wetness. Results shows that including retention basins and pumping stations along with operational rules significantly improves agree-ment with observed discharge. It shows systematic sensitivity and improvements across AMC scenarios with NSE values from -0.129 to +0.77, RMSE from 3.380 to 1.52 m³ s⁻¹, peak discharge errors from −6.20 to −0.49 m³ s⁻¹, and volume bias from −0.67 to +0.04. This shows that even with careful calibration of surface parameters (e.g., roughness and runoff coefficients), models that exclude infrastructure (e.g., pumps and retention basins) fail to accurately reproduce peak flows and recession behaviour. A targeted routing-focused cali-bration (R4) further reduced the remaining peak timing mismatch under saturated condi-tions, but introduced increased volume bias, indicating that residual discrepancies are primarily linked to simplified representation of fast urban conveyance pathways rather than surface parameterisation alone. Flood response is not determined by rainfall alone. Initial wetness and how infrastructure is operated can strongly and unpredictably change flood behaviour. Overall, the findings emphasise that for a complex and engineered urban environment, reliable urban flood simulations requires the combined consideration of hydrodynamic processes, hydrological initial conditions, and operational behaviour. The study provides practical guidance on the limits of calibration-only approaches and identifies when explicit representation of infra-structure and operational processes is essential for robust modelling.

Abstract: Wastewater treatment plants (WWTPs) are increasingly considered a key and importante infrastructure for environmental protection and combating climate change in regions suffering from severe water scarcity. This work aims to provide a comprehensive and integrated evaluation of the performance of WWTPs in arid and hyperarid contexts, based on two representative experiences in the Algerian Sahara. The evaluation is based on an analysis of treatment performance (COD, BOD₅, TSS), operational stability, and the agricultural suitability of the wastewater (electrical conductivity, SAR, RSC), in addition to the indirect effects on groundwater protection. The results show high and stable organic matter removal rates (>85-90%), demonstrating the effectiveness of biological processes under harsh and hostile climatic conditions. Despite this, residual salinity and sodium carbonate remain the two main factors limiting the extent of long-term agricultural reuse, even with effective treatment. The international comparative analysis highlights the systemic nature of this separation in hyperarid environments and seeks to confirm the need to consider wastewater treatment plants as truly integrated environmental barriers.

Abstract: Early warning systems support hazard detection, forecasting, and communication, yet U.S. Gulf Coast warning practice often treats hazards, coastal ecosystem change, and vulnerability constraints as separate domains. This paper maps operational early warning systems for climate hazards across Louisiana, Texas, Mississippi, Alabama, and Florida, and assesses whether ecosystem protective functions and social vulnerability are integrated into warning thresholds, dissemination design, and response planning. Web of Science Core Collection and Scopus were searched (timespan limit 2020 to 2026), alongside targeted searches of NOAA/NWS/NHC, FEMA IPAWS, CDC/ATSDR Social Vulnerability Index, IOOS (GCOOS), USGS, and state coastal agencies including CPRA. Searches ran from 15 September 2025 to 18 January 2026. Three independent reviewers screened records and resolved disagreements by consensus. Data were charted using a standardized matrix covering hazard focus, geography, lead organizations, products, dissemination channels, ecosystem indicators, equity features, and governance arrangements. 861 records were identified; 440 duplicates were removed and 421 abstracts were screened. Full text was unavailable for 300 records, leaving 121 reports assessed for eligibility. Ninety were excluded for lacking U.S. Gulf Coast focus and six Spanish language reports were excluded, resulting in 25 sources for charting and synthesis. Socio-ecological integration was inconsistently operationalized, with uneven documentation of how ecosystem condition and vulnerability constraints informed thresholds, accessible messaging, and preparedness supports. End-to-end warning effectiveness can be strengthened through interoperable interfaces between monitoring programs, warning operations, and emergency management, paired with equity and accessibility workflows that translate forecasts into feasible protective actions.

Abstract: Plastic pollution constitutes a critical planetary health challenge, undermining the integrity of Earth systems while generating cascading harms to human health, livelihoods, and social equity particularly in low- and middle-income countries. Conventional top-down regulatory and technological responses have proven insufficient to address the complexity of plastic pollution, often excluding those most affected from decision-making and solution design. This paper examines how democratizing plastic governance through grassroots leadership can advance planetary health by simultaneously protecting ecosystems, improving human well-being, and strengthening socio-ecological resilience. Drawing on empirical evidence from the #RestorationX10000 initiative led by Community Action Against Plastic Waste (CAPws), this paper documents implementation processes and outcomes achieved between 2021 and 2025 across 71 impacted communities in 21 countries spanning Africa, Asia-Pacific, and Latin America. The initiative was designed to empower 10,000 youths and women as community leaders, practitioners, and advocates by equipping them with leadership, technical, and policy engagement skills to drive systemic change in plastic governance and circular economy practice. Using a transdisciplinary, community-based action research approach aligned with planetary health principles, the initiative integrates capacity building, citizen science, circular economy interventions (collection, sorting, repair, reuse, repurposing, and recycling), and policy advocacy. Quantitative and qualitative evidence demonstrates that grassroots-led interventions can simultaneously reduce plastic leakage, create decent green livelihoods, and strengthen environmental governance. We argue that inclusive, community-centered plastic governance is not only an environmental intervention but a planetary health strategy, offering policy-relevant insights for national plastic action plans, extended producer responsibility frameworks, and global negotiations toward a legally binding instrument on plastic pollution.

Abstract: This paper translates a GIS-based multi-criteria evaluation (GIS–MCE) developed for the Food and Agriculture Organization (FAO) Hand-in-Hand Initiative (HiH), into a scien-tific manuscript on aquaculture zoning and site suitability in the Republic of Benin. We integrate sub-models on market accessibility, demand (population and asset wealth in-dex), biophysical conditions (water balance, soils, slope), and input availability (crop and livestock systems) to produce suitability indices for three fish farming systems: (i) ex-tensive to semi-intensive small-scale and integrated ponds; (ii) peri-urban intensive closed systems (tanks, ponds, RAS); and (iii) intensive open tilapia cages in waterbodies. A marked south–north gradient emerges, with southern and central communes showing highest suitability for non intensive systems. Priority communes include Bassila, Bantè, Ouessè, Savè, Kétou, Djidja, Agbangnizoun, Zangnanado, Ouinhi, Lalo, Bonou, and Ad-johoun. Intensive closed systems concentrate around Abomey-Calavi, Cotonou, and Porto-Novo due to superior market access. For open cage farming, southern permanent waterbodies—particularly Lakes Ahémé and Nokoué and selected reservoirs such as Zagnanando, Azili, and Ilauko—appear promising, though environmental risks (eu-trophication, pollution) warrant caution. We discuss methodological assumptions, data constraints, and limitations (e.g., absence of formal MCDA methods), and conclude with policy recommendations emphasizing environmental safeguards, support to traditional systems (e.g., whédo), improved feed and seed access, and strengthened biosecurity and monitoring frameworks for sustainable aquaculture expansion.

Abstract: This study investigates ionospheric total electron content (TEC) and differential code bias (DCB) based on GPS and Galileo data collected aboard the training ship (HANS) from DOY 249 to 300 in 2024. The estimated TEC was compared with the Center for Orbit Determination in Europe (CODE) global ionospheric map (GIM), resulting in a mean difference of –2.41 TEC units (TECU) and a root mean square (RMS) error of 6.53 TECU. Furthermore, although inland GNSS stations in South Korea were incorporated to stabilize receiver DCB estimation, the results still exhibited significant temporal fluctuations. The Pearson correlation revealed a moderate negative relationship between the receiver DCB change and the Dst index, with a correlation coefficient (R) of –0.35. It suggests that geomagnetic disturbances influence receiver DCB changes. The R between receiver DCB and the Kp index was approximately 0.18, while that with the F10.7a index was about –0.26, both indicating a relatively weak correlation. In addition, receiver DCB changes exhibited a positive correlation (R ~ 0.33) with vertical TEC (VTEC). Multipath errors showed negligible correlation with receiver DCB changes. Therefore, we suggest that the significant variability in the receiver DCB on the HANS has a strong correlation with the Dst and VTEC. It can also be attributed to the complex interaction of several factors.

Abstract: The Santorini volcano, Greece, attracts global scientific interest and constitutes a top tourist destination. The 17th century BCE eruption, known as the Minoan event, was likely the largest ever occurred in the Holocene. The evaluation of an enriched collection of documentary sources combined with scientific observations showed that during historical times 14 small-to-moderate eruptive episodes were reported from the 2nd century BCE up to 1950 CE. Among them two little-known episodes occurring in 1667 CE and 1773 CE were uncovered and analyzed based on European documentary sources. For the first time a reliability score has been assigned to each one of the 14 episodes. The completeness of the recorded eruption history after the 14th century CE looks like ten times higher than in the previous period but it remains unclear whether this reflects real eruption rate or reporting incompleteness. The eruptions occurring after the 17th century CE are characterized by lower size, in terms of Volcanic Explosivity Index (VEI), than in the previous period. However, this may be due to the incomplete record of earlier eruptions of low VEI magnitude.

Abstract: Addressing climate change and food security, this article evaluates ground silicate rocks (remineralizers) as tools for atmospheric CO2 capture and food and nutrition security. The experiments were conducted under controlled conditions using leaching columns (to quantify the leached carbon) and pots (to evaluate the growth and nutrition of three agricultural crops). Five rock types (basalt, kamafugite, chlorite-muscovite calc-schist, hydrothermalized calc-silicate, and biotite-actinolite schist) were applied to a Red Oxisol (S) at 20 t ha⁻¹, with and without organic matter (OM) at 40 t ha⁻¹. The study involved 84 experimental units, including S, S+R, S+OM, S+R+OM, and S, S+OM and NPK controls. Results showed that rock-OM mixtures significantly improved soil pH and electrical conductivity, enhancing the growth and nutritional content of three agricultural crops (beans, arugula, and carrots) compared to controls. Nutrient dynamics varied across leached liquids and in the soils of the columns and pots. While OM increased leached carbon, the direct influence of silicate rocks on CO2 capture was more pronounced in S+R treatments. The findings underscore the vital role of local, innovative strategies in providing sustainable solutions to global challenges.

Abstract: Soil organic carbon (SOC) is essential for ecosystem stability and long-term carbon storage in alpine grasslands, yet the relative importance and interactions of hydrothermal and biotic controls remain poorly understood at regional scales. In this study, we quantified surface SOC (0–20 cm) across the Yellow River Source Region (YRSR) on the northeastern Tibetan Plateau, a climate-sensitive alpine headwater system characterized by strong hydrothermal gradients and freeze–thaw dynamics. Field-based SOC measurements were integrated with multi-source remote sensing and reanalysis data that describe thermal conditions, moisture processes, vegetation productivity, soil properties, topography, and human influence. A two-step screening approach was applied using Boruta and variance inflation factor filtering, followed by modeling with random forest. The model outputs were interpreted using Shapley Additive Explanations (SHAP). SOC displayed significant spatial heterogeneity across the region. Vegetation productivity, moisture availability, and thermal conditions were identified as the dominant nonlinear drivers of SOC variation. Moisture processes were found to function as a central mediator, regulating SOC both directly and indirectly through vegetation and thermal pathways. These findings highlight the critical role of hydrothermal stability in sustaining soil carbon stocks and provide a quantitative foundation for sustainable grassland management strategies in the face of climate warming.

Abstract: Industrial coastal basins that host heavy industry can concentrate metal-bearing dust in school environments. We performed a screening multi-matrix assessment across six schools in Quintero–Puchuncaví (central Chile). We measured As, Cd, Cr, Cu, Ni, Pb, and Mn in surface soils (2023 winter only), indoor settled dust, and settleable particulate matter (SPM) collected in winter (July 2023) and summer (November 2023). Concentrations were determined by ICP-OES/ICP-MS and interpreted with enrichment factors and the geoaccumulation index. An U.S. EPA screening framework was used to estimate non-carcinogenic hazard (HQ) and incremental lifetime cancer risk (ILCR) for ingestion, inhalation, and dermal contact, as well as general assessment for non-carcinogenic risk (HI) and carcinogenic risk (Risk). SPM carried the strongest anthropogenic signal (EF up to 9900 for Cd, 408 for Cu, and 143 for Pb) and the highest summer loads (Cu >5000 mg kg−1; Ni >1000 mg kg−1). Cu dominated non-carcinogenic hazard (HQ up to 137), whereas ILCR was driven by Ni, As, and Cr, exceeding 10−4 and reaching 10−3 at inland schools in summer. Indoor dust showed intermediate burdens, indicating indoor accumulation of outdoor-derived metals, while soils acted as longer-term reservoirs. Despite the limited sample size, the results motivate emission control, dust mitigation in schools, and targeted follow-up monitoring.

Abstract: The beaded periwinkle (Cenchritis muricatus) inhabits supratidal rocky environments characterized by strong gradients in salinity, desiccation, and hydrodynamic disturbance. Preliminary observations suggested that individuals leave dry rocks more frequently when surrounded by seawater than freshwater, prompting an exploratory investigation of potential environmental cues underlying this behavior. Field-based pilot experiments in which periwinkles were placed on isolated dry rocks surrounded by either seawater or freshwater were conducted, while additional treatments varied rock height, surface char- acteristics, water depth, and salinity. Across experiments, periwinkles migrated away from rocks surrounded by seawater more frequently than those surrounded by freshwa- ter, although effect sizes varied and interactions with other factors were inconsistent. High variance and limited replication constrained statistical inference, and analyses are there- fore interpreted descriptively. Despite these limitations, results suggest that movement is not driven by immediate habitat benefits but may reflect sensitivity to salinity-associated contextual cues linked to large-scale disturbance risk. Escape behavior may be adaptive over long temporal scales associated with storm exposure, even if it appears maladaptive under experimentally constrained conditions. These findings highlight the importance of experimental scale in behavioral ecology and motivate future studies incorporating con- nected substrates, refuge gradients, and field-based validation to better resolve how su- pratidal gastropods respond to changing coastal conditions.

Abstract: Lokoja, the capital of Kogi State, Nigeria, is a rapidly growing mid-sized city located at the confluence of the Niger and Benue Rivers. While this location has driven urban expansion, it has simultaneously increased the city’s exposure to environmental risks, particularly flooding and ecosystem degradation. Despite their growing importance, cities of this scale remain underrepresented in African urban research. Using multi-decadal Landsat imagery (2000, 2010, 2020, and 2024), Random Forest supervised classification, and PyLandStats landscape metrics, this study examines the spatio-temporal dynamics of urban growth and landscape fragmentation in Lokoja. Results reveal a non-linear urban trajectory characterized by rapid expansion (2000–2010), partial consolidation (2010–2020), and renewed densification with intensified fragmentation (2020–2024). Urban land cover expanded from 6,668 ha in 2000 to 19,371 ha in 2010, declined to 12,883 ha in 2020, and increased again to 15,985 ha by 2024, representing a net growth of approximately 140%. Urban expansion has imposed severe ecological costs. Dense forest cover declined by 99.7% (from 373 ha to 1 ha), while woodland areas were reduced by 73.9%. Core habitat declined from 23% to 13.8% of the landscape, falling below the 15–20% threshold associated with ecological functionality. Edge density increased by 121%, amplifying urban heat island effects, surface runoff, and biodiversity loss. Although grassland cover increased by 77.1%, this reflects secondary succession rather than ecological recovery, given an estimated loss of 3,000 ha of original vegetation. The study recommends enforcing development restrictions below 10 m elevation with 100 m riparian buffers, restoring 500 ha of native riparian corridors, mandating a minimum of 20% urban tree canopy cover, and institutionalizing community-based monitoring of green spaces. These findings contribute empirical evidence on the sustainability challenges of mid-sized African cities and offer transferable planning strategies for ecologically sensitive urban regions.

Abstract: This study addresses the critical challenge of optimizing outdoor thermal comfort for the aging population in old residential communities within China's hot-summer and cold-winter climate zones. Against the backdrop of urban regeneration and rapid demographic aging, it investigates how key landscape elements—ground reflectance, greening type, and pergola condition—influence the microclimate of community public spaces. The research employed an integrated methodology centered on numerical simulation. Using the ENVI-met software and an L₉(3⁴) orthogonal experimental design, it simulated the microclimatic effects of nine combined scenarios on typical summer and winter days for a case study in Nanjing. The comprehensive thermal comfort index, Physiological Equivalent Temperature (PET), was used as the primary evaluation indicator to assess the thermal comfort performance for elderly occupants, with the assistance of air temperature, wind speed, and relative humidity, and the results were analyzed via range analysis and ANOVA. The key findings indicate that: (1) Greening type and pergola condition are the dominant factors affecting microclimate and annual thermal comfort across seasons, while ground reflectance has a comparatively minor influence. (2) The combination of deciduous trees with lawn achieves the optimal cross-seasonal PET gain. It provides effective shading and cooling in summer while allowing beneficial solar penetration for warming in winter, substantially outperforming evergreen-dominated configurations. (3) The presence of a pergola consistently enhances comfort by providing essential shade in summer and acting as a windbreak in winter. The combination dominated by deciduous trees + lawn and pergola yield an overall PET gain 1.0967 ℃ higher than that of evergreen trees + shrub without pergola. This study provides evidence-based, elderly-specific landscape design strategies to inform the thermal environment optimization of public spaces in old residential areas undergoing renewal.