Earth and Environmental Sciences

Abstract Climate change is amplifying climate-sensitive health risks in the Mediterranean, yet public understanding of these threats remains poorly characterized. This study assessed how adults in Greece perceive the importance and health impacts of climate change at social levels and how perceptions differ by sociodemographic characteristics. A cross-sectional survey was conducted in Greece in 2023, when the country was severely affected by severe floods and wildfires. Using proportional quota sampling, 406 adults completed a questionnaire on climate change beliefs, worry, perceived importance and perceived impacts on 13 climate-sensitive health outcomes. Associations with sex, age and education were examined using logistic regression. Almost all respondents were at least somewhat sure that climate change is occurring (95%) and reported being very or somewhat worried (93%). Women had higher odds than men of being very worried (adjusted odds ratio [aOR] = 2.63; 95% CI: 1.64–4.28) and perceiving moderate or great harm to themselves, their community, their country and future generations (aORs = 2.07–2.33). Higher education was associated with greater worry and perceived community harm, and postgraduate education with perceived harm to future generations. Older age was associated with higher perceived community harm in adjusted models. Regarding specific health outcomes, harm from wildfires (74.0%), harm from storms and floods (61.7%) and heat-related illness (50.9%) were most reported as already affected a great deal by climate change and expected to worsen in the future. In contrast, perceived impacts were more modest and uncertainty higher for vector-borne and foodborne diseases, healthcare disruption, hunger and violence or conflict. Concern about climate-related health impacts is high, especially among women and more educated adults. However, gaps in understanding of specific health risks persist. Targeted, equity-oriented climate–health communication, integrated into adaptation planning, is needed to strengthen understanding and support for public health action.

Abstract Numerous studies have compared public perceptions of climate and environmental change with instrument-derived meteorological records, yet methodological inconsistencies have limited clarity on the degree of alignment between the two. This systematic review synthesizes 204 peer-reviewed articles published between 2010 and 2025 to assess (i) how studies define and use perception recall periods, (ii) the temporal coverage and type of instrumental climate data used and (iii) the methods used to compare perceived and observed trends. Most studies focused on rainfall and temperature and were concentrated in Africa and Asia. We find that triangulation-based comparisons overwhelmingly dominate literature, with only a small subset employing statistical tests capable of assessing the strength or significance of alignment. Across studies, temporal mismatches between perception recall periods and climate data were common, and more than 100 studies did not report recall periods at all. Despite these limitations, many studies reported alignment between perceptions and instrumental records. Overall, our review recommends clearer reporting standards and broader adoption of statistical approaches that explicitly align respondents’ perception recall periods and duration lived in the study region with instrumental data to produce more robust and comparable assessments of perception–climate relationships.

Abstract This study presents a physics-guided CFD–machine learning (ML) framework for predicting cavitation damage on ogee spillways with ski-jump buckets. High-fidelity 3D CFD simulations, validated against experimental data, were used to generate a dataset of 769 cavitation index (Cv) samples under diverse hydraulic conditions across four prototype spillways in Vietnam. Thirteen regression models, including linear, shallow nonlinear, and advanced ensemble learning algorithms, were systematically evaluated. The results demonstrate that boosting-based models, particularly LightGBM and CatBoost, outperform other approaches in terms of accuracy, robustness, and generalization. Feature importance analysis using permutation importance and SHAP consistently identifies the nondimensional surface coordinate (X/L) as the dominant factor governing cavitation development, highlighting the critical role of spillway geometry. Furthermore, partial dependence analysis reveals a physically interpretable threshold behavior of flow intensity (F ≈ 4.6), corresponding to a transition from acceleration-dominated to aeration-controlled regimes. The predicted cavitation-prone zones show strong agreement with CFD results. Owing to its computational efficiency and physical interpretability, the proposed framework provides a reliable decision-support tool for cavitation risk assessment, design optimization, and long-term safety management of spillway systems.

Co-production of climate services is widely recognised as a valuable way to integrate relevant and reliable climate information into decision-making contexts. Yet the extent of information and evidence that may be relevant to a climate-sensitive decision can be very large, and it is not always feasible to involve all stakeholders throughout the process of constructing climate information. Pragmatic approaches are required, particularly in resource-constrained contexts to ensure scientifically defensible climate information can be provided to guide adaptation decisions. The Climate Information Distillation Framework (CIDF) is a multidisciplinary approach that builds on recent research with the aim of supporting climate service development across a range of applications, sectors and contexts. Here we apply the theoretical framework to the Nepal hydropower sector. Focusing on present day uncertainties and projected future changes to extreme rainfall, we show that a CIDF can be applied in situations where opportunities for co-production are limited but where there is a need to consider diverse perspectives and objectives. The benefits and challenges of using this framework are discussed, highlighting that while climate information rarely dominates in adaptation decision-making processes, appropriate framing, synthesis, transparency and communication of uncertain climate information can valuably support adaptation decisions and policy. Providing the case study of hydropower development in Nepal, within a complex economic and development context, we find that applying the theoretical framework must be done with humility and flexibility to support real-world decision-making.

A substantial body of evidence exists demonstrating that exposure to per- and polyfluoroalkyl substances (PFASs) poses a risk to human health. Data from epidemiological studies of those exposed occupationally or environmentally demonstrate adverse health risks, and these health effects are concordant with data from toxicological studies. Systematic reviews, conducted by agencies, organizations, and independent scientists that synthesize and integrate these data streams, have concluded that a range of health risks arise from PFAS exposure, including different types of cancer, especially kidney and testicular cancer, metabolic alterations such as increased liver enzymes and increased cholesterol, immune dysfunction such as reduced vaccination efficiency, reproductive and developmental outcomes such as low birth weight and reduced duration of breast feeding, and forms of endocrine disruption. Despite this, myths and misinformation surrounding these health risks slow efforts to protect public health from the hazards of PFAS exposure. This work addresses the most predominant of these myths and counters them with accumulated evidence from epidemiological and toxicological studies, demonstrating that exposure to PFASs poses a risk to human health.

Abstract Shallow groundwater (SGW) is an additional water source for low-income farmers in water-scarce regions, yet its potential remains underexplored compared to surface-water resources, particularly at small scales. This study adopts a participatory approach to analyze the use of SGW for agricultural activities, the techniques employed, and the factors that facilitate or hinder its effective utilization in Zambezia, a Province in central Mozambique. Data were collected through interviews, field visits, and open discussions with members of farmer groups across six communities in the province. The findings reveal that the communities rely on SGW for both agricultural and domestic purposes, primarily by digging traditional shallow dug wells. However, limited water availability during the dry season constrains the utilization of SGW aquifers, concentrating agricultural production in the rainy season to satisfy crop water requirements. Additionally, limited technical and financial resources restrict the use of advanced techniques such as well casing, lining, and mechanized augering, which are essential for safer and deeper well construction. This limitation prevents access to larger volumes of groundwater. To increase SGW utilization, it is essential to adopt techniques that enable safer and deeper excavations and to invest in alternative methods that improve the structural integrity and protection of wells.

Abstract Soil fertility degradation has become a major challenge resulting in low crop productivity and food insecurity in sub-Saharan Africa. Indigenous scattered trees such as Acacia abyssinica, Albizia schimperiana, Croton macrostachyus and Olea africana are purposively maintained on farmlands to improve soil fertility and crop productivity. However, there has been limited evidence on the effects of those trees on soil properties. Hence, the study aimed to examine effects of A. abyssinica, A. schimperiana, C. macrostachyus and O. africana trees and radial distances on soil physicochemical properties in Baskura Watershed, Northwest Ethiopia. Soil samples were collected from 0-20 cm depth at three radial distances (under canopy, canopy edge and outside the canopy) of each tree trunk. The collected data was analyzed using analysis of variance. Results revealed significant (p<0.05) differences in soil pH, soil organic carbon (SOC), total nitrogen (TN), available phosphorus (Av P), cation exchange capacity (CEC) and exchangeable potassium (Ex K) among tree species and radial distances. The average values of soil pH (6.36 and 6.25), SOC (3.46 and 2.93%), Av P (10.42 and 8.66 mg kg-1), CEC (38 and 36.66 cmol kg-1) and Ex K (2.34 and 2.31 cmol kg-1) were higher under the canopies of C. macrostachyus and A. abyssinica, respectively. However, the highest TN (0.46%) was recorded under the canopy of A. abyssinica. Moreover, soil nutrients decreased with increasing distance from the tree trunks. The contribution of tree species to soil properties improvement follows the order C. macrostachyus>A. abyssinica >A. schimperiana >O. Africana. Hence, integrating indigenous trees such as C. macrostachyusand and A. abyssinica on croplands can improve soil properties and nutrient availability. However, further study need to be conducted to examine effects of tree species on soil properties, litter quality and decomposition dynamics under different agroecological conditions.

ABSTRACT A clean, well‐organized and comprehensive dataset developed in accordance with the FAIR principles (Findable, Accessible, Interoperable and Reusable), provides a solid foundation for a new research initiative and supports open science. In this context, also legacy, deep boreholes data represent a valuable and unrepeatable source of geological and geophysical information to recover. This article presents the digitalization of legacy well documentation originally available only as scanned images, resulting in a dataset of 30 files, derived by an accurate digitalization workflow carried out on exploration boreholes documentation. The latter was collected across the offshore area between Pesaro‐Fano in the Adriatic Sea, a region that has experienced significant seismic activity, including the 9 November 2022 Mw 5.5 Fano‐Pesaro earthquake sequence. Such data were originally gathered as scanned images extracted from vintage raster files (PDF format), publicly available on the Italian ViDEPI Project website ( www.videpi.com ). Despite their findability and accessibility are straightforward, their interoperability and reusability are severely limited, due to variable image quality, non‐editable contents and obsolete stratigraphic nomenclature. Such wells are named from northwest to southeast as follows: Boheme 01, Tamara 01, Pesaro Mare 03, Pesaro Mare 04, Malachite 01, Cornelia 01 and Elga 01. The digitalization was carried out at high fidelity with respect to the original data, reassessing the relevant well stratigraphy by taking into account all the handwritten comments found inside the original images. Only minor reinterpretations based on lithology, depositional environment and age, were made. Some local formation names were updated, aligned and thus correlated with modern and officially recognized regional stratigraphic units. The digitalization also includes spontaneous potential, resistivity and sonic logs of the Tamara 01 and Boheme 01 wells. Such digitized data were successively stored in open and standard formats (CSV and LAS). Nowadays, these datasets are freely accessible and carry substantial significance as a foundation of earthquake studies, for enhancing geological and geophysical models, depicting the stratigraphic, structural and geophysical characteristics of the study area. Notable potential use includes, among others, seismotectonic studies, gas and CO 2 storage, basinal analysis, stratigraphic and paleogeographic investigations within the region. Digitizing such legacy data reduces the risk of data loss, improves modernization and accessibility, supports interoperability and collaboration. It also enables easier quality control and facilitates future reuse, especially when integrated with other data sources such as seismic surveys, production data and reservoir models.

Abstract This study evaluates stage-specific inefficiencies and CO2 reduction potentials in food-waste upcycling into animal feed. Using 43 months of input–output data from two Japanese dry-feed producers, we apply a network data envelopment analysis with a hybrid link structure that reflects the nondiscretionary nature of residue collection and the discretionary use of residues in production. To address seasonal heterogeneity, we incorporate a meta-frontier decomposition that benchmarks each month against best-practice observations within the same seasonal group. The results reveal persistent inefficiencies across both collection and production stages. Substantial theoretical CO2 reduction potentials—primarily associated with heavy oil and diesel use—could be realized through closer alignment with these best-practice months. Sensitivity analyses using alternative link specifications, seasonal definitions, outlier exclusions, measurement-error perturbations, returns-to-scale assumptions, and variable reduction generally support the stability of the main qualitative findings. The integrated framework helps identify stage-specific improvement opportunities and clarify how efficiency gains in upcycling systems may relate to CO2 reductions.

Deep to ultra-deep tight sandstone reservoirs are characterized by low porosity, low permeability, and strong heterogeneity, and their productivity is jointly controlled by geological, engineering, and development factors. However, the high-dimensional nonlinearity of these variables and their complex interactions make it difficult for conventional methods to accurately identify dominant controls and achieve reliable productivity prediction. To address this issue, this study developed an integrated workflow combining data preprocessing, multidimensional correlation-based factor screening, single-factor trend analysis based on simple linear regression, and machine-learning-based prediction. Pearson, Spearman, and Kendall correlation coefficients were jointly employed to identify the dominant controlling factors, and multiple predictive algorithms were systematically compared to select the optimal model for productivity prediction at the single-well level and the block-level sample-set level. The results indicate that stable water cut, shut-in time, fracturing-fluid volume, and oil saturation are the core factors controlling productivity. Among the tested models, XGBoost exhibited the best predictive performance, with overall predictive accuracy exceeding 95%. At the block-level sample-set level, errors in initial productivity prediction were mainly associated with excessively high stable water cut, whereas deviations in first-year cumulative production prediction were closely related to high water cut and insufficient shut-in time. These findings demonstrate that the proposed framework provides an effective tool for productivity evaluation and prediction within the present dataset and development conditions.

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