Earth and Environmental Sciences

with 92.8% Faradaic efficiency and 97.3% selectivity toward GA, achieving a 6.8-fold enhancement in cumulative GA production over the coupled strategy. The system sustains operation for 300 h with negligible decay. Scalability tests using 100.0 g of real-world PET yielded 81.1 g PTA (93.8% yield) and 23.1 g GA (58.4% yield). Techno-economic analysis estimates a potential profit of ∼US$515.5 per ton of waste PET, demonstrating this strategy as a promising pathway for high-value upcycling of diverse polyester and polyol feedstocks.

Recent literature suggests that climate change can impact sexual and reproductive health and rights (SRHR) outcomes, especially since climate-related events may exacerbate persistent inequalities based on gender, disability status, sexual orientation, and age, among others. Climate change can also impact health infrastructure with an impact on SRHR access and outcomes. However, data are scarce when it comes to certain topical areas, types of evidence, and in using an intersectional approach. Based on a prior expert consultation exercise, we conducted a consensus and priority setting process to develop a list of priority research questions at the intersection of climate change and SRHR. For this, in 2024, we completed an iterative process over three rounds consisting of online surveys and consultations, following modified Delphi and Child Health and Nutrition Research Initiative (CHNRI) methodologies for which 100 people were included. For round one, 56 people responded to the 17-question survey framed around topical areas in SRHR, research methodologies, and intersectionality; 39 people participated in the online consultation. The round two survey had 36 respondents and 41 participants to the online consultation. The third round included a survey with a list of 31 questions that respondents were asked to prioritize. A final list of ten questions emerged which highlighted important areas where there continue to be gaps in evidence, including maternal and perinatal health, contraception and abortion access, and gender-based violence. Other critical areas include intersectional issues regarding gender and poverty and comprehensive sex education. The list can serve as a starting point to guide the SRHR research community to generate the evidence needed for policy action.

This study addresses the persistent challenges of ambiguous oil-water relationships and poorly defined effective reservoir distribution in high-mud-content sandstone reservoirs of the III Oil Group (Zhujiang Formation, Member 1) within the Wenchang X-3 structure. Utilizing high-precision reprocessed 3D seismic data, we integrate detailed well-seismic calibration with sedimentary models to train a deep learning algorithm for the automatic identification of seismic facies characterized by “tidal sand” reflections. This approach enables the rapid and quantitative mapping of three distinct phases of sand, clarifying their boundaries, stacking patterns, and overall sedimentary architecture. Focusing on the prediction of effective reservoirs, the analysis begins with rock particle support and mineral composition. A dry rock frame model is established based on the Hashin-Shtrikman (HS) bounds, deriving quantitative relationships between key elastic parameters (e.g., P- and S-wave impedance) and reservoir properties including mud content, porosity, and oil saturation. This work culminates in the first development of a specialized rock physics template for marine high-mud-content sandstone in this region. To overcome the lack of diagnostic elastic responses, dozens of seismic and geological attributes were screened and optimized using machine learning. An innovative sensitive factors of reservoirs was constructed based on the Vp/Vs ratio and the P-wave impedance (Ip), which field application confirms to be highly accurate for identifying effective reservoirs. This research is the first to reveal a three-phase, superimposed tidal sand system in the Shenhu Uplift, explaining its planar distribution, migration history, and thereby resolving prior oil-water contradictions. The proposed predictor effectively reduces the interpretative complexity of high-mud-content sandstone reservoirs, clearly delineates their 3D spatial distribution, and provides a reliable technical foundation for validating this category of lithologic traps.

Shear strength of rock, characterized by the internal friction angle ( φ ) and cohesion ( c ), is the basis for the design and stability evaluation of foundation, rock slope and underground excavation. Obtaining φ and c through traditional methods such as laboratory or on-site tests is usually time-consuming and costly. This study adopts three algorithm-optimized machine learning models: Particle Swarm optimized (PSO) Support Vector Machine (SVM), Dung Beetle optimized (DBO) Random Forest (RF) and Adaptive Moment estimation optimized (ADAM) Feedforward Neural Network (FNN) to predict φ and c . The inputs of the model are three easily obtainable indices: point load strength index I s(50) , Schmidt hammer rebound number S rn , and ultrasonic S-wave pulse velocity V s , and the outputs are φ and c . The training data of the model comes from the laboratory tests of 63 groups of quartzite specimens in Malaysia. This study also uses R 2 and four error indicators - mean absolute error (MAE), average absolute percentage error (MAPE), root mean square error (RMSE) and relative percentage error (REP) - to evaluate the performance of the model. The results show that the R 2 of the three models is more than 0.85, and the relative error of φ and c is basically less than 10%. Among them, the PSO-SVM model has the best predictive performance, with the highest R 2 and the lowest error index (within 5% relative error). DBO-RF and ADAM-FNN models also have good prediction accuracy. This shows that all three models can predict the rock shear strength under a limited amount of data based on three portable indices, saving costs for engineering projects.

Convolutional neural networks (CNNs) have been widely applied to mineral prospectivity mapping; however, their “black-box” nature limits geological interpretability and practical acceptance. To address this issue, this study proposes an explainable convolutional neural network (Ex-CNN) framework that integrates deep learning with SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP). The Yemaquan iron ore concentration area in Qinghai Province, northwestern China, was selected as a case study. Seven predictive factors of metallogenic prediction factors were constructed, including stratigraphy, intrusive bodies, magnetic anomalies, gravity anomalies, apparent polarizability, and apparent resistivity. These multi-source datasets were standardized into spatial grids and input into the CNN model to capture nonlinear relationships between geological features and mineralization. The results demonstrate that the proposed Ex-CNN model outperforms conventional machine learning and non-explainable deep learning models. Interpretability analysis based on SHAP, permutation importance, and PDP consistently indicates that apparent resistivity is the most influential factor controlling mineralization, reflecting the key role of hydrothermal alteration and structural disruption. Stratigraphy and intrusive bodies provide important geological constraints, while magnetic and gravity anomalies contribute auxiliary information. Several high-prospectivity targets were delineated in underexplored areas, confirming the effectiveness of the proposed framework. This study demonstrates that integrating explainable AI with deep learning significantly enhances both predictive performance and geological interpretability, providing reliable support for intelligent mineral exploration.

This study investigates the mechanical behavior and microstructural evolution of undisturbed Jurica smectite-rich clays from the Querétaro Valley, central Mexico, through consolidated–undrained triaxial testing and scanning electron microscopy (SEM). The clays are classified as high-plasticity (CH) soils, with natural water content of about 30%, porosity of ∼66%, and a mineral assemblage dominated by Ca–and Na–Fe–smectites, with accessory volcanic-derived phases. Triaxial tests were conducted under confining pressures of 147–540 kPa on specimens with natural moisture (Ns) and re-saturated (Rs) conditions, and in both vertical (V) and horizontal (H) orientations. The results suggest that mechanical behavior is anisotropic and depends on confinement and saturation conditions. Vertically oriented specimens tend to exhibit higher strength and stiffness under low to intermediate confinement, although this relationship becomes less systematic at higher confining pressures. Re-saturated specimens show reduced stiffness and increased pore-pressure sensitivity relative to natural moisture conditions. Strength and pre-yield modulus increase with confining pressure, while friction angles tend to decrease at higher pressures, suggesting a progressive transition toward more ductile deformation, associated with pore collapse and particle rearrangement. A certain degree of variability in the results may be attributed to the inherent heterogeneity of the undisturbed smectite-rich clay fabric. SEM observations document stress-induced microstructural reorganization, including pore alignment, folding of flocculated (clay-silt) domains, localized deformation bands, and development of secondary porosity. Based on these observations, deformation is interpreted in terms of two conceptual modes: (i) distributed laminar sliding within clay domains, and (ii) localized shear associated with rigid inclusions and pore collapse. The results suggest a coupling between smectite microfabric and mechanical response, with potential implications for subsidence-prone lacustrine clays of urbanized volcanic-lacustrine basins.

Abstract Shallow moonquakes have been considered unique due to their large magnitudes and affinities with intraplate earthquakes. However, the small number of detections (<80 events) has prevented detailed characterization. In this study, I identified a pair of repeating shallow moonquakes by analyzing a recently updated moonquake data set. Relative‐phase assessment revealed that these events exhibit a consistent fault‐slip direction despite their occurrence at opposite tidal phases. This differs from what was observed for repeating deep moonquakes, which are closely related to tides, implying that tidal stress does not dominantly control fault‐slip initiation of the repeating shallow moonquakes. Also, the identified repeating shallow moonquakes exhibit a similar relationship between seismic moment and the spatial scale of the slip area to earthquakes. This may indicate that earthquake‐like fault physics operates on the Moon, albeit with a different driving mechanism than on Earth.

Cilia-induced vortical flows are critical for regulating oxygen (O 2 ) and metabolite exchange across coral-water interfaces. While this active ventilation affects the coral tissue microenvironment, its role in thermal stress remains poorly understood. Using high-speed imaging of cilia beating, particle image velocimetry with O 2 -sensitive nanoparticles, and a mechanistic transport model, we quantified how ciliary dynamics in the reef-building coral Porites lutea respond to acute warming in darkness. Moderate warming (~35°C) enhanced ciliary activity and advective transport yet paradoxically thickened the concentration boundary layer with O 2 -depleted water, exposing tissues to transient hypoxia. At higher temperatures, ventilation failed to meet rising metabolic demands and anoxic regions expanded rapidly. Above ~37°C, ciliary coordination collapsed and vortical flows dissipated, shifting transport to a diffusion-limited regime accelerating coral mortality. These results identify ciliary beating as a key regulator of thermal tolerance and early indicator of critical physiological tipping points for reef-building corals in a warming, deoxygenating ocean.

Channel cutoff is a fundamental mechanism by which meandering rivers of the Amazon reshape their courses, yet it remains unclear how their frequency and type vary across the river basin. Using satellite images from 1984 to 2021, we identified 1132 cutoffs across all single-threaded channels wider than 90 m. We find that cutoff types are unevenly distributed across the basin: Neck cutoffs ( n = 594) dominate low-slope floodplains, while chute cutoffs ( n = 456) are more common in steeper regions. Bimodal distributions of cutoff geometries indicate fundamentally different processes associated with neck and chute cutoffs. Results show that floodplain slope is a first-order control on cutoff type across the river basin, while water discharge and landcover act as secondary controls. The planform geometry of chute cutoffs is more scale-free than necks. This basin-wide perspective illustrates how cutoff type changes systematically across the Amazon, suggesting that floodplain properties control lentic habitat availability.

How cloud droplets evaporate when mixed with the dry surrounding air is fundamental to cloud optical properties and lifetime. We find from observations in cumulus clouds made during the ESCAPE field campaign that this mixing process appears strongly inhomogeneous-like, where a subset of droplets evaporate completely as mixing proceeds, rather than all droplets partially evaporating. We visualize the microphysical properties in a two-dimensional evaporation-phase-relaxation space and find that a diffusive turbulent-evaporation model is able to capture the dynamic evolution of the entrainment process. The results indicate that the first evaporating droplets humidify the region around the cloud so that the unmixed dry air rarely reaches the core, explaining why most mixing events appear inhomogeneous. A mixing slope parameter also confirms the nature of the mixing process. On the basis of the inhomogeneous mixing model, we propose a simple parameterization of cloud optical properties suitable for coarse-resolution models.

Accurate wetland mapping is critical for ecosystem monitoring and management, yet acquiring dense pixel-level annotations is prohibitively costly. In practice, only sparse point labels are typically available. Existing deep learning-based models face significant challenges in capturing accurate wetland extents under such weak supervision, particularly when coupled with the strong seasonal dynamics of wetlands, which, meanwhile, makes single-date imagery insufficient and causing substantial omission and commission errors when mapping. Although powerful foundation models like the Segment Anything Model (SAM) provide promising generalization from point prompts, it is intrinsically designed for static natural images, resulting in spatially fragmented masks in heterogeneous wetland environments and cannot exploit satellite image time series. To address these challenges, we propose WetSAM, a novel SAM-based framework that effectively leverages satellite image time series to enhance wetland mapping from sparse point annotations. WetSAM adopts a dual-branch design: (1) The temporal branch extends SAM to learn temporal contexts from satellite image time series via hierarchical adapters and a dynamic temporal aggregation module. This branch equips SAM with the ability to capture and model temporal features of wetlands, allowing it to learn complex temporal patterns and phenological changes; (2) The spatial branch reconstructs distinct boundaries via a temporal-constrained region-growing strategy, iteratively expanding sparse points into reliable dense pseudo-labels; (3) A bidirectional consistency regularization enforces minimizing the discrepancy of the predictions from two segmentation heads of two branches. We validate the effectiveness of WetSAM across eight diverse global locations, each covering an area of around 5000 k m 2 and with various wetland types and geographical features. WetSAM reaches an average F1-score of 85.58%, considerably outperforming other state-of-the-art algorithms. Results demonstrate that WetSAM achieves accurate, structurally consistent segmentation from sparse labels. With minimal labeling effort, our framework shows strong generalization ability and holds promise for scalable, low-cost wetland mapping at high spatial resolutions.

The Indo-Pacific Convergence Zone (IPCZ) is an important center of global marine biodiversity. However, the origins of marine species in the IPCZ remain unclear. Understanding the origin areas of marine surface organisms in the IPCZ is critical for developing ocean conservation strategies and designing marine protected areas. Particle-backtracking simulation, as an essential numerical tracking technique, can reveal the sources of objects by tracing their dispersal pathways. In this study, numerical particle-backtracking simulations initialized at different dates using the ocean currents from an ocean circulation model with data assimilation were carried out to infer the potential ‘origin’ areas of IPCZ marine surface organisms. The resulting distribution of particle trajectories indicates that coastal areas are potential sites of origin or spawning grounds for some marine surface organisms. Specially, the IPCZ coastal regions may harbor IPCZ and peripheral Eastern Indian Ocean organisms, whereas the Eastern Pacific coastal zones potentially sustain peripheral Western Pacific organisms. The newly defined terrestrial intensity (TI) confirms that the IPCZ exhibits a higher proportion of organisms with a coastal ‘origin’ than the peripheral regions. Additionally, variations in initial ocean currents have no significant influences on TI in the IPCZ yet cause some differences in peripheral areas, potentially due to ocean currents driven by monsoons or other events characterized by interannual variability. • Possible “origins” of IPCZ marine organisms are inferred via particle backtracking. • The coastal regions are potential origin sites or spawning grounds. • IPCZ coastal regions may harbor IPCZ and peripheral Eastern Indian Ocean organisms. • Eastern Pacific coastal zones potentially sustain peripheral WP organisms. • Ocean currents play important roles in transporting particles.

The collapse, retreat or stability of the West Antarctic Ice Sheet (WAIS) during previous warm periods would offer powerful constraints on its possible behaviour in a warmer polar future. The last interglacial (LIG) is the period for which the most constraining data is expected to persist. However most of the evidence is circumstantial and fragmentary, allowing us to hypothesise but not define possible conformations of the ice sheet. Farfield sea level data suggest that an Antarctic sea level contribution of one to a few metres must have occurred. Ice core and glacial geologic evidence show that significant ice remained in some parts of WAIS. Limited genomic evidence suggest that seaways existed between the Weddell, Amundsen and Ross Seas, most likely in the LIG, while ice core data imply that the Ronne Ice Shelf survived. Ice sheet modelling scenarios consistent with such evidence show very significant ice loss in the Amundsen Sea sector. We highlight two possible configurations of collapsed ice that are consistent with the range of geological and genetic evidence available, with maximum Antarctic Ice Sheet sea level contributions of 4 or 6 m. Such hypothesised conformations need to be challenged by new and emerging data from ice cores, cosmogenic studies and genomic databases. The constraints provided by the data should be used to restrict the ranges of parameters in models used to assess future rates and extents of ice sheet loss under different climate change scenarios. • Critical review of evidence for Last Interglacial Antarctic Ice Sheet configuration. • Evaluates glacial geology, marine sediment, ice core and genomic evidence. • Highlights possible configurations consistent with available evidence. • Suggests priorities for efforts to understand past Antarctic ice sheet instability.