Atmospheric and Oceanic Sciences

Terraces are a defining feature of Mediterranean mountain landscapes, enabling agriculture on steep slopes while providing multiple ecosystem services. Land suitability analysis (LSA) can guide authorities and land users to sustainably manage and expand these environments. It typically requires fully labelled datasets, but in many real-world applications only a fraction of positive examples is available, with the rest unlabelled. This study aims to present an integrated predictive modelling framework that combines GIS with data-driven Machine Learning (ML) techniques, capable of learning from Positive and Unlabelled (PU) datasets for LSA. The proposed framework was applied to develop a terrace suitability map for Cyprus’ Troodos Mountains. A 5-m DEM was processed to extract the mountain area, with elevation ≥ 500 m and slopes ≥ 15%, defining the study area. Crop plots registered under the Single Area Payment Scheme of the European Common Agricultural Policy were used to classify the study area into Terrace-Present (TP) and Terrace-Absent (TA) cells, with TP serving as labelled positive and TA as unlabelled samples. A two-step ML approach was applied, first identifying reliable negatives from TA cells, then using these with TP cells for suitability prediction. The developed PU-classifier was evaluated under the selected completely-at-random (SCAR) assumption, achieving a Recall of 84.6%, Precision of 81.5% and an F1 score of 83%. Feature importance analysis identified land cover, terrain slope and tree cover density as the most influential parameters for terrace suitability. Comparative analysis between 2017 and 2024 revealed abandonment of terraced agricultural land (29% decrease) as well as revitalisation (12% increase). The resulting suitability map and accompanying data layers are accessible through a Google Earth Engine application, aiming to support informed decision-making for sustainable landscape planning.

Algal organic matter (AOM) is a critical precursor for aquatic photochemical reactions, yet how molecular composition relates to the contrasting photoreactivity of extracellular and intracellular organic matter (EOM/IOM) remains insufficiently understood. This study systematically analyzed the photoreactivity of EOM and IOM derived from representative Chlorophyta and Cyanobacteria. Integrating optical spectroscopy and ultrahigh-resolution mass spectrometry (FT-ICR MS), we characterized the molecular features associated with their photosensitization capacity. EOM, dominated by humic-like substances, functions as a more effective photosensitizer with reactive species (RS) quantum yields up to 22.5-fold higher than protein-like IOM. At the taxonomic level, Chlorophyta-EOM outperformed Cyanobacteria-EOM, whereas Cyanobacteria-IOM exhibited pigment-mediated reactivity. NaBH 4 reduction coupled with FT-IR and 2D-COS suggested that EOM photoreactivity was more sensitive to borohydride-sensitive chromophoric moieties, whereas IOM appeared less affected by borohydride treatment. FT-ICR MS revealed EOM contained relatively lignin/CRAM-like and unsaturated molecular features, while irradiated IOM evolved into more saturated and recalcitrant residues, indicative of photochemical aging. These findings provide molecular-level insights into the divergent environmental fates of algal fractions, highlighting EOM as an important contributor to oxidative self-purification, while irradiation of IOM was associated with photochemical aging and the formation of more saturated residual molecular signatures.

Abstract Dolomite is among the most common carbonate minerals in ancient rocks but its formation is rarely observed today, a longstanding puzzle known as the “Dolomite Problem.” Recent advances in experimental mineralogy, geochemistry, and global abundance reconstructions have begun to resolve this problem. One hypothesis is that dolomitization should be reframed as a two‐stage process involving the nucleation and growth of protodolomite, followed by thermally driven recrystallization. Levenson et al. (2026, https://doi.org/10.1029/2025GL120386 ) provide geochemical evidence for this model using clumped isotope thermometry, finding formation temperatures in Mesozoic dolomites consistent with progressive burial alteration. They suggest that the scarcity of dolomite in younger rocks reflects insufficient time at elevated burial temperatures. Alternative pathways to dolomite formation, including at low temperatures and shallow burial depths, also deserve continued consideration as the community works toward a more complete resolution of the Dolomite Problem.

ABSTRACT This study compares eight third‐round NDC submissions to assess ambition, data systems, sectoral coverage, and adaptation planning. Using qualitative content analysis and cross‐country benchmarking, it evaluates institutional readiness, sectoral alignment, financing feasibility, and monitoring, reporting, and verification (MRV) quality. Results show rising ambition but persistent implementation gaps. Lower‐income countries emphasize vulnerability and equity yet face data and financing constraints. Sectoral pathways and baselines vary, limiting comparability. NDC4.0 should strengthen MRV systems and define clear, harmonized sectoral trajectories.

Underwater 3D scene reconstruction is highly challenging due to severe light scattering, colour attenuation, and the presence of dynamic elements such as swimming fish. In this paper, we present PRISM-Splat, an efficient and robust 3D Gaussian Splatting framework driven jointly by physical models and semantic features. To fundamentally separate the scattering medium from the underlying scene geometry, we introduce a physics-driven hybrid Gaussian representation derived from the radiative transfer equation (RTE). Rather than applying rigid geometric constraints, we propose a confidence-aware depth prior that adaptively guides the structural optimisation, effectively preventing geometric collapse in severely degraded regions. Furthermore, to address the persistent issue of dynamic underwater distractors, we integrate a multi-view semantic feature field that intrinsically filters out moving objects without requiring explicit priors. Extensive experiments on both authentic and simulated datasets demonstrate that PRISM-Splat achieves superior performance in high-fidelity 3D reconstruction and accurate colour restoration compared to state-of-the-art methods.

Despite widespread recognition of dermal exposure to hazardous compounds transferred from consumer products, limited information exists on the friction-mediated transfer of low molecular weight (LMW) chemicals. These chemicals, readily soluble in skin secretions, may pose prolonged dermal exposure risks. To address the knowledge gap, we investigated the frictional transfer of chemicals (formamide, benzothiazole, p -bis(2-hydroxyisopropyl)benzene ( p HPB), and bis(2-ethylhexyl) terephthalate (DEHT)) from mats to rubbing fabrics under dry and skin-secretion conditions to simulate clothing-mediated dermal exposure. Gas-phase emissions were also measured. Under dry conditions, transfer efficiencies inversely correlated with molecular weights, namely, for formamide (0.072–0.4%), benzothiazole (0.047–0.072%), p HPB (0.0054–0.035%), and DEHT (0.00037–0.0083%). Skin secretions facilitated friction-mediated chemical transfer, with the extent of facilitation correlating with log K ow . Sweat increased formamide accumulation 5.6–9.0-fold, while sebum boosted DEHT transfer up to 110-fold. Chemical transfer was amplified under intensified mechanical conditions (duration, load, and sliding speed) and modulated by mat surface roughness. In 30 min exercise simulations, formamide (an LMW chemical) transfer efficiency through friction (0.16–1.6%) was comparable to that through gas-phase emission (0.026–0.23%). These findings indicate that frictional contact is a significant pathway for LMW chemical transfer from mats to clothing, providing critical insight for dermal exposure.