Earth and Environmental Sciences

Abstract COP28 marked the first time that climate negotiations at the highest level, i.e. an official Conference of the Parties’ (COP) decision, indicated clear energy-related milestones for urgent climate action by adopting specific renewable energy and energy efficiency targets for 2030, and phasing down fossil fuels by 2050. These milestones are meant to serve as yardsticks to guide the path towards mitigating climate change and meeting the Paris Agreement (PA) goals. We assess the alignment of COP28 energy outcomes with the PA goals using several leading Integrated Assessment Models (IAMs) featuring high technological, sectoral, and regional detail. This detailed assessment could inform country- and technology-specific target design, which is becoming increasingly important in a fragmented world and under growing uncertainty regarding the prioritization of climate protection in governmental agendas. We additionally use and quantify novel transformation narratives such as “electrification” versus “combustion”, which could help develop a new generation of demand-side COP mitigation targets.

Accurate long-term global urban monitoring is essential for understanding socio-economic dynamics. To overcome limitations of single-source data at macroscopic scales, this study proposes an automated framework that fuses nighttime light (VIIRS-DNB) and vegetation indices (MODIS NDVI). We develop the Nightlight-Vegetation Rectified Index (NVRI) to suppress blooming effects in vegetated regions and amplify urban–rural contrasts. An adaptive arid grid identification mechanism further mitigates the desert blooming issue. A local adaptive thresholding strategy based on multi-scale grids and the Kneedle algorithm replaces conventional global thresholds. With spatiotemporal post-processing, we generate an annual global urban extent product (NTL Urban, 2012–2024). National-scale validation shows high agreement with MGUP and MODIS urban products. Urban extents also exhibit stronger linear correlations with GDP and population than traditional optical products, highlighting their value for macro socio-economic analysis. Despite residual limitations from blooming effects, lighting preferences, and sensor resolution, our framework offers a reproducible, scalable approach for global urban monitoring and NTL-based socio-economic research.

Urban canopy schemes are essential for urban climate modeling, yet their performance depends on the level of detail in Urban Canopy Parameters (UCPs). In this study, we evaluate the ICON TERRA_URB urban scheme against dense urban observations (near-surface sites, higher-level sites, and eddy-covariance towers) and satellite surface-temperature products, focusing on air and surface temperature, sensible and latent heat fluxes, and wind speed over Zurich and Basel during summer 2023 using four experimental configurations at 500 m resolution. These include simulations without an urban canopy scheme (No TU), TERRA_URB with spatially uniform UCPs (Constant TU), local-climate-zone-based spatially varying parameters (LCZ TU), and city-specific urban canopy parameters (Real TU). Results show that activating TERRA_URB, regardless of parameter detail, provides the largest improvement by reducing the nocturnal cold bias, increasing urban surface temperatures, reproducing slightly higher nocturnal wind speeds, and improving surface energy flux partitioning. Spatially varying UCPs further refine the simulations. In particular, Real TU best captures the nocturnal urban signal, improving agreement with observed nighttime temperatures and sensible heat fluxes, although some biases remain in city centers. Their added benefit is modest compared to the main gain from activating the urban scheme itself. A trade-off emerges aloft, as TERRA_URB tends to overestimate warming, with No TU often performing better at most higher-level sites, although this may also reflect coupled boundary-layer processes beyond the urban scheme itself. These results suggest that while bulk urban canopy schemes effectively capture surface exchanges, improved boundary-layer representation likely requires multi-layer canopy approaches.

Introduction Engineering professionals are central to climate action because their decisions shape infrastructure, energy, water, transport, industrial, and built-environment systems that influence greenhouse gas emissions and climate resilience. This study assessed the awareness, training, engagement, and perceived responsibility of South African engineering professionals registered with the Engineering Council of South Africa (ECSA) in relation to climate change mitigation and adaptation. Methods An exploratory mixed-methods cross-sectional survey was conducted, combining quantitative analysis of closed-ended responses with thematic analysis of open-ended responses. The final dataset comprised 783 valid responses. Results Although 72.1% ( n = 545) of respondents had not received formal climate-related training, trained respondents were more likely to report participation in climate-related projects than untrained respondents. Among trained respondents, 67.8% ( n = 143) reported participation, compared with 34.5% ( n = 188) of untrained respondents. A Chi-square test confirmed a statistically significant association between training and participation [χ 2 (1, N = 756) = 68.43, p < 0.001]. The Mann-Whitney U-test also showed that trained respondents had stronger perceptions of professional responsibility in climate action ( U = 48,985, p < 0.001). Discussion Survey and thematic findings showed widespread but not unconditional support for ECSA to strengthen climate-related training, collaboration, guidance, accreditation, and professional conduct frameworks while avoiding unnecessary regulatory burdens. The study highlights the need to expand climate-related professional development and regulatory guidance to enhance engineering professionals' engagement in mitigation and adaptation and support South Africa's climate resilience objectives.

Synthetic aperture radar (SAR) and light detection and ranging (LiDAR) are two fundamental active remote sensing technologies. Their synergistic use can significantly enhance the effectiveness of remote sensing applications. However, existing studies primarily focus on product-level integration, and direct mapping between SAR images and LiDAR remains a challenge due to cross-dimensional modality differences. In this paper, a deep learning-based SAR-to-LiDAR (SAR2LiDAR) matching framework is proposed, which enables direct matching between SAR images and LiDAR point clouds. The framework features a dataset generation method that creates accurate 2D-3D correspondences between SAR images and LiDAR point clouds. It explicitly models the geometric and scattering visibility relationships of SAR imaging, and employs an autoencoder-based deep learning model with specific global feature representation and cross-modal alignment capabilities. The proposed model is trained and evaluated using airborne LiDAR datasets from five urban regions: Barcelona, Hong Kong, Los Angeles, Tarragona, and Girona. Extensive qualitative and quantitative experiments demonstrate that the proposed SAR2LiDAR framework achieves accurate and robust matching performance across diverse urban scenarios, attaining an average recall@1 of 95.64%, which significantly outperforms the best competing methods by 13%. Moreover, an average recall@1 of 91.68% on out-of-distribution (OOD) test regions demonstrates the strong generalization capability of the proposed framework and its potential for large-scale remote sensing applications.

Elkhorn coral (Acropora palmata) is a threatened reef-building species that plays a critical role in Caribbean coastal ecosystems. Efficient, large-scale monitoring of A. palmata is essential for evaluating restoration success, yet traditional in situ surveys remain costly and spatially constrained. In this study, we acquired high-resolution (1.8 cm) uncrewed aerial vehicle (UAV) imagery of a coral reef within the United States Virgin Islands’ (USVI) St. Croix East End Marine Park (STXEEMP) and applied deep learning object detection to identify individual A. palmata colonies. We utilized two convolutional neural network architectures, FasterRCNN and MaskRCNN. FasterRCNN was used as an initial screening tool to identify the optimal imagery dataset from several candidates. After identifying the dataset, we used MaskRCNN with an iterative annotation refinement procedure in which initial model predictions were used to augment the training data and achieved an F1 score of 0.78. Detection accuracy was strongly influenced by colony size and apparent water depth, with markedly high accuracy for corals wider than 0.3 m (F1 = 0.87) and located in shallower waters (F1 = 0.81). Beyond detection, MaskRCNN’s polygon outputs enabled the measurement of the individual colony area and the generation of high-resolution coral density maps. These products complement broader-scale prediction and mapping approaches and provide fine-scale, management-relevant information. Although this study was conducted at a single reef site during one acquisition period, the results suggest that UAV-based deep learning workflows offer a promising approach for coral reef monitoring that could support restoration assessments and conservation decision-making, pending validation across additional sites, seasons, and environmental conditions.

With the wide application of generative models in the field of SAR image inpainting, inadequate reconstruction quality of scattering characteristics and insufficient global coherence of semantic logic remain the core challenges of such tasks. To address these issues, this paper proposes a Feature-Encoding Diffusion Model (FEDM). Guided by local valid regions, the proposed model accurately learns the microwave backscattering distribution law of ground features through a SAR-specific Variational Auto-Encoder (SAR-VAE), thus improving the reconstruction accuracy of backscattering statistics. Meanwhile, it integrates semantic embedding and cross-attention mechanism to strengthen the semantic constraints of SAR scenes, ensuring the logical rationality of the ground feature layout. With progressive diffusion generation and sliding window strategy, the model achieves high-quality reconstruction with coherent semantics and consistent global spatial structure for large-scale missing regions. Experiments on public datasets including OSdataset, SEN1-2, SRSDD-v1.0 and MRSSC show that the proposed method achieves excellent performance in terms of scattering characteristic reconstruction quality and globally coherent generation of semantic logic, and realizes high-quality SAR image inpainting.

Study region The study region is the catchments that overlie, and are adjacent to, the Cambrian Limestone Aquifer in northern Australia. Study focus The Cambrian Limestone Aquifer is the largest water resource in the Northern Territory, and the only reliable water resource in the region. Water resources development has historically only occurred in the Daly and Roper catchments, but there are now increasing development pressures on the water resources in other catchments. The groundwater discharge through springs is culturally, ecologically and economically important and protected through water allocation plans. The springs in the Daly and Roper have been well characterised but the other sparsely populated, undeveloped catchments, have not had the same level of investigation. This study aims to use remotely sensed evapotranspiration to identify groundwater discharge areas across ∼750,000 km 2 of northern Australia. New hydrological insights for the region The current conceptualisation of the CLA has discharge occurring in the Roper, Daly and Nicholson catchments. This study has identified small groundwater discharge locations at the basin margins in the Victoria, Limmen Bight, McArthur, Robinson and Calvert catchments. These additional groundwater discharge locations need to be assessed for their source aquifer before development occurs in these regions to ensure the protection of their cultural, ecological and economic values.

Soil microbial communities play a pivotal ecological role in contaminated environments. However, conventional metagenomic approaches struggle to distinguish between “potential function holders” and “in situ metabolically active executors”. Here, we employed a method combining fluorescent d -amino acid labeling, fluorescence-activated cell sorting, and metagenomics (FDAA-FACS-Metagenomics) to capture and profile active microbes in complex soils. The secondary addition of As(V) and Sb(V) enhanced the community’s reductive activity toward these metalloids, reshaping the active assemblages. Clostridium was markedly enriched, and several low-abundance members were activated as true executors of the reduction process. MAGs recovered via FDAA-FACS revealed an active core community with functional partitioning: some taxa participated directly in As(V)/Sb(V) reduction, while others contributed to community stability through tolerance and metabolic support. Notably, a Desulfitobacteriaceae genome (MAG29) harbored both arrAB and anrAB gene clusters, a complete Wood-Ljungdahl carbon fixation pathway, and nitrogen fixation genes. These genomic features suggest the potential for a multifunctional metabolic lifestyle involving metalloid reduction, carbon fixation, and nitrogen transformation. Such metabolic versatility may enable MAG29 to contribute to coupled carbon–nitrogen cycling and metalloid transformation under contaminated environmental conditions. These findings emphasize the important ecological roles of rare, metabolically active microbes in metalloid transformation and soil ecosystem functioning.

Phytoplankton functional groups distribution and dynamics builds nutrients and carbon energy pathways in the ocean. They respond fast to changes in their environment, and the understanding of their dynamics relies on regular sampling to resolve daily to weekly scales. The Bonifacio Cyclonic Gyre (BCG) is an area prone to intermittent phytoplankton blooms triggered by westerly winds, and close to coastal areas capable of fuelling open waters with nutrients from flooding. To study phytoplankton evolution in this dynamical area, the distribution of phytoplankton functional groups in the surface waters of the western Mediterranean Sea was investigated semi-continuously (30 min) using an automated CytoSense flow cytometer coupled to a FerryBox, onboard the ferry Le Carthage, between October 20, 2016 and January 12, 2017 and along the route from Tunis (Tunisia) to Genova (Italy). The BCG signature was identified by its surface temperature anomaly and evidenced significant higher abundances of the RedPicoProk, the RedPico, the HsNano and the OraNano over the full sampling period, except for the RedNano1 and RedNano2 and the OraPicoProk. Their estimated chlorophyll a concentration per group were higher within the gyre than outside, but did not affect the carbon/chlorophyll a ratio. A high carbon/chlorophyll a ratio can indicate recently uplifted cells that are not yet adapted to high light conditions. In this case, the observed ratios suggest that the cells were not recently upwelled, but instead had time to adjust their photo physiology to surface conditions. During 19–20 December, extreme overflow occurred over Corsica and Sardinia, considered as one of the highest rainfall events of the past 20 years. The runoff was evidenced by the low salinity intrusion that lasted several days after the storm. After the runoff and within the Bonifacio gyre region, biomasses of all groups increased and remained high until the end of the sampling, except for the RedPico. The runoff triggered an early bloom end of December that persisted for several weeks, being trapped and repleted by the uplift of the isopycnals from the BCG. Regular observation of the sea surface temperature and salinity together with phytoplankton community structure provides essential insights into the fast response of phytoplankton communities to an extreme event at unprecedented resolution.

Seagrass meadows rank among the most productive coastal ecosystems, yet our understanding of their nutrient-acquisition strategies in oligotrophic environments remain fragmented. Current research tends to focus on isolated processes, such as leaf or root uptake, without adequately integrating morpho-physiological traits, sediment heterogeneity, or microbial interactions. This fragmentation constrains our ability to predict seagrass responses to global environmental change. This review synthesizes seagrass nutritional ecology and shows that species in oligotrophic systems employ complementary strategies, including high-affinity foliar absorption, extensive root proliferation, and internal resorption. Crucially, these traits exhibit high plasticity and are modulated by intricate plant-microbe mutualisms, such as nitrogen fixation and phosphorus solubilization. We further demonstrate how sediment properties and hydrodynamic create spatial heterogeneity that dictates site-specific nutrient bioavailability. We propose three priority research directions: (1) deciphering the genetic and epigenetic drivers of phenotypic plasticity adaptation to oligotrophy; (2) applying stable isotope tracers and comparative genomics to quantify the net functional benefits of the seagrass microbiome; and (3) assessing long-term impacts of synergistic stressors (e.g., warming and eutrophication) on meadow structure and biogeochemical functions. By treating oligotrophic environments as natural laboratories, this review informs restoration efforts and the need to safeguard ecosystem services globally, particularly blue carbon sequestration and biodiversity conservation.

The accurate classification of seabed sediment and benthic covers in shallow-water environments remains a key challenge for marine activities and oceanographic research. However, coastal areas of shallow waters are influenced by complex dynamic environments, making it difficult to obtain authentic sediment and benthic-cover samples. Therefore, to address the problem of few-shot classification of seabed sediment and benthic covers, a few-shot classification algorithm of seabed sediment and benthic covers based on the fusion model of airborne LiDAR bathymetry (ALB) and multispectral images is proposed in this article. Based on the extracted features, a scale-invariant feature transform-progressive sample consensus (SIFT-PROSAC) algorithm and perspective transform model were constructed to achieve feature fusion. Then, multi-modal feature selection is realized using a formal concept analysis-Relief-F (FCA-Relief-F) algorithm. Finally, a graph attention network-prototype network (GAT-PN) model was established to classify five types of sediment and benthic cover (coral reef, stone, sand, vegetation, and coastal zone). To validate the effectiveness of the proposed method, experimental data from actual measurements at Ganquan Island in the Xisha Islands of China were used. Compared to other classical classifiers, the GAT-PN algorithm achieves a higher classification accuracy, with an overall accuracy (OA) and Kappa coefficient of 97.50% and 0.97, respectively. The findings of this study provide effective technical support for marine engineering and related fields.

Cova Eirós (Triacastela, Lugo, NW Iberia) holds the most comprehensive palaeolithic archaeological sequence in the northwestern Iberian Peninsula, with stratigraphic levels containing rich and well-preserved fauna and lithic industries spanning roughly from -at least- 41 ka calBP to 14 ka calBP. Within this chronological timespan, several significant climatic events took place, most notably the transition between Marine Isotopic Stages (MIS) 3 and 2, a period during which Neanderthal populations went extinct and were subsequentially replaced by Anatomically Modern Humans (AMH). In this context, the zooarchaeological record from levels 3 (~42–39 ka calBP), 2 (~36–35 ka calBP) and 1 (>17 ka BP) provides a key dataset for reconstructing the climatic and environmental conditions under which these hominin groups lived, as well as establishing a palaeoclimatic sequence encompassing MIS 3, the immediate transition to MIS 2, and a fully developed MIS 2. To achieve this, a multi-proxy approach based on the faunal record has been employed, integrating methods for palaeoclimatic reconstruction (Bioclimatic Model), palaeoenvironmental reconstruction (Habitat Weighting) and a traditional zooarchaeological analysis. This comprehensive methodology aims to infer the ecological conditions in which both Neanderthals and AMH populations thrived and elucidating their interactions with the surrounding animal communities across different time periods. Results indicate that throughout the sequence, climatic conditions were consistently colder and slightly drier than today, yet the region maintained a relatively humid and wooded environment, which may have functioned as a refuge area. Moreover, the zooarchaeological evidence reveals shifts in territorial exploitation and subsistence strategies among hunter-gatherer groups, with a progressive decrease in occupation intensity over time.

Atmospheric oxidation of nitrogen-containing volatile chemical products (N-VCPs) significantly affects air quality, with the formation of carcinogenic nitrosamines being particularly concerning. Benzimidazoles (BZIs) are high-production chemicals and a type of N-VCP. However, their atmospheric oxidation mechanisms and subsequent impacts remain unclear. Here, using the simplest benzimidazole (BZI) as a representative compound, we employed theoretical calculations and toxicological modeling to investigate the • OH-initiated reaction mechanism of BZI and assess the toxicity of their oxidation products (OPs). The calculated reaction rate constant for the BZI + • OH reaction is 1 × 10 –11 cm 3 molecule –1 s –1 at 298 K and 1 atm, which is comparable to those of other studied N-VCPs. The • OH-addition intermediate is dominantly formed in the BZI + • OH reaction, which further leads to the formation of organonitrates, alkoxy radicals, hydroperoxides, and carcinogenic nitrosamines under conditions where NO, HO 2 ·, and O 2 coexist. Toxicity predictions indicate that almost all OPs are more toxic than BZI. This study clarifies a previously unrecognized pathway to the formation of nitrosamines via the • OH-addition intermediate, beyond the conventional N–H H-abstraction intermediate, thereby refining the reaction diversity (two distinct pathways) and benefiting the assessment of the atmospheric impact caused by the oxidation of N-VCPs.

Late Cretaceous phosphogenic systems are widely documented along the Tethyan and Atlantic margins. However, the paleoenvironmental conditions controlling phosphorite formation along the Brazilian Equatorial Margin remain poorly understood. This study reconstructs the paleoenvironmental, depositional, and phosphogenic evolution of the Itamaracá, Gramame, and Jandaíra formations along the equatorial South Atlantic margin during the late Campanian–early Maastrichtian, integrating microfacies, foraminiferal assemblages, and quantitative paleoenvironmental proxies. The results reveal distinct but interconnected depositional systems governed by variations in nutrient flux, water-mass stratification, and bottom-water oxygenation. The upper Campanian stratigraphic sequence limit KCa7 records intensified upwelling, enhanced productivity, and the onset of dysoxic conditions, triggering the initial stages of phosphogenesis in both basins. The early Maastrichtian stratigraphic sequence limit KMa1 interval corresponds to maximum flooding, extreme sedimentary condensation, expansion of the oxygen minimum zone, leading to pervasive phosphatization in the basal Gramame Formation analogous to the classical Tethyan and North African phosphorite systems. In contrast, the early Maastrichtian stratigraphic sequence limit KMa2 reflects improved ventilation, oscillatory nutrient supply, and reduced phosphogenic intensity. These findings support regional and intercontinental correlations between the Late Cretaceous phosphatic deposits of northeastern Brazil and phosphogenic systems developed in the Neo-Tethyan domain. They further refine the role of upwelling-driven productivity and redox instability in controlling the development of Campanian–Maastrichtian carbonate–phosphate systems.