Atmospheric and Oceanic Sciences

Abstract. This paper describes the data quality of the first weather radar with a solid state power amplifier (SSPA) in use at the German Meteorological Service. The new transmitter has been integrated into the existing C-Band radar at the Observatory Hohenpeissenberg in October 2023. The resulting setup is unique: most of the radar hardware (wave guides, pedestal, antenna, radome) is shared between the magnetron and solid state transmitters. The same weather situation can therefore be observed with both transmitter types with a small time difference of around five minutes, while most elements of uncertainty from the hardware can be disregarded for their comparison. A two pulse scheme is investigated with an un-modulated short pulse and a long pulse with non-linear frequency modulation. The scheme provides similar spatial resolution compared to the magnetron system. We show the results of the comparison of the data from both transmitters, focusing on reflectivity, Doppler moments and dual-polarization data. Magnetron and SSPA transmitters provide comparable data quality in areas with a signal-to-noise ratio (SNR) >20 dB. For lower SNR, the SSPA outperforms the magnetron transmitter. This is especially noticeable in ranges above 130 km from the radar. Data at the transition between the modulated long pulse and the un-modulated gap filler short pulse are investigated in detail. It is shown that the matching works well and a simple approach with fixed offsets is sufficient to provide a smooth transition. Range sidelobes are investigated with examples originating from strong clutter targets and an intense convective cell. For targets stronger than 55 dBZ, range sidelobes reach levels in many radar moments (including dual-polarization moments) that resemble meteorological echoes. They influence the whole length of the pulse (30 km in the presented case). The effect on radar products and possible mitigation approaches still have to be investigated. In general, SSPA transmitters for weather radars are assessed as viable in terms of data quality and are considered as an option to replace magnetron transmitters in the DWD weather radar network.

Polycyclic aromatic hydrocarbons (PAHs) are toxic, carcinogenic organic contaminants widely distributed throughout the marine environment, directly affecting marine life and human health. Surveying these compounds in sentinel organisms, such as seabirds, is an effective way to assess environmental quality. In this sense, this research aimed to (i) develop and validate a high-performance liquid chromatography (HPLC) analytical method for the quantification of the 16 priority PAHs in blood plasma; (ii) establish an easy and quick protocol to reduce sample handling and potential contamination; and (iii) apply the method to Brown booby ( Sula leucogaster ) blood samples from specimens collected along the coast of the state of Rio de Janeiro in 2025. The performance parameters of the developed method met well-established criteria and the Limits of Quantification (LOQ) achieved herein (0.06 - 4.48 ng mL -1 ) are consistent with the main goal and comparable to recent literature. The analyzed samples (n=13) presented low PAH concentrations (from -1 ), consistent with chronic background exposure and no evidence of acute events. The contamination profile suggests exposure to low molecular weight PAH, possibly due to Brown booby trophic position and habitat. This research delivers an initial database of an ongoing monitoring project for future assistance in the event of oil spills or other acute contamination sources.

This paper presents an experimental investigation of pipe–soil interaction using geotechnical centrifuge modelling. The tests simulated the response of rigid pipe ‘elements’ at free-span ‘shoulders’ subjected to horizontal cyclic loading representative of pipeline movements during in-line Vortex-Induced Vibration (VIV). A natural carbonate silty sand from the North West Shelf of Australia was used, and the effects of vertical load, cyclic displacement amplitude, installation method, and loading history were investigated. The response was assessed in terms of changes in pipe embedment (or load), horizontal cyclic stiffness and soil material damping. The results show that the mobilised stiffness decreases with increasing displacement amplitude and increases with increasing vertical load as expected, but that stiffness values are substantially lower than those predicted with state-of-practice calculations. Soil damping increases nonlinearly with cyclic amplitude and is strongly load-dependent, with lower vertical loads producing more damping. The findings demonstrate that strain and load dependent soil behaviour governs pipe-soil interaction during in-line VIV and should be explicitly accounted for in pipeline free-span assessment.

Synthetic Aperture Radar (SAR) possesses the capacity for all-weather imaging and is widely applied in target detection. However, robust SAR target detection remains challenging due to the limited availability of task-relevant labeled samples that jointly cover target categories, depression angles, and complex target–background contexts. In this paper, we propose a Data-Augmented Gather-and-Distribute Network (DA-GDNet) for SAR image target detection. By jointly optimizing at both the data and architectural levels, the proposed approach enhances the model’s capacity for target detection in complex backgrounds. Specifically, we design a SAR image data augmentation strategy that integrates three-dimensional modeling with deep learning. Meanwhile, we incorporate a Gather–Distribute (GD) mechanism and a Spatial Feature Enhancement Module (SFEM) to achieve efficient multi-scale feature fusion and enhance the saliency of target regions. Experimental results on the MSTAR dataset and ATRNet-STAR dataset demonstrate that DA-GDNet not only improves detection accuracy and robustness, but also significantly strengthens the model’s adaptability to variations in depression angles and complex backgrounds.

Region-level weakly supervised hyperspectral target detection (HTD) using multiple-instance learning (MIL) reduces annotation costs but encounters challenges such as bag label ambiguity, boundary over-smoothing, and test-time computational latency. To address these issues, we propose Hyper-VMIL, a spatial–spectral topology-regularized variational hypergraph network. Hyper-VMIL formulates latent target localization as variational inference over dual-path hypergraphs: a boundary-aware spatial hypergraph modeling geometric patch continuity and a dynamic spectral-manifold hypergraph capturing non-local material similarity. Node-adaptive gating dynamically balances spatial and spectral evidence to mitigate over-smoothing near target boundaries. Furthermore, a confidence-aware continuous posterior refinement (CTPR) mechanism reduces the confirmation bias associated with conventional hard pseudo-label binarization. Finally, a teacher–student distillation strategy transfers contextual topology into a lightweight single-spectrum student detector. Benchmark experiments on simulated ASTER and airborne MUUFL Gulfport and Avon datasets show that Hyper-VMIL achieves competitive performance against 15 baseline methods. Notably, Hyper-VMIL supports dual inference modes: Context Mode provides improved detection accuracy (+4.6% average NAUC over VMIL-ECM on MUUFL), while Pixel Mode enables single-spectrum inference (1.25μs single-instance latency and an amortized streaming throughput of 0.015μs per pixel) suitable for onboard real-time deployment.

Introduction Operational decarbonization in prefabricated construction requires decision support that links factory production, transport logistics, energy prices, grid carbon intensity, and carbon-trading rules. This study develops an environmental systems engineering framework for carbon-management decision support in prefabricated component production and delivery. Methods The framework represents the supply chain as a coupled production–transport system in which steam-curing intensity, time-of-use electricity pricing, time-varying grid carbon factors, diesel transportation emissions, and stepped carbon trading jointly shape operational choices. A tri-objective model is formulated to minimize project completion time, energy and fuel costs, and net carbon-trading costs. The model is solved using the Bi-layer Cooperative Evolutionary Algorithm with Q-Learning (BCEA-QL), which jointly searches production and delivery decisions. Results Computational tests on nine synthetic test instances, including a recent reinforcement-learning-assisted baseline, show that BCEA-QL achieves the highest HV on all nine instances, with up to 6.88% higher hypervolume on large-scale instances. A 25-group metropolitan metro precast case further shows that, relative to a time-oriented schedule, a carbon-oriented schedule reduces energy and fuel costs by approximately 23% and yields only a small carbon-trading credit. A post-processing delay-cost analysis identifies manager-dependent switching thresholds near 57 and 212 CNY/h. Discussion The results indicate that coordinated production and delivery scheduling can help environmental managers interpret operational carbon-management trade-offs under asynchronous price–carbon signals, without implying universal carbon reduction or full field validation.

Introduction This study provides the first comprehensive bibliometric analysis of research on climate change and children’s psychological wellbeing (2001–2025), examining whose children’s experiences are studied and which voices shape this rapidly expanding field through a climate justice lens. Methods We analyzed 1,217 documents from Web of Science and Scopus using co-occurrence, citation, and co-authorship analyses. Results The field exhibited explosive growth (22.52% annually), surging from fewer than 10 publications before 2010 to 262 in 2025. However, profound geographic inequalities revealed epistemic injustice: the United States (740 publications, 60.8%), United Kingdom (396, 32.5%), and Australia (351, 28.8%) dominated production while Small Island Developing States, Sub-Saharan Africa, and climate-vulnerable regions remained severely underrepresented despite experiencing disproportionate impacts. Nine thematic clusters emerged, with eco-anxiety, climate education, and pro-environmental behavior—reflecting Western individualistic frameworks—dominating discourse, while structural themes addressing colonialism, displacement, and systemic violence remained underdeveloped. Collaboration networks exhibited US–Australia–Europe centrality with limited Global South engagement. Discussion This concentration constitutes epistemic injustice wherein nations bearing greatest historical responsibility for emissions while experiencing buffered impacts dominate knowledge production, risking universalization of Western experiences while marginalizing the majority of the world’s climate-affected children. Addressing these inequalities requires systemic transformation: redirecting funding to Global South institutions, diversifying editorial boards, valuing diverse epistemologies, ensuring equitable partnerships, and decolonizing theoretical frameworks. Such transformation represents not merely academic fairness but a climate justice imperative essential for developing knowledge systems that center marginalized voices and inform interventions responsive to realities shaping climate vulnerability globally.

Treated wastewater (TWW) is increasingly used as an alternative irrigation source to alleviate water scarcity in agriculture; however, its effects on soil microbial communities and potential microbial contamination remain a concern. This study evaluated the chemical and microbial quality of TWW for irrigation and its effects on selected soil chemical properties and microbial communities using tomato ( Solanum lycopersicum ) as a test crop. A 90-day greenhouse pot experiment was conducted using two tomato cultivars (Roma and Rodade), irrigated with TWW from four wastewater treatment plants (Bochum, Polokwane, Mankweng, and Lebowakgomo), with tap water serving as the control. Soil samples collected at crop maturity were analysed for pH (water and potassium chloride), electrical conductivity (EC), beneficial microorganisms ( Actinomycetes , Pseudomonas putida , and phosphate-solubilising bacteria), pathogenic bacteria ( Escherichia coli , Salmonella , Staphylococcus , Shigella , Vibrio cholerae , Enteric bacteria, and Pseudomonas aeruginosa ), and total bacterial and fungal counts. Irrigation water source was the main factor influencing soil chemical and biological properties, while tomato cultivar effects were negligible. Compared with the pre-irrigation soil, TWW irrigation increased soil EC by 109%–528% and soil pH by 24.5%–41.6%, whereas tap water reduced EC by 68.9%. Beneficial microbial populations increased under TWW irrigation, with Actinomycetes , P. putida , and phosphate-solubilising bacteria increasing by 131%–136%, 117%–124%, and 88%–96%, respectively. Total fungal and bacterial populations increased by up to 195.5% and 131.7%, respectively. However, TWW irrigation also elevated populations of several pathogenic bacteria, with E. coli , enteric bacteria, Salmonella , and Staphylococcus increasing by up to 72%, 141%, 73%, and 75%, respectively. In irrigation water, pathogen concentrations frequently exceeded the FAO and WHO guideline of 1,000 CFU/100 mL for unrestricted irrigation. Overall, TWW enhanced soil microbial activity and beneficial microorganisms, demonstrating its potential as a sustainable irrigation source in water-scarce regions. Nevertheless, routine monitoring and improved wastewater treatment are required to minimise pathogen transfer and support safe agricultural reuse practices.

With the backdrop of the coexistence of sustainable development of marine economy and ecological environment restrictions, how to enhance the efficiency of marine environmental governance (MEGE) has become a significant topic whereby coastal regions should attain green transformation. The research object in this paper is 11 coastal provincial administrative units in mainland China between 2013 and 2021, and marine environmental governance is divided into two stages of production and governance. MEGE and its stage efficiency are measured with the help of the two-stage super-efficiency Network SBM model, and the efficiency growth is characterized with the help of the Network Malmquist index. It is on this basis that, using the technology–organization–environment (TOE) framework, six antecedent conditions of digital economy, technological innovation, marine industrial structure, marine industrial agglomeration, environmental regulation, and degree of openness are chosen and fsQCA is applied to determine the multiple configuration paths of high MEGE growth. The findings indicate the following: (1) The MEGE in the coastal regions of China is improving in general, although the differences in the region remain evident. The efficiency characteristics of the production stage and the governance stage differ, and the inter-provincial differentiation of the governance stage is more pronounced. (2) No single condition is required to achieve high MEGE growth but four equifinal configurations created by the combined action of several conditions can be further reduced to three types, namely technology–environment driven, technology–organization–environment synergistic and technology–organization driven. (3) There exists apparent synergy, compensation, and substitution relationships between technology, organization, and environmental conditions. The most important aspect of high-efficiency growth is not that all positive conditions are on a high level simultaneously, but that a complex of conditions corresponding to the foundation of regional development is created. (4) High and non-high MEGE growth have a strong causal asymmetry and the effective driving routes in various regions are also different. This paper extends the study of the efficiency of marine environmental governance in the two dimensions of internal stage structure and configuration mechanism, and applies accurate, path adaptation, and regional differentiation governance to coastal regions.

Although climate warming affects photosynthetic carbon sequestration in coastal wetland plants, the seasonality of this effect has not been assessed. We investigated the growth traits and photosynthetic properties of Phragmites australis by using open-top chambers (OTCs) to conduct a warming experiment in the coastal wetlands of the Yellow River Delta during a single growing season. The OTCs significantly elevated temperatures by ~1 °C across the growing season, and the effects of warming on stem diameter, net photosynthetic rate (Pn), and water use efficiency (WUE) were characterized by a significant month × warming interaction. Early-season carboxylation efficiency (φ) increased by 71%, but a significant late-season decline of Pn by 49% accompanied by a rise of intercellular CO2 concentrations (Ci) and decline of stomatal limitation (Ls) led to a seasonal shift from stomatal to non-stomatal (biochemical) limitation of growth. A consistent increase in plant height and Ci across all months and concomitant decrease in Ls indicated that the additive effects of warming were independent of phenological stage. The results revealed that the phenological mediation of warming responses is trait specific. Carbon cycle models should therefore adopt trait-specific parameterizations to accurately project the impact of the wetland carbon sink under future warming.

Synthetic Aperture Radar (SAR) enables high-resolution sea-surface wind speed retrieval. However, the enhanced spatial resolution of SAR imagery introduces substantial challenges, from small-scale contamination sources that significantly degrade retrieval accuracy. Particularly in coastal regions, non-wind-related backscatter signals, such as ships and oil slicks, can severely bias wind speed estimates at sub-kilometer scales. In this study, the first-order moment (average, m1) and second-order moment (variance, m2) are computed from the normalized radar cross-section (NRCS) within sub-images of Sentinel-1 SAR data acquired in Interferometric Wide (IW) mode. Analysis reveals that clean-sea-surface signals in both VV and VH polarizations cluster around an approximately linear empirical trend, m2 = 2m1 + b, in the m1-m2 statistical feature space, whereas the examined contamination types deviate from this trend and occupy separable regions. Based on this characteristic, a quality-control framework is proposed for the systematic separation of clean sea surface from image noise (border noise and inter-swath stripe noise) and non-ocean targets (land contamination, bright targets, and dark spots). Validation using independent SAR data from the Taiwan Strait was conducted separately for native 10 m and height-adjusted 3 m buoy observations. For the native 10 m observations, the RMSE and MBE were essentially unchanged at 1.5 m/s and −0.3 m/s, respectively. For the height-adjusted nearshore observations, the RMSE decreased from 3.2 m/s to 2.1 m/s and the MBE changed from −1.5 m/s to −1.1 m/s.

Marine Functional Connectivity (MFC) science – the study of marine organism movements and their ecological consequences across scales – is emerging as a critical framework for understanding how marine biodiversity underpins spatial linkages and interdependencies across ocean basins, depth gradients and land–sea systems. Despite major advances in seabed mapping, ocean observation and hydrological modeling, knowledge of functional connectivity at sea remains fragmented and insufficient to effectively inform management. Marine species transport energy, nutrients, biomass and matter across ecosystems and political boundaries, making MFC essential for conservation, climate resilience and sustainable ocean governance. Recent decades have produced substantial progress in observing and quantifying MFC through genetics, telemetry, natural tags, dispersal modeling and remote sensing. However, major gaps persist across taxa, habitats, depths, regions and temporal scales. Global change further complicates MFC assessment by altering ocean conditions, species distributions and ecosystem dynamics. Addressing these gaps requires harmonized global observations, open-access data sharing, standardized metrics and integration of biodiversity data into global ocean models and Digital Twins of the Ocean. Advances in predictive modeling, ecological network analysis, food-web theory and socio-ecological frameworks are improving MFC integration into marine spatial planning and ecosystem management. Yet connectivity processes remain insufficiently incorporated into governance and decision-making frameworks, particularly across jurisdictions and in Areas Beyond National Jurisdiction. Future progress will depend on stronger international collaboration, transdisciplinary research, technological innovation and closer integration between science, policy and management.

Abstract Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with substantial mortality. While fine particulate matter (PM2.5) is a recognized risk factor for multiple infectious diseases, its role in the prognosis of SFTS remains unexplored. In a cohort of 3583 hospitalized SFTS patients, we found that long-term pre-admission exposure to ambient PM2.5 was significantly associated with an increased risk of SFTS-attributable death, with a corresponding hazard ratio of 1.16 (95% CI: 1.04 and 1.30) per 10 μg/m3 increment. Exploratory mediation analysis suggested that viral load partially explains this association. In a model-based counterfactual burden assessment, we estimated that 137 SFTS-attributable deaths in Xinyang during 2010–2022 might have been potentially avoidable under a hypothetical scenario in which annual PM2.5 concentrations complied with the Chinese National Ambient Air Quality Standards. Complementary experimental studies showed that PM2.5 pre-exposure exacerbated SFTSV infection, increasing viral replication and tissue injury. Transcriptomic analyses implicated mitochondrial redox metabolism and reactive-oxygen-species-related pathways, while ROS/MDA measurements and N-acetylcysteine intervention supported PM2.5-induced oxidative priming as a plausible upstream contributing mechanism for enhanced SFTSV replication and disease severity. In summary, this study provides epidemiological evidence linking long-term PM2.5 exposure to poorer SFTS prognosis and suggests a biologically plausible role of oxidative stress-related redox imbalance. These findings highlight the potential relevance of air-quality improvement for reducing severe outcomes of SFTS in endemic regions.

Although semantic 3D city models are internationally available and becoming increasingly detailed, the incorporation of material information remains largely untapped. However, a structured representation of materials and their physical properties could substantially broaden the application spectrum and analytical capabilities for urban digital twins. At the same time, the growing number of repeated mobile laser scans of cities and their street spaces yields a wealth of observations influenced by the material characteristics of the corresponding surfaces. To leverage this information, we propose radiometric fingerprints of object surfaces by grouping LiDAR observations reflected from the same semantic object under varying distances, incident angles, environmental conditions, sensors, and scanning campaigns. Our study demonstrates how 312.4 million individual beams acquired across four campaigns using five LiDAR sensors on the Audi Autonomous Driving Dataset (A2D2) vehicle can be automatically associated with 6368 individual objects of the semantic 3D city model. The model comprises a comprehensive and semantic representation of four inner-city streets at Level of Detail (LOD) 3 with centimeter-level accuracy. It is based on the CityGML 3.0 standard and enables fine-grained sub-differentiation of objects. The extracted radiometric fingerprints for object surfaces reveal recurring intra-class patterns that indicate class-dominant materials. The semantic model, the method implementations, and the developed geodatabase solution 3DSensorDB are released under: https://github.com/tum-gis/sensordb

Object detection in remote sensing imagery remains challenging due to vast scale variations, complex backgrounds, and the prevalence of small, densely packed targets. Existing CNN-based detectors are limited by restricted receptive fields, while Transformer-based methods incur prohibitive computational overhead for high-resolution inputs. In this paper, we propose SFSMamba-DETR, a detection framework that integrates state space models with Dual-Scale Window Attention for efficient and accurate remote sensing object detection. Specifically, we design a Selective Feature Scanning (SFS) module that uses the Mamba-based 2D Selective Scan mechanism to model long-range spatial dependencies with linear computational complexity. To capture both fine-grained local patterns and broader contextual cues simultaneously, we introduce a Dual-Scale Window Attention (DSWA) mechanism that operates at two complementary window scales with multi-kernel convolution bridging. These modules are orchestrated within a Cross-scale Feature Aggregation Module (CFAM) that performs hierarchical multi-scale fusion in a hybrid encoder. Extensive experiments on three primary benchmarks (MAR20, UCAS-AOD, and the Jilin-1 Satellite Aircraft Detection Dataset), together with supplementary results on DOTA and DIOR, demonstrate that SFSMamba-DETR achieves strong detection accuracy while maintaining competitive inference speed.

Oriented object detection (OOD) in remote sensing images (RSIs) suffers from insufficient feature representation caused by arbitrary rotation angles and small spatial resolutions. Existing spatial–frequency fusion paradigms merely implement single-granularity feature interaction, either global image-level frequency compensation or local instance-level feature refinement, and fail to simultaneously capture global scene semantic consistency and local object fine-grained discriminability. In this paper, we propose a unified dual-level spatial–frequency collaborative detector (DSCDet) for remote sensing OOD tasks. Different from previous decoupled designs, the proposed DSCDet constructs a complete spatial–frequency collaborative fusion paradigm that shares a generic wavelet-based frequency extraction mechanism and cross-feature fusion module, which is adaptively deployed at both image-level and instance-level granularities. Specifically, our method introduces Haar wavelet transform to extract multi-scale frequency mutation features. On this basis, a generic cross-domain attention fusion (GCDAF) is constructed with granularity-dependent positional encoding constraints. The core difference between dual granularity fusion lies in geometric positional encoding, where image-level fusion adopts global scene positional embedding to maintain overall semantic stability, and instance-level fusion leverages local pairwise instance positional embedding to optimize fine-grained target feature interaction. The unified dual-level fusion architecture comprehensively integrates global semantic integrity and local target specificity, forming a robust and universal spatial–frequency feature representation system. Extensive experiments on three public remote sensing datasets, including DOTA-v1.0, DOTA-v1.5 and DIOR-R, demonstrate that the proposed DSCDet achieves competitive and superior performance against state-of-the-art OOD detectors.

Conventional heavy metal remediation technologies are constrained by low efficiency, secondary pollution risks, and limited scalability. Dissolved organic matter (DOM), with its green, cost-effective complexation properties, has become a promising pathway for pollution control. Existing patent bibliometric studies in this field suffer from single-database bias, limited causal quantification of policy impacts, and incomplete depiction of global–local technological heterogeneity. To address these gaps, this study maps the technological innovation landscape of DOM interactions with four typical heavy metals (Cd, Pb, Cu, Zn) during 2004–2024, using a complementary dual-database framework combining Derwent and IncoPat. We integrate a three-dimensional “time–region–technology” analytical framework with interrupted time series analysis (ITSA), after standardized data processing including family deduplication and citation normalization. Cross-validation confirms that China contributes the largest share of global patent output (46.6% in Derwent, 55.0% in IncoPat). Three milestone environmental policies in China exert sequentially intensifying causal effects on patent growth (all p < 0.05), forming a closed-loop mechanism of policy orientation, funding support, technology transfer, and international diffusion. We identify a pronounced global–local technological divergence: global frontier innovation centers on digital basic research, whereas local innovation in China prioritizes engineering applications. Core patents advance the field through cross-domain technology adaptation, and the representative technical paradigm (exemplified by patent CN101168852A) has been industrially validated. These findings provide empirical support for engineering translation and policy optimization in DOM-based heavy metal remediation.