Atmospheric and Oceanic Sciences

Hyperspectral image (HSI) clustering aims to partition pixels into distinct clusters by leveraging spectral and spatial features, thereby providing crucial support for the interpretation and information extraction of hyperspectral data. However, due to high spectral variability, complex spatial distribution, and noise interference, HSI clustering still faces considerable challenges. Graph-based clustering represents a prominent learning framework and achieves competitive performance on HSI analysis. However, most existing methods ignore spatial information and suffer from high computational cost, rendering them incapable of effectively dealing with large-scale HSIs. To address the aforementioned challenges, this paper proposes an anchor-level spectral–spatial graph clustering (ASSGC) model for HSIs. The proposed ASSGC employs a band-wise median strategy within each superpixel to generate representative anchors to suppress noise and outlier effects. A novel distance metric is designed to integrate spectral features and spatial positions to effectively identify neighbors and construct a spectral–spatial joint affinity matrix at the anchor-level, thereby reducing computational burden and memory consumption. Subsequently, spectral clustering is applied to obtain anchor labels, which are propagated to the corresponding superpixels to achieve full-image clustering. Experiments on four HSI datasets yield ACC of 64.13% on Indian Pines, 71.33% on Pavia University, 87.86% on Salinas, and 99.23% on Salinas A, demonstrating that the proposed ASSGC outperforms several existing state-of-the-art methods while maintaining low time complexity.

Dissolved organic matter (DOM) plays a central role in soil carbon (C) cycling as the most mobile and reactive C fraction in forests, regulating the microbial metabolism, nutrient availability, and C export. However, molecular-level DOM responses to environmental stressors such as warming and nitrogen (N) deposition remain poorly constrained, particularly under their combined influences. Thus, we investigated how 14 years of soil warming, N-addition, and combined heat + N influence soil-derived DOM quantity and chemistry. Using solution-state NMR spectroscopy and Fourier transform ion cyclotron resonance mass spectrometry, we resolved DOM composition across molecular size, biochemical class, mobility, and oxidation state. While the DOM quantity remained unchanged, warming enhanced microbial processing and oxidative transformation, yielding DOM enriched in oxidized, structurally complex compounds, whereas N-addition suppressed decomposition, limiting the release of plant-derived biopolymers and shifting DOM toward more microbial-derived constituents. Heat + N produced the most compositionally diverse DOM, with molecular shifts more closely resembling warming-induced responses, indicating that temperature-driven decomposition dominates under interacting stressors. These results demonstrate that chronic warming and N addition influence C cycling through distinct, yet nonadditive molecular pathways not captured by single-factor studies. This underscores the necessity of multifactor experiments to accurately capture the current and future ecosystem responses to interacting environmental stressors.

Advanced oxidation processes based on peracetic acid (PAA) have emerged as a sustainable strategy for water treatment; however, developing efficient, stable, and environmentally friendly catalysts remains challenging. In this study, a functional cobalt-based catalyst (CPBCx) was fabricated by immobilizing cobalt ions onto phytic-acid-modified biochar to active PAA for the degradation of sulfamethoxazole (SMX). The effect of pyrolysis temperature on the catalytic performance was investigated, with CPBC8 showing the highest SMX degradation efficiency, under the conditions of a CPBC8 dosage of 200 mg/L, a PAA concentration of 0.2 mM, and an initial SMX concentration of 5 mg/L, and a 99.0% removal of SMX was achieved within 10 min. Moreover, the removal efficiency remained above 90% after five consecutive cycles. Mechanistic analysis revealed that biochar, acting as an efficient electron shuttle, enhanced electron transfer and accelerated the Co2+/Co3+ redox cycle, thereby shifting the SMX degradation pathway from a radical-dominated route to a non-radical one dominated by singlet oxygen (1O2). Density functional theory (DFT) calculations identified the vulnerable attack site (N11) on the SMX molecule. Transformation products and degradation pathways were elucidated using ultra-performance liquid chromatography coupled with time-of-flight mass spectrometry (UPLC-TOF-MS), and the identified intermediates exhibited low ecotoxicity. Furthermore, the CPBC8 composite demonstrated sustained degradation rates, good stability, and environmental compatibility for practical application. This study provides a sustainable and efficient solution for applying biochar-based PAA advanced oxidation processes in water treatment.

Landfill leachate represents a long-term source of contamination that may significantly affect groundwater and receiving water bodies through the migration of organic, inorganic, and toxic pollutants. This study evaluated the long-term migration of landfill leachate and its potential environmental impacts using the LandSim Release 2 probabilistic software model applied to two municipal waste landfills in the Republic of Serbia: the regional sanitary landfill “Gigoš” in Jagodina and the sanitary landfill “Meteris” in Vranje. The modelling framework integrated laboratory leachate analyses, hydrogeological conditions, engineered barrier system characteristics, and receptor-oriented contaminant transport assessment. Model validation was performed through comparison of simulated and laboratory-measured concentrations. Two scenarios were analyzed for each site: an engineered sanitary landfill scenario with a functional containment system and a conservative barrier-failure scenario representing complete loss of engineered barrier functionality. Ten representative leachate parameters were included, covering nitrogen compounds, inorganic ions, toxic substances, and heavy metals/metalloids. The results showed that engineered protection systems significantly delay contaminant migration and reduce receptor concentrations, while barrier-failure conditions lead to earlier pollutant breakthrough and higher environmental risk. The simulations demonstrated that under the engineered sanitary landfill scenario, receptor concentrations of all analyzed contaminants remained below the corresponding maximum allowable concentrations, with contaminant migration occurring only after several centuries. In contrast, the conservative barrier-failure scenario resulted in substantially earlier contaminant breakthrough, with nitrogen compounds and phenols representing the greatest environmental concern due to their rapid migration and exceedance of regulatory thresholds, while the “Meteris” landfill generally exhibited higher receptor concentrations than the “Gigoš” landfill. These findings highlight the importance of predictive modelling and long-term monitoring for sustainable landfill management and groundwater protection.

Dissolved oxygen (DO) is a critical water quality parameter in aquaculture systems. Low DO events can stress, limit the growth of, or even cause mortality of aquatic life in aquaculture systems and require rapid management decisions. This study presents a process-based approach for short-term DO forecasting that is intended to support rapid deployment and transferability across various aquaculture systems. Future DO is computed using a mass-balance equation driven by daily stream metabolism and reaeration coefficients estimated from the previous 24 h of weather and water observations. These coefficients are combined with the next day’s observed water temperature, atmospheric pressure, photosynthetically active radiation, and salinity to predict DO 24 h ahead under idealized measured-input conditions with a ten-minute resolution. Model performance was evaluated across multiple aquaculture ponds with varying aeration techniques by assessing prediction accuracy of daily DO minimums using a safety-based metric and full-day DO trajectories using root mean square error. The model successfully predicted 91.77% of DO drops below 6 mg/L within 1 mg/L in a consistently aerated artificial pond and achieved high success in a natural watershed system. Performance was reduced in systems with highly variable aeration. Prediction accuracy was the highest in surface locations away from aerators. These results indicate that a minimal-history process-based framework can identify low DO risk under idealized measured-input conditions, particularly in surface locations away from aerators and in systems with constant or natural aeration.

The genetic mechanism of the Lower Triassic Dongping carbonate-hosted Mn deposit in South China remains debated, particularly regarding depositional redox conditions and the role of organic matter in Mn carbonate formation. Here we present an integrated sedimentological and geochemical study based on new drill-core materials from the Lower Triassic Mn-bearing succession of the Shipao Formation in the Youjiang Basin. The ore bodies are mainly stratiform and locally exhibit stromatolitic fabrics. Mn-bearing minerals are dominated by kutnohorite, which commonly contains calcite or dolomite nuclei. The Mn-bearing interval is carbonate-dominated and characterized by persistently low TOC contents. Weak authigenic enrichment of redox-sensitive trace metals (Mo, U, and V) in Mn-bearing carbonates, indicates deposition under predominantly oxic to at most suboxic bottom-water conditions. Paleo-productivity proxies (biogenic Ba) indicate relatively high surface-water primary productivity during deposition of the Mn-bearing succession. Notably, the limited accumulation of organic matter despite high paleo-productivity suggests that bottom waters remained oxygenated. Moreover, the predominance of positive Ce anomalies (Ce/Ce*) in Mn carbonates indicated they experienced the Mn-oxide precipitation stage. The negative correlation between Mn contents and Mn carbonate δ 13 C values indicates a significant contribution of organic-derived DIC, likely mixed with seawater and/or carbonate-derived DIC, during Mn carbonate precipitation. These observations support a two-stage sedimentary–diagenetic model in which Mn oxides initially formed and accumulated in an oxic water column and were subsequently reduced during early diagenesis in anoxic pore waters, generating Mn 2+ - and HCO -3 -rich fluids that promoted Mn carbonate precipitation. Petrographic observations further suggest that authigenic carbonate substrates, and possibly microbial influence inferred from stromatolitic fabrics, facilitated Mn carbonate nucleation and growth. This process-based model refines the organo-diagenetic transformation framework for the Dongping deposit and provides improved facies- and mechanism-oriented criteria for exploration of Mn carbonates within the Shipao Formation, with broader implications for carbonate-hosted Mn systems in South China.

Effective forest management depends on accurately monitoring changes in forest health. The Asian longhorned beetle (ALB) has caused extensive mortality in broadleaf trees worldwide. ALB typically manifests as a distinctive treetop-dieback phenotype that progresses downward in damaged trees. Although LiDAR is widely used for plant-stress detection, two challenges persist: (1) non-specific structural responses that hinder the identification of damage-specific phenotypes and (2) limited transferability of structural metrics across age- and size-heterogeneous stands. We address these challenges with a within-tree ratio-based framework that targets the treetop-dieback phenotype with internal normalization. Two poplar plots (young, old) were selected to represent age- and size-related heterogeneity. Based on field evidence (oviposition pits, frass holes, exit holes), each tree was labeled as healthy, lightly damaged, or severely damaged. For each tree, we vertically segmented the point cloud at 50% and 80% of total height and computed crown volume (V), point density (PD), and leaf area index (LAI) of each segment. We then derived upper-to-lower ratio metrics, using the lower crown as an internal normalizer, to capture top-down dieback while normalizing size- and age-related heterogeneity. We also defined a family of combination ratios that aggregate ratios at 50% and 80% heights. Using Linear Discriminant Analysis (LDA), we evaluated separability of each metric under two strategies: (1) a 70/30 random split by trees and (2) a plot-transfer test (train: Stand young ; test: Stand old ). The results indicated that the proposed metrics, Summary Ratio of Volume (SRV = V u p p e r 50 % V l o w e r 50 % + V u p p e r 20 % V l o w e r 80 % ), achieved the highest overall accuracy (OA): 76.23% in random split, and 70.58% in plot-transfer, significantly outperforming other proposed metrics and existing LiDAR indices. By coupling phenotype-focused features with internal normalization, the approach enables precise detection of treetop dieback and improves transferability across age and size heterogeneity.

Tesla valves have emerged as promising passive flow-regulation devices for sustainable water systems because they provide directional flow control without moving parts, external energy input, or complex maintenance requirements. This review systematically examines the fundamental mechanisms, structural evolution, and engineering applications of Tesla valves in water-related systems. The underlying rectification behavior is analyzed from the perspectives of flow separation, recirculation, jet interaction, vortex evolution, and mechanism switching under varying hydraulic conditions. Recent advances in geometric optimization, multistage configurations, three-dimensional architectures, topology optimization, and data-driven design approaches are summarized to illustrate the transition from classical Tesla geometries to next-generation passive flow-control structures. Current applications in microfluidic systems, water-quality monitoring, thermo-hydraulic devices, pressure-regulation networks, and hydraulic safety enhancement are critically reviewed. The analysis indicates that Tesla-valve performance is governed by coupled interactions among geometry, flow regime, fluid properties, and operating conditions, while multifunctional designs increasingly integrate flow regulation, mixing enhancement, heat transfer, and pressure management. Finally, key challenges related to performance standardization, realistic operating conditions, manufacturability, and system-level integration are discussed. Tesla valves are expected to play an increasingly important role in intelligent and energy-efficient water infrastructure, supporting the development of next-generation sustainable water and fluid-management systems.

Microplastics (MPs) pollution has become a serious environmental threat in the 21 st century. Plastics are widely used but improperly disposed of, resulting in large amounts of plastic waste that end up in landfills and/or water bodies, contaminating the environment (terrestrial, aquatic, atmospheric, and biotic ecosystems) through various pathways. MPs (particles <5 mm in size), derived from primary and secondary sources, are a heterogeneous mixture of plastic particles of varying shapes, sizes, colors, and chemical composition. Commonly found plastic types include polyethylene terephthalate (PET), polyethylene (PE), polyvinyl chloride (PVC), polypropylene (PP), polystyrene (PS), polycarbonate (PC), polyurethane (PU), and polyester, etc. MPs are ecotoxic and long-lasting (they may persist for 100 to 1000 years) and adversely affect ecosystems, human health, and all other forms of life (aquatic life, wildlife, livestock, birds, etc.). This paper comprehensively reviews existing knowledge on MPs pollution, pathways, fate, and distribution across different ecosystems, their ecotoxic effects, mitigation and remediation strategies, knowledge gaps, and future research recommendations. Several research efforts have focused on developing standardized, efficient analytical techniques for detecting, measuring, and mitigating MPs pollution across different ecosystems, but significant research gaps remain. There is a lack of standard, efficient, and cost-effective analytical techniques suitable for large-scale applications to detect and measure MPs. Sufficient monitoring data on MPs is also scarce and not readily available. The majority of terrestrial sources of MPs remain overlooked and receive inadequate attention in current research efforts. Several knowledge gaps persist regarding current and future environmental and human health risks posed by MPs pollution. Policy measures for the mitigation and remediation of MPs pollution have been discussed.

Traditional Unmanned Aerial Vehicle (UAV) oblique photogrammetry for 3D real-scene modeling of historic cultural districts suffers from data gaps, insufficient texture, and poor accuracy in complex alleyway environments, hindering the widespread adoption of UAV technology. To address these challenges, this paper establishes a distortion region identification algorithm based on image grayscale variation range parameters. Then, through fusing UAV oblique photogrammetry, close-range smartphone photogrammetry, and Real-Time Kinematic (RTK) positioning technology, it ultimately constructs a 3D real-scene reconstruction technical framework. To validate the method’s effectiveness and reliability, a field experiment was conducted in the Zaoerxiang Historic Cultural District of Zhanggong District, Ganzhou City, Jiangxi Province, China. The experimental results demonstrate that the proposed algorithm can effectively identify distortions in the modeling results from UAV images. After fusing smartphone images from distorted regions and RTK measurements from ground control points (GCPs), the discrepancies in X, Y, and Z coordinates between the results and verification points mostly fall within 10 to 25 mm, while the differences from the measured lengths using a steel tape measure and a leveling rod were within the range of 10 to 20 mm. Furthermore, compared to approaches that rely solely on UAV images or on the fusion of UAV and all ground-based images for modeling, the method proposed in this paper restores building texture information in occluded areas and improves the accuracy of 3D real-scene modeling while simultaneously reducing data-processing and storage requirements and enhancing operational efficiency. It provides a referenceable technical framework for digital preservation, restoration planning, and smart cultural tourism of historic districts.

The rapid development of deep learning methods has significantly improved the effectiveness of object detection in Earth Observation (EO) imagery. However, standard metrics such as Mean Average Precision (mAP) do not fully reflect their utility in operational analyses. This paper proposes a multi-stage methodology for evaluating vehicle detection models, combining classical evaluation with functional analysis encompassing object counting, density estimation, and occupancy index. The research was conducted on high-resolution imagery (WorldView, Pleiades) and the xView dataset, evaluating five YOLO variants alongside transformer-based and two-stage detectors under three training strategies, including fine-tuning. The results show that models achieving high mAP values (up to 0.952) can simultaneously produce significant errors in object count estimation. Models trained exclusively on xView exhibit a substantial performance drop (mAP@0.50 ≈ 0.45) under domain shift conditions. The best results were obtained using a fusion-based approach combining YOLOv9 and YOLOv12, which reduced the mean relative error to 0.14 and the counting error to 13 objects, maintaining a low density error (0.0023). Functional validation across 20 parking areas confirmed the stability of the proposed approach. The findings confirm that functional analysis constitutes a critical complement to classical evaluation in remote sensing applications.

As unmanned aerial vehicles (UAVs) become central to traffic inspection, urban security, and emergency response, UAV-based environmental perception requires both high accuracy and real-time efficiency. However, UAV imagery remains challenging due to three primary factors: detail loss, where small targets occupy minimal pixels and weak edges are diluted by downsampling; ineffective cross-scale fusion, where semantic gaps between shallow and deep features lead to scale misalignment and small-object suppression; and environmental interference, where clutter, occlusion, and dense layouts cause localization drift. To address these challenges, we propose an optimized efficient detector built upon the YOLOv8s framework, incorporating multi-scale feature enhancement and saliency-guided cross-layer fusion. Specifically, we integrate RFCAConv and RGCSP modules into the backbone to strengthen local detail and spatial structure modeling. Furthermore, we design a Multi-Scale Adaptive Fusion Module (MSAFM) to align deep and shallow cues through dual-pooling and adaptive channel recalibration. To handle complex backgrounds, a Saliency-Guided Contextual Attention Module (CASM) is introduced to emphasize target regions, alongside a dynamic detection head for adaptive feature modulation. Evaluated on the VisDrone2019 dataset, our method achieves 48.3% mAP@0.5 and 29.0% mAP@[0.5:0.95], outperforming YOLOv8s by 10.2 and 6.3 points, respectively, while keeping the model compact with 7.2M parameters and a 14.4 MB model size.

The accelerating decline of coral reefs has stimulated restoration strategies that extend beyonzd the coral host to its symbiotic partners. Reef-building corals are holobionts whose resilience depends on interactions among the host, photosynthetic algae of the Symbiodiniaceae family, and diverse microbial communities. Experimental studies show that thermotolerant algal strains and beneficial microbes can improve coral performance under heat stress in controlled settings, yet these advances have not translated into routine restoration practice. We argue that the main barriers are not biological feasibility but gaps in knowledge and design. Three linked challenges constrain translation: (1) disconnected treatment of algal and bacterial symbionts, (2) insufficient functional validation relative to profiling, and (3) the absence of a clear pathway from discovery to carefully governed deployment under context-dependent conditions, especially in data-limited reef regions. To address these constraints, we propose a staged framework that begins with context-specific profiling, applies mechanistic and ecological criteria to candidate selection, embeds functional validation before mesocosm and nursery trials, and integrates delivery, monitoring, risk assessment, and governance into pilot deployment. A profiling-driven, evidence-based strategy is essential if microbial and algal symbionts are to become prospective restoration tools for coral reef restoration, particularly where resources, baseline data, and regulatory capacity remain limited.

Fire-induced transformation and isotopic fractionation of soil organic carbon (SOC) among density fractions remain poorly understood when investigating SOC turnover in postfire vegetation recovery. To specifically focus on the heating-induced processes, laboratory-controlled pyrolysis of forest soils was studied in a temperature gradient (simulating fire intensities) by combining density fractionation, molecular biomarker, and δ 13 C analysis. Results showed that increasing heating intensity reduced SOC content, enhanced carbon aromatization, and generated substantial pyrogenic carbon (PyC). The free light fraction (fLF) exhibited higher SOC loss and lower PyC yield compared to the heavy fraction. Preferential loss of light isotopes ( 12 C) enriched 13 C in residual pools, elevating δ 13 C in bulk soil from −26.0‰ to −21.8‰. The most pronounced 13 C enrichment occurred in fLF due to extensive SOC loss, and this enriched carbon was readily solubilized into dissolved organic matter (DOM). Notably, the isotopic fractionation during heating significantly exceeded typical microbial-induced fractionation of <3‰. DOM extracted from soils heated at 400 °C featured aromatic and phenolic-C structures, indicating PyC origins. In contrast, DOM from the 550 to 700 °C treatments contained mostly carboxyl and carbonyl-C, derived from highly oxidized SOC. These 13 C-enriched components intensified fractionation between DOM and residual organic carbon. This study clarifies mechanisms of fire-driven SOC redistribution and isotopic fractionation, highlighting the critical role of wildfire in soil carbon cycling.

Anthropogenic pollution in developed coastal areas often causes widespread seagrass loss. In Cockburn Sound, Western Australia, industrial run-off drastically reduced cover of Posidonia spp. by 77% in the 1960s–1990s. Despite significant water quality improvements, natural recovery remains limited, potentially due to legacy pollution and phytotoxic hydrogen sulfide (H 2 S) production in sediments. Using a novel multidisciplinary approach combining metabolomics, nutrient (carbon, nitrogen, phosphorus), and δ 34 S isotope analysis, we assessed whether capping existing sediment with clean, dredged material could support Posidonia australis restoration. Seagrass was transplanted into 15 garden beds across three treatments: i) Experimental control (no sediment capping); ii) Capped (capped sediment); iii) Capped + wrack (capped sediment mixed with dried seagrass leaf material). Within two weeks, sulfur cycle-related metabolites were up-regulated in seagrass growing in capped sediment which was likely due to elevated H 2 S intrusion into the leaves. Up-regulation of tocopherols suggested that P. australis activated vitamin E -related pathways to mitigate stress. Overall, sediment capping impaired seagrass health and failed to reduce conditions promoting H 2 S intrusion into plant tissue, likely because of the fine texture of the dredged material. Careful sediment assessment and modification are essential before repurposing such material for seagrass restoration.

The interaction between salt solutions and expansive soils is critical for engineering in chemically aggressive environments. However, the effect of iodide salts on pore water distribution in expansive soils remains poorly understood. This study investigated the transverse relaxation time (T2) characteristics of pore water in expansive soils under varying KI concentrations (0–20%), moisture content (8.7–26.0%), and dry density (1.26–1.79 g/cm3) using nuclear magnetic resonance (NMR). All T2 curves exhibited a single peak. Increasing moisture content from 8.7% to 26.0% resulted in increases of approximately 63% in T2 at peak and 408–439% in peak area. Increasing KI concentration decreased both T2 at peak by up to 33.3% and peak area by up to 44.0% within the tested range, attributed to diffuse double-layer compression and signal loss. Increasing moisture content broadened the T2 distribution and linearly increased T2 at peak and peak area, indicating water gradually occupied larger pore spaces as moisture content rose. T2 at peak was independent of dry density, while the peak area showed a linear relationship with dry density, consistent with mass balance. The observed systematic linear relationships among T2 at peak, peak area, and the three experimental variables suggest that NMR is a promising tool for the quantitative assessment of salt solution effects on pore water in expansive soils. These findings provide a theoretical basis for evaluating salt-affected expansive soils in coastal and arid regions.

Understanding turbidity in coastal systems is essential to ensure the sustainable management of these ecosystems, which are increasingly under pressure from natural factors and human activities. Thus, this study aims to develop a local Sentinel-2-based turbidity model for the Aveiro lagoon (Portugal) by combining Sentinel-2 records with in situ measurements. A field campaign synchronized with a Sentinel-2 overpass was conducted across the lagoon channels on 28 May 2025, to capture spatial variability by measuring near-surface turbidity and Secchi depth, for correspondence with the spectral records of satellite. Remote Sensing Reflectance (Rrs) and turbidity were derived using various algorithms integrated within the ACOLITE software (v20250114.0). Additionally, new turbidity models were developed and empirically adjusted based on the Rrs data, with their performance quantified through the coefficient of determination (R2) and Root Mean Square Error (RMSE). The results showed that the existing algorithms are not directly suitable for the Aveiro lagoon, as they underestimate the highest turbidity values. The ratio between 665 and 560 nm bands (RGratio) proved to be the most suitable spectral index, performing best in estimating turbidity (R2 = 0.822 and RMSE = 1.77 NTU). This study highlights the importance of locally calibrated models over standard ACOLITE algorithms for turbidity retrieval in shallow coastal lagoons, while emphasizing that the proposed model was calibrated for the tidal, wind, and river discharge conditions sampled during the campaign and has not yet been independently validated.

In window tracking mode, stray light and detector readout noise can submerge star spot signals in star sensor images. The resulting degradation reduces centroid extraction accuracy and may even cause extraction failure, thereby preventing precise attitude determination. This study uses the self-supervised spatiotemporal denoising model ASTERIS as the baseline. ASTERIS integrates 3D spatiotemporal inputs with a global attention mechanism for joint noise modeling, thereby providing stronger denoising and restoration capability than conventional methods such as multi-frame stacking. However, ASTERIS lacks adaptive compensation for subpixel jitter in on-orbit star images and has difficulty preserving the high-frequency morphology of star spots, affecting denoising performance and centroiding accuracy. To address these limitations, this study introduces two improvements: First, frame-by-frame spatial deformable convolution is incorporated into the decoder upsampling stage to adaptively compensate for subpixel offsets, actively suppress background noise, and lower the parameter count. Second, a complex-valued frequency domain loss with a high-frequency weighted mask is designed to jointly constrain the amplitude and phase spectra, thereby preserving high-frequency star spot details. Experimental results show that, for star images with extremely low signal-to-noise ratios, the proposed method improves the peak signal-to-noise ratio by approximately 17.8 dB and reduces the centroid localization error to approximately 0.1 pixels. This performance is substantially better than that of the original ASTERIS model, which improves the peak signal-to-noise ratio by approximately 9.5 dB and yields an error of approximately 0.4 pixels, and the multi-frame stacking method, which improves the peak signal-to-noise ratio by approximately 6.0 dB and yields an error of approximately 0.5 pixels. Under the simulated strong noise conditions considered in this study, the proposed method achieves effective centroid extraction, demonstrating its potential for on-orbit star sensor data processing. Future work will further address its engineering deployment.

In late 2024, a mortality event affecting wild dusky grouper ( Epinephelus marginatus ) was reported across multiple islands of the Azores archipelago, Portugal. Following the previous report of the initial detection of nervous necrosis virus (NNV) infection, biological material was submitted to the National Reference Laboratory, where the outbreak was confirmed and the virus was comprehensively characterized through molecular approaches. Phylogenetic analyses based on RNA1 and RNA2 genomic segments consistently classified the detected virus within genotype I (RGNNV), the most widespread betanodavirus genotype. Maximum likelihood reconstructions revealed strong clustering of the Azorean sequences within the RGNNV lineage, with high bootstrap support and no evidence of genomic reassortment. To further investigate the origin and spread of the outbreak, a spatiotemporal and time-resolved phylogenetic analysis was conducted using the INSaFLU/Nextstrain framework. This approach identified a well-supported monophyletic cluster corresponding to the Azorean outbreak, suggesting a recent common ancestor and local expansion. Comparative analyses indicated closest genetic similarity to strains previously reported in the Indo-Pacific, supporting the hypothesis of long-distance viral dissemination followed by regional emergence, rather than direct transmission from geographically closer European outbreaks. This study provides the first spatiotemporal reconstruction of an NNV outbreak in the Azores and highlights the potential epidemiological interface between aquaculture-associated viral reservoirs and wild marine populations. These findings underscore the need for enhanced surveillance and integrated management strategies to mitigate the impact of viral diseases on vulnerable marine species.

Conservation science has made extraordinary progress in documenting the ecological systems we are losing. It has made far less progress in understanding the human affective systems that determine whether those ecological systems will be protected, restored, and sustainably governed over time. This paper introduces Eco-Affective Health as a theoretical framework and proposes six testable hypotheses for understanding the affective dimensions of human-nature disconnection and reconnection in conservation contexts. The framework rests on a foundational reorientation: emotions are not downstream reactions to ecological conditions but constitute the primary regulatory mechanism through which humans perceive, interpret, and respond to the living world. This positioning generates specific, empirically testable predictions about how eco-affective states, including solastalgia, soliphilia, awe, ecological identity, place attachment, and collective efficacy, shape conservation outcomes across individual, community, and governance scales. The framework introduces the solastalgia-soliphilia axis as a diagnostic and enabling tool for conservation monitoring, conceptualizes affective tipping points as analogues to ecological early-warning signals, and proposes the Symbiocene as a health-oriented evaluative horizon for conservation decisions. Three integrated tables map eco-affective constructs to testable predictions, formalize six falsifiable hypotheses, and translate the framework into a proposed expansion of conservation monitoring systems. The paper argues that the social feasibility of conservation outcomes, the durability of stewardship across populations, and the legitimacy of conservation governance all depend fundamentally on affective infrastructure that conservation science currently lacks the conceptual tools to measure, design for, or predict. Addressing this gap is not a theoretical nicety. It is a prerequisite for conservation science adequate to the scale of the biodiversity crisis.

The microwave imager (MWRI-RM) uses the dual-point calibration on the feed port surface method to achieve real-time on-orbit calibration. Within one calibration cycle, the feed rotates under the cold-space reflector to receive the cosmic background brightness temperature as the cold calibration point; the feed then rotates under the onboard blackbody to receive blackbody radiation as the hot calibration point. Therefore, the emissivity of the onboard blackbody directly affects the on-orbit calibration accuracy of the microwave imager, making it necessary to conduct blackbody correction tests on the ground to calibrate the blackbody observation model. Ground vacuum test results show that the emissivity of the onboard blackbody in the 10.65–183 GHz frequency range is better than 0.9975, and after emissivity correction, the error when the microwave imager observes the onboard blackbody is better than 0.2 K.

Abstract Recently, Hua et al. (2026, https://doi.org/10.1029/2025GL120881 ) combined observations during a 4‐day storm period with numerical simulations and concluded that the abrupt electron loss in the field‐line‐curvature scattering (FLCS) region played a leading role in shaping the outer boundary of radiation belt (RB); the authors conjectured that the simple yet fundamental FLCS process, neglected in previous global radiation belt models, is sufficient to explain the dynamics of the outer electron belt boundary. In this commentary, we discuss the importance of the FLCS‐induced loss mechanism in the light of (a) existing information about the energetic electron precipitation and (b) contradictory conclusions of another recent study (Huang et al., 2025, https://doi.org/10.1029/2025ja033965 ). While in a qualitative sense we agree with the conjecture by Hua et al. (2026, https://doi.org/10.1029/2025GL120881 ) accurate quantification of the FLCS effects, based on a more realistic field model is still needed. Possible pathways for solving this challenging problem are outlined.

Abstract Household carbon emissions in low-and middle-income countries may be systematically underestimated when non-purchased biomass fuels are excluded from empirical analysis. This issue is particularly relevant in Pakistan, where poorer and rural households continue to rely on firewood, dung cakes, and agricultural residues obtained outside formal market transactions. This paper examines how incorporating non-purchased fuels changes the level and distribution of household energy-related carbon emissions in Pakistan. Using the 2018 Pakistan Household Budget Survey, we combine source-based emissions accounting with descriptive analysis and selection-adjusted quantile regression. This framework accounts for both the non-random incidence of non-purchased fuel use and heterogeneity across the consumption distribution. The results show that reliance on non-purchased fuels is concentrated among poorer and rural households and is closely associated with lower income, limited access to modern energy, household composition, and housing characteristics. Selection effects are also important, supporting the use of selection-corrected methods. The findings show that excluding nonmarket fuels understates household carbon emissions, particularly at the lower end of the welfare distribution. They highlight the need to incorporate non-purchased energy use into household emissions measurement and distributional environmental analysis in biomassdependent economies.

Ammonia (NH 3 ) is a promising carbon-free fuel, but its industrial application is challenged by low reactivity and substantial nitrogen oxide (NO x ) emissions. This study proposes a simulation-guided reactant optimization strategy to achieve high-efficiency NH 3 combustion with exceptional nitrogen (N 2 ) selectivity. Chemical kinetic modeling using Chemkin-Pro elucidated intrinsic mechanistic bottlenecks: while hydrogen (H 2 ) acts as a promoter, excessive H 2 functions as a radical scavenger by preferentially consuming OH radicals, whereas excess oxygen (O 2 ) accelerates the deep oxidation of amine intermediates toward NO x . Guided by these mechanistic insights, we experimentally validated this strategy over a CuO catalyst, where surface reactions closely mirrored gas-phase radical trends. Specifically, H 2 functioned as a critical chemical promoter, generating surface-active species that triggered low-temperature ignition. Furthermore, the catalyst exhibited robust activity under O 2 -lean conditions (X O2 = 0.7), likely due to the rapid saturation of surface active sites, effectively suppressing deep oxidation pathways without sacrificing reactivity. Under the optimized conditions (X H2 = 3 and X O2 = 0.7), the CuO system achieved complete NH 3 conversion at 500 °C with > 99% N 2 selectivity at 600 °C. These results demonstrate that synergistically coupling radical-controlled fuel composition with selective catalytic oxidation provides a robust pathway for developing eco-friendly NH 3 power systems.