Earth and Environmental Sciences

Urban Heat Islands (UHIs) and intensifying heat stress in cities pose growing risks to public health, outdoor thermal comfort, and climate resilience. Shade is a low-energy and cost-effective strategy for reducing outdoor heat exposure. While a variety of shade infrastructures have been developed to protect people from direct sunlight during hot weather, shelter shade remains largely overlooked in urban studies compared with building and vegetation shade. This study examined the spatial-temporal pattern, demographic equity, and supply-demand relationship of shelter shade in Singapore, a compact tropical city with high outdoor activity levels. A Geographic Information System (GIS)-based simulation model was used to estimate hourly shelter shade coverage ratio on the typical hot day. Shelter shade exposure and equity across socio-economic groups were assessed using a population-weighted exposure model and a Gini index. Shelter shade supply-demand analysis was conducted using a four-quadrant analysis and a priority index. Key findings include: (1) considerable spatial-temporal variation in shelter shade, with coverage peaking at midday and concentrated in the Central and West Regions (average: 0.052 and 0.051, respectively); (2) the elderly and Malays exhibited lower average shelter shade exposure compared to the total population (0.328 and 0.333, respectively); and (3) remarkable supply-demand mismatch (25% of the study area), mainly in central, eastern, and northeastern areas with intensive population activities but limited shelter distribution. The study proposes a shelter shade planning framework to support more equitable and effective shading, informing targeted design guidelines and heat-resilient transport and public space planning.

Soil salinization severely constrains sustainable agricultural development. Accurate monitoring of soil salinity in farmland is therefore of great significance for saline – alkali land management and food security. In this study, typical saline farmland in Caofeidian District, Tangshan, Hebei, China, was selected as the study area, and three experimental plots with different salinization levels were investigated. Considering that the spectral response of soil salinity is relatively weak and that traditional multispectral satellite remote sensing data have limited capability to finely characterize the spatial heterogeneity of salinity, a UAV hyperspectral soil salinity inversion model based on a stacking ensemble framework was developed. Spectral bands were first preprocessed using a combination of logarithmic transformation and standard normal variate transformation. Subsequently, a two-stage feature selection strategy, CARS – SHAP, was proposed to identify informative spectral bands. Specifically, the Competitive Adaptive Reweighted Sampling (CARS) method was first used to select 50 major spectral bands based on regression coefficient weights to reduce redundancy, and multi-model SHAP analysis was then applied to identify 10 key salinity-sensitive spectral bands with stable contributions across different models, thereby improving feature selection stability and prediction consistency under different salinization conditions. Finally, a soil salinity inversion model was constructed using CatBoost, XGBoost, and a multilayer perceptron (MLP) as base learners and partial least squares regression (PLSR) as the meta-learner within the stacking framework. Experimental results demonstrate that the proposed model outperforms conventional single models in both prediction accuracy and stability, achieving an R2 of 0.925 and an RMSE of 0.560 g kg−1 on the validation set. The results provide technical support for high-precision remote sensing monitoring of farmland soil salinity and precision agricultural management.

Predicting canopy traits non-destructively is important for understanding crop growth and improving phenotyping efficiency. Hyperspectral reflectance provides detailed spectral information, but the role of band selection in regression-based trait prediction at the canopy scale remains unclear. In this study, we evaluated the effects of different band-selection algorithms on the prediction accuracy of aboveground biomass (AGB), leaf area index (LAI), and canopy cover (CC) in soybeans using canopy hyperspectral reflectance in the visible to near-infrared (VNIR) range from 501 to 801 nm. The dataset included multiple sites, years, cultivars, and irrigation treatments. We compared a full-band partial least squares regression (PLS) model with three band-selection methods (PLS-Variable Importance in Projection (VIP), Bootstrapped least absolute shrinkage and selection operator (LASSO) (BoLASSO), and an ensemble approach). Model performance was assessed using Kennard–Stone validation and leave-one-year-out cross-validation. The results showed that the effectiveness of band selection depended on the target trait. Full-band PLS performed well for AGB under Kennard–Stone validation, whereas BoLASSO achieved comparable accuracy to PLS for LAI and CC using a reduced number of selected bands. Leave-one-year-out cross-validation showed that year-to-year transferability was more difficult for AGB than for LAI and CC. The selected wavelengths were located mainly in the visible, red-edge, and near-infrared regions. These results indicate that band-selection strategies should be tailored to the target trait and that selected VNIR bands can provide candidate spectral regions for simplified sensing of soybean canopy traits.

Remote Sensing Image-Text Retrieval (RSITR) is the task of learning a shared representation to measure the semantic similarity between remote sensing (RS) images and their textual descriptions. This technology is critical for applications such as disaster assessment and urban management. However, this task is highly challenging due to the varying degrees of alignment reliability between information-dense RS images and their sparse textual descriptions, ranging from explicit mismatches to weak correspondences, thereby fundamentally limiting model retrieval accuracy and robustness. Mainstream embedding-based methods typically rely on discrete supervision paradigms that fail to handle this continuous spectrum of reliability alignment. To resolve this, we propose the continuous reliability-calibrated alignment (CRCA) framework, which pioneers a reliability-aware learning paradigm. The core of our approach is a new supervision framework featuring two key innovations: reliability-calibration weighting (RCW) module, which assigns continuous weights to each pair, with a confidence-gated triplet fusion (CGTF) loss for stable and discriminative learning. To provide a robust foundation for RCW’s assessment, we first employ attentive token condensation (ATC) to purify features by filtering background noise. Furthermore, to deepen the model’s fine-grained semantic understanding, we introduce a Text-guided visual reconstruction (TVR) auxiliary task that compels the model to learn robust local region–word correspondences. Extensive experiments on the RSICD and RSITMD benchmarks demonstrate that CRCA achieves highly competitive performance, with remarkable mR scores of 38.62% and 50.80%, respectively.

Abstract Field‐scale runoff prediction is critical for managing nutrient losses. Ford et al. (2022, https://doi.org/10.1029/2022gl100667 ) present an innovative hybrid modeling and regionalization framework that integrates cluster analysis, National Water Model (NWM) outputs, and machine learning to extend edge‐of‐field (EOF) runoff prediction across the Great Lakes region. In this commentary, we highlight a methodological challenge common in EOF event prediction: when runoff events are rare relative to non‐events, accuracy‐based evaluation can obscure poor event detection. We show that the primary gains in runoff event detection stem from training strategies tailored to datasets dominated by non‐runoff days, with the inclusion of additional soil and meteorological information providing further, complementary improvements while maintaining reasonable overall accuracy. Our results reinforce the promise of the Ford et al. framework, while emphasizing the need for event‐centered evaluation when developing EOF prediction models.

ABSTRACT This study investigates the relationship between the Azores High and Indian summer monsoon during June and September. An opposite correlation pattern is observed during June and September, with significant positive correlation over the Gangetic Plain and north Peninsular India during June, and significant negative correlation over Central India during September. A diagnosis based on monthly ERA5 reanalyzed circulation products archived on finer grids reveals that the sustenance of positive rainfall anomalies over the Gangetic Plain and north Peninsular India during June is supported by the strengthened Azores High near its mean position, zonal extension and northward shift of the Tibetan Anticyclone from its mean position, and strengthening of the Asian jet over the Eurasian region, whereas the sustenance of negative rainfall anomalies over Central India during September is supported by the Azores High, which is shifted to northeast of the North Atlantic, that is, away from its mean position, mid‐latitude waves propagating at higher latitude, hence not affecting Indian summer monsoon rainfall due to their absence towards north India, weakened Tibetan Anticyclone, and weakening of the Asian jet over the Eurasian region.

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.

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.

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.

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.

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.

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.

The global demand for Critical Raw Elements (CRE) has increased owing to their role in clean energy solutions. This demand is expected to double or quadruple by 2050; thus, there is need for enhanced exploration, including new sites, to enable new ore discoveries. This study investigates the distribution of CREs in the volcanic rocks of the Kenya Rift, based on a review of various geochemical and petrological studies. Notable concentrations of chromium (Cr), vanadium (V), rubidium (Rb), zirconium (Zr), niobium (Nb), and barium (Ba) occur in the volcanic rocks of the Kenya rift system. The northern Kenya rift volcanic centers are identified for the economic exploration of Cr and Nb within mafic rocks. Additionally, V concentration up to twice the average concentration of the upper continental crust, can be considered for pilot studies to identify ore minerals. Critical elements such as Rb, Nb, and Zr enriched through fractional crystallization processes, and ongoing geothermal, tectonic and volcanic activities within Olkaria region in CKR are potential sites for future explorations. The northern Kenya rift basalts exhibit elevated concentrations of light rare earth elements (LREE), particularly La and Ce. In contrast, more evolved volcanic rocks, such as trachytes and rhyolites, demonstrate increased overall rare earth elements (REE) and yttrium concentrations, highlighting different magmatic processes along the Kenya rift. La, Ce, Nd, and Y occur at concentrations above 100 mg/kg in felsic volcanic rocks within the Kenya Rift. Siderophile critical elements such as Co, Ni, W, and platinum group metals (PGMs) are notably depleted, suggesting that the magmas underwent substantial differentiation processes that preferentially removed these elements from the melt. Similarly, the low levels of chalcophile critical elements (Sb, Ga, Ge, and Bi) indicate limited sulfide saturation and minimal hydrothermal alteration during volcanic activity. Generally, tectonic, magmatic, and surficial processes facilitate the formation and evolution of critical elements, and sedimentary basins within the Kenya rift represent potential sites for the accumulation of these essential elements. These findings provide direction for resource exploration and evaluation within the Kenya rift system.

Abstract