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The peroxymonosulfate (PMS)-triggered radical and nonradical active species can synergistically guarantee selectively removing micropollutants in complex wastewater; however, realizing this on heterogeneous metal-based catalysts with single active sites remains challenging due to insufficient electron cycle. Herein, we design asymmetric Co–O–Bi triple-atom sites in Co-doped Bi₂O₂CO₃ to facilitate PMS oxidation and reduction simultaneously by enhancing the electron transfer between the active sites. We propose that the asymmetric Co–O–Bi sites result in an electron density increase in the Bi sites and decrease in the Co sites, thereby PMS undergoes a reduction reaction to generate SO₄•- and •OH at the Bi site and an oxidation reaction to generate ¹O₂ at the Co site. We suggest that the synergistic effect of SO₄•-, •OH, and ¹O₂ enables efficient removal and mineralization of micropollutants without interference from organic and inorganic compounds under the environmental background. As a result, the Co-doped Bi₂O₂CO₃ achieves almost 99.3% sulfamethoxazole degradation in 3 min with a k-value as high as 82.95 min−1 M−1, which is superior to the existing catalysts reported so far. This work provides a structural regulation of the active sites approach to control the catalytic function, which will guide the rational design of Fenton-like catalysts.

Recent climate change has caused an increase in warming-driven erosion and sediment transport processes on the Tibetan Plateau (TP). Yet a lack of measurements hinders our understanding of basin-scale sediment dynamics and associated spatiotemporal changes. Here, using satellite-based estimates of suspended sediment, we reconstruct the quantitative history and patterns of erosion and sediment transport in major headwater basins from 1986 to 2021. Out of 13 warming-affected headwater regions, 63% of the rivers have experienced significant increases in sediment flux. Despite such intensified erosion, we find that 30% of the total suspended sediment flux has been temporarily deposited within rivers. Our findings reveal a pronounced spatiotemporal heterogeneity within and across basins. The recurrent fluctuations in erosion-deposition patterns within river channels not only result in the underestimation of erosion magnitude but also drive continuous transformations in valley morphology, thereby endangering local ecosystems, landscape stability, and infrastructure project safety. Climate change intensifies erosion and sediment transport in rivers of the Tibetan Plateau. Satellite data unveil unprecedented patterns of sediment deposition in rivers. Pronounced spatiotemporal heterogeneities within and across basins are found.

Atmospheric methane is a potent greenhouse gas that plays a major role in controlling the Earth’s climate. The causes of the renewed increase of methane concentration since 2007 are uncertain given the multiple sources and complex biogeochemistry. Here, we present a metadata analysis of methane fluxes from all major natural, impacted and human-made aquatic ecosystems. Our revised bottom-up global aquatic methane emissions combine diffusive, ebullitive and/or plant-mediated fluxes from 15 aquatic ecosystems. We emphasize the high variability of methane fluxes within and between aquatic ecosystems and a positively skewed distribution of empirical data, making global estimates sensitive to statistical assumptions and sampling design. We find aquatic ecosystems contribute (median) 41% or (mean) 53% of total global methane emissions from anthropogenic and natural sources. We show that methane emissions increase from natural to impacted aquatic ecosystems and from coastal to freshwater ecosystems. We argue that aquatic emissions will probably increase due to urbanization, eutrophication and positive climate feedbacks and suggest changes in land-use management as potential mitigation strategies to reduce aquatic methane emissions.

Supra‐ and sub‐permafrost groundwater are the two main components of groundwater in permafrost regions. However, due to the lack of groundwater observational data, the spatial‐temporal differentiation of these groundwater components in permafrost basins remains unclear. Based on flow data from 17 hydrological stations in five permafrost rivers within the Eurasian Arctic and Qinghai‐Tibet Plateau permafrost regions, this study tries to determine the proportion of supra‐ and sub‐permafrost groundwater through the corresponding relationship between baseflow separation and baseflow index. The results showed that the annual average contribution of supra‐ and sub‐permafrost groundwater in river runoff to total streamflow in the Yangtze River source basin was 36.81% and 14.56%, respectively. Correspondingly, the Yellow River source basin was 36.58% and 24.46%, the Ob River basin was 37.05% and 26.83%, the Yenisei River basin was 28.80% and 36.56%, and the Lena River basin was 39.13% and 9.54%. Over the past 50–80 years, the ratio of sub‐permafrost groundwater discharge to river runoff and the flux of sub‐permafrost groundwater have shown an increasing trend in all study basins, which was significantly affected by air temperature and permafrost area. Relative contribution of supra‐permafrost groundwater exhibits a significant positive correlation with precipitation and permafrost area. Air temperature has both positive and negative effects on supra‐permafrost groundwater discharge, leading to a rising or falling trend of supra‐permafrost groundwater discharge. In the future, it is necessary to further explore the complex effects of groundwater discharge variations on streamflow in permafrost regions under climate warming. Key Points Based on the relationship between baseflow and baseflow index, groundwater components can be separated in permafrost regions Air temperature exhibits a significant positive and negative correlation with sub‐ and supra‐permafrost groundwater components, respectively The relative proportions of supra‐ and sub‐permafrost groundwater show spatial and temporal heterogeneity

Few-shot object detection (FSOD) remains a critical challenge in computer vision, where the limited training data significantly hinder model performance. Existing methods suffer from poor robustness and accuracy, primarily due to scale sparsity and inadequate feature extraction. In this paper, we propose MDA-HAPP, a novel framework built on a transfer learning architecture and a two-stage object detection approach, specifically designed to address these issues. The key innovations of MDA-HAPP include 1. MultiScale-DynaAttention, a novel attention module that enhances feature extraction by integrating multi scale convolutions into channel attention and applying a dynamic pooling ratio to spatial attention, with residual connections to improve robustness; 2. hierarchical adaptive-pyramid pooling, designed based on a spatial pyramid pooling (SPP) structure, extracts multiscale features from intermediate layers and dynamically adjusts pooling strategies. These features are then fed into a dual-branch detection head for comprehensive results.The experimental results on the PASCAL VOC and COCO datasets show that MDA-HAPP achieves significant improvements across different K-shot settings. Specifically, the model demonstrates an up to 9.8% gain in AP75 on PASCAL VOC for K-shot values of 10 and an up to 3.7% improvement on COCO for K-shot values of 30. These results confirm its superior performance in FSOD and highlight its potential for real-world applications.

The Qinghai‐Tibetan Plateau (QTP) has undergone significant warming, wetting, and greening (WWG) over decades, alongside substantial alterations in hydrological regimes. These changes present great challenges for safeguarding water resources and ecosystems downstream. However, the lack of field observation and systematic research has obscured our understanding of how hydrological processes respond to the combined influences of climate‐permafrost‐vegetation. This study focuses on the source regions of the Yangtze River, one of the highest permafrost‐covered basins on the QTP, and employs a process‐based hydrological model to quantify the effects of WWG on hydrological processes. We show that the increasing precipitation dominates subsurface runoff changes while rising temperature primarily affects surface runoff changes by reducing the frozen duration (−52 days/century) and thickening the active layer (+2.4 cm/year). Greening vegetation primarily affects transpiration and interception evaporation. Warming, wetting, and greening will cause a transition in runoff dynamics from surface runoff dominance to subsurface runoff dominance in permafrost basins, and reduce the risk of both flooding and water shortage indicated by the decreased maximum low flow duration and maximum high flow duration of 11.0 and 5.0 days/year, respectively. Moreover, cold permafrost regions exhibit a greater propensity for generating runoff, as indicated by a higher annual increase in runoff coefficient (0.005/year) and total runoff (4.81 mm/year), compared to warm permafrost regions (with increase of 0.001/year and 1.20 mm/year, respectively). These findings enhance the understanding of hydrological changes due to WWG and provide insights for water resources management in permafrost regions under climate change. Plain Language Summary The Qinghai‐Tibetan Plateau (QTP) has been experiencing significant changes in its climate, becoming warmer, wetter, and greener over the years. These changes have led to major shifts in water flow and availability, posing challenges for managing water resources downstream. However, our understanding of how these changes in climate, permafrost, and vegetation interact and affect hydrological processes has been limited due to a lack of field data and systematic research. This study uses a hydrological model to better understand these effects. It shows that increasing precipitation mainly impacts subsurface runoff and rising air temperatures affect surface runoff by reducing the frozen period and thickening the active layer. Greening vegetation primarily affects transpiration and interception evaporation. Overall, the WWG will shift runoff patterns from surface to subsurface dominance in permafrost areas, reducing the risk of both floods and water shortages. Additionally, colder permafrost regions are more likely to generate runoff compared to warmer permafrost regions. These insights help improve our understanding of how water processes are changing due to the combined effects of a warmer, wetter, and greener climate on the QTP, and they provide valuable information for managing water resources in these regions as the climate continues to change. Key Points Precipitation dominates subsurface runoff changes in permafrost regions, and air temperature affects surface runoff via freeze‐thaw processes Runoff process in permafrost regions will shift from surface runoff to subsurface runoff dominance due to warming, wetting, and greening (WWG) Lower evapotranspiration, thinner active layer, and shorter thaw duration led to higher runoff coefficients and runoff increase rates in cold permafrost (CP) regions

Permafrost carbon pool destabilization causes substantial fluvial export of soil carbon, yet the export patterns and magnitudes are not well understood. Here we investigated the radiocarbon (14C) in dissolved organic and inorganic carbon (DOC and DIC, respectively) exported from a mid-sized river in the central Qinghai-Tibet Plateau (QTP) permafrost region. We utilized the radiocarbon dating technique to reveal the ages of riverine dissolved carbon and a statistical model to partition the riverine carbon from different age categories. DOC and DIC showed bomb-depleted 14C signatures corresponding to millennial ages. Seasonally, 14C-depleted DOC and DIC ages were associated with active layer thaw and flow path deepening. Spatially, older DOC and DIC were found in the valley sites correlated with warmer permafrost and higher groundwater flow. Further, isotopic mixing models suggested that 83 ± 27% of riverine DOC was derived from active layer and permafrost layer aged carbon. DIC export was comprised of a smaller portion of aged carbon (47.3 ± 2.6%) but a much larger flux of aged carbon due to higher annual DIC export. Interestingly, approximately 56% of annual aged DOC and DIC were exported in the short summer season (July to September). The monsoon climate-induced overlap of high discharge and maximum active layer thaw depth in summer enhanced the remarkably rapid fluvial export of millennial-aged carbon. Annual aged carbon yields in YRSR (275 ± 90 and 1661 ± 91 kg km−2 yr−1 for DOC and DIC, respectively) are much larger than those of Kolyma River (160 ± 89 and 234 ± 105 kg km−2 yr−1 for DOC and DIC, respectively). These results suggest a unique old carbon loss pattern in the QTP permafrost region compared to higher latitude permafrost regions with a non-monsoonal climate. As climate warms, more old carbon export is expected, which may affect the permafrost carbon pool and the river biogeochemical processes.

Global warming causes permafrost thawing, transferring large amounts of soil carbon into rivers, which inevitably accelerates riverine CO 2 release. However, temporally and spatially explicit variations of riverine CO 2 emissions remain unclear, limiting the assessment of land carbon-climate feedback. Using new and published 5685 riverine CO 2 partial pressure data in the Arctic and Tibetan Plateau, we show that current riverine CO 2 emission across the Northern Hemisphere permafrost zone is 200 ± 15 Tg C yr⁻ 1 . The emission offsets 28.1 ± 2.1% of the land carbon uptake in the Northern Hemisphere permafrost zone, with large regional variability of 13.1 to 63.1%. Our findings suggest that CO 2 emissions increased at a rate of 0.42 ± 0.16 Tg C yr⁻ 1 during 2000 to 2020, and this is primarily driven by increased precipitation and accelerated permafrost thawing under climate change. This study highlights increased riverine carbon emission and strengthening of the permafrost carbon feedback to climate after incorporating carbon release from rivers. Climate warming causes riverine CO 2 emissions increasing at 0.42 ± 0.16 Tg C yr⁻ 1 in the Northern Hemisphere permafrost regions during 2000–2020.

Climate change is expected to alter the hydrologic cycle through precipitation and evapotranspiration. The hydrologic intensity is more complex under climate change in high mountainous regions. The spatial and temporal patterns of hydrological intensity were analyzed. The results showed that the degree of annual variations of hydrological intensity increased between 2001 and 2015 compared to 1980 and 2000. The slope of hydrological intensification showed a significant downward trend along the elevation gradient from 1980 to 2000, but no significant elevation-dependent pattern existed from 2001 to 2015. The variation of hydrologic intensity in spring before 2000 and after 2000 was most significant, while the change of hydrologic intensity in summer was not significant. Precipitation, air temperature, net radiation, and vapor pressure deficit (VPD) were significantly correlated with the hydrological intensity before 2000, while only precipitation and air temperature were significantly correlated with hydrological intensity after 2000. The spatial correlation coefficient between hydrological intensity and vertically integrated moisture flux at different altitudes was distributed more homogeneity after 2000. Local meteorological factors and large-scale circulation can influence the elevation-dependent precipitation variation. Under climate change, more attention should be paid to no elevation-dependent changes in hydrological intensity and societal decision-making in mountainous regions.

Drivable road segmentation aims to sense the surrounding environment to keep vehicles within safe road boundaries, which is fundamental in Advance Driver-Assistance Systems (ADASs). Existing deep learning-based supervised methods are able to achieve good performance in this field with large amounts of human-labeled training data. However, the process of collecting sufficient fine human-labeled data is extremely time-consuming and expensive. To fill this gap, in this paper, we innovatively propose a general yet effective semi-supervised method for drivable road segmentation with lower labeled-data dependency, high accuracy, and high real-time performance. Specifically, a main encoder and a main decoder are trained in the supervised mode with labeled data generating pseudo labels for the unsupervised training. Then, we innovatively set up both auxiliary encoders and auxiliary decoders in our model that yield feature representations and predictions based on the unlabeled data subjected to different elaborated perturbations. Both auxiliary encoders and decoders can leverage information in unlabeled data by enforcing consistency between predictions of the main modules and those perturbed versions from auxiliary modules. Experimental results on two public datasets (Cityspace and CamVid) verify that our proposed algorithm can almost reach the same performance with high FPS as a fully supervised method with 100% labeled data with only utilizing 40% labeled data in the field of drivable road segmentation. In addition, our semi-supervised algorithm has a good potential to be generalized to all models with an encoder–decoder structure.