In 2023, the Naiman trona deposit in Inner Mongolia was discovered, representing the second-largest trona deposit in the world and the largest in Asia, with proven trona resources estimated at approximately 2.077 billion tons. This discovery presents a significant opportunity for the restructuring of China’s soda ash industry. The global and domestic development status of trona was comprehensively reviewed, highlighting the necessity for China’s soda ash industry to significantly increase its reliance on trona to enhance its international competitiveness. Considering the distinctive geological characteristics and resource distribution of the Naiman trona deposit, characterized by high associated oil and gas reserves, co-occurrence with salt and soda, deep burial, and steep inclination, three major engineering challenges were posed during the development process. \mathrm{①} Hydrocarbons are enriched within the crystal lattices and fracture networks of trona and halite layers, as well as within the pore spaces and bedding fractures of the surrounding mudstone, resulting in severe oil emulsification during solution mining. \mathrm{②} Nearly half of the Naiman trona deposit is composed of low-to-moderate salinity trona, for which cost-effective and high-efficiency salt-soda separation technologies have yet to be developed. \mathrm{③} The engineering complexity of horizontal well construction is markedly increased by deep burial depth, steep formation dip, and the presence of funnel-shaped structural blocks. Future research should prioritize the development of high-performance and environmentally benign demulsifiers, the integrated and coordinated utilization of co-occurring salt and soda resources, and high-precision horizontal well construction technologies enabled by multidisciplinary integration, thereby supporting the efficient and sustainable exploitation of the Naiman trona deposit.

Studying the supply-demand relationship of ecosystem services and implementing zoned management are crucial for reconciling ecological protection with socioeconomic development in ecologically fragile regions. This study takes Naiman Banner, a typical sandy land area in Northern China, as a case study. We quantified the supply of key ecosystem services (e.g., water yield, carbon sequestration, and soil conservation) from 2000 to 2020 using the InVEST model, while the demand was assessed based on socioeconomic data. Spatiotemporal patterns were analyzed using hotspot-cold spot analysis, and the driving mechanisms behind the comprehensive ecosystem service supply-demand ratio were investigated using the GeoDetector model. The results revealed three key findings. First, over the two decades, the overall supply capacity of ecosystem services in Naiman Banner increased by 19.81%, whereas the demand decreased by 11.09%. Consequently, the overall supply-demand ratio improved significantly by 39.73%, indicating a substantial enhancement in ecological sustainability. Spatially, the comprehensive supply-demand ratio exhibited a distinct pattern of ‘surplus in the north and south with a deficit in the center’, primarily shaped by the regional landscape configuration and the intensity of human activities. Second, factor detection identified that the proportion of forest land was the primary driver of hotspot areas (high supply-demand ratio), underscoring the critical role of afforestation and forest conservation. Conversely, the proportion of sandy land was identified as the core driver of cold spot areas (low supply-demand ratio), highlighting the impact of desertification. Third, based on an integrated analysis of the comprehensive supply-demand ratio, its spatial matching relationship, and the proportional areas of hotspots and cold spots, a systematic ecological management zoning scheme was developed. Naiman Banner was categorized into six secondary and ten tertiary zones, which were classified into four major types: ecological conservation, ecological restoration, ecological enhancement, and ecological development. Targeted and differentiated management strategies were proposed for each zone. This research provides a scientific basis for precise ecological protection and sustainable socio-economic development in Naiman Banner, offering a replicable framework for similar arid and semi-arid regions.

Chinese Maifanite, a natural mineral from Naiman Banner,Tongliao City, Inner Mongolia Autonomous Region, has potential applications in water purification, environmental remediation, healthcare, and agricultural improvement. Weathering processes alter the physicochemical properties of Maifanite, thereby influencing its heavy metal adsorption performance. This research systematically investigated Maifanite samples collected from different depths in the southern mountainous mining area of Naiman Banner, conducted comprehensive analyses including weathering degree assessment, heavy metal adsorption experiments, and prevention and control mechanism studies combining physicochemical characterization. The research reveals that Naiman Banner Maifanite primarily undergoes physical weathering. Based on macroscopic characteristics, Chemical Index of Alteration (CIA), and weathering coefficient K_f , deep-layer samples were identified as slightly weathered, while surface samples exhibited moderate to strong weathering. Weathering increased the specific surface area of Maifanite by 66.81%, reduced pores smaller than 3.6 nm, and increased pores between 3.6~4.0 nm from 12.36% to 40.05%. Additionally, weathering caused the destruction of quartz and plagioclase crystals, leaching of alkaline elements (Na, Mg, Si, K, Ca). The zero potential point shifted markedly from 7.09 to below 2. Notably, deep-layer Maifanite demonstrated preferential adsorption toward anionic chromium (Cr)-a characteristic groundwater contaminant-with a maximum theoretical adsorption capacity of 0.90 mg/g. Conversely, surface-weathered Maifanite exhibited enhanced adsorption capacity for cationic cadmium (Cd)-a typical soil pollutant-reaching 5.57 mg/g. The adsorption process, comprising three distinct stages (surface diffusion, mesopore diffusion, and micropore diffusion), achieved equilibrium within 24 hours. Multilayer heavy metal adsorption mechanisms were predominantly governed by electrostatic interactions with additional contributions from surface hydroxyl complexation. In conclusion, natural geological weathering induces significant physicochemical modifications in Maifanite, including the observed zero potential point shift, which collectively enhance its capacity for comprehensive heavy metal prevention and control in both soil and groundwater systems. This research provides crucial theoretical foundations for the sustainable development and utilization of Naiman Banner Maifanite in groundwater purification applications.

With increasing demands for timeliness and update frequency in wind power prediction, the limitations of methods based on Numerical Weather Prediction (NWP) have become increasingly evident. The development of artificial intelligence meteorological models has provided an efficient source of meteorological forecasts for wind power prediction. Selecting an offshore wind farm in Yangjiang, Guangdong as the service object, the Pangu AI meteorological model and the numerical weather prediction model CMA-GD were applied to conduct simulation calculations for a total of 17 months in 2023 and 2024. Four sets of power prediction experiments were designed, which used different meteorological data forecast sources, different power prediction methods, and a wind speed correction method, to compare and analyze the improvement of wind power prediction. The experimental results show that the Pangu model achieves accuracy comparable to or even surpassing that of numerical weather prediction models. When using the generated site power curve for power prediction, the short-term power prediction accuracy based on the Pangu model meteorological forecast exceeds that based on CMA-GD, and even in some months of 2024, the prediction performance is comparable to that of using machine learning algorithms for power prediction. According to the November 2023 experimental results, the power prediction model based on corrected Pangu model forecasts and the ResMLP algorithm achieved the best power prediction performance, with a 24-hour prediction accuracy of 0.714 and a 96-hour prediction accuracy of 0.654. Applying this model to the period from January to June 2024 yielded stable results that met the grid assessment standards. This study provides a referable solution for applying the Pangu or other artificial intelligence meteorological models in wind power prediction operations.

Naiman Banner is located in an arid and semi-arid region and represents a typical agro-pastoral ecotone. Agricultural production in this area has long been constrained by complex natural conditions and the fragmented spatial distribution of cropland, resulting in an unclear understanding of the current state of farmland utilization. This, in turn, limits the region’s capacity for agricultural resource management and targeted policy implementation. Conducting an analysis of farmland utilization in Naiman Banner is therefore crucial for promoting high-quality agricultural development in agro-pastoral transitional zones and supporting the national food security strategy. In this study, Naiman Banner was selected as the research area. Based on Landsat 8 time-series imagery, the spatial distribution of major grain crops from 2020 to 2024 was extracted using the random forest method. Additionally, Jilin-1 high-resolution remote sensing imagery was used in combination with a multi-scale segmentation algorithm and the U-Net model to obtain cropland field parcels and road data for 2020 and 2024, respectively. On this basis, an evaluation index system for assessing the level of farmland infrastructure construction in the region was established. By integrating the extracted spatial distribution of major grain crops, this study focuses on analyzing the spatiotemporal patterns of major grain crops, the level of farmland infrastructure construction, and their interrelationship in Naiman Banner. Results showed a steady increase in the planting area of major grain crops over the five-year period, with a cumulative net growth of 22 673.46 hm², representing a 12.9% increase. Planting areas gradually shifted from general farmland to well-facilitated farmland. Meanwhile, agricultural infrastructure was continuously improved, with significantly higher development levels observed in well-facilitated farmland areas compared to general farmland. Further analysis revealed that regions with higher levels of agricultural infrastructure exhibited higher utilization rates of staple crops, demonstrating clear spatial consistency and a positive correlation. These findings provide provide technical support and decision-making references for regional agricultural resource management, optimization of infrastructure allocation, and food security assurance.

Microbes are known to show a great spatiotemporal distribution, and exert extensive and intensive geological agents in both modern days and Earth history. These features make the microbes play important roles on great changes of Earth environments, enabling important and wide applications in geoengineering including the pollutant remediation, decrease of atmospheric CO₂, geohazards prevention, as well as toxicity decrease. This necessitates the cross-disciplinary construction from microbial Earth to microbial geoengineering.It is well known that microbes, the engineer of elemental geochemical cycles, have played the key roles in the geoengineering fields including carbon sink, ecological remediation and the agriculture practice. The carbon pump and the microbial carbon pump, the important mechanisms to transport the atmospheric CO₂ into the sediments or seawater, are documented to mainly regulate by the microbial communities either in the sea or on the land. Microbes are widely involved into, and known as the engineer of, the geochemical cycles of greenhouse gases including CH₄, CO₂ and N₂O. These microbial processes could be exploited in the geoengineering to promote the carbon sink or decrease the carbon release. Microbial transformation of a series of metal ions as well as the degradation on organics has been widely used in the ecological remediation of polluted environments. Microbial release of elements including carbon, nitrogen, phosphor etc., from a variety of minerals is applied in agriculture practice. The artificial microbial mixtures on the basis of natural communities could be used as the nature-based fertilizers in the farming practice. Microbial roles, played on the precipitation and erosion of minerals, could also be applied into rocks and soils engineering, deep Earth engineering and mining industry. The microbial application to these geoengineerings will greatly save the costs, remarkably promote efficiency and noticeably protect the natural environments. Microbial transformation of the expansive clay minerals into no-expansive ones could be applied into the oil recovery by water flooding as well as the rocks and soils geotechnical engineering. Carbonate factory is known to be primarily induced by microbial communities via the precipitation of calcium carbonate from the fluids which could be introduced into the building of artificial islands in the sea, the filling and repairing of rock cracks, cementation of coarse grains in a variety of geoengineering. Microbial erosion of minerals could be exploited into the mining industry via the release of metals of economic significance from ores. The presence of the so-called deep biosphere, featured by the dominance of extreme environment microbes, will exert positive and negative effects on the underground storage of dangerous materials including the nuclear wastes, CO₂ and hydrogen gas. The investigations on the microbial roles on these materials as well as the storage containers are of in particular importance.Whilst most microbial geoengineering has been conducted to prevent and control the geohazards that have come into being in natural environments, microbes could further provide the early warning of some geohazards including the biotic or ecological crisis, climatic and environmental disasters, as well as landslides due to their sensitive response to minor environmental changes. To construct the early warning geoengineering via the on-site filed observatory network is of importance so that we could take some measures to prevent the occurrence of the geohazards, or make the positive use of the microbial roles but suppress the negative roles.

The Horqin Sandy Lands, located in the ecologically sensitive transitional belt between semi-arid and semi-humid climatic zones, support a complex mosaic of ecosystems characterized by inherent vulnerability and represent one of the most severely desertified regions in northern China. Despite its environmental significance, comprehensive studies that systematically integrate the biogeographical mechanisms underlying desertification with rigorous assessments of control efficacy in this region remain notably scarce. The region's comparatively substantial groundwater resources are fundamentally underpinned by an extensive and porous sandy sedimentary stratum, measuring 80 to 200 meters in thickness, which serves as a critical aquifer system. The period from 1985 to 2000 witnessed remarkable progress in desertification control, marked by a significant reversal in land degradation trends. Nevertheless, the subsequent phase post-2000 has been defined by a pronounced operational bottleneck, manifesting as a markedly decelerated rate of reduction in desertified land area and a stagnating trajectory in the recovery of biomass. In the context of ongoing climate change, the foremost challenge confronting sustainable desertification management in the Horqin Sandy Lands is escalating water resource stress, which threatens the longevity of past restoration gains and future initiatives. This review systematically synthesizes and critically evaluates a substantial body of research pertaining to the geographical foundations of ecological vulnerability, the dynamic processes and mechanisms of desertification and ecological recovery, and the intricate interrelationships between biological productivity fluxes and groundwater dynamics. By constructing a comprehensive synthesis from these diverse research strands, this review aims to elucidate the complete narrative of desertification control tracing its foundations, achievements, and contemporary challenges， thereby providing a robust theoretical framework and empirical evidence base to inform the scientific management of soil and water resources and to guide the sustainable restoration of degraded landscapes in the Horqin Sandy Lands.

The Naiman trona deposit in Inner Mongolia is a supergiant deposit discovered in recent years in China, with identified resources of 2.077 billion tons, making it the largest trona deposit so far discovered in China and even in Asia. The deposit is located in the Naiman Sag on the southwestern margin of the Songliao Basin and is hosted in strata of the Lower Cretaceous Yixian-Jiufotang formations. It is characterized by high ore grade, great ore-layer thickness, relatively deep burial, and complex mineralogical composition. Vertically, it consists of interbedded trona ore layers, rock-salt layers and mudstones, forming as many as 118 sedimentary cycles, while horizontally it exhibits a lenticular distribution. Seven Na-carbonate minerals, including trona, nahcolite and natrocalcite, and eight associated minerals, including halite, anhydrite and searlesite, have been identified. A key scientific question is why large-scale trona enrichment took place in the Naiman area during the Early Cretaceous greenhouse period under an extensional tectonic regime. The ore-forming mechanism may be investigated from the following aspects: conducting in-depth studies on the role of fault systems as channels for hydrothermal fluid migration in an extensional setting, and on the effects of continuous basin subsidence on the formation and preservation of ore layers; resolving the material contributions of volcanic rock weathering, deep magmatic-hydrothermal fluids and high atmospheric CO₂ concentrations by means of geochemical tracing techniques; precisely constraining the timing of mineralization by integrating radiometric dating with biostratigraphic methods; and elucidating the controlling mechanisms of paleoclimate and paleoenvironment on trona sedimentary cycles through a combination of paleoclimatic-paleogeographic proxies and sedimentary cycle analysis. Through this integrated metallogenic research framework that couples deep tectonic architecture, material sources, metallogenic timing and sedimentary paleoclimate-paleoenvironment, the formation mechanism and metallogenic model of the Naiman supergiant trona deposit can be revealed, which is of great fundamental scientific significance. Meanwhile, the research results will support exploration of trona deposits in the Songliao Basin and surrounding areas and promote the upgrading of China’s soda ash industry, thus having important strategic and practical significance.

As a water conservation area in the Horqin agro-pastoral ecotone, Naiman Banner boasts strontium and metasilicic acid-rich groundwater, which provides opportunities for groundwater health research and mineral water development. This study selected the southern mountainous area of Naiman Banner, Inner Mongolia, as the target area. Based on hydrogeological surveys and test data from 37 groups of groundwater samples, hydrogeochemical analysis methods were employed to reveal the spatial differentiation patterns and formation mechanisms of strontium-rich and metasilicic acid groundwater. Results indicated that the groundwater in the study area is neutral weak alkaline, with HCO₃-Ca as the main hydrochemical type. The strontium content ranges from 0.24 to 1.83 mg/L, and the metasilicic acid content ranges from 14.9 to 29.9 mg/L. The strontium rich metasilicic acid composite groundwater is distributed around the Maifanshi mining area in the area. The weathering and dissolution of carbonate rocks and silicate rocks, as well as the alternating adsorption of cations, promote the enrichment of strontium in groundwater. Indoor experiments have shown that the leaching of vermiculite is beneficial for the formation of metasilicic groundwater. The study also indicates that the pore fissure aquifer in the area has the potential for industrialized development of strontium and metasilicic acid-rich mineral water, providing scientific basis for rural revitalization and coordinated utilization of geological resources.

Oceanic Mesoscale Eddies (ME) carry more than 90% of the kinetic energy in the upper global ocean, playing vital roles in the material and energy transport. They are highly active in the South China Sea (SCS), with complex dynamics for their generation and dissipation, which are received an increasingly attention from physical oceanographers. Through comprehensive analysis of extensively relevant literatures, it is found that the understanding of the three-dimensional structural characteristics of ME in the SCS more clear, the mechanisms of generation mainly include local wind stress, intrusion of the Kuroshio from the Luzon Strait, the westward propagation of Rossby wave in the Pacific Ocean, and a combination of multiple factors. ME’s dissipation is mainly caused by instability during their propagation or interaction with internal waves. It is shown that there is the ability to reconstruct and predict ME for those popular numerical models, data assimilation, and artificial intelligence technologies, but their accuracy still needs to be further improved. It aims to provide a systematic reference for the comprehensive understanding of ME dynamical processes and the improvement of their operational forecasting skills in the SCS. We suggest that combining dynamics theory, advanced ocean numerical models and data assimilation, big data and artificial intelligence to optimize ME simulation, is one of the key points for eddy research and forecasting in the future.

Based on the strategy of rural revitalization and the perspective of earth sciense, combining the local demands of industrial development transformation in Naiman Banner, Inner Mongolia, this study utilizes enterprise big data obtained from the Tianyancha database, GIS-based spatial distribution analysis, and big data analytics to reveal the spatiotemporal evolution characteristics of industrial development in Naiman Banner and explore the influencing factors of its spatial pattern. The results indicate that the spatial distribution of industries in Naiman Banner has gradually evolved into a belt-shaped pattern, centered around Daqintala Town and extending in a north-south orientation across the banner. Population distribution, transportation infrastructure, and land use significantly influence the industrial spatial pattern, with industries tending to cluster in urban areas near national and provincial highways, a trend more pronounced in densely populated regions. Based on the analytical results and practical industrial development conditions, this study proposes scientific recommendations for structural transformation and spatial optimization of industrial development in Naiman Banner, offering actionable insights for local policymaking.

The farming-pastoral zone in Naiman Banner is located in the hinterland of Horqin Sandy Land. The research on the formation mechanism and background values of groundwater hydrochemistry supports the allocation of water resources and the green development of agriculture and animal husbandry. Based on hydrogeological survey and hydrogeochemical analysis, combined with self-organizing map neural network (SOM) and K-means clustering hybrid algorithm, this study revealed the characteristics of groundwater chemical composition, main controlling factors, and environmental background values. Results indicated significant spatial heterogeneity in groundwater chemistry, with HCO₃-Ca·Mg as the predominant hydrochemical type and weakly alkaline characteristics. Groundwater chemical evolution is primarily driven by dissolution-precipitation of carbonate minerals and weathering of silicate minerals, and controlled by alternating positive cation adsorption. The apparent background values of Total Fe (TFe), F^-, TDS and NO₃-N, key indicators affecting the quality of groundwater in Naiman Banner, were 0.42~0.56 mg/L, 0.34~0.38 mg/L, 181~188 mg/L and 0.22~1.58 mg/L, respectively, which were estimated by using a coupled approach of hydrogeochemical graphic method, Grubbs test and SOM. The high background of TFe may be related to siderite dissolution, while the F^- enrichment is controlled by fluorite dissolution and alternating positive cation adsorption. This research elucidates the groundwater background values and hydrochemical formation mechanisms in the farming-pastoral zone of Naiman Banner, providing scientific support for the optimization management of regional water resources, pollution prevention, and ecological conservation.

Dynamic changes in land use are vital for ecological balance and sustainable development. Based on the case of Naiman Banner in Tongliao City, Inner Mongolia, time-series remote sensing data were examined to monitor changes in land use and conduct spatiotemporal analysis to support the sustainable development of this region. Given the complexities of this agro-pastoral region, the seasonal characteristics of time-series satellite imagery were utilized to produce high-precision land use products. Based on which, this study analyzes the spatiotemporal changes in land use in Naiman Banner over the past 40 years and assesses the ecological impacts of various land use changes. The results indicate differentiated spatiotemporal characteristics in land use, with the northern region exhibiting trends of desertification recovery, urban expansion, and an increase in arable land, while the southern mountainous areas show a trend of returning farmland to grass and forest. Factor contribution analysis reveals that changes in agricultural land use, ecological recovery from desertification, and deceased surface water significantly affect soil moisture content in the area, underscoring the importance of water-saving agriculture, ecological restoration of sandy areas, and water resource protection for sustainable agricultural development. This study provides important data for land resource management in Naiman Banner and offers scientific evidence for sustainable development strategies in this region, facilitating the coordinated advancement of ecological environments and economic development.

Under the backdrop of resource competition among world powers and policy adjustments in resource-rich countries, the strategic attributes and significance of copper resources have become increasingly prominent. The competition among major powers over resources has intensified, and the copper resource industry chain and supply chain are facing significant uncertainties and risks. Studying the resilience of the copper resource industry chain and supply chain trade network is of great significance for ensuring the security of copper resources and the stability of the global copper resource industry chain and supply chain. By comprehensively considering all products in the entire copper resource industry chain and supply chain, including upstream mining, midstream smelting, and downstream processing, an analysis framework for the resilience of the international trade network is constructed. Through describing the capabilities and characteristics of network nodes and structures, the evolution characteristics of the resilience of the copper resource industry chain and supply chain network are depicted from both dynamic and static dimensions. The research shows that at the node resilience level, China has a significant advantage in the upstream and midstream, but the downstream is showing a downward trend. Resource-endowed countries such as the Democratic Republic of the Congo and Chile have high resilience in the upstream and midstream due to their resource advantages, but their performance in the downstream is not outstanding. Industrial powers such as Germany and the United States have seen a decline in their upstream resilience, but their overall resilience across the entire chain is relatively prominent, and they still hold important positions in the upstream, midstream, and downstream. At the static structural resilience level, resilience in all links is showing a downward trend, and recovery capabilities are all inadequate, with stronger resistance capabilities in the upstream and downstream. At the dynamic structural resilience level, the polarization effect of the upstream and downstream networks has weakened, and their stability capabilities need to be improved. Midstream trade is increasingly concentrated on important links, leading to a decline in resistance to attacks. In the entire chain, the motivation for re-establishing trade links stems primarily from a country's existing status as a trade hub. Trade hub countries dominate the establishment of potential new links and have stronger reconstruction capabilities.

The high heterogeneity of terrestrial underlying surfaces and the complex coupling characteristics of ecohydrological processes lead to unclear multi-scale coupling evolution mechanisms of terrestrial ecohydrological processes, imperfect coupling simulation systems for multi-factor interactions, including hydrology, soil, vegetation and human activities, as well as insufficient cognition of the resource and environmental effects and derivative risks induced by the multi-scale variations in ecohydrological processes. These issues have become the core bottleneck for clarifying and solving a series of hydrological, ecological, resource, and environmental problems, and also restrict the refined management of regional water resources and the sustainable development of ecological environments.To solve the above issues, this paper breaks through the coupling and effects of typical terrestrial ecological and hydrological systems, coupling from the perspective of the multi-element coupling cycle, energy cycle, and biological process. It proposes the future research on the multi- change process of inland river basin hydrological and ecological and its water resources effects, the multi-scale change mechanism of the coupling process of hydrological and ecological in the Loess plateau, the multi-scale measurement and change mechanism of the hydrological and ecological processes in the lake basin, and the multi-scale change mechanism of the hydrological and ecological processes the Great Bay Area and its social and economic risks and the resource and ecological environment effects of the changes in the terrestrial hydrological and ecological processes. Revealing the typical terrestrial-scale coupling mechanism of inland river basins, the Loess Plateau, lake basins, the Great Bay Area and other typical terrestrial hydrological and ecological processes developing the monitoring methods of key hydrological and ecological parameters such as evapotranspiration, and developing the coupling simulation technology of terrestrial hydrological and ecological processes, elating the resource and environmental effects and its socio-economic risks of the changes in typical terrestrial hydrological and ecological processes under the background of global change, and providing a scientific basis the rational use of regional water resources, ecological environment protection and global change response.

The Chinese continental shelf, a region heavily influenced by human activities, has experienced significant ecological and environmental changes. Studying this area is of great scientific and practical importance for advancing oceanographic knowledge and promoting research on marine environmental protection and resource utilization. In recent years, intensified human activities have caused notable changes in the water environment, with Harmful Algal Blooms (HABs) showing new characteristics and shifts in dominant species. This paper provides a systematic review of research on water environmental changes and HABs occurrences in the Chinese continental shelf, offering a comprehensive analysis of major ecological and environmental challenges and outlining prospects for future research. The findings indicate that the marine ecological environment in this region is increasingly influenced by phosphorus limitation and the roles of organic nitrogen and phosphorus, accompanied by a shift in dominant HABs species from diatoms to dinoflagellates, and, in some areas, the simultaneous occurrence of HABs and green tides. These changes are driven by both ongoing alterations in terrestrial inputs and intrinsic self-regulatory mechanisms within the marine ecosystem, reflecting the cumulative effects of long-term environmental changes. Notably, there is a significant time lag between reductions in terrestrial pollution and decreases in HABs frequency, complicating the evaluation of management effectiveness and challenging the scientific determination of ecological risk thresholds or tipping points. Despite considerable progress in understanding the evolution of water environments and risks of HABs in China, significant challenges remain, including insufficient long-term and systematic observational data, the requirement to expand the research scope, and incomplete knowledge of land-sea interaction mechanisms. Future research should focus on elucidating land-sea coupling processes, enhancing marine environmental monitoring networks, improving data processing and technological innovation, and clarifying the thresholds for nitrogen and phosphorus management while developing integrated strategies that encompass monitoring, tracing, calculation, and management. These efforts are crucial for advancing marine environmental research in the Chinese continental shelf.

Against the backdrop of China’s “carbon neutrality” goal and green sustainable development supporting rural revitalization, accelerating the construction of wind- and solar-dominated renewable energy facilities requires scientifically grounded development strategies. This study conducts a refined assessment of the technically exploitable potential of solar and wind power generation in Naiman Banner by integrating high-precision satellite-derived solar radiation, high-resolution wind speed data, land use, and protected area boundaries, combined with advanced photovoltaic and wind power generation models. The results indicate that Naiman Banner possesses abundant wind and solar resources. The total rooftop photovoltaic potential reaches 13.5 TWh, with a spatial pattern characterized by higher potential in the southern and northeastern regions, and lower in the west and east. The total potential of optimally tilted fixed-angle photovoltaics is estimated at 24 TWh, mainly concentrated in the northwest, central, and northeastern areas, and lower in the south. At 100 meters height, the wind power generation is 18.4 TWh, with the highest values in the west, moderate in the central-southern region, and lower in the north. At 140 meters, the wind power generation increases to 20.8 TWh, with the highest values in the western and southeastern regions, and generally high potential across most other areas. The findings of this case study provide valuable insights for optimizing the planning and siting of PV installations, fostering the co-development of solar and wind energy, and advancing the rural revitalization strategy in Naiman Banner.

Temperature and precipitation are key climatic factors regulating ecohydrological processes in arid and semi-arid regions of China. However, the impacts of their synergistic effects on soil microbial processes remain to be further clarified. Based on long-term observational data from the Naiman National Field Scientific Observation and Research Station, this study focused on meadow aeolian sandy soil in the Horqin Sandy Land to quantitatively evaluate the synergistic effects of temperature and precipitation on soil microbial biomass carbon. The results indicated that the synergistic effect of temperature and precipitation is mediated by soil moisture as a critical intermediate, which regulates the dynamics of soil microbial biomass carbon through the core pathway: “temperature-precipitation synergy—soil moisture response—soil microbial feedback”. Furthermore, a soil moisture content of 0.019 m³/m³ can serve as the critical threshold for distinguishing different types of temperature-precipitation synergistic interactions. Under the scenario of “increased temperature and increased precipitation”, rising temperatures dominate and enhance soil evaporation. Precipitation supplementation fails to offset evaporative losses, resulting in drought stress that inhibits the accumulation of microbial biomass carbon. In contrast, under the scenario of “decreased temperature and decreased precipitation”, reduced temperatures weaken evaporation, while decreased precipitation suppresses deep water infiltration—thus maintaining surface soil moisture at an optimal state and facilitating the accumulation of microbial biomass carbon. This study not only deepens the understanding of how the synergistic effects of multiple climatic factors modulate soil microbial processes in arid and semi-arid regions but also provides significant insights for elucidating soil carbon cycle processes in sandy land ecosystems.

The evaporites in the lower part of the Jiufotang Formation of Naiman Sag，Kailu Basin, were widely developed, forming the world’s second-largest trona deposits, which serve as important deep-time records of the Early Cretaceous palaeoclimate and palaeoenvironment in Northeast Asia. However, the genesis of the trona deposit and its source-to-sink processes are still unclear. In order to investigate the formation processes of soda ash, we carried out a multi-scaled investigation of the sedimentary route system, involving regional “basin-mountain coupling”, core-scaled sedimentary cyclothemes and thin section-scaled analysis of mineral assemblages. And the material sources, facies distribution, key interfaces and sedimentary differentiation mechanisms of the Naiman trona deposit were explored. The results show that the Naiman trona deposit is similar to the Green River Formation in the United States, and the evaporites were precipitated in the central depression where black shales were also widely developed. Evaporite facies conform approximately to a bull’s eye pattern with a zonation of terrigenous clasts, alkaline earth carbonate, surrounding a basin centre accumulation of Na-carbonate and halite, indicating that the depositional differentiation is controlled by the solubility and geomorphic elevation of the basin. The formation of Naiman trona deposits mainly involves two processes: firstly, the Na⁺-HCO{}_{\mathrm{3}}^{-} rich brine accumulated in the lake during the humid period with large amounts of Na⁺, Ca²⁺, Mg²⁺, HCO{}_{\mathrm{3}}^{-}, Cl^- leached from the intermediate-basic volcanic rocks around the periphery of the basin. Secondly, the lake basin was highly dried under extreme arid climate conditions, and the lake was saturated with trona-halite, which were precipitated sequentially in the center of the depression. The giant trona deposits are characterized by interbedded halite, trona, gypsum, and black shale, reflecting frequent fluctuations of the lake level and cyclicity of trona accumulation. The annual varves and Milankovitch cycle of evaporites in Naiman Sag indicate that the trona-accumulating period was about 1.2 Ma, much smaller than the depositional time of the lower part of the Jiufotang Formation (2.5 Ma). The source-to-sink depositional pattern of trona deposits in the Naiman Sag will provide a reference for the exploration and development of similar Na-carbonate evaporite deposits.

The Naiman Sag is located in the southwestern part of the Songliao Basin. It remains underexplored, and the conditions for hydrocarbon accumulation and the main controlling factors are not yet well understood. This study based on a comprehensive review of previous studies and integrates data from drilling, seismic profiles, reservoir lithology and petrophysics. It analyzes the petroleum geological characteristics, controlling factors for hydrocarbon accumulation, and resource potential of the Naiman Sag. The results indicate the primary hydrocarbon source rocks in the region are the Shahai, lower Jiufotang, and Yixian formations. These are mainly concentrated in the northern part of the sag and are generally at a low-to-mature stage (R_O<1.0%). Among them, the lower Jiufotang Formation, developed in semi-deep lake to deep lake facies, contains high-quality source rocks with high organic matter abundance (average TOC of 2.79%) and predominantly of Type I and Type II₁ kerogen. These source rocks are the primary contributors to hydrocarbons in the region. Biomarker characteristics indicate that the source rocks formed in a high-salinity, strongly reducing lacustrine environment, with mixed contributions from lower aquatic organisms and higher terrestrial plants. The hydrocarbons in the region are primarily heavy oil and wet gas, with heavy oils not undergoing significant biodegradation and mainly controlled by low maturity. The natural gas is characterized as sapropelic kerogen-derived gas in the low-to-mature stage, directly resulting from kerogen cracking. Hydrocarbon accumulation is primarily controlled by favorable source-reservoir configurations, dominant depositional facies, and advantageous lithology, demonstrating a trinity of “facies-lithology-structure” coupling. Additionally, deep fluid activities may play an important role in the accumulation process by not only promoting secondary hydrocarbon generation but also contributing to the enrichment of associated noble gases such as hydrogen and helium. Overall, the central and deeper “sweet-spot” zones of the sag, with abundant hydrocarbon supply, proximal traps, and active fluid movement, show significant exploration potential. Future studies should focus on the unconventional accumulation mechanisms under multi-factor coupling. This will deepen the understanding of the petroleum system in the Naiman Sag.

With the rapid advancement of wind and solar power generation technologies, the proportion of wind and solar energy within power systems continues to rise. However, the output of these two energy sources is directly influenced by weather conditions, exhibiting significant randomness, volatility, and intermittency. This poses severe challenges to power dispatch and the secure, stable operation of the grid. High-precision power forecasting technologies at short- and medium-term time scales are crucial for addressing these challenges and achieving efficient utilization of wind and solar power generation. This paper systematically reviews the developmental trajectory, core technologies, and latest research advancements in short- and medium-term meteorological forecasting for wind and solar energy. First, a comprehensive analysis of relevant domestic and international literature indicates that traditional wind and solar energy forecasting methods—such as numerical weather prediction techniques, statistical approaches (including time series analysis and machine learning), and hybrid/statistical post-processing methods combining both—no longer fully meet current demands. Significant improvements in forecast accuracy and reliability have been achieved through synergistic optimization in key areas: high-resolution rapid-cycle forecasting, energy-specific physical process optimization, and hybrid data assimilation method of Ensemble Kalman Filtering (EnKF) and four-dimensional variational assimilation (4D-Var). Furthermore, the introduction of next-generation dynamic frameworks and advanced artificial intelligence (AI) large models, the assimilation and fusion of multi-source data, and the application of emerging technologies like “digital twins” offer new avenues for further refining forecast outcomes. Finally, the paper analyses the challenges confronting current forecasting techniques and outlines future development directions which include but not limited to challenges posed by forecasting extreme weather events，complex terrain physical and dynamical representations, enhancing model interpretability, and subseasonal to seasonal-scale forecasting; Deep integration of artificial intelligence with physical models to develop “physical-information neural networks” by embedding physical laws; Collective probabilistic forecasting that combines traditional physical models with large AI model ensembles will become widespread; Disruptive technologies like quantum computing are poised to advance ultra-high-resolution meteorological simulations; Achieving deep coupling between forecasting technologies and scenarios such as the grid-based automatic power generation control to build closed-loop intelligent decision-making systems. This review aims to provide a technical reference for researchers and engineering technicians in the field of energy meteorology.

Currently, the increasingly severe climate change, frequent occurrence of extreme weather events, growing pressure on food security, and the accelerated development of smart agriculture have posed severe challenges to the development of agricultural meteorology. Systems science provides solutions for agricultural meteorology to address these challenges, and developing systematic agricultural meteorology is an urgent need to promote the development of agricultural meteorology. Based on the systematic characteristics of the research object of agricultural meteorology, this article proposes the connotation and theoretical framework of systematic agricultural meteorology and looks forward to the research prospects. The results show that systematic agricultural meteorology is a science that uses system analysis methods to study the interaction mechanisms, dynamic evolution laws, and impacts and optimization paths of various elements within the agricultural meteorological system. It focuses on the interrelationships among various elements in the agricultural meteorological system and emphasizes the application of cybernetics, computer technology, and mathematical theories. Through systematic observation, analysis, description, simulation, and optimization, it builds agricultural meteorological models to achieve state expression, dynamic simulation, and regulatory improvement of the agricultural meteorological system, promoting the coordinated development of the agricultural meteorological system. The key scientific issues of systematic agricultural meteorology include multi-source data scale conversion, multi-factor synergy mechanisms, and multi-process coupling model construction. The research goal is to optimize the structure of the agricultural meteorological system and collaboratively achieve high-yield, high-quality, high-efficiency, and sustainable agricultural production. Systematic agricultural meteorology is a new form and new engine for the development of agricultural meteorology. Through theoretical innovation, technology integration, and service reconfiguration, it promotes the transformation of agricultural production from “relying on nature” to the intelligent paradigm of “knowing nature and working accordingly”, realizing the paradigm shift from empirical judgment to intelligent decision-making, and providing scientific support for addressing the challenges of food security under climate change and developing new quality productive forces in agricultural meteorology.

Accurate retrieval of snow depth from passive microwave observations remains a major challenge because of the pronounced nonlinearity between microwave brightness temperature and snow physical properties, which is further modulated by complex terrain and the temporal evolution of snowpacks. Conventional retrieval approaches, including physically based radiative transfer models, are often constrained by high computational costs and strong sensitivity to uncertain input parameters, resulting in degraded performance under heterogeneous snow conditions and across different snow climate regimes. To overcome these limitations, this study develops a passive microwave snow depth retrieval model that integrates Wavelet transform with a Residual Convolutional Neural Network (Wavelet-ResNet-CNN) to better represent multi-scale features and complex nonlinear relationships. Specifically, the wavelet transform is applied to multi-frequency brightness temperature observations to extract scale-dependent information, allowing the model to capture both large-scale snow depth variability and localized perturbations associated with rapid snow accumulation and melt processes. The resulting multi-scale features are then ingested into a residual convolutional neural network, in which residual connections facilitate deep feature propagation and enhance nonlinear fitting capability while mitigating performance degradation in deep architectures. The proposed model is evaluated using representative ground-based stations spanning different snow climate classes in the Northern Hemisphere to assess its applicability under diverse snow conditions at the site scale. Model performance is quantified using multiple statistical metrics, including Root Mean Square Error (RMSE), correlation coefficient (R), mean bias, and Nash-Sutcliffe Efficiency (NSE), and is benchmarked against conventional convolutional neural network and residual convolutional neural network models. The results show that the Wavelet-ResNet-CNN achieves an RMSE of 0.23 m, an R of 0.93, a mean bias of 0.01 m, and an NSE of 0.86, indicating consistent improvements over the reference deep learning models across all evaluation metrics. Additional analyses demonstrate that the proposed model outperforms traditional deep learning approaches across a wide range of snow depths, with particularly notable gains during periods of deep snow accumulation and rapid snow depth changes. Furthermore, compared with existing snow depth retrieval products, the model exhibits enhanced temporal responsiveness and improved spatial transferability across different snow climate types, highlighting its potential for robust passive microwave snow depth monitoring in regions with diverse snow regimes.

The Tibetan Plateau (TP), the most massive elevated landform in the Northern Hemisphere, serves as a powerful atmospheric heat source that anchors the Asian monsoon system and profoundly influences East Asian climate patterns. Although extensive research has yielded fruitful results regarding the TP’s thermal anomalies and their climatic effects, a systematic review of its dynamic mechanisms in driving multi-scale precipitation and extreme events over the Middle and Lower reaches of the Yangtze River (MLYR) is still lacking. Centered on the “thermal forcing-circulation response-precipitation processes” framework, this paper systematically reviews the mechanisms by which the TP’s atmospheric heat source affects precipitation in the MLYR. The review first addresses methods for quantifying the TP heat source, highlighting their uncertainties and spatiotemporal characteristics. We then comprehensively elucidate the core physical mechanisms through which the TP heat influences key atmospheric circulation systems via two pathways: “background state modulation” and “direct thermal forcing.” At upper levels, the TP’s thermal forcing governs the intensity and position of the South Asian High (SAH) and the seasonal migration of the subtropical westerly jet. At mid-levels, it acts as a primary source of Rossby wave trains that remotely influence the Western Pacific Subtropical High (WPSH). At lower levels, the “Sensible Heat-driven Air Pump” (SHAP) effect reshapes the monsoonal flow and establishes critical water vapor transport channels. Subsequently, the review synthesizes how these multi-level circulation adjustments translate into specific precipitation patterns over the MLYR. Two distinct configurations for persistent heavy rainfall are identified: the PSAH type, characterized by robust vertical coupling between an eastward-extended SAH and a westward-extended WPSH sustained by the rainfall-related ascent anomaly, and the NSAH type, which features a westward-extended WPSH but with a westward-retreated SAH. Furthermore, we introduce a novel framework of “scenario-dependent roles” to describe the TP’s function in extreme events. Specifically, it acts as a “primary driver” under weak external forcing (e.g., neutral ENSO), a “synergistic amplifier” when phase-locked with favorable oceanic conditions, or a “passive modulator” when strong external forcings like La Niña dominate. Our synthesis reveals that the TP’s thermal forcing is not an isolated, static driver, but rather exhibits a complex synergistic interaction with large-scale background states, such as tropical sea surface temperatures. The combination of these multiple forcing factors ultimately determines the triggering and amplification of extreme precipitation events. Finally, future research priorities are outlined to bridge existing knowledge gaps. These include advancing from analyzing the heat source’s total intensity to disentangling the differential climatic impacts of its vertical profile; moving beyond correlation to quantitative attribution of the TP’s role in specific extreme events; and overcoming data scarcity by developing a benchmark heat source dataset through the fusion of multi-source data. Addressing these challenges is crucial for advancing scientific understanding and enhancing the predictive capability for precipitation variability and extremes in the MLYR.

Plant community stability is the central ecological issue that underpins the structural integrity, functional continuity, and service sustainability of steppe ecosystems. Its influencing mechanisms have become a frontier focus in ecological and botanical research amid accelerating global climate change and intensifying anthropogenic activities. Building upon previous research, this review synthesizes current understanding of the theoretical connotation and characterization parameters of stability, the diversity-stability relationship, and the influencing mechanisms of plant community stability in steppe ecosystems. It further identifies critical knowledge gaps and provides an outlook on potential research priorities and development directions. Studies have shown that under the impacts of climate change and anthropogenic activities, the stability of plant communities in steppe ecosystems is collectively regulated by multiple biotic mechanisms—such as overyielding effects (via selection and complementarity), insurance effects, asynchrony, compensation effects, portfolio effects, and statistical averaging effects—along with abiotic factors such as soil nutrient availability. These mechanisms exhibit distinct ecosystem type specificity and significant spatio-temporal scale dependence. Future research needs to establish a systematic paradigm of “multiple disturbance factors-multiple regulatory processes-multiple spatio-temporal scales and organizational dimensions” to further unravel the intrinsic linkages between the structure, function, and stability of steppe ecosystems under global change, aiming to provide a reference for the sustainable development and adaptive management practices of steppe ecosystems.

Accurately assessing the spatiotemporal dynamics of vegetation Net Primary Productivity (NPP) in sandy ecosystems and quantifying the roles of climate change and human activities are crucial for elucidating the impacts of environmental change on the carbon pool in these ecosystems and for developing strategies to enhance carbon sinks. Using MOD17A3HGF data products, we systematically analyzed the spatiotemporal patterns of vegetation NPP and their driving mechanisms in the Horqin region from 2001 to 2023, employing trend analysis, stability analysis, partial correlation analysis, and partial derivatives. The results indicated that: ① From 2001 to 2023, the vegetation net primary productivity in the Horqin region exhibited a spatial pattern of being higher in the northwest and southeast and lower in the central region, with a significant increase at a rate of 4.78 g C/(m²⋅a). An impressive 89.3% of the region experienced significant recovery, with 72.6% maintaining stable condition. The recovery rate of net primary productivity was stronger in fixed sands than that in mobile sands. ② A warming and humidification trend characterized the climatic conditions in the Horqin region. The magnitude of climate changes was generally higher in desertified areas than in non-desertified ones. ③ Vegetation net primary productivity showed positive correlations with annual precipitation, and annual mean temperature, but was negatively correlated with annual mean solar radiation, with precipitation dominating the variation in vegetation net primary productivity. ④ In vegetation restoration areas, regions where net primary productivity changes were jointly driven by climate change and human activities, and those primarily driven by human activity accounted for 82.2% and 16.6%, respectively. However, in areas of vegetation degradation, the dominant influence of human activity remains non-negligible. Net primary productivity in mobile sand areas was predominantly influenced by climate change, whereas in fixed sand areas, it was substantially affected by anthropogenic activities. Our findings robustly endorse the following management strategies that facilitate the natural restoration of mobile sands by adapting to the prevailing trends of climate warming and humidification and enhancing the carbon sequestration and carbon sink capacity of sands through the regulation of agricultural practices. These insights provide a scientific foundation for targeted implementation of desertification control and ecological restoration efforts in the Horqin region.

In the context of climate change, glacier surges and the associated hazards are occurring with increasing frequency. Due to the difficulties in subglacial monitoring, multiple interpretations remain regarding the control mechanisms of glacier surges. The enthalpy-balance model is a recently proposed, generalized framework that integrates glacier mass balance, thermodynamics, and subglacial hydrology, and it can account for surging processes across a wide range of glacier types. We integrate previously published datasets with additional surface velocity and surface elevation data of the South Rimo Glacier (SRG) derived from remote-sensing satellite observations. On this basis, glacier basal sliding velocities and stress variations are calculated. Furthermore, an enthalpy balance model is employed to estimate glacier enthalpy production during different periods. Using these datasets and methods, we systematically analyze the spatiotemporal characteristics and key processes of SRG before and after its surge events from 2000 to 2024. The results indicate that: ① The 2018-2020 surge of SRG involved substantial water participation and was likely triggered by thermally controlled basal meltwater generation and the infiltration of surface meltwater into the glacier bed, which altered subglacial water pressure and induced rapid glacier motion; ② During the surge, the dominant enthalpy source in SRG was frictional heat generated by basal sliding. The mean annual enthalpy production attributable to frictional heating was approximately 1.0 W/m², 3.2 W/m², and 0.5 W/m² during the pre-surge, surge, and post-surge stages, respectively, with the highest mean annual value occurring in 2019 (~6.2 W/m²). In contrast, enthalpy losses were primarily associated with the discharge of subglacial basal meltwater; ③ The terminus position reached during this surge did not extend as far as that of the previous surge (1989-1999), suggesting that the intensity of the current surge was weaker than that of the last event; ④ The enthalpy balance model effectively explains the acceleration and deceleration phases of glacier surging. The surging behavior of the South Rimo Glacier, eastern Karakoram, is governed by a coupled hydro-thermal mechanism, but as to when the surge reaches the critical threshold to start and when it stops, detailed subglacial hydrological observation data are needed for analysis.

The dynamic coupling between the Low-Level Jet (LLJ) and cloud-precipitation systems acts as a vital nexus linking boundary layer processes with mesoscale weather systems, representing a central challenge in understanding extreme precipitation generation and predictability. Previous studies demonstrate that LLJs function as a “moisture conduit and dynamic engine,” critically governing cloud initiation, organization, and precipitation efficiency. This is accomplished by amplifying low-level moisture flux and convergence, modulating vertical wind shear and instability energy, and altering boundary layer turbulent mixing and momentum transport. Conversely, cloud and precipitation processes release latent heat that substantially adjusts thermal and pressure gradients and secondary circulations, while modifying moisture loading and surface flux budgets. These interactions further modulate the jet’s intensity, position, and vertical structure, establishing a multi-scale bidirectional feedback loop in which complex topography exerts a significant modulating influence.This study systematically reviews multi-source coordinated observation systems, elucidating their potential to resolve the dynamic structure, moisture transport, and vertical microphysical processes of LLJs with high fidelity. It also discusses key advancements in convection-resolving regional models and data assimilation techniques regarding the realistic simulation of LLJ diurnal cycles and the quantitative characterization of the thermodynamic environment, including moisture convergence and shear. Despite these advances, multi-source coordination faces persistent challenges related to retrieval uncertainties—particularly the coupling of vertical air motion with particle fall speed in vertically pointing measurements and spectral broadening induced by wind shear, as well as ambiguities in interpreting radar reflectivity within complex microphysical contexts. Meanwhile, numerical simulation necessitates tighter integration with in-situ calibration and artificial intelligence to advance the systematic synthesis of complex non-linear coupling mechanisms. Finally, it is recommended that future research prioritize high-resolution three-dimensional network observations oriented towards “process closure,” deep fusion of cross-platform datasets for optimized parameterizations, cross-scale high-resolution modeling with explicit uncertainty quantification, and interpretable AI-assisted diagnosis. Collectively, these strategies aim to deepen the mechanistic understanding and enhance predictive capabilities regarding the complex interactions between LLJs and cloud-precipitation systems.

Surface ozone (O₃) and terrestrial vegetation has profound and complex interactions that exert strong influences on atmospheric chemistry, ecosystem productivity, and regional climate. As a phytotoxic oxidant, O₃ enters plant tissues through stomata, triggering oxidative stress and impairing photosynthetic functions. These physiological disruptions further reduce crop yields and ecosystem productivity, posing substantial threats to global food security and terrestrial carbon sink. Current syntheses estimate that present-day O₃ pollution reduces global land sink by 1.5%~5%, with hotspots in eastern China and eastern U.S. Vegetation is not merely a passive recipient of O₃ stress; rather, it actively modulates surface O₃ concentrations through dual roles as both a sink and a source. On the one hand, stomatal uptake represents a major pathway for O₃ dry deposition, accounting for approximately 45% of global O₃ removal. On the other hand, vegetation emits Biogenic Volatile Organic Compounds (BVOCs), such as isoprene and monoterpenes, which act as key precursors for O₃ formation under sunlight. Recent modeling studies suggest that BVOCs contribute more than 10% to summertime O₃ concentrations in polluted regions such as China and U.S. A critical feedback arises from O₃-vegetation coupling: elevated O₃ induces stomatal closure as a protective response, which reduces the stomatal uptake of O₃, weakens dry deposition, and ultimately enhances ambient O₃ levels. In addition, O₃-induced stomatal closure suppresses transpiration, altering surface energy partitioning by reducing latent heat flux and increasing sensible heat flux. These biophysical responses promote regional warming and drying, with consequent impacts on regional climate and hydrology, including temperature, humidity, precipitation, and runoff. For example, O₃-vegetation interactions have been shown to increase summer surface temperatures by 0.2~0.8 K and reduce relative humidity by 3%~9% in eastern China. Despite progress in experimental and modeling approaches, substantial uncertainties persist. These include limitations of controlled-exposure experiments, inconsistencies in O₃ damage parameterizations, and incomplete understanding of interactions between O₃ stress and co-occurring drivers such as elevated CO₂, nitrogen deposition, and extreme climate events. Future research should prioritize integrated multi-scale observational networks, multifactor manipulative experiments, and advanced Earth system models that tightly couple atmospheric chemistry with dynamic vegetation processes. Improved quantification of the O₃-vegetation-climate interaction is essential for developing coordinated strategies that jointly address air quality improvement and climate change adaptation.

Under the background of global warming, Meiyu precipitation over the Yangtze River basin has increasingly deviated from its traditional characteristics of persistent light rainfall, exhibiting pronounced dual extremes of heavy rainfall and dry heat. Such complex changes are difficult to accurately characterize using a single variable. To comprehensively describe the evolving features of Meiyu, this study employs the Deviation Degree of Misty Rain (D2MR), a multidimensional index developed based on daily station observations, to characterize Meiyu variability from multiple perspectives, and its sub-indices are further used to identify the dominant Meiyu type in each year. Meanwhile, based on ERA5 reanalysis data and datasets from the Hadley Centre, physically meaningful potential predictors are systematically analyzed, and key preceding factors influencing D2MR and its sub-indices are identified. Prediction models are then constructed using the interannual increment approach. The results indicate that the proposed models exhibit significant and stable predictive performance for the Meiyu extremeness index and its sub-indices, with correlation coefficients exceeding 0.72. The models successfully capture the interannual variability and the significant upward trend of D2MR during 1963—2025, and effectively reproduce the characteristics of Meiyu in extreme years, accurately reflecting the increasing extremeness of Meiyu in recent decades. In particular, the models independently predict the occurrences of heavy-rainfall-dominated Meiyu in 2020 and 2024, dry-heat-dominated Meiyu in 2022 and 2025, and the alternating heat-rainfall Meiyu characteristics in 2023. As the frequency of extreme Meiyu events continues to increase, exerting greater socio-economic impacts across the Yangtze River Basin, the characterization and prediction of Meiyu extremity provide essential scientific support for disaster prevention, agricultural planning, and enhance our understanding of regional climate variability.

To investigate the differences in microclimatic and ecological environmental effects of centralized photovoltaic (PV) power stations under diverse climatic backgrounds, high-altitude desert PV stations located in Qinghai Province were selected as the focal point of this research. These sites were chosen to represent three typical climatic backgrounds: hyper-arid, arid, and semi-arid zones. Micro-meteorology factors and vegetation evolution characteristics inside and outside the PV arrays were analyzed by employing paired inside-outside observations and long-time-series NDVI retrieval. It was indicated by the results that distinct, non-linear responses are exhibited by the microclimatic and ecological effects of PV stations along the aridity gradient. Water availability was identified as the core regulatory factor modulating these interactions. Specifically, a significant "heat island effect" with no observable vegetation recovery was observed in the hyper-arid zone, primarily attributed to severe moisture deficits. A transitional state was demonstrated in the arid zone, characterized by nighttime warming and slight humidification effects, accompanied by a discernible trend toward vegetation recovery. In stark contrast, positive ecosystem feedback mechanisms were displayed in the semi-arid zone; here, soil moisture conservation was significantly facilitated by the shading and wind-blocking effects of PV modules. Improved moisture status was found to enable rapid vegetation restoration, by which the physical warming effects of the panels were subsequently offset through the mechanism of transpirational cooling. The evolutionary mechanism by which the ecological impacts of PV stations transition from purely physical disturbances to active ecological regulation is elucidated in this study. The feasibility of achieving synergy between large-scale photovoltaic development and ecological restoration, provided that moisture conditions are suitable, is empirically confirmed. In the future, the implementation of stable, long-term, and large-scale eco-meteorological observations, coupled with the development of ecological mechanism models specifically tailored for PV parks, is considered essential. A deeper, mechanistic understanding of the ecological footprints of photovoltaics will be facilitated by these efforts, thereby providing robust scientific evidence and support for optimizing decision-making in sustainable energy planning.

Aerosols are key factors influencing the energy balance of the Earth-atmosphere system and atmospheric environmental changes. High-precision inversion of their optical parameters has long been a topic of interest in atmospheric environmental remote sensing. Passive remote sensing of aerosol optics primarily utilizes their scattering effects in the visible/near-infrared wavelength bands. However, this approach typically relies on solar scattered radiation as the radiation source, rendering it inapplicable at night and during high-latitude winters (polar nights). Infrared hyperspectral remote sensing technology can resolve the unique spectral absorption and scattering fingerprint characteristics of aerosols, serving as another means to achieve aerosol remote sensing and providing a powerful supplement to traditional visible/near-infrared inversion techniques. This paper systematically elaborates on the fundamental principles of infrared hyperspectral inversion for aerosol microphysical parameters, focusing on key technologies such as physical mechanisms and detection instruments, radiative transfer models, and inversion algorithms. Particular emphasis is placed on discussing the key parameters of existing hyperspectral radiometers, the characteristics of hyperspectral forward radiative transfer models, and the advantages and disadvantages of different inversion methods. Based on this, the paper further outlines future research directions, proposing a focus on developing high-precision, high-efficiency forward radiative transfer models, utilizing the rich information within hyperspectral data to improve inversion algorithms, and actively advancing multi-platform collaborative detection technologies.

Sandy braided river sedimentation is characterized by high-frequency migration, rapid reworking, and multi-stage superimposition, with complex hydrodynamics driving frequent channel changes that produce intricate internal architectures. To reveal the sedimentary architecture and evolution of sandy braided rivers under seasonal flow control, this study integrated trench profile measurements with UAV-based oblique photography to systematically document geomorphic changes in the Ba River across different periods. Analysis of trench profiles enabled examination of sedimentary processes and architectural characteristics, leading to the establishment of a sedimentary model.The results show that: \mathrm{①} Discharge variation is a key control on sedimentary evolution. During flood periods, wide channels cover nearly the entire riverbed, driving downstream sand migration and rapid deposition to form lobate or linguoid bars. During normal flow periods, decreased water levels confine flow to inter-bar channels, slowing bar evolution and transforming bar morphology into irregular composite forms. \mathrm{②} Under seasonal hydrodynamic fluctuations, the scale and lithofacies characteristics of sandy braided river deposits show significant seasonal differences. The flood period develops four main lithofacies: pebbly sandstone (with abundant gravels) (Gm), large-scale planar cross-bedding sandstone (Sp2), and large-scale trough cross-bedding sandstone (St2). The normal water period develops three main lithofacies: small-scale planar cross-bedding sandstone (Sp1), small-scale trough cross-bedding sandstone (St1), and parallel-laminated sandstone (Sh). During the flood period, deeply incised main channels (CHm) develop, with bar accretion dominated by downstream accretion, forming thick linguoid or lobate bars over 1 m thick. During the normal water period, narrow and shallow secondary channel bars (CHs) develop, with multiple bar accretion styles including topset progradation, downstream accretion, and lateral accretion. Individual layers are less than 0.5 m thick and often appear as multi-stage superimpositions in profiles. \mathrm{③} Based on the relationship between discharge and sedimentary architecture characteristics, a two-stage “construction-reworking” evolutionary model for seasonal sandy braided rivers has been established. During flood period, characterized by high-energy flows transporting coarse-grained sediments to form large lobate bars through downstream and vertical accretion, accompanied by intense incision of main channels. During normal water period, characterized by falling water levels that expose previously formed bars: bar heads undergo erosion and retreat, bar centers experience multi-stage “cutting-filling-recutting” modifications, while bar tails undergo continuous accretion. Under cyclic flow variations, the geomorphic morphology of sandy braided rivers exhibits periodic changes, resulting in a complex sedimentary architecture characterized by interbedded and amalgamated flood-season accretion units and normal-flow-season reworking units.

Rare Earth Elements and Yttrium (REY) are one of the most critical strategic resources in the world today. However, the intensive exploitation and supply of conventional rare metal deposits—primarily those associated with alkaline igneous rocks and ion-adsorption clays, have led to mounting challenges for the rare earth industry, including declining resource security and increasing environmental pressure. This situation underscores the urgent need to seek alternative rare earth resources. Sedimentary phosphate rocks and deep sea REY-rich sediments have emerged as promising alternatives. They are widely distributed, possess large reserves, and are enriched in heavy rare earth elements. In recent years, considerable research have focused on the REY resource potential and mineralization mechanism of these two deposits. They found that there are several economically valuable the mineral concentrated area, distributing globally. Several studies have established mining and utilization models and developed REY extraction strategies. In terms of the ore formation mechanism, current knowledge suggests that the enrichment of REY in deep-sea sediments and phosphorus deposits is closely tied to phosphorus-enrichment, although these deposits have certain differences in terms of occurrence form, mineralization environment, and rare earth source, etc. Sorption at mineral-solution interface along with early diagenesis, are considered as the key processes to REY enrichment. However, Most of these studies were published in the past 15 years, and their systematicness and depth still fall short. For example, despite their potential, commercial development remains constrained by technical, environmental, and economic challenges—including mining equipment limitations, ecological risks, and uncertain market revenues. As a result, large-scale industrial extraction from deep-sea sediments has yet to be realized. Additionally, REY enrichment mechanisms is poorly understood. In the future, multidisciplinary collaboration will be essential. Collaborative research involving multiple disciplines and multiple technical methods will enable more precise estimation of resource reserves and contribute to the metallogenic enrichment theories. This paper provides a comprehensive overview of recent advances in the understanding of the rare earth resource replacement potential and offers perspectives for future research directions in this field.

The sequestration of Soil Organic Carbon (SOC) constitutes a pivotal component of global climate change mitigation strategies. While the “microbial carbon pump” and biotic anabolism have traditionally dominated conceptual paradigms of humification, emerging evidence suggests that this biocentric view may underestimate the contribution of abiotic geochemical pathways. This review systematically delineates the role of the non-enzymatic Maillard reaction as a critical geochemical bridge linking mineral weathering processes to long-term SOC persistence.We propose a mechanistic framework of “mineral interfacial catalysis-melanoidin formation-organo-mineral complexation”to elucidate this abiotic stabilization trajectory. Specifically, soil minerals, particularly Fe/Mn oxides and phyllosilicates, act as natural catalysts by providing Lewis acid sites that lower the apparent activation energy for the condensation and polymerization of reducing sugars and amino compounds. This process transforms labile precursors into chemically recalcitrant, aromatic-rich polymers commonly referred to as melanoidin-like substances. These reactions are thermodynamically and kinetically favored under conditions of elevated temperature, alkaline to neutral pH, and intermediate to fluctuating moisture regimes, potentially representing a dominant stabilization pathway in subsurface horizons or anaerobic environments where microbial activity is energetically or kinetically constrained.The resulting melanoidin-type products exhibit a pronounced dual-protection capacity, beyond their inherent structural heterogeneity and low biological accessibility, they form robust associations with mineral matrices through physical confinement, ligand exchange, and polyvalent cation bridging. By acting as a non-enzymatic hub that integrates plant-derived carbon inputs and microbial metabolites into persistent organo-mineral complexes, the Maillard reaction challenges conventional theories of humic substance formation and provides a mechanistic framework for an underexplored abiotic pathway of carbon stabilization.Despite growing recognition of its potential importance, current understanding remains constrained by a reliance on simplified laboratory proxies, a scarcity of in situ field evidence, and the absence of diagnostic molecular biomarkers capable of distinguishing abiotic melanoidins from microbially derived necromass. Consequently, the quantitative contribution of this abiotic module to long-term SOC persistence remains poorly constrained. Future research should prioritize resolving environmental controls, multi-factor interactions, and identifying the molecular fingerprints of soil Maillard products in natural ecosystems. Incorporating mineral-mediated, non-enzymatic stabilization processes into terrestrial carbon cycle models will be essential for accurately capturing the coupled “climate change-rock weathering-carbon stability”continuum and for informing strategies that integrate geological and ecological carbon sequestration.

Observational data indicate that, against the background of global warming, extreme high temperature events in China have increased significantly over the past few decades. Clarifying the differential mechanisms by which anthropogenic aerosols from different sources influence these trends in China is of great importance for predicting future changes. Based on historical simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6), including all-forcing, anthropogenic aerosol single-forcing, and greenhouse gas single-forcing experiments, the influence of anthropogenic aerosol emissions from different sources on the trends of extreme high temperature in China and the underlying physical mechanisms are systematically investigated. This study reveals that during 1950-1979, increased anthropogenic aerosol emissions in both East Asia and Europe collectively contributed to the reduction trend in the extreme high temperature in China. East Asian aerosols primarily induced surface cooling by reflecting and scattering shortwave radiation. While European aerosols modified the thermal structure of the atmosphere in the source region, triggering an eastward-propagate atmospheric wave train, resulting in upper-level convergence, positive geopotential height anomalies, and cold advection associated with northerlies in East Asia. During 1980-2009, the reduction in European anthropogenic aerosol emissions excited an anti-phase atmospheric teleconnection pattern, leading to the upper-level divergence, negative geopotential height anomalies, and warm advection associated with southerlies in East Asia. This counteracted the cooling effect in northern China induced by the continuous increased East Asian aerosol emissions, resulting in an “increase in the north and decrease in the south” spatial pattern of the extreme high temperature trends. During 2010-2020, the combined effect of reduced anthropogenic aerosol emissions in both East Asia and Europe further accelerated the increase trend of the extreme high temperature in China. This study clearly elucidates the fundamental differences in the mechanisms by which anthropogenic aerosol forcing from different sources influences the extreme high temperature changes in China: East Asian aerosols primarily exert a direct impact by altering local radiative forcing, whereas European aerosols indirectly modulate the extreme high temperature trends by influencing East Asian atmospheric circulation through atmospheric teleconnection processes. These findings enhance the understanding of the remote climatic effects of anthropogenic aerosol forcing and provide a theoretical basis for improving future predictions of the extreme high temperature events.

Basin-range coupling is a key process for energy and material exchange within the continental lithosphere, fundamental to understanding plate tectonics, resource distribution, and geological hazards. Its related physical modeling research has undergone a notable evolution from geometric similarity to dynamic similarity, and from single tectonic simulation to multi-process coupling. By reviewing the development of this field, it is pointed out that the differences in stage division are essentially driven by theoretical breakthroughs and technological innovations. Regarding experimental materials, modeling materials have evolved from basic substances such as quartz sand and silicone to composite functional materials capable of accurately matching the rheological behavior of geological prototypes. This paper further compares the density, friction angle/viscosity, and key dimensionless parameters of brittle, ductile, and plastic materials, and discusses the differences in material behavior under hypergravity conditions as well as the requirements for dynamic similarity. Based on the type of driving force, experimental models can be classified into three categories: compression, extension, and strike-slip. These models respectively elucidate the dynamic mechanisms of foreland basins, rift basins, and strike-slip transfer zones, consistently highlighting the controlling roles of pre-existing structures and rheological layering. By comparing three representative basin-mountain systems under the same dimensions, this paper reveals the complementarity between parametric experiments and case studies, along with the inherent limitations of physical modeling. Currently, the integration of cutting-edge technologies such as hypergravity centrifugation, CT scanning, digital volume correlation, and numerical modeling is driving a profound transformation of physical modeling towards real-time, three-dimensional, and quantitative analysis. Addressing the limitations of scale effects, time compression, laboratory apparatus influences, and lack of thermal effects, this paper proposes four technological development directions: high-resolution observation and fine modeling, intelligent continuous monitoring and parameter inversion, full-process multi-field coupled simulation, and multi-source data assimilation. Accordingly, a closed-loop research paradigm is constructed: “3D printing of initial models—hypergravity centrifugation for stress environment—digital volume correlation for four-dimensional strain field capture—artificial intelligence for automatic extraction of structural elements—numerical modeling for parameter inversion and evolutionary trend prediction”. In the future, this field will pay more attention to full-process simulation of “tectonics-geomorphology-sedimentation” and deep integration of artificial intelligence recognition technologies, thereby providing critical experimental support and theoretical frameworks for a deeper understanding of the operating mechanisms of Earth’s dynamic systems.

With the rapid development of acoustic, optical, electrochemical, and other multi-type sensor technologies, in situ high-frequency monitoring has enabled continuous observations of water level, discharge, and multiple water quality parameters with high temporal and spatial resolution. This technological advancement has fundamentally transformed the traditional paradigms of hydrological and water quality monitoring, which have long been constrained by low sampling frequency, discontinuous data, and limited ability to capture short-term hydrological events. As hydrological and water resources research increasingly emphasizes process-based mechanistic understanding and real-time management, in situ high-frequency monitoring has emerged as a critical tool for resolving rapid system dynamics and improving the accuracy of decision-making.This review systematically summarizes the evolution and applications of in situ high-frequency monitoring technology in the field of hydrology and water resources. The development of monitoring systems spans distinct technological stages, progressing from early manual observations and automated sampling to sensor-based, multi-parameter, and intelligent monitoring networks. Particular emphasis is placed on the pivotal role of advanced sensors in achieving long-term, high-resolution observations of hydrological and hydrochemical processes.A comprehensive synthesis of representative studies demonstrates that in situ high-frequency monitoring has significantly enhanced process characterization accuracy and response timeliness across a broad range of applications. These include watershed-scale hydrological process tracing and water source apportionment, analysis of solute transport and material migration mechanisms, water pollution control and early warning, refined water resources management, and forecasting of flash floods and mountain torrent disasters. High-frequency datasets effectively capture short-duration hydrological events, rapid water source transitions, and abrupt changes in pollutant fluxes—phenomena frequently missed by conventional low-frequency sampling—thereby providing essential data support for understanding complex hydrological and biogeochemical dynamics.Despite these notable advantages, current in situ high-frequency monitoring systems still face several challenges, including limited coverage of monitoring parameters, insufficient long-term sensor stability, performance degradation under complex environmental conditions, high costs for equipment deployment and maintenance, and the growing demand for efficient processing and interpretation of massive high-frequency datasets. Future development should prioritize the design of low-cost, multi-parameter integrated sensors and the deep integration of Internet of Things (IoT) technologies to improve data transmission efficiency and intelligent processing capabilities. These advancements will drive in situ high-frequency monitoring toward greater intelligence, precision, and operational efficiency, further consolidating its position as a core component of modern hydrological and water resources monitoring systems.

Riverine chemical weathering is a vital part of the global biogeochemical cycle, and its fluctuations are key to fully understanding and evaluat the effects of climate change on the Earth system. However, large uncertainties still remain regarding the long-term evolution of chemical weathering and its spatial responses under future climate scenarios. In this study, the Lechuga-Crespo model was integrated with the Random Forest algorithm, and future climate scenario datasets from CMIP6 were employed to systematically assess the long-term dynamic variations and geographical characteristics of global Ionic fluxes from rock Chemical Weathering (ICWR), including Ca²⁺, Mg²⁺, Na⁺, Alkalinity (HCO{}_{\mathrm{3}}^{-}, CO{}_{\mathrm{3}}^{\mathrm{2}-}), SO{}_{\mathrm{4}}^{\mathrm{2}-} and Cl^-, during the period from 1980 to 2100. The results reveal that global ICWR exhibits significant variations under different climate scenarios. In particular, under the high-emission scenario (SSP5-8.5), the global total ICWR is projected to increase substantially. By the end of the 21st century, the global total ionic flux is expected to rise by approximately 35% compared with the historical baseline, reaching about 6.6×10⁹ Mg/a in 2100. From a spatial perspective, Southeast Asia, the South Asian subcontinent, and sub-Saharan Africa are identified as the core high-value regions of future chemical weathering intensity, indicating strong regional heterogeneity in the response of weathering processes to climate change. Among the climatic drivers, precipitation is determined to be the most influential factor controlling ICWR variability, accounting for approximately 32%~33% of the overall contribution, with nearly 99% of the global land area exhibiting a significant increasing trend associated with precipitation changes. Further investigation into weathering mechanisms shows that the differential responses of various lithologies to climate change are primarily governed by their mineral composition and structural properties. Both carbonate rocks and silicate clastic rocks exhibit accelerated weathering trends under the context of global warming, highlighting the important role of lithological characteristics in regulating chemical weathering intensity.By introducing long-term dynamic simulations and future scenario projections and integrating multiple climatic drivers, this study provides a more systematic and comprehensive assessment of future ICWR changes. The multidimensional analytical framework established here offers a robust scientific basis for understanding the role of rock chemical weathering in the global carbon cycle and its environmental responses to ongoing climate change.

To improve the accuracy of lithology identification in uranium mine borehole strata and address the limitations of traditional ensemble learning models in achieving satisfactory lithology recognition performance, this study proposes a PCA-optimized Stacking ensemble learning model. The methodology begins by quantitatively evaluating the linear correlation strength between various logging parameters and the target lithology using Pearson correlation coefficients. This statistical analysis is further refined by incorporating geophysical mechanisms of well logging, leading to the selection of six logging parameters that demonstrate strong lithological relevance as input features. These parameters effectively capture essential petrophysical characteristics while minimizing redundancy in the feature set. Simultaneously, the model employs a dual-faceted approach to base learner selection. The Pearson correlation coefficients of prediction errors among base learners are systematically calculated to assess their error interdependence. Additionally, the Q-statistic matrix is utilized to evaluate the correlation and diversity of prediction outcomes. From this, a combination of base models with strong error complementarity (i.e., low correlation) and significant prediction mode differences (i.e., low Q-value) is selected. To optimally consolidate the predictive information, the PCA algorithm is utilized to perform a weighted fusion of the prediction results derived from the diverse base models. The resulting principal components, which represent the most discriminative fused features, then form the refined input feature space for training the second-layer meta-model, thereby achieving a sophisticated and high-accuracy multi-level ensemble learning framework. Experimental results show that the PCA-optimized Stacking model achieves a recognition accuracy of 97.19%, significantly outperforming traditional Stacking models and all individual models. The experimental results indicate that the recognition accuracy of the PCA-optimized Stacking model reaches 97.19%, significantly outperforming both the traditional Stacking model and all individual models. Furthermore, the study provides valuable insights and a novel technical framework for lithology identification in sandstone-type uranium mine borehole strata, contributing to more reliable and efficient geological interpretation in uranium exploration and mining operations.