Kai Li

Industrial emissions of H 2 S and PH 3 from submerged arc furnaces significantly hinder CO resource recovery and ecological environmental protection. While CuO-based catalysts exhibit positive removal performance toward these toxic contaminants, the atomic-level adsorption behaviors and underlying catalytic oxidation mechanisms remain poorly understood. This study employs density functional theory calculations to systematically examine the reaction system. The results show that PH 3 exhibits preferential adsorption at the Cu 3 f active site with a highest binding energy ( − 42.96 kJ mol − 1 ) , surpassing those of H 2 S ( − 36.53 kJ mol − 1 ) and O 2 ( − 34.43 kJ mol − 1 ). Transition state calculations uncover distinct reaction pathways: H 2 S is oxidized to H 2 SO 4 through hydroxyl ( − OH ) and sulfite ( − SO 3 ) intermediates, while PH 3 is converted to H 3 PO 4 via hydrogen transfer and P–O bond formation. Notably, although PH 3 adsorbs more strongly on the catalyst surface, its catalytic oxidation process is associated with a higher activation energy barrier (113.8 kJ mol − 1 ) compared to H 2 S (86.3 kJ mol − 1 ). These findings elucidate the fundamental surface reaction processes and provide theoretical support for developing high-efficiency Cu-based catalysts for simultaneous desulfurization and dephosphorization in industrial off-gas treatment.

Drought represents one of the most critical environmental hazards in arid and semi-arid regions, exerting profound impacts on ecological security and sustainable development. Nevertheless, many existing drought indices exhibit delayed responses to precipitation and soil moisture anomalies, thereby constraining their ability to characterize the rapid onset and evolution of drought events. To address this limitation, we propose the Standardized Temperature–Vegetation Drought Index (STVDI), which integrates precipitation, evapotranspiration, temperature, and vegetation dynamics within a Euclidean space framework and explicitly incorporates lag-response analysis. Taking the Mongolian Plateau (MP)—a key transition zone from taiga forest to desert steppe—as the study region, we constructed a 1 km resolution STVDI dataset spanning 2000–2021. Results reveal that over 88% of the MP is highly susceptible to flash droughts, with an average lag time of only 0.52 days, underscoring the index’s capacity for rapid drought detection. Spatial analysis indicates that drought severity peaks during March and April, with moderate drought conditions concentrated in central Mongolia and severe droughts prevailing across southwestern Inner Mongolia. Although trend analysis suggests a slight long-term alleviation of drought intensity, nearly 70% of the MP is projected to experience further intensification in the future. This study delivers the first high-resolution, low-lag drought monitoring dataset for the MP and advances theoretical understanding of drought propagation and lag mechanisms in arid and semi-arid ecosystems.

The Mongolian Plateau, a region where nomadic and agrarian civilizations intersect, exemplifies regional sustainable development and natural resource utilization through the spatiotemporal distribution of cultivated land. However, large-scale, long-term, high-precision extraction of cultivated land has not been systematically conducted in this area. This study integrated remote sensing technology with machine learning methodologies to develop an automated extraction process based on spectral, textural, and topographical features. We monitored changes in cultivated land across eight time periods from 1990 to 2023 within the Selenge River Basin, utilizing Google Earth Engine and 3527 scenes derived from Landsat and Sentinel satellite imagery. The area of cultivated land fluctuated between 6332.78 km² and 14,799.22 km², representing 2.26% to 5.29% of the total area. Cultivated land exhibited a significant decline prior to 2005 and gradually increased after 2010, largely influenced by agricultural policy reforms. Traditional nomadic areas showed a spatial pattern of reconstruction, characterized by a significant transformation to agricultural land. The overall accuracy exceeded 90%, and kappa coefficients remained above 0.83. Consistency checks and comparisons of different integration methods further validate the feasibility and reliability of the research methods and results. This approach holds promise for application across the entire Mongolian Plateau and other arid and semi-arid regions for monitoring cultivated land dynamics.

Acquiring the spatiotemporal patterns of cropland disturbance is of great significance for regional sustainable agricultural development and environmental protection. However, effective monitoring of cropland disturbances remains a challenge owing to the complexity of the terrain landscape and the reliability of the training samples. This study integrated automatic training sample generation, random forest classification, and the LandTrendr time-series segmentation algorithm to propose an efficient and reliable medium-resolution cropland disturbance monitoring scheme. Taking the Amur state of Russia in the Amur river basin, a transboundary region between Russia and China in east Asia with rich agriculture resources as research area, this approach was conducted on the Google Earth Engine cloud-computing platform using extensive remote-sensing image data. A high-confidence sample dataset was then created and a random forest classification algorithm was applied to generate the cropland classification probabilities. LandTrendr time-series segmentation was performed on the interannual cropland classification probabilities. Finally, the identification, spatial mapping, and analysis of cropland disturbances in Amur state were completed. Further cross-validation comparisons of the accuracy assessment and spatiotemporal distribution details demonstrated the high accuracy of the dataset, and the results indicated the applicability of the method. The study revealed that 2815.52 km² of cropland was disturbed between 1990 and 2021, primarily focusing on the southern edge of the Amur state. The most significant disturbance occurred in 1991, affecting 1431.48 km² and accounting for 50.84% of the total disturbed area. On average, 87.98 km² of croplands are disturbed annually. Additionally, 2495.4 km² of cropland was identified as having been disturbed at least once during the past 32 years, representing 83% of the total disturbed area. This study introduced a novel approach for identifying cropland disturbance information from long time-series probabilistic images. This methodology can also be extended to monitor the spatial and temporal dynamics of other land disturbances caused by natural and human activities.

The Mongolian Plateau (MP), situated in the transitional zone between the Siberian taiga and the arid grasslands of Central Asia, plays a significant role as an Ecological Barrier (EB) with crucial implications for ecological and resource security in Northeast Asia. EB is a vast concept and a complex issue related to many aspects such as water, land, air, vegetation, animals, and people, et al. It is very difficult to understand the whole of EB without a comprehensive perspective, that traditional diverse studies cannot cover. Big data and artificial intelligence (AI) have enabled a shift in the research paradigm. Faced with these requirements, this study identified issues in the construction of EB on MP from a big data perspective. This includes the issues, progress, and future recommendations for EB construction-related studies using big data and AI. Current issues cover the status of theoretical studies, technical bottlenecks, and insufficient synergistic analyses related to EB construction. Research progress introduces advances in scientific research driven by big data in three key areas of MP: natural resources, the ecological environment, and sustainable development. For the future development of EB construction on MP, it is recommended to utilize big data and intelligent computing technologies, integrate extensive regional data resources, develop precise algorithms and automated tools, and construct a big data collaborative innovation platform. This study aims to call for more attention to big data and AI applications in EB studies, thereby supporting the achievement of sustainable development goals in the MP and enhancing the research paradigm transforming in the fields of resources and the environment.

The transboundary region along the Heilongjiang River, encompassing the Russian Far East and Northeast China, possesses abundant agricultural natural resources crucial for global food security. In the face of the challenge of disruptions in the global food supply chain, the precise monitoring and exploitation of agricultural resources in the Heilongjiang Basin becomes imperative. This study employed deep learning to classify crop status in 2023 in the Heilongjiang Basin using Sentinel-2 satellite remote sensing images at a 10 m resolution. Various vegetation indices, including the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), the Enhanced Vegetation Index (EVI), the Modified Soil Adjusted Vegetation Index (MSAVI), and others, were computed and analyzed for different crops. The Google Earth Engine (GEE) platform was utilized for validation point sampling based on plot objects. The random forest (RF) classification method was successfully employed to classify and identify major crops in the study area (wheat, maize, rice, and soybean), as well as wetlands, tree cover, grassland, water, and constructed land, with an overall classification accuracy of 86%. Tree cover dominated the land cover, constituting 62%, while wheat, maize, rice, and soybeans accounted for 7% of the total area. Of these, soybeans occupied the largest area (57,646.60 hectares), followed by rice (53,209.53 hectares), maize (39,998.37 hectares), and wheat (8782.31 hectares). This study demonstrated that sample selection based on plot objects facilitates efficient sample labeling, providing insights into crop classification in other, potentially larger, areas. This method simultaneously distinguishes wetland, cultivated land, and forest features, supporting further integrated investigations for more natural resources.

1. Land cover refers to the natural or artificial elements that cover the surface of the Earth, including water bodies, vegetation, buildings, sandy areas, and bare soil. Different regions (latitud...

Earthquakes are one of the major natural disaster threats worldwide and directly cause substantial economic losses and many casualties every year. Research on the resource and environmental carrying capacity in earthquake-prone areas is urgently required for regional earthquake relief efforts and post-disaster reconstruction. This study considered Ganzi Tibetan Autonomous Prefecture (Ganzi Prefecture) in Sichuan Province, China, focusing on the impact of the Luding 6.8 Magnitude Earthquake in Ganzi Prefecture in 2022. An evaluation system for the resource and environmental carrying capacity of earthquake-prone areas was established. A total of 23 indicators were selected that cover ecological, social economical, and geological aspects, and the weight of each index was determined by the Analytic Hierarchy Process. The relative ranking of the resource and environmental carrying capacities of each county and city were calculated using the weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Consequently, the post-disaster reconstruction strategy of Ganzi Prefecture was evaluated and analyzed. The results show that the resource and environmental carrying capacities of each administrative area differ regionally. Evidence shows that the resource and environmental carrying capacity in southeastern Ganzi Prefecture is generally higher than in the northwest, owing to the joint influence of the social economy and ecological and geological environment. This study provides carrying capacity assessment data and support methods for earthquake-prone areas.

Land-use and land-cover changes in the Eastern European Plain have important implications for regional and global ecological environments, food security, and socio-economic development. Here, three 30 m resolution global land cover data products (FROM_GLC, GlobeLand30, and GLC_FCS30) from the Eastern European Plain were analyzed and evaluated for component similarity, type confusion degree, spatial consistency, and accuracy verification. The research found that the three products provided consistent descriptions of land-cover types in the East European Plain. There was a strong correlation in the type area between the different products, with a correlation coefficient >0.85. Medium-to-high-consistency areas represented 92.31% of the total plains area. The low-consistency areas were mainly concentrated on Yuzhny Island, Kola Peninsula, and Pechora River Basin. The comparison revealed high consistency among the three products in identifying forest, cropland, water, and permanent ice/snow types. However, the consistency was poor for shrubs, wetlands, and bare land. Using the GLCVSS_V1 validation dataset, the highest overall accuracy among the assessed land cover data products was observed in the FROM_GLC (73.96%), followed by GlobeLand30 (69.80%) and GLC_FCS30 (67.29%). The FROM_GLC dataset is suitable for studying forests, tundra, water, and providing an overall representation of the region’s land cover. The GLC_FCS30 dataset is more suitable for agricultural research. The differences between products arise from the differences in classification systems, algorithms, and data correction. In the future, it will be necessary to utilize the advantages of different products for data fusion, focusing on areas with high heterogeneity and easily confused types, and improving the reliability of land-cover data products.

Accurate and timely estimation of grass yield is crucial for understanding the ecological conditions of grasslands in the Mongolian Plateau (MP). In this study, a new artificial neural network (ANN) model was selected for grassland yield inversion after comparison with multiple linear regression, K-nearest neighbor, and random forest models. The ANN performed better than the other machine learning models. Simultaneously, we conducted an analysis to examine the spatial and temporal characteristics and trends of grass yield in the MP from 2000 to 2020. Grassland productivity decreased from north to south. Additionally, 92.64% of the grasslands exhibited an increasing trend, whereas 7.35% exhibited a decreasing trend. Grassland degradation areas were primarily located in Inner Mongolia and the central Gobi region of Mongolia. Grassland productivity was positively correlated with land surface temperature and precipitation, although the latter was less sensitive than the former in certain areas. These findings indicate that ANN model-based grass yield estimation is an effective method for grassland productivity evaluation in the MP and can be used in a larger area, such as the Eurasian Steppe.