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The evaluation of regional water resource carrying capacity has been repeatedly conducted to provide a scientific basis for the local water resource management and the sustainable development, in particular in the water-limited regions. However, the definition of regional water resource carrying capacity and its evaluation method are still arguable. Through a case study of Inner Mongolia, located in the arid and semi-arid northern China, this paper developed an improved method to calculate regional water resource carrying capacity by the combination of the water supply-demand analysis and the S-shaped curve threshold analysis. The spatial and temporal patterns of the regional water resource carrying capacity in Inner Mongolia during 2000–2019 was evaluated at three scales, namely the province scale, the basin scale and the city scale. The results showed that the average regional water resource carrying capacity of the whole province was 0.25 (the full mark is 1.00); at the basin scale, the Yellow River Basin had the lowest regional water resource carrying capacity (0.17) among all the basins, showing that the utilization of the water resources was unreasonable; at the city scale, the average regional water resource carrying capacities in Hulunbuir and Xilingol were both over 0.25, while those in Alxa, BayanNur and Wuhai were below 0.1; Hulunbuir had 25.48 billion m3 water surplus, while BayanNur suffered from an average water deficit of 4.51 billion m3 from 2000 to 2019. This paper has provided a reasonable way to measure the regional water resource carrying capacity using an improved method by incorporating S-shaped curve threshold analysis, which may have a wider application for the clustering and optimization of regional water management. In addition, the spatial and temporal patterns of regional water carrying capacity are beneficial for policymakers in the implementation of the effective water usage. • Regional water resource carrying capacity is evaluated by water supply-demand analysis and temporal threshold analysis. • The evaluation method is improved by considering temporal threshold at different regional scales. • "S"-shaped logistic curves are used to simulate the temporal pattern of water resource carrying capacity. • Spatial and temporal water resource carrying capacity analysis benefits policymakers. [ABSTRACT FROM AUTHOR]

The development of coal resources is necessary, but it has a huge negative impact on land, ecology, and the environment. With the increasing awareness of environmental protection and the requirements of related regulations, the design and practice of reclamation projects run through the mining life cycle and continue for a long time after the coal production. High-precision monitoring of mining disturbance and reclamation, quantifying the degree and time of vegetation disturbance and restoration, is of great significance to minimize the environmental effect of mining. Remote sensing, widely used as efficient monitoring tool, but there is not enough research on disturbance and reclamation monitoring taking into account large-scale areas and high temporal and spatial accuracy. Especially when mining sites remain unknown, how to distinguish the disturbance of coal mining and other human activities affecting the surface land cover has become a challenge. Therefore, this paper proposed a method to reconstruct the time series of mining disturbance and reclamation in a large area by using the POI (point of interest) and Landsat time series images using multiple buffer analysis methods. The process includes: (1) Retrieval of POI in the study area based on the public mining list using Python crawler, and buffering 100 km for preliminary extraction of potential mining areas; (2) Using spectral index mask and random forest algorithm to accurately extract the exposed coal on the Google Earth Engine (GEE) platform; (3) Buffering 10 km to identify the occurrence of disturbance and reclamation, using pixel-based temporal trajectory identification of LandTrendr algorithm under GEE. The method successful detect the change points of surface coal mining disturbance and reclamation in eastern Inner Mongolia of China. The results show that: (1) The method can effectively identify the extent of surface coal mining disturbance and reclamation, and the overall extraction accuracy is 81%. (2) Surface coal mining disturbance in eastern Inner Mongolia was concentrated in 2006–2011. By 2020, the total disturbed area is 627.8 km2, with an average annual disturbance of 18.5 km2, and the annual maximum disturbance to the ground reached 64.6 km2 in 2008. With the total reclaimed area being 236.3 km2, the reclamation rate is about 37.6%. This study provides a systematic solution and process for monitoring the disturbance and reclamation of surface coal mining in a large range with little known about the mines' location. It can effectively identify the mining disturbance and reclamation process which can also be extended to other areas, providing a quantitative assessment of mining disturbance and reclamation, which can support further ecological restoration decision-making. [Display omitted] • A remote sensing procedure of surface coal mine classification, disturbance and reclamation monitoring is developed. • Landsat, random forest, web scraping, and Google Earth Engine has been used to detect 73 surface coal mining sites. • 0.94% of the total study area has been disturbed by surface mining. • The developed procedure is able to reveal the surface coal mine reclamation pattern. [ABSTRACT FROM AUTHOR]

Soil wind erosion is an important ecological environmental problem that is widespread in arid and semi-arid regions. Currently, related studies are mainly focused on spatiotemporal characteristics or analysis of effector mechanisms, and they do not facilitate direct servicing of management decisions. In this paper, we used the Xilingol typical steppe in Inner Mongolia, China, as a study site to develop a decision framework for a comprehensive understanding of soil wind erosion and to promote sustainable management of steppes. In this study, we used the Revised Wind Erosion Equation model to simulate soil wind erosion. We combined this model with linear trend analysis to evaluate the ecological effects of soil wind erosion and wind erosion intensity, and delineated the gravity center migration path. We used the constraint line method to reveal the mechanisms by which climatic factors affected soil wind erosion, achieved the spatial visualization of wind-breaking and sand-fixing service flow, and proposed decision-based regional sustainable development suggestions. The results showed that long-term soil wind erosion will lead to soil coarsening and loss of soil nutrients. Soil wind erosion in the study site showed worsening trends and risks, and as such, ecological environment conservation and management are urgently required. The study framework promotes a clear understanding of the complex relationship of human–earth systems. The study results will aid in the ecological remediation of steppe landscapes and the prevention of desertification and will provide a foundation for win–win management of ecological conservation and economic development in arid and semi-arid regions. • A decision-oriented management framework was developed from the perspective of soil wind erosion prevention and control. • Long-term soil wind erosion will lead to soil coarsening and loss of soil nutrients. • The constraint line method can help to reveal the complex effects of meteorological factors on soil wind erosion. • The study achieved spatial visualization of wind-breaking and sand-fixing service flow in grassland ecosystem. [ABSTRACT FROM AUTHOR]

The "Joint Prevention and Control of Regional Air Pollution in China" has been put forward to solve serious regional air pollution. However, there is no geographical division covering the whole country that could reveal the similarities and differences among regions to support implementing this policy. This paper applied an air quality model and a multiregional input-output model to analyze the impacts and patterns of atmospheric transmission and trade transfer of regional air pollution, then compared the differences among typical provinces in the northern, central and southern regions. The social network analysis method was used to analyze the interacting pattern of regional atmospheric transmission and trade transfer, and 31 provinces in mainland China were divided into 5 clusters to reveal the interprovincial relationship of atmospheric transmission; then, the 31 provinces were divided into 3 clusters to reveal the interprovincial relationship of trade transfer. Based on these results, the provincial geographical division of "Nine Atmospheric Environmental Transmission Zones" covering mainland China was carried out. In addition, the key regions linking atmospheric transmission and trade transfer were also found. For example, Inner Mongolia is a typical key linkage region. From an atmospheric transmission perspective, it is linked to the northeast area. However, from a trade transfer perspective, it has a close relationship with the southern provinces. The linkage among provinces provides support for the interregional interaction mechanism of air pollution and the formulation of "Joint Prevention and Control of Regional Air Pollution in China". Image 1 • The impact of atmospheric and trade transfer on regional pollution were discussed. • The atmospheric transmission is location-sensitive and trade transfer is not. • The social network analysis was used in air atmospheric environmental zoning. • The 31 provinces were classified into 9 atmospheric environmental transmission zones. • There are four provinces being nodes connecting atmosphere and trade transfer. [ABSTRACT FROM AUTHOR]