Search Results (5)

Climate change is profoundly altering species’ geographical distributions, with particularly pronounced effects on the richness patterns of invasive alien plants. As China represents a global hotspot for biological invasions, accurately projecting these shifts is imperative for formulating proactive and effective management strategies. This study integrated occurrence records for 321 invasive plant species with seven key environmental predictors within a MaxEnt modeling framework, supplemented by ArcGIS v10.8 spatial analysis, to simulate potential species richness distributions under current climatic conditions and three future periods (2050s, 2070s, and 2090s) across three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585). The optimized models exhibited strong predictive performance (mean AUC = 0.972 ± 0.037; mean TSS = 0.877 ± 0.115), with 92.1% of species achieving AUC > 0.9. Annual precipitation metrics emerged as the predominant drivers, with precipitation of the driest month (Bio14, 37.6%), annual precipitation (Bio12, 15.6%), and minimum temperature of the coldest month (Bio6, 13.8%) exerting the strongest influence on species distributions. Contemporary invasive plant richness hotspots are concentrated in southern and southwestern China. Under future climate scenarios, substantial range shifts are anticipated: suitable habitats are projected to expand significantly for 58 species (a mean change of +145.8%), while contracting for 24 species (a mean change of −50.4%). Notably, the centroid of maximum species richness is projected to undergo a pronounced north-northwestward displacement, migrating from its current location in Xiangcheng District, Hubei Province, to Lushi County, Henan Province, by the 2090s under the SSP585 scenario. This trajectory coincides with a marked expansion of areas characterized by medium and high species richness, a trend that is particularly accentuated under the high-emission pathway. In conclusion, this study provides a robust, spatially explicit assessment of the future dynamics of invasive plant richness in China, highlighting a significant north-northwestward redistribution under climate change. These findings establish a critical scientific baseline for prioritizing regional monitoring efforts and implementing preemptive control measures in areas facing heightened invasion risk. Full article

This study uses a sample of pegmatite (LS-1) from the Longquanping deposit in Lushi County, Henan Province, to evaluate its potential as a valuable source of HPQ. This investigation uses various analytical techniques to assess the quality of quartz and its suitability for industrial applications. The methods used in this study include optical microscopy, scanning electron microscopy (SEM), Raman spectroscopy, X-ray fluorescence spectrometry (XRF), laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), and inductively coupled plasma mass spectrometry (ICP-MS) to analyze the petrographic, mineralogical, and trace element characteristics of quartz before and after purification and comprehensively evaluate the potential of quartz in these rocks as an HPQ raw material ore. The optical and scanning electron microscopic observations reveal several impurities and associated minerals in quartz, including feldspar, biotite, magnetite, sphene, and large number of fluid inclusions composed of both gas and liquid phases. The content of trace element in raw quartz ore in the LS-1 sample as determined by LA-ICP-MS analysis ranges from 41.61 to 256.13 ppm, with the main impurity elements being Al, Ti, Li, Na, K, and Ca. After purification, the SiO₂ contents and total trace elements contents of the LS-1 refined quartz sand was 99.997 wt.% and 29.29 ppm, respectively, with Al (13.29 ppm), Ti (4.20 ppm), Li (1.15 ppm), and Na (10.32 ppm). The major trace element contents of Al and Ti in the quartz concentrates were lower than the upper limit of the HPQ standard and thus belonged to the high-end products (4N8). Results of this study show that quartz from a pegmatite in the Lushi region has the potential to be purified HPQ. This study underscores the importance of thorough mineralogical and elemental analyses in assessing the suitability of quartz raw material deposits for HPQ production. Full article

The terrain in the loess hilly area of western Henan is fragmented, with steep slopes and weak soil erosion resistance. The substantial soil erosion in this region results in plenty of problems, including decreased soil productivity and ecological degradation. These problems significantly hinder the social and economic development in the region. Soil conservation planning and ecological development require accurate soil erosion surveys. However, the studies of spatio-temporal patterns, evolution, and the driving force of soil erosion in this region are insufficient. Therefore, based on a multi-stage, unequal probability, systematic area sampling method and field investigation, the soil erosion of the loess hilly area of western Henan was quantitatively evaluated by the Chinese Soil Loss Equation (CSLE) in 2022. The impact forces of soil erosion were analyzed by means of a geographic detector and multiple linear regression analysis, and the key driving factors of the spatio-temporal evolution of soil erosion in this region were revealed. The results were as follows. (1) The average soil erosion rate of the loess hilly area in western Henan in 2022 was 5.94 t⋅ha⁻¹⋅a⁻¹, with a percentage of soil erosion area of 29.10%. (2) High soil erosion rates mainly appeared in the west of Shangjie, Xingyang, and Jiyuan, which are related to the development of production and construction projects in these areas. The areas with a high percentage of soil erosion area were in the north (Xinan and Yima), west (Lushi), and southeast (Songxian and Ruyang) of the study area. Moreover, areas with the most erosion were found in forest land, cultivated land, and areas with a slope above 25°. (3) At the landscape level, the number and density of patches of all land types, except orchard land, increased significantly, and the boundary perimeter, landscape pattern segmentation, and degree of fragmentation increased. (4) The geographical detector and multiple linear regression analysis indicated that the driving forces of soil erosion are mainly topographic and climatic (slope length, elevation, precipitation, and temperature). Soil erosion was significantly influenced by the density of landscape patches. These maps and factors influencing soil erosion can serve as valuable sources of information for regional soil conservation plans and ecological environment improvements. Full article

Wide area landslide detection is a major international research hotspot in the field of geological hazards, and the integration of multi-temporal optical satellite images and spaceborne interferometric synthetic aperture radar (InSAR) appears to be an effective way to realize this. In this paper, a technical framework is presented for wide area landslide detection: (i) multi-temporal satellite optical images are used to detect landslides with distinguishable geomorphological features; (ii) Generic Atmospheric Correction Online Service (GACOS) assisted InSAR stacking is employed to generate annual surface displacement rate maps in radar line of sight using satellite SAR images from both ascending and descending tracks, which are in turn utilized to automatically detect active landslides from ground motion using hotspot analysis, and (iii) the distribution characteristics of the detected landslides are investigated by examining their relationships with topographic and hydrological factors. Three expressways in Sichuan Province, China—namely the Yakang (Ya’an-Kangding), Yaxi (Ya’an-Xichang), and Lushi (Luding-Shimian) expressways—and their surrounding regions (a total area of approximately 20,000 square kilometers) were chosen as the study area. A total of 413 landslides were detected, among which 320 were detected using multi-temporal satellite optical images, and 109 were detected using GACOS-assisted InSAR stacking. It should be noted that only 16 landslides were detected by both approaches; these landslides all exhibited not only obvious geomorphological features but also ground motion. A statistical analysis of the topographic and hydrological factors shows that of the detected landslides: 81% are distributed at elevations of 1000–2500 m, over 60% lie within the elevation range of 100~400 m, and 90% present with medium and steep slopes (20°~45°), and 80% are located within areas seeing an annual rainfall of 950~1050 mm. Nine landslides were found to pose potential safety hazards to the expressways. The research findings in this paper have directly benefitted the Sichuan expressways; equally important, it is believed that the technical framework presented in this paper will provide guidance for hazard mitigation and the prevention of transportation hazards in the future. Full article

Low-quality input data (such as sparse rainfall gauges, low spatial resolution soil type and land use maps) have limited the application of physically-based distributed hydrological models in operational practices in many data-sparse regions. It is necessary to quantify the uncertainty in the deterministic forecast results of distributed models. In this paper, the TOPographic Kinematic Approximation and Integration (TOPKAPI) distributed model was used for deterministic forecasts with low-quality input data, and then the Hydrologic Uncertainty Processor (HUP) was used to provide the probabilistic forecast results for operational practices. Results showed that the deterministic forecasts by TOPKAPI performed poorly in some flood seasons, such as the years 1997, 2001 and 2008, despite which the overall accuracy of the whole study period 1996–2008 could be acceptable and generally reproduced the hydrological behaviors of the catchment (Lushi basin, China). The HUP model can not only provide probabilistic forecasts (e.g., 90% predictive uncertainty bounds), but also provides deterministic forecasts in terms of 50% percentiles. The 50% percentiles obviously improved the forecast accuracy of selected flood events at the leading time of one hour. Besides, the HUP performance decayed with the leading time increasing (6, 12 h). This work revealed that deterministic model outputs had large uncertainties in flood forecasts, and the HUP model may provide an alternative for operational flood forecasting practices in those areas with low-quality data. Full article