Since the Quaternary period, tectonic uplift and river erosion in the northeastern Ordos Basin (northwest China) have exhumed numerous coal seams, creating the conditions for the development of coal fires following their spontaneous combustion or other types of ignition (e.g., lightning strikes). Coal fires activity is testified by the widespread occurrence of combustion metamorphic rocks. In this study, thin section analyses, scanning electron microscopy, X-ray diffraction (XRD), X-ray fluorescence (XRF), and inductively coupled plasma mass spectrometry (ICP-MS) were used to investigate in detail the mineralogical and geochemical characteristics of combustion metamorphic rocks in the Jurassic succession of the northeastern Ordos Basin. The samples collected in localities distributed over an area of about 8000 km² were analyzed to determine their mineral association, revealing the presence of tridymite, cristobalite, mullite, and cordierite that are typically produced in pyrometamorphic reactions. XRF and ICP-MS analyses revealed that combustion metamorphic rocks are iron-enriched. Investigations in the study area also highlighted the occurrence of a peculiar, porous, and permeable white sandstone that appears often associated with clinkers or coal seams. It is composed of quartz and feldspar grains and cemented by kaolinite. It is here suggested that the white color of this sandstone could be due to coal fire-related kaolinization of a sandstone protolith produced by the acidic low-temperature hydrothermal circulation of rain waters during times of coal fire activity.

The Jialing River watershed has seen substantial changes in habitat quality and land use throughout the last 20 years. It is yet unknown, however, how the habitat quality will react to these changes in land use. In this work, multi-scenario simulations for 2030 were carried out using the PLUS and InVEST models, and the land use changes and habitat quality evolution in the Jialing River basin from 2000 to 2020 were evaluated. In this study, the following facts were determined: (1) The study area’s southern region is mainly farmland, whereas the northern part is predominantly forestland. The most significant changes were a decrease of 1.46% in the grassland and an increase of 1.07% in the construction land. (2) The northern area typically has greater habitat quality than the southern region, at habitat quality indices of 0.5401, 0.5338, and 0.5084 for the year 2000, the year 2010, and the year 2020, respectively, demonstrating a pattern of steady decline. (3) Converting farmland to forestland can successfully increase habitat quality, but the conversion of grassland and forestland to farmland is the primary cause of the decline of habitat quality. (4) Global Moran’s indices of −0.7809, −0.7537, and −0.6376 for 2000, 2010, and 2020, respectively, were found in the correlation study between habitat quality and land use intensity, showing a high negative link. The local indicators of spatial association (LISA) maps indicated that the northern region had high land use intensity with low habitat quality, while the southern region had low land use intensity with low habitat quality. (5) The outcomes of the multi-scenario simulations show that, except under the ecological conservation scenario (0.5123) where habitat quality improved, there was a certain degree of deterioration under the business-as-usual (0.4381), farmland conservation (0.4941), and sustainable development (0.4909) scenarios. For future sustainable development, strict control of the encroachment on farmland and forestland is recommended, alongside the adoption of proactive measures to improve habitat quality.

Conodonts are jawless vertebrates deposited in marine strata from the Cambrian to the Triassic that play an important role in geoscience research. The accurate identification of conodonts requires experienced professional researchers. The process is time-consuming and laborious and can be subjective and affected by the professional level and opinions of the appraisers. The problem is exacerbated by the limited number of experts who are qualified to identify conodonts. Therefore, a rapid and simple artificial intelligence method is needed to assist with the identification of conodont species. Although the use of deep convolutional neural networks (CNN) for fossil identification has been widely studied, the data used are usually from different families, genera or even higher-level taxonomic units. However, in practical geoscience research, geologists are often more interested in classifying species belonging to the same genus. In this study, we use five fine-grained CNN models on a dataset consisting of nine species of the conodont genusHindeodus. Based on the cross-validation results, we show that using the Bilinear-ResNet18 model and transfer learning generates the optimal classifier. Area Under Curve (AUC) value of 0.9 on the test dataset was obtained by the optimal classifier, indicating that the performance of our classifier is satisfactory. In addition, although our study is based on a very limited taxa of conodonts, our research principles and processes can be used as a reference for the automatic identification of other fossils.

Sea level fall is considered one of the significant factors leading to the end-Permian mass extinction (EPME). We studied the relative sea level changes in the Beifengjing and Shangsi sections, and the results indicate that a sea level fall occurred in the Upper Yangtze region during the Permian–Triassic transition. Considering that there is no significant change in fossil abundance in the strata following the two sea level falls observed in the Beifengjing section, we conclude that the reduction in shallow marine habitat for sea level fall solely was insufficient to cause the mass extinction. However, sea level fall did exacerbate the input of terrestrial debris into the ocean, leading to the deterioration of the marine environment. We propose that the combined adverse effects of volcanic eruptions, sea level falls, and other events exceeded the threshold for biological survival, ultimately resulting in the catastrophic EPME.

Soil erosion is one of the most widespread environmental issues globally, posing serious threats to ecosystems and land resources. This study employs precipitation, soil, digital elevation model, and land-use data from 2000 to 2020 to quantitatively analyze the spatiotemporal patterns of land-use change and soil erosion in the Kangding River Basin through GIS-based spatial analysis and the RUSLE (Revised Universal Soil Loss Equation) model, and to evaluate soil stability across the watershed. Furthermore, using Geographical Detector (including single-factor detection and dual-factor interaction detection) and the SHAP (SHapley Additive exPlanations) algorithm to analyze the optimal machine learning model enables the assessment of the contribution of each driving factor to soil erosion. The results revealed that: (1) From 2000 to 2020, the areas of woodland and water body exhibited a decreasing trend, while cropland and construction land expanded steadily.(2) The soil erosion modulus in the Kangding River Basin first increased and then declined during the study period, rising from 16.32 t·hm ^-2 ·a ^-1 in 2000 to a peak of 21.85 t·hm ^-2 ·a ^-1 in 2005, and subsequently decreasing to 11.16 t·hm ^-2 ·a ^-1 by 2020.(3) The predominant erosion intensity was slight and very slight, with an increase in erosion severity between 2000 and 2005, followed by a gradual decrease thereafter.(4) The results of the geographical detector and SHAP analysis indicate that slope, land use, and vegetation coverage were the three most influential driving factors affecting soil erosion in the basin. These findings provide a scientific basis for comprehensive watershed management and land use planning in the Kangding River Basin, offering important theoretical support for soil and water conservation in the region.

For the purpose of providing crucial theoretical support for guaranteeing food security and reaching low-carbon emissions, this study examines the spatiotemporal evolution and influencing factors of the eco-efficiency of cultivated land use (ECLU) across 125 cities in the Yangtze River Economic Belt (YREB) from 2005 to 2022. Utilizing models such as Super-SBM, spatial autocorrelation, standard deviational ellipse, and regionally weighted regression, we investigate the spatiotemporal characteristics and influencing factors. The results indicate that (1) from 2005 to 2022, the overall ECLU in the YREB has shown a notable increase, demonstrating an “N”-shaped trend of “rise-decline-rise”, although trends vary at the city level, with upstream areas exhibiting higher efficiency than downstream and midstream areas. (2) Furthermore, a significant positive correlation exists in the ECLU among the cities, exhibiting pronounced spatial differentiation; the centroid displays a migration trajectory from “southwest-northeast-southwest”, with the long axis of the ellipse consistently oriented in the “southwest-northeast” direction. (3) Additionally, the influencing factors of the ECLU exhibit significant spatial heterogeneity across different years and regions, revealing substantial regional disparities among the cities in the YREB. Future efforts should focus on exploring differentiated regional pathways, increasing investment in agricultural technology, and enhancing farmers’ environmental awareness to promote the improvement of the ECLU.