Fengtian Yang

To reveal the impacts of long-term coal mining on groundwater flow fields and the environment, the Jinjitan Coal Mine in Shaanxi Province, China, was selected as the study area. A systematic investigation on the evolution of flow fields and migration of pollutants under long-term coal mining impact was conducted using methods including Self-Organizing Map (SOM) clustering analysis, water isotope analysis, and numerical simulation. The results show that groundwater in the study area is mainly derived from atmospheric precipitation, and the mine water is a mixture of 55% coal seam water and 45% Quaternary groundwater; long-term coal mining has led to two types of Quaternary groundwater level changes (stable and continuously declining); The groundwater level of the Jurassic aquifer shows an overall decline. However, where the aquifer is locally recharged by the Quaternary groundwater, the groundwater level shows a decline-then-rise variation. The overall groundwater flow field maintains the pattern of runoff from northeast to southwest and discharge into the Yuxi River, with no local or regional groundwater depression cones observed; numerical simulation results indicate an average groundwater level drop of 1.36 m over an 10-year period, with a maximum decrease of 4.89 m; the migration scope of oxygen demand and petroleum pollutants caused by domestic sewage pond leakage is limited, their maximum long-term (3650 d) migration distances are 253.47 m and 254.27 m, respectively, and their concentrations are far below the limit of the Class III groundwater quality standard. The research findings provide a scientific basis for the environmental protection, rational development, and utilization of groundwater in the Jinjitan Coal Mine area.

To explore the hydrogeochemical characteristics and dominant water–rock interaction processes of thermal water in Lindian geothermal field (northern Songliao Basin, NE China), this study analyzed 16 thermal water samples (1900–3000 m depth) and 3 shallow groundwater samples using hydrochemical indices, water isotopes, and statistical methods (Pearson Correlation and Principle Component Analysis). Results show that the thermal water originates from precipitation and exhibits an “oxygen shift” indicating a long-time water–rock interaction under low to medium reservoir temperature. The thermal water is alkaline with a high TDS and dominated by Na⁺, Cl⁻, and HCO₃⁻, and its hydrochemical facies changes from HCO₃·Cl–Na to Cl·HCO₃–Na and Cl–Na along the groundwater flow path. Leaching of halite, silicates, and carbonates is the primary process controlling solute accumulation. The geothermal reservoir is in a relatively closed, strong reducing environment, and thermal water reached water–rock equilibrium with respect to Na-, K-, Ca-, and Mg-alumino silicates. Principle Component Analysis identifies three key controlling factors, including mineral leaching, organic matter degradation, and sulfate reduction/mineral precipitation. This study establishes a hydrogeochemical baseline for the initial exploitation stage, providing a scientific basis for predicting long-term water quality changes and formulating differentiated sustainable development strategies for the Lindian geothermal field.

As a fundamental metric for assessing carbon sequestration, Net Primary Productivity (NPP) and the mechanisms driving its spatiotemporal dynamics constitute a critical research domain within global change science. This research centered on the Huang–Huai–Hai Plain (HHHP), combining 2001–2023 MODIS-NPP data with natural (landform, temperature, precipitation, soil) and socio-economic (population density, GDP density, land use) drivers. Trend analysis, coefficient of variation, and Hurst index were applied to clarify the spatiotemporal evolution of NPP and its future trends, while geographic detectors and structural equation models were used to quantify the contribution of drivers. Key findings: (1) Across the HHHP, the multi-year average NPP ranged between 30.05 and 1019.76 gC·m⁻²·a⁻¹, with higher values found in Shandong and Henan provinces, and lower values concentrated in the northwestern dam-top plateau and central plain regions; 44.11% of the entire region showed a statistically highly significant increasing trend. (2) The overall fluctuation of NPP was low-amplitude, with a stable center of gravity and the standard deviation ellipse retaining a southwest-to-northeast direction. (3) Future changes in NPP exhibited persistence and anti-persistence, with 44.98% of the region being confronted with vegetation degradation risk. (4) NPP variations originated from the synergistic impacts of multiple elements: among individual elements, precipitation, soil type, and elevation had the highest explanatory capacity, while synergistic interactions between two elements notably enhanced the explanatory capacity. (5) Climate variation exerted the strongest influence on NPP (direct coefficient of 0.743), followed by the basic natural environment (0.734), whereas human-related activities had the weakest direct impact (−0.098). This research offers scientific backing for regional carbon sink evaluation, ecological security early warning, and sustainable development policies.

Baiyangdian Lake, recognized as the largest freshwater body in northern China, plays a vital role in maintaining the regional eco-environment. Prior studies have pointed out the contamination of sediments with heavy metals, raising concerns about eco-environmental challenges. Therefore, it is imperative to evaluate the current pollution levels and ecological threats related to heavy metals found in the sediments of Baiyangdian Lake as well as in its inflow rivers. In May 2022, surface sediments with a depth of less than 20 cm were analyzed for Cu, Zn, Pb, Cr, Ni, As, Cd, and Hg to determine the pollution status, identify sources of pollution, and evaluate potential ecological risks. A range of evaluation methods used by predecessors such as geo-accumulation index (I_geo), enrichment factor (EF), ecological risk index (RI), sediment quality guidelines (SQGs), positive matrix factorization (PMF), absolute principal component score-multiple linear regression model (APCS-MLR), chemical mass balance (CMB), and UNMIX model were analyzed. After comparison, multi-methods including the geo-accumulation index (Igeo), absolute principal component score-multiple linear regression model (APCS-MLR), ecological risk index (RI), and sediment quality guidelines (SQGs) were utilized this time, leading to a better result. Findings reveal that pollution levels are generally low or non-existent, with only 1.64% of sampling sites showing close to moderate pollution levels for Cu, Pb, and Zn, and 4.92% and 1.64% of sites exhibiting close to moderate and moderate pollution levels for Cd, respectively. The main contributors to heavy metal presence are pinpointed as industrial wastewater discharge, particularly Cu, Zn, Pb, Cd, and Hg. The ecological risks are also relatively low, with 4.92%, 1.64%, and 1.64% of sampling sites demonstrating close to moderate, moderate, and strong risks in the inflow rivers, respectively. Additionally, only one site shows moderate potential biological toxicity, while the rest display non-toxicity. These findings will update our cognition and offer a scientific basis for pollution treatment and ecosystem enhancement for government management.

This study explores the potential of machine learning algorithms for earthquake prediction, utilizing fluid chemical anomaly data from hot springs. Six hot springs, located within an active fault zone along the southeastern coast of China, were carefully chosen as hydrochemical monitoring sites for an extended period of two and a half years. Using this data, a prediction model integrating six algorithms was developed to forecast M ≥ 5 earthquakes in Taiwan. The model's performance was validated against recorded earthquake events, and the factors influencing its predictive capability were analyzed. Our comprehensive analysis conclusively demonstrates the superiority of machine learning algorithms over traditional statistical methods for earthquake prediction. Additionally, including sampling time in the data sets significantly improves the model's predictive performance. However, it is important to note that the model's predictive performance varies across different hot spring and indicators type, highlighting the importance of identifying optimal indicators for specific scenarios. The model parameters, including the anomaly detection rate (P) and earthquake response time threshold (M), significantly impact the model's predictive capabilities. Therefore, adjustments are needed to optimize the model's performance for practical use. Despite limitations such as the inability to differentiate pre‐earthquake anomalies from post‐earthquake anomalies and pinpoint the precise location of earthquakes, this study successfully showcases the potential of machine learning algorithms in earthquake prediction, paving the way for further research and improved prediction methods.

The Dongying geothermal resources are an important part of the deep geothermal resources in Tianjin Binhai New Area, and these resources will determine whether the demand target can be met. There is an urgent need to find favorable target areas for Dongying geothermal resources and to develop Dongying geothermal resources safely, stably and efficiently. The recoverable potential of the Dongying geothermal reservoir in different positions is calculated by simulating and predicting the water level, which is as an important index of zoning evaluation. By using the analytic hierarchy process (AHP), the comprehensive indexes of geothermal exploration and development regionalization are quantified. A grade-two evaluation system, which considers the development constraints, has been established for the delineation of deep geothermal resources’ exploration and development prospect target areas. The zoning evaluation results show that the excellent prospect target area of Dongying geothermal resources, for their exploration and development, is 314.33 km², the general prospect target area is 745.77 km², and the bad prospect target area is 879.31 km². The quantitative zoning evaluation method can provide references for the optimization of the exploration and development target area of deep geothermal resources with low prospecting accuracy in key areas of China.

Dongying’s Paleogene geothermal resources are an important part of the geothermal resources of the Tianjin coastal region. The extent of the geothermal fluid resources and the supply relationship have become increasingly important, and will determine whether demand targets can be met. Dongying’s Paleogene formation in the Tianjin coastal regions is widely distributed to the east of the Cangdong fracture, but it is absent west of the Cangdong fracture. On the basis of introducing the geological characteristics and depositional characteristics of the Dongying formation, we analyzed the hydraulic conductivity of the Cangdong fracture to the Dongying formation geothermal reservoir from the aspects of geological condition, dynamic of groundwater level and hydrologic geochemistry. Based on the hydrochemical information and the isotope data gained during the water quality evaluation and isotope data analysis process, we discovered the main chemical composition, hydrogen and oxygen isotope data and geothermal fluid age are significantly different between the Dongying formation geothermal reservoir and the overlying and underlying geothermal reservoirs. It is inferred that the hydraulic conductivity of the Cangdong fracture to the Dongying formation geothermal reservoir in this area is weak, and along the Haihe fracture, where the Haihe fracture intersects with the Cangdong fracture, there is a certain hydraulic conductivity. In addition, there is no obvious hydraulic connection between the Dongying formation and the upper and lower geothermal reservoirs.

The geothermal resources in mountainous areas are usually controlled by neotectonic faults. To minimize the risk in site selection for geothermal drilling, the controlling mechanism must be identified. Based on the neotectonic control theory for geothermal resources, the occurrence of the geothermal resources in the mountainous area in southeastern Zhangjiakou city was investigated. The investigation was divided into three stages. Firstly, field investigation for neotectonics was carried out to characterize the kinematics and the stress state of the faults, including fault occurrences, tectonic stages, and paleostress analysis, and in total 19 neotectonic faults were confirmed. Based on Riedel shear model and present stress field, their conductivity for geothermal water was inferred; secondly, geophysical and geochemical surveys were applied to identify the stress state and characterize the occurrence of the potential water conductive faults. The results showed that the combined use of magnetotelluric sounding (MT), controllable source audio magnetotelluric sounding (CSAMT), and 2D seismologic survey is effective in delineating fault occurrence, strata distribution, and water conductivity within 1,000 m depth, while measurement of ²²²Rn activity in soil gas across the neotectonic faults is effective to investigate the groundwater conductivity of the faults, and all the neotectonic faults in the study area striking clockwise from N5°E to N25°W are considered to be water conductive; thus, the areas at the intersection zones of neotectonic faults in the basin in groundwater runoff and discharge regions are promising targets for geothermal exploration. This conceptual model was verified by the geothermal fields already discovered in the study area and proved to be reasonable, and then a potential drilling site was predicted and proved to be successful. It is suggested that this workflow for geothermal exploration is suitable for Zhangjiakou city and may also be applied to other mountainous areas.