Fine particulate matter (PM_2.5) is a major pollutant in Guanzhong Urban Agglomeration (GUA) during the winter, and GUA is one of China’s regions with the highest concentrations of PM_2.5. Daily surface PM_2.5 maps with a spatial resolution of 1 km × 1 km can aid in the control of PM_2.5 pollution. Thus, the Random Forest and eXtreme Gradient Boosting (RF-XGBoost) model was proposed to fill the missing aerosol optical depth (AOD) at the station scale before accurately estimating ground-level PM_2.5 using the recently released MODIS AOD product derived from Multi-Angle Implementation of Atmospheric Correction (MAIAC), high density meteorological and topographic conditions, land-use, population density, and air pollutions. The RF-XGBoost model was evaluated using an out-of-sample test, revealing excellent performance with a coefficient of determination (R²) of 0.93, root-mean-square error (RMSE) of 12.49 μg/m³, and mean absolution error (MAE) of 8.42 μg/m³. The result derived from the RF-XGBoost model indicates that the GUA had the most severe pollution in the winter of 2018 and 2019, owing to the burning of coal for heating and unfavorable meteorological circumstances. Over 90% of the GUA had an annual average PM_2.5 concentrations decrease of 3 to 7 μg/m³ in 2019 compared to the previous year. Nevertheless, the air pollution situation remained grim in the winter of 2019, with more than 65% of the study area meeting the mean PM_2.5 values higher than 35 μg/m³ and the maximum reaching 95.57 μg/m³. This research would be valuable for policymakers, environmentalists, and epidemiologists, especially in urban areas.

Eutrophication is a significant factor that damages the water ecosystem’s species balance. The total phosphorus (TP) concentration is a vital water quality indicator in assessing surface water eutrophication. This paper predicts the spatial distribution of TP concentration using remote sensing, measured data, and the partial least squares regression (PLSR) method. Based on the correlation analysis, the models were built and tested using the TP concentration and Sentinel-2 Multispectral Instrument (MSI) and Landsat-8 Operational Land Imager (OLI) image spectra. The results demonstrated that the best technique based on band combinations of the Sentinel-2 and Landsat-8 images achieved good precision. The coefficient of determination (R²), root mean square error of prediction (RMSEP), and residual prediction deviation (RPD) were 0.771, 0.023 mg/L, and 2.086 for Sentinel-2 images and 0.630, 0.032 mg/L, and 1.644 for Landsat-8 images, respectively. The TP concentration maps were interpolated using the inverse distance weighting method, and the inversion results obtained from the images were in good agreement. The western and northwestern regions of Taihu Lake, where significant cyanobacterial blooms occurred, had TP concentrations greater than 0.20 mg/L; nevertheless, the central and eastern regions had amounts ranging from 0.05 to 0.20 mg/L. In order to prove the extensibility of the model, the optimal algorithm was applied to the Sentinel-2 and Landsat-8 images in 2017. The optimal algorithm based on Landsat-8 images has a better verification effect (RMSEP = 0.027 mg/L, and R = 0.879 for one Landsat-8 image), and the optimal algorithm based on Sentinel-2 images has moderate verification effect (RMSEP = 0.054 mg/L and 0.045 mg/L, and R = 0.771 and 0.787 for two Sentinel-2 images). The interpolation and inversion maps are in good agreement, indicating that the model is suitable for the Landsat-8 and Sentinel-2 images, which can be complementary for higher temporal resolutions. Monitoring water quality using multiple remote sensing images can provide the scientific basis for water quality dynamic monitoring and prevention in China.

The adverse combination of excessive mining practices and the resulting land subsidence is a significant obstacle to the sustainable growth and stability of regions associated with mining activities. The Lakhra coal mines, which contain some of Pakistan’s largest coal deposits, have been overlooked in land subsidence monitoring, indicating a considerable oversight in the region. Subsidence in mining areas can be spotted early when using Interferometric Synthetic Aperture Radar (InSAR), which can precisely monitor ground changes over time. This study is the first to employ the Small Baseline Subset (SBAS)-InSAR and stacking-InSAR techniques to identify land subsidence at the Lakhra coal mines. This research offers critical insights into subsidence mechanisms in the study area, which has never been previously investigated for ground deformation monitoring, by utilizing 150 Sentinel-1A (ascending) images obtained between January 2018 and September 2023. A total of 102 deformation spots were identified using SBAS-InSAR, while stacking-InSAR detected 73 deformation locations. The most extensive cumulative subsidence in the Lakhra coal mine was −114 mm, according to SBAS-InSAR, with a standard deviation of 6.63 mm. In comparison, a subsidence rate of −19 mm/year was reported using stacking-InSAR with a standard deviation of 1.17 mm/year. The rangeland covered 88.8% of the total area and exhibited the most significant deformation values, as determined by stacking and SBAS-InSAR techniques. Linear regression showed that there was not a strong correlation between subsidence and topographic factors. As detected by optical remote sensing data, the subsidence locations were near or above the mines in the research area, indicating that widespread mining in Lakhra coal mines was the cause of subsidence. Our findings suggest that SAR interferometric time series analysis is helpful for proactively identifying and controlling subsidence difficulties in mining regions by closely monitoring activities, hence reducing negative consequences on operations and the environment.

Knowledge sharing is influenced not only by the attributes of the knowledge itself and the hierarchical differences among knowledge holders but also by micro-level factors such as interpersonal communication and psychological dynamics. As the demands of the new economic environment evolve, the role of knowledge sharing among team members in knowledge management has become increasingly critical. A growing body of research emphasizes the importance of sharing knowledge resources as a means of gaining strategic advantages in enterprises. Previous studies have shown that leadership behavior plays a crucial role in fostering knowledge sharing among team members. However, there is limited research on the specific pathways and mechanisms through which leadership behavior influences knowledge sharing. Additionally, existing research lacks a comprehensive examination of the structure and measurement methods of leader–member exchange (LMX), as well as its impact on employee work outcomes. This study addresses this gap by examining trust relationships through the lenses of social identity and emotional interaction, with trust serving as a mediating variable to explore the impact of leader–subordinate interactions on knowledge sharing behavior. Drawing on the relevant literature, the study focuses on 358 employees, investigating the relationship between LMX as the independent variable, team knowledge sharing as the dependent variable, and employee trust as the mediating factor. The research findings are as follows: (1) The quality of the LMX relationship significantly influences employees’ team knowledge sharing; higher-quality LMX relationships are associated with increased knowledge sharing among subordinates. (2) Employee trust in both leaders and colleagues has a substantial impact on team knowledge sharing, with higher levels of trust positively affecting knowledge sharing behaviors. (3) Employee trust serves as a mediating factor in the relationship between LMX and team knowledge sharing. This study delves into the dynamics between LMX, team knowledge sharing, and trust, providing practical recommendations for strengthening these relationships.

The farmland around the industrial areas in the Upper Yellow River is crucial for agricultural production but is vulnerable to contamination from the surrounding industries. This research focused on analyzing the spatial distribution and environmental risks of heavy metal pollution in the farmland around the Ganhe Industrial Park in the Qinghai–Tibet Plateau. A total of 138 surface soil samples were collected, and the concentration of seven heavy metals (Cd, As, Pb, Cr, Cu, Ni, and Zn) was analyzed using the random forest (RF) model. Pollution indicators, including the pollution index and Nemero index, were used to evaluate the pollution levels of soil heavy metals. The human health and ecological risks were estimated using the hazard index (HI) and the potential ecological risk index (RI). Cd and Zn were identified as the primary soil pollutants in the study area, with Cd being more concentrated than other heavy metals. Heavy metal contamination was most severe in the central–eastern region of the study area, with a ring-shaped distribution, which correlated with the presence of zinc smelting and chemical plants. Furthermore, the study revealed that soil heavy metal contamination posed a health threat to the local population, with children being particularly vulnerable to non-carcinogenic risks when the HI was 1.21 and to potential carcinogenic risks when the CR was 2.27 × 10⁻⁵. Additionally, heavy metal pollution caused a moderate to high ecological risk in 56.4% of the samples. The results highlighted the severe impact of soil heavy metal pollution on the delicate ecosystem of the Upper Yellow River and Qinghai–Tibet Plateau. The government should take action to improve soil environment management and prevent heavy metal pollution to protect the health of the local population and the ecological environment.

The contamination of farmlands with hazardous metals from mining puts the safety of agricultural commodities at risk. For remediation, it is crucial to map the spatial distribution of contaminated soil. Typical sampling-based procedures are time-consuming and labor-intensive. The use of visible, near-infrared, and short-wave infrared reflectance (VNIR-SWIR) spectroscopy to detect soil heavy metal pollution is an alternative. With the aim of investigating a methodology of detecting the most sensitive bands using VNIR-SWIR spectra to find lead (Pb) anomalies in agriculture soil near mining activities, the area in Xiaoqinling Mountain, downstream from a series of active gold mines, was selected to test the feasibility of utilizing VNIR-SWIR spectroscopy to map soil Pb. A total of 115 soil samples were collected for laboratory Pb analysis and spectral measurement. Partial least squares regression (PLSR) was adopted to estimate the soil Pb content by building the prediction model, and the model was optimized by finding the optimal number of bands involved. The spatial distribution of Pb concentration was mapped using the ordinary kriging (OK) interpolation method. This study found that five spectral bands (522 nm, 1668 nm, 2207 nm, 2296 nm, and 2345 nm) were sensitive to soil Pb content. The optimized prediction model’s coefficient of determination (R²), residual prediction deviation (RPD), and root mean square error (RMSE) were 0.711, 1.860, and 0.711 ln(mg/kg), respectively. Additionally, the result of OK interpolation was convincing and accurate (R² = 0.775, RMSE = 0.328 ln(mg/kg)), comparing maps from estimated and ground truth data. This study proves that it is feasible to use VNIR-SWIR spectral data for in situ estimation of the soil Pb content.