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.

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.

In recent years, coal mining has shifted from surface to underground multi-seam and multi-panel operations, leading to enhanced ground deformation and elevated risks of secondary geo-hazards. However, the deformation mechanisms and spatiotemporal evolution of mining-induced ground movement in high-intensity repeated mining areas require further investigation. To gain further insight, this study focuses on elucidating the deformation mechanisms and hazard-chain evolution induced by downward multi-seam and multi-panel mining in the Hongyan coal mine, located in the loess gully region. An integrated InSAR-based multi-source monitoring and modeling framework was adopted, systematically combining InSAR, historical satellite imagery, UAV-based surveys, and ground observations with numerical simulations to characterize the spatiotemporal evolution of mining-induced deformation and examine the coupling processes within the hazard chain. The monitoring results show a strong spatiotemporal correlation between mining activities and ground deformation: subsidence basins and temporal variations correspond closely to the mining sequence, and the spatial distribution of fissures aligns with the advancing working faces. The analysis indicates mining-induced stress redistribution and stratum instability are the root causes of subsidence. Subsidence characteristics are affected by topography, mining sequence, and the cumulative impacts of multi-seam mining, leading to stepwise subsidence and subsidence basins. The overlying loess’s topography and characteristics affect the subsidence distribution. The “stress arch” formed in the goaf evolves with the multi-panel mining process, gradually collapsing during continuous mining and leading to stratum instability. Initially spreading stress and preventing rock movement, the upper residual pillars aggravate stratum damage following critical stratum failure. Mining exerts spatiotemporal control over hazard development, with the hazard chain evolving upward from the mining horizon, driven by fissure propagation and subsidence as the core processes, and reinforced by a bottom-up chain reaction and feedback among successive hazards. This study provides scientific insights for the planning and hazard prevention of multi-seam mining in loess gully regions.

Based on the maximum entropy modeling (Maxent) and ArcGIS tool, this study assessed the potential of marginal land and analyzed the impact of environmental variables for Jerusalem artichoke (Helianthus tuberosus L.) in Shaanxi Province, China. The results showed that the dominant land type used for the growth of Jerusalem artichoke was moderately dense grassland. Additionally, significant environmental variables of Jerusalem artichoke and their suitable range in Shaanxi Province were average slope (SLP, 0–5°C), average soil depth (DPT, 1.50–1.60 m), max temperature of the warmest month (Bio5, 30–31°C), annual mean temperature (Bio1, 16.5–18.0°C), precipitation of the wettest quarter (Bio16, 0.01–0.02 m), July solar radiation (SR7, 1.66–1.67 × 10⁷ W/m²), precipitation seasonality (Bio15, 50–60%), precipitation of the driest quarter (Bio17, 0–0.005 m), and isothermality (Bio3, 265–275). Furthermore, the suitable area was mainly distributed in southern (mainly Hanzhong, Ankang, and Shangluo) and northern (mainly Yan’an and Yulin) parts of Shaanxi Province, covering around 8.81 × 10¹⁰ m² and accounting for 42.8% of the total area of the Shaanxi Province. This study can provide a reference for the rational planting of Jerusalem artichoke in Shaanxi Province.