Soil contamination by organic pollutants such as polycyclic aromatic hydrocarbons (PAHs), pesticides, pharmaceuticals, and petroleum hydrocarbons has emerged as a global environmental concern due to their persistence, bioaccumulation, and potential health risks. Biochar, a carbon-rich material derived from the pyrolysis of biomass, has attracted increasing attention as an environmentally friendly and cost-effective amendment for remediating contaminated soils. This review systematically summarizes recent advances in the application of biochar for the remediation of organic pollutants in soils to guide the development of more effective biochar-based strategies for sustainable soil remediation. The physicochemical properties of biochar influencing pollutant interactions are discussed, including surface area, pore structure, functional groups, and aromaticity. Mechanisms such as adsorption, sequestration, microbial interaction enhancement, and catalytic degradation are elucidated. Moreover, this review highlights the influence of feedstock types, pyrolysis conditions, biochar modification strategies, and environmental factors on biochar performance. The analysis reveals that biochar performance is strongly dependent on feedstock selection, pyrolysis conditions, and post-modification strategies, which jointly determine pollutant immobilization efficiency and long-term stability. Current challenges, such as long-term stability, pollutant desorption, and ecological impacts, are critically examined. Finally, future perspectives on the design of engineered biochar and its integration with other remediation technologies are proposed. Rationally engineered biochar, particularly when integrated with biological or physicochemical remediation technologies, demonstrates strong potential for efficient and sustainable soil remediation.

Trichloroethylene (TCE), a volatile organic compound commonly used as a solvent, is frequently detected in contaminated groundwater. In the zero-valent iron (ZVI) Fenton process, TCE can be eventually dechlorinated into non-toxic products, which is mainly caused by hydroxyl radicals derived from H₂O₂. However, some key factors in the dechlorination of TCE in the zero-valent iron Fenton process have not been studied clearly. In the present study, the effects of the initial TCE concentration, initial H₂O₂ concentration, dosage of ZVI, initial pH, and temperature on TCE degradation in the ZVI Fenton process were studied. In addition, the structure and surface morphology of the ZVI used in this study were analyzed through scanning electron microscopy (SEM), N₂ adsorption–desorption, and X-ray diffractometry (XRD). The experimental results demonstrated that the dosage of ZVI and initial H₂O₂ concentration had obvious impacts on TCE degradation. At a ZVI dosage of 2 g/L and an initial H₂O₂ concentration of 0.53 mol/L, more than 97% of TCE could be degraded within 24 h at 25 °C. We found that the ZVI Fenton process could efficiently degrade TCE at a broad pH range and room temperature, making it applicable to groundwater remediation. TCE degradation was associated with Fe²⁺ concentration. Spectroscopic analyses indicated that the oxide film formed on the ZVI surface was associated with Fe²⁺ concentration in enhanced TCE dechlorination. The ZVI Fenton process could work at a wide range of TCE concentrations (0–200 mg/L).

Long-term monitoring of wetland degradation, recovery, and spatial resilience requires methods that can jointly exploit multi-temporal remote sensing observations and spatially heterogeneous environmental information. This study develops an intelligent remote sensing framework for monitoring the disturbance–recovery dynamics and spatial resilience of typical wetland landscapes in Northeast China by coupling Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) with geographically weighted random forest (GWRF). Annual wetland landscape state sequences for 2000–2022 are constructed from multi-temporal Landsat observations and associated geospatial variables. LandTrendr time-series segmentation is used to identify disturbance timing, disturbance magnitude, recovery rate, and recovery duration, thereby converting long-term wetland change trajectories into quantitative indicators of degradation and recovery. At the GIS grid scale, remotely sensed vegetation and wetness indicators are integrated with wetland patch structure, hydrological conditions, topography, land-cover change, and anthropogenic pressure. GWRF then models local nonlinear relationships under spatial-distance weighting to generate a spatially explicit wetland resilience index and reveal geographically varying dominant controls. The LandTrendr–GWRF model achieved an R² of 0.872, an RMSE of 0.057, and an MAE of 0.039, outperforming RF, GWR, XGBoost, and standalone GWRF benchmarks. The mean wetland resilience index was 0.641, with low-, medium-, and high-resilience areas accounting for 27.86%, 44.37%, and 27.77% of the study area, respectively. Significant spatial clustering was detected (Moran’s I = 0.421, Z = 6.92, p = 0.001). Hydrological regulation, landscape connectivity, fragmentation constraint, and anthropogenic disturbance pressure emerged as the four dominant spatial response types. High-resilience cells formed contiguous clusters where hydrological recharge and landscape connectivity were strong, while low-resilience cells clustered where fragmentation and construction pressure were concentrated. Disturbance magnitude and recovery duration differed markedly among the four response types, with connectivity-dominated cells returning to the pre-disturbance state within 4.4 years and anthropogenic pressure-dominated cells requiring 11.7 years. The proposed framework extends wetland remote sensing from static condition mapping toward process-oriented monitoring of degradation, recovery, connectivity, and resilience, providing a practical approach for long-term wetland restoration assessment and spatial management.