Biochar, a porous carbonaceous material derived from the pyrolysis of biomass under oxygen-limited conditions, offers several advantages for environmental remediation, including a high specific surface area, ease of preparation, and abundant raw material sources. However, the application of pristine biochar is limited by its inherent physicochemical shortcomings, such as a lack of active functional groups and limited elemental compositions. To overcome these limitations, metal-modified biochars have garnered increasing attention. In particular, iron-manganese (Fe-Mn) modification significantly enhances the adsorption capacity, redox potential, and microbial activity of biochar, owing to the synergistic interactions between Fe and Mn. Iron-manganese-modified biochar (FM-BC) has demonstrated effective removal of heavy metals, organic matter, phosphate, and nitrate through mechanisms including mesoporous adsorption, redox reactions, complexation, electrostatic interactions, and precipitation. Moreover, FM-BC can improve soil physicochemical properties and support plant growth, highlighting its promising potential for broader environmental application. This review summarizes the preparation methods, environmental remediation mechanisms, and practical applications of FM-BC and discusses future directions in mechanism elucidation, biomass selection, and engineering implementation. Overall, FM-BC, with its tunable properties and multifunctional capabilities, emerges as a promising and efficient material for addressing complex environmental pollution challenges.

The continuous and effective monitoring of the water quality of small rural rivers is crucial for rural sustainable development. In this work, machine learning models were established to predict the water quality of a typical small rural river based on a small quantity of measured water quality data and UAV hyperspectral images. Firstly, the spectral data were preprocessed using fractional order derivation (FOD), standard normal variate (SNV), and normalization (Norm) to enhance the spectral response characteristics of the water quality parameters. Second, a method combining the Pearson’s correlation coefficient and the variance inflation factor (PCC–VIF) was utilized to decrease the dimensionality of features and improve the quality of the input data. Again, based on the screened features, a back-propagation neural network (BPNN) model optimized using a mixture of the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm was established as a means of estimating water quality parameter concentrations. To intuitively evaluate the performance of the hybrid optimization algorithm, its prediction accuracy is compared with that of conventional machine learning algorithms (Random Forest, CatBoost, XGBoost, BPNN, GA–BPNN and PSO–BPNN). The results show that the GA–PSO–BPNN model for turbidity (TUB), ammonia nitrogen (NH₃-N), total nitrogen (TN), and total phosphorus (TP) prediction exhibited optimal accuracy with coefficients of determination (R²) of 0.770, 0.804, 0.754, and 0.808, respectively. Meanwhile, the model also demonstrated good robustness and generalization ability for data from different periods. In addition, we used this method to visualize the water quality parameters in the study area. This work provides a new approach to the refined monitoring of water quality in small rural rivers.

The promotion of pollutant oxidation degradation efficiency by adding organic catalysts has obtained widespread attention in recent years. Studies have shown that organic substances promote the process of traditional oxidation reactions by accelerating the redox cycle of transition metals, chelating transition metals, activating oxidants directly to generate reactive oxygen species such as hydroxyl and sulfate radical, or changing the electron distribution of the target pollutant. Based on the promotion of typical organic functional groups on the chemical oxidative process, a metal-organic framework has been developed and applied in the field of chemical catalytic oxidation. This manuscript reviewed the types, relative merits, and action mechanisms of common organics which promoted oxidation reactions so as to deepen the understanding of chemical oxidation mechanisms and enhance the practical application of oxidation technology.