Chinese Journal of Environmental Engineering•2026•DOI: 10.12030/j.cjee.202511020
With the increasing number of oil pipelines crossing rivers, the potential risks of oil leakage and surface spreading to river ecosystems and water environments are becoming more severe. Scenario-based simulation of oil spill diffusion is a prerequisite for effective interception point placement and leakage risk prevention. Numerous factors influence oil spill diffusion, including environmental conditions, river hydrology, and accessibility of emergency resources. This study integrates these factors and multiple dynamic processes to design eight typical scenarios for oil spill diffusion simulation, considering emergency resource locations, river hydrological regimes, and leakage modes. A case study is conducted on an oil pipeline crossing a river in northwest China. Results indicate that the diffusion distance and affected area are primarily controlled by water conditions and emergency resource accessibility. In emergency management, the efficiency of maintenance and repair resources during high-water months should be prioritized. Mechanistically, external forces such as hydraulic and wind forces have a greater influence on diffusion distance, surpassing internal forces like gravity, viscosity, and surface tension within a short time. For river crossings near emergency resources, internal force effects should be considered in oil spill diffusion simulations. When emergency resource arrival times are long, the diffusion distance based on Fay's theory is relatively small and can be neglected in engineering practice. This study provides a computational basis and methodological reference for risk assessment and emergency response to potential oil spills from pipelines crossing rivers, enhancing the scientific and effective nature of risk prevention and emergency handling.
Environmental Chemistry•2026•DOI: 10.7524/j.issn.0254-6108.2025041502
Microplastics (MPs), defined as plastic particles smaller than 5 mm, are ubiquitous environmental contaminants with documented presence in urban, rural, marine, remote, and polar atmospheres. The atmosphere serves as a primary medium for their long-range transport, raising concerns regarding climate interactions and human health. This review synthesizes recent advances in atmospheric MPs research, encompassing sampling strategies, pretreatment protocols, analytical techniques, occurrence characteristics, and ecological ramifications. Passive and active sampling methods are delineated, with active samplers enabling quantitative flux measurements. Pretreatment typically involves sequential steps of sieving, density separation, digestion, staining, and filtration to isolate MPs from complex matrices. Identification relies on visual inspection, micro-Fourier transform infrared spectroscopy (μ-FTIR), micro-Raman spectroscopy, laser direct infrared imaging (LDIR), and mass spectrometry. Reported atmospheric MPs predominantly exhibit dimensions below 700 μm, with fibrous morphologies being most prevalent. Color distribution is dominated by black, followed by white and transparent particles. Over 20 polymer types have been identified, with textiles, tire wear, and dust identified as principal sources. Atmospheric MPs can influence solar radiation balance, cloud formation processes, and pose risks to flora, fauna, and human health. However, research remains nascent; standardization of sampling and analytical protocols, along with comprehensive toxicological assessments, are critical knowledge gaps requiring urgent attention.
Journal of Environmental Engineering Technology•2026•DOI: 10.13205/j.hjgc.202608006
Organic waste is a potential phosphorus reservoir, and understanding the dynamics of available phosphorus (AP) during its resource utilization is critical for efficient phosphorus recovery. Composting, a key route for organic waste valorization, involves complex transformations of phosphorus alongside organic matter degradation and humification. However, the long duration and high cost of composting experiments, coupled with multifactorial influences, hinder efficient elucidation of AP dynamics via conventional methods. This study compiled data from 33 publications, constructing a dataset of 647 samples. Data preprocessing included iterative imputation, one-hot encoding, and standardization. A stacking ensemble learning model was developed to predict AP generation during composting. The optimal ensemble comprised XGBoost and SVR as base learners and ElasticNet as the meta-learner, achieving R² values of 0.954 and 0.928 on training and test sets, respectively, with low overall error. SHAP analysis revealed that key factors influencing AP content, in descending order of importance, were feedstock type, bulking agent type, turning interval, pH, electrical conductivity (EC), and C/N ratio. Notably, livestock manure as feedstock and straw-based bulking agents contributed positively to AP predictions. Partial dependence plots indicated that lower pH and C/N ratios generally favored AP accumulation throughout composting. During the initial stage, higher moisture content and lower EC enhanced AP; in the thermophilic phase, higher temperatures corresponded to higher AP; and during cooling and maturation, maintaining moisture below 48% and C/N below 14, while extending composting beyond 43 days, promoted AP accumulation. This study demonstrates accurate AP prediction via stacking ensemble learning and identifies critical factors, offering support for optimizing phosphorus management in composting engineering.