Soil pollution is a serious threat to human life and development. Different remedial measures are applied to soils with different levels of contamination. The degree of soil contamination in different areas is generally evaluated and categorised based on the analysis of samples. Regional soil sampling sites are generally sparse because of the cost of sampling and other factors, which makes it difficult to accurately assess the extent of regional soil contamination. In this study, a spatial classification model was established for the Di(2-ethylhexyl) phthalate (DEHP) pollution level using a Bi-directional Long Short-Term Memory (Bi-LSTM) neural network considering that the sampling information gradually diminishes with increasing distance between the sampling and prediction points. In this study, a method is proposed for the prediction of the spatial distribution of soil pollution categories based on sparse samples. We also established a model for the spatial distribution of organic pollution categories. The analysis of an actual contaminated area shows that the DEHP concentrations at different locations can be effectively predicted with the proposed method by categorising the contamination levels of specific DEHP samples. The results show that the method can be used to classify the degree of light/severe DEHP contamination. The results are in good agreement with the actual situation, verifying the validity of the method. This method is important for the rapid assessment of the spatial distribution of soil contamination levels based on sparse sampling.

Understanding the groundwater movement patterns and regulating functions of the karst vadose zone is essential in addressing water scarcity and protecting the ecological environment in the karst area of southwest China. A laboratory-scale experimental model of a typical karst vadose zone was constructed and used to simulate the water flow process under the influence of four factors: transfer zone thickness, surface slope, karstification degree, and rainfall intensity. A corresponding distributed model was subsequently developed to simulate the laboratory experiments. The discharge recession process, the regulating capacity, and the division of fast and slow flow were quantitatively analyzed by the recession coefficient, the regulating coefficient, and the percentage of fast flow and that of sinkhole flow. As the transfer zone thickness increases from 40 cm to 120 cm, the vadose zone regulating coefficient rises from 0.49 to 0.53, while fast flow decreases from 87.7% to 78.1%, indicating that the enhanced regulating capacity is mainly governed by the slow flow system. The evident difference in growth rates between the percentage of fast flow (an increase of 9.1%) and that of sinkhole flow (an increase of 48.7%) indicates that the decrease in regulating capacity resulting from an increase in surface slope is primarily due to enhanced water loss through sinkholes. When the structure of the karst vadose zone remains constant, the regulating coefficient decreases exponentially with increasing rainfall intensity and gradually approaches a constant value, which represents the maximum regulating capacity of the karst vadose zone under its current structural conditions.

Understanding the spatiotemporal variability of nanoplastics (NPs) in porous media is vital for environmental risk assessment, yet quantitative in-media analysis of NP distributions during transport remains limited. To address this, we innovatively applied low-field nuclear magnetic resonance (LF-NMR) as a non-invasive approach to dynamically monitor magnetic polystyrene nanoplastic (MPSNP) transport in saturated quartz sand. By establishing the relationship between LF-NMR transverse relaxation rate [1/T_2,I − 1/T_2,0] and MPSNP concentrations, we reconstructed spatiotemporal concentration profiles via T₂ inversion. This methodology enabled systematic evaluation of the effects of ionic strength (IS), flow velocity, initial concentration, and flow direction. Three mathematical models were further applied to analyze MPSNP transport behavior. Results revealed IS as the dominant factor; increasing IS (0.001 to 1 mM) dropped mass recovery from 85.7% to 0%, the migration front no longer advanced at IS > 5 mM. Lower flow rates, higher initial concentrations, and horizontal flow also enhanced retention. The two types of two-site kinetic models provide a better fit for the features of the breakthrough curves. This novel use of LF-NMR demonstrates its robust capability to resolve spatial transport heterogeneity, underscoring that flow velocity, flow direction, and ionic strength are critical regulatory parameters that should be carefully accounted for when evaluating nanoplastic transport in porous media.