The DPSIR model is a conceptual model established by the European Environment Agency to solve environmental problems. It provides an overall framework for analysis of environmental problems from five aspects: driving force (D), pressure (P), state (S), impact (I), and response (R). Through use of the DPSIR model framework, this paper presents the SEEC model approach for evaluating watershed ecological security. The SEEC model considers four aspects: socioeconomic impact (S), ecological health (E), ecosystem services function (E), and control management (C). Through screening, 38 evaluation indicators of the SEEC model were determined. The evaluation results showed that the ecological security index of the study area was >80, indicating a generally safe level. The lowest score was mainly attributable to the low rate of treatment of rural domestic sewage. The water quality status was used to evaluate the applicability of the SEEC model, and the calculation results indicated that the higher the score of the ecological security evaluation results, the better the water quality status. The findings show that the SEEC model demonstrates satisfactory applicability to evaluation of watershed ecological security.

Charcoal, a byproduct resulting from incomplete combustion of biomass in fire events, can modify the physical properties of soil due to its high porosity and large surface area. To evaluate the impact of fire-deposited charcoal on soil hydraulic characteristics, soil–charcoal mixtures were analyzed to investigate the effects of different application doses (wt%: 0, 1%, 3%, 5%, 10% and 20%) of charcoal on soil bulk density (BD), porosity (total, capillary, and non-capillary), residual moisture after free drainage (RM), saturated water content (SC), and saturated hydraulic conductivity (Ks) of loamy and sandy soils collected from subtropical forests in south China. The results showed that the impact of charcoal on soil’s physical and hydraulic properties depends on the soil type and the application dose. The incorporation of charcoal significantly decreased the BD of sandy soil (p < 0.001), while a significant decrease in BD in loamy soil was only observed as a result of the higher application doses (10% and 20%) (p < 0.001). Charcoal application doses of 5% or higher led to a significant increase in the total porosity (TP) of sandy soil (p < 0.001) and doses of 3% and 20% resulted in a significant increase in the TP of loamy soil (p < 0.001). The capillary porosity (CP) of both sand and loamy soils significantly increased when charcoal was applied at doses of 3% or higher (p < 0.001). The minimum charcoal application dose that significantly increased the RM in sandy soil was 5%, while for loamy soil, the minimum effective dose was 10%. Charcoal applied at a dose of 3% significantly increased the Ks of sandy soil (p < 0.001), while no significant effect on Ks was observed for loamy soil (p > 0.05). Collectively, our findings suggest that fire-derived charcoal enhances the soil water-retention capacity in subtropical forests, with the effects becoming more pronounced at higher application doses and being particularly notable in sandy soil compared to loamy soil.

Identifying the source and impact pathways of soil heavy-metal pollution is critical for its assessment and remediation. Numerical simulation has been widely used to simulate soil heavy-metal pollution processes and predict risks. However, traditional numerical simulation software requires a large number of parameters, which are difficult to obtain in site-scale studies. This study proposes a rapid method for identifying soil heavy-metal pollution processes using the TOUGH2/EOS7 software. It has automatic calibration and uncertainty analysis capabilities, which can effectively reduce the demand for parameters. This study established a method, including model selection, simulation, validation, and error analysis, to verify the effectiveness of the proposed method. This study identified the most realistic scenario for chromium pollution and simulated its release over 20 years, and the results met accuracy requirements with a best-case fit of 0.9998. The results showed that the method can quickly identify the source and impact pathways of soil heavy-metal pollution, providing strong evidence for environmental damage assessment.