In this study, a grid-based network environ analysis (FEM-NEA) framework was developed to identify the ecological risks of the internal components within a landscape scale mining ecosystem with multiple land types. In this framework, an information-based network analysis is applied for addressing ecological risk assessments (ERA) based on control allocation (CA) and energy flow matrix, which can quantificationally reflect the relationship between soil ecosystems and ecological communities. In order to improve the computational accuracy and efficiency, the FEM and information-based network analysis are combined to further quantify the flow risk among different components within each grid as well as the whole ecosystem. Compared with the existing NEA-ERA model, various risk factors and receptors are compatible with the proposed FEM-NEA framework and both direct and indirect effects can be taken into consideration altogether. By taking an abandoned mining area of the Yanshan mountain as an example, risk propagation between all component of the ecosystem concerning both direct risk and integral risk dynamic were quantified. The results showed that contaminated soil normally poses risks to surrounding ecological environment, thereby affecting local vegetation and microorganisms. Afterward, the risks are passed throughout the ecosystem, forming threats to herbivores and predators via the food chain. Here, the probability of risk is ranked as follows: village> farmland> bare land> woodland. Moreover, the influence of input energy and data attributes on the prediction results are also discussed. When the input energy rises by 50%, the control allocation (CA) from herbivores to soil microorganisms correspondingly increases by 1.00% but the value from herbivores to carnivores decreases by 0.71%. This highlights the robustness of the proposed ecological risk assessment framework (FEM-NEA). In general, the FEM-NEA results not only presents the macro-scale risk distribution caused by the interaction of each component, but also reflects the potential migration direction of the risk center. Findings can provide a new perspective and method for assessing ecological risks and also help support remediation techniques for contaminated areas with different land types.

在本研究中，开发了基于网格的网络环境分析 (FEM-NEA) 框架，以识别具有多种土地类型的景观规模采矿生态系统内部组件的生态风险。在该框架中，基于控制分配（CA）和能量流矩阵，应用基于信息的网络分析来解决生态风险评估（ERA），可以量化反映土壤生态系统与生态群落之间的关系。为了提高计算精度和效率，FEM和基于信息的网络分析相结合，进一步量化每个网格内不同组件之间以及整个生态系统的流动风险。与现有的 NEA-ERA 模型相比，各种风险因素和受体与提议的 FEM-NEA 框架兼容，可以同时考虑直接和间接影响。以燕山某废弃矿区为例，量化了生态系统各组成部分之间的直接风险和整体风险动态的风险传播。结果表明，受污染的土壤通常会对周围的生态环境构成风险，从而影响当地的植被和微生物。之后，风险在整个生态系统中传递，通过食物链对食草动物和捕食者构成威胁。这里，风险概率排序为：村庄>农田>裸地>林地。此外，还讨论了输入能量和数据属性对预测结果的影响。当输入能量增加 50% 时，从食草动物到土壤微生物的控制分配（CA）相应增加 1.00%，但从食草动物到食肉动物的控制分配（CA）减少 0.71%。这突出了拟议生态风险评估框架 (FEM-NEA) 的稳健性。总的来说，FEM-NEA结果不仅呈现了各分量相互作用引起的宏观风险分布，还反映了风险中心的潜在迁移方向。研究结果可以为评估生态风险提供新的视角和方法，也有助于支持不同土地类型污染区域的修复技术。00% 但从食草动物到食肉动物的价值下降了 0.71%。这突出了拟议生态风险评估框架 (FEM-NEA) 的稳健性。总的来说，FEM-NEA结果不仅呈现了各分量相互作用引起的宏观风险分布，还反映了风险中心的潜在迁移方向。研究结果可以为评估生态风险提供新的视角和方法，也有助于支持不同土地类型污染区域的修复技术。00% 但从食草动物到食肉动物的价值下降了 0.71%。这突出了拟议生态风险评估框架 (FEM-NEA) 的稳健性。总的来说，FEM-NEA结果不仅呈现了各分量相互作用引起的宏观风险分布，还反映了风险中心的潜在迁移方向。研究结果可以为评估生态风险提供新的视角和方法，也有助于支持不同土地类型污染区域的修复技术。