Ecological risk assessment of heavy metal contamination of mining area soil based on land type changes: An information network environ analysis

• Published in: Ecological modelling 2021-09, Vol.455, p.109633, Article 109633
• Main Authors: Lu, Jingzhao, Lu, Hongwei, Wang, Weipeng, Feng, SanSan, Lei, Kaiwen
• Format: Article
• Language: English
• Subjects: Ecological risk assessment; Heavy metals; Mining ecosystems; Network environ analysis (NEA); The finite element method (FEM)
• ISSN: 0304-3800; 1872-7026
• DOI: 10.1016/j.ecolmodel.2021.109633

•FEM-NEA framework proposed for risk assessment at landscape-scale ecosystem.•Combined with GIS/RS technology, this model solved the spatial mismatch problem.•The direct- and integral- risk is based on the controlled distributions (CA) of ecosystem.•The risk trigger of four sub-ecosystems: village>farmland>woodland>bare land.•The sensitive analysis indicated that the FEM-NEA framework is robust 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 ri