In a recent article published in the journal Mining, researchers presented a comprehensive approach to managing and assessing the risks associated with post-mining environments, particularly within abandoned coal and lignite mines. The closure of these mining operations presents long-term risks not only environmentally but also socio-economically.
Study: Spatial Decision Support System for Multi-Risk Assessment of Post-Mining Hazards. Image Credit: Parilov/Shutterstock.com
These risks necessitate sophisticated risk management strategies to mitigate potential hazards. The study's focal point is a European project funded by the Research Fund for Coal and Steel, which aims to enhance the understanding and management of multi-risk situations in former mining areas.
A multidisciplinary methodology is employed, merging historical, geological, topographical, environmental, and socioeconomic data to inform decision-makers at various levels.
By integrating a spatial decision support system (sDSS), the research establishes a framework for effective risk assessment and potential mitigation strategies tailored to the needs of stakeholders.
Background
Mining activities have significantly shaped many regions around the world, leaving a legacy of abandoned sites associated with various potential hazards. The article highlights that the number of closed and abandoned mines continues to grow, leading to a pressing need for effective rehabilitation and risk management strategies.
It is estimated that there are over one million abandoned mines globally, representing a critical industrial legacy marked by numerous environmental and socio-economic challenges. The research emphasizes the importance of continued risk assessment and robust management strategies to tackle the multi-hazard states of these former mining sites.
Considering the inherent risks—natural, technological, and post-mining—this research aims to provide a structured approach to risk mitigation. The primary distinction drawn is between traditional mining operations and the post-mining landscape, which often becomes susceptible to residual hazards if not properly managed.
The Current Study
The methodology developed in this study integrates a variety of multi-risk assessments tailored to the specific conditions of post-mining areas. Central to this approach is the creation of a spatial decision support system (sDSS) that employs a multi-hazard, multi-risk framework.
This system utilizes expert knowledge to assign weights to various hazards, which include post-mining hazards, natural hazards, and technical risks. Each hazard's interaction is analyzed, ultimately leading to the formation of a spatial multi-hazard index.
The system also incorporates social vulnerability and exposure factors to create a comprehensive risk map that reflects the specific conditions of individual regions.
In its practical application, the methodology was tested through a case study conducted in the southern Ruhr area of Germany, a region historically impacted by coal mining activities. Data collection involved the use of open-source and publicly available datasets, ensuring compliance with data protection regulations.
The study employed various hazards—including subsidence, sinkholes, gas emissions linked to mining, and mining-induced floods—assessing their potential impacts through spatial analysis.
The socio-economic vulnerability of the affected communities was also quantified through a Vulnerability Index (VI), which integrates factors like employment rates, population demographics, environmental conditions, and infrastructure quality. This multifaceted data was then rasterized into a spatial format to facilitate further analysis and visualization.
Results and Discussion
The results highlighted the effectiveness of the developed sDSS in generating spatial multi-risk maps for the study area. Two distinct scenarios were analyzed to assess possible interactions among the identified hazards.
Maps produced during this assessment delineated areas of varying risk levels, providing a visual representation of how different hazards affected the region.
The spatial assessment showed significant variations in risk distribution, particularly highlighting urban areas with dense populations and historical mining activities as having higher vulnerability.
The discussion in the article underscores the critical role that expert knowledge plays in the weighting and evaluation of risk factors. It points out that localized expertise is essential, as the specific context of each mining site can significantly alter the assessment process.
Additionally, the integration of GIS and statistical tools proved to be a powerful combination in visualizing data and assisting stakeholders in understanding the complexities of post-mining risks. The scalable nature of the sDSS allows it to be tailored to different mining sites across Europe, enhancing its usability for varying contexts.
Conclusion
The study concludes by emphasizing the developmental significance of the sDSS in assessing risks related to post-mining hazards. By combining a wide array of data, including geological, topographical, environmental, and socio-economic factors, the methodology established a novel framework that enhances the decision-making process for local stakeholders.
The validation achieved in the case study illustrates that the sDSS can effectively support risk mitigation strategies, land-use planning, and monitoring efforts.
Furthermore, the article anticipates that future advancements, potentially involving the use of artificial intelligence, could enhance the analytical capabilities of the system, thereby improving the overall assessment process for post-mining regions.
The article reinforces the view that understanding the socio-economic dimensions of hazards is essential to ensure that risk assessments address the needs and well-being of local communities affected by former mining activities.
Ultimately, the research advocates for the continued development and application of such decision support systems to aid in the responsible management of post-mining landscapes.
Journal Reference
Haske B., Al Heib M., et al. (2025). Spatial Decision Support System for Multi-Risk Assessment of Post-Mining Hazards. Mining 5(1):17. doi: 10.3390/mining5010017. https://www.mdpi.com/2673-6489/5/1/17