STINGS is funded by EIT RAWMATERIALS
EIT RawMaterials, initiated and funded by the EIT (European Institute of Innovation and Technology), a body of the European Union, is the largest and strongest consortium in the raw materials sector worldwide. Its vision is to develop raw materials into a major strength for Europe. Its mission is to boost competitiveness, growth and attractiveness of the European raw materials sector via radical innovation, new educational approaches and guided entrepreneurship.
EIT RawMaterials unites more than 120 partners from leading industry, universities and research institutions from more than 20 EU countries. Partners of EIT RawMaterials are active across the entire raw materials value chain; from exploration, mining and mineral processing to substitution, recycling and circular economy. They collaborate on finding new, innovative solutions to secure the supplies and improve the raw materials sector in Europe.
The Innovation Projectread more »
Currently, there is a lack of a detailed operational methodology that comprises the management of environmental risks related to tailing operations, containing specific guidance on the appropriate approaches, tools and techniques with due consideration to economic issues, and the way they should be used. This is true both at European level but also internationally.
The proposed integrated methodology in STINGS aims towards the development of measurement components based on the latest, innovative technology and high-performance monitoring methods and has important differences with the previous tools of environmental risk management in this field. It will provide specific guidance on the issues that need to be considered when assessing the environmental impacts from tailing operations, accounting for different operational and environmental settings found across the world and specifically for the test cases. STINGS will identify the physical and chemical processes that affect environmental risks and establish integrated modelling and monitoring methods that should be implemented in order to make reliable environmental impact predictions. It will also establish an integrated risk assessment methodology considering the uncertainty inherent in the data collection and the limitations and assumptions inherent in environmental impact prediction Tools.