| EPSRC Reference: |
EP/Z535436/1 |
| Title: |
Integrating artificial intelligence technologies for subsurface pollution fate prediction and risk assessment |
| Principal Investigator: |
Bortone, Dr I |
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| Department: |
Faculty of Engineering & Applied Science |
| Organisation: |
Cranfield University |
| Scheme: |
Standard Research TFS |
| Starts: |
01 February 2025 |
Ends: |
31 January 2027 |
Value (£): |
192,856
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Summary on Grant Application Form |
Dr Bortone is a researcher in soil and groundwater sciences and environmental engineering, with expertise in fluid flow and hydrogeochemical process modelling, who aspires to learn information and communication technology (ICT) research skills and bring a data enabled decision making approach into her discipline.
Soil and groundwater pollution poses significant and costly environmental and public health threats, which climate change will exacerbate. Pollution control and mitigation have become an absolute priority for government and stakeholders worldwide, who have requested advanced environmental risk assessment (ERA) systems for continuous risk prediction and monitoring to improve environmental protection. Accurate prediction and risk assessment of pollution fate, considering the multiple factors and interactions among physical, chemical, and environmental systems in the subsurface are crucial for effective pollution risk management. However, current ERA procedures inadequately depict and account for soil and groundwater characteristics, pollution geospatial distribution, chemical temporal changes and associated risks, often lacking precision and reliability in addressing their spatiotemporal aspects.
There is a need to tackle the challenges related to multifactor and dynamic risk assessment, as well as the constraints of conventional tools, which only account for a single environmental medium at a time. Similarly, our ability to integrate complex interactions between contaminants, soil, and groundwater into comprehensive prediction models to support effective environmental management and decision making needs to be enhanced.
This project proposes to leverage artificial intelligence (AI) technologies to address these requirements, as AI has demonstrated its effectiveness in handling complex multifactor challenges. AI methods support process-based simulations by accounting for variabilities and eventual uncertainties while also mitigating the high "human" and resource costs of traditional methods.
Throughout her career, the proponent has developed advanced calculation codes, multifaceted datasets and multicriteria analysis to describe flow transport in heterogeneous media with single and multiple compounds, by integrating physicochemical and biological processes applied in environmental engineering and water management. With the specialised ICT training proposed for the Hopping scheme, Dr Bortone aims to employ AI-methods to effectively address the challenges of accurately analysing complex real-world subsurface systems, considering the numerous, nonlinear, and interrelated factors involved.
Specifically, she will develop an AI-powered open-source tool that combines 3D geostatistical and spatial analysis of Geographical Information Systems (GIS) with AI techniques to predict soil and groundwater pollutant transport and assess the associated environmental risks in advanced multifactor ERA.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.cranfield.ac.uk |