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Artificial intelligence monitoring system for soil contamination based on Laser-induced breakdown spectroscopy (LIBS)

   School of Engineering and the Built Environment (SEBE)

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  Dr A Kerrouche, Dr D Barreto  No more applications being accepted  Self-Funded PhD Students Only

About the Project

Heavy metals are considered as one of the most important sources of contamination released by human activities in urban and agricultural soils, sediments and groundwater. Soil contamination refers to heavy metals of biological toxicity, including mercury (Hg), cadmium (Cd), arsenic (As), etc. With the development of the global economy worldwide, soil pollution has gradually increased in recent years, very little is known about the awareness of human exposure hazards and the extent to which human contact with such contaminated soil or water may affect public health.

Extra heavy metals in soil are generated from many sources including sewage irrigation, industrial waste and the use of pesticides and fertilizers in agriculture. In general, the analysis of polluted soils is conducted using inductively coupled plasma optical emission spectroscopy (ICP-OES) or atomic absorption spectroscopy (AAS). These traditional laboratory-based methods are time consuming, very expensive and require highly trained personnel3,4. This limits their use in areas such as 3rd world countries with limited resources to rapidly identify the risks. There is therefore an urgent need to provide fast, cost-effective and in-situ sensing solutions for toxins associated with contaminated soil and polluted water.

This project aims to use innovative optical approach based on laser-induced breakdown spectroscopy (LIBS) to develop a prototype system to address these global environmental challenges.

Academic qualifications

A first degree (at least a 2.1) ideally in Electronic/Optical Engineering, Computer Science, Bioinformatics with a good fundamental knowledge of Image processing and bioinformatics.

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Essential attributes:

· Experience of fundamental Strong machine learning background

· Competent in Matlab, Python, Java and/or C++

· Knowledge of Biomedical Optics

· Good written and oral communication skills

· Strong motivation, with evidence of independent research skills relevant to the project

· Good time management

Desirable attributes:

Practical research expertise in an optical lab and/or a clean-room environment

Funding Notes

Self funded students only


Fei Li (June 27th 2018). Heavy Metal in Urban Soil: Health Risk Assessment and Management, Heavy Metals, Hosam El-Din M. Saleh and Refaat F. Aglan, IntechOpen, DOI: 10.5772/intechopen.73256. Available from: https://www.intechopen.com/books/heavy-metals/heavy-metal-in-urban-soil-health-risk-assessment-and-management Science Communication Unit, University of the West of England, Bristol (2013). Science for Environment Policy In-depth Report: Soil Contamination: Impacts on Human Health. Report produced for the European Commission DG Environment, September 2013. Available at: http://ec.europa.eu/science-environment-policy
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