Chemical contaminant monitoring of water resources destined for human consumption is of utmost importance to human health. Contamination of drinking water with toxic and/or endocrine disrupting compounds such as pesticides can cause serious health effects including cancer, infertility, diabetes, obesity and neurological disorders. Fast and efficient ways to alert water providers of chemical contamination issues are urgently required.
This project will explore the generation of toxicological data alongside chemical analysis of water samples for the development of predictive machine learning models. These models can then be applied to the chemical contaminant analysis of water resources and provide a new generation of rapid alert systems for water providers.
According to the United Nations’ Goal 6 ‘‘Access to safe water, sanitation and hygiene is the most basic human need for health and well-being.’’ However, at the moment and for at least 3 billion people, the quality of the water they rely upon is unknown due to a lack of efficient monitoring. This project will take large strides in precisely addressing this critical challenge. In particular, this project will seed the basis of building faster and more innovative strategies for the monitoring of chemical water quality.
Within the endocrine disruptor research group of Professor Lisa Connolly at Queen’s University Belfast, a wide range of environmental contaminants and persistent organic pollutants such as pesticides, perfluorinated compounds, brominated flame retardants, bisphenols will be analysed using mammalian cell culture in state of the art toxicological in vitro bioassays. These assays will be used to test the biological activity of the chemicals in order to assess potential toxic and endocrine disrupting mammalian health effects. Multiple toxic and endocrine disrupting end-points will be assessed in the assays using high content analysis, a cutting edge technological platform which can provide multiple toxicological data endpoints within a single assay, including for example, mammalian cell toxicity, hormone receptor disruption, oxidative stress markers, nuclear and mitochondrial effects. Details such as CAS, purity and supplier of the test chemicals will also be catalogued. In addition, datasets for chemical water monitoring of drinking water resources will be provided by Northern Ireland Water.
Prof Michalis Matthaiou’s team within the ECIT institute at Queen’s University Belfast will integrate the toxicology data, chemical profiles and the Northern Ireland Water chemical monitoring results to produce predictive machine learning algorithms. The developed algorithms will provide the basis of a rapid alert system for chemical water monitoring which can be used by drinking water providers. It will also create an algorithmic platform which can be further used for identifying geographical areas of concerning water quality in Northern Ireland.
The integration of toxicology with predictive machine learning provides a more powerful chemical water monitoring strategy with potential global impact for the protection of human and environmental health. The combination of benefits delivered by this innovatively collaborative project will impact human, environmental and economic health locally, internationally and globally.
Specific skills/experience required: Cell biology.
Start Date: 1 October 2023
Duration: 3 years
How to apply: Applications must be submitted online via: https://dap.qub.ac.uk/portal/user/u_login.php
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