Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.622049
Title: Characterising the functional ecology of slow sand filters through environmental genomics
Author: Haig, Sarah-Jane
ISNI:       0000 0004 5360 7658
Awarding Body: University of Glasgow
Current Institution: University of Glasgow
Date of Award: 2014
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Abstract:
Today the water industry faces a huge challenge in supplying a sustainable, energy efficient and safe supply of drinking water to an increasing world population. Slow sand filters (SSFs) have been used for hundreds of years to provide a safe and reliable source of potable drinking water, with minimal energy requirements. However, a lack of knowledge pertaining to the treatment mechanisms, particularly the biological processes, underpinning SSF operation, has meant SSFs are still operated as “black boxes”. This lack of knowledge pertaining to the underlying ecology and ecophysiology limits the design and optimisation of SSFs. This thesis represents the most comprehensive microbial community survey of full-scale SSFs to-date. Using traditional microbiological methods alongside up-to-date molecular techniques and extensive water quality analyses, specific taxa and community metrics are linked to changes in water quality production. Furthermore, it has been verified that laboratory scale SSFs can mimic the microbial community and water quality production of full-scale filters. This allowed rigorous experiments pertaining to operational differences, pathogen and novel contaminant removal to be performed. This has revealed, for the first time, that multiple trophic interactions within SSFs are integral to optimal performance. This thesis has shown that SSFs are phylogenetically and metabolically diverse systems capable of producing high quality water, with the ability to adapt to remove novel contaminants. Using the information gathered, improvements to filter maintenance and operation can be achieved. Future work will apply the microbial and macrobial community dynamics and impact of novel contaminants on filter performance discovered in this thesis into predictive models for water quality.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.622049  DOI: Not available
Keywords: QR Microbiology ; TD Environmental technology. Sanitary engineering
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