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Appears in Collections:Biological and Environmental Sciences eTheses
Title: Risk-based modelling of faecal indicator organism export from agricultural landscapes
Author(s): Porter, Kenneth
Supervisor(s): Oliver, David
Quilliam, Richard
Reaney, Sim
Keywords: Agricultural waste management
Diffuse pollution
Faecal indicator organism
Microbial die-off
Survival curves
Decision support
E. coli
Hydrological connectivity
Non-point source pollution
microbial transport
uncertainty analysis
Issue Date: Sep-2018
Publisher: University of Stirling
Citation: Porter, K.D., Reaney, S.M., Quilliam, R.S., Burgess, C. and Oliver, D.M., 2017. Predicting diffuse microbial pollution risk across catchments: The performance of SCIMAP and recommendations for future development. Science of the Total Environment, 609, pp.456-465.
Porter, K.D., Quilliam, R.S., Reaney, S.M. and Oliver, D.M., 2019. High resolution characterisation of E. coli proliferation profiles in livestock faeces. Waste Management, 87, pp.537-545.
Abstract: Microbial contamination of watercourses can threaten ecosystem services related to clean water; for example, recreational bathing, shellfish harvesting and potable water supplies. This is because pathogens associated with faeces from warm blooded animals can cause gastrointestinal illness in exposed human beings. Microbial water quality impacts from point sources associated with wastewater transfer and treatment have been reduced through engineering solutions. However, as these sources of contamination have been reduced diffuse sources have become more important. Diffuse pollution describes water quality impacts originating from accumulations of many small, spatially distributed, inputs. These sources of pollution are difficult to manage because their loading and connectivity to sensitive receptors varies spatially and temporally. The Sensitive Catchment Integrated Mapping Analysis Platform (SCIMAP) is a risk-based approach that has been developed to map sources of diffuse sediment and conservative nutrient pollution allowing for efficient targeting of mitigation efforts which are often expensive and occupy valuable productive land. SCIMAP has been well received within the regulatory community in the United Kingdom and its development to account for diffuse microbial pollution is therefore timely. The primary goal for this thesis was to explore SCIMAP’s application to microbial pollution, highlight areas for improvement and work towards a new SCIMAP framework that accounts for microbial diffuse pollution. An initial application of SCIMAP, as it exists, revealed that the time-integrated approach currently employed may be inappropriate for sources of microbial pollution that are likely to vary temporally due to microbial die off. Furthermore, an enhanced description of land use incorporating spatial distributions of the numbers and types of livestock may improve SCIMAP’s 4 performance. Spatial variations in microbial source loading arising from differences in the persistence of E. coli (an indicator of faecal pollution) in the faeces of different livestock was investigated within a controlled environment facility. This controlled experiment provided a novel non-linear description of E. coli growth in ovine and 2 types of bovine faeces for a period of 30 days post defecation. Potential variation in rainfall induced E. coli release from faecal matrices associated, with beef cattle, dairy cattle and sheep were explored using rainfall simulation. An asymptotic model of E. coli release with increasing rainfall depth was developed and no difference was discovered in the profile of release from sheep, beef cattle and dairy cattle. Finally lessons from these investigations were combined to propose a framework for an evolution of SCIMAP allowing for a better description of microbial source and transfer risk. This new version of SCIMAP will provide a decision support tool allowing for more efficient targeting of mitigation efforts reducing microbial impacts to important ecosystem services relying on clean water.
Type: Thesis or Dissertation

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