Ecology, physiology and performance in high-rate anaerobic digestion

The design demands on water and sanitation engineers are rapidly changing. The global population is set to rise from 7 billion to 10 billion by 2083. Urbanisation in developing regions is increasing at such a rate that a predicted 56% of the global population will live in an urban setting by 2025. Compounding these problems, the global water and energy crises are impacting the Global North and South alike. High-rate anaerobic digestion offers a low-cost, low-energy treatment alternative to the energy intensive aerobic technologies used today. Widespread implementation however is hindered by the lack of capacity to engineer high-rate anaerobic digestion for the treatment of complex wastes such as sewage. This thesis utilises the Expanded Granular Sludge Bed bioreactor (EGSB) as a model system in which to study the ecology, physiology and performance of high-rate anaerobic digestion of complex wastes. The impacts of a range of engineered parameters including reactor geometry, wastewater type, operating temperature and organic loading rate are systematically investigated using lab-scale EGSB bioreactors. Next generation sequencing of 16S amplicons is utilised as a means of monitoring microbial ecology. Microbial community physiology is monitored by means of specific methanogenic activity testing and a range of physical and chemical methods are applied to assess reactor performance. Finally, the limit state approach is trialled as a method for testing the EGSB and is proposed as a standard method for biotechnology testing enabling improved process control at full-scale. The arising data is assessed both qualitatively and quantitatively. Lab-scale reactor design is demonstrated to significantly influence the spatial distribution of the underlying ecology and community physiology in lab-scale reactors, a vital finding for both researchers and full-scale plant operators responsible for monitoring EGSB reactors. Recurrent trends in the data indicate that hydrogenotrophic methanogenesis dominates in high-rate anaerobic digestion at both full- and lab-scale when subject to engineered or operational stresses including low-temperature and variable feeding regimes. This is of relevance for those seeking to define new directions in fundamental understanding of syntrophic and competitive relations in methanogenic communities and also to design engineers in determining operating parameters for full-scale digesters. The adoption of the limit state approach enabled identification of biological indicators providing early warning of failure under high-solids loading, a vital insight for those currently working empirically towards the development of new biotechnologies at lab-scale.

Methods matter: computational modelling in public health policy and planning

This work is aimed at understanding and unifying information on epidemiological modelling methods and how those methods relate to public policy addressing human health, specifically in the context of infectious disease prevention, pandemic planning, and health behaviour change. This thesis employs multiple qualitative and quantitative methods, and presents as a manuscript of several individual, data-driven projects that are combined in a narrative arc. The first chapter introduces the scope and complexity of this interdisciplinary undertaking, describing several topical intersections of importance. The second chapter begins the presentation of original data, and describes in detail two exercises in computational epidemiological modelling pertinent to pandemic influenza planning and policy, and progresses in the next chapter to present additional original data on how the confidence of the public in modelling methodology may have an effect on their planned health behaviour change as recommended in public health policy. The thesis narrative continues in the final data-driven chapter to describe how health policymakers use modelling methods and scientific evidence to inform and construct health policies for the prevention of infectious diseases, and concludes with a narrative chapter that evaluates the breadth of this data and recommends strategies for the optimal use of modelling methodologies when informing public health policy in applied public health scenarios.