Anna Laino

Newcastle University

PhD researcher

 

Academic and industrial affiliations: Newcastle University, Scottish Water, Northumbrian Water Ltd.

Academic supervisors: Russell Davenport, Sadegh Soudjani

Title of research Project: Quantification of wastewater treatment resilience metrics

 

The purpose of this project is to develop a metric (or metrics) to quantify the resilience of wastewater treatment plants. This research project aims to understand how wastewater treatment systems will cope and recover under extreme events. Climate change is expected to influence variability in environmental conditions, influencing both the wastewater network and treatment systems. It is recognised that both stress and shock events, driven by climatic change, population growth, ageing infrastructure etc. will increase the variability of flows and loads of influent entering treatment works, and will increase the occurrence of internal asset failures. The current asset base and system operation may be incapable of responding to these events in order to prevent failure, and as such these events have the potential to cause service disruption and environmental pollution. Climate change, also, will impact the environment where the water will be discharged, for example a river, because a longer period of low river flow, in addition to increasing water temperatures, will reduce dilution capacity and so water quality. More resilient treatment processes should be able to of coping with fluctuating flows and loads, with a reduction in final effluent failures due to shocks and stresses, and consequently there would reduce impacts on the environment. Furthermore, the developing of comprehensive study of stressors which could affect wastewater treatment will help to understand potential vulnerabilities. There is a difference between the unknown stressors (i.e. black swan events in this project) and how they affect the performance of the wastewater treatment plant during its lifetime, and, the known stressors, where the challenge is to properly combine available tools and models.

 

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