[WATER] Development of a quantitative risk assessment model for diffuse pollution from estuarine landfill sites associated with climate change

Lead Research Organisation: Queen Mary University of London
Department Name: Geography

Abstract

Under current predictions for climate change the hydrology of rivers and estuaries is likely to change with sea level rise, coastal flooding and increased precipitation, resulting in saline incursion and bank erosion in the upper estuaries of many river systems (IPCC 2007). This poses a significant threat to the many landfill sites situated on/near river and estuary banks, where the potential for diffuse discharge of pollutants has not been quantified and is poorly understood. Landfill managers have a duty of care towards the adjacent aquatic environment. Yet, currently there are few tools for assessing the risk to rivers and estuaries associated with changes to hydrological regime due to climate change. The main aim of the PhD project will be: To develop a quantitative risk assessment model (RAM) for diffuse pollution from estuarine landfill sites associated with climate change. Research objectives (ROs): 1. To assess the current release of diffuse pollution and potential for landfill failure in upper estuarine landfill sites 2. To understand the change in contaminant behaviour following saline intrusion 3. To understand the change in geotechnical properties of landfill materials and landfill stability following saline intrusion 4. To develop a quantitative RAM to assess the release of diffuse pollutants to the aquatic environment following saline intrusion of landfill sites Methodological approach: ROs 1-3 will be addressed by combining field- and laboratory- based investigations. This will improve fundamental understanding of diffuse pollution from bankside landfill sites and will also provide baseline data and input parameters for model development under RO 4. 1) Field-based investigations (Year 1): To address RO 1, the student will conduct environmental quality and geotechnical investigations of the historic Crossways landfill site on Rainham Marshes in the Thames Estuary. Sampling, environmental analysis of soil/sediment and pore water, and a geotechnical assessment of landfill failure will be carried out following EA guidelines, based on current conditions. Datasets will be examined to explore relationships between environmental and geotechnical parameters and to determine pathways of diffuse pollutant release. 2) Laboratory-based investigations (Year 2): To address RO 2, sorption behaviour of pollutants, under current and saline conditions, will be determined using batch sorption experiments. To address RO 3 changes to soil structure, geotechnical properties and mineralogy will be observed before and after saline intrusion using a monolith test, where landfill material will be suspended in a seawater solution. Temporal changes to analyte concentrations will be measured in the surrounding solution, soil structure and mineralogy will be observed using scanning electron microscopy. 3) Risk assessment modelling (Year 3): Geotechnical models assess the risk of failure of a structure (such as the current Hungary wall collapse), but cannot predict the behaviour of the contaminants in the released sediments. Here, an environmental RAM, coupled to a geotechnical and hydrodynamic model, will allow for a better assessment of risk and a determination of controls on potential contaminant release. Training: Research training and PPD are offered through formal taught courses within QMUL. The student will also receive subject specific training; sample collection, environmental analysis, field/survey techniques, regulatory approach to landfill*, geotechnical site investigations* and risk assessment modelling* (*at Arcadis). The student will attend two PG taught courses in 1) multivariate stats (UCL) and 2) Biogeochemical cycling (QMUL). Outputs: Outputs will include; an improved understanding of the potential for landfill failure and release of diffuse pollutants following climate change, protocols for risk assessment associated with climate change, and a quantitative RAM.

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