Diagnostic strategy and risk assessment framework for complex chemical mixtures - Cipullo, Sabrina (Cranfield University, 2018)
Environmental contamination comprises a complex mixture of both organic and inorganic contaminants. Understanding their distribution, behaviour and chemical interactions provides the evidence necessary to make informed decision and implement robust remediation strategies. However most of the current risk assessment frameworks, used to manage land contamination, are based on the total contaminant concentration rather than the concentration likely to pose significant risk, the bioavailable concentration. Further to this, the exposure assessments embedded within the frameworks do not explicitly address the partitioning and bioavailability of chemical mixtures. This inability may contribute to an overestimation of both the eco-toxicological effects of the fractions and their mobility in air and water; leading to an overestimation of health and environmental effects. In turn, this may limit the efficacy of the risk assessment frameworks to inform targeted and proportionate remediation strategies. The aim of this PhD study was to address this gap by delivering an integrated risk assessment framework for sites contaminated with complex chemical mixtures. Specifically, this PhD study investigated the fate and behaviour of complex mixtures of petroleum hydrocarbons, metals and metalloids in soils and its implication for partitioning, bioavailability and risk assessment through a 12 month mesocosms study. Further to this, an integrated approach, where contaminants bioavailability and distribution changes along with a range of microbiological indicators and ecotoxicological bioassays, was used to provide multiple lines of evidence to support the risk characterisation and assess the remediation end-point over a 6 month study. From the empirical data obtained from the two mesocosm studies, two Machine Leaning (ML) approaches have been developed to provide a quick and reliable tool to assess multi-contaminated sites with Visible and Near-Infrared Spectroscopy (Vis-NIRS), and to predict bioavailability and toxicity changes occurring during bioremediation. Overall this PhD study shed light on the behaviour of bioavailability, and toxicity of complex chemical mixtures in soils genuinely contaminated. This was supported through a comprehensive and integrated analytical framework providing the necessary lines of evidence to evaluate the implications for risk assessment and identify the end point remediation. The developed framework can significantly help to identify optimal remediation strategies and contribute to change the over-conservative nature of the current risk assessments.
Keywords: Contaminated land; bioavailablility; toxicity; bioremediation; machine learning