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Abstract:
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Environmental risk analysis borrows from most of the main themes of the larger area of quantitative risk analysis, including essentially all of the data-scientific aspects. This introductory overview will discuss how risk is defined and quantified in environmental risk assessments, and is designed to give the audience a sense of and flavor for the foundations of data science in modern environmental risk assessment. It begins with an overview of data analytics for risk and potency estimation. This includes classic measures such as median effective dose, no-observed-adverse-effect levels, incremental slope measures, and modern methods such as benchmark risk analysis. The discussion then moves to more contemporary methods such as uncertainty factors, uncertainty assessment, Monte Carlo methods, input sensitivity, correlation ratios, and model sensitivity.
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