Non-targeted analysis (NTA) is a powerful tool for identifying emerging chemical contaminants.
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NTA can directly support chemical risk characterization but must provide quantitative estimates.
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Quantitative NTA methods exist but do not fully consider estimation uncertainty.
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Effects of experimental recovery on estimation uncertainty remain largely unexplored in NTA studies.
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Integration of NTA estimates with available hazard metrics may facilitate provisional safety evaluations.
Abstract
Chemical risk assessments follow a long-standing paradigm that integrates hazard, dose–response, and exposure information to facilitate quantitative risk characterization. Targeted analytical measurement data directly support risk assessment activities, as well as downstream risk management and compliance monitoring efforts. Yet, targeted methods have struggled to keep pace with the demands for data regarding the vast, and growing, number of known chemicals. Many contemporary monitoring studies therefore utilize non-targeted analysis (NTA) methods to screen for known chemicals with limited risk information. Qualitative NTA data has enabled identification of previously unknown compounds and characterization of data-poor compounds in support of hazard identification and exposure assessment efforts. In spite of this, NTA data have seen limited use in risk-based decision making due to uncertainties surrounding their quantitative interpretation. Significant efforts have been made in recent years to bridge this quantitative gap. Based on these advancements, quantitative NTA data, when coupled with other high-throughput data streams and predictive models, are poised to directly support 21st-century risk-based decisions. This article highlights components of the chemical risk assessment process that are influenced by NTA data, surveys the existing literature for approaches to derive quantitative estimates of chemicals from NTA measurements, and presents a conceptual framework for incorporating NTA data into contemporary risk assessment frameworks.
