Superfund Research Program
Novel Analytical and Computational Strategies for Exposure Assessment of Complex Mixtures
Project Leader: Erin S. Baker (University of North Carolina-Chapel Hill)
Grant Number: P42ES027704
Funding Period: 2022-2027
- Project Summary
Project Summary (2022-2027)
The comprehensive assessment of hazardous substances in complex environmental samples is essential in understanding the “environmental exposome” and identifying potential human health and environmental risks. Although targeted analyses are commonly used to measure between 10 and 100 specific substances per study, their precise parameters and limited coverage are not suitable for evaluating other potentially hazardous substances that may be present in the samples. This limitation has showcased the importance of untargeted measurements as hundreds of new chemicals are being introduced annually that need to be assessed. Since untargeted analyses can focus on all detected features, they are able to evaluate those with statistical significance between sample type and location, in addition to features with extremely high abundance. The information from the untargeted studies therefore provides the evaluation of novel and legacy hazardous substances in addition to their metabolites, intermediates and degradants which can be more hazardous than the parent compounds. However, untargeted measurements are greatly challenged by how to optimize instruments for broad characterization and then how to analyze all the “big” data that is generated by the new analytical methods. Thus, both analytical and computational developments are necessary.
By combining ion mobility spectrometry (IMS)-derived structural information, mass spectrometry (MS)-derived high-resolution m/z measurements and new data processing algorithms, the research team is creating a uniform workflow for evaluation of complex environmental mixtures in the untargeted studies of samples obtained before, during and after environmental emergencies. To enable comprehensive analytical characterization, the researchers couple the multidimensional IMS-MS analyses with steps including sample concentration, extraction, and liquid chromatography (LC) separations to allow an in-depth characterization of the mixtures. The information obtained from the untargeted IMS-MS and LC-IMS-MS studies includes molecular properties such as m/z, Kendrick Mass Defect (KMD), retention time (RT) and collision cross section (CCS). As these values have shown utility in targeted studies for molecular classification, they are combined with a targeted library of >3,000 environmental chemicals from the center’s previous research and processed with cheminformatics and machine learning algorithms to annotate and classify the unknown features from the untargeted studies.
The team also utilizes both the targeted and untargeted studies to enable better disaster-related evaluation of potential chemical exposures by creating a list containing thousands of hazardous substances for rapid characterization with automated solid phase sample cleanup and IMS-MS. This automated SPE-IMS-MS platform provides 10 s sample-to-sample throughput and when coupled with cloud-based data assessment, it enables the rapid chemical analyses of complex environmental samples from disaster situations that may involve chemical spills.