# Project 1 > **NIH NIH P42** · TEXAS A&M UNIVERSITY · 2022 · $207,249 ## Abstract Project 1 Abstract 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 of the “big” data that are 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, we aim to create 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, we will 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 will include 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 will be combined with our targeted library of >3,000 environmental chemicals from the past funding period and processed with cheminformatics and machine learning algorithms to annotate and classify the unknown features from the untargeted studies. We will also utilize 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 will provide 10 s sample-to-sample throughput and when coupled with cloud-based data assessment, it will enable the rapid chemical analyses of complex... ## Key facts - **NIH application ID:** 10349751 - **Project number:** 2P42ES027704-06 - **Recipient organization:** TEXAS A&M UNIVERSITY - **Principal Investigator:** Erin S Baker - **Activity code:** P42 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $207,249 - **Award type:** 2 - **Project period:** 2022-09-20 → 2027-06-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10349751 ## Citation > US National Institutes of Health, RePORTER application 10349751, Project 1 (2P42ES027704-06). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10349751. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*