Project 1

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 environmental samples from disaster
situations that may involve chemical spills.

Grant Number: 5P42ES027704-09
NIH Institute/Center: NIH
Principal Investigator: Erin Baker