Application of Nontarget High Resolution Mass Spectrometry Data to Quantitative Source Apportionment

Abstract

High resolution mass spectrometry (HRMS) analyses provide expansive chemical characterizations of environmental samples. To date, most research efforts have developed tools to expedite labor- and time-intensive contaminant identification efforts. However, even without chemical identity, the richness of nontarget HRMS data sets represents a significant opportunity to chemically differentiate samples and delineate source contributions. To develop this potential, we evaluated the use of unidentified HRMS detections to define sample uniqueness and provide additional statistical resolution for quantitative source apportionment, overcoming a critical limitation of existing approaches based on targeted contaminants. By creating a series of sample mixtures that mimic pollution sources in a representative watershed, we assessed the fidelity of HRMS source fingerprints during dilution and mixing. This approach isolated 8–447 nontarget compounds per sample for source apportionment and yielded accurate source concentration estimates (between 0.82 and 1.4-fold of actual values), even in multisource systems with <1% source contributions. Furthermore, we mined the nontarget data to identify five source-specific chemical end-members amenable to apportionment. While additional development studies are needed to fully evaluate the myriad factors affecting method accuracy and capabilities, this study provides a conceptual foundation for novel applications of nontarget HRMS data to confidently distinguish and quantify source impacts in complex systems.

Runoff of the Tire-Wear Compound, Hexamethoxymethyl-Melamine into Urban Watersheds

“…Furthermore, HMMM readily leaches from tire material, confirming that HMMM is a chemical constituent of tire wear particles (Peter et al 2018). HMMM has been used as an indicator for the presence of leachates from tire wear particles in road runoff (Seitz and Winzenbacher 2017;Peter et al 2019;Warner et al 2019;Hou et al 2019;Tian et al 2020).…”

Assessing Emissions from Pharmaceutical Manufacturing Based on Temporal High-Resolution Mass Spectrometry Data

“…Only a few studies have exploited the wealth of information contained in unidentified HRMS signals. For example, Chiaia-Hernandez, et al 23 applied non-target time pattern analysis to identify profiles of anthropogenic origin in lake sediments, and Peter, et al 24 used non-target HRMS signatures to differentiate samples from different sources. However, to the best of our knowledge, so far no study has attempted to assess the contribution of industrial-type time profiles on the total detected emission profiles, nor have long time course data of WWTP effluent samples been systematically investigated for industrial discharges.…”

Source Water Apportionment of a River Network: Comparing Field Isotopes to Hydrodynamically Modeled Tracers

“…This study compared two techniques from two fields of study in order to arrive at estimates of source water fraction (SWF) within a natural system. The hydrodynamic model provided estimated flows and mixing patterns to complement a field-based isotope approach that would otherwise be unsubstantiated (as opposed to previous field-based studies [21,22]). Similarly, the isotope findings act as a field-validation for a numerically-based method of finding SWF in surface-water systems.…”

“…However, because this study was performed using a depth-averaged model we cannot investigate sensitivity to the water sample being taken near the top of the water column, but we can assume that the vertical mixing past the confluence is thorough [13]. Although the model accurately predicts cross-sectional flows and stage, the same issue arises as in the study on the model watershed by Peter et al [22]: the exact flow and mixing patterns are unknown. The calibrated hydrodynamic model coupled with a transport model provides more spatiotemporally specific output that has proven to be reliable within the bounds of the field data approach this study used.…”

“…Other studies have used isotope signatures to identify which tributaries have dominant effects on geochemical properties of the mainstem [18], to investigate snowmelt versus rainfall contribution [19], and to identify water origin [20]. Our study aimed to use two methods to find the SWF at each location within the study area over time; one method used is similar to that of Halder et al [21] and Marchina et al [20], which evaluates stable hydrogen ( 2 H) and oxygen ( 18 O) isotopes at sample locations and compares those to the tributary source waters of interest.. Another direct application of using physical properties of tributary water to find the distribution of source waters in a river network was presented by Peter et al [22] in a physically modeled hypothetical watershed. In the study, high-resolution mass spectrometry of organic contaminants was used to confirm the source hydrology of a hypothetical river system with known flows, and thus known mixtures.…”