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

Enhanced industrial wastewater monitoring: method development for non-target screening of highly polar substances using ZIC-HILIC-HRMS

Non-target screening (NTS) plays a major role in the monitoring and management of water bodies. While the NTS of moderate to non-polar substances is well-established, the screening of highly polar chemicals remains challenging. In this study, a robust separation method for highly polar substances using zwitterionic hydrophilic interaction liquid chromatography coupled with high-resolution mass spectrometry (ZIC-HILIC-HRMS) was developed. This method was specifically designed for the NTS of industrial wastewater, with the objective of capturing a wide range of polar contaminants in each acquisition run. Method validation included assessing key parameters such as repeatability, reproducibility, linearity, and limit of detection (LOD). For repeatability and reproducibility, the average %RSD of intensity and retention time across all substances in different matrices-solvent, influent, and effluent-remained below 6% and 1%, respectively (n = 10). The method demonstrated good linearity (R2 > 0.99) for 75% of the substances, while LODs varied between 0.1 and 40 µg/L depending on the compound tested. The method was then applied for NTS analysis of untreated wastewater at various locations within a chemical industrial park. Additionally, the overall influent and effluent of an industrial wastewater treatment plant (WWTP) were monitored over a 10-day period. Principal component analysis (PCA) was performed to interpret the data, identifying irregularities in the wastewater content. Moreover, the method demonstrated the WWTP's ability to achieve an average removal efficiency of approximately 90% for this category of substances in this period, while also detecting their degradation products in the effluent. Finally, the method was successfully integrated into the daily monitoring routine of the WWTP, ensuring continuous surveillance and improved management of wastewater treatment processes.

Aerosolomics based approach to discover source molecular markers: A case study for discriminating residential wood heating vs garden green waste burning emission sources

Biomass burning is a significant source of particulate matter (PM) in ambient air and its accurate source apportionment is a major concern for air quality. The discrimination between residential wood heating (RWH) and garden green waste burning (GWB) particulate matter (PM) is rarely achieved. The objective of this work was to evaluate the potential of non-targeted screening (NTS) analyses using HRMS (high resolution mass spectrometry) data to reveal discriminating potential molecular markers of both sources. Two residential wood combustion appliances (wood log stove and fireplace) were tested under different output conditions and wood moisture content. GWB experiments were carried out using two burning materials (fallen leaves and hedge trimming). PM samples were characterized using NTS approaches with both LC- and GC-HRMS (liquid and gas chromatography-HRMS). The analytical procedures were optimized to detect as many species as possible. Chemical fingerprints obtained were compared combining several multivariate statistical analyses (PCA, HCA and PLS-DA). Results showed a strong impact of the fuel nature and the combustion quality on the chemical fingerprints. 31 and 4 possible markers were discovered as characteristic of GWB and RWH, respectively. Complementary work was attempted to identify potential molecular formulas of the different potential marker candidates. The combination of HRMS NTS chemical characterization with multivariate statistical analyses shows promise for uncovering organic aerosol fingerprinting and discovering potential PM source markers.

Application of Nontarget High-Resolution Mass Spectrometry Fingerprints for Qualitative and Quantitative Source Apportionment: A Real Case Study

High-resolution mass spectrometry (HRMS) provides extensive chemical data, facilitating the differentiation and quantification of contaminants of emerging concerns (CECs) in aquatic environments. This study utilizes liquid chromatography-HRMS for source apportionment in Chebei Stream, an urban water stream in Guangzhou, South China. Initially, 254 features were identified as potential CECs by the nontarget screening (NTS) method. We then established 1689, 1317, and 15,759 source-specific HRMS fingerprints for three distinct sources, the mainstream (C3), the tributary (T2), and the rain runoff (R1), qualitatively assessing the contribution from each source downstream. Subsequently, 32, 55, and 3142 quantitative fingerprints were isolated for sites C3, T2, and R1, respectively, employing dilution curve screening for source attribution. The final contribution estimates downstream from sites C3, T2, and R1 span 32-96, 12-23, and 8-23%, respectively. Cumulative contributions from these sources accurately mirrored actual conditions, fluctuating between 103 and 114% across C6 to C8 sites. Yet, with further tributary integration, the overall source contribution dipped to 52%. The findings from this research present a pioneering instance of applying HRMS fingerprints for qualitative and quantitative source tracking in real-world scenarios, which empowers the development of more effective strategies for environmental protection.

Single extraction and integrated non-target data acquisition with data mining workflow for analysis of hazardous substances in agricultural plant products

Identifying contaminants in agricultural plant food products (APFPs) is a major problem. In this study, we developed a single-step extraction and integrated non-target data acquisition (INDA) workflow for increasing hazardous substances coverage. D-optimal experimental designs were applied to optimize filter plate extraction (FPE) for one-single extraction of multipolar hazardous substances. The vDIA mode was used to collect all precursor ion fragments within the range to supplement data loss caused by DDA mode. The underlying principle of vDIA is to increase the utilization rate of MS2 spectra that are likely to identify a maximum number and minimum amounts of hazardous substances. Compared with traditional DDA mode alone, a combination of the two modes increased the rate of identification of hazardous substances by 18.5%. The molecular network of hazardous substance provided by GNPS could enable some metabolites and structure-related products to discover potentially hazardous substance.

Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis

Mass-Suite (MSS) is a Python-based, open-source software package designed to analyze high-resolution mass spectrometry (HRMS)-based non-targeted analysis (NTA) data, particularly for water quality assessment and other environmental applications. MSS provides flexible, user-defined workflows for HRMS data processing and analysis, including both basic functions (e.g., feature extraction, data reduction, feature annotation, data visualization, and statistical analyses) and advanced exploratory data mining and predictive modeling capabilities that are not provided by currently available open-source software (e.g., unsupervised clustering analyses, a machine learning-based source tracking and apportionment tool). As a key advance, most core MSS functions are supported by machine learning algorithms (e.g., clustering algorithms and predictive modeling algorithms) to facilitate function accuracy and/or efficiency. MSS reliability was validated with mixed chemical standards of known composition, with 99.5% feature extraction accuracy and ~ 52% overlap of extracted features relative to other open-source software tools. Example user cases of laboratory data evaluation are provided to illustrate MSS functionalities and demonstrate reliability. MSS expands available HRMS data analysis workflows for water quality evaluation and environmental forensics, and is readily integrated with existing capabilities. As an open-source package, we anticipate further development of improved data analysis capabilities in collaboration with interested users.

Target and suspect screening of pesticide residues in soil samples from peach orchards using liquid chromatography quadrupole time-of-flight mass spectrometry

Agricultural soil contamination by pesticide residues has become a serious issue of increasing concern due to their high persistence and toxicity to non-target species. However, as the world's largest peach producer, national scale surveys on pesticide residues in peach orchard soils are scarce in China. In this study, a target and suspect screening method covering over 200 pesticides commonly used in peach orchards was developed using ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry in MS^E. An identification strategy using different data processing parameters was developed to identify the pesticide occurrence in soil. The method was applied to soil samples from typical peach orchards in 12 regions across China. The present work also discusses in detail the frequency of occurrence, concentration of pesticides, spatial distribution of multiresidues, and relationship between pesticide occurrence and soil properties. In the tested soil samples, 21 herbicides (level 1), 31 fungicides (level 2a), 24 insecticides (level 2a), and 3 growth regulators (level 2a) were identified. The total concentrations of quantifiable herbicides in the soil samples ranged from 1.05 to 327 ng/g. Only in 5.4% of the soil samples, no pesticide residues were present. By contrast, more than 86% of the total contained multiple residues. This study represents the first large-scale survey of pesticides in soil from peach orchards and provides comprehensive and accurate information on the pesticide residue status for risk assessment.

Unravelling the occurrence of trace contaminants in surface waters using semi-quantitative suspected non-target screening analyses

Several classes of anthropogenic chemicals such as pesticides and pharmaceuticals are frequently used in human-related life activities and are discharged into the aquatic environment. These compounds can exert an unknown effect on aquatic life and humans if the water is used for human consumption. Thus, unravelling their occurrence in the aquatic system is crucial for the well-being of life and monitoring purposes. To this end, we used nanoflow-liquid and ion-exchange chromatography hyphenated with orbitrap high-resolution tandem mass spectrometry to detect several thousands of features (chemical entities) in surface water. Later, the features were narrowed down to a few focused lists using a stepwise filtering strategy, for which the structural elucidation was made. Accordingly, the chemical structure was confirmed for 83 compounds from different application areas, mainly being pharmaceuticals, pesticides, and other multiple application industrial compounds and xenobiotic degradation products. The compounds with the highest concentration were lamotrigine (27.6 μg/L), valsartan (14.4 μg/L), and ibuprofen (12.7 μg/L). Some compounds such as prosulfocarb, fluopyram, and tris(3-chloropropyl) phosphate were found to be the most abundant and widespread contaminants. Of the 32 sampling sites, nearly half of the sites (47%) contained more than 30 different compounds. Two sampling sites were far more contaminated than other sites based on the estimated concentration and the number of identified contaminants they contained. Our triplicate analysis revealed a low relative standard deviation between replicates, advocating for the added value in analysing more sampling sites instead of sample repetition. Overall, our study elucidated the occurrence of organic contaminants from a variety of sources in the aquatic environment. Furthermore, our findings highlighted the role of suspected non-target screening in exposing a snapshot of the chemical composition of surface water and the localized possible contamination sources.

Assessing Reliability of Non-targeted High-Resolution Mass Spectrometry Fingerprints for Quantitative Source Apportionment in Complex Matrices

Effective management of contaminated sites requires differentiating and deconvoluting contaminant source impacts in complex environmental systems. The existing source apportionment approaches that use targeted analyses of preselected indicator chemicals are limited whenever target analytes are below the detection limits or derived from multiple sources. However, non-targeted analyses that leverage high-resolution mass spectrometry (HRMS) yield rich datasets that deeply characterize sample-specific chemical compositions, providing additional potential end-members for source differentiation and apportionment. Previous work demonstrated that HRMS fingerprints can define sample uniqueness and support accurate, quantitative source concentration estimates. Here, using two aqueous film-forming foams as representative complex sources, we assessed the qualitative fidelity and quantitative accuracy of HRMS source fingerprints in increasingly complex background matrices. Across all matrices, HRMS-derived source concentration estimates were 0.81 ± 0.11-fold and 0.64 ± 0.24-fold of actual in samples impacted solely by analytical matrix effects (MEs) or by sample processing recovery and analytical MEs, respectively. Isotopic internal standards were not easily paired to individual unidentified non-target features, but bulk internal standard-based abundance corrections improved apportionment accuracy in higher matrix samples (to 0.90 ± 0.12-fold of actual) and/or informed concentration estimate relative errors. HRMS fingerprint mining could identify, based on the dilution behavior, effective individual chemical end-members across 16 homologous series. Although method development is needed, the results further demonstrate the potential applications of non-targeted HRMS data for source apportionment and other quantitative outcomes.

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

Hexamethoxymethyl-melamine (HMMM) is used as a crosslinking agent in resins and plastics and in the manufacture of tires. In the present study, surface water samples were collected from two rivers adjacent to high traffic highways in the Greater Toronto Area in Ontario, Canada. Composite samples collected from the Don River and Highland Creek during rain events and a period of rapid snowmelt were preconcentrated using solid phase extraction and analyzed using liquid chromatography with high-resolution mass spectrometry. Elevated concentrations (> 1 µg/L) of HMMM were detected in surface waters during rain events in October of 2019 and during snow melt in early March of 2020. There were lower average concentrations of HMMM detected during rain events in the winter and spring of 2020. Temporal profiles of changes in the concentrations of HMMM in composite samples collected every 3 h during a rain event in October 2019 closely corresponded to the hydrograph profiles at the sampling sites, with the HMMM concentrations peaking > 6 h after the peak in water levels. This work contributes to the literature showing that HMMM is a ubiquitous contaminant of urban watersheds and that runoff from roads is a vector for the transport of this compound into urban surface waters.

Evaluation of Sample Preparation Methods for Non-Target Screening of Organic Micropollutants in Urban Waters Using High-Resolution Mass Spectrometry

Non-target screening (NTS) has gained interest in recent years for environmental monitoring purposes because it enables the analysis of a large number of pollutants without predefined lists of molecules. However, sample preparation methods are diverse, and few have been systematically compared in terms of the amount and relevance of the information obtained by subsequent NTS analysis. The goal of this work was to compare a large number of sample extraction methods for the unknown screening of urban waters. Various phases were tested for the solid-phase extraction of micropollutants from these waters. The evaluation of the different phases was assessed by statistical analysis based on the number of detected molecules, their range, and physicochemical properties (molecular weight, standard recoveries, polarity, and optical properties). Though each cartridge provided its own advantages, a multilayer cartridge combining several phases gathered more information in one single extraction by benefiting from the specificity of each one of its layers.

Coupling Suspect and Nontarget Screening with Mass Balance Modeling to Characterize Organic Micropollutants in the Onondaga Lake-Three Rivers System

Characterizing the occurrence, sources, and fate of organic micropollutants (OMPs) in lake-river systems serves as an important foundation for constraining the potential impacts of OMPs on the ecosystem functions of these critical landscape features. In this work, we combined suspect and nontarget screening with mass balance modeling to investigate OMP contamination in the Onondaga Lake-Three Rivers system of New York. Suspect and nontarget screening enabled by liquid chromatography-high-resolution mass spectrometry led to the confirmation and quantification of 105 OMPs in water samples collected throughout the lake-river system, which were grouped by their concentration patterns into wastewater-derived and mixed-source clusters via hierarchical cluster analysis. Four of these OMPs (i.e., galaxolidone, diphenylphosphinic acid, N-butylbenzenesulfonamide, and triisopropanolamine) were prioritized and identified by nontarget screening based on their characteristic vertical distribution patterns during thermal stratification in Onondaga Lake. Mass balance modeling performed using the concentration and discharge data highlighted the export of OMPs from Onondaga Lake to the Three Rivers as a major contributor to the OMP budget in this lake-river system. Overall, this work demonstrated the utility of an integrated screening and modeling framework that can be adapted for OMP characterization, fate assessment, and load apportionment in similar surface water systems.

Treading Water: Tire Wear Particle Leachate Recreates an Urban Runoff Mortality Syndrome in Coho but Not Chum Salmon

Tire tread wear particles (TWP) are increasingly recognized as a global pollutant of surface waters, but their impact on biota in receiving waters is rarely addressed. In the developed U.S. Pacific Northwest, acute mortality of adult coho salmon (Oncorhynchus kisutch) follows rain events and is correlated with roadway density. Roadway runoff experimentally triggers behavioral symptoms and associated changes in blood indicative of cardiorespiratory distress prior to death. Closely related chum salmon (O. keta) lack an equivalent response. Acute mortality of juvenile coho was recently experimentally linked to a transformation product of a tire-derived chemical. We evaluated whether TWP leachate is sufficient to trigger the acute mortality syndrome in adult coho salmon. We characterized the acute response of adult coho and chum salmon to TWP leachate (survival, behavior, blood physiology) and compared it with that caused by roadway runoff. TWP leachate was acutely lethal to coho at concentrations similar to roadway runoff, with the same behaviors and blood parameters impacted. As with runoff, chum salmon appeared insensitive to TWP leachate at concentrations lethal to coho. Our results confirm that environmentally relevant TWP exposures cause acute mortalities of a keystone aquatic species.

Toxicity Testing of Effluent-Dominated Stream Using Predictive Molecular-Level Toxicity Signatures Based on High-Resolution Mass Spectrometry: A Case Study of the Lubbock Canyon Lake System

Current aquatic toxicity assessments usually focus on targeted analyses coupled with toxicity testing to determine the impacts of complex mixtures on aquatic organisms. However, based on this approach alone, it is sometimes difficult to explain observed toxicity from the selected chemical analytes. Recent analytical advances such as high-resolution mass spectrometry (HRMS) can improve the characterizations of the chemical composition of complex mixtures, but the intensive labor required to produce confident identifications limits its utility in high-throughput screening. In the present study, we evaluated a rapid workflow to predict potential toxicity signatures of complex water samples based on high-throughput, tentative HRMS identifications derived from database matching, followed by identification of chemical-ligand interactions and pathway identification. We tested the workflow with water samples from the effluent-dominated Lubbock Canyon Lake System (LCLS). Results across all sites showed that predicted toxicity signatures had little variation when correcting for HRMS false-positive rates. The most common pathways across sites were gonadotropin-releasing hormone receptor and α-adrenergic receptor signaling. Alterations to the predicted pathways were successfully observed in larval zebrafish exposures to LCLS water samples. These results may allow researchers to better utilize rapid assessments of HRMS data for the assessment of adverse impacts on aquatic organisms.

Evaluation of Nontarget Long-Term LC-HRMS Time Series Data Using Multivariate Statistical Approaches

The use of liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) has steadily increased in many application fields ranging from metabolomics to environmental science. HRMS data are frequently used for nontarget screening (NTS), i.e., the search for compounds that are not previously known and where no reference substances are available. However, the large quantity of data produced by NTS analytical workflows makes data interpretation and time-dependent monitoring of samples very sophisticated and necessitates exploiting chemometric data processing techniques. Consequently, in this study, a prioritization method to handle time series in nontarget data was established. As proof of concept, industrial wastewater was investigated. As routine industrial wastewater analyses monitor the occurrence of a limited number of targeted water contaminants, NTS provides the opportunity to detect also unknown trace organic compounds (TrOCs) that are not in the focus of routine target analysis. The developed prioritization method enables reducing raw data and including identification of prioritized unknown contaminants. To that end, a five-month time series for industrial wastewaters was utilized, analyzed by liquid chromatography-time-of-flight mass spectrometry (LC-qTOF-MS), and evaluated by NTS. Following peak detection, alignment, grouping, and blank subtraction, 3303 features were obtained of wastewater treatment plant (WWTP) influent samples. Subsequently, two complementary ways for exploratory time trend detection and feature prioritization are proposed. Therefore, following a prefiltering step, featurewise principal component analysis (PCA) and groupwise PCA (GPCA) of the matrix (temporal wise) were used to annotate trends of relevant wastewater contaminants. With sparse factorization of data matrices using GPCA, groups of correlated features/mass fragments or adducts were detected, recovered, and prioritized. Similarities and differences in the chemical composition of wastewater samples were observed over time to reveal hidden factors accounting for the structure of the data. The detected features were reduced to 130 relevant time trends related to TrOCs for identification. Exemplarily, as proof of concept, one nontarget pollutant was identified as N-methylpyrrolidone. The developed chemometric strategies of this study are not only suitable for industrial wastewater but also could be efficiently employed for time trend exploration in other scientific fields.

More Than a First Flush: Urban Creek Storm Hydrographs Demonstrate Broad Contaminant Pollutographs

Stormwater runoff clearly impacts water quality and ecological health of urban receiving waters. Subsequent management efforts are often guided by conceptual models of contaminant "first flushes", defined by disproportionate concentrations or mass loads early in the storm hydrograph. However, studies examining the dynamics of contaminant transport and receiving water hydrology have primarily focused on "traditional" stormwater contaminants and point sources, with less evaluation of chemically complex nonpoint pollution sources. Accordingly, we conducted baseflow and storm sampling in Miller Creek, a representative small, urban watershed in the Puget Sound region (WA, USA). We comprehensively characterized organic contaminant profiles and dynamics via targeted quantification of 35 stormwater-derived chemicals, complementary nontarget HRMS analyses, and surrogate chemical metrics of ecological health. For quantified analytes, total daily baseflow loads were 0.8-3.4 g/day and storm event loads were ∼80-320 g/storm (∼48 h interval), with nine contaminants detected during storms at >500 ng/L. Notably, urban creek "pollutographs" were much broader than relatively sharp storm hydrographs and exhibited transport-limited (rather than mass-limited) source dynamics, with immediate water quality degradation during low-intensity precipitation and continued mobilization of contaminant mass across the entire hydrograph. Study outcomes support prioritization of source identification and focused stormwater management efforts to improve water quality and promote ecosystem function in small urban receiving waters.

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

Tributary source water provenance is a primary control on water quality and ecological characteristics in branching tidal river systems. Source water provenance can be estimated both from field observations of chemical characteristics of water and from numerical modeling approaches. This paper highlights the strengths and shortcomings of two methods. One method uses stable isotope compositions of oxygen and hydrogen from water in field-collected samples to build a mixing model. The second method uses a calibrated hydrodynamic model with numerical tracers released from upstream reaches to estimate source-water fraction throughout the model domain. Both methods were applied to our study area in the eastern Sacramento–San Joaquin Delta, a freshwater tidal system which is dominated by fluvial processes during the flood season. In this paper, we show that both methods produce similar source water fraction values, implying the usefulness of both despite their shortcomings, and fortifying the use of hydrodynamic tracers to model transport in a natural system.

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

This study presents a nontarget approach to detect discharges from pharmaceutical production in municipal wastewater treatment plant (WWTP) effluents and to estimate their relevance on the total emissions. Daily composite samples were collected for 3 months at two WWTPs in Switzerland, measured using liquid chromatography high-resolution mass spectrometry, and time series were generated for all features detected. The extent of intensity variation in the time series was used to differentiate relatively constant domestic inputs from highly fluctuating industrial emissions. We show that an intensity variation threshold of 10 correctly classifies compounds of known origin and reveals clear differences between the two WWTPs. At the WWTP receiving wastewater from a pharmaceutical manufacturing site, (i) 10 times as many potential industrial emissions were detected as compared to the WWTP receiving purely domestic wastewater; (ii) for 11 pharmaceuticals peak concentrations, >10 μg/L and up to 214 μg/L were quantified, which are clearly above typical municipal wastewater concentrations; and (iii) a pharmaceutical not authorized in Switzerland was identified. Signatures of potential industrial emissions were even traceable at the downstream Rhine monitoring station at a >4000-fold dilution. Several of them occurred repeatedly, suggesting that they were linked to regular production, not to accidents. Our results demonstrate that small wastewater volumes from a single industry not only left a clear signature in the effluents of the respective WWTP but also influenced the water quality of one of Europe's most important river systems. Overall, these findings indicate that pharmaceutical production is a relevant emission source even in highly developed countries with a strong focus on water quality, such as Switzerland.

Developing Unique Nontarget High-Resolution Mass Spectrometry Signatures to Track Contaminant Sources in Urban Waters

Diffuse pollution in urban receiving waters often adversely impacts both humans and ecosystems. Identifying such pollution sources is challenging and limits the effectiveness of management actions intended to reduce risk. Here, we evaluated the use of nontarget analysis via high-resolution mass spectrometry (HRMS) to develop chemical fingerprints/signatures for source tracking. Specifically, we applied nontarget HRMS to characterize and differentiate two urban chemical sources: roadway runoff and wastewater influent. We isolated 112 and 598 nontarget compounds (both known and unidentified chemicals) that co-occurred in all roadway runoff and wastewater influent samples, respectively, and were unique relative to other sampled sources. For example, methamphetamine, often considered wastewater derived, was detected in all samples, implying that individual wastewater indicators may lack sufficient specificity in urban receiving waters impacted by multiple sources. Hierarchical cluster analysis differentiated source types, and normalized abundance profiling prioritized nontarget compounds with consistent relative abundance patterns across field sites for a given source. Hexa(methoxymethyl)melamine, 1,3-diphenylguanidine, and polyethylene glycols co-occurred in roadway runoff across geographic areas and traffic intensities, supporting continued development of a universal roadway runoff fingerprint based on ubiquitous compounds. This study provides a proof-of-concept for isolating nontarget source fingerprints to track diffuse contamination in urban receiving waters.