Response to "Letter to the Editor: Optimism for Nontarget Analysis in Environmental Chemistry"

Estimating LoD-s Based on the Ionization Efficiency Values for the Reporting and Harmonization of Amenable Chemical Space in Nontargeted Screening LC/ESI/HRMS

Nontargeted LC/ESI/HRMS aims to detect and identify organic compounds present in the environment without prior knowledge; however, in practice no LC/ESI/HRMS method is capable of detecting all chemicals, and the scope depends on the instrumental conditions. Different experimental conditions, instruments, and methods used for sample preparation and nontargeted LC/ESI/HRMS as well as different workflows for data processing may lead to challenges in communicating the results and sharing data between laboratories as well as reduced reproducibility. One of the reasons is that only a fraction of method performance characteristics can be determined for a nontargeted analysis method due to the lack of prior information and analytical standards of the chemicals present in the sample. The limit of detection (LoD) is one of the most important performance characteristics in target analysis and directly describes the detectability of a chemical. Recently, the identification and quantification in nontargeted LC/ESI/HRMS (e.g., via predicting ionization efficiency, risk scores, and retention times) have significantly improved due to employing machine learning. In this work, we hypothesize that the predicted ionization efficiency could be used to estimate LoD and thereby enable evaluating the suitability of the LC/ESI/HRMS nontargeted method for the detection of suspected chemicals even if analytical standards are lacking. For this, 221 representative compounds were selected from the NORMAN SusDat list (S0), and LoD values were determined by using 4 complementary approaches. The LoD values were correlated to ionization efficiency values predicted with previously trained random forest regression. A robust regression was then used to estimate LoD values of unknown features detected in the nontargeted screening of wastewater samples. These estimated LoD values were used for prioritization of the unknown features. Furthermore, we present LoD values for the NORMAN SusDat list with a reversed-phase C18 LC method.

Water, Water Everywhere, but Every Drop Unique: Challenges in the Science to Understand the Role of Contaminants of Emerging Concern in the Management of Drinking Water Supplies

The protection and management of water resources continues to be challenged by multiple and ongoing factors such as shifts in demographic, social, economic, and public health requirements. Physical limitations placed on access to potable supplies include natural and human-caused factors such as aquifer depletion, aging infrastructure, saltwater intrusion, floods, and drought. These factors, although varying in magnitude, spatial extent, and timing, can exacerbate the potential for contaminants of concern (CECs) to be present in sources of drinking water, infrastructure, premise plumbing and associated tap water. This monograph examines how current and emerging scientific efforts and technologies increase our understanding of the range of CECs and drinking water issues facing current and future populations. It is not intended to be read in one sitting, but is instead a starting point for scientists wanting to learn more about the issues surrounding CECs. This text discusses the topical evolution CECs over time (Section 1), improvements in measuring chemical and microbial CECs, through both analysis of concentration and toxicity (Section 2) and modeling CEC exposure and fate (Section 3), forms of treatment effective at removing chemical and microbial CECs (Section 4), and potential for human health impacts from exposure to CECs (Section 5). The paper concludes with how changes to water quantity, both scarcity and surpluses, could affect water quality (Section 6). Taken together, these sections document the past 25 years of CEC research and the regulatory response to these contaminants, the current work to identify and monitor CECs and mitigate exposure, and the challenges facing the future.

Isotope-coded derivatization with designed Girard-type reagent as charged isobaric mass tags for non-targeted profiling and discovery of natural aldehydes by liquid chromatography-tandem mass spectrometry

Aldehyde-containing metabolites are reactive electrophiles that have attracted extensive attention due to their widespread occurrence in organisms and natural foods. Herein we described a newly-designed Girard's reagent, 1-(4-hydrazinyl-4-oxobutyl)pyridin-1-ium bromide (HBP), as charged tandem mass (MS/MS) tags to facilitate selective capture, sensitive detection and semi-targeted discovery of aldehyde metabolites via hydrazone formation. After HBP labeling, the detection signals of the test aldehydes were increased by 21-2856 times, with the limits of detection were 2.5-7 nM. Upon isotope-coded derivatization with a pair of labeling reagents, HBP-d₀ and its deuterium-labeled counterpart HBP-d₅, the aldehyde analytes were converted to hydrazone derivatives, which generated characteristic neutral fragments of 79 Da and 84 Da, respectively. The isobaric HBP-d₀/HBP-d₅ labeling based LC-MS/MS method was validated by relative quantification of human urinary aldehydes (slope=0.999, R² > 0.99, RSDs ≤ 8.5%) and discrimination analysis between diabetic and control samples. The unique isotopic doubles (Δm/z = 5 Da) by dual neutral loss scanning (dNLS) provided a generic reactivity-based screening strategy that allowed non-targeted profiling and identification of endogenous aldehydes even amidst noisy data. The LC-dNLS-MS/MS screening of cinnamon extracts led to finding 61 possible natural aldehydes and guided discovery of 10 previously undetected congeners in this medicinal plant.

Nontargeted screening reveals fluorotelomer ethoxylates in indoor dust and industrial wastewater

Concerns regarding the persistence, bioaccumulation behaviour, and toxicity of perfluorooctanoic acid and perfluorooctane sulfonic acid have resulted in the creation of thousands of replacement perfluoroalkyl substances (PFAS). This study reports on the discovery of fluorotelomer ethoxylates (FTEO) in indoor dust (9/15 samples), and industrial effluents (14/37 samples) using gas chromatographic cyclic ion mobility mass spectrometry (GC-cIMS). By filtering the detected unknowns by mass and collision-cross section, a series of FTEO homologues were revealed with the formula F-(CF₂)_n(C₂H₄O)_xH, where n = 6,8,10, and x = 4-12. The highest concentrations were observed in samples collected from healthcare facilities, consistent with the potential use of these compounds in anti-fog products, sprays used to prevent condensation on eyeglasses. FTEOs were also detected in c. 40 % of industrial effluent samples, with the highest concentrations in electroplating facilities, manufacturers of cosmetics and personal care products, and linen cleaning services for healthcare and work uniforms. These results suggest that FTEOs may well be widespread pollutants that are more persistent than previously thought, underlining the need for further study of their occurrence and potential impact to human health and the environment.

From Centroided to Profile Mode: Machine Learning for Prediction of Peak Width in HRMS Data

Centroiding is one of the major approaches used for size reduction of the data generated by high-resolution mass spectrometry. During centroiding, performed either during acquisition or as a pre-processing step, the mass profiles are represented by a single value (i.e., the centroid). While being effective in reducing the data size, centroiding also reduces the level of information density present in the mass peak profile. Moreover, each step of the centroiding process and their consequences on the final results may not be completely clear. Here, we present Cent2Prof, a package containing two algorithms that enables the conversion of the centroided data to mass peak profile data and vice versa. The centroiding algorithm uses the resolution-based mass peak width parameter as the first guess and self-adjusts to fit the data. In addition to the m/z values, the centroiding algorithm also generates the measured mass peak widths at half-height, which can be used during the feature detection and identification. The mass peak profile prediction algorithm employs a random-forest model for the prediction of mass peak widths, which is consequently used for mass profile reconstruction. The centroiding results were compared to the outputs of the MZmine-implemented centroiding algorithm. Our algorithm resulted in rates of false detection ≤5% while the MZmine algorithm resulted in 30% rate of false positive and 3% rate of false negative. The error in profile prediction was ≤56% independent of the mass, ionization mode, and intensity, which was 6 times more accurate than the resolution-based estimated values.

Non-target analysis of organic waste amended agricultural soils: Characterization of added organic pollution

Amendment of soil with organic urban and animal wastes can keep arable soil fertile without the need for synthetic fertilizers. However, pollutants present in these types of waste might be carried into the soil with unintended consequences for the environment. We studied an experimental agricultural plot, which had been amended with either synthetic inorganic fertilizers, human urine, manure, or wastewater treatment sludge at very high rates. We applied chemical non-target analysis to characterize present organic micropollutants, intending to compare treatments and highlight suspects of environmental concern. Soil samples were prepared by pressurized liquid and purified with solid-phase extraction before analysis with nanoflow ultra-high performance liquid chromatography coupled to high-resolution Orbitrap tandem mass spectrometry. Automated elucidation with two mass spectral libraries, multiple large chemical databases and environmental NORMAN suspect lists was able to annotate (level 3 and level 2) ∼ 20% of the 2306 detected features. A following principal component- and differential-analysis could separate the soil treatment groups' pollution profiles and highlight high relative abundance features. From cattle manure, natural compounds such as bile acids and steroids were found. Human urine led to pollution with common pharmaceuticals such as metoprolol and propranolol. The highest number was added by wastewater treatment sludge, with 25 significant contaminants, spanning blood pressure regulators, antidepressants, synthetic steroids and sleep medication. Furthermore, using Kendrick mass defect plots, a series of polypropylene glycols could be revealed in the soil. Non-target analysis appears to be a promising method to characterize organic pollutants in soils.

Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis

Non-target analysis (NTA) employing high-resolution mass spectrometry is a commonly applied approach for the detection of novel chemicals of emerging concern in complex environmental samples. NTA typically results in large and information-rich datasets that require computer aided (ideally automated) strategies for their processing and interpretation. Such strategies do however raise the challenge of reproducibility between and within different processing workflows. An effective strategy to mitigate such problems is the implementation of inter-laboratory studies (ILS) with the aim to evaluate different workflows and agree on harmonized/standardized quality control procedures. Here we present the data generated during such an ILS. This study was organized through the Norman Network and included 21 participants from 11 countries. A set of samples based on the passive sampling of drinking water pre and post treatment was shipped to all the participating laboratories for analysis, using one pre-defined method and one locally (i.e. in-house) developed method. The data generated represents a valuable resource (i.e. benchmark) for future developments of algorithms and workflows for NTA experiments.

Measurement(s)	chemical • drinking water
Technology Type(s)	high resolution mass spectrometry • non-target analysis • Interlaboratory
Factor Type(s)	method
Sample Characteristic - Environment	laboratory environment

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.15028665

Predicting the Mass Spectra of Environmental Pollutants Using Computational Chemistry: A Case Study and Critical Evaluation

Organic pollutants can be identified by comparing their electron ionization (EI) mass spectra with those in libraries or obtained from authentic standards. Nevertheless, libraries are incomplete; standards may be unavailable or too costly, or their synthesis may be too time-consuming. This study evaluates the performance of quantum chemical electron ionization mass spectrometry (QCEIMS) vis-à-vis competitive fragmentation modeling (CFM) for suspect screening and unknown identification. EI mass spectra of 35 compounds, including halogenated organics, organophosphorus flame retardants (OPFRs), and disinfection byproducts were computed. Computational results were compared with EI mass spectra compiled in the NIST Library as well as collision-induced dissociation (CID) mass spectra obtained from radical cations M^•+ generated by charge-exchange atmospheric pressure chemical ionization (APCI). The results indicate that QCEIMS performs equivalently or better than CFM. Average match factors between computed and experimental (NIST) EI mass spectra were 656 vs 503 for the halogenated organics, and on average, QCEIMS predicted 55% of the products generated by CID vs 17% predicted by CFM. QCEIMS predicted 37% of the OPFR CID products whereas CFM predicted 29%. QCEIMS performed comparably to a commercial combinatorial fragmentation method for suspect screening of a dust sample, identifying 19/20 targets. Examples of unknown pollutants, whose reference spectra were unavailable at the time of discovery, are also presented. The computational results suggest that QCEIMS can help guide the analyst in obtaining authentic standards and raise the possibility that, with advances in computing, an unknown may eventually be confirmed in hours as opposed to the days or months required to obtain authentic standards.

Open, High-Resolution EI+ Spectral Library of Anthropogenic Compounds

To address the lack of high-resolution electron ionisation mass spectral libraries (HR-[EI+]-MS) for environmental chemicals, a retention-indexed HR-[EI+]-MS library has been constructed following analysis of authentic compounds via GC-Orbitrap MS. The library is freely provided alongside a compound database of predicted physicochemical properties. Currently, the library contains over 350 compounds from 56 compound classes and includes a range of legacy and emerging contaminants. The RECETOX Exposome HR-[EI+]-MS library expands the number of freely available resources for use in full-scan chemical exposure studies and is available at: https://doi.org/10.5281/zenodo.4471217.

PFAS Exposure Pathways for Humans and Wildlife: A Synthesis of Current Knowledge and Key Gaps in Understanding

We synthesize current understanding of the magnitudes and methods for assessing human and wildlife exposures to poly- and perfluoroalkyl substances (PFAS). Most human exposure assessments have focused on 2 to 5 legacy PFAS, and wildlife assessments are typically limited to targeted PFAS (up to ~30 substances). However, shifts in chemical production are occurring rapidly, and targeted methods for detecting PFAS have not kept pace with these changes. Total fluorine measurements complemented by suspect screening using high-resolution mass spectrometry are thus emerging as essential tools for PFAS exposure assessment. Such methods enable researchers to better understand contributions from precursor compounds that degrade into terminal perfluoroalkyl acids. Available data suggest that diet is the major human exposure pathway for some PFAS, but there is large variability across populations and PFAS compounds. Additional data on total fluorine in exposure media and the fraction of unidentified organofluorine are needed. Drinking water has been established as the major exposure source in contaminated communities. As water supplies are remediated, for the general population, exposures from dust, personal care products, indoor environments, and other sources may be more important. A major challenge for exposure assessments is the lack of statistically representative population surveys. For wildlife, bioaccumulation processes differ substantially between PFAS and neutral lipophilic organic compounds, prompting a reevaluation of traditional bioaccumulation metrics. There is evidence that both phospholipids and proteins are important for the tissue partitioning and accumulation of PFAS. New mechanistic models for PFAS bioaccumulation are being developed that will assist in wildlife risk evaluations. Environ Toxicol Chem 2021;40:631-657. © 2020 SETAC.

Statistical profiling for identifying transformation products in an engineered treatment process

This study demonstrated statistical profiling consisting of the analysis of variance (ANOVA) and fold change to efficiently identify transformation products of an organic model compound (i.e., carbamazepine, CBZ) in ozonation. To this end, liquid chromatography (LC)-quadrupole time-of-flight mass spectrometry (QTOF-MS) was employed to measure the accurate masses of CBZ transformation products. Subsequently, statistical profiling was applied to differentiating features that are uniquely present in the ozonated samples from those in blanks and control (i.e., CBZ sample without ozonation). The identified transformation products had significant statistical power (i.e., power, 1-β > 0.8) in post hoc power analysis, which suggests that the profiling procedure can be an efficient means of reducing false negative in data analysis. 2-quinazolinone was newly reported here as a tentative transformation of CBZ during ozonation. In addition, a transformation product with one less carbon than CBZ, often called "anomalous" transformation product, was also found. While statistical profiling was applied to a model experiment, such an approach can be further utilized to screen many features with a higher data complexity such as non-targeted screening (NTS) and non-target analysis (NTA) for environmental samples.

Automated Isotopic Profile Deconvolution for High Resolution Mass Spectrometric Data (APGC-QToF) from Biological Matrices

An isotopic profile matching algorithm, the isotopic profile deconvoluted chromatogram (IPDC), was developed to screen for a wide variety of organic compounds in high-resolution mass spectrometry (HRMS) data acquired from instruments with resolution power as low as 22 000 fwhm. The algorithm initiates the screening process by generating a series of C/Br/Cl/S isotopic patterns consistent with the profiles of approximately 3 million molecular formulas for compounds with potentially persistent, bioaccumulative, and toxic (PBT) properties. To evaluate this algorithm, HRMS data were screened using these seed profiles to isolate relevant chlorinated and/or brominated compounds. Data reduction techniques included mass defect filtering and retention time prediction from estimated boiling points predicted using molecular formulas and reasonable elemental conformations. A machine learning classifier was also developed using spectrometric and chromatographic variables to minimize false positives. A scoring system was developed to rank candidate molecular formulas for an isotopic feature. The IPDC algorithm was applied to a Lake Michigan lake trout extract analyzed by atmospheric pressure gas chromatography-quadrupole time-of-flight (APGC-QToF) mass spectrometry in positive and negative modes. The IPDC algorithm detected isotopic features associated with legacy contaminants and a series of unknown halogenated features. The IPDC algorithm resolved 313 and 855 halogenated features in positive and negative modes, respectively, in Lake Michigan lake trout.

Calculation of Electron Ionization Mass Spectra with Semiempirical GFNn-xTB Methods

In this work, we have tested two different extended tight-binding methods in the framework of the quantum chemistry electron ionization mass spectrometry (QCEIMS) program to calculate electron ionization mass spectra. The QCEIMS approach provides reasonable, first-principles computed spectra, which can be directly compared to experiment. Furthermore, it provides detailed insight into the reaction mechanisms of mass spectrometry experiments. It sheds light upon the complicated fragmentation procedures of bond breakage and structural rearrangements that are difficult to derive otherwise. The required accuracy and computational demands for successful reproduction of a mass spectrum in relation to the underlying quantum chemical method are discussed. To validate the new GFN2-xTB approach, we conduct simulations for 15 organic, transition-metal, and main-group inorganic systems. Major fragmentation patterns are analyzed, and the entire calculated spectra are directly compared to experimental data taken from the literature. We discuss the computational costs and the robustness (outliers) of several calculation protocols presented. Overall, the new, theoretically more sophisticated semiempirical method GFN2-xTB performs well and robustly for a wide range of organic, inorganic, and organometallic systems.