InSpectra - A platform for identifying emerging chemical threats

Prioritization strategies for non-target screening in environmental samples by chromatography - High-resolution mass spectrometry: A tutorial

Non-target screening (NTS) using chromatography coupled to high-resolution mass spectrometry (HRMS), has become fundamental for detecting and prioritizing chemicals of emerging concern (CECs) in complex environmental matrices. The vast number of generated features (m/z, retention time, and intensity) necessitate effective prioritization strategies to identify environmentally and toxicologically relevant CECs. Since compound identification remains a major bottleneck in NTS, prioritization is critical to focus identification efforts where they matter most. This tutorial presents seven prioritization strategies: (1) Target and suspect screening for identifying known or suspected compounds using reference libraries. (2) Data quality filtering to apply quality control measures to reduce noise and the number of false positives. (3) Chemistry-driven prioritization using HRMS data properties to prioritize specific compound classes (e.g., halogenated substances, transformation products). (4) Process-driven - using spatial, temporal, or process-based comparisons (pre- and post-technical processes) to identify key features. (5) Effect-Directed Analysis (EDA) and Virtual Effect-Directed Analysis (vEDA) prioritization to link chemical features to biological effects. (6) Prediction-based prioritization such as quantitative structure-property relationships (QSPR) and machine learning to estimate risk or concentration levels, and (7) Pixel- or tile-based analysis where the chromatographic image (2D data) is used to pin-point regions of interest or for comparison of larger sample sets. By integrating these prioritization strategies, this tutorial provides a structured foundation to evaluate both identified and unidentified features, prioritize high-risk compounds, and advance environmental risk assessment and regulatory decision-making.

Analysis strategy of contamination source using chemical fingerprint information based on GC-HRMS: A case study of landfill leachate

With the increasing prevalence of emerging contaminants (ECs) in the environment, gaining a deeper understanding of the chemical information pertaining to the contamination source is a crucial step toward effective prevention and control of these ECs. This study presents a novel strategy for analyzing the chemical information of contamination sources using gas chromatography-high resolution mass spectrometry (GC-HRMS) and demonstrates it on landfill leachate, a common and representative environmental contamination source. Initially, a non-targeted screening approach using HRMS was used to characterize a total of 5344 organic compounds with identification confidence levels 1 and 2 in 14 landfill leachate samples. Leveraging this as a base data set, the similarity analysis was first performed, and the classification fingerprints exhibited a pronounced level of similarity. Second, 169 characteristic marker contaminants with important and significant differences were identified in the 3 groups of landfill leachate with different solid waste compositions (mostly kitchen waste, mostly plastic & daily chemical product waste, and proportion average) by difference analysis. Finally, 101 hazardous chemicals (HCs) were screened in the data set. The results demonstrated that a class of contamination source exhibited certain common characteristics, while different groups of samples had their own distinct contamination signatures. This work offers a unique perspective on the interpretation of chemical information from contamination sources, aiming to provide a valuable reference for environmental pollution management.

Identification of polar bioactive substances in the Upper Rhine using effect-directed analysis

Effect-Directed Analysis (EDA) was used to identify bioactive compounds in surface and well water from the Upper Rhine, and to evaluate their properties against the criteria set for Persistent, Mobile and Toxic (PMT) and very persistent and very mobile (vPvM) substances. A multi-layered solid-phase extraction was implemented to enrich a broad range of polar substances from the collected samples. The extracts were fractionated into 108 fractions and tested in the transthyretin (TTR)-binding assay measuring displacement of fluorescently labeled thyroxine (FITC-T4 TTR-binding assay) and the Aliivibrio fischeri bioluminescence (AFB) bioassay. Bioactive fractions guided the identification strategy using high-resolution mass spectrometry. Chemical features were systematically annotated using library databases and suspect lists, incorporating an automated assessment of the quality of each annotation. Based on this assessment, each chemical feature was assigned a specific identification confidence level. Identification of bioactive compounds was facilitated by using bioassay specific suspect lists that were extracted from an in-house developed database of positive and negative TTR-binding compounds and from a recently published database of active inhibitors of AFB. This resulted in the identification and confirmation of ten bioactive substances, including four evaluated as PMT and vPvM substances (diclofenac, trifloxystrobin acid, 6:2 FTSA and PFOA), and one as a potential PMT substance (4-aminoazobenzene). This study demonstrates the effectiveness of EDA in the identification of PMT/vPvM substances in the aquatic environment, facilitating their prioritization for comprehensive environmental risk assessment and possible regulation.

Non-target analysis of Danish wastewater treatment plant effluent: Statistical analysis of chemical fingerprinting as a step toward a future monitoring tool

In an attempt to discover and characterize the plethora of xenobiotic substances, this study investigates chemical compounds released into the environment with wastewater effluents. A novel non-targeted screening methodology based on ultra-high resolution Orbitrap mass spectrometry and nanoflow ultra-high performance liquid chromatography together with a newly optimized data-processing pipeline were applied to effluent samples from two state-of-the-art and one small wastewater treatment facility. In total, 785 molecular structures were obtained, of which 38 were identified as single compounds, while 480 structures were identified at a putative level. Most of these substances were therapeutics and drugs, present as parent compounds and metabolites. Using R packages Phyloseq and MetacodeR, originally developed for bioinformatics, significant differences in xenobiotic presence in the wastewater effluents between the three sites were demonstrated.

Apportioning sources of chemicals of emerging concern along an urban river with inverse modelling

Concentrations of chemicals in river water provide crucial information for assessing environmental exposure and risks from fertilisers, pesticides, heavy metals, illicit drugs, pathogens, pharmaceuticals, plastics and perfluorinated substances, among others. However, using concentrations measured along waterways (e.g., from grab samples) to identify sources of contaminants and understand their fate is complicated by mixing of chemicals downstream from diverse diffuse and point sources (e.g., agricultural runoff, wastewater treatment plants). To address this challenge, a novel inverse modelling approach is presented. Using waterway network topology, it quantifies locations and concentrations of contaminant sources upstream by inverting concentrations measured in water samples. It is computationally efficient and quantifies uncertainty. The approach is demonstrated for 13 contaminants of emerging concern (CECs) in an urban stream, the R. Wandle (London, UK). Mixing (the forward problem) was assumed to be conservative, and the location of sources and their concentrations were treated as unknowns to be identified. Calculated CEC source concentrations, which ranged from below detection limit (a few ng/L) up to 1μg/L, were used to predict concentrations of chemicals downstream. Using this approach, >90% of data were predicted within observational uncertainty. Principal component analysis of calculated source concentrations revealed signatures of two distinct chemical sources. First, pharmaceuticals and insecticides were associated with a subcatchment containing a known point source of treated effluent from a wastewater treatment plant. Second, illicit drugs and salicylic acid were associated with multiple sources, interpreted as input from untreated sewage including Combined Sewer Overflows (CSOs), misconnections, runoff and direct disposal throughout the catchment. Finally, a simple algorithmic approach that incorporates network topology was developed to design sampling campaigns to improve resolution of source apportionment. Inverse modelling of contaminant measurements can provide objective means to apportion sources in waterways from spot samples in catchments on a large scale.

Workflow to facilitate the detection of new psychoactive substances and drugs of abuse in influent urban wastewater

The complexity around the dynamic markets for new psychoactive substances (NPS) forces researchers to develop and apply innovative analytical strategies to detect and identify them in influent urban wastewater. In this work a comprehensive suspect screening workflow following liquid chromatography - high resolution mass spectrometry analysis was established utilising the open-source InSpectra data processing platform and the HighResNPS library. In total, 278 urban influent wastewater samples from 47 sites in 16 countries were collected to investigate the presence of NPS and other drugs of abuse. A total of 50 compounds were detected in samples from at least one site. Most compounds found were prescription drugs such as gabapentin (detection frequency 79%), codeine (40%) and pregabalin (15%). However, cocaine was the most found illicit drug (83%), in all countries where samples were collected apart from the Republic of Korea and China. Eight NPS were also identified with this protocol: 3-methylmethcathinone 11%), eutylone (6%), etizolam (2%), 3-chloromethcathinone (4%), mitragynine (6%), phenibut (2%), 25I-NBOH (2%) and trimethoxyamphetamine (2%). The latter three have not previously been reported in municipal wastewater samples. The workflow employed allowed the prioritisation of features to be further investigated, reducing processing time and gaining in confidence in their identification.

Non-target screening in water analysis: recent trends of data evaluation, quality assurance, and their future perspectives

This trend article provides an overview of recent advancements in Non-Target Screening (NTS) for water quality assessment, focusing on new methods in data evaluation, qualification, quantification, and quality assurance (QA/QC). It highlights the evolution in NTS data processing, where open-source platforms address challenges in result comparability and data complexity. Advanced chemometrics and machine learning (ML) are pivotal for trend identification and correlation analysis, with a growing emphasis on automated workflows and robust classification models. The article also discusses the rigorous QA/QC measures essential in NTS, such as internal standards, batch effect monitoring, and matrix effect assessment. It examines the progress in quantitative NTS (qNTS), noting advancements in ionization efficiency-based quantification and predictive modeling despite challenges in sample variability and analytical standards. Selected studies illustrate NTS's role in water analysis, combining high-resolution mass spectrometry with chromatographic techniques for enhanced chemical exposure assessment. The article addresses chemical identification and prioritization challenges, highlighting the integration of database searches and computational tools for efficiency. Finally, the article outlines the future research needs in NTS, including establishing comprehensive guidelines, improving QA/QC measures, and reporting results. It underscores the potential to integrate multivariate chemometrics, AI/ML tools, and multi-way methods into NTS workflows and combine various data sources to understand ecosystem health and protection comprehensively.

Is Nontarget Analysis Ready for Regulatory Application? Influence of Peak-Picking Algorithms on Data Analysis

The use of peak-picking algorithms is an essential step in all nontarget analysis (NTA) workflows. However, algorithm choice may influence reliability and reproducibility of results. Using a real-world data set, the aim of this study was to investigate how different peak-picking algorithms influence NTA results when exploring temporal and/or spatial trends. For this, drinking water catchment monitoring data, using passive samplers collected twice per year across Southeast Queensland, Australia (n = 18 sites) between 2014 and 2019, was investigated. Data were acquired using liquid chromatography coupled to high-resolution mass spectrometry. Peak picking was performed using five different programs/algorithms (SCIEX OS, MSDial, self-adjusting-feature-detection, two algorithms within MarkerView), keeping parameters identical whenever possible. The resulting feature lists revealed low overlap: 7.2% of features were picked by >3 algorithms, while 74% of features were only picked by a single algorithm. Trend evaluation of the data, using principal component analysis, showed significant variability between the approaches, with only one temporal and no spatial trend being identified by all algorithms. Manual evaluation of features of interest (p-value <0.01, log fold change >2) for one sampling site revealed high rates of incorrectly picked peaks (>70%) for three algorithms. Lower rates (<30%) were observed for the other algorithms, but with the caveat of not successfully picking all internal standards used as quality control. The choice is therefore currently between comprehensive and strict peak picking, either resulting in increased noise or missed peaks, respectively. Reproducibility of NTA results remains challenging when applied for regulatory frameworks.