Employing complementary multivariate methods for a designed nontarget LC-HRMS screening of a wastewater-influenced river

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.

Enhanced efficiency of MS/MS all-ion fragmentation for non-targeted analysis of trace contaminants in surface water using multivariate curve resolution and data fusion

Data-independent acquisition-all-ion fragmentation (DIA-AIF) mode of mass spectrometry can facilitate wide-scope non-target analysis of contaminants in surface water due to comprehensive spectral identification. However, because of the complexity of the resulting MS2 AIF spectra, identifying unknown pollutants remains a significant challenge, with a significant bottleneck in translating non-targeted chemical signatures into environmental impacts. The present study proposes to process fused MS1 and MS2 data sets obtained from LC-HRMS/MS measurements in non-targeted AIF workflows on surface water samples using multivariate curve resolution-alternating least squares (MCR-ALS). This enables straightforward assignment between precursor ions obtained from resolved MS1 spectra and their corresponding MS2 spectra. The method was evaluated for two sets of tap water and surface water contaminated with 14 target chemicals as a proof of concept. The data set of surface water samples consisting of 3506 MS1 and 2170 MS2 AIF mass spectral features was reduced to 81 components via a fused MS1-MS2 MCR model that describes at least 98.8% of the data. Each component summarizes the distinct chromatographic elution of components together with their corresponding MS1 and MS2 spectra. MS2 spectral similarity of more than 82% was obtained for most target chemicals. This highlights the potential of this method for unraveling the composition of MS/MS complex data in a water environment. Ultimately, the developed approach was applied to the retrospective non-target analysis of an independent set of surface water samples.

Searching for pharmaceutically active products and metabolites in environmental waters of Peru by HRMS-based screening: Proposal for future monitoring and environmental risk assessment

The presence of pharmaceutical active products (PhACs) in the aquatic environment is a matter of current concern, and there is an increasing trend to include these compounds in water quality monitoring programs and environmental risk assessments. Several studies have reported the presence of PhACs in environmental waters worldwide, but only a few studies have focused on Latin American countries. Thus, available information on the occurrence of parent pharmaceuticals, especially their metabolites, is very scarce. Peru is one of the less monitored countries in terms of contaminants of emerging concern (CECs) in waters, and only one study has been found, which was focused on the quantification of selected PhACs in urban wastewater and surface water. The aim of this work is to complement the previous data reported on PhACs in the aquatic environment by application of a wide-scope high-resolution (HRMS)-based screening, making use of target and suspect approaches. In the present work, 30 pharmaceuticals, drugs or other compounds (sweeteners, UV filters, etc.) and 21 metabolites have been identified, with antibiotics (and metabolites) being the most prevalent compounds. The use of liquid chromatography (LC) coupled to ion mobility-HRMS allowed the tentative identification of parent compounds and metabolites, for which the analytical reference standard was not available, with a high level of confidence in their identification. Based on the results obtained, a strategy for the monitoring of PhACs and relevant metabolites in environmental waters from Peru and for subsequent risk assessment is proposed. Our data will also help to focus future studies to evaluate the removal efficiency of wastewater treatment plants and the impact of treated water in receiving water bodies.

Non-target Analysis and Chemometric Evaluation of a Passive Sampler Monitoring of Small Streams

Complex multivariate datasets are generated in environmental non-target screening (NTS) studies covering different sampling locations and times. This study presents a comprehensive chemometrics-based data processing workflow to reveal hidden data patterns and to find a subset of discriminating features between samples. We used ANOVA-simultaneous component analysis (ASCA) to disentangle the influence of spatial and seasonal effects as well as their interaction on a multiclass dataset. The dataset was obtained by a Chemcatcher passive sampler (PS) monitoring campaign of three small streams and one major river over four sampling periods from spring to summer. Monitoring of small streams is important as they are impacted by non-point source introduction of organic micropollutants (OMPs). The use of a PS provides a higher representativeness of sampling, and NTS broadens the range of detectable OMPs. A comparison of ASCA results of target analysis and NTS showed for both datasets a dominant influence of different sampling locations and individual temporal pollution patterns for each river. With the limited set of target analytes, general seasonal pollution patterns were apparent, but NTS data provide a more holistic view on site-specific pollutant loads. The similarity of temporal pollution patterns of two geographically close small streams was revealed, which was not observed in undecomposed data analysis like principal component analysis (PCA). With a complementary partial least squares-discriminant analysis (PLS-DA) and Volcano-based prioritization strategy, 223 site- and 45 season-specific features were selected and tentatively identified.

Non-target trend analysis for the identification of transformation products during ozonation experiments of citalopram and four of its biodegradation products

During ozonation in wastewater treatment plants, ozone reacts with emerging pollutants, which are partially removed through the secondary treatment, as long as, with their biotransformation products, triggering the formation of ozonation transformation products (TPs). Although the transformation of parent compounds (PCs) and their metabolites has been reported in the literature, the probable transformation of biotransformation products has not been investigated so far. This study evaluates the fate of citalopram (CTR) and four of its biotransformation products (DESCTR, CTRAM, CTRAC and CTROXO) during ozonation experiments. A Gaussian curve-based trend analysis was performed for the first time for the automated detection of TPs in ozone concentrations ranging from 0.06 to 12 mg/L. In total 46 ozonation TPs were detected; 7 TPs of CTR, 10 of DESCTR, 9 of CTRAM, 12 of CTRAC and 8 of CTROXO and were structurally elucidated based on their high resolution tandem mass spectra interpretation and tandem mass spectra similarity with the respective PC. Results have demonstrated that the examined compounds follow common transformation pathways in reaction with ozone and that common TPs were formed through the ozonation of different structurally-alike compounds. Moreover, the toxicity of the identified TPs was predicted with an in-house risk assessment program.

Wide-scope screening of pharmaceuticals, illicit drugs and their metabolites in the Amazon River

Only a limited number of households in the Amazon are served by sewage collection or treatment facilities, suggesting that there might be a significant emission of pharmaceuticals and other wastewater contaminants into freshwater ecosystems. In this work, we performed a wide-scope screening to assess the occurrence of pharmaceuticals, illicit drugs and their metabolites in freshwater ecosystems of the Brazilian Amazon. Our study included 40 samples taken along the Amazon River, in three of its major tributaries, and in small tributaries crossing four important urban areas (Manaus, Santarém, Macapá, Belém). More than 900 compounds were investigated making use of target and suspect screening approaches, based on liquid chromatography coupled to high-resolution mass spectrometry with ion mobility separation. Empirical collision-cross section (CCS) values were used to help and confirm identifications in target screening, while in the suspect screening approach CCS values were predicted using Artificial Neural Networks to increase the confidence of the tentative identification. In this way, 51 compounds and metabolites were identified. The highest prevalence was found in streams crossing the urban areas of Manaus, Macapá and Belém, with some samples containing up to 30 - 40 compounds, while samples taken in Santarém showed a lower number (8 - 11), and the samples taken in the main course of the Amazon River and its tributaries contained between 1 and 7 compounds. Most compounds identified in areas with significant urban impact belonged to the analgesics and antihypertensive categories, followed by stimulants and antibiotics. Compounds such as caffeine, cocaine and its metabolite benzoylecgonine, and cotinine (the metabolite of nicotine), were also detected in areas with relatively low anthropogenic impact and showed the highest total prevalence. This study supports the need to improve the sanitation system of urban areas in the Brazilian Amazon and the development of follow-up studies aimed at quantifying exposure levels and risks for Amazonian freshwater biodiversity.