Maryam Vosough

Feature prioritization is a critical step in high-dimensional liquid chromatography−high-resolution mass spectrometry (LC-HRMS)-based non-target screening (NTS), yet alternative statistical prioritization strategies have not been systematically compared in this context. We implemented a multi-method workflow combining robustness-aware prioritization, operationalized through within-method feature-selection stability assessment, with explicit evaluation of cross-method support, and applied it to 24 municipal wastewater influent samples collected from four wastewater treatment plants (WWTPs) across six sampling periods. After pre-processing, 3,636 ESI(+) and 2,053 ESI(−) features were evaluated. Principal component analysis (PCA) and self-organizing maps (SOM) revealed a pronounced exploratory temporal gradient with weaker site-related structure. Features were prioritized using partial least-squares-discriminant analysis/variable importance in projection (PLS-DA/VIP) with model population analysis (MPA), support vector machine-recursive feature elimination (SVM-RFE) with repeated stratified cross-validation, and false discovery rate (FDR)-adjusted univariate volcano filtering. The resulting prioritized sets showed limited and contrast-dependent overlap, demonstrating substantial method dependence. Cross-method support was summarized using consensus levels (CL1−CL3), representing selection by one, two, or three approaches, respectively. Temporal comparisons contained no CL3 features, whereas spatial contrasts showed greater convergence, reaching 50.0% CL3 at one WWTP in ESI(+). Design-preserving sample-composition and parameter-sensitivity analyses showed that the main agreement patterns were generally retained, although individual feature support varied. Association fingerprints with physicochemical parameters and quantified biomarkers also differed across consensus levels. Overall, combining feature-selection stability with cross-method comparison provides a transparent basis for prioritizing LC-HRMS NTS candidates for downstream identification and wastewater-based epidemiology interpretation.

The widespread presence of pharmaceuticals and antibiotics in aquatic environments poses a serious ecological and public health risk, necessitating effective removal strategies. This study investigates a Z-scheme Ag/Ag₃PO₄/g-C₃N₄ photocatalyst for the simultaneous photodegradation of sulfamethoxazole, trimethoprim, naproxen, and diclofenac under visible light, a topic that has not been previously reported. The photocatalyst was comprehensively characterized using XRD, XPS, FTIR, BET, FESEM-EDX, TEM, EIS, PL, and DRS. Among the synthesized variants, Ag/Ag₃PO₄/g-C₃N₄ (80%) exhibited the highest photocatalytic efficiency. Optimal operational parameters (pH 6, 30 min irradiation, and 1.170 g L⁻¹ catalyst dosage) were established using response surface methodology based on a central composite design. Under these conditions, complete pollutant removal was achieved, with a total organic carbon (TOC) reduction of 88.62% after 180 min. Kinetic studies followed a pseudo-first-order model, and scavenger tests identified photogenerated holes (h⁺) as the dominant reactive species. Transformation products were identified using LC/MS. The photocatalyst retained high activity after five reuse cycles, confirming its stability and reusability. The superior performance is attributed to the Z-scheme mechanism and silver-induced surface plasmon resonance (SPR). The integration of advanced nanostructuring, heterojunction interface engineering, and statistical optimization enables efficient degradation of pharmaceutical mixtures at environmentally relevant concentrations in real water samples, highlighting its strong potential for sustainable water purification applications. Graphical abstract

Significant progress in high-throughput analytical techniques has paved the way for novel approaches to integrating data sets from different compartments. This study leverages nontarget screening (NTS) via liquid chromatography-high-resolution mass spectrometry (LC-HRMS), a crucial technique for analyzing organic micropollutants and their transformation products, in combination with biological indicators. We propose a combined multivariate data processing framework that integrates LC-HRMS-based NTS data with other high-throughput data sets, exemplified here by 18S V9 rRNA and full-length 16S rRNA gene metabarcoding data sets. The power of data fusion is demonstrated by systematically evaluating the impact of treated wastewater (TWW) over time on an aquatic ecosystem through a controlled mesocosm experiment. Highly compressed NTS data were compiled through the implementation of the region of interest-multivariate curve resolution-alternating least-squares (MCR-ALS) method, known as ROIMCR. By integrating ANOVA-simultaneous component analysis with structural learning and integrative decomposition (SLIDE), the innovative SLIDE-ASCA approach enables the decomposition of global and partial common, as well as distinct variation sources arising from experimental factors and their possible interactions. SLIDE-ASCA results indicate that temporal variability explains a much larger portion of the variance (74.6%) than the treatment effect, with both contributing to global shared space variation (41%). Design structure benefits include enhanced interpretability, improved detection of key features, and a more accurate representation of complex interactions between chemical and biological data. This approach offers a greater understanding of the natural and wastewater-influenced temporal patterns for each data source, as well as reveals associations between chemical and biological markers in an exemplified perturbed aquatic ecosystem.

In this study, simultaneous photocatalytic degradation of different parabens (methyl-, ethyl-, propyl-, and butyl paraben) and UV filters (benzophenone-3,

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.

UV filters as an important class of emerging organic pollutants are continuously released into and transported between the aquatic environments. So, the removal of these compounds from aquatic environments is of great importance. This study was conducted to evaluate the simultaneous photodegradation of three widely used UV filter compounds (4-methylbenzylidene camphor, 2-ethylhexyl 4-(dimethylamino) benzoate, ethylhexyl methoxycinnamate), in an aqueous environment under sunlight and Ag@AgCl photocatalyst integrated with plasmonic effect. The plasmonic Ag@AgCl nanocomposite was constructed via photochemical conversion and photoreduction. The enhanced photocatalytic performance can be attributed to the surface plasmon resonance effect of the silver nanoparticles and the hybrid effect caused by AgCl. For the monitoring of the target compounds’ degradation before and after photodegradation, an optimized method based on membrane-protected micro-solid-phase extraction coupled with gas chromatography-mass spectrometry (GC-MS) was employed. The simultaneous degradation of selected UV filters was also further investigated in contaminated real samples (river water) and the results showed that the matrix constituents could diminish the photocatalytic degradation efficiency. Graphical abstract

The use of liquid chromatography coupled to 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 non-target 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 a time dependent monitoring of samples very sophisticated and necessitates exploiting chemometric data processing techniques. Consequently, in this study a prioritisation method to handle time series in non-target data was established. As proof of concept, industrial wastewater was investigated. As routine industrial wastewater analyses monitor 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 prioritisation method enables reducing raw data and including identification of prioritised unknown contaminants. To that end, a five-months’ time series for industrial wastewaters was utilised, analysed 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 prioritisation are proposed. Therefore, following a pre filtering step, principal component analysis (PCA; feature wise) and group wise PCA (GPCA) of the matrix (temporal wise) were used to annotate trends of relevant wastewater contaminants. With sparse factorisation of data matrices using GPCA, groups of correlated features/mass fragments or adducts were detected, recovered and prioritised. 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 non target pollutant was identified as N methylpyrrolidone. The developed chemometric strategies of this study are not only suitable for industrial wastewater but could also be efficiently employed for time trend exploration in other scientific fields.

A novel sunlight-activated double-shell Cu^@Cu₂O/SiO₂ (m-pCu^@Cu₂O/SiO₂) photocatalyst is presented via a combined precipitation and sol-gel methods with a mesoporous silica outer shell. After applying several characterization techniques on the m-pCu^@Cu₂O/SiO₂, it was tested in the photodegradation of ciprofloxacin (CIP). The experimental results demonstrated a higher photocatalytic activity of the double-shell m-pCu^@Cu₂O/SiO₂ nanophotocatalyst than the core-shell pCu^@Cu₂O nanophotocatalyst under the sunlight irradiation. When the content of pCu^@Cu₂O was 30 wt.%, it showed the highest activity. The Cu nanoparticles exhibited the surface plasmonic resonance (SPR) effect which increased the light absorption in the visible region of light. It also caused the rapid separation of the photoexcited e⁻/h⁺ pairs. Furthermore, the mesoporous structure of outer shell silica favors the transfer of reactants, resulting in the improved photoactivity performance for the supported pCu^@Cu₂O catalyst. Central composite design (CCD) based on RSM (response surface methodology) approach was used to optimize four of the most important experimental variables. The photodegraded intermediates were identified by HPLC-Mass.

The field of high resolution mass spectrometry has undergone a rapid progress in the last years due to instrumental improvements leading to a higher sensitivity and selectivity of instruments. A variety of qualitative screening approaches, summarized as non-target screening, have been introduced and have successfully extended the environmental monitoring of organic micropollutants. Several automated data processing workflows have been developed to handle the immense amount of data that are recorded in short time frames by these methods. Most data processing workflows include similar steps, but underlying algorithms and implementation of different processing steps vary. In this study the consistency of data processing with different software tools was investigated. For this purpose, the same raw data files were processed with the software packages MZmine2, enviMass, Compound Discoverer and XCMS online and resulting feature lists were compared. Results show a low coherence between different processing tools, as overlap of features between all four programs was around 10 % and for each software between 40 – 55 % of features did not match with any other program. The implementation of replicate and blank filter was identified as one of the sources of observed divergences. However, there is a need for a better understanding and user-instructions on the influence of different algorithms and settings on feature extraction and following filtering steps. In future studies it would be of interest to investigate how final data interpretation is influenced by different processing software. With this work we want to encourage more awareness on data processing as a crucial step in the workflow of non-target screening.