Enhancing spectral quality in complex environmental matrices: Supporting suspect and non-target screening in zebra mussels with ion mobility

Effect-directed analysis of endocrine and neurotoxic effects in stormwater depending discharges

The investigation of pollutant inputs via stormwater runoff and subsequent effects in receiving waters is becoming increasingly urgent in view of climate change with accompanying extreme weather situations such as heavy rainfall events. In this study, two sampling areas, one urban and one rural but dominated by a highway, were investigated using effect-directed analysis to identify endocrine and neurotoxic effects and potentially responsible substances in stormwater structures and receiving waters. For this purpose, a transgenic yeast cell assay for the simultaneous detection of estrogenic, androgenic, and progestogenic effects (YMEES) was performed directly on high-performance thin-layer chromatography (HPTLC) plates. Concomitantly, estrogens were analyzed by GC-MS/MS and other micropollutants typical for wastewater and stormwater by LC-MS/MS. Discharges from the combined sewer overflow (CSO) contribute a large portion of the endocrine load to the studied water body, even surpassing the load from a nearby wastewater treatment plant (WWTP). An effect pattern similar to the CSO sample was shown in the receiving water after the CSO with lower intensities, consisting of an estrogenic, androgenic, and progestogenic effect. In contrast, after the WWTP, only one estrogenic effect with a lower intensity was detected. Concentrations of E1, 17α-E2, 17β-E2, EE2, and E3 in the CSO sample were 2000, 410, 1100, 560, and 2700 pg/L, respectively. HPTLC-YMEES and GC-MS/MS complement each other very well and help to elucidate endocrine stresses. An Acetylcholinesterase (AChE) inhibitory effect could not be assigned to a causative compound by suspect and non-target analysis using LC-HRMS. However, the workflow showed how information from HPTLC separation, effect-based methods, and other meta-information on the sampling area and substance properties can contribute to an identification of effect-responsible substances. Overall, the study demonstrated that effect-based methods in combination with HPTLC and instrumental analysis can be implemented to investigate pollution by stormwater run-off particularly regarding heavy rain events due to climate change.

Progress, applications, and challenges in high-throughput effect-directed analysis for toxicity driver identification - is it time for HT-EDA?

The rapid increase in the production and global use of chemicals and their mixtures has raised concerns about their potential impact on human and environmental health. With advances in analytical techniques, in particular, high-resolution mass spectrometry (HRMS), thousands of compounds and transformation products with potential adverse effects can now be detected in environmental samples. However, identifying and prioritizing the toxicity drivers among these compounds remain a significant challenge. Effect-directed analysis (EDA) emerged as an important tool to address this challenge, combining biotesting, sample fractionation, and chemical analysis to unravel toxicity drivers in complex mixtures. Traditional EDA workflows are labor-intensive and time-consuming, hindering large-scale applications. The concept of high-throughput (HT) EDA has recently gained traction as a means of accelerating these workflows. Key features of HT-EDA include the combination of microfractionation and downscaled bioassays, automation of sample preparation and biotesting, and efficient data processing workflows supported by novel computational tools. In addition to microplate-based fractionation, high-performance thin-layer chromatography (HPTLC) offers an interesting alternative to HPLC in HT-EDA. This review provides an updated perspective on the state-of-the-art in HT-EDA, and novel methods/tools that can be incorporated into HT-EDA workflows. It also discusses recent studies on HT-EDA, HT bioassays, and computational prioritization tools, along with considerations regarding HPTLC. By identifying current gaps in HT-EDA and proposing new approaches to overcome them, this review aims to bring HT-EDA a step closer to monitoring applications.

Non-target analysis for water characterization: wastewater treatment impact and selection of relevant features

Non-target analyses were conducted to characterize and compare the molecular profiles (UHPLC-HRMS fingerprint) of water samples from a wastewater treatment plant (WWTP). Inlet and outlet samples were collected from three campaigns spaced 6 months apart in order to highlight common trends. A significant impact of the treatment on the sample fingerprints was shown, with a 65-70% abatement of the number of features detected in the effluent, and more polar, smaller and less intense molecules found overall compared to those in WWTP influent waters. Multivariate analysis (PCA) associated with variations of the features between inlets and outlets showed that features appearing or increasing were correlated with effluents while those disappearing or decreasing were correlated with influents. Finally, effluent features considered as relevant to a potentially adverse effect on aqueous media (i.e. those which appeared or increased or slightly varied from the influent) were highlighted. Three hundred seventy-five features common with the 3 campaigns were thus selected and further characterized. For most of them, elementary composition was found to be C, H, N, O (42%) and C, H, N, O, P (18%). Considering the MS2 spectra and several reference MS2 databases, annotations were proposed for 35 of these relevant features. They include synthetic products, pharmaceuticals and metabolites.

Complementary methods for structural assignment of isomeric candidate structures in non-target liquid chromatography ion mobility high-resolution mass spectrometric analysis

Non-target screening with LC/IMS/HRMS is increasingly employed for detecting and identifying the structure of potentially hazardous chemicals in the environment and food. Structural assignment relies on a combination of multidimensional instrumental methods and computational methods. The candidate structures are often isomeric, and unfortunately, assigning the correct structure among a number of isomeric candidate structures still is a key challenge both instrumentally and computationally. While practicing non-target screening, it is usually impossible to evaluate separately the limitations arising from (1) the inability of LC/IMS/HRMS to resolve the isomeric candidate structures and (2) the uncertainty of in silico methods in predicting the analytical information of isomeric candidate structures due to the lack of analytical standards for all candidate structures. Here we evaluate the feasibility of structural assignment of isomeric candidate structures based on in silico-predicted retention time and database collision cross-section (CCS) values as well as based on matching the empirical analytical properties of the detected feature with those of the analytical standards. For this, we investigated 14 candidate structures corresponding to five features detected with LC/HRMS in a spiked surface water sample. Considering the predicted retention times and database CCS values with the accompanying uncertainty, only one of the isomeric candidate structures could be deemed as unlikely; therefore, the annotation of the LC/IMS/HRMS features remained ambiguous. To further investigate if unequivocal annotation is possible via analytical standards, the reversed-phase LC retention times and low- and high-resolution ion mobility spectrometry separation, as well as high-resolution MS² spectra of analytical standards were studied. Reversed-phase LC separated the highest number of candidate structures while low-resolution ion mobility and high-resolution MS² spectra provided little means for pinpointing the correct structure among the isomeric candidate structures even if analytical standards were available for comparison. Furthermore, the question arises which prediction accuracy is required from the in silico methods to par the analytical separation. Based on the experimental data of the isomeric candidate structures studied here and previously published in the literature (516 retention time and 569 CCS values), we estimate that to reduce the candidate list by 95% of the structures, the confidence interval of the predicted retention times would need to decrease to below 0.05 min for a 15-min gradient while that of CCS values would need to decrease to 0.15%. Hereby, we set a clear goal to the in silico methods for retention time and CCS prediction.

Identification and characterization of quaternary ammonium compounds in Flemish indoor dust by ion-mobility high-resolution mass spectrometry

Quaternary ammonium compounds (QACs) are a class of surfactants commonly used in disinfecting and cleaning products. Their use has substantially increased during the COVID-19 pandemic leading to increasing human exposure. QACs have been associated with hypersensitivity reactions and an increased risk of asthma. This study introduces the first identification, characterization and semi-quantification of QACs in European indoor dust using ion-mobility high-resolution mass spectrometry (IM-HRMS), including the acquisition of collision cross section values (^DTCCS_N2) for targeted and suspect QACs. A total of 46 indoor dust samples collected in Belgium were analyzed using target and suspect screening. Targeted QACs (n = 21) were detected with detection frequencies ranging between 4.2 and 100 %, while 15 QACs showed detection frequencies > 90 %. Semi-quantified concentrations of individual QACs showed a maximum of 32.23 µg/g with a median ∑QAC concentration of 13.05 µg/g and allowed the calculation of Estimated Daily Intakes for adults and toddlers. Most abundant QACs matched the patterns reported in indoor dust collected in the United States. Suspect screening allowed the identification of 17 additional QACs. A dialkyl dimethyl ammonium compound with mixed chain lengths (C16:C18) was characterized as a major QAC homologue with a maximum semi-quantified concentration of 24.90 µg/g. The high detection frequencies and structural variabilities observed call for more European studies on potential human exposure to these compounds. For all targeted QACs, drift tube IM-HRMS derived collision cross section values (^DTCCS_N2) are reported. Reference ^DTCCS_N2 values allowed the characterization of CCS-m/z trendlines for each of the targeted QAC classes. Experimental CCS-m/z ratios of suspect QACs were compared with the CCS-m/z trendlines. The alignment between the two datasets served as an additional confirmation of the assigned suspect QACs. The use of the 4bit multiplexing acquisition mode with consecutive high-resolution demultiplexing confirmed the presence of isomers for two of the suspect QACs.