Photo-assisted transformation of furosemide: Exploring transformation pathways, structure database and suspect and non-target workflows for comprehensive screening of unknown transformation products in wastewaters and landfill leachates.

In recent years, transformation products-(TPs) of pharmaceuticals in the environment have received considerable attention. In this context, here, a customized overview of transformation of Furosemide-(FRS) in aqueous matrices treated by photo-oxidation is provided as a proof of concept. Hence, the primary goal of the study was to display an integrated strategy by combining the target (parent-molecule) and suspect screening-(SS) approaches (TPs) in order to build an in-house High-Resolution mass spectrometry (HRMS) database able to provide reference information (chromatographic/spectral) for environmental investigations in complex matrices (wastewaters/landfill leachates). Data analysis was performed by optimizing a SS workflow. Additional confirmation for the proposed structural elucidation was provided by correlating retention time to the proposed structure employing three prediction models. This approach was applied for the tentative identification of 35 TPs of FRS, 28 of which are reported herein for the first time. Finally, SS and non-target analysis (NTA) have been successfully applied for retrospective screening of FRS and its TPs in real samples. The findings demonstrated that SS allows the proper identification of TPs of FRS in complex matrices proving its outstanding importance compared to NTA. In total, six TPs were identified by SS with potential ecotoxicological implications for two of them according to in silico risk assessment.

A Comprehensive Non-targeted Analysis Study of the Prenatal Exposome.

Recent technological advances in mass spectrometry have enabled us to screen biological samples for a very broad spectrum of chemical compounds allowing us to more comprehensively characterize the human exposome in critical periods of development. The goal of this study was three-fold: (1) to analyze 590 matched maternal and cord blood samples (total 295 pairs) using non-targeted analysis (NTA); (2) to examine the differences in chemical abundance between maternal and cord blood samples; and (3) to examine the associations between exogenous chemicals and endogenous metabolites. We analyzed all samples with high-resolution mass spectrometry using liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF/MS) in both positive and negative electrospray ionization modes (ESI+ and ESI-) and in soft ionization (MS) and fragmentation (MS/MS) modes for prioritized features. We confirmed 19 unique compounds with analytical standards, we tentatively identified 73 compounds with MS/MS spectra matching, and we annotated 98 compounds using an annotation algorithm. We observed 103 significant associations in maternal and 128 in cord samples between compounds annotated as endogenous and compounds annotated as exogenous. An example of these relationships was an association between three poly and perfluoroalkyl substances (PFASs) and endogenous fatty acids in both the maternal and cord samples indicating potential interactions between PFASs and fatty acid regulating proteins.

A quest to identify suitable organic tracers for estimating children's dust ingestion rates.

Chemical exposure via dust ingestion is of great interest to researchers and regulators because children are exposed to dust through their daily activities, and as a result, to the many chemicals contained within dust. Our goal was to develop a workflow to identify and rank organic chemicals that could be used as tracers to calculate children's dust ingestion rates. We proposed a set of criteria for a chemical to be considered a promising tracer. The best tracers must be (1) ubiquitous in dust, (2) unique to dust, (3) detectable as biomarkers in accessible biological samples, and (4) have available or obtainable ADME information for biomarker-based exposure reconstruction. To identify compounds meeting these four criteria, we developed a workflow that encompasses non-targeted analysis approaches, literature and database searching, and multimedia modeling. We then implemented an ad hoc grading system and ranked candidate chemicals based on fulfillment of our criteria (using one small, publicly available dataset to show proof of concept). Initially, five chemicals (1,3-diphenylguanidine, leucine, piperine, 6:2/8:2 fluorotelomer phosphate diester, 6:2 fluorotelomer phosphate diester) appeared to satisfy many of our criteria. However, a rigorous manual investigation raised many questions about the applicability of these chemicals as tracers. Based on the results of this initial pilot study, no individual compounds can be unequivocally considered suitable tracers for calculating dust ingestion rates. Future work must therefore consider larger datasets, generated from broader measurement studies and literature searches, as well as refinements to selection criteria, to identify robust and defensible tracer compounds.

Applications of Machine Learning to In Silico Quantification of Chemicals without Analytical Standards.

Non-targeted analysis provides a comprehensive approach to analyze environmental and biological samples for nearly all chemicals present. One of the main shortcomings of current analytical methods and workflows is that they are unable to provide any quantitative information constituting an important obstacle in understanding environmental fate and human exposure. Herein, we present an in silico quantification method using mahine-learning for chemicals analyzed using electrospray ionization (ESI). We considered three data sets from different instrumental setups: (i) capillary electrophoresis electrospray ionization-mass spectrometry (CE-MS) in positive ionization mode (ESI+), (ii) liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF/MS) in ESI+ and (iii) LC-QTOF/MS in negative ionization mode (ESI-). We developed and applied two different machine-learning algorithms: a random forest (RF) and an artificial neural network (ANN) to predict the relative response factors (RRFs) of different chemicals based on their physicochemical properties. Chemical concentrations can then be calculated by dividing the measured abundance of a chemical, as peak area or peak height, by its corresponding RRF. We evaluated our models and tested their predictive power using 5-fold cross-validation (CV) and y randomization. Both the RF and the ANN models showed great promise in predicting RRFs. However, the accuracy of the predictions was dependent on the data set composition and the experimental setup. For the CE-MS ESI+ data set, the best model predicted measured RRFs with a mean absolute error (MAE) of 0.19 log units and a cross-validation coefficient of determination (Q2) of 0.84 for the testing set. For the LC-QTOF/MS ESI+ data set, the best model predicted measured RRFs with an MAE of 0.32 and a Q2 of 0.40. For the LC-QTOF/MS ESI- data set, the best model predicted measured RRFs with a MAE of 0.50 and a Q2 of 0.20. Our findings suggest that machine-learning algorithms can be used for predicting concentrations of nontargeted chemicals with reasonable uncertainties, especially in ESI+, while the application on ESI- remains a more challenging problem.

Investigating the presence and persistence of volatile methylsiloxanes in Arctic sediments.

Volatile methylsiloxanes (VMS) have been identified as contaminants of emerging concern in aquatic systems. Here, we report on the presence of VMS in sediment and wastewater from Arctic regions in 2014 to 2016 and model their persistence in Adventfjorden in Longyearbyen, Svalbard. Total concentrations of VMS in sediment were dominated by D4 and D5 and ranged from 0.0024 to 1.7 ng g-1 at Svalbard (Longyearbyen), from 4.0 to 43 ng g-1 in Greenland (Nuuk) and from 0.19 to 21 ng g-1 in the Canadian Archipelago. Concentrations in wastewater samples from Svalbard ranged from 12 to 156 ng L-1. Large variability in reported values of the partition ratio between organic carbon and water (KOC) and enthalpy of sorption (ΔHOC; often estimated from enthalpy of phase change between octanol and water, ΔHOW) of VMS has resulted in high uncertainty in evaluating persistence in aquatic systems. We evaluated previously reported KOC and ΔHOC values from the literature in predicting measured VMS concentrations in sediment and wastewater in scenarios using a fugacity-based multimedia model for VMS concentrations in Svalbard. We tested two different model scenarios: (1) KOC and ΔHOW measurements for three cyclic VMS previously reported by Kozerski et al. (Environ. Toxicol. Chem., 2014, 33, 1937-1945) and Xu and Kropscott (Environ. Chem., 2014, 33, 2702-2710) and (2) the KOC and ΔHOC measurements from Panagopoulos et al. (Environ. Sci. Technol., 2015, 49, 12161-12168 and Environ. Sci. Technol. Lett., 2017, 4(6), 240-245). Concentrations of VMS in sediment predicted from concentrations in wastewater in scenario 2 were in good agreement with measured concentrations, whereas in scenario 1, predicted concentrations were 2 to 4 orders of magnitude lower. Such large discrepancies indicate that the differences in the predicted concentrations are more likely to be attributed to KOC and ΔHOC than to uncertainty in environmental parameters or emission rates.