qPeaks: A Linear Regression-Based Asymmetric Peak Model for Parameter-Free Automatized Detection and Characterization of Chromatographic Peaks in Non-Target Screening Data.

We present qPeaks (quality peaks), a novel, user-parameter-free algorithm for peak detection and peak characterization applicable to chromatographic data. The algorithm is based on a linearizable regression model that analyzes asymmetric peaks and estimates the specific uncertainties associated with the peak regression parameters. The uncertainties of the parameters are used to derive a data quality score DQSpeak, rendering low reliability results more transparent during processing and allowing for the prioritization of generated features. High DQSpeak chromatographic peaks have a lower chance of being classified as false-positive and show higher repeatability over multiple measurements. The high efficiency of the algorithm makes it particularly useful for application within processing routines of nontarget screening through chromatography coupled with high-resolution mass spectrometry. qPeaks is integrated into the qAlgorithms nontarget screening processing toolbox and appends a parameter-free chromatographic peak detection and characterization step to it. With qAlgorithms, now high-resolution mass spectra are centroided using the qCentroids algorithms, centroids are clustered to form extracted ion chromatograms (EICs) with the qBinning algorithm, and chromatographic peaks are found on the generated EICs with qPeaks. However, all tools from qAlgorithms can also be used independently.

Evaluation of GC-EI&CI-TOFMS for Nontarget Analysis of Industrial Wastewater Using Hydrophilic-Lipophilic-Balanced SPME.

The evaluation of nontarget analysis (NTA) techniques for the monitoring of wastewater is important as wastewater is an anthropogenic pollution source for aquatic ecosystems and a threat to human and environmental health. This study presents the proof-of-concept NTA of industrial wastewater samples. A prototype hydrophilic-lipophilic-balanced (HLB) SPME and gas chromatography interfaced with time-of-flight high-resolution mass spectrometry (GC-TOFMS) with electron ionization (EI) and chemical ionization (CI) in parallel are employed. The HLB-SPME consists of a poly(divinylbenzene-co-N-vinylpyrrolidone) structure, allowing the extraction of hydrophilic as well as lipophilic substances. As the combination of parallel CI and EI data provides a comprehensive data set as a unique feature, this study is strongly focused on the compound identification procedure and confidence reporting of exemplary substances. Furthermore, the use of three different CI reagent ions, including [N2H]+/[N4H]+, [H3O]+, and [NH4]+, enables a broad range of analytes to be ionized in terms of selectivity and softness. The complementary information provided by EI and CI data allows a level 3 identification or higher in 69% of cases. The polarity coverage based on the physicochemical properties of the analytes (such as volatility, water solubility, hydrophilicity, and lipophilicity) was visualized by using Henry's law and octanol-water partitioning constants. In conclusion, the presented approach is shown to be valuable for water analysis and allows enhanced and accelerated compound identification compared to utilizing only one type of ionization.

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.

qBinning: Data Quality-Based Algorithm for Automized Ion Chromatogram Extraction from High-Resolution Mass Spectrometry.

Due to the complexity and volume of data generated through non-target screening (NTS) using chromatographic couplings with high-resolution mass spectrometry, automized processing routines are necessary. The processing routines usually consist of many individual steps that are user-parameter-dependent and, thus, require labor-intensive optimization. Additionally, the effect of variations in raw data quality on the processing results is unclear and not fully understood. Within this work, we present qBinning, a novel algorithm for constructing extracted ion chromatograms (EICs) based on statistical principles and, thus, without the need to set user parameters. Furthermore, we give the user feedback on the specific qualities of the generated EICs using a scoring system (DQSbin). The DQSbin measures reliability as it correlates with the probability of correct classification of masses into EICs and the degree of overlap between different EIC construction algorithms. This work is a big step forward in understanding the behavior of NTS data and increasing the overall transparency in the results of NTS.

Impact of Chemical and Physical Properties of Organic Solvents on the Gas and Hydrogen Formation during Laser Synthesis of Gold Nanoparticles.

Laser ablation in liquids has been established as a scalable preparation method of nanoparticles for various applications. Particularly for materials prone to oxidation, it is established to suppress oxidation by using organic solvents as a liquid medium. While this often functionalizes the nanoparticles with a carbon shell, the related chemical processes that result from laser-induced decomposition reactions of the organic solvents remain uncertain. Using a systematic series of C6 solvents complemented by n-pentane and n-heptane during the nanosecond laser ablation of gold, the present study focuses on the solvent-dependent influence on gas formation rates, nanoparticle productivity, and gas composition. Both the permanent gas and hydrogen formation was found to be linearly correlated with ablation rate, ΔHvap , and pyrolysis activation energy. Based on this, a decomposition pathway linked to pyrolysis is proposed allowing the deduction of first selection rules for solvents that influence the formation of carbon or permanent gases.

Determination of anti-SARS-CoV-2 virustatic pharmaceuticals in the aquatic environment using high-performance liquid chromatography high-resolution mass spectrometry.

The Covid-19 pandemic has affected the global population since 2019. The rapid development and approval of vaccines has brought relief. Yet, effective cures are still being researched. Even if the pandemic situation may end, SARS-CoV-2 will remain and, thus, continued application of the drugs will lead to emissions of the active ingredients into the aquatic environment, as with other anthropogenic micropollutants. However, a general method for trace analysis of antiviral drugs is still missing. To this purpose, favipiravir, remdesivir, its active metabolite GS-441524, molnupiravir and its active metabolite EIDD-1931 were selected as representative analytes. A method was developed based on solid phase extraction and high-performance liquid chromatography combined with electrospray ionization quadrupole time-of-flight high-resolution mass spectrometry. Optimization comprised the choice of chromatographic columns, elution gradient, mass spectrometry and tandem mass spectrometry parameters. Solid phase extraction proved suitable for increase in limits of detection and quantitation. amelioration of the limits of detection and quantitation. Matrix effects were investigated applying the optimized method to a wastewater sample with added virustatics. All five compounds could be separated with reversed phase chromatography, whereas EIDD-1931 profited from hydrophilic interaction liquid chromatography. The optimized method yielded limits of detection and quantification of 2.1·10-1, 6.9·10-1 µg·L-1 for favipiravir, 1.8·10-3, 5.5·10-3 µg·L-1 for remdesivir, 1.9·10-3, 7.6·10-3 µg·L-1 for GS-441524, 2.9·10-3, 8.7·10-3 µg·L-1 for molnupiravir, and 1.3·10-1, 3.8·10-1 µg·L-1 for EIDD 1931. The method was first applied to compound stability testing at pH 2.8 and 9.7. At pH 2.8, remdesivir, GS-441524 and molnupiravir proved stable, whereas about 14% of EIDD-1931 and favipiravir were degraded. All five antiviral compounds were almost completely decomposed at pH 9.7. The application of the method was further demonstrated for potential transformation product detection on favipiravir ozonation monitoring.

Comparison of gas chromatographic techniques for the analysis of iodinated derivatives of aromatic amines.

Some aromatic amines (AA) have been classified as carcinogens to humans. After entering the body, mainly through tobacco smoke, they can be detected in urine. Thus, their trace analysis as biomarkers in biofluids is of high relevance and can be achieved with gas chromatography (GC-MS), usually after derivatization. This study compares three gas chromatographic methods for the analysis of ten iodinated derivatives of AA: GC-MS in single-ion monitoring (SIM) mode with (1) electron ionization (GC-EI-MS) and (2) negative chemical ionization (GC-NCI-MS), and (3) GC-EI-MS/MS in multiple reaction monitoring (MRM) mode using electron ionization. All methods and most analytes showed good coefficients of determination (R2 > 0.99) for broad linear ranges covering three to five orders of magnitude in the picogram-per-liter to nanogram-per-liter range, with one and two exceptions for (1) and (2) respectively. Excellent limits of detection (LODs) of 9-50, 3.0-7.3, and 0.9-3.9 pg/L were observed for (1), (2), and (3) respectively, and good precision was achieved (intra-day repeatability < 15% and inter-day repeatability < 20% for most techniques and concentration levels). On average, recoveries between 80 and 104% were observed for all techniques. Urine samples of smokers and non-smokers were successfully analyzed, and p-toluidine and 2-chloroaniline could be found at significantly (α = 0.05) higher concentrations among smokers.

Liquid-phase microextraction of aromatic amines: hollow fiber-liquid-phase microextraction and parallel artificial liquid membrane extraction comparison.

Aromatic amines (AA) are carcinogenic compounds that can enter the human body through many sources, one of the most important being tobacco smoke. They are excreted with the urine, from which they can be extracted and measured. To that end, hollow fiber-liquid-phase microextraction (HF-LPME) and parallel artificial liquid membrane extraction (PALME) were optimized for the analysis of representative aromatic amines, as alternatives to liquid-liquid extraction (LLE). Relevant extraction parameters, namely organic solvent, extraction time, agitation speed, and acceptor solution pH, were studied, and the two optimized techniques-HF-LPME: dihexyl ether, 45 min, 250 rpm, and pH 1; PALME: undecane, 20 min, 250 rpm and pH 1-were compared. Comparison of the optimized methods showed that significantly higher recoveries could be obtained with PALME than with HF-LPME. Therefore, PALME was further validated. The results were successful for nine different AA, with regression coefficients (R2) of at least 0.991, limits of detection (LOD) of 45-75 ng/L, and repeatability and peak area relative standard deviations (RSD) below 20%. Furthermore, two urine samples from smokers were measured as proof of concept, and 2-methylaniline was successfully quantified in one of them. These results show that PALME is a great green alternative to LLE. Not only does it use much smaller volumes of toxic organic solvents, and sample-enabling the study of samples with limited available volumes-but it is also less time consuming and labor intensive, and it can be automated.

Isotope-labeling in situ derivatization and HS-SPME arrow GC-MS/MS for simultaneous determination of fatty acids and fatty acid methyl esters in aqueous matrices.

Fatty acids (FAs) and fatty acid methyl esters (FAMEs) co-occur in many samples, and analysis of both substance classes is frequently of high interest. To this end, this study introduces the first method for simultaneous determination of FAs and FAMEs including fully automated solvent-free solid-phase microextraction (SPME) arrow headspace extraction combined with isotope-labeling in situ FA derivatization with deuterated methanol (CD3OD). By using the chromatographic isotope effect (ΔRt = 0.03 min) and the + 3 m/z mass shift, FAs can be selectively differentiated from the FAMEs during gas chromatography tandem-mass spectrometry (GC-MS/MS) operated in the multiple reaction monitoring (MRM) aquisition mode. Additionally, an approach is presented to predict the retention times of deuterated compounds. Optimization of the derivatization conditions was accomplished by design of experiments and found to be 20 min, 50 °C, 4 v/v% CD3OD, and pH 2.1. During method validation, FAs and FAMEs were calibrated in different concentration ranges by standard addition in five real matrices and ultrapure water leading to good linearities and method detection limits for FAs ranging from 1-30 µg L-1 and for FAMEs from 0.003-0.72 µg L-1. FAs and FAMEs were detected in real samples from surface water, wastewater treatment plant effluent, and three different bioreactor samples and could be quantified in concentrations ranging from 2-1056 µg L-1 for FAs and 0.01-14 µg L-1 for FAMEs.

Aromatic amines leachate from cigarette butts into aquatic environments: Is there risk for water organisms?

There are many toxics, such as aromatic amines (AAs), in cigarette butts (CBs). As CBs are the most abundant litter worldwide, these chemicals may leach into water bodies. In the present work, for the first time, the levels of AAs leachates from CBs in distilled water (DW) and river water (RW) samples were evaluated at different exposure times ranging from 15 min to 30 days. The mean leachate levels of AAs in DW and RW samples were in the range of 0.2-566 and 0.2-596 ng L-1, respectively, with overall mean values of 569 and 556 ng L-1. There was no significant difference (p > 0.05) between the total AAs levels as well as the level of each examined AA in DW and RW samples. Aniline (ANL) had the highest leaching rate from CBs into water. The mean leachates of AAs from CBs into water were ranked as: ANL> 1-naphthylamine (1-NA)> 2-naphthylamine (2-NA) > 2,6-dimethylaniline (2, 6-DMA)> ∑toluidine (∑TOL)> o-anisidine (o-ASD)> ∑aminobiphenyl (∑ABP). Ecological risk assessment showed that ∑7AAs, ANL, p-TOL, o-TOL, 2-NA, and ∑ABP had medium risks to sensitive crustaceans and fish. As AAs are not the only hazardous chemicals which may leach from CBs into aquatic environments, restrictions on littering CBs into the environment are required due to the release of different toxics ultimately causing adverse effects on aquatic organisms.

Enrichment and quantification of 18 polycyclic aromatic hydrocarbons from intermediates for plastics production by a generic liquid chromatography column switching.

The polycyclic aromatic hydrocarbon concentration in plastic products is regulated in (European Union) No. 1272/2013. However, this only covers the end products and not intermediate substances. Therefore, a generic method was developed to analyze the polycyclic aromatic hydrocarbons listed by the Environmental Protection Agency and the European Union. This method is based on direct large volume injection from solutions of plastic additives followed by liquid chromatography coupled to fluorescence detection. The additives Irganox 1010, ureido methacrylate, and cetyl methacrylate 1618F were used as examples for method development. Two serially coupled columns allowed the matrix to be removed on the first column and the analytes to be separated on the second column. The columns were connected by an intermediate valve. The valve allowed the matrix to be diverted after the first column and water to be dosed upstream of the second column via an additional pump. This allowed samples in aqueous or organic media to be focused at the column head. An injection volume of 100 µl and online aqueous dilution of 1:3 led to a limit of detection below 1 ng/ml for 15 polycyclic aromatic hydrocarbons. Moreover, concentrations between 1.6 and 10.3 ng/ml were found in the three plastic additives.

Differentiating between Acidic and Basic Surface Hydroxyls on Metal Oxides by Fluoride Substitution: A Case Study on Blue TiO2 from Laser Defect Engineering.

Both oxygen vacancies and surface hydroxyls play a crucial role in catalysis. Yet, their relationship is not often explored. Herein, we prepare two series of TiO2 (rutile and P25) with increasing oxygen deficiency and Ti3+ concentration by pulsed laser defect engineering in liquid (PUDEL), and selectively quantify the acidic and basic surface OH by fluoride substitution. As indicated by EPR spectroscopy, the laser-generated Ti3+ exist near the surface of rutile, but appear to be deeper in the bulk for P25. Fluoride substitution shows that extra acidic bridging OH are selectively created on rutile, while the surface OH density remains constant for P25. These observations suggest near-surface Ti3+ are highly related to surface bridging OH, presumably the former increasing the electron density of the bridging oxygen to form more of the latter. We anticipate that fluoride substitution will enable better characterization of surface OH and its correlation with defects in metal oxides.

How to Couple LC-IRMS with HRMS─A Proof-of-Concept Study.

Compound-specific stable isotope analysis (CSIA) is a unique analytical technique for determining small variations in isotope ratios of light isotopes in analytes from complex mixtures. A problem of CSIA using gas chromatography (GC) and liquid chromatography-isotope ratio mass spectrometry (LC-IRMS) is that any structural information of the analytes is lost due to the processes involved in determining the isotope ratio. To obtain the isotopic composition of, for example, carbon from organic compounds, all carbon in each analyte is quantitatively converted to CO2. For GC-IRMS, open split GC-IRMS-MS couplings have been described that allow additional acquisition of structural information of analytes and interferences. Structural analysis using LC-IRMS is more difficult and requires additional technical and instrumental efforts. In this study, LC was combined for the first time with simultaneous analysis by IRMS and high-resolution mass spectrometry (HRMS), enabling the direct identification of unknown or coeluting species. We have thoroughly investigated and optimized the coupling and showed how technical problems, arising from instrumental conditions, can be overcome. To this end, it was successfully demonstrated that a consistent split ratio between IRMS and HRMS could be obtained using a variable postcolumn flow splitter. This coupling provided reproducible results in terms of resulting peak areas, isotope values, and retention time differences for the two mass spectrometer systems. To demonstrate the applicability of the coupling, we chose to address an important question regarding the purity of international isotope standards. In this context, we were able to confirm that the USGS41 reference material indeed contains substantial amounts of pyroglutamic acid as suggested previously in the literature. Moreover, the replacement material, USGS41a, still has significant amounts of pyroglutamic acid as impurity, rendering some caution necessary when using this material for isotopic calibration.

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.

Reaction of Chlorine Dioxide with Saturated Nitrogen-Containing Heterocycles and Comparison with the Micropollutant Behavior in a Real Water Matrix.

Chlorine dioxide (ClO2) is a very selective oxidant that reacts with electron-rich moieties such as activated amines and thus can degrade specific N-containing micropollutants. N-containing heterocycles (NCHs) are among the most frequent moieties of pharmaceuticals. In this study, the reactions of ClO2 with ritalinic acid and cetirizine, two abundant micropollutants, and model compounds representing their NCH moiety were investigated. The pH-dependent apparent reaction rates of all NCHs with ClO2 were measured and modeled. This model showed that neutral amines are the most important species having reaction rates between 800 and 3200 M-1 s-1, while cationic amines are not reactive. Ritalinic acid, cetirizine, and their representative model compounds showed a high stoichiometric ratio of ≈5 moles ClO2 consumption per degraded ritalinic acid and ≈4 moles ClO2 consumption per degraded cetirizine, respectively. Investigation of chlorine-containing byproducts of ClO2 showed that all investigated NCHs mostly react by electron transfer and form above 80% chlorite. The reactions of the model compounds were well comparable with cetirizine and ritalinic acid, indicating that the model compounds indeed represented the reaction centers of cetirizine and ritalinic acid. Using the calculated apparent reaction rate constants, micropollutant degradation during ClO2 treatment of surface water was predicted for ritalinic acid and cetirizine with -8 to -15% and 13 to -22% error, respectively. The results indicate that in ClO2-based treatment, piperidine-containing micropollutants such as ritalinic acid can be considered not degradable, while piperazine-containing compounds such as cetirizine can be moderately degraded. This shows that NCH model compounds could be used to predict micropollutant degradation.

Sorption and Mobility of Charged Organic Compounds: How to Confront and Overcome Limitations in Their Assessment.

Permanently charged and ionizable organic compounds (IOC) are a large and diverse group of compounds belonging to many contaminant classes, including pharmaceuticals, pesticides, industrial chemicals, and natural toxins. Sorption and mobility of IOCs are distinctively different from those of neutral compounds. Due to electrostatic interactions with natural sorbents, existing concepts for describing neutral organic contaminant sorption, and by extension mobility, are inadequate for IOC. Predictive models developed for neutral compounds are based on octanol-water partitioning of compounds (Kow) and organic-carbon content of soil/sediment, which is used to normalize sorption measurements (KOC). We revisit those concepts and their translation to IOC (Dow and DOC) and discuss compound and soil properties determining sorption of IOC under water saturated conditions. Highlighting possible complementary and/or alternative approaches to better assess IOC mobility, we discuss implications on their regulation and risk assessment. The development of better models for IOC mobility needs consistent and reliable sorption measurements at well-defined chemical conditions in natural porewater, better IOC-, as well as sorbent characterization. Such models should be complemented by monitoring data from the natural environment. The state of knowledge presented here may guide urgently needed future investigations in this field for researchers, engineers, and regulators.

High-performance thin-layer chromatography in combination with an acetylcholinesterase-inhibition bioassay with pre-oxidation of organothiophosphates to determine neurotoxic effects in storm, waste, and surface water.

Pesticides such as organothiophosphates (OTPs) are neurotoxically active and enter the aquatic environment. Bioassays, using acetylcholinesterase (AChE), a suitable substrate and reactant, can be applied for the photometric detection of AChE-inhibiton (AChE-I) effects. The oxidized forms of OTPs, so-called oxons, have higher inhibition potentials for AChE. Therefore, a higher sensitivity is achieved for application of oxidized samples to the AChE assay. In this study, the oxidation of malathion, parathion, and chlorpyrifos by n-bromosuccinimide (NBS) was investigated in an approach combining high-performance thin-layer chromatography (HPTLC) with an AChE-I assay. Two AChE application approaches, immersion and spraying, were compared regarding sensitivity, precision, and general feasibility of the OTP effect detection. The oxidation by NBS led to an activation of the OTPs and a strong increase in sensitivity similar to the oxons tested. The sensitivity and precision of the two application techniques were similar, although the spray method was slightly more sensitive to the oxidized OTPs. The 10% inhibition concentrations (IC10) for the spray approach were 0.26, 0.75, and 0.35 ng/spot for activated malathion, parathion, and chlorpyrifos, respectively. AChE-I effect recoveries in samples from a stormwater retention basin and receiving stream were between 69 and 92% for malathion, parathion, and chlorpyrifos. The overall workflow, including sample enrichment by solid-phase extraction, HPTLC, oxidation of OTPs, and AChE-I assay, was demonstrated to be suitable for the detection of AChE-I effects in native water samples. An effect of unknown origin was found in a sample from a stormwater retention basin.

Peak broadening caused by using different micro-liquid chromatography detectors.

Advancements in column technology resulted in smaller particles and more efficient phases. In parallel, the use of columns with reduced dimensions is becoming more common. This means the effective column volume is also decreased, thereby making the systems more susceptible to effects of band broadening due to extra-column volume. Despite these trends and the fact that a growing number of miniaturized liquid chromatography systems are being offered commercially, manufacturers often stick to the modular concept with dedicated units for pumps, column oven, and detectors. This modular design results in long connection capillaries, which leads to extra-column band broadening and consequently prevents the exploitation of the intrinsic efficiency of state-of-the-art columns. In particular, band broadening post column has a considerable negative effect on efficiency. In this study, mass flow and concentration-dependent detectors were examined for their influence on band broadening using a micro-LC system. A mass spectrometric detector, an evaporative light scattering detector, two UV detectors, and a previously undescribed fluorescence detector were compared. The influence on efficiency is compared using plate height vs linear velocity data and peak variance. It is shown that an increase in the inner diameter after the post-column transfer capillary leads to significant loss in plate height. Comparing the UV detectors, it could be shown that the dispersion was reduced by 38% by the reduction of the post-column volume. The largest variance was found for the evaporative light scattering detector, which was 368% higher compared to the variance of the detector with the least effect on band broadening.

Target, suspect and non-target screening analysis from wastewater treatment plant effluents to drinking water using collision cross section values as additional identification criterion.

The anthropogenic entry of organic micropollutants into the aquatic environment leads to a potential risk for drinking water resources and the drinking water itself. Therefore, sensitive screening analysis methods are needed to monitor the raw and drinking water quality continuously. Non-target screening analysis has been shown to allow for a more comprehensive investigation of drinking water processes compared to target analysis alone. However, non-target screening is challenging due to the many features that can be detected. Thus, data processing techniques to reduce the high number of features are necessary, and prioritization techniques are important to find the features of interest for identification, as identification of unknown substances is challenging as well. In this study, a drinking water production process, where drinking water is supplied by a water reservoir, was investigated. Since the water reservoir provides surface water, which is anthropogenically influenced by wastewater treatment plant (WWTP) effluents, substances originating from WWTP effluents and reaching the drinking water were investigated, because this indicates that they cannot be removed by the drinking water production process. For this purpose, ultra-performance liquid chromatography coupled with an ion-mobility high-resolution mass spectrometer (UPLC-IM-HRMS) was used in a combined approach including target, suspect and non-target screening analysis to identify known and unknown substances. Additionally, the role of ion-mobility-derived collision cross sections (CCS) in identification is discussed. To that end, six samples (two WWTP effluent samples, a surface water sample that received the effluents, a raw water sample from a downstream water reservoir, a process sample and the drinking water) were analyzed. Positive findings for a total of 60 substances in at least one sample were obtained through quantitative screening. Sixty-five percent (15 out of 23) of the identified substances in the drinking water sample were pharmaceuticals and transformation products of pharmaceuticals. Using suspect screening, further 33 substances were tentatively identified in one or more samples, where for 19 of these substances, CCS values could be compared with CCS values from the literature, which supported the tentative identification. Eight substances were identified by reference standards. In the non-target screening, a total of ten features detected in all six samples were prioritized, whereby metoprolol acid/atenolol acid (a transformation product of the two ß-blockers metoprolol and atenolol) and 1,3-benzothiazol-2-sulfonic acid (a transformation product of the vulcanization accelerator 2-mercaptobenzothiazole) were identified with reference standards. Overall, this study demonstrates the added value of a comprehensive water monitoring approach based on UPLC-IM-HRMS analysis.