From Molecular Descriptors to Intrinsic Fish Toxicity of Chemicals: An Alternative Approach to Chemical Prioritization.

The European and U.S. chemical agencies have listed approximately 800k chemicals about which knowledge of potential risks to human health and the environment is lacking. Filling these data gaps experimentally is impossible, so in silico approaches and prediction are essential. Many existing models are however limited by assumptions (e.g., linearity and continuity) and small training sets. In this study, we present a supervised direct classification model that connects molecular descriptors to toxicity. Categories can be driven by either data (using k-means clustering) or defined by regulation. This was tested via 907 experimentally defined 96 h LC50 values for acute fish toxicity. Our classification model explained ≈90% of the variance in our data for the training set and ≈80% for the test set. This strategy gave a 5-fold decrease in the frequency of incorrect categorization compared to a quantitative structure-activity relationship (QSAR) regression model. Our model was subsequently employed to predict the toxicity categories of ≈32k chemicals. A comparison between the model-based applicability domain (AD) and the training set AD was performed, suggesting that the training set-based AD is a more adequate way to avoid extrapolation when using such models. The better performance of our direct classification model compared to that of QSAR methods makes this approach a viable tool for assessing the hazards and risks of chemicals.

Occurrence of tire and road wear particles in urban and peri-urban snowbanks, and their potential environmental implications.

According to estimates put forward in multiple studies, tire and road wear particles are one of the largest sources to microplastic contamination in the environment. There are large uncertainties associated with local emissions and transport of tire and road wear particles into environmental compartments, highlighting an urgent need to provide more data on inventories and fluxes of these particles. To our knowledge, the present paper is the first published data on mass concentrations and snow mass load of tire and polymer-modified road wear particles in snow. Roadside snow and meltwater from three different types of roads (peri-urban, urban highway and urban) were analysed by Pyrolysis Gas Chromatography Mass Spectrometry. Tire particle mass concentrations in snow (76.0-14,500 mg/L meltwater), and snow mass loads (222-109,000 mg/m2) varied widely. The concentration ranges of polymer-modified particles were 14.8-9550 mg/L and 50.0-28,800 mg/m2 in snow and meltwater, respectively. Comparing the levels of tire and PMB particles to the total mass of particles, showed that tire and PMB-particles combined only contribute to 5.7% (meltwater) and 5.2% (mass load) of the total mass concentration of particles. The large variation between sites in the study was investigated using redundancy analysis of the possible explanatory variables. Contradictory to previous road studies, speed limit was found to be one of the most important variables explaining the variation in mass concentrations, and not Annual Average Daily Traffic. All identified variables explained 69% and 66%, for meltwater and mass load concentrations, respectively. The results show that roadside snow contain total suspended solids in concentrations far exceeding release limits of tunnel and road runoff, as well as tire particles in concentrations comparable to levels previously reported to cause toxicity effects in organisms. These findings strongly indicate that roadside snow should be treated before release into the environment.

From Centroided to Profile Mode: Machine Learning for Prediction of Peak Width in HRMS Data.

Centroiding is one of the major approaches used for size reduction of the data generated by high-resolution mass spectrometry. During centroiding, performed either during acquisition or as a pre-processing step, the mass profiles are represented by a single value (i.e., the centroid). While being effective in reducing the data size, centroiding also reduces the level of information density present in the mass peak profile. Moreover, each step of the centroiding process and their consequences on the final results may not be completely clear. Here, we present Cent2Prof, a package containing two algorithms that enables the conversion of the centroided data to mass peak profile data and vice versa. The centroiding algorithm uses the resolution-based mass peak width parameter as the first guess and self-adjusts to fit the data. In addition to the m/z values, the centroiding algorithm also generates the measured mass peak widths at half-height, which can be used during the feature detection and identification. The mass peak profile prediction algorithm employs a random-forest model for the prediction of mass peak widths, which is consequently used for mass profile reconstruction. The centroiding results were compared to the outputs of the MZmine-implemented centroiding algorithm. Our algorithm resulted in rates of false detection ≤5% while the MZmine algorithm resulted in 30% rate of false positive and 3% rate of false negative. The error in profile prediction was ≤56% independent of the mass, ionization mode, and intensity, which was 6 times more accurate than the resolution-based estimated values.

Assessment of human exposure to selected pesticides in Norway by wastewater analysis.

Pesticides offer many benefits for humanity and agriculture, but at the same time pose a potential risk to human health because of their widespread use and high biological activity. Human biomonitoring (HBM) studies are the main tool to investigate human exposure to pesticides and other chemicals, but face limitations such as sampling biases, long time to complete and high costs. Wastewater-based epidemiology (WBE) is an alternative approach that is centered on the chemical analysis of biomarkers of (pesticide) exposure in urban wastewater. The present study used WBE to assess human exposure to selected classes of pesticides, triazines, pyrethroids and organophosphates, in Norway. Untreated wastewater samples were collected from four cities, covering approximately 20% of the Norwegian population. The highest population weighted mass loads (mg/day/1000 inhabitants) were for alkyl phosphates and the lowest for triazines. Some differences were observed for the two metabolites, 2-isopropyl-6-methyl-4-pyrimidinol (IMPY) and 3-(2,2-dichlorovinyl)-2,2-dimethyl-(1-cyclopropane) carboxylic acid (DCCA), which were higher in the rural city of Hamar. WBE figures were comparable with HBM findings for the specific metabolite of chlorpyrifos and chlorpyrifos methyl (3,5,6-trichloro-2-pyridinol; TCPY) but were different for the alkyl phosphates. Pyrethroid intake was calculated and was lower than the acceptable daily intake in all the cities, indicating low risk for human health. This is the most extensive WBE study performed to date to assess national human exposure to pesticides. This study demonstrated that WBE has the potential to be a useful complementary biomonitoring tool for assessing population-wide exposure to pesticides, overcoming some of the limitations of HBM.

Concentration and Distribution of Naphthenic Acids in the Produced Water from Offshore Norwegian North Sea Oilfields.

Naphthenic acids (NAs) constitute one of the toxic components of the produced water (PW) from offshore oil platforms discharged into the marine environment. We employed liquid chromatography (LC) coupled to high-resolution mass spectrometry with electrospray ionization (ESI) in negative mode for the comprehensive chemical characterization and quantification of NAs in PW samples from six different Norwegian offshore oil platforms. In total, we detected 55 unique NA isomer groups, out of the 181 screened homologous groups, across all tested samples. The frequency of detected NAs in the samples varied between 14 and 44 isomer groups. Principal component analysis (PCA) indicated a clear distinction of the PW from the tested platforms based on the distribution of NAs in these samples. The averaged total concentration of NAs varied between 6 and 56 mg L-1, among the tested platforms, whereas the concentrations of the individual NA isomer groups ranged between 0.2 and 44 mg L-1. Based on both the distribution and the concentration of NAs in the samples, the C8H14O2 isomer group appeared to be a reasonable indicator of the presence and the total concentration of NAs in the samples with a Pearson correlation coefficient of 0.89.

Self Adjusting Algorithm for the Nontargeted Feature Detection of High Resolution Mass Spectrometry Coupled with Liquid Chromatography Profile Data.

Nontargeted feature detection in data from high resolution mass spectrometry is a challenging task, due to the complex and noisy nature of data sets. Numerous feature detection and preprocessing strategies have been developed in an attempt to tackle this challenge, but recent evidence has indicated limitations in the currently used methods. Recent studies have indicated the limitations of the currently used methods for feature detection of LC-HRMS data. To overcome these limitations, we propose a self-adjusting feature detection (SAFD) algorithm for the processing of profile data from LC-HRMS. SAFD fits a three-dimensional Gaussian into the profile data of a feature, without data preprocessing (i.e., centroiding and/or binning). We tested SAFD on 55 LC-HRMS chromatograms from which 44 were composite wastewater influent samples. Additionally, 51 of 55 samples were spiked with 19 labeled internal standards. We further validated SAFD by comparing its results with those produced via XCMS implemented through MZmine. In terms of ISs and the unknown features, SAFD produced lower rates of false detection (i.e., ≤ 5% and ≤10%, respectively) when compared to XCMS (≤11% and ≤28%, respectively). We also observed higher reproducibility in the feature area generated by SAFD algorithm versus XCMS.

Performance of Three-Dimensional Rainbow Trout (Oncorhynchus mykiss) Hepatocyte Spheroids for Evaluating Biotransformation of Pyrene.

The aquatic bioconcentration of a chemical is typically determined using conventional fish tests. To foster the approach of alternatives to animal testing, a combination of computational models and in vitro substrate depletion bioassays (e.g., primary hepatocytes) can be used. One recently developed in vitro assay is the three-dimensional (3D) hepatic spheroid model from rainbow trout (Oncorhynchus mykiss). The aim of the present study was to evaluate the metabolic competence of the 3D spheroids from rainbow trout when exposed to pyrene, using 2 different sampling procedures (SP1 and SP2). The results were compared with previously published intrinsic clearance (CL) results from S9 fractions and primary hepatocyte assays. Extraction of pyrene using SP1 suggested that the spheroids had depleted 33% of the pyrene within 4 h of exposure, reducing to 91% after 30 h. However, when applying SP2 a substantial amount (36%) of the pyrene was bound to the exposure vial within 2 h, decreasing after 6 h of exposure. Formation of hydroxypyrene-glucuronide (OH-PYR-Glu) was obtained throughout the study, displaying the metabolic competence of the 3D spheroids. The 2 sampling procedures yielded different CLin vitro , where pyrene depletion using SP2 was very similar to published studies using primary hepatocytes. The 3D spheroids demonstrated reproducibile, log-linear biotransformation of pyrene and displayed formation of OH-PYR-Glu, indicating their metabolic competence for 30 h or more. Environ Toxicol Chem 2019;38:1738-1747. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Machine learning combined with non-targeted LC-HRMS analysis for a risk warning system of chemical hazards in drinking water: A proof of concept.

Guaranteeing clean drinking water to the global population is becoming more challenging, because of the cases of water scarcity across the globe, growing population, and increased chemical footprint of this population. Existing targeted strategies for hazard monitoring in drinking water are not adequate to handle such diverse and multidimensional stressors. In the current study, we have developed, validated, and tested a machine learning algorithm based on the data produced via non-targeted liquid chromatography coupled with high resolution mass spectrometry (LC-HRMS) for the identification of potential chemical hazards in drinking water. The machine learning algorithm consisted of a composite statistical model including an unsupervised component (i.e. principal component analysis PCA) and a supervised one (i.e. partial least square discrimination analysis PLS-DA). This model was trained using a training set of 20 drinking water samples previously tested via conventional suspect screening. The developed model was validated using a validation set of 20 drinking water samples of which 4 were spiked with 15 labeled standards at four different concentration levels. The model successfully detected all of the added analytes in the four spiked samples without producing any cases of false detection. The same validation set was processed via conventional trend analysis in order to cross validate the composite model. The results of cross validation showed that even though the conventional trend analysis approach produced a false positive detection rate of ≤5% the composite model outperformed that approach by producing zero cases of false detection. Additionally, the validated model went through an additional test with 42 extra drinking water samples from the same source for an unbiased examination of the model. Finally, the potentials and limitations of this approach were further discussed.

The effect of extraction methodology on the recovery and distribution of naphthenic acids of oilfield produced water.

Comprehensive chemical characterization of naphthenic acids (NAs) in oilfield produced water is a challenging task due to sample complexity. The recovery of NAs from produced water, and the corresponding distribution of detectable NAs are strongly influenced by sample extraction methodologies. In this study, we evaluated the effect of the extraction method on chemical space (i.e. the total number of chemicals present in a sample), relative recovery, and the distribution of NAs in a produced water sample. Three generic and pre-established extraction methods (i.e. liquid-liquid extraction (Lq), and solid phase extraction using HLB cartridges (HLB), and the combination of ENV+ and C8 (ENV) cartridges) were employed for our evaluation. The ENV method produced the largest number of detected NAs (134 out of 181) whereas the HLB and Lq methods produced 108 and 91 positive detections, respectively, in the tested produced water sample. For the relative recoveries, the ENV performed better than the other two methods. The uni-variate and multi-variate statistical analysis of our results indicated that the ENV and Lq methods explained most of the variance observed in our data. When looking at the distribution of NAs in our sample the ENV method appeared to provide a more complete picture of the chemical diversity of NAs in that sample. Finally, the results are further discussed.

Assessing sample extraction efficiencies for the analysis of complex unresolved mixtures of organic pollutants: A comprehensive non-target approach.

The comprehensive extraction recovery assessment of organic analytes from complex samples such as oil field produced water (PW) is a challenging task. A targeted approach is usually used for recovery and determination of compounds in these types of analysis. Here we suggest a more comprehensive and less biased approach for the extraction recovery assessment of complex samples. This method combines conventional targeted analysis with a non-targeted approach to evaluate the extraction recovery of complex mixtures. Three generic extraction methods: liquid-liquid extraction (Lq), and solid phase extraction using HLB cartridges (HLB), and the combination of ENV+ and C8 (ENV) cartridges, were selected for evaluation. PW was divided into three parts: non-spiked, spiked level 1, and spiked level 2 for analysis. The spiked samples were used for targeted evaluation of extraction recoveries of 65 added target analytes comprising alkanes, phenols, and polycyclic aromatic hydrocarbons, producing absolute recoveries. The non-spiked samples were used for the non-targeted approach, which used a combination of the F-ratio method and apex detection algorithm. Targeted analysis showed that the use of ENV cartridges and the Lq method performed better than use of HLB cartridges, producing absolute recoveries of 53.1 ± 15.2 for ENV and 46.8 ± 13.2 for Lq versus 19.7 ± 6.7 for HLB. These two methods appeared to produce statistically similar results for recoveries of analytes, whereas they were both different from the produced recoveries via the HLB method. The non-targeted approach captured unique features that were specific to each extraction method. This approach generated 26 unique features (mass spectral ions), which were significantly different between samples and were relevant in differentiating each extract from each method. Using a combination of these targeted and non-targeted methods we evaluated the extraction recoveries of the three extraction methods for analysis of PW.

Exploring the Potential of a Global Emerging Contaminant Early Warning Network through the Use of Retrospective Suspect Screening with High-Resolution Mass Spectrometry.

A key challenge in the environmental and exposure sciences is to establish experimental evidence of the role of chemical exposure in human and environmental systems. High resolution and accurate tandem mass spectrometry (HRMS) is increasingly being used for the analysis of environmental samples. One lauded benefit of HRMS is the possibility to retrospectively process data for (previously omitted) compounds that has led to the archiving of HRMS data. Archived HRMS data affords the possibility of exploiting historical data to rapidly and effectively establish the temporal and spatial occurrence of newly identified contaminants through retrospective suspect screening. We propose to establish a global emerging contaminant early warning network to rapidly assess the spatial and temporal distribution of contaminants of emerging concern in environmental samples through performing retrospective analysis on HRMS data. The effectiveness of such a network is demonstrated through a pilot study, where eight reference laboratories with available archived HRMS data retrospectively screened data acquired from aqueous environmental samples collected in 14 countries on 3 different continents. The widespread spatial occurrence of several surfactants (e.g., polyethylene glycols ( PEGs ) and C12AEO-PEGs ), transformation products of selected drugs (e.g., gabapentin-lactam, metoprolol-acid, carbamazepine-10-hydroxy, omeprazole-4-hydroxy-sulfide, and 2-benzothiazole-sulfonic-acid), and industrial chemicals (3-nitrobenzenesulfonate and bisphenol-S) was revealed. Obtaining identifications of increased reliability through retrospective suspect screening is challenging, and recommendations for dealing with issues such as broad chromatographic peaks, data acquisition, and sensitivity are provided.

Combining a Deconvolution and a Universal Library Search Algorithm for the Nontarget Analysis of Data-Independent Acquisition Mode Liquid Chromatography-High-Resolution Mass Spectrometry Results.

Nontarget analysis is considered one of the most comprehensive tools for the identification of unknown compounds in a complex sample analyzed via liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). Due to the complexity of the data generated via LC-HRMS, the data-dependent acquisition mode, which produces the MS2 spectra of a limited number of the precursor ions, has been one of the most common approaches used during nontarget screening. However, data-independent acquisition mode produces highly complex spectra that require proper deconvolution and library search algorithms. We have developed a deconvolution algorithm and a universal library search algorithm (ULSA) for the analysis of complex spectra generated via data-independent acquisition. These algorithms were validated and tested using both semisynthetic and real environmental data. A total of 6000 randomly selected spectra from MassBank were introduced across the total ion chromatograms of 15 sludge extracts at three levels of background complexity for the validation of the algorithms via semisynthetic data. The deconvolution algorithm successfully extracted more than 60% of the added ions in the analytical signal for 95% of processed spectra (i.e., 3 complexity levels multiplied by 6000 spectra). The ULSA ranked the correct spectra among the top three for more than 95% of cases. We further tested the algorithms with 5 wastewater effluent extracts for 59 artificial unknown analytes (i.e., their presence or absence was confirmed via target analysis). These algorithms did not produce any cases of false identifications while correctly identifying ∼70% of the total inquiries. The implications, capabilities, and the limitations of both algorithms are further discussed.

Analysis of stimulant drugs in the wastewater of five Nordic capitals.

Wastewater-based epidemiology is an efficient way to assess illicit drug use, complementing currently used methods retrieved from different data sources. The aim of this study is to compare stimulant drug use in five Nordic capital cities that include for the first time wastewater samples from Torshavn in the Faroe Islands. Currently there are no published reports that compare stimulant drug use in these Nordic capitals. All wastewater samples were analyzed using solid phase extraction and ultra-high performance liquid chromatography coupled to tandem mass spectrometry. The results were compared with data published by the European Monitoring Centre for Drugs and Drug Addiction based on illicit drugs in wastewater from over 50 European cities. Confirming previous reports, the results showed high amphetamine loads compared with other European countries. Very little apparent abuse of stimulant drugs was detected in Torshavn. Methamphetamine loads were the highest from Helsinki of the Nordic countries, indicating substantial fluctuations in the availability of the drug compared with previous studies. Methamphetamine loads from Oslo confirmed that the use continues to be high. Estimated cocaine use was found to be in the lower range compared with other cities in the southern and western part of Europe. Ecstasy and cocaine showed clear variations between weekdays and weekends, indicating recreational use. This study further demonstrates geographical trends in the stimulant drug market in five Nordic capitals, which enables a better comparison with other areas of the continent.

Determination of cannabinoid and synthetic cannabinoid metabolites in wastewater by liquid-liquid extraction and ultra-high performance supercritical fluid chromatography-tandem mass spectrometry.

For the first time, ultra-high performance supercritical fluid chromatography (UHPSFC) coupled to tandem mass spectrometry has been used to determine cannabinoid and synthetic cannabinoid residues in wastewater. Combined with a downscaled version of the classic liquid-liquid extraction, the proposed method allows for the quantification of Δ9-tetrahydrocannabinol, three of its major metabolites (the monohydroxylated, the dehydroxylated, and the carboxylated species) and four synthetic cannabinoid metabolites (from the JWH-series) at low ng L-1 levels. Limits of quantification are in the 1-59 ng L-1 range, with recovery between 62 and 122% in ultrapure water and between 59 and 138% in wastewater. The applicability of the developed methodology was confirmed by the analysis of real wastewater, where cannabis metabolites could be positively quantified in all the samples analyzed. It is, therefore, a fast and simple alternative to common solid-phase extraction-liquid chromatography-mass spectrometry procedures for the determination of these low polar substances in water. Copyright © 2017 John Wiley & Sons, Ltd.

A high-throughput solid-phase microextraction and post-loop mixing large volume injection method for water samples.

This article presents a novel approach for the analysis of 13 drugs in wastewater for use in wastewater-based epidemiology (WBE) studies. Sample preparation remains one of the principal bottlenecks in modern high-throughput analysis by ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). The proposed methodology is based on the micro-extraction of small volumes (1 ml) of wastewater using a HLB 96-well microplate and both large volume injection (LVI) and post-loop mixing injection (PLM). With this configuration, the limits of quantification (LOQ) were below the reported environmental concentrations of the target compounds in wastewater. Furthermore, both the complexity of collecting, transporting and storing the wastewater sample, sample preparation time, cost and amount of solvent used are all diminished, enhancing the suitability of this methodology for future WBE studies. A new workflow is also proposed in order to create a virtual specimen library bank for WBE by using high-resolution mass spectrometry (HRMS). The method was validated and the limits of quantification were between 0.2 and 6.3 ng L-1. The relative standard deviations (RSD) for a standard mixture at 200 ng L-1 (n = 6) was between 3.4 and 14.4% while the recoveries for the 13 drug target residues (DTR) were between 92 and 110%. The developed and validated method was finally successfully applied to 10 wastewater samples collected from Oslo, Norway.

Two stage algorithm vs commonly used approaches for the suspect screening of complex environmental samples analyzed via liquid chromatography high resolution time of flight mass spectroscopy: A test study.

LC-HR-QTOF-MS recently has become a commonly used approach for the analysis of complex samples. However, identification of small organic molecules in complex samples with the highest level of confidence is a challenging task. Here we report on the implementation of a two stage algorithm for LC-HR-QTOF-MS datasets. We compared the performances of the two stage algorithm, implemented via NIVA_MZ_Analyzer™, with two commonly used approaches (i.e. feature detection and XIC peak picking, implemented via UNIFI by Waters and TASQ by Bruker, respectively) for the suspect analysis of four influent wastewater samples. We first evaluated the cross platform compatibility of LC-HR-QTOF-MS datasets generated via instruments from two different manufacturers (i.e. Waters and Bruker). Our data showed that with an appropriate spectral weighting function the spectra recorded by the two tested instruments are comparable for our analytes. As a consequence, we were able to perform full spectral comparison between the data generated via the two studied instruments. Four extracts of wastewater influent were analyzed for 89 analytes, thus 356 detection cases. The analytes were divided into 158 detection cases of artificial suspect analytes (i.e. verified by target analysis) and 198 true suspects. The two stage algorithm resulted in a zero rate of false positive detection, based on the artificial suspect analytes while producing a rate of false negative detection of 0.12. For the conventional approaches, the rates of false positive detection varied between 0.06 for UNIFI and 0.15 for TASQ. The rates of false negative detection for these methods ranged between 0.07 for TASQ and 0.09 for UNIFI. The effect of background signal complexity on the two stage algorithm was evaluated through the generation of a synthetic signal. We further discuss the boundaries of applicability of the two stage algorithm. The importance of background knowledge and experience in evaluating the reliability of results during the suspect screening was evaluated.

Statistical Variable Selection: An Alternative Prioritization Strategy during the Nontarget Analysis of LC-HR-MS Data.

Liquid chromatography coupled to high resolution mass spectrometry (LC-HR-MS) has been one of the main analytical tools for the analysis of small polar organic pollutants in the environment. LC-HR-MS typically produces a large amount of data for a single chromatogram. The analyst is therefore required to perform prioritization prior to nontarget structural elucidation. In the present study, we have combined the F-ratio statistical variable selection and the apex detection algorithms in order to perform prioritization in data sets produced via LC-HR-MS. The approach was validated through the use of semisynthetic data, which was a combination of real environmental data and the artificially added signal of 31 alkanes in that sample. We evaluated the performance of this method as a function of four false detection probabilities, namely: 0.01, 0.02, 0.05, and 0.1%. We generated 100 different semisynthetic data sets for each F-ratio and evaluated that data set using this method. This design of experiment created a population of 30 000 true positives and 32 000 true negatives for each F-ratio, which was considered sufficiently large enough in order to fully validate this method for analysis of LC-HR-MS data. The effect of both the F-ratio and signal-to-noise ratio (S/N) on the performance of the suggested approach were evaluated through normalized statistical tests. We also compared this method to the pixel-by-pixel as well as peak list approaches. More than 92% of features present in the final feature list via the F-ratio method were also present in the conventional peak list generated by MZmine. However, this method was the only approach successful in the classification of samples, and thus prioritization, when compared to the other evaluated approaches. The application potential and limitations of the suggested method are discussed.

Increased levels of the oxidative stress biomarker 8-iso-prostaglandin F2α in wastewater associated with tobacco use.

Wastewater analysis has been demonstrated to be a complementary approach for assessing the overall patterns of drug use by a population while the full potential of wastewater-based epidemiology has yet to be explored. F2-isoprostanes are a prototype wastewater biomarker to study the cumulative oxidative stress at a community level. In this work, 8-iso-prostaglandin F2α (8-iso-PGF2α) was analysed in raw 24 h-composite wastewater samples collected from 4 Norwegian and 7 other European cities in 2014 and 2015. Using the same samples, biomarkers of alcohol (ethyl sulfate) and tobacco (trans-3'-hydroxycotinine) use were also analysed to investigate any possible correlation between 8-iso-PGF2α and the consumption of the two drugs. The estimated per capita daily loads of 8-iso-PGF2α in the 11 cities ranged between 2.5 and 9.9 mg/day/1000 inhabitants with a population-weighted mean of 4.8 mg/day/1000 inhabitants. There were no temporal trends observed in the levels of 8-iso-PGF2α, however, spatial differences were found at the inter-city level correlating to the degree of urbanisation. The 8-iso-PGF2α mass load was found to be strongly associated with that of trans-3'-hydroxycotinine while it showed no correlation with ethyl sulfate. The present study shows the potential for 8-iso-PGF2α as a wastewater biomarker for the assessment of community public health.

A two stage algorithm for target and suspect analysis of produced water via gas chromatography coupled with high resolution time of flight mass spectrometry.

Gas chromatography coupled with high resolution time of flight mass spectrometry (GC-HR-TOFMS) has gained popularity for the target and suspect analysis of complex samples. However, confident detection of target/suspect analytes in complex samples, such as produced water, remains a challenging task. Here we report on the development and validation of a two stage algorithm for the confident target and suspect analysis of produced water extracts. We performed both target and suspect analysis for 48 standards, which were a mixture of 28 aliphatic hydrocarbons and 20 alkylated phenols, in 3 produced water extracts. The two stage algorithm produces a chemical standard database of spectra, in the first stage, which is used for target and suspect analysis during the second stage. The first stage is carried out through five steps via an algorithm here referred to as unique ion extractor (UIE). During the first step the m/z values in the spectrum of a standard that do not belong to that standard are removed in order to produce a clean spectrum and then during the last step the cleaned spectrum is calibrated. The Dot-product algorithm, during the second stage, uses the cleaned and calibrated spectra of the standards for both target and suspect analysis. We performed the target analysis of 48 standards in all 3 samples via conventional methods, in order to validate the two stage algorithm. The two stage algorithm was demonstrated to be more robust, reliable, and less sensitive to the signal-to-noise ratio (S/N), when compared to the conventional method. The Dot-product algorithm showed lower potential in producing false positives compared to the conventional methods, when dealing with complex samples. We also evaluated the effect of the mass accuracy on the performances of Dot-product algorithm. Our results indicated the crucial importance of HR-MS data and the mass accuracy for confident suspect analysis in complex samples.