77 results found
Ng KT, Rapp-Wright H, Egli M, et al., 2020, High-throughput multi-residue quantification of contaminants of emerging concern in wastewaters enabled using direct injection liquid chromatography-tandem mass spectrometry., Journal of Hazardous Materials, Vol: 398, Pages: 1-14, ISSN: 0304-3894
A rapid quantitative method for 135 contaminants of emerging concern (CECs) in untreated wastewater enabled with direct injection liquid chromatography-tandem mass spectrometry is presented. All compounds were analysed within 5 min on a short biphenyl cartridge using only 10 μL of filtered sample per injection. Up to 76 compounds were monitored simultaneously during the gradient (including mostly two transitions per compound and stable isotope-labelled analogues) while yielding >10 data points per peak. Evaluation of seven solid phase extraction sorbents showed no advantage for wastewater matrix removal. Excellent linearity, range, accuracy and precision was achieved for most compounds. Matrix effects were <11 % and detection limits were <30 ng L-1 on average. Application to untreated wastewater samples from three wastewater treatment works in the UK, USA and Mexico, enabled quantification of 56 compounds. Banned and EU 'watch-list' substances are critically discussed, including pesticides, macrolide antibiotics, diclofenac, illicit drugs as well as multiple pharmaceuticals and biocides. This high-throughput method sets a new standard for the speedy and confident determination of over a hundred CECs in wastewater at the part-per-trillion level, as demonstrated by performing over 260 injections per day.
Irlam R, Hughes C, Parkin M, et al., 2020, Trace multi-class organic explosives analysis in complex matrices enabled using LEGO®-inspired clickable 3D-printed solid phase extraction block arrays, Journal of Chromatography A, Vol: 1629, ISSN: 0021-9673
The development of a new, lower cost method for trace explosives recovery from complex samples is presented using miniaturised, click-together and leak-free 3D-printed solid phase extraction (SPE) blocks. For the first time, a large selection of ten commercially available 3D printing materials were comprehensively evaluated for practical, flexible and multiplexed SPE using stereolithography (SLA), PolyJet and fused deposition modelling (FDM) technologies. Miniaturised single-piece, connectable and leak-free block housings inspired by Lego® were 3D-printed in a methacrylate-based resin, which was found to be most stable under different aqueous/organic solvent and pH conditions, using a cost-effective benchtop SLA printer. Using a tapered SPE bed format, frit-free packing of multiple different commercially available sorbent particles was also possible. Coupled SPE blocks were then shown to offer efficient analyte enrichment and a potentially new approach to improve the stability of recovered analytes in the field when stored on the sorbent, rather than in wet swabs. Performance was measured using liquid chromatography-high resolution mass spectrometry and was better, or similar, to commercially available coupled SPE cartridges, with respect to recovery, precision, matrix effects, linearity and range, for a selection of 13 peroxides, nitramines, nitrate esters and nitroaromatics. Mean % recoveries from dried blood, oil residue and soil matrices were 79 ± 24%, 71 ± 16% and 76 ± 24%, respectively. Excellent detection limits between 60 fg for 3,5-dinitroaniline to 154 pg for nitroglycerin were also achieved across all matrices. To our knowledge, this represents the first application of 3D printing to SPE of so many organic compounds in complex samples. Its introduction into this forensic method offered a low-cost, ‘on-demand’ solution for selective extraction of explosives, enhanced flexibility for multiplexing/design alteration and po
Sheikholeslami MN, Gómez-Canela C, Barron LP, et al., 2020, Untargeted metabolomics changes on Gammarus pulex induced by Propranolol, Triclosan, and Nimesulide pharmaceutical drugs, Chemosphere, ISSN: 0045-6535
The presence of pharmaceuticals and personal care products (PPCPs) in natural water resources due to incomplete removal in Wastewater Treatment Plants (WWTPs) is a serious environmental concern at present. In this work, the effects of three pharmaceuticals (propranolol, triclosan, and nimesulide) on Gammarus pulex metabolic profiles at different doses and times of exposure have been investigated by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). The complex data sets generated in the different exposure experiments were analyzed with the ROIMCR procedure, based on the selection of the MS regions of interest (ROI) data and on their analysis by the Multivariate Curve-Resolution Alternating Least Squares (MCR-ALS) chemometrics method. This approach, allowed the resolution and identification of the metabolites present in the analyzed samples, as well as the estimation of their concentration changes due to the exposure experiments. ANOVA Simultaneous Component Analysis (ASCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were then conducted to assess the changes in the concentration of the metabolites for the three pharmaceuticals at the different conditions of exposure. The three tested pharmaceuticals changed the concentrations of metabolites, which were related to different KEGG functional classes. These changes summarize the biochemical response of Gammarus pulex to the exposure by the three investigated pharmaceuticals. Possible pathway alterations related to protein synthesis and oxidative stress were observed in the concentration of identified metabolites.
Ng K, Rapp-Wright H, Egli M, et al., 2020, HIGH-THROUGHPUT ANALYSIS OF INTERNATIONAL WASTEWATERS FOR CONTAMINANTS OF EMERGING CONCERN ENABLED USING DIRECT INJECTION LIQUID CHROMATOGRAPHY-TANDEM MASS SPECTROMETRY, Journal of Hazardous Materials, ISSN: 0304-3894
A rapid quantitative method for 135 contaminants of emerging concern (CECs) in untreated wastewater enabled with direct injection liquid chromatography-tandem mass spectrometry is presented. All compounds were analysed within 5 min on a short biphenyl cartridge using only 10 μL of filtered sample per injection. Up to 76 compounds were monitored simultaneously during the gradient (including mostly two transitions per compound and stable isotope-labelled analogues) while yielding >10 data points per peak. Evaluation of seven solid phase extraction sorbents showed no advantage for wastewater matrix removal. Excellent linearity, range, accuracy and precision was achieved for most compounds. Matrix effects were <11% and detection limits were <30 ng L -1 on average. Application to untreated wastewater samples from three wastewater treatment works in the UK, USA and Mexico, enabled quantification of 56 compounds. Banned and EU ‘watch-list’ substances are critically discussed, including pesticides, macrolide antibiotics, diclofenac, illicit drugs as well as multiple pharmaceuticals and biocides. This high-throughput method sets a new standard for the speedy and confident determination of over a hundred CECs in wastewater at the part-per-trillion level, as demonstrated by performing over 260 injections per day .
Miller TH, Ng KT, Bury ST, et al., 2019, Biomonitoring of pesticides, pharmaceuticals and illicit drugs in a freshwater invertebrate to estimate toxic or effect pressure, Environment International, Vol: 129, Pages: 595-606, ISSN: 0160-4120
Multiple classes of environmental contaminants have been found in aquatic environments, globally. Understanding internalised concentrations in the organism could further improve the risk assessment process. The present study is concerned with the determination of several contaminant classes (107 compounds) in Gammarus pulex collected from 15 sites covering 5 river catchments across Suffolk, UK. Quantitative method performance was acceptable for 67 compounds including pharmaceuticals, pesticides, illicit drugs and drugs of abuse. A total of 56 compounds were detectable and ranged from <LOQ to 45.3 ng g-1, with cocaine and lidocaine being the most frequently detected compounds present in all biota samples (n=66). For surface water, 50 compounds were detectable and ranged from <LOQ to 382.2 ng L-1. Additionally, some pesticides currently not approved for use were detected, including fenuron that reached a maximum of 16.1 ng g-1. The internal concentrations of pesticides were used to estimate toxic pressure which showed that for the measured pesticides toxic pressure was low ranging from logTU ≤-7 to ≤-2. This methodology was extended to pharmaceuticals and drugs of abuse in a novel approach that proposed the use of pharmacological data (human therapeutic plasma concentrations) to estimate the likelihood of an effect (or effect pressure) to occur based on the internal exposure of the organism. The quantified effect pressure ranged from logEU ≤-9 to ≤1 with haloperidol showing the largest likelihood for an effect. The approach showed that several pharmaceuticals have the potential to elicit effects but further investigation surrounding thresholds for effects would be required. This new approach presented showed potential to be used to improve risk assessment for pharmaceuticals in the environment.
Irlam RC, Parkin MC, Brabazon DP, et al., 2019, Improved determination of femtogram-level organic explosives in multiple matrices using dual-sorbent solid phase extraction and liquid chromatography-high resolution accurate mass spectrometry, TALANTA, Vol: 203, Pages: 65-76, ISSN: 0039-9140
Identification and trace quantification of multiple explosives residues, their precursors and transformation products in complex samples remains very challenging. For solid phase extraction (SPE) and liquid chromatography-high resolution accurate mass spectrometry-based methods (LC-HRMS), interferences from co-extracted matrix components can significantly affect recovery during extraction and/or detector signal. The aim of this work was to develop a new, improved and more generalisable extraction approach to trace explosives analysis in a range of matrices using dual-sorbent SPE with LC-HRMS. Recoveries of 44 organic explosives from model solutions were optimised and compared for seven different sorbents (Oasis HLB, HyperSep Retain PEP and Isolute ENV+, HyperSep SAX, HyperSep NH2, Strata Alumina-N and Bond Elut CN). On average, Oasis HLB and Isolute ENV+ yielded the best recoveries (>80 %). For three sorbents, mean recoveries remained ≤1 %, which made them potentially suitable for matrix removal when used in series with more analyte-selective sorbents. To evaluate matrix effects, a range of aqueous (river- and wastewater), solid (soil), dirty (road sign swabs), oily (oven hood swabs) and biological (dried blood) samples were selected based on complexity and forensic relevance. With the exception of river water, matrix effects were lowest using dual-sorbent SPE, with little/no compromise in recovery. Quantitative method performance assessment is presented for 14 selected explosives, representative of different classes, molecular weights and volatilities, and across three different matrices (i.e. untreated wastewater, cooking oil residues and dried blood). Limits of detection improved by ~10-fold over a single sorbent approach, enabling fg sensitivity in many cases. Finally, application of the method to untreated wastewater enabled detection of new explosives traces for the first time, which could be used to help identify clandestine manufacture or sources of en
Munro K, Martins CPB, Loewenthal M, et al., 2019, Evaluation of combined sewer overflow impacts on short-term pharmaceutical and illicit drug occurrence in a heavily urbanised tidal river catchment (London, UK), Science of the Total Environment, Vol: 657, Pages: 1099-1111, ISSN: 0048-9697
The occurrence of pharmaceutical and illicit drug residues potentially arising from combined sewer overflows (CSOs) in the Central London portion of the Thames Estuary is presented. Approximately 39 million tonnes of untreated sewage enter the River Thames at 57 CSO points annually. Differential analysis of influents and effluents in a major wastewater treatment plant identified seven potential drug-related CSO markers based on removal rates. Three were present in influent at concentrations >1 μg L−1 (caffeine, cocaine and benzoylecgonine). During dry weather, analysis of hourly samples of river water revealed relatively consistent concentrations for most drugs, including CSO markers, over a tidal cycle. River water was monitored over a week in January and July and then daily across six consecutive weeks in November/December 2014. Out of 31 compounds monitored, 27 drug residues were determined in the River Thames and, combined, ranged between ~1000–3500 ng L−1. Total drug concentration generally declined during extended periods of drier weather. For CSO markers, short-term increases in caffeine, cocaine and benzoylecgonine concentration were observed ~24 h after CSO events (especially those occurring at low tide) and generally within one order of magnitude. Timings of elevated occurrence also correlated well with ammonium ion and dissolved oxygen data following CSOs. This work also represents an important study of pharmaceutical occurrence before a major 'super Sewer’ infrastructure upgrade in London aiming to reduce CSOs by 95%.
Gallidabino MD, Irlam RC, Salt MC, et al., 2019, Targeted and non-targeted forensic profiling of black powder substitutes and gunshot residue using gradient ion chromatography – high resolution mass spectrometry (IC-HRMS), ANALYTICA CHIMICA ACTA, Vol: 1072, Pages: 1-14, ISSN: 0003-2670
A novel and simplified gradient IC-HRMS approach is presented in this work for forensic profiling of ionic energetic material residues, including low-order explosives and gunshot residue (GSR). This new method incorporated ethanolic eluents to facilitate direct coupling of IC and HRMS without auxiliary post-column infusion pumps that are traditionally used to assist with gas phase transfer. Ethanolic eluents also enabled better integration with an in-service protocol for direct analysis of high-order organic explosives by ICHRMS, without requiring solvent exchange before injection. Excellent method performance was achieved, enabling both full scan qualitative and quantitative analysis, as required. In particular, linearity for 19 targeted compounds yielded R2 > 0.99 across several orders of magnitude, with trace analysis possible at the low-mid pg level. Reproducibility and mass accuracies were also excellent, with peak area %RSDs < 10 %, tR %RSDs < 0.4% and δm/z < 3 ppm. The method was applied to targeted analysis of latent fingermarks and swabbed hand sweat samples to determine contact with a black-powder substitute containing nitrate, benzoate and perchlorate. When combined with principal component analysis (PCA), the effect of time since handling on recorded signals could be interpreted further in order to support forensic investigations. In a second, non-targeted application, PCA using full scan IC-HRMS data enabled classification of GSR from three different types of ammunition. An additional 20 markers of GSR were tentatively identified in silico, in addition to the 15 anions detected during targeted analysis. This new approach therefore streamlines and adds consistency and flexibility to forensic analysis of ionic energetic material. Furthermore, it also has implications for targeted, non-targeted and suspect screening applications in other fields by expanding the separation space to low molecular weight inorganic and organic anions.
Shimko KM, O'Brien JW, Barron L, et al., 2019, A pilot wastewater-based epidemiology assessment of anabolic steroid use in Queensland, Australia, Drug Testing And Analysis, Vol: 11, Pages: 937-949, ISSN: 1942-7603
Anabolic‐androgenic steroids are synthetic compounds prohibited due to their performance enhancing characteristics. The use of these substances is known to cause health‐related issues, which highlights the importance of being able to evaluate the scale of consumption by the general population. However, most available research on the analysis of anabolic steroids is focussed on animals and athletes in connection with doping. The potential of wastewater‐based epidemiology as an intelligence tool for the assessment of community level use of anabolic steroids is presented herein. A liquid chromatography tandem mass spectrometry method was developed for the analysis of ten anabolic‐androgenic steroids and 14 endogenous hormones in influent wastewater. The validated method was applied to sixteen 24‐hour composite wastewater influent samples that were collected over a period of five years from two wastewater treatment plants in Queensland, Australia. Nine investigated compounds were found to be present at concentrations between 14‐611 ng L‐1 which translated into 3‐104 mg excreted per 1000 individuals per day. It was concluded that the developed analytical method is suitable for the analysis of AAS in wastewater matrix. Additionally, both the inclusion of metabolites and further investigation into deconjugation by enzymatic hydrolysis would aid in understanding and evaluating community anabolic steroid use. For the first time, this study presents the application of wastewater‐based epidemiology on anabolic‐androgenic steroids in Australia.
Gallidabino MD, Barron LP, Weyermann C, et al., 2019, Quantitative profile-profile relationship (QPPR) modelling, A novel machine learning approach to predict and associate chemical characteristics of unspent ammunition from gunshot residue (GSR), Vol: 144, Pages: 1128-1139, ISSN: 0003-2654
Evidence association in forensic cases involving gunshot residue (GSR) remains very challenging. Herein, a new in silico approach, called quantitative profile-profile relationship (QPPR) modelling, is reported. This is based on the application of modern machine learning techniques to predict the pre-discharge chemical profiles of selected ammunition components from those of the respective post-discharge GSR. The obtained profiles can then be compared with one another and/or with other measured profiles to make evidential links during forensic investigations. In particular, the approach was optimised and successfully tested for the prediction of GC-MS profiles of smokeless powders (SLPs) from organic GSR in spent cases, for nine ammunition types. Results showed a high degree of similarity between predicted and experimentally measured profiles, after adequate combination and evaluation of fourteen machine learning techniques (median correlation of 0.982). Areas under the curve (AUCs) of 0.976 and 0.824 were observed after receiver operating characteristic (ROC) analysis of the results obtained in the comparisons between predicted-predicted and predicted-measured profiles, respectively, in the specific case that the ammunition types of interest were excluded from the training dataset (i.e., extrapolation). Furthermore, AUCs of 0.962 and 0.894 were observed in interpolation mode. These values were close to those of the comparison of the measured SLP profiles between themselves (AUC = 0.998), demonstrating excellent potential to correctly associate evidence in a number of different forensic scenarios. This work represents the first time that a quantitative approach has successfully been applied to associate a GSR to a specific ammunition.
Barron LP, 2019, Spatio-temporal assessment of illicit drug use at large scale: evidence from seven years of international wastewater monitoring, Addiction, ISSN: 0965-2140
Background and aims: Wastewater-based epidemiology is an additional indicator of drug use that is gaining reliability to complement the current established panel of indicators. The aims of this study were to (i) assess spatial and temporal trends of population-normalized mass loads of benzoylecgonine, amphetamine, methamphetamine, and 3,4-methylenedioxymethamphetamine (MDMA) in raw wastewater over seven years (2011-2017); (ii) address overall drug use by estimating the average number of combined doses consumed per day in each city. Design: Analysis of daily composite raw wastewater samples collected over one week per year from 2011 to 2017. Setting: Catchment areas of 143 wastewater treatment plants in 120 cities in 37 countries. Methods: Parent substances (amphetamine, methamphetamine, and MDMA) and the metabolites of cocaine (benzoylecgonine) and of Δ9-tetrahydrocannabinol (11-nor-9-carboxy-Δ9-tetrahydrocannabinol) were measured in wastewater using liquid chromatography-tandem mass spectrometry. Daily mass loads (mg/day) were normalized to catchment population (mg/1000 people/day) and converted to the number of combined doses consumed per day. Spatial differences were assessed worldwide, and temporal trends were discerned at European level by comparing 2011-2013 drug loads versus 2014-2017 loads. Findings: Benzoylecgonine was the stimulant metabolite detected at higher loads in southern and western Europe, and amphetamine, MDMA, and methamphetamine in the east and the north-centre of Europe. In other continents methamphetamine showed the highest levels in the United States and Australia and benzoylecgonine in South America. Over the reporting period, benzoylecgonine loads increased in general across Europe, amphetamine and methamphetamine levels fluctuated and MDMA underwent an intermittent upsurge. Conclusions: The analysis of wastewater to quantify drug loads provides updated and objective estimates of drug use that globally correspond to prevalence
Miller TH, Gallidabino MD, MacRae JI, et al., 2019, Prediction of bioconcentration factors in fish and invertebrates using machine learning, Science of the Total Environment, Vol: 648, Pages: 80-89, ISSN: 0048-9697
The application of machine learning has recently gained interest from ecotoxicological fields for its ability to model and predict chemical and/or biological processes, such as the prediction of bioconcentration. However, comparison of different models and the prediction of bioconcentration in invertebrates has not been previously evaluated. A comparison of 24 linear and machine learning models is presented herein for the prediction of bioconcentration in fish and important factors that influenced accumulation identified. R2 and root mean square error (RMSE) for the test data (n = 110 cases) ranged from 0.23–0.73 and 0.34–1.20, respectively. Model performance was critically assessed with neural networks and tree-based learners showing the best performance. An optimised 4-layer multi-layer perceptron (14 descriptors) was selected for further testing. The model was applied for cross-species prediction of bioconcentration in a freshwater invertebrate, Gammarus pulex. The model for G. pulex showed good performance with R2 of 0.99 and 0.93 for the verification and test data, respectively. Important molecular descriptors determined to influence bioconcentration were molecular mass (MW), octanol-water distribution coefficient (logD), topological polar surface area (TPSA) and number of nitrogen atoms (nN) among others. Modelling of hazard criteria such as PBT, showed potential to replace the need for animal testing. However, the use of machine learning models in the regulatory context has been minimal to date and is critically discussed herein. The movement away from experimental estimations of accumulation to in silico modelling would enable rapid prioritisation of contaminants that may pose a risk to environmental health and the food chain.
Aliferi A, Ballard D, Gallidabino MD, et al., 2018, DNA methylation-based age prediction using massively parallel sequencing data and multiple machine learning models, Forensic Science International-Genetics, Vol: 37, Pages: 215-226, ISSN: 1872-4973
The field of DNA intelligence focuses on retrieving information from DNA evidence that can help narrow down large groups of suspects or define target groups of interest. With recent breakthroughs on the estimation of geographical ancestry and physical appearance, the estimation of chronological age comes to complete this circle of information. Recent studies have identified methylation sites in the human genome that correlate strongly with age and can be used for the development of age-estimation algorithms. In this study, 110 whole blood samples from individuals aged 11-93 years were analysed using a DNA methylation quantification assay based on bisulphite conversion and massively parallel sequencing (Illumina MiSeq) of 12 CpG sites. Using this data, 17 different statistical modelling approaches were compared based on root mean square error (RMSE) and a Support Vector Machine with polynomial function (SVMp) model was selected for further testing. For the selected model (RMSE = 4.9 years) the mean average error (MAE) of the blind test (n = 33) was calculated at 4.1 years, with 52% of the samples predicting with less than 4 years of error and 86% with less than 7 years. Furthermore, the sensitivity of the method was assessed both in terms of methylation quantification accuracy and prediction accuracy in the first validation of this kind. The described method retained its accuracy down to 10 ng of initial DNA input or ~2 ng bisulphite PCR input. Finally, 34 saliva samples were analysed and following basic normalisation, the chronological age of the donors was predicted with less than 4 years of error for 50% of the samples and with less than 7 years of error for 70%.
Gallidabino MD, Hamdan L, Murphy B, et al., 2018, Suspect screening of halogenated carboxylic acids in drinking water using ion exchange chromatography – high resolution (Orbitrap) mass spectrometry (IC-HRMS), TALANTA, Vol: 178, Pages: 57-68, ISSN: 0039-9140
Retrospective in silico screening of analytical data for the identification of new or emerging disinfection by-products in drinking waters could be useful to assess quality and potential hazards, as well as help implement mitigation procedures more rapidly. Herein, the first study coupling ion exchange chromatography (IC) with high resolution mass spectrometry (HRMS) for the determination of halogenated carboxylic acid disinfectant by-products is reported. Separation was achieved using a Metrohm A Supp 5 column and a Na2CO3/NaHCO3 gradient eluent from 1/0.31 to 10/3.1 mM. A variety of solid phase extraction (SPE) sorbents were tested for added selectivity to organic ions and Isolute ENV+ cartridges were selected because of their best overall extraction performance. Method LODs were in the μg L−1 concentration range, with R2 ≥ 0.99 for all the analytes, and isobaric ions could be easily discriminated using HRMS. The method was applied to municipal drinking water. Targeted quantitative analysis revealed the presence of 10 haloacetic acids at levels not exceeding the limits set by WHO and USEPA. Furthermore, suspect screening for additional halogenated carboxylic acids via retrospective HRMS data analysis also indicated the presence of other iodinated HAAs and chlorinated propionic acids, of which one (i.e. monochloropropionic acid) is discussed here for the first time. Most importantly, several potential suspects could be eliminated from further consideration through HRMS data analysis alone. To our knowledge, this represents the first time that a retrospective IC-HRMS screen of halogenated carboxylic acids in drinking water has been reported.
McEneff GL, Murphy B, Webb T, et al., 2018, Sorbent Film-Coated Passive Samplers for Explosives Vapour Detection Part A, Materials Optimisation and Integration with Analytical Technologies, Vol: 8, ISSN: 2045-2322
A new thin-film passive sampler is presented as a low resource dependent and discrete continuous monitoring solution for explosives-related vapours. Using 15 mid-high vapour pressure explosives-related compounds as probes, combinations of four thermally stable substrates and six film-based sorbents were evaluated. Meta-aramid and phenylene oxide-based materials showed the best recoveries from small voids (~70%). Analysis was performed using liquid chromatography-high resolution accurate mass spectrometry which also enabled tentative identification of new targets from the acquired data. Preliminary uptake kinetics experiments revealed plateau concentrations on the device were reached between 3–5 days. Compounds used in improvised explosive devices, such as triacetone triperoxide, were detected within 1 hour and were stably retained by the sampler for up to 7 days. Sampler performance was consistent for 22 months after manufacture. Lastly, its direct integration with currently in-service explosives screening equipment including ion mobility spectrometry and thermal desorption mass spectrometry is presented. Following exposure to several open environments and targeted interferences, sampler performance was subsequently assessed and potential interferences identified. High-security building and area monitoring for concealed explosives using such cost-effective and discrete passive samplers can add extra assurance to search routines while minimising any additional burden on personnel or everyday site operation.
McEneff GL, Richardson A, Webb T, et al., 2018, Sorbent Film-Coated Passive Samplers for Explosives Vapour Detection Part B, Deployment in Semi-Operational Environments and Alternative Applications, Vol: 8, ISSN: 2045-2322
The application of new sorbent-film coated passive samplers for capture of bulk commercial and military explosives vapours in operationally relevant spaces such as luggage, rooms, vehicles and shipping containers is presented. Samplers were easily integrated with in-service detection technologies with little/no sample preparation required. Ethylene glycol dinitrate (EGDN) was detected within 4 h in a container holding a suitcase packed with 0.2 kg Perunit 28E. Within a 22,000 dm3 room, 1 kg of concealed Seguridad was detected within 24 h and in an adjoining room within 7 days. Exposed samplers also successfully captured components of 1 kg TNT after 72 h and 1 kg concealed Perunit 28E after 6 h in both a furnished room and a large, partially filled shipping container. For the latter, samplers captured detectable residues outside the container after 24 h and were stable during wet weather for 72 h. A one-week trial at three operationally relevant venues including a university, a theatre and a government building revealed a nuisance positive rate of <1.4% (n = 72). Finally, two alternative applications are presented for extraction of liquid samples and use a particulate contact swab showing flexibility for a range of different search activities.
Miller TH, Bury NR, Owen SF, et al., 2018, A review of the pharmaceutical exposome in aquatic fauna, ENVIRONMENTAL POLLUTION, Vol: 239, Pages: 129-146, ISSN: 0269-7491
Pharmaceuticals have been considered ‘contaminants of emerging concern’ for more than 20 years. In that time, many laboratory studies have sought to identify hazard and assess risk in the aquatic environment, whilst field studies have searched for targeted candidates and occurrence trends using advanced analytical techniques. However, a lack of a systematic approach to the detection and quantification of pharmaceuticals has provided a fragmented literature of serendipitous approaches. Evaluation of the extent of the risk for the plethora of human and veterinary pharmaceuticals available requires the reliable measurement of trace levels of contaminants across different environmental compartments (water, sediment, biota - of which biota has been largely neglected). The focus on pharmaceutical concentrations in surface waters and other exposure media have therefore limited both the characterisation of the exposome in aquatic wildlife and the understanding of cause and effect relationships. Here, we compile the current analytical approaches and available occurrence and accumulation data in biota to review the current state of research in the field. Our analysis provides evidence in support of the ‘Matthew Effect’ and raises critical questions about the use of targeted analyte lists for biomonitoring. We provide six recommendations to stimulate and improve future research avenues.
Mollerup CB, Mardal M, Dalsgaard PW, et al., 2018, Prediction of Collision Cross Section and Retention Time for Broad Scope Screening in Gradient Reversed-Phase Liquid Chromatography-Ion Mobility-High Resolution Accurate Mass Spectrometry, Journal of Chromatography A, ISSN: 0021-9673
Exact mass, retention time (RT), and collision cross section (CCS) are used as identification parameters in liquid chromatography coupled to ion mobility high resolution accurate mass spectrometry (LC-IM-HRMS). Targeted screening analyses are now more flexible and can be expanded for suspect and non-targeted screening. These allow for tentative identification of new compounds, and in-silico predicted reference values are used for improving confidence and filtering false-positive identifications. In this work, predictions of both RT and CCS values are performed with machine learning using artificial neural networks (ANNs). Prediction was based on molecular descriptors, 827 RTs, and 357 CCS values from pharmaceuticals, drugs of abuse, and their metabolites. ANN models for the prediction of RT or CCS separately were examined, and the potential to predict both from a single model was investigated for the first time. The optimized combined RT-CCS model was a four-layered multi-layer perceptron ANN, and the 95th prediction error percentiles were within 2 minutes RT error and 5% relative CCS error for the external validation set (n = 36) and the full RT-CCS dataset (n = 357). 88.6% (n = 733) of predicted RTs were within 2 minutes error for the full dataset. Overall, when using 2 minutes RT error and 5% relative CCS error, 91.9% (n = 328) of compounds were retained, while 99.4% (n = 355) were retained when using at least one of these thresholds. This combined prediction approach can therefore be useful for rapid suspect/non-targeted screening involving HRMS, and will support current workflows.
van Nuijs ALN, Lai FY, Been F, et al., 2018, Multi-year interlaboratory exercises for the analysis of illicit drugs and metabolites in wastewater, development of a quality control system, ISSN: 0165-9936
This study presents the development of a worldwide inter-laboratory testing scheme for the analysis of seven illicit drug residues in different matrices (standard solutions, tap- and wastewater). By repeating this exercise for six years with participation of 37 laboratories from 25 countries, the testing scheme was substantially improved based on experiences gained across the years (e.g. matrix type, sample conditions, spiking levels). From the exercises (pre-)analytical issues (e.g. pH adjustment, filtration), were revealed for some analytes which resulted in formulation of best-practice protocols, both for inter-laboratory setup and analytical procedures. The results illustrate the effectiveness of the inter-laboratory testing scheme in assessing laboratory performance in the framework of illicit drug analysis in wastewater. The exercise proved that measurements of laboratories were of high quality (>80% satisfactory results for 6 out of 7 analytes) and that analytical follow-up is important to assist laboratories in improving robustness of wastewater-based epidemiology results.
Rapp-Wright H, McEneff G, Murphy B, et al., 2017, Suspect screening and quantification of trace organic explosives in wastewater using solid phase extraction and liquid chromatography-high resolution accurate mass spectrometry, Journal of Hazardous Materials, Vol: 329, Pages: 11-21, ISSN: 0304-3894
The first comprehensive assessment of 34 solid phase extraction sorbents is presented for organic explosive residues in wastewater prior to analysis with liquid chromatography-high resolution accurate mass spectrometry (LC-HRMS). A total of 18 explosives were selected including nitramines, nitrate esters, nitroaromatics and organic peroxides. Three polymeric divinylbenzene-based sorbents were found to be most suitable and one co-polymerised with n-vinyl pyrrolidone offered satisfactory recoveries for 14 compounds in fortified wastewater (77–124%). Limits of detection in matrix ranged from 0.026–23 μg L−1 with R2 ≥ 0.98 for most compounds. The method was applied to eight 24-h composite wastewater samples from a London wastewater works and one compound, 2,4-dinitrotoluene, was determined over five days between 332 and 468 g day−1 (225–303 ng L−1). To further exploit the suspect screening capability, 17 additional explosives, precursors and transformation products were screened in spiked wastewater samples. Of these, 14 were detected with recoveries from 62 to 92%, highlighting the broad applicability of the method. To our knowledge, this represents the first screen of explosives-related compounds in wastewater from a major European city. This method also allows post-analysis detection of new or emerging compounds using full-scan HRMS datasets to potentially identify and locate illegal manufacture of explosives via wastewater analysis.
Miller TH, Bury NR, Owen SF, et al., 2017, Uptake, biotransformation and elimination of selected pharmaceuticals in a freshwater invertebrate measured using liquid chromatography tandem mass spectrometry, Chemosphere, Pages: 389-400, ISSN: 0045-6535
Methods were developed to assess uptake and elimination kinetics in Gammarus pulex of nine pharmaceuticals (sulfamethazine, carbamazepine, diazepam, temazepam, trimethoprim, warfarin, metoprolol, nifedipine and propranolol) using targeted LC-MS/MS to determine bioconcentration factors (BCFs) using a 96 h toxicokinetic exposure and depuration period. The derived BCFs for these pharmaceuticals did not trigger any regulatory thresholds and ranged from 0-73 L kg−1 (sulfamethazine showed no bioconcentration). Metabolism of chemicals can affect accurate BCF determination through parameterisation of the kinetic models. The added selectivity of LC-MS/MS allowed us to develop confirmatory methods to monitor the biotransformation of propranolol, carbamazepine and diazepam in G. pulex. Varying concentrations of the biotransformed products; 4-hydroxypropranolol sulphate, carbamazepine-10,11-epoxide, nordiazepam, oxazepam and temazepam were measured following exposure of the precursor compounds. For diazepam, the biotransformation product nordiazepam was present at higher concentrations than the parent compound at 94 ng g−1 dw. Overall, the results indicate that pharmaceutical accumulation is low in these freshwater amphipods, which can potentially be explained by the rapid biotransformation and excretion.
Vidaki A, Ballard D, Aliferi A, et al., 2017, DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing, Forensic Science International-Genetics, Pages: 225-236, ISSN: 1872-4973
The ability to estimate the age of the donor from recovered biological material at a crime scene can be of substantial value in forensic investigations. Aging can be complex and is associated with various molecular modifications in cells that accumulate over a person's lifetime including epigenetic patterns. The aim of this study was to use age-specific DNA methylation patterns to generate an accurate model for the prediction of chronological age using data from whole blood. In total, 45 age-associated CpG sites were selected based on their reported age coefficients in a previous extensive study and investigated using publicly available methylation data obtained from 1156 whole blood samples (aged 2-90 years) analysed with Illumina's genome-wide methylation platforms (27K/450K). Applying stepwise regression for variable selection, 23 of these CpG sites were identified that could significantly contribute to age prediction modelling and multiple regression analysis carried out with these markers provided an accurate prediction of age (R2 =0.92, mean absolute error (MAE)=4.6 years). However, applying machine learning, and more specifically a generalised regression neural network model, the age prediction significantly improved (R2 =0.96) with a MAE=3.3 years for the training set and 4.4 years for a blind test set of 231 cases. The machine learning approach used 16 CpG sites, located in 16 different genomic regions, with the top 3 predictors of age belonged to the genes NHLRC1, SCGN and CSNK1D. The proposed model was further tested using independent cohorts of 53 monozygotic twins (MAE=7.1 years) and a cohort of 1011 disease state individuals (MAE=7.2 years). Furthermore, we highlighted the age markers' potential applicability in samples other than blood by predicting age with similar accuracy in 265 saliva samples (R2 =0.96) with a MAE=3.2 years (training set) and 4.0 years (blind test). In an attempt to create a sensitive and accurate age prediction test, a next gener
Ryu Y, Barceló D, Barron LP, et al., 2016, Comparative measurement and quantitative risk assessment of alcohol consumption through wastewater-based epidemiology, An international study in 20 cities, Vol: 565, Pages: 977-983, ISSN: 0048-9697
Quantitative measurement of drug consumption biomarkers in wastewater can provide objective information on community drug use patterns and trends. This study presents the measurement of alcohol consumption in 20 cities across 11 countries through the use of wastewater-based epidemiology (WBE), and reports the application of these data for the risk assessment of alcohol on a population scale using the margin of exposure (MOE) approach. Raw 24-h composite wastewater samples were collected over a one-week period from 20 cities following a common protocol. For each sample a specific and stable alcohol consumption biomarker, ethyl sulfate (EtS) was determined by liquid chromatography coupled to tandem mass spectrometry. The EtS concentrations were used for estimation of per capita alcohol consumption in each city, which was further compared with international reports and applied for risk assessment by MOE. The average per capita consumption in 20 cities ranged between 6.4 and 44.3 L/day/1000 inhabitants. An increase in alcohol consumption during the weekend occurred in all cities, however the level of this increase was found to differ. In contrast to conventional data (sales statistics and interviews), WBE revealed geographical differences in the level and pattern of actual alcohol consumption at an inter-city level. All the sampled cities were in the “high risk” category (MOE
Barron LP, McEneff GL, 2016, Gradient liquid chromatographic retention time prediction for suspect screening applications, A critical assessment of a generalised artificial neural network-based approach across 10 multi-residue reversed-phase analytical methods, Vol: 147, Pages: 261-270, ISSN: 0039-9140
For the first time, the performance of a generalised artificial neural network (ANN) approach for the prediction of 2492 chromatographic retention times (tR) is presented for a total of 1117 chemically diverse compounds present in a range of complex matrices and across 10 gradient reversed-phase liquid chromatography-(high resolution) mass spectrometry methods. Probabilistic, generalised regression, radial basis function as well as 2- and 3-layer multilayer perceptron-type neural networks were investigated to determine the most robust and accurate model for this purpose. Multi-layer perceptrons most frequently yielded the best correlations in 8 out of 10 methods. Averaged correlations of predicted versus measured tR across all methods were R2=0.918, 0.924 and 0.898 for the training, verification and test sets respectively. Predictions of blind test compounds (n=8-84 cases) resulted in an average absolute accuracy of 1.02±0.54 min for all methods. Within this variation, absolute accuracy was observed to marginally improve for shorter runtimes, but was found to be relatively consistent with respect to analyte retention ranges (~5%). Finally, optimised and replicated network dependency on molecular descriptor data is presented and critically discussed across all methods. Overall, ANNs were considered especially suitable for suspects screening applications and could potentially be utilised in bracketed-type analyses in combination with high resolution mass spectrometry.
Gómez-Canela C, Miller T, Bury NR, et al., 2016, Targeted metabolomics of Gammarus pulex following controlled exposures to selected pharmaceuticals in water, Science of the Total Environment, Vol: 562, Pages: 777-788, ISSN: 0048-9697
The effects of pharmaceuticals and personal care products on aquatic organisms represent a significant current concern. Herein, a targeted metabolomics approach using liquid chromatography-high resolution mass spectrometry (LC-HRMS) is presented to characterise concentration changes in 29 selected metabolites following exposures of aquatic invertebrates, Gammarus pulex, to pharmaceuticals. Method performance revealed excellent linearity (R2>0.99), precision (0.1-19 %) and lower limits of detection (0.002-0.20 ng) for all metabolites studied. Three pharmaceuticals were selected representing the low, middle and high range of measured acute measured toxicities (of a total of 26 compounds). Gammarids were exposed to both the no-observed-adverse-effect-level and the lowest-observed-adverse-effect-level of triclosan (0.1 and 0.3 mg L-1), nimesulide (0.5 and 1.4 mg L-1) and propranolol (100 and 153 mg L-1) over 24 hrs. Quantitative metabolite profiling was then performed. Significant changes in metabolite concentrations relative to controls are presented and showed distinct clustered trends for each pharmaceutical. Approximately 37 % (triclosan), 33 % (nimesulide) and 46 % (propranolol) of metabolites showed statistically significant time-related effects. Observed changes are also discussed with respect to internal concentrations of the three pharmaceuticals measured using a method based on pulverised liquid extraction, solid phase extraction and LC-MS/MS. Potential metabolic pathways that may be affected by such exposures are also discussed. This represents the first study focussing on quantitative, targeted metabolomics of this lower trophic level benthic invertebrate that may elucidate biomarkers for future risk assessment.
Miller TH, Baz-Lomba JA, Harman C, et al., 2016, The First Attempt at Non-Linear in Silico Prediction of Sampling Rates for Polar Organic Chemical Integrative Samplers (POCIS), Environmental science & technology, Vol: 50, Pages: 7973-7981, ISSN: 0013-936X
Modeling and prediction of polar organic chemical integrative sampler (POCIS) sampling rates (Rs) for 73 compounds using artificial neural networks (ANNs) is presented for the first time. Two models were constructed: the first was developed ab initio using a genetic algorithm (GSD-model) to shortlist 24 descriptors covering constitutional, topological, geometrical and physicochemical properties and the second model was adapted for Rs prediction from a previous chromatographic retention model (RTD-model). Mechanistic evaluation of descriptors showed that models did not require comprehensive a priori information to predict Rs. Average predicted errors for the verification and blind test sets were 0.03 ± 0.02 L d-1 (RTD-model) and 0.03 ± 0.03 L d-1 (GSD-model) relative to experimentally determined Rs. Prediction variability in replicated models was the same or less than for measured Rs. Networks were externally validated using a measured Rs data set of six benzodiazepines. The RTD-model performed best in comparison to the GSD-model for these compounds (average absolute errors of 0.0145 ± 0.008 L d-1 and 0.0437 ± 0.02 L d-1, respectively). Improvements to generalizability of modeling approaches will be reliant on the need for standardized guidelines for Rs measurement. The use of in silico tools for Rs determination represents a more economical approach than laboratory calibrations.
Miller T, McEneff GL, Stott LC, et al., 2016, Assessing the reliability of uptake and elimination kinetics modelling approaches for estimating bioconcentration factors in the freshwater invertebrate, Gammarus pulex Thomas H. Miller, Gillian L. McEneff, Lucy C. Stott, Stewart F. Owen, Nicolas R. Bury, Leon P. Barron, Science of the Total Environment, Vol: 547, Pages: 396-404, ISSN: 0048-9697
This study considers whether the current standard toxicokinetic methods are an accurate and applicable assessment of xenobiotic exposure in an aquatic freshwater invertebrate. An in vivo exposure examined the uptake and elimination kinetics for eight pharmaceutical compounds in the amphipod crustacean, Gammarus pulex by measuring their concentrations in both biological material and in the exposure medium over a 96 h period. Selected pharmaceuticals included two anti-inflammatories (diclofenac and ibuprofen), two beta-blockers (propranolol and metoprolol), an anti-depressant (imipramine), an anti-histamine (ranitidine) and two beta-agonists (formoterol and terbutaline). Kinetic bioconcentration factors (BCFs) for the selected pharmaceuticals were derived from a first-order one-compartment model using either the simultaneous or sequential modelling methods. Using the simultaneous method for parameter estimation, BCF values ranged from 12 to 212. In contrast, the sequential method for parameter estimation resulted in bioconcentration factors ranging from 19 to 4533. Observed toxicokinetic plots showed statistically significant lack-of-fits and further interrogation of the models revealed a decreasing trend in the uptake rate constant over time for rantidine, diclofenac, imipramine, metoprolol, formoterol and terbutaline. Previous published toxicokinetic data for 14 organic micro-pollutants were also assessed and similar trends were identified to those observed in this study. The decreasing trend of the uptake rate constant over time highlights the need to interpret modelled data more comprehensively to ensure uncertainties associated with uptake and elimination parameters for determining bioconcentration factors are minimised.
Weston-Ford KA, Moseley ML, Hall LJ, et al., 2016, The retrieval of fingerprint friction ridge detail from elephant ivory using reduced-scale magnetic and non-magnetic powdering materials, Science and Justice, Vol: 56, Pages: 1-8, ISSN: 1355-0306
An evaluation of reduced-size particle powdering methods for the recovery of usable fingermark ridge detail from elephant ivory is presented herein for the first time as a practical and cost-effective tool in forensic analysis. Of two reduced-size powder material types tested, powders with particle sizes ≤ 40 μm offered better chances of recovering ridge detail from unpolished ivory in comparison to a conventional powder material. The quality of developed ridge detail of these powders was also assessed for comparison and automated search suitability. Powder materials and the enhanced ridge detail on ivory were analysed by scanning electron microscopy and energy dispersive X-ray spectroscopy and interactions between their constituents and the ivory discussed. The effect of ageing on the quality of ridge detail recovered showed that the best quality was obtained within 1 week. However, some ridge detail could still be developed up to 28 days after deposition. Cyanoacrylate and fluorescently-labelled cyanoacrylate fuming of ridge detail on ivory was explored and was less effective than reduced-scale powdering in general. This research contributes to the understanding and potential application of smaller scale powdering materials for the development of ridge detail on hard, semi-porous biological material typically seized in wildlife-related crimes.
Zapata F, de la Ossa MªÁF, Gilchrist E, et al., 2016, Progressing the analysis of Improvised Explosive Devices, Comparative study for trace detection of explosive residues in handprints by Raman spectroscopy and liquid chromatography, Vol: 161, Pages: 219-227, ISSN: 0039-9140
Concerning the dreadful global threat of terrorist attacks, the detection of explosive residues in biological traces and marks is a current need in both forensics and homeland security. This study examines the potential of Raman microscopy in comparison to liquid chromatography (ion chromatography (IC) and reversed-phase high performance liquid chromatography (RP-HPLC)) to detect, identify and quantify residues in human handmarks of explosives and energetic salts commonly used to manufacture Improvised Explosive Devices (IEDs) including dynamite, ammonium nitrate, single- and double-smokeless gunpowders and black powder. Dynamite, ammonium nitrate and black powder were detected through the identification of the energetic salts by Raman spectroscopy, their respective anions by IC, and organic components by RP-HPLC. Smokeless gunpowders were not detected, either by Raman spectroscopy or the two liquid chromatography techniques. Several aspects of handprint collection, sample treatment and a critical comparison of the identification of compounds by both techniques are discussed. Raman microscopy and liquid chromatography were shown to be complementary to one another offering more comprehensive information for trace explosives analysis.
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