Time since last discharge of firearms and spent ammunition elements: state of the art and perspectives

The estimation of the time since last discharge of firearms or spent ammunition elements (e.g., casings) may provide crucial information in the investigation of a shooting incident and, eventually, the following trial. Herein, an exhaustive review of the methods described in the literature is reported, with the aim to evaluate their potential and limitations from a forensic perspective. This work, in particular, highlighted the fact that a number of investigations have been carried out in the field during the last century (with an especially high rate in the last 30 years), but the implementation of related procedures in forensic laboratories is still rare. The situation has been discussed and a series of propositions have been forwarded, in order to overcome challenges and facilitate the implementation of dating approaches in real casework

Forensic profiling of smokeless powders (SLPs) by gas chromatography–mass spectrometry (GC-MS): a systematic investigation into injector conditions and their effect on the characterisation of samples

Smokeless powders (SLPs) are composed of a combination of thermolabile and non-thermolabile compounds. When analysed by GC-MS, injection conditions may therefore play a fundamental role on the characterisation of forensic samples. However, no systematic investigations have ever been carried out. This casts doubt on the optimal conditions that should be adopted in advanced profiling applications (e.g. class attribution and source association), especially when a traditional split/splitless (S/SL) injector is used. Herein, a study is reported that specifically focused on the evaluation of the liner type (L type) and inlet temperature (T inj). Results showed that both could affect the exhaustiveness and repeatability of the observed chemical profiles, with L type being particularly sensitive despite typically not being clarified in published works. Perhaps as expected, degradation effects were observed for the most thermolabile compounds (e.g. nitroglycerin) at conditions maximising the heat transfer rates (L type = packed and T inj ≥ 200 °C). However, these did not seem to be as influential as, perhaps, suggested in previous studies. Indeed, the harshest injection conditions in terms of heat transfer rate (L type = packed and T inj = 260 °C) were found to lead to better performances (including better overall %RSDs and LODs) compared to the mildest ones. This suggested that implementing conditions minimising heat-induced breakdowns during injection was not necessarily a good strategy for comparison purposes. The reported findings represent a concrete step forward in the field, providing a robust body of data for the development of the next generation of SLP profiling methods. Graphical abstract: (Figure presented.).</p

Prevalence and characterisation of microfibres along the Kenyan and Tanzanian coast

Microplastic pollution is ubiquitous, with textiles being a major source of one of the dominant microplastic types—microfibres. Microfibres have been discovered in the aquatic environment and marine biota, demonstrating direct infiltration in the environment. However, the impact of non-plastic microfibres has been overlooked until recently despite their prevalence and the ecotoxicological risk posed by chemical dyes and finishes used during processing. During an expedition from Lamu to Zanzibar (East Africa), a citizen science strategy was employed to innovate, educate and influence microfibre pollution reform through the Flipflopi project, a circular economy effort to stop the use of single-use plastic. Simple sampling methods were developed to replace costly equipment, which local citizens could use to partake in the collection and sampling of surface water samples from the previously understudied Kenyan and Tanzanian coast. To maintain the reliability of samples and to minimise contamination, a forensic science strategy was embedded throughout the methodology of the study, collection and analysis of the samples. A total of 2,403 microfibres from 37 sites were recovered and fully characterised with 55% found to be of natural origin, 8% regenerated cellulosic and 37% synthetic microfibres. Natural microfibres were in higher abundance in 33 of the 37 sampled sites. Congruent with recent studies, these findings further support the need for greater understanding of the anthropogenic impact of natural microfibres

Prediction of bioconcentration factors in fish and invertebrates using machine learning

© 2018 The Authors 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.Biotechnology and Biological Sciences Research Council (BBSRC) CASE industrial scholarship scheme (Reference BB/K501177/1), iNVERTOX project (Reference BB/P005187/1) and AstraZeneca Global SHE research programme. This work was additionally supported by the Francis Crick Institute which receives its core funding from Cancer Research UK (FC001999), the UK Medical Research Council (FC001999), and the Wellcome Trust (FC001999)

Suspect screening of halogenated carboxylic acids in drinking water using ion exchange chromatography – high resolution (Orbitrap) mass spectrometry (IC-HRMS)

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

Cartridge Case and Bullet Comparison : Examples of Evaluative Reporting

This paper presents examples of how to report results when using a probabilistic approach in the field of firearms. Three European Institutes who have adopted a Likelihood Ratio (LR) -based approach in their practice were asked to produce the evaluation section of a report based on a fictious case. Background information about the Institutes, their conclusion scales, and reporting formats is presented. The article ends with a brief overview of recent developments within the ENFSI (European Network of Forensic Science Institutes) community where guidelines for evaluative reports have been formulated and are now published. The current collaborative approach between Institutes, presented in this article, offers an opportunity to discuss practical issues related to this approach. More specifically, this is an opportunity to introduce the ENFSI guidelines and its tenets, providing more tools for firearms/toolmarks examiners to progress toward a LR-based approach

Machine learning for environmental toxicology

Recent advances in computing power have enabled the application of machine learning (ML) across all areas of science. A step change from a data-rich landscape to one where new hypotheses, relationships, and knowledge is emerging as a result. While ML is related to artificial intelligence (AI), they are not the same. ML is a branch of AI involving the application of statistical algorithms to enable a system to learn. Learning can involve data interpretation, identification of patterns and decision making. However, application and acceptance of ML within environmental toxicology, and more specifically for our viewpoint, environmental risk assessment (ERA), remains low. ML is an example of a disruptive research technology, which is urgently needed to cope with the complexity and scale of work required