Chlorinated organic compounds in concrete as specific markers for chlorine gas exposure.

The formation of chlorinated organic compounds in concrete debris exposed to reactive chlorine was studied to search for markers specific to chlorine gas exposure. Concrete materials of different origins were exposed to a range of species of reactive chlorine including bleach, humid and dry chlorine gas at different concentrations. Chlorinated organic compounds in concrete extracts were analysed by targeted gas and liquid chromatography-tandem mass spectrometry (GC-MS/MS and LC-MS/MS) and by non-targeted screening using the corresponding high-resolution techniques (GC-HRMS and LC-HRMS). Overall, different levels and species of chlorinated organic compounds namely chlorophenols, chlorobenzenes, chloromethoxyphenols, chloromethylbenzenes and chloral hydrate were identified in these chlorinated concrete extracts; two examples of diagnostic markers for neat chlorine exposure were trichloromethylbenzene and tetrachlorophenol. The old concrete samples from the 1930s and 1950s had the most chlorinated organic compounds after exposure to neat chlorine gas. Lignin or lignin degradation products were identified as probable candidates for phenolic precursor molecules in the concrete samples. Multivariate data analysis (OPLS-DA) shows distinct patterns for bleach and chlorine exposure. The chlorinated chemicals and specific markers for chlorine gas discovered in our research assist other laboratories in forensic investigations of chlorine gas attacks.

Route Determination of Sulfur Mustard Using Nontargeted Chemical Attribution Signature Screening.

Route determination of sulfur mustard was accomplished through comprehensive nontargeted screening of chemical attribution signatures. Sulfur mustard samples prepared via 11 different synthetic routes were analyzed using gas chromatography/high-resolution mass spectrometry. A large number of compounds were detected, and multivariate data analysis of the mass spectrometric results enabled the discovery of route-specific signature profiles. The performance of two supervised machine learning algorithms for retrospective synthetic route attribution, orthogonal partial least squares discriminant analysis (OPLS-DA) and random forest (RF), were compared using external test sets. Complete classification accuracy was achieved for test set samples (2/2 and 9/9) by using classification models to resolve the one-step routes starting from ethylene and the thiodiglycol chlorination methods used in the two-step routes. Retrospective determination of initial thiodiglycol synthesis methods in sulfur mustard samples, following chlorination, was more difficult. Nevertheless, the large number of markers detected using the nontargeted methodology enabled correct assignment of 5/9 test set samples using OPLS-DA and 8/9 using RF. RF was also used to construct an 11-class model with a total classification accuracy of 10/11. The developed methods were further evaluated by classifying sulfur mustard spiked into soil and textile matrix samples. Due to matrix effects and the low spiking level (0.05% w/w), route determination was more challenging in these cases. Nevertheless, acceptable classification performance was achieved during external test set validation: chlorination methods were correctly classified for 12/18 and 11/15 in spiked soil and textile samples, respectively.

Synthesis route attribution of sulfur mustard by multivariate data analysis of chemical signatures.

A multivariate model was developed to attribute samples to a synthetic method used in the production of sulfur mustard (HD). Eleven synthetic methods were used to produce 66 samples for model construction. Three chemists working in both participating laboratories took part in the production, with the aim to introduce variability while reducing the influence of laboratory or chemist specific impurities in multivariate analysis. A gas chromatographic/mass spectrometric data set of peak areas for 103 compounds was subjected to orthogonal partial least squares - discriminant analysis to extract chemical attribution signature profiles and to construct multivariate models for classification of samples. For one- and two-step routes, model quality allowed the classification of an external test set (16/16 samples) according to synthesis conditions in the reaction yielding sulfur mustard. Classification of samples according to first-step methodology was considerably more difficult, given the high purity and uniform quality of the intermediate thiodiglycol produced in the study. Model performance in classification of aged samples was also investigated.

On the use of spectra from portable Raman and ATR-IR instruments in synthesis route attribution of a chemical warfare agent by multivariate modeling.

Collecting data under field conditions for forensic investigations of chemical warfare agents calls for the use of portable instruments. In this study, a set of aged, crude preparations of sulfur mustard were characterized spectroscopically without any sample preparation using handheld Raman and portable IR instruments. The spectral data was used to construct Random Forest multivariate models for the attribution of test set samples to the synthetic method used for their production. Colored and fluorescent samples were included in the study, which made Raman spectroscopy challenging although fluorescence was diminished by using an excitation wavelength of 1064 nm. The predictive power of models constructed with IR or Raman data alone, as well as with combined data was investigated. Both techniques gave useful data for attribution. Model performance was enhanced when Raman and IR spectra were combined, allowing correct classification of 19/23 (83%) of test set spectra. The results demonstrate that data obtained with spectroscopy instruments amenable for field deployment can be useful in forensic studies of chemical warfare agents.