Requirements for Chromium Reactors for Use in the Determination of H Isotopes in Compound-Specific Stable Isotope Analysis of Chlorinated Compounds

Current trends and challenges in the analysis of marine environmental contaminants by isotope ratio mass spectrometry

An increasing number of organic and inorganic pollutants are being detected in the marine environment, posing a severe threat to the ecosystem and human health, even in trace concentrations. Isotope ratio mass spectrometry (IRMS) is one of the critical methods for determining the origin and fate of environmental pollutants and characterising their transformation processes. It has been used for a relatively long time for ecological monitoring of some well-studied industrial hydrocarbons at contaminated sites. However, the method still faces many analytical challenges. This review provides a comprehensive overview of recent technical advances concerning IRMS analysis of various contaminants and discusses typical pitfalls encountered in marine environment analysis. Particular attention is given to the study of sampling techniques and sample preparation for examination, often the keys to successful research given the complexity of marine matrices and the diverse and numerous nature of contaminants. Prospects for developing IRMS to monitor pollution sources and pollutant transformation in the marine environment are outlined.

Determination of Stable Hydrogen Isotopic Composition and Isotope Enrichment Factor at Low Hydrogen Concentration

Determination of stable hydrogen isotopic compositions (δ2H) is currently challenged to achieve a high detection limit for reaching the linear range where δ2H values are independent of concentration. Therefore, it is difficult to assess precise δ2H values for calculating the hydrogen isotope enrichment factor (εH) and for field application where the concentrations of contaminants are relatively low. In this study, a data treatment approach was developed to obtain accurate δ2H values below the linear range. The core concept was to use a logarithmic function to fit the δ2H values below the linear range and then adjust the δ2H values below the linear range into the linear range by using the fitted logarithmic equation. Moreover, the adjusted δ2H values were calibrated by using laboratory reference materials, e.g., n-alkanes. Tris(2-chloroethyl) phosphate (TCEP) and hexachlorocyclohexane (HCH) isomers were selected as examples of complex heteroatom-bearing compounds to develop the data treatment approach. This data treatment approach was then tested using δ2H values from a TCEP transformation experiment with OH radicals. Comparable δ2H values and εH between the low-concentration experiment and the reference experiment were obtained using the developed approach. Therefore, the developed data treatment approach enables a possibility of determining the hydrogen isotopic compositions of organic components in low concentrations. It is especially valuable for determining organic contaminants in environmental samples, which are usually present in low concentrations.

Multi-element isotopic evidence for monochlorobenzene and benzene degradation under anaerobic conditions in contaminated sediments

Industrial chemicals are frequently detected in sediments due to a legacy of chemical spills. Globally, site remedies for groundwater and sediment decontamination include natural attenuation by in situ abiotic and biotic processes. Compound-specific isotope analysis (CSIA) is a diagnostic tool to identify, quantify, and characterize degradation processes in situ, and in some cases can differentiate between abiotic degradation and biodegradation. This study reports high-resolution carbon, chlorine, and hydrogen stable isotope profiles for monochlorobenzene (MCB), and carbon and hydrogen stable isotope profiles for benzene, coupled with measurements of pore water concentrations in contaminated sediments. Multi-element isotopic analysis of δ¹³C and δ³⁷Cl for MCB were used to generate dual-isotope plots, which for 2 locations at the study site resulted in Λ_C/Cl(130) values of 1.42 ± 0.19 and Λ_C/Cl(131) values of 1.70 ± 0.15, consistent with theoretical calculations for carbon-chlorine bond cleavage (Λ_T = 1.80 ± 0.31) via microbial reductive dechlorination. For benzene, significant δ²H (122‰) and δ¹³C (6‰) depletion trends, followed by enrichment trends in δ¹³C (1.6‰) in the upper part of the sediment, were observed at the same location, indicating not only production of benzene due to biodegradation of MCB, but subsequent biotransformation of benzene itself to nontoxic end-products. Degradation rate constants calculated independently using chlorine isotopic data and carbon isotopic data, respectively, agreed within uncertainty thus providing multiple lines of evidence for in situ contaminant degradation via reductive dechlorination and providing the foundation for a novel approach to determine site-specific in situ rate estimates essential for the prediction of remediation outcomes and timelines.

Implications of regression bias for multi-element isotope analysis for environmental remediation

Measuring changes in the stable isotope ratios of multiple elements (e.g. Δδ¹³C, Δδ³⁷Cl, and Δδ²H) during the (bio)transformation of environmental contaminants has provided new insights into reaction mechanisms and tools to optimize remediation efforts. Dual-isotope analysis, wherein changes in one isotopic system are plotted against another (to derive an interpretational parameter expressed as Λ), is a key tool in multi-element isotopic assessment. To date, most dual-isotope analyses use ordinary linear regression (OLR) for the calculation, which can be subject to regression attenuation and thus an inherent artifact that depresses slope values, expressed as Λ. Here, a series of Monte Carlo simulations were constructed to represent common data conditions and variations within dual-isotope data to test the degree of bias when deriving Λ using OLR compared to an alternative regression technique, the York method. The degree of bias was quantified compared to the modeled or "true" Λ value. For all simulations, the York method provided the least bias in slope estimates (<1%) over all data conditions tested. In contrast, OLR produced unbiased estimates only under a limited set of conditions, which was validated through a mathematical model proof. Both the mathematical model and simulations show that bias of at least 5% in OLR occurs when the extent of enrichment in the x-variable (X_M) is equal to or less than ≈15 times the 1σ precision in the isotope measurement (σ_X), for both Cl/C and C/H plots. The results give practitioners tools to evaluate whether bias is present in data and to estimate the extent to which this negatively impacts the interpretations and predictions of remediation potential for new and previously published datasets. This study demonstrates that integration of such robust statistical tools is essential for dual-isotope interpretations widely used in contaminant hydrogeology but relevant to other disciplines including environmental chemistry and ecology.