Field survey of Canadian background soils: Implications for a new mathematical gas chromatography-flame ionization detection approach for resolving false detections of petroleum hydrocarbons in clean soils.

The reference method for the Canada-wide standard (CWS) for petroleum hydrocarbons (PHCs) in soil provides laboratories with methods for generating accurate and reproducible soil analysis results. The CWS PHC tier 1 generic soil-quality guidelines apply to 4 carbon ranges/fractions: F1 (C6-C10), F2 (C10-C16), F3 (C16-C34), and F4 (>C34). The methods and guidelines were developed and validated for soils with approximately 5% total organic carbon (TOC). However, organic soils have much higher TOC levels because of biogenic organic compounds (BOCs) originating from sources such as plant waxes and fatty acids. Coextracted BOCs can have elevated F2-F4 concentrations, which can cause false exceedances of PHC soil guidelines. The present study evaluated false PHC detections in soil samples collected from 34 background sites. The list of analytes included soil type, TOC, polycyclic aromatic hydrocarbons (PAHs), F2, F3, F4, F3a (C16-C22), and F3b (C22-C34). Soils with 3% to 41% TOC falsely exceeded the CWS PHC 300 mg/kg F3 coarse soil guideline. It was previously demonstrated that clean peat had F2:F3b ratios of less than 0.10, while crude oil spiked peat and spiked sand had higher ratios of greater than 0.10. In the present background study, all of the clean organic soils with at least 300 mg/kg F3 had F2:F3b ratios of less than 0.10, which indicated false guideline exceedances. Clean inorganic soils had low F3 concentrations, resulting in high F2:F3b ratios of greater than 0.10. Validation field studies are required to determine if the F2:F3b 0.10 PHC presence versus absence threshold value is applicable to crude oil- and diesel-contaminated sites.

Is it clean or contaminated soil? Using petrogenic versus biogenic GC-FID chromatogram patterns to mathematically resolve false petroleum hydrocarbon detections in clean organic soils: a crude oil-spiked peat microcosm experiment.

The Canadian Council of Ministers of the Environment (CCME) reference method for the Canada-wide standard (CWS) for petroleum hydrocarbon (PHC) in soil provides chemistry analysis standards and guidelines for the management of contaminated sites. However, these methods can coextract natural biogenic organic compounds (BOCs) from organic soils, causing false exceedences of toxicity guidelines. The present 300-d microcosm experiment used CWS PHC tier 1 soil extraction and gas chromatography-flame ionization detector (GC-FID) analysis to develop a new tier 2 mathematical approach to resolving this problem. Carbon fractions F2 (C10-C16), F3 (C16-C34), and F4 (>C34) as well as subfractions F3a (C16-C22) and F3b (C22-C34) were studied in peat and sand spiked once with Federated crude oil. These carbon ranges were also studied in 14 light to heavy crude oils. The F3 range in the clean peat was dominated by F3b, whereas the crude oils had approximately equal F3a and F3b distributions. The F2 was nondetectable in the clean peat but was a significant component in crude oil. The crude oil­spiked peat had elevated F2 and F3a distributions. The BOC-adjusted PHC F3 calculation estimated the true PHC concentrations in the spiked peat. The F2:F3b ratio of less than 0.10 indicated PHC absence in the clean peat, and the ratio of greater than or equal to 0.10 indicated PHC presence in the spiked peat and sand. Validation studies are required to confirm whether this new tier 2 approach is applicable to real-case scenarios. Potential adoption of this approach could minimize unnecessary ecological disruptions of thousands of peatlands throughout Canada while also saving millions of dollars in management costs.

Multivariate analysis of the geochemistry and mineralogy of soils along two continental-scale transects in North America.

Soils collected in 2004 along two North American continental-scale transects were subjected to geochemical and mineralogical analyses. In previous interpretations of these analyses, data were expressed in weight percent and parts per million, and thus were subject to the effect of the constant-sum phenomenon. In a new approach to the data, this effect was removed by using centered log-ratio transformations to 'open' the mineralogical and geochemical arrays. Multivariate analyses, including principal component and linear discriminant analyses, of the centered log-ratio data reveal the effects of soil-forming processes, including soil parent material, weathering, and soil age, at the continental-scale of the data arrays that were not readily apparent in the more conventionally presented data. Linear discriminant analysis of the data arrays indicates that the majority of the soil samples collected along the transects can be more successfully classified with Level 1 ecological regional-scale classification by the soil geochemistry than soil mineralogy. A primary objective of this study is to discover and describe, in a parsimonious way, geochemical processes that are both independent and inter-dependent and manifested through compositional data including estimates of the elements and corresponding mineralogy.

Compositional data analysis for elemental data in forensic science.

Discrimination of material based on elemental composition was achieved within a compositional data (CoDa) analysis framework in a form appropriate for use in forensic science. The methods were carried out on example data from New Zealand nephrite. We have achieved good separation of the in situ outcrops of nephrite from within a well-defined area. The most significant achievement of working within the CoDa analysis framework is that the implications of the constraints on the data are acknowledged and dealt with, not ignored. The full composition was reduced based on collinearity of elements, principal components analysis (PCA) and scalings from a backwards linear discriminant analysis (LDA). Thus, a descriptive subcomposition was used for the final discrimination, using LDA, and proved to be more successful than using the full composition. The classification based on the LDA model showed a mean error rate of 2.9% when validated using a 10 repeat, three-fold cross-validation. The methods presented lend objectivity to the process of interpretation, rather than relying on subjective pattern matching type approaches.