Compositional data from a soil survey over north Canberra, Australian Capital Territory, are used to develop and test an empirical soil provenancing method. Mineralogical data from Fourier Transform InfraRed spectroscopy (FTIR) and Magnetic Susceptibility (MS), and geochemical data from X-Ray Fluorescence (XRF; for total major oxides) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS; for both total and aqua regia-soluble trace elements), are performed on the survey’s 268 topsoil samples (0-5 cm depth; 1 sample per km2). Principal components (PCs) are calculated after imputation of censored data and centred logratio transformation. The sequential provenancing approach is underpinned by (i) the preparation of interpolated raster grids of the soil properties (including PCs); (ii) the explicit quantification and propagation of uncertainty; (iii) the intersection of the soil property rasters with the values of the evidentiary sample (± uncertainty); and (iv) the computation of cumulative provenance rasters (‘heat maps’) for the various analytical techniques. The sequential provenancing method is tested in the north Canberra soil survey with three ‘blind’ samples representing simulated evidentiary samples. Performance metrics of precision and accuracy indicate that the FTIR and MS (mineralogy), as well as XRF and total ICP-MS (geochemistry) analytical methods offer the most precise and accurate provenance predictions. Inclusions of PCs in provenancing adds marginally to the performance. Maximising the number of analytes/analytical techniques is advantageous in soil provenancing. Despite acknowledged limitations and gaps, it is concluded that the empirical soil provenancing approach can play an important role in forensic and intelligence applications. <b>Citation:</b> de Caritat, P., Woods, B., Simpson, T., Nichols, C., Hoogenboom, L., Ilheo, A., Aberle, M.G. and Hoogewerff, J. (2021), Forensic soil provenancing in an urban/suburban setting: A sequential multivariate approach. <i>J Forensic Sci</i>, 66: 1679-1696. https://doi.org/10.1111/1556-4029.14727

Understanding the near surface migration patterns and rates of efflux of CO₂ is important for developing effective monitoring and verification programs for the geological storage of CO₂. Soil flux surveys are a well-established technique for characterising surface CO₂ emission sources from controlled release sites, CO₂storage sites or natural CO₂seeps. The performance of four interpolation methods; arithmetic mean (AM), two minimum variance unbiased estimators (MVUE), and a newly developed geostatistical cubic surface were evaluated using 21 soil flux surveys conducted over two controlled release experiments in 2012 and 2013, at the Ginninderra controlled release facility, Australia. Data was binned to approximate a regular sampling grid for improved performance of the whole-of-field AM and MVUE averaging techniques. The AM and MVUE methods were highly sensitive to deviations in the statistical distribution of the data, and performed inconsistently across the two experiments. These two methods proved ill-suited for application to CO₂ leak quantification due to their inflexible sampling and distribution requirements. The cubic technique provided the best net emission estimates across both experiments, and when applied at different bin sizes, estimating the true release rate to within 20% for the 2012 experiment and 45% below the release rate for the 2013 experiment. The cubic method is well-suited for CO₂ leak quantification because it is not limited by assumptions of the data’s spatial or statistical distribution. Net H₂O emissions of 29 kg/d were observed coincident with the high CO₂ flux zones in the field. The interpolation methods were applied with similar results on soil flux surveys taken from a natural seepage site in Qinghai, China. Gravity currents appear to describe the observed soil flux and soil gas behavior at Ginninderra, i.e. the observed lateral migration of CO₂in the subsurface. Subsurface migration was also strongly influenced by the relative depth of the groundwater. Thus the low water table and greater vadose zone in the 2013 experiment is suspected to facilitate greater lateral CO₂ migration and explain the poor closure of the CO₂ balance. <b>Citation:</b> I.F. Schroder, P. Wilson, A.F. Feitz, J. Ennis-King, <i>Evaluating the Performance of Soil Flux Surveys and Inversion Methods for Quantification of CO2 Leakage</i>, Energy Procedia, Volume 114, 2017, Pages 3679-3694, ISSN 1876-6102, https://doi.org/10.1016/j.egypro.2017.03.1499.

Soil is a ubiquitous material at the Earth's surface with potential to be a useful evidence class in forensic and intelligence applications. Compositional data from a soil survey over North Canberra, Australian Capital Territory, are used to develop and test an empirical soil provenancing method. Mineralogical data from Fourier Transform InfraRed spectroscopy (FTIR) and geochemical data from X-Ray Fluorescence (XRF; for total major oxides) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS; for both total and aqua regia-soluble trace elements) are obtained from the survey's 268 topsoil samples (0–5 cm depth; 1 sample per km2). The simultaneous provenancing approach is underpinned by (i) the calculation of Spearman's correlation coefficients (rS) between an evidentiary sample and all the samples in the database for all variables generated by each analytical method; and (ii) the preparation of an interpolated raster grid of rS for each evidentiary sample and method resulting in a series of provenance rasters (“heat maps”). The simultaneous provenancing method is tested on the North Canberra soil survey with three “blind” samples representing simulated evidentiary samples. Performance metrics of precision and accuracy indicate that the FTIR (mineralogy) and XRF (geochemistry) analytical methods offer the most precise and accurate provenance predictions. Maximizing the number of analytes/analytical techniques is advantageous in soil provenancing. Despite acknowledged limitations, it is concluded that the empirical soil provenancing approach can play an important role in forensic and intelligence applications. <b>Citation:</b> de Caritat, P, Woods, B, Simpson, T, Nichols, C, Hoogenboom, L, Ilheo, A, et al. Forensic soil provenancing in an urban/suburban setting: A simultaneous multivariate approach. <i>J Forensic Sci</i>. 2022; 67: 927–935. https://doi.org/10.1111/1556-4029.14967

<div>Soil is a complex and spatially variable material that has a demonstrated potential to be a useful evidence class in forensic casework and intelligence operations. Here, the capability to spatially constrain searches and prioritise resources by triaging areas as low and high interest is advantageous. Conducted between 2017 and 2021, a forensically relevant topsoil survey (0-5 cm depth; 1 sample per 1 km2) has been carried out over Canberra, Australia, with the aims of documenting the distribution of chemical elements in an urban/suburban environment, and of acting as a testbed for investigating various aspects of forensic soil provenancing. Geochemical data from X-Ray Fluorescence (XRF; for total major oxides) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS; for trace elements) following a total digestion (HF + HNO3) were obtained from the survey’s 685 topsoil samples (plus 138 additional quality control samples and six “Blind” simulated evidentiary samples). Using those “Blind” samples, we document a likelihood ratio approach where for each grid cell the analytical similarity between the grid cell and evidentiary sample is attributed from a measure of overlap between both Cauchy distributions, including appropriate uncertainties. Unlike existing methods that base inclusion/exclusion on an arbitrary threshold (e.g., ± three standard deviations), our approach is free from strict binary or Boolean thresholds, providing an unconstrained gradual transition dictated by the analytical similarity. Using this provenancing model, we present and evaluate a new method for upscaling from a fine (25 m x 25 m) interpolated grid to a more appropriate coarser (500 m x 500 m) grid, in addition to an objective method using Random Match Probabilities for ranking individual variables to be used for provenancing prior to receiving evidentiary material. Our results show this collective procedure generates more consistent and robust provenance maps between two different interpolation algorithms (e.g., inverse distance weighting, and natural neighbour), grid placements (e.g., grid shifts to the north or east) and theoretical users (e.g., different computer systems, or forensic geoscientists).</div> <b>Citation:</b> Michael G. Aberle, Patrice de Caritat, James Robertson, Jurian A. Hoogewerff, A robust interpolation-based method for forensic soil provenancing: A Bayesian likelihood ratio approach,<i> Forensic Science International</i>, Volume 353, 2023, 111883, ISSN 0379-0738. https://doi.org/10.1016/j.forsciint.2023.111883.