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Forensic soil provenancing in an urban/suburban setting: a simultaneous multivariate approach

  • Patrice de Caritat
  • , Brenda Woods
  • , Timothy Simpson
  • , Christopher Nichols
  • , Lissy Hoogenboom
  • , Adriana Ilheo
  • , Michael G. Aberle
  • , Jurian Hoogewerff

    Research output: Contribution to journalArticlepeer-review

    80 Downloads (Pure)

    Abstract

    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.
    Original languageEnglish
    Pages (from-to)927-935
    Number of pages9
    JournalJournal of Forensic Sciences
    Volume67
    Issue number3
    DOIs
    Publication statusPublished - May 2022

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 16 - Peace, Justice and Strong Institutions
      SDG 16 Peace, Justice and Strong Institutions

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