<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.

Geochemical surveys deliver fundamental data, information and knowledge about the concentration and spatial distribution of chemical elements, isotopes and compounds in the natural environment. Typically near-surface sampling media, such as soil, sediment, outcropping rocks and stream or groundwater, are used. The application of such datasets to fields such as mineral exploration, environmental management, and geomedicine has been widely documented. In this presentation I reflect on a sabbatical experience with the Australian Federal Police (AFP) in 2017-2018 that allowed me to extend the interpretation of geochemical survey data beyond these established applications. In particular, with my collaborators we explore ways in which geochemical survey data and maps can be used to indicate the provenance of an evidentiary sample collected at a crime scene or obtained for instance from items belonging to a suspect intercepted at border entry. Because soils are extremely diverse mineralogically, geochemically and biologically, it should theoretically be possible to exclude very large swathes of territory (>90%) from further provenancing investigation using soil data. In a collaboration between Geoscience Australia (GA), the AFP and the University of Canberra (UC), a recent geochemical survey of the urban/suburban Canberra region in southeastern Australia is being used as a testbed for developing different approaches to forensic applications of geochemical surveys. A predictive soil provenancing method at the national scale was also developed and tested for application where no actual detailed, fit-for-purpose geochemical survey data exist. Over the next few years, GA, AFP and UC are collaborating with Flinders University to add biome data from soil and soil-derived dust to further improve the provenancing technique. This Abstract was presented at the 2021 Goldschmidt Conference (https://conf.goldschmidt.info/goldschmidt/2021/meetingapp.cgi)

<div>The ubiquitous nature of dust, along with localised chemical and biological signatures, makes it an ideal medium for provenance determination in a forensic context. Metabarcoding of dust can yield biological communities unique to the site of interest, similarly, geochemical and mineralogical analyses can uncover elements and minerals within dust than can be matched to a geographic location. Combining these analyses presents multiple lines of evidence as to the origin of collected dust samples. In this work, we investigated whether the time an item spent at a site collecting dust influenced the ability to assign provenance. We then integrated dust metabarcoding of bacterial and fungal communities into a framework amenable to forensic casework, (i.e., using calibrated log-likelihood ratios to predict the origin of dust samples) and assessed whether current soil metabarcoding databases could be utilised to predict dust origin. Furthermore, we tested whether both metabarcoding and geochemical/mineralogical analyses could be conducted on a single sample for situations where sampling is limited. We found both analyses could generate results capable of separating sites from a single swabbed sample and that the duration of time to accumulate dust did not impact site separation. We did find significant variation within sites at different sampling time periods, showing that bacterial and fungal community profiles vary over time and space – but not to the extent that they are non-discriminatory. We successfully modelled soil and dust samples for both bacterial and fungal diversity, developing calibrated log-likelihood ratio plots and used these to predict provenance for dust samples. We found that the temporal variation in community composition influenced our ability to predict dust provenance and recommend reference samples be collected as close to the sampling time as possible. Thus, our framework showed soil metabarcoding databases are capable of being used to predict dust provenance but the temporal variation in metabarcoded communities will need to be addressed to improve provenance estimates.&nbsp;</div> <b>Citation:</b> Nicole R. Foster, Duncan Taylor, Jurian Hoogewerff, Michael G. Aberle, Patrice de Caritat, Paul Roffey, Robert Edwards, Arif Malik, Michelle Waycott and Jennifer M. Young, The secret hidden in dust: Uncovering the potential to use biological and chemical properties of the airborne soil fraction to assign provenance and integrating this into forensic casework, <i>Forensic Science International: Genetics,</i> (2023) doi:https://doi.org/10.1016/j.fsigen.2023.102931

<div>Environmental DNA (eDNA), elemental and mineralogical analyses of soil have been shown to be specific to their source material, prompting consideration of the use of dust for forensic provenancing. Dust is ubiquitous in the environment and is easily transferred to items belonging to a person of interest, making dust analysis an ideal tool in forensic casework. The advent of Next Generation Sequencing technologies means that metabarcoding of eDNA can uncover microbial, fungal, and even plant genetic fingerprints in dust particles. Combining this with elemental and mineralogical compositions offers multiple, complementary lines of evidence for tracing the origin of an unknown dust sample. This is particularly pertinent when recovering dust from a person of interest to ascertain where they may have travelled. Prior to proposing dust as a forensic trace material, however, the optimum sampling protocols and detection limits need to be established to place parameters around its utility in this context. We tested several approaches to collecting dust from different materials and determined the lowest quantity of dust that could be analysed for eDNA, geochemistry and mineralogy, whilst still yielding results capable of distinguishing between sites. We found that fungal eDNA profiles could be obtained from multiple sample types and that tape lifts were the optimum collection method for discriminating between sites. We successfully recovered both fungal and bacterial eDNA profiles down to 3&nbsp;mg of dust (the lowest tested quantity) and recovered elemental and mineralogical compositions for all tested sample quantities. We show that dust can be reliably recovered from different sample types, using different sampling techniques, and that fungal, bacterial, and elemental and mineralogical profiles, can be generated from small sample quantities, highlighting the utility of dust as a forensic provenance material.</div> <b>Citation:</b> Nicole R. Foster, Belinda Martin, Jurian Hoogewerff, Michael G. Aberle, Patrice de Caritat, Paul Roffey, Robert Edwards, Arif Malik, Priscilla Thwaites, Michelle Waycott, Jennifer Young, The utility of dust for forensic intelligence: Exploring collection methods and detection limits for environmental DNA, elemental and mineralogical analyses of dust samples, <i>Forensic Science International </i>, Volume 344, 2023, 111599, ISSN 0379-0738, https://doi.org/10.1016/j.forsciint.2023.111599. ISSN 0379-0738,