A robust interpolation-based method for forensic soil provenancing: A Bayesian likelihood ratio approach.

Soil is a complex and spatially variable material that has a demonstrated potential as a useful evidence class in forensic casework and intelligence operations. Here, the capability to spatially constrain police search areas 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) was carried out over Canberra, Australia, aiming to document 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) of the fused XRF beads 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 the two 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, an objective method using Random Match Probabilities for ranking individual variables to be used for provenancing prior to receiving evidentiary material was demonstrated. Our results show this collective procedure generates more consistent and robust provenance maps when applied to two different interpolation algorithms (e.g., inverse distance weighting, and natural neighbour), with different grid placements (e.g., grid shifts to the north or east) and by different theoretical users (e.g., different computer systems, or forensic geoscientists).

The secret hidden in dust: Assessing the potential to use biological and chemical properties of the airborne fraction of soil for provenance assignment and forensic casework.

The airborne fraction of soil (dust) is both ubiquitous in nature and contains localised biological and chemical signatures, making it a potential medium for forensic intelligence. Metabarcoding of dust can yield biological communities unique to the site of interest, similarly, geochemical analyses can uncover elements and minerals within dust that can be matched to a geographic location. Combining these analyses presents multiple lines of evidence as to the origin of dust collected from items of interest. In this work, we investigated whether bacterial and fungal communities in dust change through time and whether they are comparable to soil samples of the same site. We integrated dust metabarcoding into a framework amenable to forensic casework, (i.e., using calibrated log-likelihood ratios) to predict the origin of dust samples using models constructed from both dust samples and soil samples from the same site. Furthermore, we tested whether both metabarcoding and geochemical/mineralogical analyses could be conducted on a single swabbed sample, for situations where sampling is limited. We found both analyses could generate results from a single swabbed sample and found biological and chemical signatures unique to sites. However, we did find significant variation within sites, where this did not always correlate with time but was a random effect of sampling. This variation within sites was not greater than between sites and so did not influence site discrimination. When modelling bacterial and fungal diversity using calibrated log-likelihood ratios, we found samples were correctly predicted using dust 67% and 56% of the time and using soil 56% and 22% of the time for bacteria and fungi communities respectively. Incorrect predictions were related to within site variability, highlighting limitations to assigning dust provenance using metabarcoding of soil.

The utility of dust for forensic intelligence: Exploring collection methods and detection limits for environmental DNA, elemental and mineralogical analyses of dust samples.

Environmental DNA (eDNA), elemental and mineralogical analyses of soil have been shown to be specific to their source material, prompting consideration of using the airborne fraction of soil (dust) for forensic intelligence work. 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 Massive Parallel Sequencing technologies means metabarcoding of eDNA can uncover bacterial, 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, elemental composition 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 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 fungi and bacteria, as well as elemental and mineralogical profiles, can be generated from small sample quantities, highlighting the utility of dust for forensic intelligence.

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

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.

A fresh scientific look at transfer and persistence: From a materials science and tribology perspective.

Knowledge of the mechanisms governing transfer, persistence, and recovery of trace evidence, together with background prevalence in the population of interest, and other task relevant information, is key for the forensic interpretation and reconstruction of what happened at the activity level. Up to now, this informational "toolkit" has largely been developed through empirical forensic studies on specific trace materials such as glass, textile fibers, and soil. Combined with the identified systemic siloing between disciplines, while valuable, such research tends to be very material-dependent, introducing specific parameters and interpretations that may have actually impeded the recognition of underlying foundational factors applicable to most material types. In Australia, there has been a renewed interest in developing a discipline-independent framework for the interpretation and/or reconstruction of trace evidence to interpret specific circumstances in casework. In this paper, we present a discipline agnostic "way of thinking" that has been anchored in foundational science underpinning the trace evidence discipline. Physical and mechanical material properties such as material geometry and surface topography, strength, stiffness, and hardness collectively influence contact interactions through underlying friction, wear, and lubrication cause and effect mechanisms. We discuss how these fundamental factors and parameters stemming from materials science and tribology may be adopted and adapted by forensic practitioners and researchers to contribute to a better understanding of transfer, persistence, and recovery mechanisms irrespective of evidence discipline and material type. Examples are provided to demonstrate the practical significance to real-life casework and academic research.

Forensic soil provenancing in an urban/suburban setting: A sequential multivariate approach.

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 centered log-ratio 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 on 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. Inclusion of PCs in provenancing adds marginally to the performance. Maximizing 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.