Georeferenced soil provenancing with digital signatures

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).

Spatial Variability of Soil Properties and Portable X-Ray Fluorescence-quantified Elements of typical Golf Courses Soils

Understanding and quantitative delineation of Portable X-Ray Fluorescence (PXRF) -quantified elements and soil properties spatial variability are important for healthy turf development for golf courses. In this study, PXRF-quantified elements and soil properties (except soil acidity and alkalinity (pH), electric conductivity (EC), and textures) of 200 soil samples were measured by PXRF analyzer at different golf courses in Lubbock, Amarillo, and Midland in Texas, and Hobbs in New Mexico. Furthermore, principal component analysis (PCA), empirical bayesian kriging (EBK) and the ordinary least square model (OLSM) were used in the study. Two kinds of components were extracted and interpreted by PCA, the results showed Zn, Ti, Fe, Rb, V, Mn and Zr were associated with the component 1, while Sr was associated with the component 2, the preliminary classification of PXRF-quantified elements was formed by PCA. The EBK approach was used to evaluate the spatial patterns of PXRF-quantified elements and soil properties. The OLSM model quantitatively related pH to EC, silt texture and the PXRF-quantified K, Ca and Sr. The integration of PCA, EBK and OLSM revealed quantitative links between soil pedogenesis and causes, spatial variability and couple relationships of PXRF-quantified elements and soil properties over golf courses.

Can analysis of a small clod of soil help to solve a murder case?

Soil forensics utilizes extensive soil information to answer legal questions and test hypotheses. The main difficulty often is the determination of different variables from a small amount of soil sample collected on the suspect. We developed a sequential mineralogical and chemical analyses to assess a limited quantity of soil vestiges (0.5 g) from a suspect's vehicle (adhered to the outside rear-view mirror and to the left front fender) involved in a murder case and compared them with the surface samples found at the victim's body disposal site at the Graciosa Road, Paraná State, Brazil. All results affirm that the suspect's vehicle could have been in contact with the edge of the Graciosa Road, approximately the place where the victim's body was located. As a result of the soil analysis and comparison, the results support the likely contact of the suspect's vehicle with the crime scene.

Predicting provenance of forensic soil samples: Linking soil to ecological habitats by metabarcoding and supervised classification

Environmental DNA (eDNA) is increasingly applied in ecological studies, including studies with the primary purpose of criminal investigation, in which eDNA from soil can be used to pair samples or reveal sample provenance. We collected soil eDNA samples as part of a large national biodiversity research project across 130 sites in Denmark. We investigated the potential for soil eDNA metabarcoding in predicting provenance in terms of environmental conditions, habitat type and geographic regions. We used linear regression for predicting environmental gradients of light, soil moisture, pH and nutrient status (represented by Ellenberg Indicator Values, EIVs) and Quadratic Discriminant Analysis (QDA) to predict habitat type and geographic region. eDNA data performed relatively well as a predictor of environmental gradients (R² > 0.81). Its ability to discriminate between habitat types was variable, with high accuracy for certain forest types and low accuracy for heathland, which was poorly predicted. Geographic region was also less accurately predicted by eDNA. We demonstrated the application of provenance prediction in forensic science by evaluating and discussing two mock crime scenes. Here, we listed the plant species from annotated sequences, which can further aid in identifying the likely habitat or, in case of rare species, a geographic region. Predictions of environmental gradients and habitat types together give an overall accurate description of a crime scene, but care should be taken when interpreting annotated sequences, e.g. due to erroneous assignments in GenBank. Our approach demonstrates that important habitat properties can be derived from soil eDNA, and exemplifies a range of potential applications of eDNA in forensic ecology.