<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>Geochemistry of soils, stream sediments, and overbank sediments, plays an important part in informing geochemical environmental baselines, mineral prospectivity, and environmental management practices. Australia has a large number of such surveys, but they are spatially isolated and often used in isolation. First released in 2020, the Levelled Geochemical Baseline of Australia focused on levelling such surveys across the North Australian Craton, so that they could be used as a seamless dataset. This data release acts as an update to the Levelled Geochemical Baseline of Australia by changing the focus to national scale and incorporating recently reanalysed legacy samples.</div><div><br></div><div>This work was undertaken as part of the Exploring for the Future program, an eight-year program by the Australian government. The Exploring for the Future program provides precompetitive information to inform decision-making by government, community and industry on the sustainable development of Australia's mineral, energy and groundwater resources. By gathering, analysing and interpreting new and existing precompetitive geoscience data and knowledge, we are building a national picture of Australia’s geology and resource potential. This leads to a strong economy, resilient society and sustainable environment for the benefit of all Australians. This includes supporting Australia’s transition to net zero emissions, strong, sustainable resources and agriculture sectors, and economic opportunities and social benefits for Australia’s regional and remote communities. The Exploring for the Future program, which commenced in 2016, was an eight year, $225m investment by the Australian Government.</div><div><br></div><div><br></div><div><br></div><div><br></div>

<div>Geochemical and mineralogical analysis of surficial materials (streams, soils, catchment samples, etc) can provide valuable information about the potential for mineral systems, and the background mineralogical and geochemical variation for a region. However, collecting new samples can be time consuming and expensive, particularly for regional-scale studies. Fortunately, Geoscience Australia has a large holding of archived samples from regional- to continental-scale geochemical studies conducted over the last 50 years, the majority collected at high sampling densities that would be cost-prohibitive today. Although all these samples have already been analysed, their vintage can mean that analyses were obtained by a variety of analytical methods, are of variable quality, and often only available for a small number of elements. As part of the Australian government’s Exploring for the Future program, funding was dedicated to re-analyse ~9,000 samples from these legacy surveys. They were re-analysed for 63 elements (total content) at a single laboratory producing a seamless, internally consistent, high-quality dataset, providing valuable new insights.</div><div><br></div><div>A large number (7,700) of these legacy samples were collected from north Queensland, predominantly in the Cape York – Georgetown area (5,472) — an area with both a wide-range of existing deposit types and known potential for many critical minerals. The sample densities of these studies, up to 1 sample per ~2.5 km2 for Georgetown, makes them directly applicable for determining local- and regional-scale areas of interest for mineral potential. Early interpretation of the Cape York – Georgetown data has identified several locations with stream sediments enriched in both heavy and light rare earth elements (maximum 4000 and 31,800 ppm, respectively), demonstrating the potential of this dataset, particularly for critical minerals. The greater sampling density means that these samples can also provide much more granular geochemical background information and contribute to our understanding of the lower density data commonly used in regional- and national-scale geochemical background studies.</div><div><br></div><div>In addition to the geochemical re-analysis of legacy surface samples, Geoscience Australia has also been undertaking mineral analysis of legacy continental-scale geochemical samples. The National Geochemical Survey of Australia (NGSA) sample archive has been utilised to provide a valuable new dataset. By separating and identifying heavy minerals (i.e., those with a specific gravity >2.9 g/cm3) new information about the mineral potential and provenance of samples can be gained. The Heavy Mineral Map of Australia (HMMA) project, undertaken in collaboration with Curtin University, has analysed the NGSA sample archive, with~81% coverage of the continent. The project has identified over 145 million individual mineral grains belonging to 163 unique mineral species. Preliminary analysis of the data has indicated that zinc minerals and native elements may be useful for mineral prospectivity. Due to the large amount of data generated as part of this HMMA project, a mineral network analysis tool has been developed to help visualise the relationship between minerals and aid in the interpretation of the data. Abstract presented to the Australian Institute of Geoscientists – ALS Friday Seminar Series: Geophysical and Geochemical Signatures of Queensland Mineral Deposits October 2023 (https://www.aig.org.au/events/aig-als-friday-seminar-series-geophysical-and-geochemical-signatures-of-qld-mineral-deposits/)

<div>This dataset comprises hydrochemistry results for groundwater, surface water, and rainwater samples collected as part of the Upper Darling Floodplain groundwater study. Associated methods, interpretation, and integration with other datasets are found in the Upper Darling Floodplain geological and hydrogeological assessment (Geoscience Australia Ecat ID:149689). This project is part of the Exploring for the Future (EFTF) program, an eight-year, $225 million Australian Government funded geoscience data and precompetitive information acquisition program. The dataset contains 68 groundwater samples, 17 surface water samples, and four rainwater samples. Groundwater samples are from the Cenozoic formations within the alluvium of the Darling River, the Great Artesian Basin, and the Murray geological basin. Surface water samples are from the Darling River, and rainwater samples were taken within the study area. Subsets of the samples were analysed for major ions and trace metals, stable isotopes of water (δ2H and δ18O), radiocarbon (14C), stable carbon isotopes (δ13C), strontium isotopes (87Sr/86Sr), sulfur hexafluoride (SF6), chlorofluorocarbon (CFC) isotopes, chlorine-36 (36Cl), noble gases, and Radon-222. The results were used to inform a range of hydrogeological questions including aquifer distribution and quality, inter-aquifer connectivity, and groundwater-surface water connectivity.&nbsp;</div><div><br></div>

<div>This guide and template details data requirements for submission of mineral deposit geochemical data to the Critical Minerals in Ores (CMiO) database, hosted by Geoscience Australia, in partnership with the United States Geological Survey and the Geological Survey of Canada. The CMiO database is designed to capture multielement geochemical data from a wide variety of critical mineral-bearing deposits around the world. Samples included within this database must be well-characterized and come from localities that have been sufficiently studied to have a reasonable constraint on their deposit type and environment of formation. As such, only samples analysed by modern geochemical methods, and with certain minimum metadata attribution, can be accepted. Data that is submitted to the CMiO database will also be published via the Geoscience Australia Portal (portal.ga.gov.au) and Critical Minerals Mapping Initiative Portal (https://portal.ga.gov.au/persona/cmmi).&nbsp;</div><div><br></div>

<div>With a higher demand for lithium (Li), a better understanding of its concentration and spatial distribution is important to delineate potential anomalous areas. This study uses a digital soil mapping framework to combine data from recent geochemical surveys and environmental covariates to predict and map Li content across the 7.6 million km2 area of Australia. Soil samples were collected by the National Geochemical Survey of Australia at a total of 1315 sites, with both top (0–10 cm depth) and bottom (on average 60–80 cm depth) catchment outlet sediments sampled. We developed 50 bootstrap models using a Cubist regression tree algorithm for both depths. The spatial prediction models were validated on an independent Northern Australia Geochemical Survey dataset, showing a good prediction with an RMSE of 3.82 mg kg-1 for the top depth. The model for the bottom depth has yet to be validated. The variables of importance for the models indicated that the first three Landsat bands and gamma radiometric dose have a strong impact on Li prediction. The bootstrapped models were then used to generate digital soil Li prediction maps for both depths, which could select and delineate areas with anomalously high Li concentrations in the regolith. The map shows high Li concentration around existing mines and other potentially anomalous Li areas. The same mapping principles can potentially be applied to other elements.&nbsp;</div> <b>Citation:</b> Ng, W., Minasny, B., McBratney, A., de Caritat, P., and Wilford, J.: Digital soil mapping of lithium in Australia, <i>Earth Syst. Sci. Data</i>, 15, 2465–2482, https://doi.org/10.5194/essd-15-2465-2023, <b>2023</b>.

<div>Alluvial sediments have long been used in geochemical surveys as their compositions are assumed to be representative of areas upstream. Overbank and floodplain sediments, in particular, are increasingly used for regional to continental-scale geochemical mapping. However, during downstream transport, sediments from heterogeneous source regions are carried away from their source regions and mixed. Consequently, using alluvial sedimentary geochemical data to generate continuous geochemical maps remains challenging. In this study we demonstrate a technique that numerically unmixes alluvial sediments to make a geochemical map of their upstream catchments. The unmixing approach uses a model that predicts the concentration of elements in downstream sediments, given a map of the drainage network and element concentrations in the source region. To unmix sedimentary chemistry, we seek the upstream geochemical map that, when mixed downstream, best fits geochemical observations downstream. To prevent overfitting we penalise the roughness of the geochemical model. To demonstrate our approach we apply it to alluvial samples gathered as part of the Northern Australia Geochemical Survey. This survey gathered samples collected over a ∼ 500,000 km2 area in northern Australia. We first validate our approach for this sample distribution with synthetic tests, which indicate that we can resolve geochemical variability at scales greater than 0.5 – 1◦ in size. We proceed to invert real geochemical data from the total digestion of fine-grained fraction of alluvial sediments. The resulting geochemical maps for two elements of potential economic interest, Cu and Nd, are evaluated in detail. We find that in both cases, our predicted downstream concentrations match well against a held-out, unseen subset of the data, as well as against data from an independent geochemical survey. By performing principal component analysis on maps generated for all 46 available elements we produce a synthesis map showing the significant geochemical domains of this part of northern Australia. This map shows strong spatial similarities to the underlying lithological map of the area. Finally, we compare the results from our approach to a geochemical map produced by kriging. We find that, unlike the method presented here, kriging generates geochemical maps that are both dampened relative to expected magnitude, as well as being spatially distorted. We argue that the unmixing approach is the most appropriate method for generating geochemical maps from regional-scale alluvial surveys.&nbsp;</div> <b>Citation:</b> Alex G. Lipp, Patrice de Caritat, Gareth G. Roberts, Geochemical mapping by unmixing alluvial sediments: An example from northern Australia, <i>Journal of Geochemical Exploration,</i> Volume 248, <b>2023</b>, 107174, ISSN 0375-6742, https://doi.org/10.1016/j.gexplo.2023.107174. (https://www.sciencedirect.com/science/article/pii/S0375674223000213)

<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,

<div>A novel method of estimating the silica (SiO2) and loss-on-ignition (LOI) concentrations for the North American Soil Geochemical Landscapes (NASGL) project datasets is proposed. Combining the precision of the geochemical determinations with the completeness of the mineralogical NASGL data, we suggest a ‘reverse normative’ or inversion approach to calculate first the minimum SiO2, water (H2O) and carbon dioxide (CO2) concentrations in weight percent (wt%) in these samples. These can be used in a first step to compute minimum and maximum estimates for SiO2. In a recursive step, a ‘consensus’ SiO2 is then established as the average between the two aforementioned estimates, trimmed as necessary to yield a total composition (major oxides converted from reported Al, Ca, Fe, K, Mg, Mn, Na, P, S, and Ti elemental concentrations + ‘consensus’ SiO2 + reported trace element concentrations converted to wt% + ‘normative’ H2O + ‘normative’ CO2) of no more than 100 wt%. Any remaining compositional gap between 100 wt% and this sum is considered ‘other’ LOI and likely includes H2O and CO2 from the reported ‘amorphous’ phase (of unknown geochemical or mineralogical composition) as well as other volatile components present in soil. We validate the technique against a separate dataset from Australia where geochemical (including all major oxides) and mineralogical data exist on the same samples. The correlation between predicted and observed SiO2 is linear, strong (R2 = 0.91) and homoscedastic. We also compare the estimated NASGL SiO2 concentrations with another publicly available continental-scale survey over the conterminous USA, the ‘Shacklette and Boerngen’ dataset. This comparison shows the new data to be a reasonable representation of SiO2 values measured on the ground over the same study area. We recommend the approach of combining geochemical and mineralogical information to estimate missing SiO2 and LOI by the recursive inversion approach in datasets elsewhere, with the caveat to validate results.</div><div><br></div><div>The major oxide concentrations, including those for the estimated SiO2 and LOI, for the NASGL A and C horizons are available for download, as CSV files. A worked example for five selected NASGL C horizon samples is also available for download, as an XLSX file.</div> <b>Citation:</b> P. de Caritat, E. Grunsky, D.B. Smith; Estimating the silica content and loss-on-ignition in the North American Soil Geochemical Landscapes datasets: a recursive inversion approach. <i>Geochemistry: Exploration, Environment, Analysis</i> 2023; 23 (3): 2023-039 doi: https://doi.org/10.1144/geochem2023-039 This article appears in multiple journals (Lyell Collection & GeoScienceWorld)

<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.