Identification and analysis of man-made geological product particles to aid forensic investigation of provenance in the built environment

摘要

Small (sub-mm) fragments of construction materials derived from geological products are common components of soil and dust samples from urban and industrial environments. These particles increase the complexity of a soil through the admixture of man-made materials with natural minerals within the soil matrix. One application of such indicators is in nuclear security investigations, where there is a requirement to determine the origin and process history of a nuclear material discovered outside of regulatory control. In such cases, analysis of trace environmental materials accumulated from locations where the material was produced, transported and stored may help to establish material provenance. Given a suitable sample, the recognition of particles derived from construction materials can aid such investigations by helping to determine potentially distinctive properties of the originating environment, such as types and potential sources of building materials. Correct identification of man-made particles is also necessary to prevent misidentification of soil mineral particle profiles, and therefore enable determination of the natural mineralogy of associated soil material. In this paper the application of automated mineralogy (based on scanning electron microscopy) analysis for the characterisation of sub-mm particles of man-made construction materials is tested. Thirty-three examples of concrete, construction blocks, cement, brick, plaster and render were analysed. Based on both the particle texture and the minerals/chemical phases present, it is shown that the different construction materials can be readily recognised and characterised. Comparison of natural and artificial cemented particles derived from sedimentary rocks and concrete, and of natural and artificial fine-grained particles derived from mudstone and brick fragments highlights how salient features can be recognised from automated mineralogy data to distinguish man-made geological products from soil mineral assemblages. Crown Copyright (C) 2019 Published by Elsevier B.V. All rights reserved.