Assessing Mineral Dust Properties Using Passive Dust Samplers and Scanning Electron Microscopy
This study presents a novel method to characterize dust particles using a passive dust sampler (PDS). Six different PDS were deployed around six different metal mine sites (Tasmania, Australia) and left in the field for 1 month. Dust particles were analyz
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Abstract This study presents a novel method to characterize dust particles using a passive dust sampler (PDS). Six different PDS were deployed around six different metal mine sites (Tasmania, Australia) and left in the field for 1 month. Dust particles were analyzed directly on the PDS using a Field Emission Scanning Electron Microscope. Backscattered electron (BSE) images were collected with a resolution of 0.5 lm per pixel and used to characterize the size and composition of dust particles. Those particles >2 lm in diameter were classified according to the range of BSE brightness values, which correspond to mineralogical compositions. Particles were grouped according to BSE brightness and categorized as organic particles, silicates, Fe silicates and oxides, and sulfides. Dust sources with unique particle size:composition relationships were identified at particular mine site domains (e.g. rock crusher, concentrator plant, tailings dam). The documented method can be used to monitor the dispersal of mineral dust and provide information on the mineralogical composition of particle size fractions relevant to occupational health risks at metalliferous mine sites.
T.L. Noble (&) R.F. Berry School of Physical Sciences, University of Tasmania, Private Bag 79, Hobart, TAS 7001, Australia e-mail: [email protected] R.F. Berry e-mail: [email protected] K. Goemann Central Science Laboratory, University of Tasmania, Private Bag 74, Hobart, TAS 7001, Australia e-mail: [email protected] B. Lottermoser Institute of Mineral Resources Engineering, RWTH Aachen University, Wüllnerstrasse 2, 52062 Aachen, Germany e-mail: [email protected] © Springer International Publishing Switzerland 2017 B. Lottermoser (ed.), Environmental Indicators in Metal Mining, DOI 10.1007/978-3-319-42731-7_18
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Introduction Atmospheric particulate matter can result from a diverse range of natural and anthropogenic sources, resulting in a complex mixture of mineral (e.g. silicates, sulfides, sulfates) and biological (e.g. organics, pollen, microbial contaminants) particulates. Mineral dust originating from mine sites is commonly rich in silicate minerals. Depending on the size and nature of dust particles, they can have significant human health impacts (Plumlee and Ziegler 2003). The most hazardous dust particles are those that are 100), “medium” (>45), and “other”. The statistical data on size, shape and average brightness was exported for these objects (typically 2000–10,000 for each image) and the data from the two images were combined. The effective diameter of each object was calculated according to Berry and Hunt (2013), which provides the best estimate of the average diameter for near spherical objects. BSE brightness is a function of mean atomic weight of elements in a material (Howell et al. 1998). However for very small particles, the apparent brightness is also a function of the grain size. To account for this grain size affect, a provisional function was used based on empirical observations
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