Spectral Differentiation of Asbestos Minerals in South Africa for Potential Use in Pollution Monitoring
Brilliant M. Petja
Researcher/ PhD candidate
University of Limpopo,
Phila C. Sibandze
Remote Sensing Researcher
ARC-Institute of Soil, Climate and Water
Abstract - Different types of asbestos have been mined in South Africa for more than ninety years. However, the mines have been decommissioned because of the negative social health impacts and environmental degradation associated with asbestos mining. A majority of the mines have been rehabilitated over the years since they were decommissioned. Despite the rehabilitation process, field evidence shows that traces of different asbestos minerals appear scattered even on the rehabilitated environments. This study investigates the feasibility of using remote sensing to differentiate different types of asbestos minerals. An ability to separate different types of asbestos will play a significant role in developing spectrally based mapping methods to detect the spread of asbestos. Analytical Spectral Devices (ASD) Field Spectrometer was used to collect spectra of asbestos minerals that occur in South Africa. Spectra of soil and water samples were also collected from the rehabilitated environments to determine the presence of asbestos minerals. The collected spectra of asbestos minerals will be used to discern the possibility of using asbestos spectral library to map distribution of asbestos minerals in the post mining environment for effective monitoring of environmental pollution.
Reflectance and emittance spectroscopy of natural surfaces are sensitive to specific chemical bonds in materials, whether solid, liquid or gas. Spectroscopy has the advantage of being sensitive to both crystalline and amorphous materials, unlike some diagnostic methods, like X-ray diffraction. The advantage of spectroscopy is that it is too sensitive to small changes in the chemistry and/or structure of a material. Variations in material composition often cause shifts in the position and shape of absorption bands in the spectrum. Spectroscopy brings also the advantage of allowing us to obtain more details about the chemistry of our natural environment based on the reflectance and emittance characteristics. Spectrometry is derived from spectro-photometry, the measure of photons as a function of wavelength (Clark, 1999). Lucas et al., (2004) shows that the use of field spectral measurements are among others for the purpose of validating and calibrating data obtained using airborne and space borne sensors. They further indicate that data acquired by airborne or spaceborne sensors cannot be considered in isolation since effective data interpretation requires a detailed understanding of the processes and interactions occurring at the Earth’s surface. In this respect, a fundamental component of understanding hyperspectral sensors is the laboratory and field measurement of the spectral reflectance of different surfaces. This study investigates the feasibility of using remote sensing to differentiate different types of asbestos minerals by conducting spectroscopy analyses. The study also investigates if traces of asbestos minerals can be detected using spectral signature analysis from collected soil and water samples on the rehabilitated environments. Separability of different types of asbestos will play a significant role in developing spectrally based mapping methods to detect the spread of asbestos.
Study Area and Geological Background
The study was conducted in Mafefe and Mathabatha areas in the Limpopo Province of South Africa (Fig. 1). The areas are situated between Wolkberg and Steelpoort Mountains which form part of Drakensberg Mountains. Asbestos mining was undertaken on mountainuous environment with a steep to relatively flat slope. However, the asbestos wastes where dumped within the villages and at valleys. According to Coetzee et al., (1992), the crocidolite and amosite asbestos in Mafefe and Mathabatha constitute the Transvaal Crocidolite-Amosite field which occupies portions of Polokoane and Letaba districts and it stretches from Chueniespoort in the west to Steelpoort river in the east. The asbestos occurs in the Banded Ironstage of the Dolomite series. The ironstone is underlain by a great thickness of dolomite of the main dolomite stage and overlain by dolomite and shale of the upper dolomite stage. The banded ironstone reaches a maximum thickness of over 800m in the centre of the field around Mohlapitse river. The thickness decreases westwards rapidly due to postdepositional erosion so that the thickness at Penge is 160m and 30m at Kromelboog. The diabase sills of up to 70m thick have been intruded into the succession. The banded ironstone succession has been subjected to folding into numerous synclines and anticlines with a general east west strike. The asbestos occupies definite stratigraphic horizons in succession which are generally known as the lower zone, the main zone, the short fibre zone and upper zone. Each zone consists of a large number of individual asbestos fibre seams or groups of fibre seams which comprise the main asbestos zone in the Malips river area (Coetzee et al., 1992).
Collection of spectral reflectance of different asbestos minerals, soil and water samples were undertaken using the ASD (Analytical Spectral Devices Inc., 2002) FieldSpecFR spectroradiometer. This instrument records the reflectance within the range 350nm to 2500nm. The sampling interval for the FieldSpecFR is, 1.4nm for the region 350-100nm and 2nm for the region 1000-2500nm. The full-width-half-maximum (FWHM) spectral resolution of the FieldSpecFR spectroradiometer is 3nm for the region 350-1000nm and 10nm for the region 1000-2500nm. Spectral reflectance were recorded for each asbestos rock type collected from the rock library of the Council for Geoscience and the soil and water samples were collected from the study area. The spectral signatures of asbestos minerals were collected to determine the spectral separability of different types and also to determine if the collected asbestos spectral profiles can be used to detect the presence of asbestos in soil and water. On recording the spectral signatures, the ASD field spectroradiometer was first calibrated with a calibration panel before measurements were recorded. The procedure was repeated continually fifteen minutes after taking the readings. This procedure involves optimizing the instrument in order to adjust it to the sensitivity of various conditions of illumination. The instrument is then calibrated using a white reference. The spectral reflectance of the targets were then recorded.
The analysis of the spectral profiles was conducted using the ASD Viewspec pro software(Analytical Spectral Devices Inc., 2002) and the output in the form of spectral reflectance averages and graphs was exported in an ascii format for further analysis. The exported data was then imported to Microsoft Excel spreadsheet for detailed analysis and interpretation. These included converting the nanometers to micrometers, plotting the graphs and computing the difference spectra.
Spectral separability of different asbestos minerals
Figure 1. Locality map of South Africa showing the study area in Limpopo Province.
Figure 2. Spectral profiles of different types of asbestos minerals occurring in Limpopo Province.
Figure 2 shows spectral signatures of different types of asbestos minerals occurring in the study area. These different types of asbestos minerals generally show a similar type of reflectance. However, they potray different percentage of reflectance at various wavelengths. Anthophylite and tremolite shows a similar reflectance and saturates (reaches 100 % reflectance) at about 1.2 – 1.4?m wavelengths. They can both be separated spectrally at about 1.5 – 1.6?m because anthophyllite does not saturate at this
region when compared to tremolite. The two minerals can be separated from the other by differencing the percentage spectral reflectance.
Figure 3 demonstrates the spectral separability of crocidolite, amosite and chrysotile. Both minerals show a similar pattern of reflectance. However, they can be clearly separated because of the reflectance level within the region of 1.8 – 2.5?m. Their mapping may be effective in this wavelength region.
Figure 3. Spectral separability of crocidolite, amosite and chrysotile.
Figure 4 shows the reflectance difference of amosite from crocidolite. These were the main minerals that were mined in the study area. Compared to crocidolite, amosite show low reflectance difference in most wavelengths which goes below 10 % in the region of 1.4 - 2.3μm.
Figure 4. Spectral differencing of amosite from crocidolite.
Possibility of detecting traces of asbestos in soil and water samples.
Figure 5 shows the spectral profiles of soil and water samples collected from the study area, together with the reflectance of crocidolite and amosite minerals. From the flowing water collected in the river (diluted and undiluted), traces of asbestos minerals cannot be clearly detected. However, wet soil samples collected from the river bed shows a possibility of detecting the presence of asbestos minerals. This will be verified by the laboratory analyses currently conducted for these collected samples.
Figure 5. Spectral signatures of collected to soil and water samples and the two asbestos minerals.
Different asbestos minerals generally follow a similar pattern of reflectance and transmittance of electromagnetic radiation. Depending on the percentage or level of reflectance, different asbestos type can be distinguished. This can also be done effectively using the spectra differencing method. On this basis, spectrally derived mineral mapping can be used as an important tool to map different minerals of the same genera with the aid of very high spectral resolution remote sensing images. Construction of this type of spectral libraries through time will contribute positively to the geo-information applications in the mining sector. Spectra differencing is presented here as the most appropriate method to conduct the spectral separability of different minerals. It helps in avoiding errors that could be conducted when undertaking visual interpretation. Through the use of reflectance spectroscopy, a possibility exists to use reflectance data to detect the presence of minerals and to monitor pollution of water bodies and streams surrounding mining areas. Through effective validation and calibration, this will contribute positively to monitoring pollution using space borne and airborne sensors.
This study demonstrated the separability of different asbestos minerals using field based reflectance spectroscopy. The potential for detecting the presence of asbestos minerals in water bodies was also examined using the remote sensing techniques. The information derived can be used as a valuable input to carry out spectrally derived mapping of minerals and pollution in the mining environment. However,
cost and accessibility of hyperspectral images may become a limiting factor.
The authors acknowledge the South African Department of Science and Technology (Pontsho Maruping, Mothibi Ramusi and Mzukisi Mazula), Agricultural Research Council – Institute for Soil, Climate and Water (Prof. Tim Simalenga) and University of Limpopo for funding this project. Data and research materials obtained from Council for Geosciences is gratefully acknowledged. We also acknowledge the guidance obtained from Dr. Gregg. A. Swayze (US Geological Survey).
- Analytical Spectral Devices Inc. (2002) FieldSpec ® Pro: User Guide. Boulder, USA.
- Clark, R. N. (1999) Chapter 1: Spectroscopy of Rocks and Minerals, and Principles of Spectroscopy, in A.N. Rencz (ed.) Manual of Remote Sensing, Volume 3, Remote Sensing for the Earth Sciences, John Wiley and Sons, New York, p 3- 58.
- Coetzee, C.B., Brabers, A.J., Malherbe, S.J., and van Biljon, W.J. (1992) Asbestos in Coetzee, C.B.(ed) Mineral resources of South Africa. 5th edition. Geological Survey, Pretoria.
- Lucas, R., Rowlands, A., Niemann, O., Merton, R. N. (2004) Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data. Book Chapter 1, in Varshney, P. K., and Arora, M. J. (eds) Hyperspectral Sensors and Applications. Springer-Verlag. p11 - 49.