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There is gold in them hills: Predicting potential acid mine drainage events through the use of chemometrics
•PCA analysis confirms pollution events of Mount Morgan mine.•High negative loadings were shown in DO and pH.•High pH was attributed to the loadings associated with the mine site.•PCA is a powerful tool to discriminate between a polluted and reference site. [Display omitted] Disused mines and mining...
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Published in: | The Science of the total environment 2018-04, Vol.619-620, p.1464-1472 |
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Main Authors: | , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | •PCA analysis confirms pollution events of Mount Morgan mine.•High negative loadings were shown in DO and pH.•High pH was attributed to the loadings associated with the mine site.•PCA is a powerful tool to discriminate between a polluted and reference site.
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Disused mines and mining legacy require significant manpower to ameliorate the contaminated environmental surroundings following their disbanding coupled with extraordinary funding to manage these issues. Water (pH, temperature, dissolved oxygen, conductance, metals, sulphate) and total suspended solids (TSS) quality are environmental parameters that are affected by legacy mining activity and often require monitoring and rapid response if events (e.g. rainfall) occur which might affect the surrounding areas. In this study, we have monitored a famous mine site in Queensland, Australia for a number of water and sediment parameters known to be associated with acid mine drainage. This study performed analysis of water and sediment over three years, as well as rainfall data. Principal component analysis (PCA) and partial least squares (PLS) regression was undertaken to investigate the data obtained. It was found that the use of PCA can predict the effect of year and site on the environmental influence of the abandoned mine site, based on the combination of chemical properties and meteorological data. |
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ISSN: | 0048-9697 1879-1026 |
DOI: | 10.1016/j.scitotenv.2017.11.063 |