In support of continuous environmental improvement in the mining industry, it is important to systematically assess the environmental impacts of mining and mineral processing operations from a life cycle perspective. Although life cycle assessment (LCA) is widely used as an environmental systems analysis tool, the application of LCA in the mining industry is still in progress. This paper carried out a cradle-to-gate LCA of an underground copper ore mine planned in Northern Norway. Based on the ReCiPe midpoint (hierarchist) life cycle impact assessment method, results of the study showed that on-site electricity use, diesel for mining trucks and blasting dominated contributions across six, four and four, respectively, of the eighteen categories assessed, and metals leaching from tailings were the primary contributors to the human toxicity and marine ecotoxicity impacts. Compared to the baseline, results of the energy-oriented scenario analysis indicated that electrification of diesel-driven mining trucks would be more environmentally beneficial as long as across impact categories. While electrodialytic tailings remediation could extract up to 64% of copper in tailings prior to disposal and significantly reduce 2 the human toxicity impact of tailings, the marine ecotoxicity impact of tailings after electrodialysis changed inconsistently across the ReCiPe hierarchist and egalitarian perspectives. It is recommended to further assess the trade-off between the benefits of electrodialytic tailings remediation (extracting more copper) and the potential impacts of deposited tailings after electrodialysis from a multi-criteria decision-analysis perspective. In a generic context, this study provides an insight in further promoting LCA as an environmental decision-support tool, especially for comparing available cleaner production options, improving the overall environmental performance of a mine, and facilitating better communication with stakeholders.
Pollution levels, pollutant distribution and potential source assessments based on multivariate analysis (chemometrics) were made for harbour sediments from two Arctic locations; Hammerfest in Norway and Sisimiut in Greenland. High levels of heavy metals were detected in addition to organic pollutants. Preliminary assessments based on Principal Component Analysis (PCA) revealed different sources and pollutant distribution in the sediments of the two harbours. Tributyltin (TBT) was, however, found to originate from point source(s) and the highest concentrations of TBT in both harbours were found adjacent to the former shipyards. Polyaromatic hydrocarbons (PAH) ratios and PCA plots revealed that the predominant source in both harbours was pyrogenic related to coal/biomass combustion. Comparison of commercial polychlorinated biphenyls (PCB) mixtures with PCB compositions in the sediments indicated relation primarily to German, Russian and American mixtures in Hammerfest; and American, Russian and Japanese mixtures in Sisimiut. PCA was shown to be an important tool for identifying pollutant sources and differences in pollutant composition in relation to sediment characteristics.
Chemometrics was used to develop a multivariate model based on 46 previously reported electrodialytic remediation experiments (EDR) of five different harbour sediments. The model predicted final concentrations of Cd, Cu, Pb and Zn as a function of current density, remediation time, stirring rate, dry/wet sediment, cell set-up as well as sediment properties. Evaluation of the model showed that remediation time and current density had the highest comparative influence on the clean-up levels. Individual models for each heavy metal showed variance in the variable importance, indicating that the targeted heavy metals were bound to different sediment fractions. Based on the results, a PLS model was used to design five new EDR experiments of a sixth sediment to achieve specified clean-up levels of Cu and Pb. The removal efficiencies were up to 82% for Cu and 87% for Pb and the targeted clean-up levels were met in four out of five experiments. The clean-up levels were better than predicted by the model, which could hence be used for predicting an approximate remediation strategy; the modelling power will however improve with more data included.
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