Applying risk-based principles of dispersive mine spoil behaviour to facilitate development of cost-effective best management practices

Bennett, John and Raine, Steve and Reardon-Smith, Kate and Dale, Glenn and Thomas, Evan (2017) Applying risk-based principles of dispersive mine spoil behaviour to facilitate development of cost-effective best management practices. Technical Report. Unpublished . [Report]

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Abstract

Dispersive spoil material on mine sites represents a significant operational, environmental and economic challenge to mining operations. Better understanding of the chemical and physical characteristic of spoil material and its behaviour under different climatic and management regimes is needed to inform site-specific management decisions.

This industry-funded project has developed, parameterised and tested a prototype Bayesian network (BN) model which integrates a range of biophysical (climate, spoil characteristics, vegetation cover) and management (landform, spoil amendment, runoff management) variables.

Where available, quantitative data were used to parameterise the model; however, in many instances, existing data were too few and it was necessary to use qualitative information (expert judgement). The process of developing the model identified serious data deficiencies which should inform future data collection strategies.

An ongoing iterative process, with targeted data collection and feedback from industry decision makers and discipline experts, will support improvements in the model, which has significant potential to inform adaptive evidence-based best practice dispersive mine spoil management.


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Item Type: Report (Technical Report)
Item Status: Live Archive
Additional Information: USQ Report.
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019)
Date Deposited: 28 Apr 2021 06:30
Last Modified: 28 Apr 2021 06:30
Uncontrolled Keywords: Bayesian Network model; model development; sensitivity analysis
Fields of Research (2008): 05 Environmental Sciences > 0502 Environmental Science and Management > 050207 Environmental Rehabilitation (excl. Bioremediation)
05 Environmental Sciences > 0503 Soil Sciences > 050399 Soil Sciences not elsewhere classified
05 Environmental Sciences > 0502 Environmental Science and Management > 050205 Environmental Management
Fields of Research (2020): 41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410405 Environmental rehabilitation and restoration
41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410404 Environmental management
41 ENVIRONMENTAL SCIENCES > 4106 Soil sciences > 410699 Soil sciences not elsewhere classified
Socio-Economic Objectives (2008): D Environment > 96 Environment > 9609 Land and Water Management > 960908 Mining Land and Water Management
D Environment > 96 Environment > 9612 Rehabilitation of Degraded Environments > 961205 Rehabilitation of Degraded Mining Environments
B Economic Development > 84 Mineral Resources (excl. Energy Resources) > 8498 Environmentally Sustainable Mineral Resource Activities > 849899 Environmentally Sustainable Mineral Resource Activities not elsewhere classified
Socio-Economic Objectives (2020): 18 ENVIRONMENTAL MANAGEMENT > 1806 Terrestrial systems and management > 180604 Rehabilitation or conservation of terrestrial environments
18 ENVIRONMENTAL MANAGEMENT > 1806 Terrestrial systems and management > 180605 Soils
25 MINERAL RESOURCES (EXCL. ENERGY RESOURCES) > 2501 Environmentally sustainable mineral resource activities > 250199 Environmentally sustainable mineral resource activities not elsewhere classified
URI: http://eprints.usq.edu.au/id/eprint/41858

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