It is necessary to trade off environmental impact and mining performance, preferably using an integrated life cycle modeling approach, while quantifying the air emissions. An integrated framework comprises of a life cycle inventory, and artificial neural network model for mining (LCAMM) is developed and applied to the real case study of an open-pit gold mine. This study aims to develop the air pollution inventory using inverse matrix method and estimation of midpoint impact assessment using defined characterization methodologies. Furthermore, this paper explores the feasibility of back-propagation artificial neural networking model to develop the carbon footprints analysis for the mine in terms of CO2 equivalent by constructing different scenarios. The inventory result shows that TSP (49.6%), PM10 (20%), PM2.5 (14.7%), N2O (7.1%), CO (6.1%), SO2 (1.3%), HCN (0.6%), CH4 (0.4%), CaO (0.2%) and VOCs (0.06%) are responsible for the total environmental load in this gold mining. Subsequently, particulate matter formation (kg PM10 eq.) significantly contributes to the midpoint impact in contrast to the other environmental impacts, mainly because of hauling, handling of ore, milling/grinding, drilling and stockpiling. The study reveals that average 13,992.25–13,993.75 tons of CO2 equivalent is produced as the carbon footprints for this mine. This study confirms that LCAMM framework can serve the basis for further analysis of pollutant’s dispersion and assist decision makers to select an appropriate remedial technique in the mining industry.
Air pollutants Mining Life cycle assessment Artificial neural network Carbon footprint Inventory
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The research was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC). The authors would like to thank Mr. Jean-Marc Lacoste, the President of the Croinor Gold Inc., for being an industrial partner providing Canadian context mining process references.
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