Abstract

Air quality in mining region has been facing many challenges due to lack of understanding of atmospheric factors and physical removal mechanism. Mining operations emit various types of pollutants which could violate the environmental guidelines. The development of an integrated approach is conceptualized in this thesis as life cycle assessment based air quality modeling system (LCAQMS) for the mining industry. LCAQMS consists of four primary models which are: (1) life cycle inventory model, (2) artificial neural network model, (3) mining-zone air dispersion model, and (4) decision analysis model. A graphical user interface (GUI) is built to integrate primary models to understand the pollutant’s fate from its generation (emission inventory) to its management (control decisions). The life cycle inventory (LCI) model is developed to determine emission inventory using inverse matrix method, and defined characterization methods are investigated to assess the environmental impact. Artificial neural network model is developed to analyze carbon footprints (CO2 equivalent) using backpropagation method. Mining-zone air dispersion model (MADM) is developed to generate the predicted concentration of air pollutants at various receptor levels by considering the deposition effect. The meteorological factors based on atmospheric stability conditions are determined by employing the Pasquill-Turner method (PTM). The decision analysis model comprises multi-criteria decision analysis (MCDA) method and air pollution control model (APCM) to provide air pollution control alternatives and optimize the cost-effective solutions, respectively. Monte Carlo simulation accomplishes the uncertainties in the system. Moreover, an environmental risk assessment (ERA) method is extended by integrating the APCM with a fuzzy set. The applicability of LCAQMS is explored through three different case studies of open-pit metal mining in North America. Inventory results first show the air emission load for each mining activity and allow to evaluate the emission impact by linking the inventory to each impact category. Then this study helps to quantify the carbon footprints for the gold and copper mines. Also, prediction of significant pollutants such as PM10, PM2.5, SO2, and NOx at ground level has been calculated. The results depict that dry deposition is a dominate physical removal mechanism in the mining area. The LCAQMS results are evaluated with the monitoring field values, particularly MADM results are statistically tested against California puff (CALPUFF) model. Additionally, atmospheric stability is examined by analyzing the relationship between modeled PM2.5 concentrations and mixing height based on seasonal variation and the diurnal cycle. In conclusion, LCAQMS can serve as a useful tool for the stakeholders to assess the impact, predict the air quality, and aid planners to minimize the pollutants at a marginal cost by suggesting control pollution techniques.