Date of Award


Publication Type

Master Thesis

Degree Name



Civil and Environmental Engineering

First Advisor

Xu, X.




The sources and processes, including re-emission of gaseous elemental mercury, affecting speciated atmospheric mercury (Hg) at Flin Flon, Manitoba were identified and quantified using the positive matrix factorisation (PMF) model and principal component analysis (PCA). The input data contain the concentrations of gaseous elemental mercury (GEM), gaseous oxidized mercury (GOM), particulate-bound mercury (PBM), PM2.5and its components (elements and ions), sulphur dioxide (SO2) as well as temperature, precipitation, relative humidity and wind speed. Eighty-one daily samples and twenty chemical species concentrations as well as meteorological parameters, measured from July 2010 to May 2011, were analysed. PMF identified six factors, namely secondary aerosol and re-emission, industrial, crustal/soil dust, road salt/biomass burning, Hg oxidation and coal combustion. Among the factors, secondary aerosol and re-emission, road salt/biomass burning and bromine source profiles contained one or two Hg forms. The bromine source and, secondary aerosol and re-emission were the dominant GEM contributing factors with average contributions of 48% and 43%, respectively. PMF most closely predicted the observed daily concentrations of PBM then GOM and PBM. PCA of the same concentration data set extracted six principal components. These were largely consistent with the PMF factors. A component identified as long-range transport of Hg with loadings on GEM and GOM only was identified by PCA. With inclusion of meteorological data in the input, the long-range transport of Hg was divided into re-emission and a new component, dispersion of GEM. Overall, PCA identified three Hg-associated components, including re-emission of GEM. The long-range transport of Hg predominantly contributed to GEM in PCA of dataset. The dispersion component’s contribution to GEM was dominant when meteorological data was included in the input. PCA most closely predicted PBM then GOM and GEM, regardless of whether or not meteorological data was included.