Tailings ponds in
the oil sands (OS) region in Alberta, Canada,
have been associated with fugitive emissions of volatile organic compounds
(VOCs) and other pollutants to the atmosphere. However, the contribution
of tailings ponds to the total fugitive emissions of VOCs from OS
operations remains uncertain. To address this knowledge gap, a field
study was conducted in the summer of 2017 at Suncor’s Pond
2/3 to estimate emissions of a suite of pollutants including 68 VOCs
using a combination of micrometeorological methods and measurements
from a flux tower. The results indicate that in 2017, Pond 2/3 was
an emission source of 3322 ± 727 tons of VOCs including alkanes,
aromatics, and oxygenated and sulfur-containing organics. While the
total VOC emissions were approximately a factor of 2 higher than those
reported by Suncor, the individual VOC species emissions varied by
up to a factor of 12. A chemical mass balance (CMB) receptor model
was used to estimate the contribution of the tailings pond to VOC
pollution events in a nearby First Nations and Metis community in
Fort McKay. CMB results indicate that Suncor Pond 2/3 contributed
up to 57% to the total mass of VOCs measured at Fort McKay, reinforcing
the importance of accurate VOC emission estimation methods for tailings
ponds.
Many environmental studies require the characterization of a large geographical region using a range of representative sites amenable to intensive study. A systematic approach to selecting study areas can help ensure that an adequate range of the variables of interest is captured. We present a novel method of selecting study sites representing a larger region, in which the region is divided into subregions, which are characterized with relevant independent variables, and displayed in mathematical variable space. Potential study sites are also displayed this way, and selected to cover the range in variables present in the region. The coverage of sites is assessed with the Quality Index, which compares the range and standard deviation of variables among the sites to that of the larger region, and prioritizes sites that are well-distributed (i.e. not clumped) in variable space. We illustrate the method with a case study examining relationships between agricultural land use, physiography and stream phosphorus (P) export, in which we selected several variables representing agricultural P inputs and landscape susceptibility to P loss. A geographic area of 110,000km was represented with 11 study sites with good coverage of four variables representing agricultural P inputs and transport mechanisms taken from commonly-available geospatial datasets. We use a genetic algorithm to select 11 sites with the highest possible QI and compare these, post-hoc, to our sites. This approach reduces subjectivity in site selection, considers practical constraints and easily allows for site reselection if necessary. This site selection approach can easily be adapted to different landscapes and study goals, as we provide an algorithm and computer code to reproduce our approach elsewhere.
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