SCIENTIFIC PROGRAMS AND ACTIVITIES
|January 22, 2017|
Lectures Celebrating New Fellows of the Royal Society of Canada
The presentation will give an overview of some of the research being
conducted at the University of Calgary in the area of satellite navigation
using the Global Positioning System (GPS). A history of GPS is given
followed by an overview of the system architecture and navigation signals.
Some of the research conducted will be discussed ranging from fundamental
error modelling, system simulation, algorithm development as well as
system integration for a wide range of application areas. Specific projects
that are discussed include the use of new GPS technology for positioning
in weak signal environments (e.g. indoors), the development of centimetre-level
positioning systems to support aircraft landing on aircraft carriers,
the use of multiple reference stations for centimetre-level positioning,
orientation determination using low-cost GPS sensors, low cost sensor
integration for personal and vehicle positioning and tracking, as well
as the integration of GPS and Inertial Measurement Units (IMUs) or for
precise vehicle positioning in urban areas.
The analysis of frequency data is rooted in models and methods for
characterizing joint distributions of discrete (categorical) variables.
These distributions have interesting mathematical representations. In
this presentation we look at two seemingly distinct representations
for frequency data that leads to markedly different forms of statistical
analysis. We begin by revisiting aspects of log-linear models for contingency
tables and suggest new applications, and then we turn to a new Bayesian
mixed membership model for clustering and classification which provides
an alternative way to model large sparse categorical data arrays. We
include applications to confidentiality, the study of disability, and
the topical classification of journal publications.
B. Sherwood Lollar, P. Morrill, S. Hirschorn, S. Mancini, J. McKelvie, M. Elsner, G. Lacrampe-Couloume, E.A. Edwards, B. Sleep
Compound Specific Isotope Analysis (CSIA), or the characterization of stable carbon and hydrogen isotope compositions of individual contaminant compounds dissolved in groundwater, provides a novel method for investigation of degradation and remediation potential at contaminated sites. For organic contaminants such as chlorinated solvents, petroleum hydrocarbons and fuel additives, degradation can involve large and reproducible kinetic isotope effects, producing systematic changes in the delta 13C values of the residual contaminant. Examples from laboratory studies and recent field applications will demonstrate that during biodegradation, the light (12C) versus heavy isotope (13C) bonds are preferentially degraded, resulting in isotopic enrichment of the residual contaminant in 13C. Even larger fractionation is often observed when delta 2H values are measured, due to the preferential rate of degradation for the light 1H-containing molecules. This large fractionation effect, often more than an order of magnitude greater than those documented for stable carbon isotopes, indicates that hydrogen isotope analysis has the potential to provide a more definitive indicator of biodegradation. This can be particularly important for organic compounds such as the aromatic hydrocarbons and MTBE, for which carbon isotope fractionation effect are typically on the order of a few permil, somewhat smaller than those observed for the chlorinated hydrocarbons.
Significant advances are being made in integrating CSIA data to quantitative
models. In many cases, stable isotope fractionation during degradation
can be modeled by a simple Rayleigh distillation model that relates
the change in observed stable isotope compositions to the extent of
degradation in the system. Stable isotope analysis can therefore provide
a direct indication of the effects of degradation on specific contaminants,
as well as a novel independent means to quantify the extent of degradation
and estimate degradation rates. In more recent developments, reactive
transport modeling and inverse modeling are being used to address contaminant
fate and transport and to constrain fractionation factors. A key issue
remains determination of the extent to which fractionation factors derived
in the laboratory under controlled conditions can be reliably applied
to more complex field systems. A series of recent field studies will
be highlighted that illustrate the range of potential applications for
CSIA - including examples of biodegradation of chlorinated solvents,
petroleum hydrocarbons and MTBE.