Quantification of motor vehicle emission factors from on road measurements

Assessment and prediction of the impact of vehicular traffic emissions on air quality and exposure levels requires knowledge of vehicle emission factors. The aim of this study was quantification of emission factors from an on road, over twelve months measurement program conducted at two sites in Brisbane: 1) freeway type (free flowing traffic at about 100 km/h, fleet dominated by small passenger cars - Tora St); and 2) urban busy road with stop/start traffic mode, fleet comprising a significant fraction of heavy duty vehicles - Ipswich Rd. A physical model linking concentrations measured at the road for specific meteorological conditions with motor vehicle emission factors was applied for data analyses. The focus of the study was on submicrometer particles; however the measurements also included supermicrometer particles, PM2.5, carbon monoxide, sulfur dioxide, oxides of nitrogen. The results of the study are summarised in this paper. In particular, the emission factors for submicrometer particles were 6.08 x 1013 and 5.15 x 1013 particles per vehicle-1 km-1 for Tora St and Ipswich Rd respectively and for supermicrometer particles for Tora St, 1.48 x 109 particles per vehicle-1 km-1. Emission factors of diesel vehicles at both sites were about an order of magnitude higher than emissions from gasoline powered vehicles. For submicrometer particles and gasoline vehicles the emission factors were 6.08 x 1013 and 4.34 x 1013 particles per vehicle-1 km-1 for Tora St and Ipswich Rd, respectively, and for diesel vehicles were 5.35 x 1014 and 2.03 x 1014 particles per vehicle-1 km-1 for Tora St and Ipswich Rd, respectively. For supermicrometer particles at Tora St the emission factors were 2.59 x 109 and 1.53 x 1012 particles per vehicle-1 km-1, for gasoline and diesel vehicles, respectively.

Integrated Marketing Communication (IMC): New Discipline with an Old Learning Approach: A Syllabi Analysis

Marketing communications as a discipline has changed significantly in both theory and practice over the past decade. But has our teaching of IMC kept pace with the discipline changes? The purpose of this paper is to explore how far the evolving concepts of IMC are reaching university learners. By doing this, the paper offers an approach to assessing how well marketing curricula are fulfilling their purpose. The course outlines (syllabi) for all IMC courses in 30 universities in Australia and five universities in New Zealand were analyzed. The findings suggest that most of what is taught in the units is not IMC. It is not directed by the key constructs of IMC, nor by the research informing the discipline. Rather, it appears to have evolved little from traditional promotion management units and is close in content and structure to many introductory advertising courses. This paper suggests several possible explanations for this, including: (1) a tacit rejection of IMC as a valid concept; (2) a lack of information about what IMC is and what it is not; and (3) a scarcity of teaching and learning materials that are clearly focused on key constructs and research issues of IMC.

Components of learning and assessment in linear algebra

Linear algebra provides theory and technology that are the cornerstones of a range of cutting edge mathematical applications, from designing computer games to complex industrial problems, as well as more traditional applications in statistics and mathematical modelling. Once past introductions to matrices and vectors, the challenges of balancing theory, applications and computational work across mathematical and statistical topics and problems are considerable, particularly given the diversity of abilities and interests in typical cohorts. This paper considers two such cohorts in a second level linear algebra course in different years. The course objectives and materials were almost the same, but some changes were made in the assessment package. In addition to considering effects of these changes, the links with achievement in first year courses are analysed, together with achievement in a following computational mathematics course. Some results that may initially appear surprising provide insight into the components of student learning in linear algebra.