Quantification of particle emission characteristics and development of an emission model for use in transport microenvironments affected by traffic emissions

Vehicle emitted particles are of significant concern based on their potential to influence local air quality and human health. Transport microenvironments usually contain higher vehicle emission concentrations compared to other environments, and people spend a substantial amount of time in these microenvironments when commuting. Currently there is limited scientific knowledge on particle concentration, passenger exposure and the distribution of vehicle emissions in transport microenvironments, partially due to the fact that the instrumentation required to conduct such measurements is not available in many research centres. Information on passenger waiting time and location in such microenvironments has also not been investigated, which makes it difficult to evaluate a passenger’s spatial-temporal exposure to vehicle emissions. Furthermore, current emission models are incapable of rapidly predicting emission distribution, given the complexity of variations in emission rates that result from changes in driving conditions, as well as the time spent in driving condition within the transport microenvironment. In order to address these scientific gaps in knowledge, this work conducted, for the first time, a comprehensive statistical analysis of experimental data, along with multi-parameter assessment, exposure evaluation and comparison, and emission model development and application, in relation to traffic interrupted transport microenvironments. The work aimed to quantify and characterise particle emissions and human exposure in the transport microenvironments, with bus stations and a pedestrian crossing identified as suitable research locations representing a typical transport microenvironment. Firstly, two bus stations in Brisbane, Australia, with different designs, were selected to conduct measurements of particle number size distributions, particle number and PM2.5 concentrations during two different seasons. Simultaneous traffic and meteorological parameters were also monitored, aiming to quantify particle characteristics and investigate the impact of bus flow rate, station design and meteorological conditions on particle characteristics at stations. The results showed higher concentrations of PN20-30 at the station situated in an open area (open station), which is likely to be attributed to the lower average daily temperature compared to the station with a canyon structure (canyon station). During precipitation events, it was found that particle number concentration in the size range 25-250 nm decreased greatly, and that the average daily reduction in PM2.5 concentration on rainy days compared to fine days was 44.2 % and 22.6 % at the open and canyon station, respectively. The effect of ambient wind speeds on particle number concentrations was also examined, and no relationship was found between particle number concentration and wind speed for the entire measurement period. In addition, 33 pairs of average half-hourly PN7-3000 concentrations were calculated and identified at the two stations, during the same time of a day, and with the same ambient wind speeds and precipitation conditions. The results of a paired t-test showed that the average half-hourly PN7-3000 concentrations at the two stations were not significantly different at the 5% confidence level (t = 0.06, p = 0.96), which indicates that the different station designs were not a crucial factor for influencing PN7-3000 concentrations. A further assessment of passenger exposure to bus emissions on a platform was evaluated at another bus station in Brisbane, Australia. The sampling was conducted over seven weekdays to investigate spatial-temporal variations in size-fractionated particle number and PM2.5 concentrations, as well as human exposure on the platform. For the whole day, the average PN13-800 concentration was 1.3 x 104 and 1.0 x 104 particle/cm3 at the centre and end of the platform, respectively, of which PN50-100 accounted for the largest proportion to the total count. Furthermore, the contribution of exposure at the bus station to the overall daily exposure was assessed using two assumed scenarios of a school student and an office worker. It was found that, although the daily time fraction (the percentage of time spend at a location in a whole day) at the station was only 0.8 %, the daily exposure fractions (the percentage of exposures at a location accounting for the daily exposure) at the station were 2.7% and 2.8 % for exposure to PN13-800 and 2.7% and 3.5% for exposure to PM2.5 for the school student and the office worker, respectively. A new parameter, “exposure intensity” (the ratio of daily exposure fraction and the daily time fraction) was also defined and calculated at the station, with values of 3.3 and 3.4 for exposure to PN13-880, and 3.3 and 4.2 for exposure to PM2.5, for the school student and the office worker, respectively. In order to quantify the enhanced emissions at critical locations and define the emission distribution in further dispersion models for traffic interrupted transport microenvironments, a composite line source emission (CLSE) model was developed to specifically quantify exposure levels and describe the spatial variability of vehicle emissions in traffic interrupted microenvironments. This model took into account the complexity of vehicle movements in the queue, as well as different emission rates relevant to various driving conditions (cruise, decelerate, idle and accelerate), and it utilised multi-representative segments to capture the accurate emission distribution for real vehicle flow. This model does not only helped to quantify the enhanced emissions at critical locations, but it also helped to define the emission source distribution of the disrupted steady flow for further dispersion modelling. The model then was applied to estimate particle number emissions at a bidirectional bus station used by diesel and compressed natural gas fuelled buses. It was found that the acceleration distance was of critical importance when estimating particle number emission, since the highest emissions occurred in sections where most of the buses were accelerating and no significant increases were observed at locations where they idled. It was also shown that emissions at the front end of the platform were 43 times greater than at the rear of the platform. The CLSE model was also applied at a signalled pedestrian crossing, in order to assess increased particle number emissions from motor vehicles when forced to stop and accelerate from rest. The CLSE model was used to calculate the total emissions produced by a specific number and mix of light petrol cars and diesel passenger buses including 1 car travelling in 1 direction (/1 direction), 14 cars / 1 direction, 1 bus / 1 direction, 28 cars / 2 directions, 24 cars and 2 buses / 2 directions, and 20 cars and 4 buses / 2 directions. It was found that the total emissions produced during stopping on a red signal were significantly higher than when the traffic moved at a steady speed. Overall, total emissions due to the interruption of the traffic increased by a factor of 13, 11, 45, 11, 41, and 43 for the above 6 cases, respectively. In summary, this PhD thesis presents the results of a comprehensive study on particle number and mass concentration, together with particle size distribution, in a bus station transport microenvironment, influenced by bus flow rates, meteorological conditions and station design. Passenger spatial-temporal exposure to bus emitted particles was also assessed according to waiting time and location along the platform, as well as the contribution of exposure at the bus station to overall daily exposure. Due to the complexity of the interrupted traffic flow within the transport microenvironments, a unique CLSE model was also developed, which is capable of quantifying emission levels at critical locations within the transport microenvironment, for the purpose of evaluating passenger exposure and conducting simulations of vehicle emission dispersion. The application of the CLSE model at a pedestrian crossing also proved its applicability and simplicity for use in a real-world transport microenvironment.

Tensor query expansion : a cognitively motivated relevance model

In information retrieval, a user's query is often not a complete representation of their real information need. The user's information need is a cognitive construction, however the use of cognitive models to perform query expansion have had little study. In this paper, we present a cognitively motivated query expansion technique that uses semantic features for use in ad hoc retrieval. This model is evaluated against a state-of-the-art query expansion technique. The results show our approach provides significant improvements in retrieval effectiveness for the TREC data sets tested.

Measuring neighbourhood sustainability performance: an indexing model for Gold Coast City, Australia

The aim of this research is to develop an indexing model to evaluate sutainability performance of urban settings, in order to assess environmental impacts of urban development and to provide planning agencies an indexing model as a decision support tool to be used in curbing negative impacts of urban development. Indicator-based sustainability assessment is embraced as the method. Neigbourhood-level urban form and transport related indicators are derived from the literature by conducting a content analysis and finalised via a focus group meeting. The model is piloted on three suburbs of Gold Coast City, Australia. Final neighbourhood level sustainability index score was calculated by employing equal weighting schema. The results of the study show that indexing modelling is a reasonably practical method to measure and visualise local sustainability performance, which can be employed as an effective communication and decision making tool.

The development and evaluation of a culturally affirmative counselling model for deaf clients in Australia

In Australia, there is only one, newly established, dedicated mental health service catering specifically for the signing *Deaf community. It is staffed by four part-time hearing professionals and based in Brisbane. There are currently no Deaf psychologists or psychiatrists and there is no valid or reliable empirical evidence on outcomes for Deaf people accessing specialised or mainstream mental health services. Further compounding these issues, is the fact that there are no sign language versions of the most common standardised mental health or psychological instruments available to clinicians in Australia. Contemporary counselling literature is acknowledging the role of the therapeutic alliance and the impact of 'common factors' on therapeutic outcomes. However, these issues are complicated by the relationship between the Deaf client and the hearing therapist being a cross-cultural exchange. The disability model of deafness is contentious and few professionals in Australia have the requisite knowledge and understanding of deafness from a cultural perspective to attend to the therapeutic relationship with this in mind. Consequently, Deaf people are severely disadvantaged by the current lack of services, resources and skilled professionals in the field of deafness and psychology in this country. The primary aim of the following program of research has been to propose a model for culturally affirmative service delivery and to provide clinicians with tools to evaluate the effect of their therapeutic work with Deaf people seeking mental health treatment. The research document is presented as a thesis by publication and comprises four specific objectives formulated in response to the lack of existing services and resources. The first objective was to explore the use of social constructionist counselling techniques and a reflecting team with Deaf clients, hearing therapists and an interpreter. Following the establishment of a pilot counselling clinic, indepth semi-structured interviews were conducted with two long-term clients following the one year pilot of this service. These interviews generated recommendations for the development of a new 'enriched' model of counselling to be implemented and evaluated in later stages of the research program. The second objective was to identify appropriate psychometric measures that could be translated into Australian Sign Language (Auslan) for research into efficacy, effectiveness and counselling outcomes. Two instruments were identified as potentially suitable; the Outcome Rating Scale (ORS), a measure of global functioning, and the Session Rating Scale (SRS), a measure of therapeutic alliance. A specialised team of bi-lingual and bi-cultural interpreters, native signers and the primary researcher for this thesis, produced the ORS-Auslan and the SRS-Auslan in DVD format, using the translation and back-translation process. The third objective was to establish the validity and reliability of these new Auslan measures based on normative data from the Deaf community. Data from the ORS-Auslan was collected from one clinical and one non-clinical sample of Deaf people. Statistical analyses revealed that the ORS-Auslan is reliable, valid and adequately distinguishes between clinical and non-clinical presentations. Furthermore, construct validity has been established using a yet to be validated sign language version of the Depression, Anxiety and Stress Scale-21 items (DASS-21), providing a platform for further research using the DASS-21 with Deaf people. The fourth objective was to evaluate counselling outcomes following the implementation of an enriched counselling service, based on the findings generated by the first objective, and using the newly translated Auslan measures. A second university counselling clinic was established and implemented over the course of one year. Practice-based evidence guided the research and the ORS-Auslan and the SRS-Auslan were administered at every session and provided outcome data on Deaf clients' global functioning. Data from six clients over the course of ten months indicated that this culturally affirmative model was an effective approach for these six clients. This is the first time that outcome data have been collected in Australia using valid and reliable Auslan measures to establish preliminary evidence for the effectiveness of any therapeutic intervention for clinical work with adult, signing Deaf clients. The research generated by this thesis contributes theoretical knowledge, professional development and practical resources that can be used by a variety of mental health clinicians in the context of mental health service delivery to Deaf clients in Australia.

Towards a model of dynamic capabilities in innovation-based competitive strategy : insights from project-oriented service firms

With the growing significance of services in most developed economies, there is an increased interest in the role of service innovation in service firm competitive strategy. Despite growing literature on service innovation, it remains fragmented reflecting the need for a model that captures key antecedents driving the service innovation-based competitive advantage process. Building on extant literature and using thirteen in-depth interviews with CEOs of project-oriented service firms, this paper presents a model of innovation-based competitive advantage. The emergent model suggests that entrepreneurial service firms pursuing innovation carefully select and use dynamic capabilities that enable them to achieve greater innovation and sustained competitive advantage. Our findings indicate that firms purposefully use create, extend and modify processes to build and nurture key dynamic capabilities. The paper presents a set of theoretical propositions to guide future research. Implications for theory and practice are discussed. Finally, directions for future research are outlined.

Towards testing a new first-order decision making model for the procurement of public sector major infrastructure

Given global demand for new infrastructure, governments face substantial challenges in funding new infrastructure and simultaneously delivering Value for Money (VfM). As background to this challenge, a brief review is given of current practice in the selection of major public sector infrastructure in Australia, along with a review of the related literature concerning the Multi-Attribute Utility Approach (MAUA) and the effect of MAUA on the role of risk management in procurement selection. To contribute towards addressing the key weaknesses of MAUA, a new first-order procurement decision making model is mentioned. A brief summary is also given of the research method and hypothesis used to test and develop the new procurement model and which uses competition as the dependent variable and as a proxy for VfM. The hypothesis is given as follows: When the actual procurement mode matches the theoretical/predicted procurement mode (informed by the new procurement model), then actual competition is expected to match optimum competition (based on actual prevailing capacity vis-à-vis the theoretical/predicted procurement mode) and subject to efficient tendering. The aim of this paper is to report on progress towards testing this hypothesis in terms of an analysis of two of the four data components in the hypothesis. That is, actual procurement and actual competition across 87 road and health major public sector projects in Australia. In conclusion, it is noted that the Global Financial Crisis (GFC) has seen a significant increase in competition in public sector major road and health infrastructure and if any imperfections in procurement and/or tendering are discernible, then this would create the opportunity, through the deployment of economic principles embedded in the new procurement model and/or adjustments in tendering, to maintain some of this higher level post-GFC competition throughout the next business cycle/upturn in demand including private sector demand. Finally, the paper previews the next steps in the research with regard to collection and analysis of data concerning theoretical/predicted procurement and optimum competition.

An effective approach to verbose queries using a limited dependencies language model

Intuitively, any ‘bag of words’ approach in IR should benefit from taking term dependencies into account. Unfortunately, for years the results of exploiting such dependencies have been mixed or inconclusive. To improve the situation, this paper shows how the natural language properties of the target documents can be used to transform and enrich the term dependencies to more useful statistics. This is done in three steps. The term co-occurrence statistics of queries and documents are each represented by a Markov chain. The paper proves that such a chain is ergodic, and therefore its asymptotic behavior is unique, stationary, and independent of the initial state. Next, the stationary distribution is taken to model queries and documents, rather than their initial distributions. Finally, ranking is achieved following the customary language modeling paradigm. The main contribution of this paper is to argue why the asymptotic behavior of the document model is a better representation then just the document’s initial distribution. A secondary contribution is to investigate the practical application of this representation in case the queries become increasingly verbose. In the experiments (based on Lemur’s search engine substrate) the default query model was replaced by the stable distribution of the query. Just modeling the query this way already resulted in significant improvements over a standard language model baseline. The results were on a par or better than more sophisticated algorithms that use fine-tuned parameters or extensive training. Moreover, the more verbose the query, the more effective the approach seems to become.

The role of cognitive and visual abilities as predictors in the Multifactorial Model of Driving Safety

Objective The current study evaluated part of the Multifactorial Model of Driving Safety to elucidate the relative importance of cognitive function and a limited range of standard measures of visual function in relation to the Capacity to Drive Safely. Capacity to Drive Safely was operationalized using three validated screening measures for older drivers. These included an adaptation of the well validated Useful Field of View (UFOV) and two newer measures, namely a Hazard Perception Test (HPT), and a Hazard Change Detection Task (HCDT). Method Community dwelling drivers (n = 297) aged 65–96 were assessed using a battery of measures of cognitive and visual function. Results Factor analysis of these predictor variables yielded factors including Executive/Speed, Vision (measured by visual acuity and contrast sensitivity), Spatial, Visual Closure, and Working Memory. Cognitive and Vision factors explained 83–95% of age-related variance in the Capacity to Drive Safely. Spatial and Working Memory were associated with UFOV, HPT and HCDT, Executive/Speed was associated with UFOV and HCDT and Vision was associated with HPT. Conclusion The Capacity to Drive Safely declines with chronological age, and this decline is associated with age-related declines in several higher order cognitive abilities involving manipulation and storage of visuospatial information under speeded conditions. There are also age-independent effects of cognitive function and vision that determine driving safety.

A conceptual model for manufacturing performance improvement

Purpose: Important performance objectives manufacturers sought can be achieved through adopting the appropriate manufacturing practices. This paper presents a conceptual model proposing relationship between advanced quality practices, perceived manufacturing difficulties and manufacturing performances. Design/methodology/approach: A survey-based approach was adopted to test the hypotheses proposed in this study. The selection of research instruments for inclusion in this survey was based on literature review, the pilot case studies and relevant industrial experience of the author. A sample of 1000 manufacturers across Australia was randomly selected. Quality managers were requested to complete the questionnaire, as the task of dealing with the quality and reliability issues is a quality manager’s major responsibility. Findings: Evidence indicates that product quality and reliability is the main competitive factor for manufacturers. Design and manufacturing capability and on time delivery came second. Price is considered as the least important factor for the Australian manufacturers. Results show that collectively the advanced quality practices proposed in this study neutralize the difficulties manufacturers face and contribute to the most performance objectives of the manufacturers. The companies who have put more emphasize on the advanced quality practices have less problem in manufacturing and better performance in most manufacturing performance indices. The results validate the proposed conceptual model and lend credence to hypothesis that proposed relationship between quality practices, manufacturing difficulties and manufacturing performances. Practical implications: The model shown in this paper provides a simple yet highly effective approach to achieving significant improvements in product quality and manufacturing performance. This study introduces a relationship based ‘proactive’ quality management approach and provides great potential for managers and engineers to adopt the model in a wide range of manufacturing organisations. Originality/value: Traditional ways of checking product quality are different types of testing, inspection and screening out bad products after manufacturing them. In today’s manufacturing where product life cycle is very short, it is necessary to focus on not to manufacturing them first rather than screening out the bad ones. This study introduces, for the first time, the idea of relationship based advanced quality practices (AQP) and suggests AQPs will enable manufacturers to develop reliable products and minimize the manufacturing anomalies. This paper explores some of the attributes of AQP capable of reducing manufacturing difficulties and improving manufacturing performances. The proposed conceptual model contributes to the existing knowledge base of quality practices and subsequently provides impetus and guidance towards increasing manufacturing performance.

A suspicious behaviour detection using a context space model for smart surveillance systems

Video surveillance systems using Closed Circuit Television (CCTV) cameras, is one of the fastest growing areas in the field of security technologies. However, the existing video surveillance systems are still not at a stage where they can be used for crime prevention. The systems rely heavily on human observers and are therefore limited by factors such as fatigue and monitoring capabilities over long periods of time. This work attempts to address these problems by proposing an automatic suspicious behaviour detection which utilises contextual information. The utilisation of contextual information is done via three main components: a context space model, a data stream clustering algorithm, and an inference algorithm. The utilisation of contextual information is still limited in the domain of suspicious behaviour detection. Furthermore, it is nearly impossible to correctly understand human behaviour without considering the context where it is observed. This work presents experiments using video feeds taken from CAVIAR dataset and a camera mounted on one of the buildings Z-Block) at the Queensland University of Technology, Australia. From these experiments, it is shown that by exploiting contextual information, the proposed system is able to make more accurate detections, especially of those behaviours which are only suspicious in some contexts while being normal in the others. Moreover, this information gives critical feedback to the system designers to refine the system.